WATER RESEARCH A Journal of the International Water Association
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Influence of sampling strategies on the monitoring of cyanobacteria in shallow lakes: Lessons from a case study in France David Pobel a, Joe¨l Robin a, Jean-Franc¸ois Humbert b,* a b
ISARA-Lyon, Equipe Ecosyste`mes et Ressources Aquatiques, 23 rue Jean Baldassini 69364 Lyon Cedex 07, France INRA, UMR 7618 BIOEMCO, Site de l’ENS, 46 rue d’Ulm, 75005 Paris, France
article info
abstract
Article history:
Sampling cyanobacteria in freshwater ecosystems is a crucial aspect of monitoring programs
Received 6 August 2010
in both basic and applied research. Despite this, few papers have dealt with this aspect, and
Received in revised form
a high proportion of cyanobacteria monitoring programs are still based on monthly or twice-
5 October 2010
monthly water sampling, usually performed at a single location. In this study, we conducted
Accepted 10 October 2010
high frequency spatial and temporal water sampling in a small eutrophic shallow lake that
Available online 20 October 2010
experiences cyanobacterial blooms every year. We demonstrate that the spatial and temporal aspects of the sampling strategy had a considerable impact on the findings of
Keywords:
cyanobacteria monitoring in this lake. In particular, two peaks of Aphanizomenon flos-aquae
Sampling strategy
cell abundances were usually not picked up by the various temporal sampling strategies
Cyanobacteria
tested. In contrast, sampling once a month was sufficient to provide a good overall estima-
Spatiotemporal dynamic
tion of the population dynamics of Microcystis aeruginosa. The spatial frequency of sampling
Microcystis aeruginosa
was also important, and the choice in the location of the sampling points around the lake was
Aphanizomenon flos-aquae
very important if only two or three sampling points were used. When four or five sampling points were used, this reduced the impact of the choice of the location of the sampling points, and allowed to obtain fairly similar results than when six sampling points were used. These findings demonstrate the importance of the sampling strategy in cyanobacteria monitoring, and the fact that it is impossible to propose a single universal sampling strategy that is appropriate for all freshwater ecosystems and also for all cyanobacteria. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Due to eutrophication and, to a lesser extent, to climatic changes (Markensten et al., 2010; Paerl and Huisman, 2009) cyanobacterial blooms seem to be increasing in freshwater ecosystems worldwide. These blooms severely disrupt the functioning of these ecosystems and potential water use. Furthermore, many cyanobacterial species are able to produce a variety of toxic metabolites, which can be harmful to both human (Kuiper-Goodman et al., 1999) and animal (Codd et al.,
2005) health. For these reasons, numerous attempts have been made in the last 20 years to elucidate the factors that control cyanobacterial blooms and toxin production, and thus to make it possible to evaluate better the health risks associated with bloom events. From all these studies, it is clear that the spatial distribution of cyanobacteria in freshwater ecosystems can display marked horizontal and vertical variations (Porat et al., 2001; Welker et al., 2003). Moreover, by means of a real-time PCR analysis of a gene involved in the biosynthesis of microcystins we have shown that considerable fluctuations
* Corresponding author. E-mail address:
[email protected] (J.-F. Humbert). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.011
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can also occur in the proportions of potentially microcystinproducing and non-producing cells during the course of Microcystis aeruginosa blooms (Briand et al., 2009). Similar results have been found for various M. aeruginosa populations located in the same geographic area (Sabart et al., 2009), which makes it difficult to manage the health risks associated with these events. All these studies indicate that the sampling strategy used for monitoring cyanobacteria is a critical aspect, both in basic research on cyanobacteria, (e.g. investigation of the factors and processes involved in the development of the blooms), and in applied research, (e.g. implementing monitoring programs of these microorganisms in freshwater ecosystems used to provide drinking water or for recreational activities). In recent years, new tools have been tested with the intention of improving cyanobacterial sampling, for example, remote sensing reconnaissance to determine the horizontal distribution of cyanobacteria in freshwater ecosystems (Hunter et al., 2009), or spectrofluorometric probes to reveal the vertical distribution of these cyanobacteria in the water column (Leboulanger et al., 2002). Moreover, these spectrofluorometric probes and other sensors have now been integrated into buoys, to provide real-time monitoring of cyanobacteria in freshwater ecosystems (Le Vu et al., in press). However, despite the great potential interest of these tools, their cost will remain prohibitive for their routine use in the foreseeable future, and most of the monitoring programs worldwide for the survey of cyanobacteria will continue to be based on more conventional methods for some years to come. Taking discrete samples of various volumes of water taken from the shoreline of ecosystems is probably the method one most often used in studies. Unfortunately, as a result of spatial and temporal differences in the distribution of cyanobacteria, this approach can often provide a very poor estimation of cyanobacterial abundance and, consequently, of the associated health risk. We therefore need to devise simple sampling strategies for the low cost monitoring of cyanobacteria in shallow lakes. In an attempt to do this, we performed intense spatiotemporal monitoring of cyanobacteria in a shallow lake known to experience cyanobacterial blooms every year.
2.
Materials and methods
2.1.
Study site
This study was performed in a shallow lake named Place (0.08 km2, 2.5 m max depth, 45 430 N, 4 140 E) located in the plain of Forez (Central France), (Fig. 1). This lake is used for extensive fish production and its trophic status is eutrophic to hypereutrophic (OCDE, 1982). M. aeruginosa blooms occur every summer.
2.2.
Data acquisition
2.2.1.
Sampling strategy and cell counting
In order to assess the variations in the horizontal distribution of cyanobacteria in this pond, we monitored six sampling points located around the lake at 1 m from the shore (V1eV6; Fig. 1). The water depth in each of these sampling points was around 1 m. Samples were taken every two days, between 09:00 and 10:00 a.m., from early June 2008 to early October 2008. The first 40-cm of the water column were sampled using a water sampler (Uwitech, Austria). This water sample was shacked and then divided into two 1-L bottles, 1 L being stored at room temperature with Lugol’s iodine solution, and the other at 4 C. In order to evaluate the diel variations in the subsurface cyanobacterial biomass, we performed a 22-h survey (from 4:30 p.m. August 4, 2009 to 2:30 p.m. August 5, 2009) at five sampling points (AeE; Fig. 1) using a BBE Algaetorch (Moldaenke, Germany). This torch is based on the same principle as the BBE spectrofluorometric probe (Beutler et al., 2002), but provides only an estimation of the concentrations of cyanobacteria and total chlorophyll in water. Every hour, the torch was immersed to a depth of 20 cm at the five sampling points, and triplicate measurements were performed in each point. The cyanobacterial cell concentrations were estimated using a Nageotte cell and an optical microscope, as described in Brient et al. (2008). For each rectangular area, we counted at least 400 cells of each cyanobacterial species..
Fig. 1 e Geographical location of the study site in France (left), and of the sampling points in the lake (right).
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2.2.2.
Meteorological data
The speed and direction of wind during our study were obtained from the Mete´o France meteorological station at St Etienne-Bouthe´on (45 32’N e 4 18’E). The wind direction rose for this station is given in Supplemental Figure 1, and shows that the two dominant wind directions were NW and SE. The direction of winds blowing from 240 e60 was classified as NW, and that of winds blowing from 60 e240 as SE.
2.3.
Data analysis
The spatial distribution of cyanobacteria in the lake was represented using Surfer (v. 7.0, Golden Software Inc.), and statistical analyses (Wilcoxon test, Spearman correlation) were performed using the R package version 2.10 (R development core Team, 2010).
3.
1007
Results
3.1. Change over time in the population dynamics of the two dominant cyanobacterial species Two cyanobacterial species, M. aeruginosa and Aphanizomenon flos-aquae, dominated the phytoplankton community during the summer of 2008. The population dynamics of these two species displayed very contrasting patterns (Fig. 2). The population dynamics of M. aeruginosa was characterized by a steady increase in the cell abundance from June to August, apart from a brief dip in the middle of July. The maximum population was reached on August 21 (264,000 cells/mL), and subsequently the cell concentration remained stable until the end of September, and then decreased in October. In contrast,
Fig. 2 e Changes over time of the concentrations of M. aeruginosa (top) and A. flos-aquae (bottom). These concentrations were estimated by calculating the average cell count for the six samples at each date. The error bars indicate the standard deviation.
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the population dynamics of A. flos-aquae was much more chaotic, with the cell abundance reaching two very high and short-lived peaks in July (400,000 cells/mL on July 17, and 560,000 cells/mL on July 23).
Our assessment of the changing population dynamics of the two cyanobacteria was obtained using a very frequent high temporal sampling regime (every two days), which would not be practicable in the context of normal monitoring programs. In
order to evaluate the impact of the sampling frequency, we simulated weekly, twice-monthly and monthly sampling frequencies to our data set. The results of these simulations are shown in Figs. 3 and 4. From these figures, we can see that changes in M. aeruginosa cell abundance over time would have been fairly accurately estimated at all these sampling frequencies. Moreover, for all sampling frequencies, the quality of the estimation of the M. aeruginosa population dynamics was not influenced by choice of the first sampling date (Fig. 3). In contrast, the population dynamics of A. flos-aquae would have been badly or even very badly estimated by using weekly, twice-monthly and monthly sampling frequencies (Fig. 4). We
Fig. 3 e Simulation of the change over time of M. aeruginosa cell concentrations found using a weekly (top), twicemonthly (middle) or monthly sampling frequency (bottom), with lags for the first sampling day of zero days (_), 2 days (ee) and 4 days (..) comparing to our first sampling day. The gray curve corresponds to the reference data.
Fig. 4 e Simulation of the change over time of the biomass of A. flos-aquae found using a weekly (top), twice-monthly (middle), or monthly sampling frequency (bottom), and with lags for the first sampling day of zero days (_), 2 days (ee) and 4 days (..) comparing to our first sampling day. The gray curve corresponds to the reference data.
3.2. Influence of sampling frequency on the estimation of the population dynamics
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would only have detected both A. flos-aquae peaks in one of the three trials testing the weekly sampling strategy, and we would never have detected these peaks with twice-monthly and monthly sampling frequencies.
3.3. Evolution of the horizontal distribution of cyanobacteria in the lake during the bloom As shown in the Video S1 (Supplemental Fig. 2), the horizontal distribution of both cyanobacteria displayed marked variations during the course of the study. Moreover, when the spatial distributions of the two species at the same sampling dates were compared, it could be seen that similar or contrasting patterns in the horizontal distribution of M. aeruginosa and A. flos-aquae cells would have been found, depending on the dates chosen (some examples are provided in Fig. 5). Supplementary data related to this article can be found online at doi: 10.1016/j.watres.2010.10.011 In order to obtain a better picture of this spatial variability in the cell concentrations of the two species, we estimated the coefficients of variation in the mean abundance for each sampling date and for each species from the results obtained at the six sampling points (Fig. 6). These coefficients were usually higher for A. flos-aquae than for M. aeruginosa (Wilcoxon test, p ¼ 3.25 1005), suggesting that the horizontal distribution of A. flos-aquae was more variable. Finally, there was no correlation (Spearman coefficient) between the coefficient of variation and the mean cell abundance for A. flos-aquae, and only a weak correlation was found for M. aeruginosa (Spearman coefficient, p ¼ 0.003 r ¼ 0.4; Supplemental Fig. 3). In order to find out whether wind speed/direction could account for the variations in the horizontal distribution of cyanobacterial cell abundance in the lake, we recorded in a first time, for each species and for each sampling date, the sampling point (out of the six) at which the highest cell
Fig. 6 e Change over time in the coefficients of variation of the mean cell abundances of M. aeruginosa (black triangle) and A. flos-aquae (white square) estimated at all six sampling points.
abundance was detected. We then constructed a table in which we related these findings to the wind direction and speed in the 5 h before the sampling, knowing that only data with wind speed values 2.0 m/s were taken into consideration. For M. aeruginosa, the highest cell abundances in the southernmost sampling points V2 and V3 were associated with winds blowing from the NW (Table 1), whereas those at the V1 and V4 sampling points were more surprisingly associated with winds from the SE. High cell abundances in the northern most sampling points V5 and V6 were equally associated with winds from NW and SE. For A. flos-aquae, the results were more complicated, and no obvious link could be seen between the direction of the wind and the distribution of the cyanobacteria (Table 1). The same analyses were performed by taking into account the wind data one and two days before sampling (instead 5e10 h before sampling), but no obvious relationship was detected (data not shown).
Fig. 5 e Spatial distribution of two cyanobacteria, M. aeruginosa and A. flos-aquae, in the lake at four sampling dates (July, 9, 17 and 23; August, 8).
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Table 1 e Relationship between wind direction and high cell abundance recorded for M. aeruginosa and A. flosaquae at the different sampling points. V1 M. aeruginosa SeE 4 NeW 2 A. flos-aquae SeE 9 NeW 6
V2
V3
V4
V5
V6
1 5
1 3
8 2
7 6
2 2
2 5
4 0
0 0
5 5
2 4
3.4. Influence of the number of sampling points on the estimated cyanobacterial cell abundances in the lake The cyanobacterial cell abundances in the shallow lake were estimated by calculating the average value for the six sampling points (see Fig. 1). In order to determine the number of sampling points required to obtain a good estimation of cyanobacterial cell abundances in the lake, we compared the
estimations of cell abundance based on using samples from just one, two, three, four or five sampling points with that based on all six. To do this, we calculated the correlation coefficients (Spearman) between the estimations based on the six sampling points and those based on one to five sampling points for each species (Fig. 7). We considered all possible combinations of points, and the results are classified in the figure on the basis of increasing order of r values within each combination of groups. For both species, we found that the estimations of cell abundances based on only one or two sampling points were generally rather badly correlated with those obtained using all six sampling points. On the other hand, it appeared that good correlations (around or >0.9) were obtained when at least three sampling points were used, but also that the variations due to the choice of the sampling points were still considerable when only three sampling points were used. In order to find out which combinations of sampling points provided the best results when only two or three sampling points were used, we classified all the possible combinations
Spearman coefficients
1
0,9
Microcystis 0,8
0,7
Microcystis - 1 point: ▲ ; 2 points: ■ ; 3 points: ♦; 4 points: ○ ; 5 points: □ V5 V4 V1 V2 V6 V3 V 1V 5 V 1V 4 V 4V 5 V 3V 5 V 5V 6 V 2V 5 V 1V 6 V 1V 2 V 4V 6 V 2V 3 V 2V 6 V 3V 4 V 1V 3 V 2V 4 V 3V 6 V 1V4V5 V 1V4V6 V 1V2V6 V 1V3V4 V 1V3V5 V 2V3V4 V 3V4V5 V 3V5V6 V 4V5V6 V 1V2V4 V 1V2V5 V 2V4V5 V 2V5V6 V 1V2V3 V 1V5V6 V 2V3V5 V 1V3V6 V 2V3V6 V 2V4V6 V 3V4V6 V1V 2V3V 6 V1V 4V5V 6 V1V 2V3V 4 V1V 2V3V 5 V1V 2V4V 5 V1V 2V4V 6 V1V 2V5V 6 V1V 3V4V 5 V1V 3V4V 6 V1V 3V5V 6 V1V2V3V4V 6 V1V2V3V5V 6 V1V2V3V4V 5 V1V2V4V5V 6 V1V3V4V5V 6 V2V3V4V5V 6
0,6
Sampling points
Spearman coefficients
1
0,9
Aphanizomenon 0,8
0,7
Aphanizomenon - 1 point: ▲ ; 2 points: ■ ; 3 points: ♦; 4 points: ○ ; 5 points: □ V3 V4 V1 V5 V2 V6 V 2V 3 V 3V 4 V 1V 3 V 1V 5 V 2V 4 V 1V 4 V 3V 5 V 4V 5 V 1V 2 V 1V 6 V 4V 6 V 3V 6 V 2V 6 V 2V 5 V 5V 6 V 2V 3V4 V 3V 5V6 V 1V 3V4 V 1V 5V6 V 4V 5V6 V 1V 2V3 V 1V 2V4 V 1V 3V5 V 3V 4V5 V 3V 4V6 V 1V 3V6 V 1V 4V5 V 2V 3V6 V 2V 4V6 V 1V 2V6 V 1V 4V6 V 2V 3V5 V 2V 4V5 V 2V 5V6 V 1V 2V5 V1V 3V 5V6 V1V 2V 3V4 V1V 4V 5V6 V1V 2V 3V5 V1V 2V 3V6 V1V 2V 4V5 V1V 2V 4V6 V1V 2V 5V6 V1V 3V 4V6 V1V 3V 4V5 V 1V3V 4V 5V6 V 2V3V 4V 5V6 V 1V2V 3V 4V5 V 1V2V 3V 4V6 V 1V2V 4V 5V6 V 1V2V 3V 5V6
0,6
Sampling points
Fig. 7 e Spearman correlation values between M. aeruginosa (top) and A. flos-aquae (bottom) cell abundances estimated from the mean values for all six sampling point values, and those estimated from only one, two, three, four or five of these six sampling points.
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Fig. 8 e Location of the sampling points providing the best (left) and worst (right) estimations of cyanobacterial cell abundances (M. aeruginosa and A. flos-aquae), compared to estimations based on six sampling points. We give the combinations for two (top) and three (bottom) sampling points. The polar plot shows the direction of the maximum daily wind speed during the study. The different line types permit to distinguish the two best or the two worst combinations of sampling points, using two or three sampling points.
of points. To do this, we added the rank of each combination of sampling points obtained for the two species (M. aeruginosa and A. flos-aquae). From Fig. 8, we can see that the best estimations obtained using only two or three sampling points were provided by combinations in which the sampling points used were on the shore opposite to the prevailing wind direction over the lake.
3.5. Diel variations in the subsurface cyanobacterial biomass in the lake Finally, we carried out a 22-h estimation of the variations in the total cyanobacterial biomass in the subsurface water (20 cm depth) of the lake, at five sampling points using the BBE torch (AeE, see Fig. 1). As shown in Fig. 9, there was a steady fall in the cyanobacterial biomass at all sampling points during the
Fig. 9 e Cyanobacterial biomass in the subsurface water of the lake over a 22-h period at five sampling points (A point A, - point B, : point C, 3 point D, and > point E). The error bars indicate the standard deviation.
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afternoon and evening, and conversely an increase late at night and in the morning. Moreover, the differences in biomass between the five sampling points were smaller during the night than during the day, as was the standard error (three measurements per sampling point). A multidimensional scaling analysis performed on the same values confirmed these observations, with all the night sampling times being grouped together, whereas the sampling times during the day were much more scattered (Data not shown).
4.
Discussion
As far as we are aware, this is the first attempt to investigate the influence of sampling strategies on the evaluation of spatial and temporal variations in cyanobacterial abundances in shallow lakes, which constitute unstable and complex ecosystems. These lakes are used by humans for numerous activities, including recreational activities and the supply of drinking water, which makes the monitoring of cyanobacteria in such ecosystems of particular importance, especially as part of the evaluation of the health risks linked to cyanobacterial blooms and their toxins. Sampling strategy is also very important in the context of basic studies, because the quality of sampling has a major impact on the quality of the final results. In this study, we found that the sampling frequency required to obtain a good estimation of the temporal evolution of the cyanobacterial abundance depends on the blooming species, M. aeruginosa or A. flos-aquae. Twice-monthly or monthly sampling provided good results for M. aeruginosa, whereas this was not often enough to monitor the chaotic population dynamics of A. flos-aquae. These findings are in contradiction with the recommendations of Codd et al. (1999), who proposed weekly or a twice-monthly sampling for species that do not form scum (A. flos-aquae for example), and more frequent sampling for scum-forming species (such as M. aeruginosa), because they can display more rapid changes in concentration. On the other hand, in agreement with these authors, our findings also demonstrate that a reactive approach to cyanobacterial sampling is called for, and that appropriate monitoring programs must be devised for each ecosystem based on what is known about how these systems function. It is clear that sampling only once or twice a month can lead to a very considerable under-estimation of cyanobacterial concentrations, and thus of the health risks associated with the bloom. As a result, a weekly sampling frequency seems to be required for cyanobacteria in small freshwater ecosystems. Our data on the variability of the spatial distribution of cyanobacteria in the lake indicate that at least three sampling points were needed to obtain a good estimation of the abundance, based on a comparison with estimations based on six sampling points. It appeared also that if only three sampling points are used, the choice of the location of these sampling points is very important for the quality of the estimation. The most reliable results were obtained using sampling points located on the opposite side of the lake shore to the main axis of the wind direction, and that adding more sampling points reduces the impact of the choice of the location of the sampling points. Such horizontal variability in the
distribution of cyanobacteria has been previously documented for many ecosystems, and also for many cyanobacterial species. For example, in a recent study, Briand et al. (2009) showed that the spatial distribution of M. aeruginosa in a large freshwater reservoir on a given date could vary from 7.103 cells/mL to 2.108 cells/mL, depending on the location of the sampling points in the reservoir. Many factors and processes can influence the horizontal distribution of cyanobacteria in a freshwater ecosystem. Among them, wind and surface currents seem to have the greatest impact. For example, the distribution of Microcystis spp. in lake Taihu (see the review paper of Qin et al., 2010) and in Lake Ontario (Hotto et al., 2007) is clearly influenced by both winds and currents. Similarly, Moreno-Ostos et al. (2009) have shown that in a Spanish reservoir currents have a marked effect on the distribution of cyanobacteria, and more globally on the phytoplankton community. In this study, we found that the horizontal distribution of M. aeruginosa in the lake was influenced more by wind direction than that of A. flos-aquae. This could be explained by the fact that M. aeruginosa colonies are located at the surface of the lake at the end of the night, and thus are more subjected to the influence of the wind than A. flos-aquae filaments, which are distributed over the entire water column. We found also that two sampling points in the lake (V5 and V6) were less influenced by wind direction than the others. This could be explained by the fact that these two sampling points are protected from the influence of winds blowing from the NW by an embankment located in the North part of the lake. Finally, we also demonstrated that in such a small lake, the impact of wind occurred at the scale of a few hours, in contrast to the previous findings of Welker et al. (2003) showing that the distribution of cyanobacteria was influenced by winds that had been blowing one or two days earlier. In addition to this variability in their horizontal distribution; the vertical distribution of cyanobacteria was also variable. Indeed, during the 22 h for which we used the BBE Torch to monitor the concentrations of cyanobacteria, we found that they were lower in the subsurface layer early at night than during the day. The greatest variations in biomass were recorded during the daytime, both at the scale of one sampling point when the three measurements were compared, and at the scale of the five sampling points monitored during this study. These findings also suggest that several sampling points are necessary to obtain an accurate assessment of the cyanobacterial biomass and that integrated sampling of the first meter of the water column reduces the variability in the estimation of the biomass due to the position of cyanobacteria in the water column. This finding is consistent with data reported by Ahn et al. (2008) showing that an integrated method was the most appropriate sampling method for Oscillatoria and Microcystis blooms. The causes of these variations in the position of cyanobacteria in the water column have been studied for different species. Several papers (Porat et al., 2001; Rabouille and Salenc¸on, 2005; Rabouille et al., 2005; Visser et al., 2005; Walsby, 1994) have shown that migrations of cyanobacteria in the water column are probably due to the dynamics of the carbon-reserve metabolism, and are strongly influenced by light, temperature, and water mixing.
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From all these findings, guidelines should be proposed for the monitoring of cyanobacteria in shallow lakes. Codd et al. (1999) propose that the choice of sampling frequency and the choice of the number and location of the sampling sites should depend on the purpose of monitoring. For example, sampling near public bathing sites was recommended in freshwater ecosystems used for recreational activities. However, this strategy might generate data relevant only to the immediate vicinity of the bathing area, which do not reflect the global distribution of cyanobacteria in the lake. This is especially true when this distribution is very varied, and could make it difficult to prevent or manage blooms. On the basis of our findings, we proposed a different sampling strategy, which does not depend on the purpose of the monitoring. In order to minimize the cost of the cyanobacteria survey, twice-monthly sampling could be the norm for monitoring, but only if it is complemented by regular visual surveys. Changes in the appearance of the water (e.g. its color) between two successive dates would lead to an immediate increase in the sampling frequency. If it is not possible to carry out this visual survey, only a weekly sampling strategy can ensure that a sporadic cyanobacterial bloom is not missed. With regard to the number of sampling points, we found that at least three sampling points were necessary to obtain an accurate assessment of the cyanobacterial biomass (based on comparison with six sampling points). However, even when three sampling points were used, we found that the choice of the location of the sampling points was also very important (Fig. 8), even though the lake was fairly rectangular in shape and its perimeter small (around 1.3 km). These findings suggest that for large lakes and also for lakes with a more complex shape, a large number of sampling points would be necessary to obtain a good estimation of the cyanobacterial abundance. Clearly such sampling is time consuming and expensive. One way to reduce these costs would be to collect a large number of samples and then pool equal volumes of these samples in the same flask, before carrying out a single analysis. In this study, as in most of the monitoring programs performed in small lakes, all samples were taken from the shoreline of the lake. This kind of sampling is suitable for small lakes, but it has been shown that for large lakes (Rogalus and Watzin, 2008) shoreline sampling may miss early warning signs of bloom development, and also lead to the overestimation of the concentration of microcystins, when compared to data obtained from offshore samples. For bigger lakes, therefore, the sampling strategy must include offshore samples. Different programs worldwide are testing alternatives to water sampling for the monitoring of cyanobacteria in freshwater ecosystems. Two main approaches have been investigated. The first one is based on the use of remote sensing, which has long been in use in marine ecosystems (see for example Bracher et al., 2009). In freshwater ecosystems, the paper of Hunter et al. (2008) has shown the potential of high resolution images for the assessment of the spatial distribution of M. aeruginosa in a shallow eutrophic lake. However, the cost of these images and the impact of meteorological conditions are limiting factors for envisaging the use of this tool in routine cyanobacteria monitoring programs. One alternative, lower-cost solution could be based, in the future, on the use of
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drones to take aerial photographs of freshwater ecosystems, but these tools are still in development. Moreover, they will be only useful for cyanobacterial species that live in the surface water of lakes. The second way of monitoring of cyanobacteria without sampling the water being investigated is the use of buoys equipped with a variety of sensors, including, for example, a submersible spectrofluorometer to quantify the biomass of the cyanobacteria. This kind of tool permits the real-time monitoring of phytoplankton, including cyanobacteria, as shown for example in the paper of Le Vu et al. (in press). The two obstacles to their use in routine cyanobacteria monitoring programs are the high price of these systems, and the fact that they only provide estimations for one sampling point. Despite this, the possible use of such buoys, combined with the spatial monitoring of cyanobacteria by water sampling looks very promising for surveying cyanobacteria in freshwater ecosystems.
5.
Conclusion
The sampling of cyanobacteria in freshwater ecosystems is a hot topic, in particular in the context of programs for surveying these toxic microorganisms in ecosystems used for the production of drinking water or for recreational activities. Paradoxically, fewer studies deal with the impact of sampling strategies on the estimation of cyanobacterial cell abundances in freshwater ecosystems. In this study, we demonstrate that the choice of sampling strategy can lead to very different estimations of the cell abundances of two blooming species in a shallow lake and also that, depending on the cyanobacterial species involved, different sampling strategies are required to obtain a good estimation of their population dynamics. All these findings suggested that monthly or twice-monthly sampling strategies at just one sampling point do not allow to provide an accurate estimation of cyanobacterial abundances, and thus of the health risks associated with the presence of toxic species in aquatic ecosystems. Moreover, although promising new technologies are being developed for monitoring freshwater cyanobacteria, their cost and some other drawbacks mean that at present they cannot replace water sampling, which will remain the basis of most of these monitoring programs for the foreseeable future.
Acknowledgment This work was funded by the Re´gion Rhoˆne-Alpes and the Conseil Ge´ne´ral de la Loire. Monika Ghosh is acknowledged for improving the English version of the manuscript. The comments and suggestions of the two anonymous reviewers were greatly appreciated.
Appendix Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.10.011.
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Ahn, C.Y., Joung, S.H., Park, C.S., Kim, H.S., Yoon, B.D., Oh, H.M., 2008. Comparison of sampling and analytical methods for monitoring of cyanobacteria-dominated surface waters. Hydrobiologia 596, 413e421. Beutler, M., Wiltshire, K.H., Meyer, B., Moldaenke, C., Luring, C., Meyerhofer, M., Hansen, U.P., Dau, H., 2002. A fluorometric method for the differentiation of algal populations in vivo and in situ. Photosynthesis Research 72, 39e53. Bracher, A., Vountas, M., Dinter, T., Burrows, J.P., Rottgers, R., Peeken, I., 2009. Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data. Biogeosciences 6, 751e764. Briand, E., Escoffier, N., Straub, C., Sabart, M., Quiblier, C., Humbert, J.-F., 2009. Spatiotemporal changes in the genetic diversity of a bloom-forming Microcystis aeruginosa (cyanobacteria) population. The ISME Journal 3, 419e429. Brient, L., Lengronne, M., Bertrand, E., Rolland, D., Sipel, A., Steinmann, D., Baudin, I., Legeas, M., Le Rouzic, B., Bormans, M., 2008. A phycocyanin probe as a tool for monitoring cyanobacteria in freshwater bodies. Journal of Environmental Monitoring 10, 248e255. Codd, G.A., Chorus, I., Burch, M., 1999. Design of monitoring programmes. In: WHO (Ed.), Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring and Management. E&F Spon ed, London & New York, p. 302e316. Codd, G.A., Lindsay, J., Young, F.M., Morrison, L.F., Metcalf, J.S., 2005. Harmful cyanobacteria. In: Huisman, J., Matthijs, H.C.P., Visser, P.M. (Eds.), Harmful Cyanobacteria. Springer, Dordrecht, pp. 1e23. Hotto, A.M., Satchwell, M.F., Boyer, G.L., 2007. Molecular characterization of potential microcytsin-producing cyanobacteria in lake Ontario embayments and nearshore waters. Applied and Environmental Microbiology 73, 4570e4578. Hunter, P.D., Tyler, A.N., Gilvear, D.J., Willby, N.J., 2009. Using remote sensing to aid the assessment of human health risks from blooms of potentially toxic cyanobacteria. Environmental Science & Technology 43, 2627e2633. Hunter, P.D., Tyler, A.N., Willby, N.J., Gilvear, D.J., 2008. The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: a case study using high spatial resolution time-series airborne remote sensing. Limnology and Oceanography 53, 2391e2406. Kuiper-Goodman, T., Falconer, I., Fitzgerald, J., 1999. Human health aspects. In: Chorus, I., Bartram, J. (Eds.), Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring and Management. WHO, pp. 125e160. Le Vu, B., Vinc¸on-Leite, B., Lemaire, B., Bensoussan, N., Calzas, M., Drezen, C., Deroubaix, J., Escoffier, N., De´gre´s, Y., Freissinet, C., Groleau, A., Humbert, J.-F., Paolini, G., Pre´vot, F., Quiblier, C., Rioust, E., Tassin, B. High-frequency monitoring of phytoplankton dynamics within the European water framework directive: application to metalimnetic cyanobacteria. Biogeochemistry, in press, doi:10.1007/s10533010-9446-1.
Leboulanger, C., Dorigo, U., Jacquet, S., Le Berre, B., Paolini, G., Humbert, J.-F., 2002. Application of a submersible spectrofluorometer for rapid monitoring of freshwater cyanobacterial blooms: a case study. Aquatic Microbial Ecology 30, 83e89. Markensten, H., Moore, K., Persson, I., 2010. Simulated lake phytoplankton composition shifts toward cyanobacteria dominance in a future warmer climate. Ecological Applications 20, 752e767. Moreno-Ostos, E., Cruz-Pizarro, L., Basanta, A., George, D.G., 2009. Spatial heterogeneity of cyanobacteria and diatoms in a thermally stratified canyon-shaped reservoir. International. Review of Hydrobiology 94, 245e257. OCDE, 1982. Eutrophisation des eaux: me´thodes de surveillance, d’e´valuation et de lutte. OCDE, 164 pp. Paerl, H.W., Huisman, J., 2009. Climate change: a catalyst for global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports 1, 27e37. Porat, R., Teltsch, B., Perelman, A., Dubinsky, Z., 2001. Diel buoyancy changes by the cyanobacterium Aphanizomenon ovalisporum from a shallow reservoir. Journal of Plankton Research 23, 753e763. Qin, B., Zhu, G., Gao, G., Zhang, Y., Li, W., Paerl, H., Carmichael, W., 2010. A drinking water crisis in Lake Taihu, China: linkage to climatic variability and lake management. Environmental Management 45, 105e112. R Development Core Team, 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0. http://www. R-project.com. Rabouille, S., Salenc¸on, M.J., 2005. Functional analysis of Microcystis vertical migration: a dynamic model as a prospecting tool. II. Influence of mixing, thermal stratification and colony diameter on biomass production. Aquatic Microbial Ecology 39, 281e292. Rabouille, S., Salenc¸on, M.J., Thebault, J.M., 2005. Functional analysis of Microcystis vertical migration: a dynamic model as a prospecting tool I - Processes analysis. Ecological Modelling 188, 386e403. Rogalus, M.K., Watzin, M.C., 2008. Evaluation of sampling and screening techniques for tiered monitoring of toxic cyanobacteria in lakes. Harmful Algae 7, 504e514. Sabart, M., Pobel, D., Latour, D., Robin, J., Salenc¸on, M.J., Humbert, J.-F., 2009. Spatiotemporal changes in the genetic diversity in French bloom-forming populations of the toxic cyanobacteria Microcystis aeruginosa. Environmental Microbiology Reports 1, 263e272. Visser, P.M., Ibelings, B.W., Mur, L.R., Walsby, A.E., 2005. The ecophysiology of the harmful cyanobacterium Microcystis features explaining its success and measures for its control. In: Huisman, J., Matthijs, H.C.P., Visser, P.M. (Eds.), Harmful Cyanobacteria. Springer, Dordrecht, pp. 109e142. Walsby, A.E., 1994. Gas vesicles. Microbiological Reviews 51, 94e144. Welker, M., Do¨hren von, H., Ta¨uscher, H., Steinberg, C.E.W., Erhard, M., 2003. Toxic Microcystis in shallow lakes Mu¨ggelsee (Germany) - dynamics, distribution, diversity. Archiv fu¨r Hydrobiologie 157, 227e248.
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Available at www.sciencedirect.com
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Ozonation and activated carbon treatment of sewage effluents: Removal of endocrine activity and cytotoxicity Daniel Stalter*, Axel Magdeburg, Martin Wagner, Jo¨rg Oehlmann Goethe University Frankfurt am Main, Biological Sciences Division, Department Aquatic Ecotoxicology, Siesmayerstrasse 70, 60323 Frankfurt, Germany
article info
abstract
Article history:
Concerns about endocrine disrupting compounds in sewage treatment plant (STP) effluents
Received 14 July 2010
give rise to the implementation of advanced treatment steps for the elimination of trace
Received in revised form
organic contaminants. The present study investigated the effects of ozonation (O3) and
4 October 2010
activated carbon treatment (AC) on endocrine activities [estrogenicity, anti-estrogenicity,
Accepted 10 October 2010
androgenicity, anti-androgenicity, aryl-hydrocarbon receptor (AhR) agonistic activity] with
Available online 16 October 2010
yeast-based bioassays. To evaluate the removal of non-specific toxicity, a cytotoxicity assay using a rat cell line was applied. Wastewater (WW) was sampled at two STPs after conventional activated sludge treatment following the secondary clarifier (SC) and after
Keywords: Micropollutants
subsequent advanced treatments: O3, O3 þ sand filtration (O3-SF), and AC. Conventional
Anti-androgens
treatment reduced estrogenicity, androgenicity, and AhR agonistic activity by 78e99%
Anti-estrogens
compared to the untreated influent WW. Anti-androgenicity and anti-estrogenicity were not detectable in the influent but appeared in SC, possibly due to the more effective
Dioxin-like xenobiotics Aryl-hydrocarbon
receptor
(AhR)
removal of respective agonists during conventional treatment. Endocrine activities after SC
activity
ranged from 2.0 to 2.8 ng/L estradiol equivalents (estrogenicity), from 4 to 22 mg/L
Yeast estrogen screen (YES)
4-hydroxytamoxifen equivalents (anti-estrogenicity), from 1.9 to 2.0 ng/L testosterone
Yeast androgen screen (YAS)
equivalents (androgenicity), from 302 to 614 mg/L flutamide equivalents (anti-androge-
GH3 cells
nicity), and from 387 to 741 ng/L b-naphthoflavone equivalents (AhR agonistic activity). In
Polyaromatic hydrocarbons (PAHs)
particular, estrogenicity and anti-androgenicity occurred in environmentally relevant
Wastewater treatment plant
concentrations. O3 and AC further reduced endocrine activities effectively (estrogenicity: 77e99%, anti-androgenicity: 63e96%, AhR agonistic activity: 79e82%). The cytotoxicity assay exhibited a 32% removal of non-specific toxicity after O3 compared to SC. O3 and sand filtration reduced cytotoxic effects by 49%, indicating that sand filtration contributes to the removal of toxicants. AC was the most effective technology for cytotoxicity removal (61%). Sample evaporation reduced cytotoxic effects by 52 (AC) to 73% (O3), demonstrating that volatile substances contribute considerably to toxic effects, particularly after O3. These results confirm an effective removal or transformation of toxicants with receptor-mediated mode of action and non-specific toxicants during O3 and AC. However, due to the limited extractability, polar ozonation by-products were neglected for toxicity analysis, and hence non-specific toxicity after O3 is underestimated. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ49 6979824882; fax: þ49 6979824748. E-mail address:
[email protected] (D. Stalter). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.008
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1.
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Introduction
The conventional activated sludge treatment of wastewater (WW) removes endocrine disrupting compounds (EDCs) very effectively (Liu et al., 2009). Nevertheless, sewage effluents considerably contribute to surface water contamination with (xeno-) hormones on trace levels and thus to widespread endocrine disruption in aquatic wildlife (Jobling and Tyler, 2006). Feminization of fish and molluscs results in population relevant effects on reproduction or development (Jobling and Tyler, 2006). Besides estrogenicity, anti-androgenicity has recently attracted augmented attention regarding its impact on aquatic ecosystems as it contributes to the feminization of fish and molluscs (Santos et al., 2008; Jobling et al., 2009). The study by Jobling et al. (2009) suggests that reproductive health effects in fish (e.g., intersexuality and vitellogenin induction) are mediated primarily by anti-androgenic combined with estrogenic mechanisms rather than by estrogenic mechanisms alone. Besides anti-androgens, anti-estrogens are known to potentially influence endocrine disrupting effects. Benzotriazole, for example, is only partly removed in sewage treatment plants (STPs) and hence might affect in vitro experiments (Harris et al., 2007). As androgenic and anti-androgenic compounds are known to influence each other’s interaction with the androgen receptor, the analysis of agonists and antagonists is important to evaluate the in vitro endocrine disrupting potential of a complex chemical mixture (Weiss et al., 2009). Advanced WW treatment technologies, like ozonation (O3) and activated carbon filtration (AC), provide effective barriers to a wide range of organic pollutants (Nowotny et al., 2007; Hollender et al., 2009; Schaar et al., 2010) and can thus reduce the emission of EDCs via STP effluents. While estrogenicity removal by advanced treatment methods has already been investigated (Huber et al., 2004; Escher et al., 2009; Stalter et al., 2010b), the impact on androgenicity, antiandrogenicity, and anti-estrogenicity remains unclear. Therefore, the present study examined the effect of O3 and AC on estrogenicity, androgenicity, and the respective antagonistic activities. Additionally, the aryl-hydrocarbon receptor (AhR) agonistic activity was analysed. AhR regulates genes involved in xenobiotic metabolism (Miller, 1999). Thus, the measurement of AhR agonists is a suitable tool to detect hazardous substances like halogenated aromatic hydrocarbons (e.g., dibenzodioxins, dibenzofurans, and biphenyls) and polyaromatic hydrocarbons (PAHs; Alnafisi et al., 2007). A vertebrate cell-based test system is applied to test for non-specific toxicity because ozonation results in potentially hazardous oxidation products (Petala et al., 2006; Benner and Ternes, 2009; Stalter et al., 2010a,b). This work is part of a comprehensive study within the Neptune project (www.neptune-eu.org), covering in vivo tests with six different test organisms (Stalter et al., 2010a,b) and a variety of in vitro bioassays.
2.
Material and methods
2.1.
Characterization of the STPs
WW samples of a treatment plant in Regensdorf (Switzerland; STP A) and a pilot treatment plant in Neuss (Germany; STP B)
were investigated. Both STPs operated experimentally with a full scale (STP A) or half scale ozonation (STP B) after the secondary clarifier (SC) subsequent to conventional activated sludge treatment and with a sand filtration step after the ozone reactor. Applied ozone doses were 0.8 (STP A) and 0.7 g O3/g DOC (dissolved organic carbon). At STP B, powdered activated carbon treatment (AC; 20 mg/L, contact time 60 min) was tested in parallel with a subsequent sand filtration step. The applied ozone and AC doses at both treatment plants were chosen because they eliminate pollutants effectively and are regarded as economically feasible (Nowotny et al., 2007; Joss et al., 2008). More detailed information is provided in the supplementary information (SI), including WW quality parameters after SC (Table S1).
2.2.
Collection and extraction of the WW samples
At STP A, six grab samples were collected from each sampling point after SC, after the ozone reactor (O3), and after the following sand filtration (O3-SF; sampling period: 08/2008e09/ 2008). At STP B, nine 24 h composite samples were collected from each sampling point after SC, O3, O3-SF, and after the parallel AC treatment subsequent to sand filtration (ACSF; sampling period: 09/2008e03/2009). WW from the treatment plant influent (INF) was sampled (n ¼ 3) at both STPs to allow an estimation of the removal effectiveness during conventional treatment. At STP B, WW was additionally sampled after treatment with different ozone doses ranging from 0.4 to 1.6 g O3/g DOC. All WW samples were processed by solid phase extraction (SPE). The detailed extraction method is described in SI 3 and the sampling points are displayed in Figure S1.
2.3.
Recombinant yeast screens
All yeast screens in this study base upon the same principle. The yeast cells contain a gene for the human estrogen receptor, the human androgen receptor or the aryl-hydrocarbon receptor, respectively, each fused to the reporter gene lacZ. The binding of agonistic ligands leads to a colour change in the assay medium, which is photometrically measurable and provides a marker for the agonistic activity. The detection of antagonistic activity requires a background concentration of the agonistic reference substance, and hence antagonistic activity in the sample leads to a reduced colour change. 96-well microtiter plates were loaded with 30 mL 80-fold concentrated methanol extracts (prepared as described in SI 3), providing a 20-fold sample concentration per well (for INF: 7.5 mL/well, providing a 5-fold concentration). These concentrations were chosen to avoid cytotoxic effects. WW extracts were evaporated to dryness and re-dissolved in the respective assay medium. Assay procedure and data analysis were conducted as described previously (Routledge and Sumpter, 1996) with modifications according to Wagner and Oehlmann (2009). The yeast-based test on aryl-hydrocarbon receptor (AhR) agonists (yeast dioxin screen, YDS) was performed in analogy to the yeast estrogen screen with a slightly modified medium according to Miller (1999). For the YDS, 1000-fold concentrated dimethyl sulfoxide (DMSO) extracts (prepared as described in SI 3) were used because Gustavsson et al. (2004) reported
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 1 5 e1 0 2 4
a significant fraction of volatile AhR agonists in STP sludge. As evaporation in the wells might lead to a loss of agonistic substances, DMSO extracts were directly added to the assay medium in volumes providing a 1e5-fold final sample concentration. Sample concentration was adapted to avoid cytotoxic effects. Total solvent portion was adjusted to 0.5% in each treatment group. In all in vitro screens, microtiter plates were sealed with gas permeable membranes (Breath-Easy, Diversified Biotech, Boston, USA) during incubation to reduce the risk of cross-contamination via volatile substances. The AhR agonist b-naphthoflavone (b-NF) was used as reference instead of 2,3,7,8-tetrachlorodibenzodioxin (TCDD). Because of its high hydrophobicity, the bioavailability of TCDD is reduced considerably when the test is conducted in plates consisting of plastic compared to glass material (Miller, 1999), and hence resulting sample activity might be overestimated. Thus, the results by Miller (1999) suggest that b-NF is a more appropriate reference substance than TCDD for the respective test system. In this study, reference equivalents (R-EQs) are interpolated nonlinearly from the appropriate doseeresponse relationship of the reference agonist or antagonist under presence of a fixed concentration of the corresponding agonist according to Wagner and Oehlmann (2009). Estradiol equivalents (E-EQs) were calculated as R-EQs for estrogenic activity, 4-hydroxytamoxifen equivalents (OHT-EQs) for anti-estrogenicity, testosterone equivalents (T-EQs) for androgenicity, flutamide equivalents (F-EQs) for anti-androgenicity, and b-NF equivalents (b-NF-EQ) for AhR agonistic activity. The applied concentrations for the reference dilution series, the background agonist concentration for the anti-screens as well as the corresponding EC50 for each test system is provided in Table S2. The doseeresponse curves of the reference dilution series are displayed in Figure S2. Each sample was analysed in eight pseudo replicates for every test system. Box and whisker plots display the range of mean activities of all samples per sampling point. If a clear removal pattern was observed, Tables S3eS8 state the mean values, standard errors, and percentage removals compared to SC for the samples from each sampling campaign.
2.4.
Cytotoxicity assay
Cytotoxic effects were analysed using the rat pituitary cell line GH3 to assess the non-specific toxicity of WW samples. This cell line was chosen because of its enhanced sensitivity compared to a rainbow trout liver cell line (RTL-W1; Lee et al., 1993) that was employed in parallel (data not shown). Six samples from the serial treatment steps at STP B were extracted at pH 2 and pH 7 and tested as DMSO extracts and methanol extracts each (SI 3). 0.5% DMSO extract was added to the cell suspension for a final 5-fold concentration. Methanol extracts were evaporated to dryness in the microtiter wells in volumes providing a 10-fold final concentration. Different dosings were chosen as 5-fold concentrated methanol extracts did not induce significant toxic effects after conventional treatment and 10-fold concentrated DMSO extracts led to maximum toxicity in all treatment groups. No dilution series were applied due to the limited sample extract volumes. Cell culturing and preparation of the cell suspension is described in SI 5.1.
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100 mL of the suspension was added to the wells of 96-well tissue culture test plates (Orange Scientific, Braine-l’Alleud, Belgium). Microtiter plates were sealed using Breath-Easy membranes and incubated for 120 h (37 C, 5% CO2). Each sample was analysed in eight pseudo replicates. After incubation, the cell activity was determined according to Palomino et al. (2002) with slight modifications. Resazurin sodium salt powder (VWR International GmbH, Darmstadt, Germany) was dissolved at 0.01% (w/v) in phosphate buffer saline and filter sterilized (0.2 mm). 30 mL resazurin solution was added to each well, incubated for 5.5 h and photometrically measured at 540 and 595 nm. The percentage resazurin reduction (calculated as described in SI 5.2) served as measurement for cell viability. Correlation between cell density and resazurin reduction is documented in Figure S3 (r2 ¼ 0.99). Cytotoxicity (percentage inhibition of cell density) was calculated according to SI 5.3. The blank (wells with culture medium, 0.5% DMSO, without cells) was defined as 100% cytotoxicity and negative control (wells with cell suspension, 0.5% DMSO) as 0%. 2,4-dichlorophenol in concentrations from 1.5 to 24 mg/L served as reference toxicant (doseeresponse curve in Figure S4).
2.5.
Statistical analysis
Statistical analyses were performed using GraphPad Prism version 5.03 for Windows (GraphPad Software, San Diego, California, USA). To test for significant differences, data were Ln-transformed and one-way ANOVA was applied with Dunnett’s post test. Percentage data were transformed by arcsin before ANOVA. In cases of unequal variances (Bartlett’s test, p < 0.05), KruskaleWallis with Dunn’s post test was used instead.
3.
Results and discussion
3.1.
Estrogenicity
Compared to the influent (13e23 ng/L E-EQ), the estrogenic activity was reduced by 78% (STP A) to 91% (STP B; Fig. 1A, B) during conventional treatment, confirming literature data, which report percentage removals between 70 and 99% (Liu et al., 2009). Despite the effective elimination of estrogenicity, remaining (xeno-) estrogens in sewage effluents potentially are of environmental relevance (Jobling et al., 2002). At both STPs, the average estrogenic activity after SC ranged from 2.0 to 2.8 ng/L E-EQ. Ozonation further diminished this activity with a percentage removal following O3-SF of 88e95% compared to SC (Fig. 1A, B). ACSF was less effective (77% average removal, Fig. 1B) than O3-SF at the applied AC dose of 20 mg/L. The remaining average estrogenic activity of all sampling campaigns after advanced treatment was 0.1e0.2 ng/L E-EQ after O3-SF and 0.4 ng/L after ACSF (Table S3, S4). Hence, final activity was below the environmental quality standard proposed for estradiol (0.5 ng/L; Moltmann et al., 2007). Therefore, estrogenicity reduction could be a relevant environmental benefit, helping to reduce the feminization of fish populations (Jobling et al., 2002) as reported by Filby et al. (2010). At STP A, the reduced estrogenic activity led to reduced vitellogenin levels in juvenile rainbow trout (Stalter et al.,
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Fig. 1 e Estrogenic (estradiol equivalents, E-EQs; A, B) and anti-estrogenic activities (4-hydroxytamoxifen equivalents, OHTEQs; C, D) during six (STP A) or nine sampling campaigns (STP B, INF: n [ 3) as determined in recombinant yeast screens. Displayed are the mean activities of eight pseudo replicates for each sample. Average endocrine activity removal is stated as % compared to INF (DINF) or SC (DSC). INF, influent; SC, after the secondary clarifier; O3, SC after ozonation; O3-SF, O3 after sand filtration; ACSF, SC after activated carbon treatment.
2010b). The removal of estrogenicity by ozonation was already investigated in previous studies (Huber et al., 2004; Escher et al., 2009; Stalter et al., 2010b) and is a result of the oxidative transformation of estrogenically active chemicals (Huber et al., 2004). Phenols are an important functional group, interacting with the estrogen receptor (Nishihara et al., 2000) and are known to be particularly susceptible to ozone attack (von Gunten, 2003). Consequently, estrogenicity removal via ozonation is to be expected. The less pronounced removal efficiency of AC treatment compared to O3 might be a result of the elevated DOC level at STP B (up to 12 mg/L, Table S1), because adsorbability of pollutants is significantly lower at higher background DOC concentrations (Nowotny et al., 2007). Finally, further in vitro studies with a steroidgenesis assay could be appropriate to evaluate potential effects on the cellular production of estradiol before and after advanced treatments to complement the present findings (Grund et al., 2010).
3.2.
Anti-estrogenicity
The anti-estrogenic activity exhibited no clear removal pattern. At STP A, OHT-EQs were elevated after ozonation and decreased to the level of SC after sand filtration (Fig. 1C).
However, these results are based on only 3 samples, due to limited sample extract volumes. At STP B, anti-estrogenicity was slightly increased after O3 and O3-SF, while in INF no antiestrogenic activity was detectable (Fig. 1D). Possibly the occurrence of anti-estrogenic compounds in INF was masked by the high estrogenicity at both STPs (Fig. 1A, B). A nonspecific quenching by influent extracts is not likely to explain the absence of antagonistic activity because the test system for the agonists works well at the same sample concentration. However, fractionation studies are required for separating agonists from antagonists to confirm masking effects, which were already observed by Weiss et al. (2009). During ozonation, the estrogenic compounds were probably more effectively removed than anti-estrogens, resulting in a slightly elevated anti-estrogenicity in O3 and O3-SF. The further increase after activated carbon treatment cannot be explained conclusively, except by a higher adsorption of estrogens compared to anti-estrogenic compounds. Nonetheless, the increased anti-estrogenicity after advanced treatments do not necessarily implicate that these methods are not applicable for pollution reduction as the reduced estrogenicity might be of higher benefit for aquatic wildlife. However, further research on potential negative effects of anti-estrogens on aquatic organisms is desirable.
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3.3.
Androgenicity
Androgenic activity from the influent (201e400 ng/L T-EQs) was reduced by more than 99% during conventional activated sludge treatment (Fig. 2A, B). At STP A, average activity after SC reached 2.0 ng/L T-EQs. After O3 and O3-SF, average activities were noticeably but not significantly elevated to 5.2 and 7.0 ng/L (Fig. 2A). At STP B, androgenicity was on an average level of 1.9 ng/L after SC, without a consistent removal during advanced treatment steps (Fig. 2B). Potential interactions between androgens and anti-androgens might explain the lack of androgenicity removal during advanced treatment steps and are discussed in the following section.
3.4.
Anti-androgenicity
Anti-androgenic activity was not detectable in the influent WW (Fig. 2C, D). After conventional treatment, the average activity ranged from 302 (STP B) to 614 mg/L F-EQs (STP A). Following O3-SF, the average removal reached 78% (STP B) to 90% (STP A, Fig. 2C, D), while ACSF was slightly less effective (63% removal, Fig. 2D). Besides estrogenic activity, antiandrogenicity is an important causative factor for the feminization of wild fish (Jobling et al., 2009). Anti-androgenic activity predicted to be present in UK rivers ranged from 0 to
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100 mg/L F-EQs (Jobling et al., 2009). Considering a potential WW dilution in the receiving water body of 1:10, the values in the present study (Table S5, S6) conform to literature data (Jobling et al., 2009). Moreover, anti-androgenic activity measured in the conventionally treated WW samples might be sufficient to induce biological responses in fish. Katsiadaki et al. (2006) determined a lowest observed effect concentration (LOEC) of 10 mg/L flutamide (concentration, where the production of a glue protein in the kidney of sticklebacks was depleted significantly) for in vivo anti-androgenic activity. Even if a dilution factor of 10 is assumed in the receiving water body, each sample after SC exceeded this effect concentration (Table S5, S6). Finally, the studies of Jobling et al. (2009) and Katsiadaki et al. (2006) provide clear indications that antiandrogenic compounds have a significant impact on aquatic organisms while this study demonstrates that anti-androgens are introduced to rivers via STP effluents in environmentally relevant concentrations. After O3 and O3-SF, activity was below 100 mg/L F-EQ in most cases, what could be a relevant benefit for aquatic wildlife. Nonetheless, if the dilution factor is below 10 in the receiving water body, remaining activity may exceed the LOEC for flutamide and hence may still be of environmental relevance. Several potential causative substances for anti-androgenicity in the environment have been identified, for example,
Fig. 2 e Androgenic (testosterone equivalents, T-EQs; A, B) and anti-androgenic activities (flutamide equivalents, F-EQs; C, D) during six (STP A) or nine sampling campaigns (STP B, INF: n [ 3) as determined in recombinant yeast screens. Displayed are the mean activities of eight pseudo replicates for each sample. Average endocrine activity removal is stated as % compared to INF (DINF) or SC (DSC). INF, influent; SC, after the secondary clarifier; O3, SC after ozonation; O3-SF, O3 after sand filtration; ACSF, SC after activated carbon treatment.
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several PAHs, butyl-benzyl-phthalate, dibutyl-phthalate, triclosan, and dichlorophene (Thomas et al., 2009; Hill et al., 2010). However, the explicit identity of compounds causing the observed anti-androgenicity in municipal WW remains unclear. The identification of anti-androgens and their contribution to the overall activity is however required to develop source control strategies. Additionally, with regard to the high hydrophobicity of the suspected compounds (Zhang and Huang, 2003; Clara et al., 2010), it could be assumed that anti-androgens primarily enter the environment bound to particulate matter (Clara et al., 2010). The analysed water samples were membrane filtrated (1.2 mm pore size) before solid phase extraction, and hence the determined activities might be underestimated. The lack of anti-androgenicity in INF is possibly a result of masking effects due to the high androgenic activity. Environmental samples contain a complex mixture of chemicals and EDCs. When endocrine activity is analysed, only the gross activity resulting from the present receptor agonists and antagonists can be measured. Accordingly, antagonistic activity could easily be masked by its agonists in the influent (Figs. 1 and 2), and androgenic activity could even increase with advanced treatment processes in the case of a more effective antagonist removal (Fig. 2A). Comparable masking effects of androgens and anti-androgens in sediment samples were already reported by Weiss et al. (2009). Consequently, the appearance of anti-androgenic activity in SC is possibly a consequence of the effective androgen removal by >99% during conventional treatment (Fig. 2), whereas anti-androgens like PAHs or phthalates are more stable (Clara et al., 2010).
3.5.
AhR agonistic activity
The aryl-hydrocarbon agonistic activity was reduced by an average of 81% (STP A)e96% (STP B) during conventional WW treatment (Fig. 3A, B). Ozonation resulted in further reduction by 80% (STP B) to 90% (STP A; Fig. 3A, B). Removal effectiveness of ACSF (average of 82%) was comparable to that of O3. The considerable removal of AhR agonistic activity after advanced WW treatment steps indicates an effective
reduction of potentially hazardous compounds like PAHs, PCBs (polychlorinated biphenyls), furans, and dioxins. A study by Macova et al. (2010) point out that AhR agonistic activity in WW may be attributed to chemicals other than dioxins, polychlorinated biphenyls (PCBs) and furans. Macova et al. (2010) reported AhR agonistic activities in STP effluents below 1 ng/L TCDD-EQs. According to a circa 10-fold lower 50% effect concentration (EC50) for TCDD compared to b-NF (Miller, 1999), the assumed TCDD-EQs should be approximately one order of magnitude lower than the calculated b-NF-EQs. Thus, the measured activities in the present study are probably about 100 times higher compared to the Australian study. Such discrepancies might be a result of a different effluent composition at the investigated treatment plants. Furthermore, evaporation of sample extracts might lead to the loss of volatile AhR agonists as reported by Gustavsson et al. (2004). Due to their high hydrophobicity, AhR agonists largely bind to particulate organic matter (Dagnino et al., 2010) and are subsequently released to a certain extend in STPs (Suares Rocha et al., 2010). Consequently, different pore sizes of the sample filters influence the results as well. In the present study, WW samples were filtered before extraction with a pore size of 1.2 mm, and hence the overall activity in the WW samples (including particulate matter) was presumably higher than analysed. Nonetheless, our data emphasize that STP effluents are potential point sources for dioxin-like chemicals and PAHs in the aquatic environment. Especially sediments in urbanized areas are often contaminated by AhR agonists (Hollert et al., 2002). According to a study by Brack et al. (2005), dioxins, PCBs, and furans contribute only to a minor extend to AhR-mediated effects in sediments, conforming to the study by Macova et al. (2010). Brack et al. (2005) identified nonpriority PAHs as major contributors to respective activities in river sediments. An effective PAH degradation with O3 has already been documented, however, possibly at the expense of an increased non-specific toxicity as a result of oxidation by-product formation (Luster-Teasley et al., 2002). However, results of the applied yeast-based reporter gene test system are not necessarily transferable to in vivo conditions as it does not take into account the potential adsorption
Fig. 3 e AhR agonistic activities (b-naphthoflavone equivalents, b-NF-EQs) during 6 (STP A) or 9 sampling campaigns (STP B; INF: n [ 3) as determined in a recombinant yeast screen. Displayed are the mean activities of eight pseudo replicates for each sample. Average b-NF-EQ removal is stated as % compared to INF (DINF) or SC (DSC). INF, influent; SC, after the secondary clarifier; O3, after ozonation; O3-SF, O3 after sand filtration; ACSF, SC after activated carbon treatment.
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1021
Fig. 4 e Percentage removal of estrogenic (A), anti-androgenic (B) and aryl-hydrocarbon agonistic activity (C) at different ozone doses.
of substances to test material, the bioconcentration ability as well as metabolic transformation and excretion of chemicals. Consequently, the yeast dioxin screen can deliver different results as test systems based upon the AhR-dependent induction of CYP1A1 (EROD assay; Sugihara et al., 2008) and hence further research might be appropriate.
3.6.
Ozone dose dependent removal of endocrine activity
Estrogenicity removal reached 90% even at the lowest ozone dose of 0.4 g O3/g DOC (Fig. 4A), confirming the high susceptibility of estrogenically active compounds to ozone attack (Huber et al., 2004). F-EQ removal was less effective at 0.4 g O3/g DOC (45%), whereas removal was elevated to >80% at doses >1.0 g O3/g DOC (O3-SF, Fig. 4B). AhR agonistic activity was least effectively reduced at 0.4 g O3/g DOC (27%), while around 80% removal was achieved at doses >0.7 g O3/g DOC (Fig. 4C). These results suggest that O3 doses of 0.4 g O3/g DOC might be too low for an effective endocrine activity removal.
3.7.
Cytotoxicity
The cytotoxicity assay revealed non-specific toxic effects after SC at an average of 89% compared to the negative control (Fig. 5A, Table S10A). Compared to SC, cytotoxicity was reduced by 32% after O3 and further reduced by 49% during
sand filtration. Activated carbon treatment reduced toxic effects most effectively by 61% compared to SC. Samples extracted at pH 7 in DMSO revealed a reduced cytotoxicity in O3 compared to pH 2 by an average of 24% (Fig. 5B; Table S10A, B). Evaporation of methanolic extracts (extracted at pH 2) led to a reduction of cytotoxic effects by 52% (AC) to 73% (O3) after advanced treatments (Fig. 5C; Table S10D), in spite of a doubled sample concentration compared to the DMSO extracts. The different dosings were required to maximize differences between the treatment steps. The results underline that the choice of extraction method and sample preparation remarkably influences cytotoxic effects. Reduced toxicity of samples extracted at pH 7 compared to pH 2 indicates the presence of toxic compounds with acidic moieties after O3. Additionally, the complete evaporation of solvent extracts in the test wells led to a considerable loss of toxic volatile substances and hence to a consistent underestimation of toxic effects, particularly in O3. The observed cytotoxic effects were a result of a mixture of the extractable fraction of contaminants present in WW. This study focused on toxic effects induced by extractable organic pollutants. Potentially toxic ozonation by-products are neglected in the present toxicity analysis because such substances are hardly extractable via conventional SPE due to high polarities (Benner and Ternes, 2009) and degradability (Petala et al., 2006). Consequently, further research on the
Fig. 5 e Reduction of cell density compared to negative control after exposure to WW extracts. A: at pH 2 extracted samples in DMSO (5-fold concentrated), B: at pH 7 extracted samples in DMSO (5-fold), C: at pH 2 extracted samples in methanol (10-fold, evaporated to dryness before test). Average cytotoxicity removal is stated as % compared to SC (DSC). Negative control [ 0%; blank [ 100%; n [ 6. SC, secondary clarifier; O3, after ozonation; O3-SF, O3 after sand filtration; ACSF, activated carbon treatment; SE, standard error.
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choice of extraction method is desirable to increase the portion of extractable polar oxidation products. The observed cytotoxicity after O3 did not argue for an effective non-specific toxicity reduction during ozonation as cytotoxicity was still on an average level of 62%, whereas in three out of six samples toxicity was >88%. These results point to the possibility that cytotoxic effects after O3 were caused by extractable oxidation products. Toxicity reduction after sand filtration of 23% possibly supports an effective byproduct removal, but non-specific toxicity was still elevated compared to ACSF by an average of 23% (Table S10A). An in vivo test with the zebra mussel e performed at the same treatment plant e led to comparable results: non-specific toxic effects occurred after O3, toxicity decreased after O3-SF, and ACSF was most effective in non-specific toxicity removal (Stalter et al., 2010a). Therefore, the applied cytotoxicity assay is possibly a sensitive in vitro tool to evaluate the non-specific toxicity reduction of advanced WW treatment methods. Reliable in vitro screening methods are preferable compared to chronic in vivo approaches due to logistical, cost and time constraints as well as ethical considerations. However, the portion of extractable polar oxidation products has to be increased, not to underestimate a potential toxication after ozonation (Stalter et al., 2010a,b). For a comprehensive evaluation of advanced WW treatment technologies, the analysis of EDC removal effectiveness is important, however, this should not be regarded as the essence of the matter. The challenge is not to confirm an effective reduction of receptor-mediated toxicity (because this is rather predictable with respect to pollutant degradation/ removal) but to test for an increase in non-specific toxicity due to by-product formation during ozonation (Petala et al., 2006; Stalter et al., 2010a,b). An appropriate in vitro bioassay, which enables a fast and reliable toxicity screening, would help to verify the by-product removal with post treatments like sand filtration and could promote an effect directed identification of toxic oxidation by-products.
4.
suggests that doses 0.4 g O3/g DOC are too low for an effective elimination of endocrine disrupting compounds. Cytotoxicity was most effectively removed during activated carbon treatment (61%), while ozonation is less efficient (32%). Volatile substances contribute considerably to cytotoxic effects, particularly after ozonation. Therefore, sample evaporation should be avoided for tests on non-specific toxicity. In the long run, on-site field studies at WW receiving water bodies (including, for example, community analyses of fish, macroinvertebrates, plants and microorganisms as well as biomarker approaches) e before and after upgrading STPs e could allow to draw environmentally relevant conclusions regarding benefits and risks of advanced WW treatment methods.
Acknowledgments The authors would like to thank Stefan Ingenhaag (Grontmij Deutsche Projekt Union GmbH, Cologne, Germany) for his technical assistance at STP Neuss. Installation and support with maintenance of the ozonation plant in Regensdorf was conducted by Daniel Rensch, Steve Brocker (Hunziker-Betatech AG), Saskia Zimmerman and Dr Christoph Ort (EAWAG, Switzerland). Furthermore we thank Prof Charles A. Miller (Tulane University, New Orleans, USA) and Prof John Sumpter (Brunel University, Uxbridge, UK) for providing the recombinant yeast strains, Prof Gu¨nter Stalla and Dr Ulrich Renner (MPI, Munich, Germany) for providing GH3 cells as well as Prof Henner Hollert (RWTH Aachen, Germany), Prof Lucy Lee (Wilfrid Laurier University,), and Prof Niels Bols (University of Waterloo, Canada) for providing RTL-W1 cells. Moreover, we thank Dr Ulrike Schulte-Oehlmann for reviewing the manuscript. This study was part of the EU project Neptune (contract no 036845, SUSTDEV-2005-3.II.3.2) and co-funded by the BAFU (Switzerland) within the Strategy MicroPoll program (contract no 05.0013.PJ/F471-0916).
Conclusions
Conventional activated sludge treatment of wastewater reduces endocrine activity effectively (78e99%), but remaining concentrations of estrogens and anti-androgens still exceed environmental quality standards or effect concentrations, respectively, and hence might be of environmental relevance. Masking effects could explain the absence of anti-androgenicity and anti-estrogenicity due to the high agonistic activities in the untreated wastewater. Accordingly, the appearance of antagonistic activities after the secondary clarifier is possibly due to the more effective removal of respective agonists during conventional treatment. These results indicate the importance of analysing agonists as well as antagonists to evaluate the in vitro endocrine disrupting potential of a complex chemical mixture. The endocrine activity and AhR agonistic activity is effectively reduced during advanced wastewater treatment steps (63e99%), apart from androgenicity and anti-estrogenicity, which were not affected consistently. The analysis of endocrine activity removal at different ozone doses
Appendix. Supplementary material Additional information on treatment plant characteristics, test systems, and test results are available at the journal’s homepage. Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.10.008.
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Surveillance of adenoviruses and noroviruses in European recreational waters A. Peter Wyn-Jones a,*, Annalaura Carducci b, Nigel Cook c, Martin D’Agostino c, Maurizio Divizia d, Jens Fleischer e, Christophe Gantzer f, Andrew Gawler g, Rosina Girones h, Christiane Ho¨ller i, Ana Maria de Roda Husman j, David Kay a, Iwona Kozyra k, Juan Lo´pez-Pila l, Michele Muscillo m, Maria Sa˜o Jose´ Nascimento n, George Papageorgiou o, Saskia Rutjes j, Jane Sellwood p, Regine Szewzyk l, Mark Wyer a a
IGES, University of Aberystwyth, Ceredigion, SY23 3DB, UK Department of Biology, Universita` di Pisa, Italy c Food and Environment Research Agency, FERA, York, UK d Faculty of Medicine, Tor Vergata University, Rome, Italy e Landesgesundheitsamt Baden-Wu¨rttemberg, Germany f Faculte´ de Pharmacie, University Henri Poincare´, Nancy, France g Environment Agency, United Kingdom h Department of Microbiology, Faculty of Biology, University of Barcelona, Spain i Bayerisches Landesamt fu¨r Gesundheit und Lebensmittelsicherheit, Germany j National Institute for Public Health and the Environment (RIVM), The Netherlands k National Veterinary Research Institute, Pulawy, Poland l Umweltbundesamt, Berlin, Germany m Istituto Superiore Sanita`, Rome, Italy n Faculdade de Farma´cia, Universidade do Porto, Porto, Portugal o Environmental Virology Laboratory, State General Laboratory, Cyprus p Environmental Virology Unit, Health Protection Agency, UK b
article info Article history: Received 27 April 2010 Received in revised form 20 August 2010 Accepted 13 October 2010 Available online 29 October 2010
abstract Exposure to human pathogenic viruses in recreational waters has been shown to cause disease outbreaks. In the context of Article 14 of the revised European Bathing Waters Directive 2006/7/EC (rBWD, CEU, 2006) a Europe-wide surveillance study was carried out to determine the frequency of occurrence of two human enteric viruses in recreational waters. Adenoviruses were selected based on their near-universal shedding and environmental survival, and noroviruses (NoV) selected as being the most prevalent gastroenteritis agent worldwide. Concentration of marine and freshwater samples was done by adsorption/elution followed by molecular detection by (RT)-PCR. Out of 1410 samples, 553 (39.2%)
Keywords:
were positive for one or more of the target viruses. Adenoviruses, detected in 36.4% of
Adenoviruses
samples, were more prevalent than noroviruses (9.4%), with 3.5% GI and 6.2% GII, some
Noroviruses
samples being positive for both GI and GII. Of 513 human adenovirus-positive samples, 63
Bathing water
(12.3%) were also norovirus-positive, whereas 69 (7.7%) norovirus-positive samples were
River water
adenovirus-negative. More freshwater samples than marine water samples were virus-
Sea water
positive. Out of a small selection of samples tested for adenovirus infectivity,
* Corresponding author. Tel.: þ44 191 384 2749. E-mail address:
[email protected] (A.P. Wyn-Jones). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.015
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Recreational water
approximately one-quarter were positive. Sixty percent of 132 nested-PCR adenovirus-
Water quality
positive samples analysed by quantitative PCR gave a mean value of over 3000 genome copies per L of water. The simultaneous detection of infectious adenovirus and of adenovirus and NoV by (RT)PCR suggests that the presence of infectious viruses in recreational waters may constitute a public health risk upon exposure. These studies support the case for considering adenoviruses as an indicator of bathing water quality. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Enteric viruses have frequently been implicated in recreational water-related gastro-intestinal (G.I.) disease (Sinclair et al., 2009). Studies in Europe and the US suggest that most infections contracted as a result of swimming, canoeing or other recreational use of sewage-polluted water may be viral in nature (e.g. Medema et al., 1995; Gray et al., 1997). Enteric viruses may cause asymptomatic or mild infections in humans, but these faecal-orally transmitted viruses may also cause more serious disease, such as hepatitis and meningitis, especially in vulnerable groups, e.g. young children (Nwachuku and Gerba, 2006). Enteric viruses are recognized as agents that can cause large outbreaks throughout the world with thousands of cases (Sarguna et al., 2007; Bucardo et al., 2007; Iijima et al., 2008; Zhang et al., 2009). Novel emerging viruses such as SARS coronavirus, human parechovirus and zoonotic influenza viruses also appear to be excreted in faeces but the evidence for enteric transmission is not always clear (Ding et al., 2004). Transmission routes for enteric viruses may be diverse such as personeperson, food- or waterborne associated with insufficient hygiene and sanitation (Koopmans et al., 2002; Wyn-Jones and Sellwood, 2001). Disease outbreaks associated with enteric viruses, such as noroviruses and astroviruses, in bathing water have been described (Hauri et al., 2005; Maunula et al., 2004). However, bathing water-related outbreaks may be easily missed due to either unidentified source or unidentified agent, or both. Enteric viruses in water may originate from discharges of raw or treated sewage, run-off of animal manure or directly from humans or animals. Viruses commonly associated with waterborne disease include the human adenoviruses (HAdVs), noroviruses (NoVs), hepatitis A and E viruses (HAV, HEV), parvoviruses, enteroviruses, and rotaviruses (RVs). In addition, sewage, especially from slaughterhouses, may contain (for example) animal adenoviruses, sapoviruses, and HEV (Hundesa et al., 2006), which may be zoonotic. Viruses originating from (un)treated sewage can contaminate bathing water after discharge into surface waters (in)directly used for recreational water activities. All are capable of infection by ingestion. Some multiply in the intestine and may cause diarrhoea and/or vomiting, while some are associated with tissues (e.g. the liver) other than the gut. The viruses responsible for waterborne infections are not usually identified at the time of a disease outbreak following recreational water activity, and robust associations between the simultaneous presence of virus in faeces of affected individuals and in the water are only occasionally demonstrated (e.g. Hoebe et al., 2004). The epidemiological picture of disease associated with recreational use of water is therefore far from complete, and measures to limit
enteric disease after exposure to recreational water are based on water quality parameters built on the detection of faecal bacterial indicator organisms (FIOs). However, it has been shown that water conforming to bacterial standards may contain high levels of human enteric viruses and that FIOs often fail to predict the risk for waterborne pathogens including enteric viruses (Gerba et al., 1979; Lipp et al., 2001). Further, several studies have shown that levels of indicator bacteria do not correlate with those of viruses, particularly when faecal indicator concentrations are low (Contreras-Coll et al., 2002). Viruses are known to be more resistant to environmental degradation than bacteria (Vasl et al., 1981; Thurston-Enriquez et al., 2003; Rzezutka and Cook, 2004; de Roda Husman et al., 2009). Together with the understanding that G.I. illness may be due to viruses rather than bacteria, this provides a case for using a viral indicator of human faecal pollution rather than to rely exclusively on bacterial parameters. Bathing water quality in the European Union (EU) has been regulated since 1976 by the Bathing Water Directive (76/160/ EEC). In 2006 this was revised (rBWD, CEU, 2006) by including enterococci (and, in fresh waters, Escherichia coli) as the principal microbial determinants which placed the microbiological parameters on a firmer scientific footing (Kay et al., 1994, 2004: Wiedenmann et al., 2006; WHO, 2003) and allowed classification of bathing waters to be undertaken with more confidence. When tested at sufficient frequency E. coli may be a useful indicator of faecal pollution and therefore of the probability of waterborne disease. However, in the EU Directive the frequency is only about once in two weeks and testing takes two days. The earlier Directive included an enterovirus parameter which stipulated that 95% of 10-L water samples taken during the bathing season should contain no (zero p.f.u.) enteroviruses. This was based on early work (described by Farrah and Bitton, 1990) which suggested that, for poliovirus, Coxsackie A and Coxsackie B viruses, between one and twenty virus infectious units might be sufficient to cause infection. The pathogenesis of enterovirus infections is now better understood, and this belief is considered unsound in determining water quality. Further, although important pathogens in many contexts, the presence of enteroviruses in water does not necessarily correlate with the presence of pathogens such as hepatitis A virus (Dubrou et al., 1991; Pina et al., 1998). The enterovirus parameter was removed during the revision of the 1976 Directive. Concentrations of some viruses in surface waters can be determined by cell culture monolayer plaque assays, but the technique is not applicable to most viruses of prime interest. Furthermore, cell culture is expensive and time-consuming, and detection of viruses is now done mainly by molecular methods such as reverse transcription RT-PCR or nucleic acid
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sequence-based amplification (NASBA) which amplify RNA/ DNA. Although mainly described as end-point assays, amplification products of both techniques can be detected by realtime methods. The major advantages of real-time detection are hands-on time and the ability to quantify amplification products, which is very important in being able to estimate the public health risks of low levels of enteric viruses in bathing water. A viral indicator may be better suited to indicate the risk of human pathogenic viruses in bathing waters. However, cell culture-based methods for viral detection are costly, require specialised skills and equipment, and have too long a turnaround time. For this reason, the EU Framework 6 Project VIROBATHE was done to devise a robust, rapid and cost-efficient method for routine compliance monitoring of enteric viruses in recreational waters. Part of the work involved Europe-wide surveillance of recreational waters to determine the frequency of target virus occurrence and, to a limited extent, serotypes and quantities. So that the virus levels could be seen in the context of compliance-related water quality, the work also included determination of FIO levels to provide general water quality data. The viruses selected as targets were adenoviruses and noroviruses. The former are shed by many individuals (often without showing symptoms), they are more environmentally robust than enteroviruses (Enriquez et al., 1995; ThurstonEnriquez et al., 2003), they have been found in surveys of polluted waters (e.g. Pina et al., 1998; Laverick et al., 2004; Lee et al., 2004; Miagostovich et al., 2008), and have been associated with outbreaks of disease in swimming pools (e.g. Papapetropolou and Vantarakis, 1995; Harley et al., 2001) and other recreational waters (Sinclair et al., 2009). Being DNA viruses, their detection by PCR does not have the problems associated with the genetic variation seen with RNA viruses. They are also more likely to be detected in recreational water samples (e.g. Pina et al., 1998; Miagostovich et al., 2008), especially if sensitive nucleic acid detection methods are used, and they may therefore provide the best indicator of viral faecal pollution. Noroviruses are the most important cause of acute viral gastroenteritis in people of all age groups and many waterborne outbreaks have been reported. Sinclair et al. (2009) reviewed 55 recreational water-related G.I. disease outbreaks of which 25 (46%) were reported as caused by noroviruses. The study reported here was performed to demonstrate that a common concentration protocol could be used across recreational waters in widely diverse geographical areas, that viruses concentrated by this protocol could be detected by a rapid molecular method, that it was possible to enumerate viruses and to investigate whether there was a range of sero/ genotypes of the target viruses present across the locations studied.
2.
Materials and methods
2.1.
Survey design
Each of the 15 Surveillance Laboratories located in nine countries selected up to two sites for study which were sampled during the EU Bathing Season 2006, and samples were concentrated and analysed for the target viruses by
molecular means. FIOs and various physico-chemical parameters were also determined. Data were sent to the coordinating Laboratory at the University of Aberystwyth for collation.
2.2.
Sampling sites
Each laboratory selected up to two sites (main site and second site) for the study (Table 1 and Fig. 1). The principal criterion for a site being chosen was its current use for recreational water activity; sites were not chosen on the basis of being EUdesignated bathing waters, nor because they had a history of pollution in the area, though several sites were known to be impacted by sewage effluent. A minimum of 80 10-L water samples from the main site was taken and up to 20 additional samples were taken in the event of (e.g.) heavy rain or when investigators considered that there was some other occurrence which may have resulted in deterioration of water quality. The second site could also be used if the main site yielded negative data in the first stages of sampling, or for taking the 20 additional samples following the 80 minimum to be taken at the main site. Thus, each laboratory could focus on one site (100 samples) or divide surveillance between the main site (80 samples) and the second site (20 samples). In practice both approaches were used, so in total, 24 sites were sampled. Sites were sampled at approximately weekly intervals from the end of May to the beginning of November 2006, which included the Bathing Season in all Member States. On each
Table 1 e Location of sampling sites. Site* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Country
Location
Cyprus France Germany
Larnaca Nancy BadenWu¨rttemberg Germany BadenWu¨rttemberg Germany Bavaria Germany Berlin Germany Berlin Italy Pisa Italy Pisa Italy Castel Gandolfo Italy Ardea (Rome) Italy Pomezia (Rome) Netherlands Durgerdam Netherlands Leerdam Poland Pulawy Portugal Porto Portugal Porto Spain Barcelona Spain Barcelona UK York UK Devon UK Devon UK Kew (London) UK Reading
*See also Fig. 1 for site locations.
Site name
Water type
Larnaca Marina Tomblaine Neckar River
Marine Fresh Fresh
Kirchentellinsfurt Lake Amper Grasslfing Wannsee Landwehrkanal San Rossore Bocca d’Arno Castel Gandolfo Lake Fosso dell’Incastro Rio Torto Kinselmeer Linge VistulaRiver Molhe South Molhe North Gava` Gava` Naburn Lock Axmouth Harbour River Kenn River Thames River Thames
Fresh Fresh Fresh Fresh Marine Marine Fresh Marine Marine Fresh Fresh Fresh Marine Marine Marine Marine Fresh Marine Marine Fresh Fresh
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Fig. 1 e Location of sampling sites.
sampling occasion, four 10-L samples (a ‘tetrad’), plus one additional sample for positive Quality Control (QC) purposes, were collected from each site. One 250 mL sample for bacterial faecal indicators was also taken. In total each laboratory processed and analysed at least 100 water samples for virus detection and 25 samples for bacterial enumeration.
2.3.
Sample processing
Many methods for the concentration and detection of enteric viruses in water samples have been described (Wyn-Jones and Sellwood, 2001). For virological water quality to be assessed on a comparable basis, a single method common to all laboratories was needed for each water type (fresh or coastal/transitional) analysed during the surveillance programme. Prior to the surveillance stage several different methods were evaluated (see Section 3.1) using HAdV2 and NoV GII.4, and the best in terms of virus recovery and capital/recurrent costs was selected. The HAdV2 was obtained from the UK Health Protection Agency (HPA) National Collection of Pathogenic Viruses (NCPV), where the virus genome was authenticated. Virus was grown and assayed by plaque assay in A549 cultures and distributed by the HPA Environmental Virology Unit to
other laboratories. Norovirus GII.4 was identified in a faecal sample from an outbreak in a care home and the identity confirmed by sequencing. End-point dilution assay by RT-PCR gave a titre of 109. The suspension was distributed at 103 which provided sufficient virus for evaluation and quality control purposes for all laboratories throughout the project. Process characterisation was done by four experienced laboratories concentrating replicate samples of water spiked with HAdV2 and analysing the concentrates for recovered virus.
2.3.1. Concentration of freshwater samples by glass wool filtration For freshwater samples a modification of the glass wool method of Vilagine`s et al. (1993) was used. The glass wool filter was made by compressing 10 g glass wool (type 725; Rantigny, Saint-Gobain, France) into a 30 cm by 3 cm polystyrene column to obtain a filter height of 6e8 cm. The filter was washed by gravity with 50 mL volumes of (in order) 1 M HCl, tap water, and 1 M NaOH, followed by tap water until the filtrate pH was neutral. Water samples (10-L) were conditioned with 1 M or 0.1 M HCl to pH 3.5 to enhance binding of the viruses to the filter and passed through the filter at a rate not exceeding 1.5 L min1. When all the sample had passed
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through the filter the virus was eluted from the glass wool by slow (20e30 min) passage of 200 mL 3% (w/v) beef extract at pH 9.5 in 0.05 M glycine buffer through the filter. The eluate was flocculated by the addition of 1 M and 0.1 M HCl until the pH reached 3.5. The resultant protein floc, containing virus, was deposited by centrifugation at 7500 g for 30 min, dissolved to a final volume of 10 mL phosphate buffered saline (PBS) and stored at 20 C pending further analysis.
2.3.2. Concentration of marine water samples by nitrocellulose membrane filtration Coastal/transitional water samples were processed by filtration through nitrocellulose membranes, elution and organic flocculation (Wyn-Jones et al., 2000). The sample, at pH 3.5, was passed through a 142 mm diameter glass fibre pre-filter and a nitrocellulose membrane in a Sartorius filter holder at a maximum rate of 1.5 L min1. The filtrate was run to waste and the virus was then eluted from the membrane by slow passage (10 min) of 200 ml skimmed milk solution (0.1% in 0.05 M glycine buffer). The eluate was flocculated by reducing its pH to 4.5 with M HCl and centrifuging as above.
2.4.
Extraction of nucleic acids from sample concentrates
Nucleic acid (NA) was extracted from 5 mL volumes of sample concentrate using the NucliSens miniMAG system (Biome´rieux, France) according to manufacturer’s instructions, with slight modifications comprising centrifugation at 1500 g for 2 min after addition of the silica suspension to reduce the chance of cross-contamination. The final 100 mL NA extract was centrifuged at 13,000 g for 1 min to pellet any remaining traces of silica which could inhibit downstream (RT)PCR reactions, the supernatant was transferred to a clean microfuge tube and was stored at 80 C if not used immediately.
2.5.
Human adenovirus PCR
For the detection of human adenovirus in the water samples the nested-PCR based on the method of Allard et al. (2001) was employed, using primers Hex1deg and Hex2deg for the first round of amplification and primers nehex3deg and nehex4deg for the second round. Additionally, an internal amplification control (IAC, see below) was incorporated in the assay, and a carryover contamination prevention system utilising uracil-Nglycosylase (UNG) in the first round PCR and dUTP (replacing dTTP) in both PCRs. The reaction incorporated a hot-start polymerase (Platinum Taq DNA polymerase, Life Technologies Inc.). The target amplicon sizes were 301 bp in the first round and 171 bp in the second round. The first round reaction conditions were as follows: 10 mL DNA, 1X Platinum Taq buffer, 1.5 mM Mgþþ, 250 mM dNTPs, 0.5 mM primer Hex1deg, 0.5 mM primer Hex2deg, 1U Platinum Taq (Life Technologies Inc.), and 1 U HK-UNG (Epicentre, Madison, Wisconsin). Five mL IAC were added in the first round. Adenovirus DNA (20 ng mL1), and ultrapure water were included as positive and negative reaction control, respectively. After 10 min at 50 C (UNG) and 10 min at 95 C (activation of Taq polymerase), cycling conditions included 45 cycles of 94 C for 30 s, 55 C for 30 s and 72 C for 1 min, followed by a final extension of 72 C for 5 min. The second round reaction conditions were: 1X
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Platinum Taq buffer, 1.5 mM Mgþþ, 100 mM dNTPs, 0.5 mM primer nehex3deg, 0.5 mM primer nehex4deg, and 1U Platinum Taq. Two mL from the first round reaction were used as target. The thermocycling conditions were 94 C for 3 min, then 45 cycles of 94 C for 30 s, 55 C for 30 s and 72 C for 1 min, followed by a final extension of 72 C for 5 min. The amplicons were electrophoresed in a 2% agarose gel stained with 10 ng mL1 ethidium bromide or equivalent nucleic acid staining methods such as SYBR-Gold, and subsequently visualised by UV transillumination.
2.6.
Norovirus RT-PCR
To detect norovirus, the nested RT-PCR based on the method of Vennema et al. (2002) was used, and comprised amplification of norovirus RNA-dependent RNA polymerase (RdRp) gene sequences by RT-PCR followed by a semi-nested PCR for each genogroup (G). Depending on the laboratory, contamination carryover prevention was also incorporated utilising uracil-N-glycosylase (UNG) in the PCR. The target amplicon sizes were 327 bp in the RT-PCR, 188 bp in the GI nested PCR, and 237 bp in the GII nested PCR. Reverse transcription PCR conditions were as follows: 1X OneStep buffer (Qiagen, UK), 400 mM each dNTP, 1X OneStep enzyme mix (Qiagen, UK), 0.5 mM primer JV12Y, 0.5 mM primer JV13i, and 50U RNasin (RNasinPlus, Promega, UK), 1U Platinum Taq (Life Technologies Inc.). Five mL IAC were added in the first round. A 10 mL sample of nucleic acid was used as target. The thermocycling conditions were 50 C for 30 min, 95 C for 15 min, then 40 cycles of 94 C for 1 min, 37 C for 1 min and 72 C for 1 min, followed by a final extension of 72 C for 10 min. The second round PCR conditions were as follows: 1X Platinum Taq buffer, 2.0 mM Mgþþ, 200 mM dATP, 200 mM dCTP, 200 mM dGTP, 400 mM dUTP, 0.4 mM primer JV12Y, 0.4 mM primer Ni-R, 1U HK-UNG and 1U Platinum Taq. One mL from the first round reaction was used as target. The thermocycling conditions were 50 C for 10 min, 95 C for 10 min, 96 C for 3 min then 40 cycles of 95 C for 1 min, 40 C for 1 min and 72 C for 1 min, followed by a final extension of 72 C for 10 min. The amplicons were electrophoresed in a 2% agarose gel stained with 10 ng mL1 ethidium bromide or equivalent nucleic acid staining methods such as SYBR-Gold, and subsequently visualised by UV transillumination.
2.7.
Internal amplification controls (IACs)
The need to guard against false negative results required the use of a novel IAC in each PCR. For adenovirus IACs, oligonucleotides were constructed which contained the adenovirus primer sequences used in each round flanking primer sequences for amplification of invA sequences from Salmonella enterica (Malorny et al., 2003, 2004). The amplicon was cloned into a plasmid (pGem T-Easy vector) by Yorkshire Bioscience Ltd. (York, UK). The resulting pADENOIAC plasmid was linearised at the unique PstI site downstream of the adenovirus IAC insert region. Yorkshire Bioscience supplied pADENOIAC in 100 mL volumes containing 1 mg mL1 plasmid DNA in 10 mM TriseHCl, 1 mM EDTA buffer pH 8.0. The IAC amplicon sizes were 384 bp in the first round and 337 bp in the second round.
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For the norovirus IAC the RNA was synthesized by the addition of complementary sequences of the first round primers JV12Y and JV13i to part of the b-globin gene, resulting in a PCR product of 369 base pairs. In this same construct sequences complementary to the GI nested-primer Ni-R and to the GII nested-primer GI were included. The construct was subsequently cloned downstream of a T7 RNA-polymerase promoter. The RNA IAC was prepared by Yorkshire Bioscience Ltd. (York, UK) using plasmid pnJV IAC which was linearised with Sal1 restriction endonucleases and purified. The resulting RNA was transcribed using the T7 RNA polymerase transcription system. Template DNA was removed from the preparation during incubation with RNase-free DNase. The RNA was purified by LiCl precipitation followed by multiple phenol/chloroform extractions. The preparation was concentrated to 1.0 mg mL1 by precipitation with ethanol and dissolving in a minimal volume of MilliQ/18.2 MU quality water. Both types of IAC were prepared in single batches by Yorkshire Bioscience, and checked and distributed to all participants by one of the participant laboratories (FERA). Amplification products of the IAC with the GI specific primers produced a PCR product of 228 base pairs, GII-specific amplification resulted in a PCR product of 277 base pairs. The working concentration of each IAC (in 10 mM TriseHCl, 1 mM EDTA buffer pH 8.0, plus 500 ng mL1 bovine serum albumin) was empirically determined as the dilution which consistently (triplicate determinations) gave a positive signal. Aliquots were stored at 20 C (adenovirus IAC) or 70 C (norovirus IAC). In a correctly functioning reaction, an IAC signal should always be produced in the absence of a target signal (high amounts of target can out-compete amplification of the IAC, but then the target signal itself shows that the reaction has worked). In this study, when an (RT)PCR of a sample produced neither IAC nor target signal, the presence of inhibitory substances derived from the water sample was assumed. Consequently, the nucleic extract was diluted ten-fold until the appearance of an IAC or target signal revealed that no inhibition was occurring.
2.8.
Infectivity determination
At least 10 adenovirus-positive (by nested-PCR) samples from each Laboratory were tested for virus infectivity by integrated cell culture-PCR (ICC-PCR, Reynolds et al., 2001; Greening et al., 2002). If any of the four test samples in a tetrad was positive by human adenovirus nested-PCR then the sample concentrate which had given the strongest PCR band was tested for infectious adenovirus by inoculation of cell cultures and nested-PCR analysis of the cultures after zero and five days’ incubation. No infectivity assay was performed if the adenovirus nested-PCR on all four concentrates was negative. At least two 25 cm2 flasks, each containing a monolayer of confluent A549 cells (European Collection of Cell Culture, ECACC, UK) were inoculated with 1 mL of sample concentrate. At least one flask was incubated for five days (T ¼ 5). One flask was analysed without incubation (T ¼ 0), to guard against detection of seed virus. One negative control with cell culture medium only was set up. Following incubation, flasks in the first set (T ¼ 5) were frozen and thawed three times and the
separated supernatant analysed by the adenovirus nested PCR. A positive nested-PCR signal after five days, coupled with a negative reaction after zero days (confirming that inoculum was not being detected) was taken as evidence of virus multiplication, and hence of infectivity.
2.9.
QPCR assay for the detection of HAdV DNA
Virus nucleic acid in at least 10 samples which were positive for adenovirus by nested-PCR from each Laboratory was quantified by real-time qPCR. The nucleic acid extracts from these samples were diluted as was found necessary to observe a signal in the PCR (see Section 2.7). Assays were done in 25-mL reaction mixtures each containing 10 mL of nucleic acid extract and 15 mL of TaqMan Universal PCR Master Mix (Applied Biosystems) containing 0.9 mM of each primer (AdF and AdR) and 0.22 5 mM of fluorogenic probe (AdP1) as previously described (Hernroth et al., 2002). Following activation of the uracil-N-glycosylase (2 min, 50 C) and activation of the AmpliTaq Gold for 10 min at 95 C, 45 cycles (15 s at 95 C and 1 min at 60 C) were performed. A pBR322 plasmid containing the HAdV 41 hexon sequence kindly donated by Dr. Annika Allard from the University of Umea˚, Sweden, was used to construct a standard containing 101e107 copies of DNA in the 10 mL added to the PCR reaction. Each dilution of standard DNA suspensions was run in triplicate. Ten mL of undiluted and a ten-fold dilution of the DNA suspensions obtained from water samples were run in duplicate. In all QPCRs the amount of DNA was defined as the mean of the data obtained. A non-template control and a nonamplification control were added to each run.
2.10.
Sequence analysis
The amplicons obtained after nested-PCR assays of HAdV or NoV were purified using the QIAquick PCR purification kit (QIAGEN, Inc.). Purified DNA was directly sequenced with the ABI PRISM Dye Terminator Cycle Sequencing Ready Reaction kit version 3.1 with Ampli Taq DNA polymerase FS (Applied Biosystems) following the manufacturer’s instructions. The conditions for the 25-cycle sequencing amplification were: denaturing at 96 C for 10 s, annealing for 5 s at 50 C and extension at 60 C for 4 min. The nested primers were used for sequencing at a concentration of 0.05 mM. The results were checked using the ABI PRISM 377 automated sequencer (PerkineElmer, Applied Biosystems). The sequences were compared with the GenBank and the EMBL (European Molecular Biology Library) using the basic BLAST program of the NCBI (The National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov/BLAST/). Alignments of the sequences were carried out using the ClustalW program of the EBI (European Bioinformatics Institute of the EMBL, http://www.ebi.ac.uk/clustalw/).
2.11.
Faecal indicator organisms
Detection of E. coli and intestinal enterococci was done according to ISO 9308-3 and ISO 7899-1 using Microtiter plates. One laboratory enumerated bacteria by colony-forming units (cfu).
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The robustness of the methods was calculated using the results obtained from the analysis of quality control samples. Nine laboratories participated in the trial of the methods for analysis of fresh waters and six laboratories participated in the trial of the methods for analysis of marine samples. Test samples comprised 1 mL aliquots of adenovirus Type 2 (containing 200 pfu of virus), and norovirus GII.4 which were added by the participants to their own water samples. A batch of adenovirus Type 2 and a batch of norovirus GII.4 were prepared, distributed into single-use ampoules and sent to each participant. On each sampling occasion 1 mL of the adenovirus Type 2 and 1 mL of the norovirus-positive control material were added to a separate 10-L quality control sample of the recreational water being tested. Negative samples were prepared from a mixture of de-ionised and tap water, or artificial seawater. Each participant analysed at least 25 sets of quality control samples. The raw data sent by each laboratory were statistically analysed according to the recommendations of Scotter et al. (2001) by the methods of Langton et al. (2002). The trial sensitivity was defined as the percentage of positive samples giving a correct positive signal, and trial specificity was defined as the percentage of negative samples giving a correct negative signal. Accordance (repeatability of qualitative data) was defined as the percentage chance of finding the same result, positive or negative, from two identical samples analysed in the same laboratory under predefined repeatability conditions, and concordance (reproducibility of qualitative data) was defined as the percentage chance of finding the same result, positive or negative, from two identical samples analysed in different laboratories under predefined repeatability conditions. These calculations take into account different replication in different laboratories by weighting results appropriately. The concordance odds ratio (COR) was the degree of inter-laboratory variation in the results, and expressed as the ratio between accordance and concordance percentages (Langton et al., 2002). The COR value may be interpreted as the likelihood of getting the same result from two identical samples, whether they are sent to the same laboratory or to two different laboratories. The closer the value is to 1.0, the higher is the likelihood of getting the same result. Confidence intervals for accordance, concordance and COR were calculated by the method of Davison and Hinckley (1997); each laboratory was considered representative of all laboratories in the “population” of laboratories, not just those participating in this analysis.
3.
Results
The study surveillance period ran from the end of May until early November 2006. Nine participant Laboratories collected samples at both of their sampling sites, whereas six Laboratories took samples from only their main site. Thirteen fresh water sites and 11 marine sites were sampled (Table 1 and Fig. 1). A total of 1410 samples was taken of which 928 were from fresh water and 482 were from marine sites (Table 1).
3.1.
Virus detection
Four experienced laboratories evaluated the concentration methods by processing replicate samples using different methods, and analysing the concentrates by (RT)PCR and, for HAdV2, monolayer plaque assay in A549 cultures. Concentration by three different methods gave mean recoveries of 49% and 37% of seeded HAdV2 from fresh and artificial sea water respectively, as measured by plaque assay. Across all evaluating laboratories, concentration of HAdV2 in spiked freshwater samples by glass wool with elution using beef extract gave a mean recovery of 57.1% (range 34.2%e78.2%), while concentration of virus in spiked artificial seawater samples with skimmed milk elution gave a mean recovery of 35.4% (range 22.5%e43.8%). The variation between laboratories’ performance made decisions on method choice based only on recovery values less than clearcut, which is why other factors such as cost were also taken into account. From the overall surveillance data 553 out of 1410 samples (39.2%) were positive for one or more of the target viruses (Fig. 2). This corresponded to 582 virus detections, some samples being positive for more than one kind of virus. Adenoviruses were detected more often than noroviruses, 513 (36.4%) samples being positive for one or more human adenovirus types, while 132 samples (9.4%) tested positive for one or both norovirus genogroups; these were divided between GI (49, 3.5% samples positive) and GII (88, 6.2%, Fig. 2). Five samples (two marine and three fresh water) were positive for both norovirus genogroups. Out of the 513 human adenovirus-positive samples, 63 (12.3%) were also positive for one or both NoV genogroups (33 out of 381 freshwater samples and 30 out of 132 marine samples). Just four samples (two fresh water and two marine), were positive for all three virus types. Interestingly, 69 samples (22 fresh water and 47 marine) were positive for one or both norovirus genogroups while testing negative for adenovirus.
3.2.
Water type
Freshwater sites showed a higher frequency of virus-positive samples than marine sites (Fig. 3). Adenoviruses were detected more often in fresh water (381 adenovirus-positive samples out of 928, 41.1%, Fig. 3) than in marine water (132 out of 482, 27.4%). Conversely, noroviruses (either GI or GII or both)
50
% positive of 1410 samples
2.12. Quality assurance e robustness of the concentration and detection methods
40
30 553
20
samples samples positive positive
513
10 132
0
49
All viruses %
HAdV %
All NoV %
NoV GI %
88
NoV GII %
Fig. 2 e Summary of virus detection in all water types.
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were detected less often in freshwater samples (58 noroviruspositive samples out of 928, 6.3%) than in marine water (79 out of 482, 16.4%, Fig. 3). Further, in marine waters the detection rate of norovirus GI was almost as high as norovirus GII (7.9% compared with 8.5%), which differs from the clinical context where GI viruses are found much less frequently than GII types in patients from gastroenteritis outbreaks, even in surveys of unaffected individuals (e.g. Verhoef et al., 2009). However, these high GI detection rates were mainly due to just four sites having higher frequencies of NoV GI.
3.3.
Variation according to site
Virus occurrence ranged widely between sites. Some Laboratories reported no viruses at all in any sample while others found many samples positive for at least one virus. Human adenoviruses were detected in all except two sites, one marine and one fresh water. Sites were chosen on the basis of their recreational use, and most were impacted by sewage effluent. Among the marine sites, 55% of samples from Pomezia, Rome, were positive for HAdV, while none was found at one of the Barcelona sites (though more samples were positive at the second site), and none was detected at Larnaca, Cyprus, where it is known no sewage is discharged. Among the freshwater sites, no HAdV was found at Kirchentellinsfurt Lake in BadenWu¨rttemberg, while 80% of samples were HAdV-positive at Amper Grasslfing in Bavaria and 91% were positive at the site at Tomblaine, Nancy, a site well recognised for its anthropogenic effects as well as its recreational activities (mainly canoeing). With respect to noroviruses, five out of 11 marine water sites, and four out of 13 freshwater sites gave samples positive for GI noroviruses, the highest recovery from a marine site being 30% of samples positive at Pomezia (Rome), and that from a freshwater site being 10% of samples positive at Reading. Genogroup II noroviruses were detected at eight marine and eight freshwater sites, the highest frequencies being 16.3% positive samples at Ardea (Rome, marine), and 15% at Durgerdam (freshwater). Overall, the data showed that adenoviruses were present at more sites than noroviruses. Some sites had more than 25% samples virus-positive in respect of both adenoviruses and noroviruses. To illustrate
3.4.
Virus infectivity by ICC-PCR
From each Laboratory, at least 10 samples that gave a strong HAdV-positive signal by nested PCR were analysed further by inoculation into cell culture and analysis of the supernatants by PCR. Fifty-one of 482 marine sample concentrates and 226 of 928 freshwater sample concentrates were tested. The results are shown in Table 2. Twenty-four (47%) of the marine water samples were found to be positive by nested PCR following inoculation of A549 cell cultures and where uninoculated control cultures remained negative, and where cultures inoculated and sampled immediately after inoculation also remained negative. Forty-six (20%) freshwater samples were positive for infectious HAdV.
3.5.
QPCR assay for the detection of HAdV DNA
A total of 132 marine and freshwater samples which had previously tested HAdV-positive by nested-PCR were further
14 12
Number of sites
Fig. 3 e Adenovirus and norovirus detection in marine and fresh waters.
the distribution of sites relative to the frequency of virus detection, Fig. 4 (marine sites) and Fig. 5 (freshwater sites) show the frequencies of positive samples divided into five groups (0%, 1e25%, 26e50%, 51e75% and 76e100% positive samples) plotted against the number of sites in each group. Thus there was, for example, one of the 11 marine sites which reported no samples being HAdV-positive, five sites in which between 1% and 25% samples were HAdV-positive, three sites between 26% and 50% and two sites with between 51% and 75% HAdV-positive (Fig. 4). There were several sites where the adenovirus frequency was in the higher categories and two freshwater sites where over 76% samples were HAdV-positive. Examination of the marine water norovirus GI data, when divided according to sites, shows that almost all the norovirus GI-positive samples (37/38) were found in four sites in Italy, the only other norovirus GI-positive marine water sample being found in one of the sites in Portugal. There was no evidence of outbreaks of norovirus-related disease in Italy in the areas local to the detection of GI virus in the environmental samples at the time when the samples were taken.
10
HAdV NoV GI
8
NoV GII
6 4 2 0 0%
1-25%
26-50%
51-75%
76-100%
% positive samples
Fig. 4 e Distribution of virus-positive sites e marine. Frequencies of positive samples divided into five groups (0%, 1e25%, 26e50%, 51e75% and 76e100% samples positive) plotted against the number of sites in each group.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 2 5 e1 0 3 8
3.7.
14
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Relationship of virus frequency to faecal indicators
Number of sites
12 10
HAdV NoV GI NoV GII
8 6 4 2 0 0%
1-25%
26-50%
51-75%
76-100%
% positive samples
Fig. 5 e Distribution of virus-positive sites e fresh water. Frequencies of positive samples divided into five groups (0%, 1e25%, 26e50%, 51e75% and 76e100% samples positive) plotted against the number of sites in each group.
analysed by the QPCR assay of Hernroth et al. (2002). Eighty (60.6%) samples were positive, with a mean value of 3260 genome copies (GC)/L of water. The percentage of positive samples was similar in both types of recreational water; 61.3% positive for fresh water with mean GC values of 558 GC/L versus 58.6% positive for marine waters with mean concentrations of 8810 GC/L.
3.6. Analysis of the sequence of the PCR products obtained Fifty-three samples were further analysed to type the HAdV present. The most frequently detected HAdV serotypes were 12 (n ¼ 4), 31 (n ¼ 8), 40 (n ¼ 4) and 41 (n ¼ 22). Serotypes 1 and 19 were observed with lower frequency. Serotypes 1, 2, 3, 12, and 31 were observed after analysing 7 samples which had been cultured in A549 cells as part of the infectivity detections. Nineteen samples were studied for determining NoV genotypes. Fifteen were confirmed as GII, with seven of them being GII.4. Four were GI, with one being GI.2. Over the last few years the most newly emerging NoV strains belong to GII.4 and show a global presence (Bull et al., 2006; Rowena et al., 2006).
Table 2 e Adenovirus infectivity over all sampling sites.
Marine (51 tested)
Fresh (226 tested)
T¼0
T¼5
Number of samples
% of those tested
e* e þ þ e e þ þ
e þ e þ e þ e þ
15 24 0 12 169 46 2 9
29 47 0 24 75 20 1 4
* Nested-PCR test result on cell culture after zero (T ¼ 0) or five (T ¼ 5) days’ incubation.
Frequencies of virus-positive samples were compared with the threshold values for E. coli and intestinal enterococci defining “good” water quality in the rBWD. The levels specified in the Directive for E. coli are 500/100 mL (coastal/transitional waters) and 1000/100 mL (inland waters), and the corresponding values for intestinal enterococci are 200/100 mL (coastal/transitional waters) and 400/100 mL (inland waters). Matching E. coli and intestinal enterococci data were available for 193 adenovirus-positive samples of which 117 (60.6%) had E. coli concentrations below the thresholds for “good” water quality whilst 151 (78.2%) had intestinal enterococci concentrations below the “good” water quality thresholds. For norovirus, matching E. coli and intestinal enterococci data were available for 52 positive samples, and the E. coli concentration in 31 (59.6%) of these was below the rBWD thresholds for “good” water quality. For intestinal enterococci, 38 (73.1%) norovirus-positive samples had concentrations below the “good” water quality thresholds. These results demonstrate the presence of PCR-detected virus in samples that would be considered “clean”, and of low illness risk, in terms of their faecal indicator organism concentration.
3.8.
Robustness of virus detection methods
The results of the robustness calculations of the virus/water detection methods are shown in Table 3. With the adenovirus/ freshwater method the trial sensitivity, or percentage of correctly identified positive samples, was 77.2%, and the concordance was lower than the accordance. A value of 1.0 lies just outside the COR 95% confidence intervals (CI), indicating that the method was not quite as reproducible as repeatable. The trial specificity, or percentage of correctly identified negative samples, was 96.1%, and 1.0 fell within the COR 95% CI, indicating that with identification of negative samples the method was as reproducible as it was repeatable. With the adenovirus/seawater method the trial sensitivity was 89.3%, and the concordance was lower than the accordance. Again, 1.0 lies just outside the COR 95% confidence intervals (CI). The trial specificity was 99.2%, and 1.0 fell within the COR 95% CI. With the norovirus/freshwater method the trial sensitivity was 91.4%, and the concordance was lower than the accordance, 1.0 lying just outside the COR 95% confidence intervals (CI). The trial specificity was 96.1%, and 1.0 fell within the COR 95% CI. With the norovirus/seawater method the trial sensitivity was 91.7%, and 1.0 fell within the COR 95% CI. The trial specificity was 92.6%, and 1.0 fell within the COR 95% CI.
4.
Discussion
This study has shown clearly that it is possible to use relatively straightforward methods for the detection of two important enteric viruses in water samples across a range of geographical sites with varying degrees of pollution. The common occurrence of adenoviruses (36.4% of samples tested) reflected the intermittent shedding of these viruses in the faeces by most adults. The difference in
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Table 3 e Statistical evaluation of methods for virus detection from recreational waters. Method A
Adenovirus/freshwater
B
Adenovirus/seawater
C
Norovirus/freshwater
D
Norovirus/seawater
Sample type
Sensitivity (%)
Specificity (%)
Positive Negative Positive Negative Positive Negative Positive Negative
77.2 N/A 89.3 N/A 91.4 N/A 91.7 N/A
N/A 96.1 N/A 99.2 N/A 96.1 N/A 92.6
(71.3e82.1)* (82.5e93.6) (87.1e94.3) (85.5e95.5)
(92.8e98.0) (95.5e99.9) (92.8e98) (86.5e96.0)
Accordance (%) 73.9 93.0 85.9 98.6 86.2 92.9 85.3 88.0
(61.2e86.5) (85.2e100) (68.9e94.9) (97.4e100) (74.4e96.1) (87e97.7) (75.6e94.9) (70.8e100)
Concordance (%) 63.5 (50.9e81.7) 92.5 (84.8e100) 79.6 (66.1e92.7) 98.3 (94.6e100) 83.9 (71.9e95.7) 92.5 (86.8e97.5) 84.6 (75.3e94.9) 85.7 (70.1e100)
COR 1.63 1.08 1.57 1.25 1.2 1.06 1.05 1.22
(1.07e2.52) (1.00e1.16) (1.01e2.29) (0.97e1.44) (1.02e1.35) (0.97e1.14) (0.81e1.38) (0.92e2.18)
*Numbers in parentheses indicate lower and upper 95% confidence intervals.
detection frequency may have been due to the greater dispersing and diluting power of the sea compared with that of the fresh waters. Alternatively, viruses may be less stable in marine waters due to the higher salt content, especially with higher temperatures (Hawley and Garver, 2008; Lo et al., 1976). The frequent detection of HAdVs by most laboratories reflected their known environmental robustness; though it was not possible to perform ICC-PCR on all the adenovirus-positive samples and thus show that all contained infectious viruses, it is known that adenoviruses can persist in an infectious state in various environments over long periods (Rzezutka and Cook, 2004). Charles et al. (2009) found a strong relation between PCR detection and infectivity of adenovirus Type 2 in groundwater over one year. Although noroviruses are spread principally by person-toperson transmission, environmental spread is also important, for instance in outbreaks associated involving drinking water (e.g. Hewitt et al., 2007) and consumption of bivalve molluscs (Lees, 2000). In this study, the high frequency of NoV GI detection in two Laboratories suggests a higher level in the environment than was demonstrated by consideration of the rest of the data for this virus. Detection of GI noroviruses in the environment is not matched by their detection in clinical samples; GI NoV strains have been detected frequently in sewage, effluent, and surface waters (da Silva et al., 2007; Katayama et al., 2008; Myrmel et al., 2006), which contributes to the view that many norovirus infections are symptomless, with GI viruses being under-represented among those found in clinical cases. It is unclear whether this relates to our data as most of the GI isolates were found in only four sites. The frequency of GII norovirus detection (approximately 6%) was as expected. It is commonly accepted that norovirusrelated disease shows a seasonal trend, with most outbreaks and sporadic cases occurring in winter. Whilst it would have been interesting to obtain a temporal distribution of environmental norovirus detection similar to that of Nordgren et al. (2009), this was not feasible in this study since it was specifically planned to be related to the EU bathing season, and in any case RT-PCR detection might not have provided resolution high enough to show temporal differences in norovirus levels. Further studies are planned using a norovirus QPCR to investigate this aspect. The performance characteristics of the methods used for concentration and detection of HAdV and NoV in both fresh and marine water samples were determined. Recovery values of 49% (seeded fresh water) and 37% (seeded artificial sea water) were considered acceptable, though variations
between laboratories prevented direct statistical comparisons of performance, and a modified method for marine water samples was developed during the project (Calgua et al., 2008). The percentage of correctly identified positive samples was around 90%, except for HAdV in freshwater, which showed a sensitivity of 77%, while the specificity of the methods was shown to be 93% or more. The sensitivity and specificity values compare well with those of some PCR-based methods for foodborne pathogen detection (Abdulmawjood et al., 2004; Malorny et al., 2004). The lower sensitivity value of the adenovirus/freshwater may be due to the fact that the HAdV concentration in the seeded sample was lower than the NoV concentration used. This may also explain the higher COR values for the HAdV-positive marine and freshwater samples. Furthermore, it should be noted that the samples used for the QC were not actually identical, whereas for the COR estimation this would be preferred. Each participant used the water from their own site(s), and this would differ from site to site and from week to week. River water, particularly, will contain varying levels of material that may reduce the effectiveness of the concentration method and/or inhibit the molecular assays. Notwithstanding this, the results demonstrate that the methods used are robust, although currently no criteria exist on lower limits of acceptability for robustness of methods for detection of viruses in water. The theoretical limit of detection of the method reported here can be estimated. If an (RT)PCR signal was obtained from an undiluted nucleic acid extract, and the assumption is made that the assay could detect one target molecule, this signifies that there was one virus equivalent in 10 mL nucleic acid extract. There were thus 10 virus equivalents in 100 mL nucleic acid extract, and on the assumption that this extract was obtained from 5 mL concentrate with no loss of target nucleic acid, this implies that there were 20 virus particles in the 10 mL concentrate. Assuming that the concentrate was derived from the original sample with no loss of virus, the conclusion is that a signal from the neat extract indicates that there were at least 20 virus particles in the 10-L water sample. If the extract had to be diluted to 101, then there were 200 virus particles in the 10-L sample. In selecting methods for concentration and detection of the target viruses practical and cost factors were considered in addition to recovery efficiency. Concentration by glass wool filtration is inexpensive requiring no specialised equipment beyond a centrifuge capable of 7000 g, and running costs are minimal. Membrane filtration is slightly more expensive, requiring a filtration stand, but again, running costs are low.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 2 5 e1 0 3 8
Both approaches contrast with (for example) ultrafiltration (high costs of filtration units and pumps, or disposable cartridges) and ultracentrifugation, which is unlikely to be found in routine environmental virology laboratories. The time taken to process samples was also an important factor; using the selected methods it was possible to process up to eight samples in one day (including controls) following familiarisation with the method. For detection, cell culture was not considered for the surveillance stage, being too slow, expensive and requiring specialised facilities; the costs and labour time spent on molecular detection was as might be expected in any laboratory equipped for PCR and related techniques. The amount of sewage discharged in the vicinity of many of the sites studied will affect the likelihood of human viruses being present in the water. Sewage input was not measured directly but the level of faecal indicators found reflects the contamination level. Viruses were found less often in sites where the sewage input was expected to be lower. The influence of organic contaminants that occur naturally in water must not be underestimated. Reaction inhibition by substances in the sample is a well-known problem associated with analysis of environmental samples (e.g. da Silva et al., 2007), and was observed in this study. The use of the IACs in both NoV and HAdV PCRs was of significant benefit in guarding against false negative reactions. In the current study the norovirus RT-PCR suffered about 5.5% of reactions failing to give a conclusive result (4.4% of freshwater samples and 7.7% of marine samples). Samples were tested at a higher dilution (up to 103) to remove inhibition and achieve a positive IAC signal. Successive dilutions were done when a higher concentration failed to give a target signal or an IAC signal. Inhibition of the adenovirus PCR was much less problematic, with PCR reactions of 0.9% of freshwater samples and 5.6% of marine water samples being inhibited. Samples from one inland major river site (Kew Bridge, UK) had often to be diluted up to 103 and consequently unexpectedly low numbers of samples positive for adenovirus (23%) were recorded. Subsequent tests with bovine serum albumin (BSA) in the PCR reaction suggest that routine incorporation of this reagent in the reaction mix may reduce enzymatic inhibition. Integrated cell culture-PCR provided a method of determining the infectivity of adenoviruses, which was particularly useful since naturally-occurring virus strains do not always grow in cell culture with the same rapidity nor with the same evidence of cellular destruction. The enteric Ad40 and Ad41 viruses cannot be grown in most cell culture systems that support the growth of adenoviruses from the other subgroups, A549, HeLa, primary human amnion and primary human embryo kidney cells (Tiemessen and Kidd, 1995). They have been shown to replicate in cell culture systems using Graham 293 cells, HEp-2 cells and HT-29 cells (Ko et al., 2003; Tiemessen and Kidd, 1995). Our data support these findings, because the presence of both Ad40 and Ad41 was shown by direct PCR, not in the cell culture-PCR assay using A549 cells. Direct inoculation of cell cultures followed by observation over an extended period would not provide a good indication of infectivity and would not be in the interests of providing a rapid test. The finding that about 20% of freshwater samples and about 47% of marine water samples contained infectious
1035
adenovirus supports laboratory observations (e.g. ThurstonEnriquez et al., 2003) that these agents are environmentally robust. The FIO levels encountered in this project exhibited a wide range. Comparisons with FIO thresholds defined in the current European Directive bathing water standards (2006/7/ EC) suggest that over 50% of samples that are relatively clean in terms of FIO concentrations and which exhibit “good” water quality, with a low associated illness risk, can be positive for adenovirus and norovirus. However, use of an adenovirus PCR, for example, as a means of determining recreational water quality would require the use of quantitative, rather than presence/absence detection. Quantitative PCRs for different types of environmental adenovirus are now available. Whether such a test would detect infectious virus may be addressed by, for example, detection of virus-specific mRNA, and also there is some evidence that in adenovirus preparations from which free DNA has been removed before analysis virus titres measured by infectivity and by QPCR are very similar (Girone`s, personal communication). It would then be necessary to determine any association between adenovirus levels and health risk, and there is thus a need for further work before the viral parameters investigated here could be used in a regulatory framework prior to epidemiological investigation to provide an appropriate evidence-base for policy development.
5.
Conclusions
A comprehensive surveillance study of EU recreational waters was done through the 2006 bathing season. It may be concluded from the results that: 1. Almost 40% of bathing water samples in Europe were viruspositive entailing a possible public health risk from bathing; 2. Adenoviruses are more prevalent than noroviruses in both marine and fresh waters and appear to be a promising viral indicator for bathing water quality; 3. A single concentration method can be used to concentrate adenoviruses and noroviruses in fresh water recreational samples and a further single method can be used for marine waters; 4. Concentration and detection methods may be used effectively even in polluted waters; 5. Though the majority of sites returned frequencies of 0e25% positive, some were so polluted that >50% of samples contained one or both target viruses; 6. Adenoviruses remain infectious in the environment, and this may be true for other pathogenic viruses such as noroviruses.
6.
The ‘Virobathe’ group
This work was performed by scientists and technicians from 16 Institutions across Europe. In addition to the Authors of this paper, those making significant contributions were as follows:
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Dr. Silvia Bofill-Mash, Ms. Pilar Clementeh, Dr. Donia Domenicad, Ms. Alexandra Duarten, Dr. Inge Gra¨berl, Dr. Wafa Hollisterp, Ms. Stephanie Huberi, Dr. Marcello Iaconellim, Dr. Giuseppina La Rosam, Prof. Beata Cuvelierk, Ms. Leslie Orgorzalyf, Dr. Nicholas Pissarides, Dr. Gabrieli Rosannad, Ms. Elyne Salagnonc, Dr. Oliver Schneidere, Ms. Arieke Docters van Leeuwenj, Dr. Marco Veranib, and Mr. Steve Wildeg.
Acknowledgements This work was funded by an EU contract number 513648 VIROBATHE, as part of the Sixth Framework Programme. The authors are grateful to Dr. Jan Vinje´ for helpful comments on the manuscript.
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Effect of selected metal ions on the photocatalytic degradation of bog lake water natural organic matter Luis A. Tercero Espinoza*, Eike ter Haseborg, Matthias Weber, Elly Karle, Rafael Peschke, Fritz H. Frimmel Water Chemistry, Engler-Bunte-Institut, Karlsruhe Institute of Technology, Engler-Bunte-Ring 1, 76131 Karlsruhe, Germany
article info
abstract
Article history:
Herein we report the photocatalytic degradation of natural organic matter from a bog lake
Received 11 April 2010
(Lake Hohloh, Black Forest, Germany) in the presence of 0, 5, and 10 mmol L1 of added Cu2þ,
Received in revised form
Mn2þ, Zn2þ and Fe3þ. The reactions were followed by size exclusion chromatography with
14 September 2010
organic carbon detection (SEC-DOC) and by measurements of low molecular weight organic
Accepted 13 October 2010
acids. Addition of Cu2þ had the largest effect of all four studied metals, leading to a retarda-
Available online 21 October 2010
tion in the molecular size changes in NOM: degradation of the larger molecular weight fraction was inhibited leading to reduced production of smaller molecular weight metabo-
Keywords:
lites. Similarly, addition of Cu2þ reduced the production of formic and oxalic acids, and
Heterogeneous photocatalysis
reduced the bioavailability of the partially degraded NOM.
Natural organic matter
ª 2010 Elsevier Ltd. All rights reserved.
Size exclusion chromatography Low molecular weight organic acids Degradation
1.
Introduction
Natural organic matter (NOM) is present in varying concentrations in all raw waters used for the production of drinking water. Its concentration is often reduced through flocculation and filtration prior to oxidation and disinfection. In spite of this, NOM is the principal natural precursor for unwanted byproducts in disinfection during water treatment (Frimmel et al., 2002; Zwiener, 2006). Thus, it is necessary to understand the behavior of NOM in established and alternative processes, especially those inducing chemical changes in the NOM. One alternative oxidation process with deployment potential in small, decentralized water treatment units is heterogeneous photocatalysis with titanium dioxide as the photocatalyst. This process is one of the so-called advanced oxidation processes
(AOP), the effectiveness of which is based largely on the oxidation potential of OH radicals (2.8 V vs. standard hydrogen electrode) and has been extensively demonstrated (Hoffmann et al., 1995; Kabra et al., 2004). The TiO2/UV process is particularly attractive for use in regions where the UV photons can be provided by sunlight. An overview of the work on solar heterogeneous photocatalysis is given by Malato et al. (2007). NOM is known to bleach and undergo changes in adsorption properties under photocatalysis with TiO2 suspensions (Bekbo¨let et al., 1996; Eggins et al., 1997) e for a recent review please see Matilainen and Sillanpa¨a¨ (2010). It was recently shown that aquatic NOM from a bog lake is progressively degraded in this process starting with the higher molecular weight fraction (Tercero Espinoza et al., 2009). Iterative calculations based on that experimental data show a cascade
* Corresponding author. Present address: Fraunhofer Institute for Systems and Innovation Research, Breslauer Str. 48, 76139 Karlsruhe, Germany. E-mail addresses:
[email protected] (L.A. Tercero Espinoza),
[email protected] (F.H. Frimmel). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.013
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in which NOM is progressively degraded and that this progression is different in homogeneous and heterogenous AOP (Tercero Espinoza and Frimmel, 2009). The work presented herein is a continuation of our efforts to characterize the photocatalytic degradation of NOM under simulated solar ultraviolet (UV) irradiation. Here we examine the effect of the presence of selected metal ions commonly found in natural waters, namely iron(III), manganese(II), copper(II) and zinc(II) on the photocatalytic degradation of NOM, taking into account changes in molecular size as measured by size exclusion chromatography with dissolved organic carbon detection (SEC-DOC), as well as the production of low molecular weight organic acids (LMWOA) and changes in the bioavailability of the DOC upon irradiation.
2.
Materials and methods
2.1.
Experiments without added metals
20 mg P25 (Degussa, Germany) was added to 40 mL of Lake Hohloh water (r(DOC) ¼ 21 mg L1) previously filtered with 0.45 mm cellulose acetate membrane filters and mixed. P25 is comprised of TiO2 (approx. 75% anatase and 25% rutile) nanoparticles with average particle diameter around 20e30 nm and a BET surface area of 50 15 m2 g1 (Doll and Frimmel, 2005, and manufacturer’s specifications). Lake Hohloh water was extensively described by Frimmel et al. (2002). Samples were sonicated for 10 min prior to irradiation. The samples were then placed in a solar UV simulator (Oriel Corp., Stratford, CT), where they were stirred, open to the atmosphere, and irradiated from above by a homogeneous light field. The estimated photon flow in the UV range (290 < l < 400 nm) was 1.4 107 mol s1 (polychromatic actinometry following Defoin et al., 1986). The sample volume was 40 mL in all experiments, with an irradiation pathlength of z3.5 cm.
2.2.
Experiments with added metals
Lake Hohloh water was mixed with an appropriate amount of CuSO4 or CuCl2$2H2O, FeCl3, ZnCl2, and MnCl2 solutions and let stand for a minimum of 3 days in the dark. This spiked water was used instead of the original lake Hohloh water in the procedure described in Section 2.1. In order to systematically explore the experimental region, we turned to a full factorial design for 4 variables in 2 levels (24 experimental runs), which comprise all possible combinations of the þ1 and
Table 1 e Variable settings for the 24 full factorial design. Variable
cadded(Cu2þ) cadded(Fe2þ) cadded(Zn2þ) cadded(Mn2þ)
Coded var.
x1 x2 x3 x4
Variable setting 1 (mmol L1)
0 (mmol L1)
þ1 (mmol L1)
0 0 0 0
5 5 5 5
10 10 10 10
Table 2 e Experimental matrix for the irradiation experiments with added metals. Each row represents one experimental run. The experimental runs were performed in random order. Runs with tirrad [ 30 min and those with tirrad [ 60 min were performed separately. cadded(Cu2þ) (mmol L1) 0 10 0 10 0 10 0 10 0 10 0 10 0 10 0 10 5 5 5
cadded(Fe3þ) (mmol L1)
cadded(Zn2þ) (mmol L1)
cadded(Mn2þ) (mmol L1)
0 0 10 10 0 0 10 10 0 0 10 10 0 0 10 10 5 5 5
0 0 0 0 10 10 10 10 0 0 0 0 10 10 10 10 5 5 5
0 0 0 0 0 0 0 0 10 10 10 10 10 10 10 10 5 5 5
1 levels shown in Table 1, plus repetitions of the “center point” (level “0” for all variables, see Box et al., 2005). The full experimental matrix is shown in Table 2.
2.3. Size exclusion chromatography with dissolved organic carbon detection (SEC-DOC) Filtered (0.45 mm) samples were analyzed using the apparatus described in detail by Huber and Frimmel (1991) using Toyopearl HW 50S resin (Tosoh Corp., Japan) as column packing and phosphate eluent (1.5 g L1 Na2HPO4$2H2O þ 2.5 g L1 KH2PO4) flowing at a rate of 1 mL min1 as the mobile phase. Samples were diluted 1:5 prior to analysis. The dimensions of the column were: length ¼ 250 mm, inner diameter ¼ 20 mm. The injection volume was 1 mL and the DOC concentration of each sample was calculated on the basis of an external calibration using potassium hydrogen phthalate as a standard. The resulting chromatograms were divided operationally into three fractions (cf. Tercero Espinoza et al., 2009), as follows: Fraction 1: 28.0 min < tr < 45.8 min, where tr is the retention time (in this case numerically equal to the elution volume in mL); the DOC contained in this fraction is commonly attributed to the relatively high molecular sized humic material (Huber et al., 1994), Fraction 2: 45.8 min < tr < 50.7 min; the DOC contained in this fraction is sometimes referred to as “building blocks”, representing medium sized molecules (Frimmel, 1998; Huber et al., 1994). Fraction 3: 50.7 min < tr < 56.5 min; this peak includes totally permeating molecules, as well as molecules which elute prematurely due to the difference in electrical conductivity between sample and buffer (Specht and Frimmel, 2000; Huber et al., 1994).
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2.4.
Ion chromatography (IC)
KD
Measurements of total metal concentrations were performed by means of inductively coupled plasma-optical emission spectroscopy (ICP-OES) using a Vista-Pro CCD simultaneous ICP-OES spectrometer (Varian) with yttrium as an internal standard.
2.6. Degradation experiments with bacteria from activated sludge The biodegradability of the irradiated samples was assessed by performing SEC-DOC measurements of filtered samples after incubation with a mixed bacterial culture following Tercero Espinoza et al. (2009). Briefly, 4 mL of activated sludge inoculum (mixed from two wastewater treatment plants, one municipal and one industrial) were added to 50 mL of the irradiated samples, which were previously filtered to remove the TiO2 particles. The samples were then incubated in the dark on a shaker (70 rpm) for 48 h at 36 . The choice of bacterial cultures reflects the expectation that the mixed inoculum from two wastewater treatment plants possesses a larger bacterial diversity and a correspondingly larger metabolic potential compared e.g. to the “native” inoculum used by Brinkmann et al. (2003).
3.
Results and discussion
3.1.
Degradation of NOM in the absence of added metals
As a base case for later experiments with added Cu2þ, Mn2þ, Zn2þ and Fe3þ, we first performed irradiation experiments in the absence of added metals (Fig. 1). A further aim was to select an adequate time scale for the ensuing experiments with added metals. To this end, the SEC-DOC chromatograms of the original and irradiated samples were divided into three fractions based on retention time (tr) as described in Section 2.3. The fractions are presented graphically in Fig. 1, and are also referred to as F1eF3 in the following presentation and discussion. In good agreement with previous results (Tercero Espinoza et al., 2009), irradiation of the samples led to a rapid decrease in F1 (0.05 mg L1 min1), initially accompanied by an increase in F2 and F3, each at a rate approximately half that of decrease of F1. Therefore, the sum of the DOC contained in F1eF3 did not change appreciably during the first hour of irradiation but decreased steadily at longer irradiation time (tirrad), indicating mineralization of the NOM.
1.0
1.3
1.7
2. 0
0 min 60 min 120 min
1. 0
Relative DOC signal
2.5. Inductively coupled plasma-optical emission spectroscopy (ICP-OES)
0.7
Irrad. time
F1
0. 0
The determination of low molecular weight organic acids on filtered samples (0.45 mm) was performed by ion exchange chromatography (IC) using a Dionex DX500 system, equipped with an IonPacAS11 column (length 250 mm, inner diameter 4 mm) as described earlier (Tercero Espinoza et al., 2009). The eluent was aqueous KOH with concentrations shown in Table S1 in the supporting information.
0.3
3.0
0.0
20
30
40
F2 F3
50
60
70
80
t r / min
Fig. 1 e SEC-DOC chromatograms showing the defined fractions (F1eF3) and their time evolution under UV irradiation in the presence of 0.5 g LL1 TiO2. The DOC signal is directly proportional to the mass of organically bound carbon leaving the SEC column at retention time tr. Notice the marked shift in the chromatograms from left to right with increasing irradiation time, pointing to the progressive degradation of the NOM (for a more detailed discussion, see e.g. Tercero Espinoza et al., 2009; Tercero Espinoza and Frimmel, 2009).
Because F2 experienced a maximum in DOC content at tirrad z 60e90 min, we chose irradiation times of 30 and 60 min for the following experiments with added metals. See the supporting information for a plot of DOC content vs. tirrad for F1eF3 (Figure S1). Through this choice of tirrad (i.e. in the early stages of the reaction, before the maximum in F2) we aimed to avoid having to distinguish between values on either side of the UV (Figure S2) and DOC maxima for F2.
3.2. Degradation of NOM in the presence of added metals In order to study the influence of added metals on the photocatalytic degradation of NOM, we performed irradiation experiments with added iron(III), copper(II), manganese(II) and zinc(II) in the range 0e10 mmol L1. The original Lake Hohloh water contained 1 mmol L1 of each Cu and Mn, approx. 2 mmol L1 Zn, and approx. 6 mmol L1 Fe, as measured by ICP-OES. No steps were taken to change this natural background concentration. Thus, the concentration of the spiked samples is given as cadded(metal ion) instead of total concentration. As expected from the irradiation experiments without added metals (Section 3.1; cf. Tercero Espinoza et al., 2009), the DOC content of F1 decreased upon irradiation, and that of F2 and F3 increased with increasing tirrad. While more than half of the DOC initially contained in F1eF3 was present as F1, this fraction was reduced to approximately half after tirrad ¼ 30 min and less than half after tirrad ¼ 60 min. The sum of F1eF3 remained nearly constant. The results for all experiments containing added metals are summarily presented in Fig. 2. For comparison, the values
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corresponding to the experimental runs without added metals are given at the bottom of the histograms (gray and black stubs). After tirrad ¼ 30 min, the DOC content of the sample without added metals (gray stub in Fig. 2, left panel) was close to the mean of all samples for F1 and F3. However, for F2, the DOC content of this sample was at the high end of the distribution, suggesting that the addition of metals led to a smaller F2. After tirrad ¼ 60 min, the value of the DOC content of the sample without metals (black stub in Fig. 2, left panel) was at the lower end of the distribution for F1, pointing to a retardation of the degradation of F1 in the presence of added metals. Congruently, this sample showed larger F2 and F3 (both close to the upper end of their respective distributions). Thus, addition of the selected metal ions appears to generally slow down the transformations of NOM in the direction F1 / F2 / F3 as observed after tirrad ¼ 60 min. This inhibitive effect may be related either to the presence of metal ions as scavengers of both electrons and OH radicals (Litter, 1999), or to the complexation of metal ions with NOM. These aspects are discussed in the following sections. It is interesting to note that not only the mean but also the shape of the histograms changes as the irradiation progresses, with peaks for the individual fractions apparently acquiring a more pronounced bimodal character after 60 min of irradiation than after 30 min, as seen in Fig. 2. For example, the mode of the DOC content of F1 after tirrad ¼ 30 min is between 5 and 5.5 mg L1. However, there are two modes visible after tirrad ¼ 60 min: one around 3.5e4.0 mg L1 and one around 4.5e5.0 mg L1. This suggests that the DOC content of these fractions, originally the same regardless of the added metals, splits into two distinct “groups” during the course of irradiation. These groups may in turn result from the action of one or more of the added metals. This aspect is explored below.
3.3.
DOC content of the individual fractions
In order to help quantify the effect of the added metals on the “internal” changes in the apparent molecular size distribution of the NOM samples, we fitted the polynomial model
y ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b4 x4 |{z} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} mean
main effects
þ b12 x1 x2 þ b13 x1 x3 þ . þ b34 x3 x4 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} firstorder or twoway interaction effects
þ
b123 x1 x2 x3 þ . þ b234 x2 x3 x4 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} secondorder or threeway interaction effects
þ
b1234 x1 x2 x3 x4 |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl} thirdorder or fourway interaction effect
to the data, where the subscripts 1e4 are defined in Table 1. We then analyzed the magnitude of the coefficients by using the techniques from Daniel (1959) and Lenth (1989) to identify the most important factors influencing the DOC content of each fraction.
3.3.1.
Fraction 1 (F1)
There was no clear effect of the added metals on the DOC content of F1 at tirrad ¼ 30 min (cf. Fig. 2, left panel). However, evaluation of the data after tirrad ¼ 60 min shows an effect of added Cu2þ. Plotting the results only as a function of cadded(Cu2þ) reveals that an increasing concentration of Cu2þ results in a larger DOC content of F1, indicating a retardation in the degradation of this fraction as compared to samples without added Cu2þ. For cadded(Cu2þ) ¼ 0 mmol L1, the DOC contained in F1 was 3.8 0.2 mg L1, while for cadded(Cu2þ) ¼ 10 mmol L1 it was 4.7 0.5 mg L1. These data are shown as box plots in Fig. 3, left panel. Note that the results in Fig. 3 are presented as if the other metals were not present. Therefore, the variability observed for cadded(Cu2þ) ¼ 0 and 10 mmol L1 also includes the variations caused by the presence/absence of the other three added metals. In spite of this, the difference observed for F1 in Fig. 3 between cadded(Cu2þ) ¼ 0 and at cadded(Cu2þ) ¼ 10 mmol L1 is statistically significant when considering a 99% confidence interval (CI) for the difference of means. The data at cadded(Cu2þ) ¼ 5 mmol L1 (center point) show the variability of true replicates of the same experiment (n ¼ 3). The experimental error associated with true replicate runs of a single experiment was, therefore, small when compared with the variability observed with cadded(Cu2þ) ¼ 0 and 10 mmol L1, indicating that
Irrad. time
30 min 60 min
F1
F2
F3
F1 + F2 + F3
2 2.5 3 3.5 4
1.5 2 2.5 3 3.5
10 Frequency
8 6 4 2 0 3
4
5
6
7
8
9
10 11 12
−1
DOC contained in fraction(s) / mg L
Fig. 2 e Frequency distribution of the DOC content of F1eF3 as a function of time (each data point corresponds to one experiment in Table 2). The gray and black stubs at the bottom of the histograms indicate the values corresponding to samples without added metals after 30 and 60 min of irradiation, respectively. Note the different bin sizes: 0.5 mg LL1 for F1 and F1 D F2 D F3 but 0.25 mg LL1 for F2 and F3.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 3 9 e1 0 4 8
F2
F3
5
4
3
2 0
5
10
0
5
c added(Cu
)
2+
10
0
5
10
−1
/ μmol L
Fig. 3 e Box plots showing the DOC content of F1, F2 and F3 after 60 min of irradiation. The box at cadded(Cu2D) [ 5 mmol LL1 (x1 [ 0) results from the repetitions at the center point (true replicate runs, n [ 3). Box plots (sometimes called box and whisker plots) summarily present all data in a distribution. The lower end of the bottom whisker in a box plot corresponds to the minimum of all observations while the top of the upper whisker represents the maximum. The bottom of the box represents the 25th percentile (1st quartile), the central line the median (50th percentile or 2nd quartile), and the top of the box the 75th percentile (3rd quartile) of the distribution (Lapin, 1997).
the influence of the other added metals on the degradation of F1, though small compared to that of Cu2þ, may not be negligible. The largest of these other influences was that of added Mn2þ. The change in DOC content with added Cu2þ was different in the absence and in the presence of added Mn2þ: rðDOCF1 Þx1 ¼þ1 rðDOCF1 Þx1 ¼1 ¼ 1:2 0:3 mg L1 for cadded (Mn2þ) ¼ 0 mmol L1 but only 0.6 0.4 mg L1 for cadded(Mn2þ) ¼ 10 mmol L1. Thus, the magnitude of the observed effect of added Cu2þ decreased with increasing cadded(Mn2þ) (first-order interaction effect). We note that the addition of Mn2þ alone did not have a significant effect on the DOC content of the fractions. Fig. 3 also reveals that the change in DOC content of F1 after 60 min of irradiation did not depend linearly on cadded(Cu2þ). Instead, it appears that already small amounts of added Cu2þ strongly retarded the degradation of F1, with the effect of cadded(Cu2þ) becoming smaller per mmol L1 as cadded(Cu2þ) increased. The presence of Cu2þ in NOM samples has been reported to interfere with wet chemical oxidation methods, such as the one used to quantify DOC in this study. In fact, using the same analytical system and the same water source,1 Brinkmann
(2003) reported reduced carbon recovery rates when adding Cu2þ at concentrations greater than 25 mmol L1. However, for c(Cu2þ) 10 mmol L1, no detection problems were observed. Furthermore, in our experiments, added Cu2þ led to higher DOC content of F1, indicating a retardation in the photocatalytic degradation of F1 rather than an inhibition in DOC detection as observed by Brinkmann (2003), which would have led to an erroneously lower value. Therefore, we rule out a biasing of our results due to systematic measurement error in the presence of added Cu(II) ions. Accordingly, we conclude that the effect of added Cu(II) ions is real and requires an explanation. Because the nature of the counterion ðSO2 4 Þ could play a role in the retarding effect observed for Cu2þ (Abdullah et al., 1990), control experiments were performed using Cl as a counterion 2þ and using SO2 4 in the absence of Cu . These results are shown in Fig. 4. Comparison of the original Lake Hohloh water with a sample spiked with 10 mmol L1 K2SO4 showed no difference between the samples, indicating that the presence of SO2 4 alone does not lead to a retardation of the photocatalytic degradation of NOM at this concentration. Furthermore, the effect of adding 10 mmol L1 CuSO4 or CuCl2$2H2O was essentially identical. Therefore, we conclude that the observed effect is due to the presence of the added Cu(II) ions. Different authors have reported the acceleration and inhibition of degradation reaction kinetics when copper(II) ions are added to titanium dioxide suspensions (e.g. Aarthi and Madras, 2007; Bideau et al., 1991; Cai et al., 2003; Chen et al., 2002; Lindner et al., 1997; Wang et al., 2007). The mechanisms proposed therein are based on the electron acceptor role of copper(II) ions for the conduction band electrons from the irradiated TiO2. This effect has been reported to be concentration dependent (Litter, 1999): at “low” concentrations (similar to those in the present study), the effect of added Cu(II) ions is often reported to be accelerating. This is explained by the ability of Cu(II) ions to
5 4 3 2 1 0 5 4 3 2 1 0
original
+ 10 μmol L−1 K2SO4
+ 10 μmol L−1 CuSO4
+ 10 μmol L−1 CuCl2
35 1
Water from Lake Hohloh exhibits moderate changes in DOC content depending on the sampling time and season. However, the quality of the NOM is essentially constant Abbt-Braun and Frimmel, (2002).
0 min 30 min 60 min
t irrad
Relative DOC signal / AU
DOC contained in fraction / mg L−1
F1
45
55
35
45
55
t r / min Fig. 4 e Control experiments for the inhibiting effect of added Cu2D.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 3 9 e1 0 4 8
accept electrons from the conduction band, thus inhibiting recombination. at “high” concentrations, the effect of added Cu(II) ions is generally inhibiting. This is attributed to the Cu(II) ions acting as scavengers for OH radicals. These two explanations require the Cu(II) ions to be free in solution. This is a reasonable assumption if the DOC present does not possess complexing moieties (e.g. phenol), as has been the case in most previous studies (Litter, 1999). However, NOM is known to have a high copper complexation capacity. Thus, the formation of NOMeCu2þ complexes may well play a key role in the inhibiting effect observed when adding Cu(II) ions to the TiO2 suspensions. Frimmel and Geywitz (1983) reported complexation capacities of isolated aquatic humic substances from different German lakes and rivers ranging from 1.8 to 6.8 mmol Cu per mg C, as measured by polarography. The complexation capacity of NOM is largely determined by the acidic functional groups present in it (Prado et al., 2006), although Cu2þ is known to be able to bind to both carboxylic and phenolic groups in humic substances (Martyniuk and Wieckowska, 2003). Lake Hohloh water has been shown to possess an acidic functional group in every 4e8th carbon (based on proton titration), with approximately every 10th of these functional groups being able to form stable bivalent metal complexes. Measurements of the copper complexation capacity of Lake Hohloh water yielded a value of z1.9 mmol Cu per mg C (Abbt-Braun and Frimmel, 2002). If all the added copper in our experiments was bound in complexes with the NOM, this would lead to (0:5 mmol Cu per mg C, a value well below the complexation capacity for this NOM. Thus, we expect most Cu2þ ions to be present as Cu2þeNOM complexes. While there is no literature information regarding the photocatalytic degradation of NOM in the presence of Cu(II) ions, indirect information on this process can be obtained from other advanced oxidation processes. Using the H2O2/UV process, Liao et al. (2001) report the inhibition of humic acid (HA) mineralization in the presence of added copper(II) ions, albeit at higher concentrations (40e60 mmol L1) than those used in this study. They proposed that HAecopper complexes are more resistant to attack by HO than HA alone. This would agree with the results presented in Fig. 3 (left panel). However, the experiments by Liao et al. (2001) are strongly convoluted with the radical scavenging effect of Cu2þ because the concentrations present in their experiments exceed by a factor of 5e8 the copper(II) complexation capacity of the humic acid used in those experiments (CCCu2þ ;AldrichHA z1 mmol per mg C; Kolokassidou et al., 2009). No other reports of the degradation of coppereNOM or coppereHA complexes by hydroxyl radicals are known to us. However, the complexing agent EDTA is known to be degraded more rapidly by HO (in the sonolysis process) than its copper complex (CueEDTA; Frim et al., 2003). Thus, it appears that a stabilization of the NOM by complexation is responsible for the inhibition in degradation observed in our experiments. This stabilization of the NOMeCu2þ complexes has practical consequences, such as a reduced formation of bromoform in bromide-containing waters (Tercero Espinoza et al., 2010). It is unclear how the addition of Mn2þ affects the effect of added Cu2þ. Uyguner and Bekbolet (2007) reported that the addition of Mn2þ ions did not significantly alter the degradation
kinetics of humic acids, in agreement with our results for the effect of added Mn2þ alone. However, the type of interaction described above has not yet been reported. A possibility would be, that the Cu2þ complexes are largely substituted by Mn2þ complexes. An analogous behavior was observed by Park et al. (2006) in the system EDTA-Cu2þ/Fe3þ, where the Cu2þ replaced the Fe3þ and changed the degradation kinetics accordingly. However, such a substitution is unlikely for the case NOM-Cu2þ/ Mn2þ because the stability of the Cu2þ complexes of humic and fulvic acids is approx. one order of magnitude higher than that of the corresponding Mn2þ complexes (Hirata, 1981). Cu2þ and Fe3þ generally bind more strongly to humic acids than Mn2þ and Zn2þ (Van Dijk, 1971). We stress, however, that complex stability and increased stability against attack by HO are not the same, as shown for example by the work of Madden et al. (1997) with EDTA complexes of several metal ions. Instead, the effect of complex formation on the degradation kinetics in irradiated TiO2 suspensions appears to depend both on the nature of the metal ion and on the nature of the complexing partner. For example, in the case of EDTA complexes, addition of Fe3þ and Zn2þ inhibit the photocatalytic degradation of the complexes to a much larger extent than addition of Cu2þ, although Cu2þ complexes of EDTA are more stable (Park et al., 2006; Madden et al., 1997). In our experiments, the formation of Cu2þ complexes was the dominating factor.
3.3.2.
Fraction 2 (F2)
Inspection of the fitted coefficients for Equation (1) applied to F2 after tirrad ¼ 30 min shows an influence of cadded(Cu2þ) and suggested small interaction effects between Cu2þ and Zn2þ as well as between Fe3þ and Mn2þ. After 60 min, the effect of added Cu2þ became more dominant. Plotting the DOC content of F2 as a function of only cadded(Cu2þ) reveals a strong, nonlinear dependence (Fig. 3, center panel). This dependence closely mirrored the one shown for F1. Thus, the finding that increasing concentrations of Cu2þ led to a lower DOC content of F2 is complimentary to the observation that adding Cu2þ led to a larger F1.
3.3.3.
Fraction 3 (F3)
Following the same procedure as above, we found a marked effect of added Cu2þ on the DOC content of F3 already after 30 min of irradiation. As already seen for F2, increasing cadded(Cu2þ) also led to a lower DOC content of F3. Analysis of the results after tirrad ¼ 60 min shows qualitatively the same results (shown in Fig. 3, right panel). Thus, the presence of the added metals, especially of copper, slows down the degradation and mineralization of NOM, apparently by stabilizing the high molecular weight material through complexation.
3.4. Formation of low molecular weight organic acids (LMWOA) We then investigated the formation of low molecular weight organic acids upon irradiation. The concentration of four out of six identified acids (formic, acetic, malonic, oxalic, succinic and glutaric acids) was quantified by using ion chromatography (IC). The quantified acids were formic, oxalic, succinic and glutaric acids.
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Table 3 e Concentration after irradiation of the quantified low molecular weight organic acids (LMWOA). r (LMWOA), mg L1
LMWOA
Formic acid Oxalic acid Succinic acid Glutaric acid
0 min
30 min
60 min
0.3 0.2 0.1 0.1 <0.1 <0.1
0.8 0.3 0.4 0.2 0.1 0.0 <0.1
1.3 0.7 0.1 0.1
0.6 0.4 0.1 0.1
tirrad
30 min 60 min
1.5 (oxalic acid) / mg L−1
The measured concentrations of all four acids after tirrad ¼ 30 min and tirrad ¼ 60 min are summarized in Table 3. The acids were present in concentrations following the order: formic > oxalic [ succinic z glutaric acid. It is worth pointing out that the LMWOA are not only formed during the course of irradiation but can also be degraded by HO. Regarding the time evolution of the concentrations, we note that both the mean and the standard deviation increased with increasing irradiation time. The increase in mean concentration shows the steady production of these acids during irradiation, while the increase in standard deviation may result from an effect of the added metals. In order to explore the effect of the added metals, we employed the same techniques as above. These could be applied to both formic and oxalic acids but not to succinic and glutaric acids because the absolute amounts produced were too small. First, we note that the range of concentration of formic acid is not very large, spanning from 0.4 to 1.2 mg L1 after tirrad ¼ 30 min. Within this range, however, there were differences between the amounts produced at different levels of added metals. The largest of these was found in experiments with cadded(Mn2þ) ¼ 10 mmol L1. Here we observed a decrease in the concentration of formic acid with increasing cadded(Cu2þ), from 1.1 0.1 mg L1 at cadded(Cu2þ) ¼ 0 mmol L1 to 0.5 0.0 mg L1 at cadded(Cu2þ) ¼ 10 mmol L1 (significant, 99% CI). In contrast, there was no clear effect of added Cu2þ in the case of no added Mn2þ. This type of interaction effect is similar to that described for the DOC content of F1, but the “direction” of the interaction is exactly the opposite. Unfortunately, we cannot offer a plausible explanation for this at the present time. At tirrad ¼ 60 min, the same qualitative results were obtained as for tirrad ¼ 30 min. Quantitatively, the formic acid concentrations were approximately twice as high (0.8e2.3 mg L1) and the effect of added Cu2þ increased proportionately. In the case of oxalic acid, the largest effect was that of added Cu2þ, as shown in Fig. 5. After 30 min of irradiation, r(oxalic acid) ¼ 0.6 0.3 mg L1 in the absence of added Cu2þ but only 0.3 0.1 mg L1 when cadded(Cu2þ) ¼ 10 mmol L1. This difference is significant considering a 99% CI. The same trend is seen at a higher level after 60 min of irradiation. We note that the effect of added Cu2þ is not linear for both 30 and 60 min of irradiation. A small interaction effect observed for the addition of Mn2þ was not statistically significant. Thus, it is apparent that the main influence on the formation of both formic and oxalic acids came from the
1
0.5
0 0 μmol L−1
5 μmol L−1 10 μmol L−1 c added(Cu2+)
Fig. 5 e Box plots showing the concentration of oxalic acid after simulated solar irradiation of NOM in the presence of TiO2 and of added Cu2D.
addition of Cu2þ, and that this influence was negative with respect to LMWOA formation. A similar behavior regarding Cu2þ was found by Brinkmann et al. (2003) in bog lake water samples irradiated with simulated solar light in the absence of TiO2 (direct photolysis), but the addition of Mn2þ and/or Zn2þ was not considered in that work. In addition, an increase in production of LMWOA in the presence of added Fe3þ, as was the case for the direct photolysis of Lake Hohloh water NOM (Brinkmann et al., 2003; Brinkmann, 2003), was not observed in our experiments.
3.5.
Bioavailability of DOC in F1eF3
The irradiated samples were incubated with a bacterial culture for a period of two days in order to examine the bioavailability of the DOC after partial degradation (cf. Tercero Espinoza et al., 2009). SEC-DOC chromatograms revealed changes in all fractions, with F1 being larger after incubation compared to before incubation, and both F2 and F3 being smaller after incubation. These results are shown in Fig. 6. We attribute the increase in F1 to the synthesis of high molecular weight organic compounds by the bacteria (Wingender et al., 1999), and the decrease of F2 and F3 to the biological degradation of the bioavailable components present in these two fractions (cf. Brinkmann et al., 2003). We note that LMWOA (contained in F2 and F3) are generally more easily available for uptake and integration into the microbial metabolism than larger molecules. Addition of the metal ions led to changes in the amount of DOC degraded by the bacteria, in particular, addition of Cu2þ was observed to lead to lower amounts of DOC being biodegraded, as shown for F2 in Fig. 7. Although Cu2þ is necessary for bacterial life and growth in trace amounts, it is known to be toxic at higher concentrations (Trevors and Cotter, 1990; Lamb and Tollefson, 1973; Domek et al., 1984). Within the range of cadded(Cu2þ) used in this study, however, only a reversible
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 3 9 e1 0 4 8
0.5 0.0 −0.5 −1.5
−1.0
ΔDOC / mg/L
1.0
1.5
3.6. acids
F1
F2
F3
Fig. 6 e Box plots showing the change in DOC content of F1eF3 for all irradiated samples (tirrad [ 60 min) after incubation with a mixed bacterial culture. The change in DOC is defined as DDOC [ DOCafter incubation L DOCbefore incubation.
inhibition (adaptation) of the added bacteria is to be expected (Trevors and Cotter, 1990). Furthermore, the inhibiting effect of Cu2þ in biological systems is observed when adding free Cu2þ, which then interacts with different biomolecules. In our case, the copper ions are initially complexed to the NOM, leaving a much lower effective concentration of free copper ions. Therefore, the observed effect cannot be explained by an inhibiting effect of the added Cu2þ on the bacteria. Instead, comparison of Fig. 7 with Fig. 3 reveals that the inhibition in the biodegradation mirrored the inhibition seen for the photocatalytic degradation of F1 to form F2 and F3: addition of Cu2þ leads to a lower DOC content of F2 and F3 so that there is less bioavailable DOC to be degraded by the bacteria in the subsequent incubation step.
Formic, succinic and glutaric acid were completely biodegraded after two days of incubation. The concentration of oxalic acid was reduced by 82 8% for tirrad ¼ 30 min and 87 10% for tirrad ¼ 60 min. Thus, the fraction of degraded oxalic acid was nearly constant although more oxalic acid was initially present before incubation in the case of tirrad ¼ 60 min. The difference in biodegradation between oxalic acid and formic, glutaric and succinic acids can be explained by their different functions in the bacterial metabolism. Formic acid is built up or utilized in several metabolic pathways present in many different microorganisms such that a direct use of formic acid is to be expected (Gra¨ntzdo¨rffer, 2000). Similarly, succinic acid is used directly in the citric acid cycle (Bailey and Ollis, 1986) and glutaric acid plays an important role in amino acid degradation. The glutaric acid is converted to acetyl coenzyme A that could be directly used in the citric acid cycle or for the build-up of fatty acids (e.g. Griffin and Trudgill, 1972). In contrast, oxalic acid is in many organisms an end product that is secreted into the environment. Only plant cells and few bacteria (e.g. Oxalobacter formigenes, Ralstonia oxalatica or Moorella thermoacetica) have been shown to be able to metabolize oxalic acid. Oxalic acid is degraded by e.g. O. formigenes to formic acid that can be utilized by many different microorganisms (Daniel et al., 2004).
4.
Conclusions
We observed an inhibition of the photocatalytic degradation of NOM by the addition of up to 10 mmol L1 of Cu2þ, also in combination with up to 10 mmol L1 of Fe3þ, Mn2þ and Zn2þ. The addition of Mn2þ was observed to change the magnitude of the effect of added Cu2þ in two cases:
−1.0
−0.8
−0.6
−0.4
a larger inhibiting effect of cadded(Cu2þ) was observed in the absence of added Mn2þ during the degradation of the DOC contained in F1 (large molecular weight material); and an inhibiting effect of cadded(Cu2þ) was only observed in the presence of added Mn2þ for the case of formic acid formation.
−1.2
ΔDOC / mg L−1
Bioavailability of low molecular weight organic
0 μmol L−1
5 μmol L−1 10 μmol L−1 cadded(Cu2+)
Fig. 7 e Effect of added Cu2D on the bioavailability of Fraction 2 after irradiation (tirrad [ 60 min). The change in DOC is defined as DDOC [ DOCafter incubation L DOCbefore incubation.
These two first-order interaction effects between added Cu2þ and added Mn2þ have the opposite “direction” for the same combination of metals and exemplify the complexity of the system at hand. No significant effects were observed when adding Fe3þ and Zn2þ to the suspensions. Our results point to a stabilization of the NOM against degradation by HO by complexation with Cu2þ, which may increase the longevity of NOM in aquatic systems. The addition of Cu2þ led to a decreased production of formic and oxalic acids. Addition of Mn2þ led to an interaction effect as described above. The amount of bioavailable DOC after irradiation was found to be directly coupled to the extent of photocatalytic degradation of the NOM. In particular, addition of Cu2þ, which was found to inhibit the photocatalytic degradation
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 3 9 e1 0 4 8
of the large molecular weight fraction F1, led to lower bioavailability of the partially degraded NOM. While the effect of the added metal ions could be quantified and a possible reason for the observed effects was suggested, the elucidation of the mechanistic basis for the observed influence of the added metal ions requires further investigation.
Acknowledgements This work was funded in part by the German Technical and Scientific Association for Gas and Water (DVGW) and by the German Research Foundation (DFG) through the Research Training Group on “Surface phenomena in aquatic systems and aqueous phases” (Graduiertenkolleg 366). We are grateful to Marta Lo´pez Furelos and Johannes Kern for their assistance in the laboratory, to Reinhard Sembritzki for the ICP-OES measurements and to Maren Daschner de Tercero for her comments on the manuscript. Numerous comments and suggestions from anonymous reviewers contributed to improving the manuscript.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.10.013.
references
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characterization by physicochemical properties and Cu(II) complexation. J. Hazard. Mater. 164, 442e447. Lamb, A., Tollefson, E., 1973. Toxic effects of cypric, chromate and chromic ions on biological oxidation. Water Res. 7, 599e613. Lapin, L., 1997. Modern Engineering Statistics. Duxbury Press, Belmont, CA. Lenth, R.V., 1989. Quick and easy analysis of unreplicated factorials. Technometrics 31, 469e473. Liao, C.H., Lu, M.C., Su, S.H., 2001. Role of cupric ions in the H2O2 oxidation of humic acids. Chemosphere 44, 913e919. Lindner, M., Theurich, J., Bahnemann, D.W., 1997. Photocatalytic degradation of organic compounds: accelerating the process efficiency. Water Sci. Technol. 35, 79e86. Litter, M.I., 1999. Heterogeneous photocatalysis: transition metal ions in photocatalytic systems. Appl. Catal. B Environ 23, 89e114. Madden, T.H., Datye, A.K., Fulton, M., Prairie, M.R., Majumdar, S. A., Stange, B.M., 1997. Oxidation of metaleEDTA complexes by TiO2 photocatalysis. Environ. Sci. Technol. 31, 3475e3481. Malato, S., Blanco, J., Alarco´n, D.C., Maldonado, M.I., Ferna´ndezIba´nez, P., Gernjak, W., 2007. Photocatalytic decontamination and disinfection of water with solar collectors. Catal. Today 122, 137e149. Martyniuk, H., Wieckowska, J., 2003. Adsorption of metal ions on humic acids extracted from brown coals. Fuel Proc. Technol. 84, 23e36. Matilainen, A., Sillanpa¨a¨, M., 2010. Removal of natural organic matter from drinking water by advanced oxidation processes. Chemosphere 80, 351e365. Park, E., Jung, J., Chung, H., 2006. Simultaneous oxidation of EDTA and reduction of metal ions in mixed Cu(II)/Fe(III)eEDTA system by TiO2 photocatalysis. Chemosphere 64, 432e436. Prado, A.G., Torres, J.D., Martins, P.C., Pertusatti, J., Bolzon, L.B., Faria, E.A., 2006. Studies on copper(II)- and zinc(II)-mixed ligand complexes of humic acid. J. Haz. Mat 136, 585e588.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 4 9 e1 0 6 2
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The role of biomass, electron shuttles, and ferrous iron in the kinetics of Geobacter sulfurreducens-mediated ferrihydrite reduction Luke H. MacDonald 2, Hee Sun Moon 1, Peter R. Jaffe´* Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
article info
abstract
Article history:
This work presents a new framework for describing biologically mediated reduction of thin
Received 17 May 2010
layers of poorly crystalline iron oxides. The research here explores the nature of the
Received in revised form
biomass to surface area relationship and the role of biogenic ferrous iron during Geobacter
29 September 2010
sulfurreducens-mediated ferrihydrite reduction, with and without an electron shuttle,
Accepted 14 October 2010
through experiments and a mathematical model. The results indicate that a saturating
Available online 6 November 2010
function of biomass most accurately describes the rate of iron reduction without electron shuttles, based on the principle of electron transfer via direct contact. This study also finds
Keywords:
that the most appropriate model of iron reduction in the presence of electron shuttles
Iron reduction kinetics
includes both a saturating function of biomass for electron transfer via direct contact and
Ferrihydrite
a first-order electron transfer to ferrihydrite via the electron shuttle, strongly supporting
Geobacter sulfurreducens
the idea that G. sulfurreducens uses both pathways simultaneously. In all experiments, G.
Electron shuttle
sulfurreducens reduced less than 60% of the total ferric iron, a phenomenon that has often
Biomass
been explained through the inhibitory effects of biogenic ferrous iron in the dissolved
Ferrous inhibition
phase. However, through experiments with spikes of ferrous sulfate, this study suggests that the role of dissolved ferrous iron is passive in this case, and does not directly inhibit the extent of iron reduction in ferrihydrite coated sand. These experiments find that solid phase ferrous iron is the most probable primary product of ferrihydrite reduction, and that the conversion of solid ferric iron to solid ferrous iron depletes a fixed pool of bioavailable ferric iron, thereby accounting for the incomplete reduction of ferric iron observed here. This is the first reported model that explicitly treats solid ferrous iron as the primary product of reduction, with aqueous ferrous iron as a passive byproduct. This simple mathematical model readily translates to other systems of microbially mediated iron reduction. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Poorly crystalline iron oxides such as ferrihydrite are highly reactive, binding with many contaminants and important
nutrients. Their nearly ubiquitous presence in sediments and soils makes the chemistry of such iron oxides an important control on contaminant transport in groundwater and sediments, as iron is the dominant redox sensitive element in
* Corresponding author. E-mail address:
[email protected] (P.R. Jaffe´). 1 Present address: School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, Korea. 2 Present address: Johns Hopkins University, Global Water Program, Baltimore, MD21205, USA. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.017
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a wide variety of sub-surface environments (Lovley et al., 2004). As a testament to the influence and complexity of iron oxide biogeochemistry, much research has focused on microbially mediated reduction of poorly crystalline iron oxides under anaerobic conditions in the sub-surface, as bacteria transform ferric iron (Fe(III)) to ferrous iron (Fe(II)), leading to discoveries about the complex nature of solid phase transformations (Hansel et al., 2005; Islam et al., 2005) and the mechanisms through which microbes drive these transformations (e.g. Lies et al., 2005). The seemingly contradictory interplay between iron reduction and contaminant solubility can lead to the direct release of sorbed contaminants such as arsenic or phosphate under some conditions (Bennett and Dudas, 2003; Burnol et al., 2007) or to the direct capture of contaminants like arsenic under other conditions (Islam et al., 2005). Iron oxidation, on the other hand, usually leads directly to exclusive contaminant capture (e.g. Blute et al., 2004). Microbially mediated iron reduction can also indirectly influence contaminant mobility when it occurs simultaneously with microbially mediated uranium reduction and precipitation (Anderson et al., 2003; Komlos et al., 2008). Despite the many recent advances in understanding the fundamentals of iron reduction, much remains undiscovered. In particular, few studies have focused on a simple and robust mathematical description of the influence of biomass on iron reducing kinetics for surface bound microbes undergoing direct electron transfer from organic carbon to ferric iron oxides. The relationship between biomass concentration and rate of iron reduction is a necessary and important component of any predictive model that describes the transport of contaminants that interact with iron oxides, especially during processes such as biostimulation, when biomass can vary by over an order of magnitude within a period of a few weeks (Moon et al., 2010). Contaminant transport and geochemical models of aqueous substrate and electron donor consumption, including Fe(III), typically use the Monod growth formulation with MichaeliseMenten uptake kinetic form (e.g. Guha and Jaffe´, 1996; Wang et al., 2003), expressed in the dual Monod form as dB S1 S2 B bB; ¼ mmax KS1 þ S1 KS2 þ S2 dt
(1)
and mmax ¼ Yvmax ;
(2)
where S1 and S2 are the concentration of the electron donor and electron acceptor (moles), B is the biomass (cells/L), mmax is the maximum growth rate (hr1), KS1 and KS2 are half-saturation constants (moles), b is a decay coefficient to describe cell death (hr1), Y is the cell yield (cells/mole), and vmax is the maximum substrate uptake rate (mole/(cells hr)). Biogeochemical models also often use this formulation to describe surface-driven processes like manganese and iron reduction (e.g. Burgos et al., 2003; Smith and Jaffe´, 1998; Wang et al., 2003). MichaeliseMenten uptake kinetics derive from laws of diffusion (Aksnes and Egge, 1991), and, although Monod kinetics were originally empirically derived, current research offers a thermodynamic explanation for Monod kinetics (Liu et al., 2003). In the above kinetic expression, the rate of reduction is proportional to biomass. However, a proportional relationship between biomass and solid phase iron reduction rate surely
cannot hold, especially at high biomass concentrations. In this surface-driven phenomena, bacteria only reduce proximal ferric ions, so the limiting control on the rate of reduction is the surface density of bacteria or, more specifically, the ratio of the ferric substrate total surface area to the combined area that all iron reducing bacteria can influence. As more bacteria load the surface of iron oxides, the gain in iron reduction rate by adding additional bacteria must diminish because the system approaches complete biomass saturation. Yet this effect may not be universal. It is not clear whether highly bioavailable, thin layers of amorphous ferrihydrite, which are similar to freshly precipitated iron found in natural sediments, will display the effects of biomass saturation in natural conditions or under bioremediation conditions. Therefore, the present study focuses on biomass concentrations near the range relevant for biostimulation field experiments, found to be from w106 to w108 cells/g sediment (Cardenas et al., 2008; Moon et al., 2010), on thin layers of amorphous ferric iron. To isolate surface effects, this study focuses on Geobacter sulfurreducens which have not been demonstrated to synthesize compounds to reduce spatially distant iron oxides. Geobacter species are thought to reduce iron oxides by direct electron transfer to the oxide surface, either via electrically conductive filamentous pili or via surface proteins. A geneknockout study of G. sulfurreducens showed that pili deficient mutants do not reduce ferric iron oxides while wild-type do (Reguera et al., 2005). The role of pili is not clear; Reguera et al. (2007) acknowledge the possible structural role that pili take to attach to surfaces and other work finds that surface cytochromes are key components of electron transfer in G. sulfurreducens (Nevin et al., 2009). Lovley (2008) provides thorough discussion of pili and proteins thought to be involved in electron transfer. Despite the incomplete information on the nature of electron transfer, all aforementioned models depend on localized electron transfer from G. sulfurreducens to iron oxides. No study has demonstrated solid ferric iron reduction at distance for G. sulfurreducens without exogenous compounds. In contrast, research demonstrates that another well-studied iron reducing bacterial genus, Shewanella, can reduce spatially distant iron oxides without exogenous compounds (Lies et al., 2005; Rosso et al., 2003). Therefore, G. sulfurreducens is an ideal candidate for studying the surfaceloading limitations of iron reduction. Although evidence does not indicate that G. sulfurreducens can synthesize compounds to reduce spatially distant ferric iron, G. sulfurreducens can use naturally occurring compounds to reduce ferric iron. Humic substances, a class of large organic molecules found in soils and sediments, can act as redox intermediates. G. sulfurreducens mediates the transfer of electrons from small organic molecules like acetate to various humics (Scott et al., 1998), which then pass electrons abiotically to ferric oxyhydroxides (Jiang and Kappler, 2008). Since the oxidation state of such humics cycle between oxidized and reduced while they ferry electrons from small organic molecules to an insoluble terminal electron acceptor, such humics are called electron shuttles. Electron shuttles increase the rate of iron reduction for iron reducing bacteria on a suite of iron oxides and oxyhydroxides (Burgos et al., 2003; Hacherl et al., 2003; Komlos and Jaffe´, 2004; Scott et al., 1998;
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Wolf et al., 2009). Research demonstrates that Monod-type kinetics apply to humic reduction (Jiang and Kappler, 2008), and the abiotic transfer of electrons from reduced humics to solid iron oxides is unlikely to depend on biomass. This raises an interesting, unanswered question: in the presence of electron shuttles, will the rate of bacterially mediated iron reduction have a different form with respect to biomass? To uncover the biomass kinetic form, this study compares experiments with and without an electron shuttle, 9,10anthraquinone-2,6-disulfonic acid (AQDS). AQDS is used as a functional analogue to naturally occurring humics but not as a structural analogue because AQDS is smaller than natural humics and may diffuse into the periplasm to dramatically accelerate reduction rates. Consequently, AQDS-driven electron shuttling is an extreme case that minimizes the impact of surface limitations on iron reduction rate, and, by adding AQDS, this research tests whether humics remove the surface limiting effect of biomass or whether surface limitations hold even in the presence of rapid electron shuttling. Roden (2006) elegantly shows the intriguing result that the initial rate of iron reduction is a saturating function of biomass when no electron shuttle is present. That work demonstrates that G. sulfurreducens and Shewanella putrefaciens achieve a maximum initial reduction rate as biomass increases in slurries of synthetic goethite, offering proof that the rate of iron reduction saturates as biomass increases. But goethite is a crystalline iron oxyhydroxyide, and the principles of biomass saturation on goethite may not apply over relevant concentrations of biomass on thin layers of amorphous iron oxides like ferrihydrite, which are more bioavailable and yield faster rates of iron reduction (Bonneville et al., 2004). Roden (2006) tested natural sediments, focused on the initial rate of reduction, and did not test for saturating effects in the presence of electron shuttles. Like that study, Bonneville et al. (2006) also showed a saturating function of biomass in the absence of electron shuttles through application of a geometric model considering idealized nanohematite spheres, another highly crystalline iron oxide. Both models focused on the initial rate of reduction. As a result, the research presented here seeks to reproduce and expand on the work of Roden (2006) and Bonneville et al. (2006) in several ways. First, this study will test whether the notion of a saturating relationship of biomass to solid ferric iron is valid for highly bioavailable, thin layers of amorphous ferrihydrite in porous media over relevant biomass concentrations. Through the development of a model the present research also seeks to extend the notion of biomass saturation to iron reduction rate at all times, not only the initial rate of reduction. Most importantly, this research will address whether the surfacesaturating relationship holds in the presence of electron shuttles, because it is not clear if the pathways are mutually exclusive, compete, or coexist (Cutting et al., 2009). This research will help resolve that issue by rigorously exploring kinetics under a saturating biomass with and without an electron shuttle. Aside from the biomass surface area kinetic relationship, another puzzle is the inability of bacteria to completely reduce solid ferric iron oxides in homogenous, closed systems. Iron reducing bacteria typically reduce a fraction of total ferric iron (e.g. Fredrickson et al., 1998; Hacherl et al., 2003), but there is no single unifying explanation for the intractability of this fraction of iron oxides. Some view sorption of biogenic Fe2þ to
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surface Fe(III) as the main mechanism for stopping ferric reduction, while other studies invoke thermodynamics. For example, Royer et al. (2002a) showed that natural organic matter and ferrozine, which enhances ferrous iron solubility, enhanced the final extent of hematite reduction reduction and concluded that sorption was the limiting factor in the total amount of Fe(II) produced. In a related study, Royer et al. (2002b) showed that spikes of aqueous Fe(II) before to inoculation with S. putrefaciens CN32 inhibited hematite reduction and greatly diminished the effects of all of the organic materials, again suggesting that sorption explains the cutoff in ferric reduction. Hacherl et al. (2003) and Roden and Urrutia (1999) found similar results. Yet later, using Mn(II) as an analogue to Fe(II), Royer et al. (2004), found that Mn(II) sorption did not inhibit hematite reduction, implying that Fe(II) sorption does not stop hematite reduction and instead suggest that thermodynamics are mainly responsible. But this conclusion depends partly on deriving a value for the thermodynamic potential of hydrated hematite, which was unknown and was indirectly calculated. Also, it is important to note that the majority of biogenic ferrous iron in those studies was in the aqueous phase, which is not true for ferrihydrite reduction where most ferrous iron is in the solid phase. Similarly, other studies proposed that the diminishing thermodynamic energy of ferrous iron production explains why ferric iron reduction stops when ferric iron is left in the system; Liu et al. (2001) show that ferric iron reduction ceases when Fe2þ production yields less energy than is needed to make adenosine5-triphosphate (ATP), the fundamental energy storing molecule of all cell metabolism. But such calculations are not a direct test of the thermodynamic limitation hypothesis since they depend on the assumption that Fe2þ is the primary product of reduction. One way to directly test the hypothesis that thermodynamics limit the extent of ferric iron reduction is to test whether the addition of dissolved ferrous iron inhibits the rate of iron reduction in the presence and absence of electron shuttles. The energy of the total reaction is the same with or without a redox intermediate, so if thermodynamics limit the extent of iron reduction this limitation should be the same with or without an electron shuttle. The work presented in this paper will use this concept test to revisit the issue of thermodynamic limitations on ferric iron reduction. Since a main goal of this study is to determine biomass saturation effects on the rate of iron reduction, the experiments here involve reactions on sand coated with thin layers of ferrihydrite rather than studying reactions on pure ferrihydrite. Coating sand particles with iron oxides ensures a more even distribution of iron oxides in the experiment than would stirring slurries of pure iron oxides, and using sand grains as a lattice for ferrihydrite prevents aggregation of iron particles and flocculation. Aggregation could have the undesirable impact of changing the surface area to biomass ratio during the course of an experiment, decreasing bioavailability (Cutting et al., 2009). Following the above motivating discussion, this work takes a new look at the kinetics of iron reduction, with the goal of using experimental results to develop a mathematical model that incorporates the surface loading of biomass and explains the cutoff in iron reduction. Specifically, through batch experiments with different biomass concentrations, experiments with and without AQDS, experiments with ferrous sulfate additions, and
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experiments with an Fe(II)-specific chelator, this research tests four questions: (i) does the rate of iron reduction on ferrihydrite saturate with respect to biomass as G. sulfurreducens load ferrihydrite surfaces, (ii) do electron shuttles shift the G. sulfurreducens biomass surface area kinetic relationship from a saturating function to some other function or are these pathways independent, (iii) does ferrous sorption inhibit the rate or extent of G. sulfurreducens mediated ferrihdyrite reduction, and (iv) do the thermodynamics of dissolved ferrous iron production limit the rate or extent of G. sulfurreducens mediated ferrihydrite reduction? Detailed mineralogy is not performed here. Iron reduction and corresponding mineral transformations are extremely complex, and biostimulation models need a simple and robust formulation for conditions in which the detailed geochemistry might not be known.
2.
Methods
2.1.
Ferrihydrite coated sand
Iron oxide coated sand consists of ASTM grade 20-30 sand (U.S. Silica), a mesh size equivalent to 0.60e0.85 mm, etched in 2 N nitric acid overnight, rinsed in DI water, wet and dry sieved, and coated in a synthetic iron oxide according to the method of Brooks et al. (1996). After this initial preparation, a 6-min rinse with deionized water removed loosely attached iron oxides that might detach, clog pore space, and aggregate. Removing loose particles better ensures a consistent surface area of iron oxides throughout the course of the experiments and ensures the thinest possible layer. Air drying was the final step in preparation of iron coated sand. Numerous studies on sand prepared by this method found the iron coating to be ferrihydrite for lengths over 30 days after synthesis (Brooks et al., 1996; Hansel et al., 2005). Ferrihydrite coated sand was loaded into test vials for iron reduction experiments. Two grams of coated sand were mixed with 18 g of uncoated sand to achieve fast reduction kinetics. Like the coated sand, 2 N nitric acid etched the uncoated sand overnight, and the etched sand underwent dry and wet sieving.
2.2.
Media
An artificial groundwater (AGW) served as the media for these iron reduction experiments, similar to that described in (Benner et al., 2002), which was designed to closely resemble naturally occurring groundwater. AGW contained 20 mg/L NaCl, 0.95 mg/L NH4Cl, 5 mg/L KCl, 50 mg/L MgSO4, 0.95 mg/L KH2PO4, 1/2 mL Wolfes mineral solution (ATCC), and 1/2 mL Wolfes vitamin solution (ATCC), equilibrated under 1% CO2, 99% N2 (Airgas, certified grade) and calcite (0.6 g/L). Visual MINTEQ calculations for batch vials with quartz and ferrihydrite reveal an ionic strength of 0.0068 and pH of 7.8, a pH slightly above experimental results, and an initial total dissolved carbonate concentration greater than 10 mM ðtotal carbonate ¼ ½H2 CO3 þ ½HCO þ ½CO2 3 Þ. All stock salts were reagent grade (Fisher, Sigma). Unlike Benner et al., the AGW of this study contains elevated vitamins and trace minerals here, which ensures no limits on iron reduction except iron availability. This solution was also equilibrated with lower levels of atmospheric CO2. Acetate served as the electron donor and organic carbon
source at 10 mM concentrations, a high level that keeps acetate from becoming rate limiting. Experiments either contained 140 mM AQDS or were without any AQDS. All media was autoclaved prior to running the experiments at 121 C for 20 min and stored anaerobically. After the addition of 6.5 mL of media with bacterial culture to 20 g prepared sand mixtures, the media purged for 15 min under 1% CO2, 99% N2 and was crimp sealed with a rubber stopper.
2.3.
Bacterial culture
G. sulfurreducens (ATCC 51573) were cultured in a basal salt media as described elsewhere (Lovley and Phillips, 1988), with 10 mM sodium acetate as the electron donor and 8.0 g/L fumarate as the electron acceptor. Initial cultures from ATCC grew to late log phase, before centrifuge harvesting at 8000 g and 4 C for 15 min and freezing at 80 C with 10% glycerol to store as seed cultures. Large batches of identical seed cultures were grown from frozen stock and again harvested and refrozen. Prior to inoculation in a batch test vial, frozen seed cultures were revived in fumarate bearing media, grown for 96 h, twice centrifuged (8000 g, 4 C, 20 min) and rinsed with sterile AGW. These cultures were then added to batch vials. Bacterial biomass concentrations were determined by epifluorescence microscopy (Lisle et al., 2004), with counts performed on 6 slides for each cell containing AGW media fixed at the moment of adding to sediment-bearing batch vials.
2.4.
Sampling and analysis of batch experiments
Identically prepared 20 mL vials were sampled in duplicate. While purging under 1% CO2, or inside an anaerobic glovebox (Coy), pH was measured in dedicated sample vials with microelectrodes contained within a 16-gauge needle (Microelectrodes, inc) and found to remain circumneutral throughout (pH ¼ 7.0 0.5). Each vial was destructively sampled inside the anaerabic glovebox by removing 3.5 mL of porewater via syringe. From this porewater, two samples of 0.5 mL were filtered (0.2 mM, Fisher) and added to 2 N HCl (final conc is 1 N HCl) for determination of Fe(II) (aq) and using 3-(2-pyridyl)-5,6bis(4-phenylsulfonic acid)-1,2,4-triazine, hereafter referred to as ferrozine Stookey (1970), in a strong HEPES buffer (35.8 g/L) titrated with 10 M NaOH to pH 7.0. This filtration proved sufficient to remove solid iron particles in random filtered samples. Analyzing for ferrous iron after adding the strong reductant hydoxylamine to a 0.25 N final concentration yielded no additional iron within detection limits. From the original 3.5 mL of porewater, 1 mL of filtered porewater was stored in a sealed container at 4 C for acetate analysis via ion chromatography (Dionex) using a Bio-Rad HPLC fast acid analysis column (100 7.8 mm). The reduced form of AQDS, AH2QDS, was measured anaerobically by combining the remaining 1.5 mL of the original 3.5 mL of filtered porewater from duplicate vials into a single cuvette (Fisher), reaching a final volume of 3 mL, for spectrophotometric measurement at 405 nm (Genesys) inside the anaerobic glovebox. Adding 10 mL of 1 N HCl to the sediment and remaining 3 mL of AGW (final concentration 0.77 N HCl) allowed for solidassociated Fe(II) via ferrozine analysis Stookey (1970). Adding up to 5 N HCl did not increase the amount of extractable Fe(II).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 4 9 e1 0 6 2
Visually, all vials appear as pure sand after the 24 h extraction, losing any trace of color from red ferric oxides or black ferrous precipitants. Adding hydroxylamine to a final concentration of 0.25 N reduces all ferric iron, allowing for total iron measurement via the ferrozine method. Thus, solid-associated ferrous iron is defined as Fe(II)s ¼ [0.77 N HCl Fe(II)] [Fe (II) (aq)]. In each batch vial at time zero for biomass experiments, acid extraction and iron analysis found 31.4 mmoles of Fe(III), or 15.7 mmoles of Fe(III) per gram of iron coated sand, which also translates to a concentration of 4.83 mM of solid ferric iron. For experiments testing the role of dissolved ferrous iron, the same analysis revealed 86.4 mmoles Fe(III), or 43.2 mmoles of Fe(III) per gram of iron coated sand (13.3 mM). The experimental analysis in this paper relies strictly on measurements from wet-chemistry, owing to the inherent difficulty in obtaining reliable solid phase data on thin layers of amorphous iron oxides bound to highly crystalline quartz grains. Even if possible, it is doubtful whether solid phase analysis would reveal any new insights because the transformation of ferrihydrite to other iron oxides under iron reducing conditions is already well-documented (Benner et al., 2002; Coker et al., 2008; Fredrickson et al., 2001; Hansel et al., 2003), and the experiments here focus on the role of biomass and ferrous iron.
2.5.
Batch design strategy: biomass surface loading
Here, a batch experiment consists of identically prepared vials, with the same initial biomass (population size), the same AQDS concentration, and the same Fe(III) content. Separate batch experiments differ in the initial bacterial biomass or AQDS content. The strategy of varying the biomass while holding all other constituents fixed highlights the relationship between biomass and solid phase iron content, with and without AQDS. Bacterial population size in these experiments was selected in a range relevant for field scale biostimulation. Cardenas et al. (2008) found iron reducing bacteria concentrations ranging from 1.93 106 cells/g sediment to 9.40 107 cells/g, and the experiments in this study range from 7.50 105 cells/g sediment to 6.30 107 cells/g. Control experiments, data not shown, revealed that no iron or AQDS reduction occurred without G. sulfurreducens. All experimental vials were incubated at 30 C under gentle shaking. While precise direct measurement of biomass after inoculation was not possible, growth models estimate that biomass concentrations remain roughly constant throughout the course of the experiments, within 5% with the possible exception of the low biomass, high AQDS experiment, which was, accordingly, not included in model fitting. See the Appendix for details.
2.6.
Batch design strategy: role of dissolved ferrous iron
To explore the role of Fe(II), spikes of ferrous sulfate at either 0.8 or 8 mM concentrations were added to batch vials at the start of microbial reduction, with and without AQDS. The purpose of these experiments are to test: 1. Does biogenic Fe2þ inhibit iron reduction thermodynamically? 2. Does biogenic Fe2þ sorb to the surfaces and inhibit iron reduction?
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These concentrations of ferrous sulfate were selected because 0.8 mM is roughly equal to the maximum biogenic aqueous ferrous iron observed, and 8 mM is an extreme case well over biogenic levels produced, so any thermodynamic or sorptive impact of biogenic ferrous iron should be evident. Complexation of sulfate did not increase overall solubility according to MINTEQ calculations, since ferric oxides, siderite, and vivianite were supersaturated under both conditions. At 8 mM FeSO4, MINTEQ calculated that 67% of Fe2þ was a free ion, 19% was complexed as FeSO4(aq), 2% as FeOHþ, 5% as FeHCO3 and 7% as Fe-acetate. At 0.8 mM FeSO4, MINTEQ calculated that 77% of Fe2þ was a free ion, 5% was complexed as FeSO4(aq), 7% as FeOHþ, 5% as FeHCO3 and 9% as Fe-acetate. Other vials contained an Fe(II) chelator, ferrozine, at a concentration of approximately 20 mM. At circumneutral pH, Ferrozine (Fz) binds to iron exclusively as FeFz3 (Huang et al., 2003), so adding 20 mM Fz creates the binding capacity for 6.65 mM dissolved ferrous iron, half the concentration of iron present in the initial ferrihydrite.
2.7.
Numerical simulation
The model equations developed in the discussion section were coded in Matlab as ordinary differential equations and integrated using the internal Matlab ode45 program, a variable step size RungeeKutta method. The model has an analytical solution when AQDS is not present, and comparing results from the ode45 integration to the analytical solution revealed a negligible difference using the model parameters in Table 3, suggesting that ode45 is an appropriate solver. Matlab optimization function lsqcurvefit, which scans the parameter space to minimize the difference between modeled results and real data, provided parameter estimates for experiments with and without AQDS. The lsqcurvefit searched for parameters using the large-scale, trust-region reflective Newton method, as this yielded the most reliable results. The parameter fits for experiments without AQDS became inputs into the model with AQDS and iron coated sand. Fits to batch experiments containing AQDS but with no iron coated sand generated parameters for experiments with AQDS (data not shown), which then became inputs to the model for experiments containing AQDS and iron coated sand. Using parameter estimates from simplified systems as inputs to the full AQDS and plus iron system allows the optimization function lsqcurvefit to focus on only one parameter, the rate constant for electron transfer from the reduced form of AQDS, AH2QDS, to ferrihydrite. A suite of experiments with 10 mM acetate and varying AQDS and biomass concentrations, but no ferrihydrite, was used to determine the firstorder kinetic parameters of G. sulfurreducens mediated AQDS reduction as used in the model (data not shown).
3.
Results
3.1.
Biomass loading
Fig. 4 presents the time series of biogenic ferrous iron from solid and aqueous phase measurements, with reduced iron as a fraction of the total ferric iron present at the start of the
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experiment. Raw data appear as symbols and model results appear as lines. The raw data in this figure reveal that increasing biomass increases the rate of iron reduction for a given biomass, showing that the electron shuttle AQDS enhances the rate of iron reduction. From the experimental data in these graphs, it is clear that adding AQDS does not result in an increase in the total amount of biogenic ferrous iron produced. During the course of the experiments shown, biomass remained roughly constant as explained in detail in the Appendix, with the possible exception of the lowest biomass experiments with AQDS, which have been removed from analysis. Experiments without AQDS show more complex dissolved ferrous iron trends after day 3, with most experiments showing a drop in dissolved ferrous iron concentration, and at least one experiment with AQDS show a similar pattern beginning between days 1 and 2 (down triangles). This pattern could be the precipitation of ferrous minerals like vivianite or siderite: þ 3Fe2þ þ 2HPO2 4 þ 8H2 O/ Fe3 ðPO4 Þ2 $8H2 O ðsÞ þ2H
(3)
þ Fe2þ þ HCO 3 /ðFeCO3 ÞðsÞ þH
(4)
Indeed, the solubility of vivianite and siderite is low, and Visual MINTEQ calculations show both solids as above saturation at 0.075 mM Fe2þ. Aqueous concentrations in the experiment climb to as high as 0.75 mM Fe2þ (x15% FeTOTAL). This suggests that the formation of both solids is thermodynamically favorable in the experiments of Fig. 4, in keeping with siderite and vivianite formation observed in other, similar experiments (Fredrickson et al., 1998; Islam et al., 2005; Liu et al., 2001). With more than 1% CO2 in the headspace plus 10 mM initial total dissolved carbonate species and with a phosphate concentration of 0.07 mM in AGW, there is more than enough carbonate and phosphate to account for the observed maximum solidassociated of roughly 50% FeTOTAL, equal to 2.45 mM Fe(II)s. Since Fe2þ stays above 0.075 mM, the system is out of equilibrium with respect to vivianite and siderite formation. Fig. 1 makes it clear that the initial rate of reduction is much higher in the presence of AQDS over the range of biomass concentrations explored. AQDS also increases the final solubility of ferrous iron, but not the maximum extent of iron reduction, so diminishing surface limitation or allowing simultaneous reduction is likely to be the key impact of adding electron-shuttling AQDS and a key part of the model developed during the discussion section below, which explains the model derivation and model results in detail. Fig. 2 shows the AH2QDS time series used to calibrate the model. Although the molecule cycles between AQDS and AH2QDS, the rate of electron transfer from AH2QDS to ferrihydrite is the rate limiting step, which is evident because AH2QDS accumulates in the experiments during the active phase of iron reduction (Fig. 2). Hence, the rate of electron transfer from acetate to AQDS exceeds the rate from AH2QDS to iron.
3.2.
Role of dissolved ferrous iron
Figs. 3 and 4 show the results of ferrous sulfate or ferrozine additions, displaying time series of ferrous iron for the test conditions referenced against a control experiment without FeSO4 or ferrozine. No measurable ferrous iron sorption or
precipitation occurred in abiotic controls (data not shown). Because the aqueous ferrous iron content remained high in the experiments with ferrous sulfate spikes (Fig. 3, panels a, b), the time series of aqueous iron shows that little sorption or precipitation of iron from ferrous sulfate occurred, except after day one in the 8 mM FeSO4 experiment. Panel a reveals a drop in the dissolved iron concentration after day one that corresponds to a rise in the solid-associated ferrous iron concentration (panel d ), while the cumulative sum of dissolved plus solid-associated ferrous iron remained relatively constant (panel g). Precipitation of ferrous iron explains this pattern. Interestingly, ferrozine addition led to almost twice the biogenic aqueous iron concentration in the presence of AQDS, but had no effect in the absence of AQDS (panel c). In both cases, as the bulk of reduced iron is in the solid phase, the total biogenic Fe(II) was roughly similar to control experiments for ferrozine additions. This is a different result than that found in, for example, (Royer et al., 2002a), which studied hematite reduction. From Fig. 3 it is not clear whether the spike of FeSO4 inhibited biogenic iron production because of the high background Fe(II) levels. However, one can determine whether a ferrous spike inhibits iron reduction by tracking the total amount of ferrous iron in the system, which includes both the dissolved and solidassociated ferrous iron, and then subtracting any ferrous iron added as ferrous sulfate and labeling the remaining ferrous iron as biogenic ferrous iron. Accordingly, Fig. 4 shows only the biogenic ferrous iron normalized as a fraction of the total iron. Fig. 4 reveals that spiking the system with the highest observed biogenic aqueous ferrous iron concentrations (0.8 mM) does not inhibit iron reduction. Furthermore, of the experiments performed here, Table 1 shows that spikes of ferrous sulfate only inhibit experiments without AQDS the ferrous sulfate concentration is 8 mM, ten times the biogenic ferrous iron concentration, and do not inhibit experiments with AQDS. Nor did adding ferrozine as an Fe(II)-specific chelator enhance the rate or extent of iron reduction, as Fig. 4 also shows.
4. Discussion: model concept and model results 4.1.
Model concept
As a starting point into developing a mathematical model to predict the rate of iron reduction while considering the role of biomass and the role of ferrous iron, this discussion begins with the concept of bioavailable iron. It is reasonable to define bioavailable ferric iron as the portion of ferrihydrite that is able to receive electrons either from bacteria directly attached to the surface or from AH2QDS, and assume that bioavailable ferric iron is a fixed fraction at time zero. Fig. 4 suggests that bioavailable ferric iron is the same with and without AQDS. The fraction of iron that is bioavailable is necessarily a function of the surface area, the surface morphology, and the degree of crystallinity. This fraction may represent only the top layer of ferric iron ions, or the first several layers, or only lewis acid sites, or ions buried deep in the crystal. Such nuances may not be possible to study in complex systems even with advanced spectroscopic techniques, and their elucidation is not undertaken here. So, the model assumes
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a
Biogenic iron ratios no AQDS
b
0.6
Ratio of biogenic solid Fe(II) 0.3 to Fe(total) 0
c
e
0
2
4
6
0
8
d
0.2
0
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4
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8
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0.2
0.1
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f
0.8
Ratio of biogenic solid & aqueous 0.4 Fe(II) to Fe(total) 0
0.6
0.3
Ratio of biogenic aqueous Fe(II) 0.1 to Fe(total) 0
Biogenic iron ratios with AQDS
0.8
0.4
0
2
4
6
0
8
Time (days)
Time (days)
,
5.02
107 cells/µM Fe(III)
,
1.76
107 cells/µM Fe(III)
,
3.84
106 cells/µM Fe(III)
,
1.39
106 cells/µM Fe(III)
,
4.26
105 cells/µM Fe(III)
Fig. 1 e Biogenic iron production with 140 mM AQDS (b,d,f) and without AQDS (a,c,e) for five biomass concentrations (< ±5%). Lines are model results. The top row depicts solid ferrous iron (a,b), the middle depicts aqueous ferrous iron (c,d), and the bottom depicts the sum of all ferrous iron (e,f).
that the rate of reduction is proportional to the concentration open surface sites, Feo(t), an assumption consistent with other published models (Burgos et al., 2003; Roden, 2006). Next, the discussion turns to the role of ferrous iron. The above results show that spiking the system with ferrous iron does not inhibit the rate of iron reduction except at levels much higher than those found during microbially driven iron reduction in these experiments, and they demonstrate that increasing the solubility of ferrous iron by adding a chelator does not enhance the rate or extent of reduction. Therefore, the sorption of Fe2þ or the thermodynamics of dissolved ferrous iron production do not limit the rate or extent of iron reduction for these experimental conditions.
To further shed light on this, one can examine the thermodynamics of Fe2þ production as if it were the primary reaction product. Then, the following conditions apply: þ CH3 COO þ 4AQDS þ 4H2 O/2HCO 3 þ 4AH2 QDS þ H ; DG1 ;
(5)
8FeðOHÞ3ðsÞ þ16Hþ þ 4AH2 QDS/8Fe2þ þ 4AQDS þ 24H2 O; DG2 ; (6) and CH3 COO þ 8FeðOHÞ3ðsÞ þ15Hþ /8Fe2þ þ 2HCO 3 þ 20H2 O; DG3 : (7)
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AH2QDS concentrations during iron reduction 150
AH2QDS (µM)
75
0
0
2
4
6
8
Time (days) , , , ,
5.02 1.76 3.84 1.39
7
10 cells/µM Fe(III) 107 cells/µM Fe(III) 106 cells/µM Fe(III) 6 10 cells/µM Fe(III)
Fig. 2 e Time series of AH2QDS during iron reduction, the reduced form of AQDS, for the experiments in Figure 1 (b,d,f). Symbols are data (< ±5%), and lines are modeled results.
8 mM FeSO4
a
0.8 mM FeSO4
10
b
1
0
0
d
6
Solid Fe(II) (mM)
3
3
0
0
e
16
h
Solid & aqueous 8 Fe(II) (mM) 0
Ferrozine
2
Aqueous 5 Fe(II) (mM)
g
Since the Gibbs free energy of reaction DG3 ¼ DG1 þ DG2 , the energy of electron transfer from acetate to ferrihydrite is the same for the system with AQDS as for the system without AQDS. Without AQDS, G. sulfurreducens derive metabolic energy from the total reaction ðDG3 Þ, but in the presence of AQDS G. sulfurreducens can derive energy from the transfer of electrons from acetate to AQDS ðDG1 Þ or from acetate directly to Fe(III) ðDG3 Þ. No study suggests that G. sulfurreducens captures energy from electron transfer between AH2QDS and Fe(III) ðDG2 Þ. But regardless of which reaction G. sulfurreducens draws energy from, from the observation that an 8 mM ferrous sulfate addition does not inhibit iron reduction in the presence of AQDS, a spike of ferrous iron more than ten times that of biogenic Fe2þ levels, one can only conclude that the thermodynamics of dissolved Fe2þ production do not limit the rate or extent of iron reduction in the batch systems under study. Viewed in this light, Fe2þ is not the primary product of iron reduction but is instead a byproduct of iron reduction, with
c
1
0
1
2
1 2 Time (days)
3
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f
Control
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1 2 Time (days)
3
AQDS control
8 mM FeSO
AQDS + 8 mM FeSO
0.8 mM FeSO4
AQDS + 0.8 mM FeSO4
Ferrozine
AQDS + Ferrozine
4
4
Fig. 3 e Effects of ferrous iron and ferrozine additions on iron reduction (< ±5%), with lines to guide the eye. The left column of panels (a,d,g) shows 8 mM FeSO4 additions, the middle column (b,e,h) shows 0.8 mM FeSO4, and the right column (c,f,i) shows ferrozine addition. The top row of pan-els (a,b,c) shows dissolved Fe(II), middle row (d,e,f) shows solid-associated Fe (II), and the bottom row (g,h,i) shows all Fe(II). Note the different scales on the y-axis.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 4 9 e1 0 6 2
Biogenic Fe(II) production after ferrous sulfate or ferrozine addition 0.3
Ratio of biogenic solid & aqueous Fe(II) to Fe(total) 0.15
0
0
1
2
3
Time (days) Control
AQDS control
8 mM FeSO4
AQDS + 8 mM FeSO4
0.8 mM FeSO4
AQDS + 0.8 mM FeSO4
Ferrozine
AQDS + Ferrozine
Fig. 4 e Biogenic ferrous iron time series from the experiments in Figure 3. This graph subtracts FeSO4 additions to strictly display only biogenic ferrous iron, referenced against control experiments with no FeSO4 or ferrozine (black symbols). Symbols are data (< ±5%), and lines are present to guide the eye.
the corollary that the direct formation of solid-associated ferrous iron is the primary product of iron reduction in this system. Indeed, other research supports this concept; Wilkins et al. (2007) found evidence to support the idea that electrons transferred from G. sulfurreducens to ferrihydrite reduce iron ions buried deep in the mineral structure, which cannot dissolve until overlying layers of ferric ions are reduced. So, while the accumulation of Fe(II) on oxide surfaces may be responsible for the cessation of iron reduction, our results show that solid-associated Fe(II) on these surfaces is not biogenic Fe2þ that has re-adsorbed, which is, for example, the conceptual model of Hansel et al. (2003). Rather, the present results suggest a direct Fe(III)s to Fe(II)s transformation. Why, then, is aqueous ferrous iron present? The answer may be rooted in the relative stability of newly minted solidassociated ferrous iron ions. Such ferrous ions may be unstable in bonds that constrained the same ions in ferric form. Ultimately, unstable ferrous ions transform to other, more stable solids, as found in other studies (e.g. Fredrickson et al., 1998; Islam et al., 2005; Liu et al., 2001), or dissolve into porewater as aqueous ferrous iron. Capturing the rates of these transformations is difficult, and in this model these transformations are simplified so that newly minted ferrous ions are modeled as either stable or unstable.
Table 1 e Effect of ferrous sulfate. Iron reduction is deemed inhibited by FeSO4 if the rate or extent of iron reduction is significantly different from the control experiment. 0.8 mM e e X X
8 mM
AQDS
Inhibited?
X X e e
X e e X
No Yes No No
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According to the above assumptions, a fraction of the open surface sites that transform to ferrous solids is subsequently released into the aqueous phase. But what happens to the pool of open surface sites when a ferrous ion is released into the porewater? It is reasonable to suppose that, provided there are sufficient ferric ions, a new site is exposed upon ferrous dissolution. This leads to a simple rule: if a newly reduced ferrous iron ion stays in the solid matrix, the amount of open surface sites is diminished by one ion but if, on the other hand, a newly reduced ferrous iron ion is released into the aqueous phase, then a new ferric iron ion is exposed and the amount of open surface sites remains the same. This approach treats dissolved ferrous iron as a byproduct of iron reduction, in keeping with the observations and discussion above. Fig. 5 schematically represents this process. To express this simple rule in a mathematical form, the percent of ferrous iron that is not freed is assigned a value, Pnf, and the percent that is freed into the aqueous phase is also assigned a value, Pf, such that Pnf þ Pf ¼ 1. To keep mass balance, the total iron in the system always exceeds the sum of the solid-associated ferrous iron produced, Feo ðt ¼ 0Þ Feo ðtÞ, plus the aqueous ferrous iron, Feaq. Since Feo is expressed as a fraction of total iron, the mass balance becomes 1 Feo ðt ¼ 0Þ Feo ðtÞ þ Feaq ðtÞ:
(8)
This conceptual model ignores atomic exchange between aqueous and solid phase. Recent experiments tracking 56Fe/ 57Fe demonstrate a near-complete exchange of Fe between aqueous Fe(II) and Fe(III) atoms in goethtite (Handler et al., 2009). Such exchange, however, would necessarily be oneto-one and, therefore unlikely to significantly affect the rate or reduction. Solid-state electron transfer from Fe(II) through solid Fe(III) may also play a role (Yanina and Rosso, 2008). Fully quantitative experimental tracking of such electron transfers is difficult, if not impossible. This one-to-one transfer occurs in at least two steps, the initial transfer from cell to surface, then one or more intra-crystal transfers. As electrons move deeper into the crystal, previously ferrous surface sites become ferric, and, therefore, bioavailable. In this way, intracrystal electron transfer effectively extends the bioavailable iron, Feo (t ¼ 0), to ions buried in the crystal, and because the model tracks bioavailable iron this intra-crystal electron transfer is implicit in the model assumptions. In reality, a onestep electron transfer from cell to surface may be faster than a multi-step electron transfer from cell to surface to subsurface ferric ions, but the model captures a global transfer rate, which is the experimentally measurable rate. Having developed a framework based on bioavailable ferric iron, the discussion now turns to surface loading effects of bacterial biomass. The model needs a function that reflects that, at some point, adding more bacteria does not increase the rate of iron reduction. The function GðBÞ ¼
B ; Kb þ B
(9)
where B is the biomass in cells per mmole Fe(III), and Kb is a constant with the same units, expresses the concept of saturation. As B/N; G/1. With this function, the model is now complete, and Table 2 presents the full model equations.
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e- + Fe(III)open
Fe2+(aq)
New Fe(III) site exposed; no net change of Fe(III)open
Fe(II)(s)
Loss of one Fe(III)open site
Fig. 5 e Conceptual model of ferric iron reduction. An open surface site (Fe(III)open) receives an electron (eL) from acetate or AQDS and transforms to either dissolved ferrous iron (Fe2+(aq)), thus exposing a new Fe(III) surface site, or transforms to solid ferrous iron (Fe(II)(s)), thus effectively eliminating one Fe(III) site from the pool of open surface sites.
In summary, the model assumptions are: the iron reduction rate is proportional to the open ferric iron sites from a pool of bioavailable ferric iron, Feo(t), which is not necessarily the first layer of ferric ions. there is a fixed amount of bioavailable ferric iron at the start of the experiment, Feo(t ¼ 0) biogenic ferrous iron that is released into the aqueous phase does not deplete the pool of open surface sites and the rate of iron reduction is proportional to Feo ðtÞ,Pnf . If Pf ¼ 1, then Pnf ¼ 0 and reduction occurs at a steady rate until the supply of ferric ions is exhausted. the amount of iron reduced cannot exceed the total amount of iron in the system (Table 2, in eq. (3)). the rate of direct electron transfer from acetate to the surface is proportional to a saturating function of biomass, G(B). the rate of AQDS reduction is proportional to biomass, B, and AQDS concentration. biomass in the experiments is assumed to remain constant (Appendix A), but the model is adaptable to allow biomass to change as a function of time. One could incorporate, for example, growth using the expression BðtÞ ¼ B0 þ Y$FeðIIÞ, where B0 is the initial biomass, Fe(II) is the biogenic iron produced, and Y is a yield coefficient. in the presence of AQDS, the bacterial population simultaneously reduces iron directly via cell-to-surface electron transfer and indirectly via electron shuttling; direct electron transfer occurs at the same rate whether or not the electron shuttle is present.
Table 2 e Model equations.
the rate of electron transfer from AH2QDS to ferric iron is proportional to the open surface sites and to the concentration of AH2QDS. the stoichiometry of iron reduction dictates that two Fe ions reduce for every AH2QDS ion oxidized Note that this model does not account for the re-precipitation of aqueous ferrous as ferrous solids because including such events may over-parameterize the model. The focus of this model is to describe iron reduction, and the experiments with ferrous sulfate spikes suggest that the sorption or precipitation of Fe2þ does not inhibit iron reduction rate at levels experienced during biologically mediated reactions.
4.2.
Model results
The solid lines in Fig. 4 show model results, optimized according to the procedure outlined in the methods section. Table 3 reports parameter values. Although the lsqcurvefit algorithm directly optimized most parameters, the lone exception was the best fit for Pf, the parameter that assigns a percent of reduced iron ions that freely dissolve into the porewater. The estimate for this parameter was biased to be low due to the experimental results, which show a drop in dissolved ferrous iron concentration after an initial rise. This pattern most likely arises from secondary precipitation of ferrous iron, perhaps nucleated by solid-associated biogenic ferrous iron that remains in the crystal as it transforms Fe(III) to Fe(II). Secondary precipitation is not included in the model, which justifies an attempt to counter the optimization bias, so the value of Pf was manually adjusted by a factor of 1.25. After
dFeo ¼ lPnf GðBÞFeo 2aPnf ½AH2 QDSðFeo $FeTOTAL Þ dt
(10)
Table 3 e Model parameter estimates.
dFeaq ¼ lPf GðBÞFeo þ 2aPf ½AH2 QDSðFeo $FeTOTAL Þ dt
(11)
Pf ¼ 0:23
(17)
1 Feo ðt ¼ 0Þ Feo ðtÞ þ Feaq ðtÞ
(12)
Pnf ¼ 0:77
(18)
Pnf þ Pf ¼ 1
(13)
l ¼ 1:24 day1
(19)
B GðBÞ ¼ Kb þ B
(14)
m ¼ 2:61107 ðmL=ðcell,dayÞÞ
(20)
Kb ¼ 1:30107 cell=mmole FeTOTAL
(21)
Feo ð0Þ ¼ 0:51 mmole Feo =mmole FeTOTAL
(22)
a ¼ 1:59103 L2 =ðday$½mM AH2 QDS$½mM FeÞ
(23)
d½AH2 QDS ¼ mBðFeTOTAL =1000Þ½AQDS dt a½AH2 QDSðFeo FeTOTAL Þ
(15)
d½AQDS ¼ mBðFeTOTAL =1000Þ½AQDS dt þa½AH2 QDSðFeo $FeTOTAL Þ
(16)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 4 9 e1 0 6 2
adjustment, the model predicts aqueous biogenic ferrous iron production that more closely matches the data during the initial part of the reduction experiment. Then, treating dissolved ferrous iron as a passive byproduct of reduction appears to do well in the short-term, before secondary precipitation, but generally overpredicts aqueous ferrous iron concentrations in the long-term, after secondary precipitation. Nevertheless, the results support the basic ferrous iron modeling concepts insofar as the model correctly captures early phase iron reduction, before any secondary precipitation occurs. The model fit for initial open sites Feo(t ¼ 0) is 0.51 mmole Feo/mmole FeTOTAL (Table 3). Dzombak and Morel (1990) recommend a value of 0.2 for the ratio of surface hydroxyl groups to total hydrous ferrous oxides, but these surface groups may not be the only ones involved in the reaction. As Wilkins et al. (2007) show, G. sulfurreducens can transfer electrons to ions deep in the ferrihydrite structure. Yanina and Rosso (2008) show that intra-crystal electron transfer in hematite, and the results here agree with this scenario. While more complex models could account for processes like isotope exchange or solidphase electron transfer, such models would not be justified from experiments that do not accurately track these processes. The bulk rate of reduction is often of interest in biogeochemical models, and the simplified conceptual model presented in this work accurately reproduces experimental data. Fig. 4 clearly presents a diminishing spread in the rate of iron reduction curves as biomass increases, and good agreement between model and data suggests that a saturating biomass function accurately represents the system. As the biomass concentration ranges from values well below Kb to values exceeding the parameter Kb, both the data and the modeled results show curves that display diminishing returns for additional bacteria. Bonneville et al. (2006) employ a similar saturation concept to describe reduction of nanohematite. Idealizing these particles as perfect spheres allows (Bonneville et al., 2006) to derive a half-saturation constant Kb from geometric considerations. Those calculations depend on the ratio of hematite sphere size to the bacterial size, where the iron particles are much smaller than the cell, while the present study involve heterogeneously coated sand particles that are much larger than the cell. Nevertheless, both studies suggest that the saturation concept is valid. Adding the electron shuttle AQDS may seem to partially remove the saturating limitation for low biomass values. Yet at high values, although the rate of reduction is greater with AQDS than without, the rate of reduction saturates. This supports the model assumption that, in the presence of AQDS, the bacterial population simultaneously reduces AQDS and ferric iron through direct electron transfer. So, at low biomass electron shuttling dominates and surface saturation is not important, but at high biomass surface saturation becomes more important. The model fits in Fig. 4 use the same parameters l, Pnf, Pf, Feo(t ¼ 0), and Kb to describe the rate of direct cell-to-surface electron transfers with and without AQDS, strengthening the case that the two pathways coexist. Without this assumption, i.e. not allowing direct iron reduction when AQDS is present, attempts to fit model results to the data produced markedly erroneous predictions (not shown). Researchers have demonstrated that the simultaneous use of two pathways for iron
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reduction can occur for another iron reducing bacteria Shewanella oneidensis (Lies et al., 2005), and the results here are the first to demonstrate simultaneous pathways for G. sulfurreducens reducing amorphous iron oxides. Although derived for specific experimental conditions, the model presented here is readily generalized to other bacteria, electron shuttles, and iron oxides. The kinetic form of the equations should remain the same but the parameter values will change. Each iron oxide should have a unique Pf, the probability of freeing a reduced iron atom. Hematite, for example, was shown to be controlled by thermodynamics (Royer et al., 2004) which is due to a difference in the energetics of the reaction and could be exacerbated by a high Pf. Each bacteria-iron oxide combination should have a unique biomass saturation constant, Kb, and maximum reduction rate constant, l. Each electron shuttle-iron oxide combination will have a different rate constant a, and each electron shuttle will have a unique reduction constant m. To generalize for biomass growth the model will accept biomass as a function of time, but needs accurate yield coefficient estimation. The model presented here can also extend to account for multiple pools of open sites, although as currently formulated it only considers fast kinetics. The kinetic profiles of Fig. 4 do not show that iron reduction has completely ceased. To account for this, the model allows for an additive, parallel reactions that includes a second pool of a intractable, slowly reducing sites and follows the exact formulation presented in Table 2. Simultaneous reactions based on two pools of would exist (equations (10)e(16)), with independent parameters and no overlap except that the mass balance would incorporate both pools (equation (2)).
5.
Conclusions
This study presents experimental evidence supporting a new model formulation for bacterially driven iron reduction that uses a biomass surface saturation effect for direct electron transfer from cells to the solid, allows simultaneous direct transfer and transfer via electron shuttle, and treats the primary product of reduction as solid-associated ferrous iron with dissolved iron as a passive byproduct of reduction. Previous experiments testing the surface saturation limitation with S. putrefaciens did not test whether electron shuttles removed the limitation, and previous studies on the role of ferrous iron implicitly or explicitly assume that Fe2þ is the primary product of reduction. The discussion and interpretation of experimental findings in this paper leads to a new model that accurately reproduces experimental trends, suggesting that: a saturating biomass to iron content function best describes iron reduction kinetics, with the half-saturation constant Kb, at 1.30 107 cell/mmole FeTOTAL, which is relevant for biostimulation studies. sorption and thermodynamics of aqueous ferrous iron production cannot explain an abrupt cutoff in iron reduction. While certainly accounting for the observed phenomena in some studies, these explanations do not fully account for the abrupt cutoff in iron reduction in the experiments presented here.
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a direct ferrihydrite Fe(III)(S) to Fe(II)(S) transformation best explains the cutoff in iron reduction, leading to a framework that describes iron reduction based on the concept of bioavailable ferric iron. electron shuttles do not remove the biomass surface saturation limitation for G. sulfurreducens mediated ferrihydrite reduction, but acts additively to reduction via direct contact. This novel result suggests that G. sulfurreducens can simultaneously use two pathways to reduce ferrihydrite: direct electron transfer and electron transfer via electron shuttle.
G. sulfurreducens grows via reaction (5) without AQDS or via reaction (3) with AQDS. Reaction (4) occurs abiotically. MINTEQ calculations give a bicarbonate concentration of 1 mM for these batch vials. Using thermodynamic tables and conventional methods to calculate energetics at pH 7 and 1 mM bicarbonate, DG0 ¼ 0.41 kJ per millimole acetate, DG1 ¼ 0.058 kJ per millimole acetate, and DG3 ¼ 0.23 kJ per millimole acetate (Bethke, 1996; Morel and Hering, 1993; Rosso et al., 2004). An assumption of equal energetic efficiency for all substrates leads to: 3þ
Fe Ycell=kJ ¼ Ycell=c
Acknowledgements This work was supported by EPA-STAR Graduate Research Fellowship #F5A20133, the School of Engineering and Applied Sciences Upton Fellowship at Princeton University, and the National Science Foundation EAR-0337687.
Appendix. Estimates of biomass change These experiments were designed so that biomass remains constant, but sediment particles prevented accurate cell counts after inoculation to directly verify constant biomass. So, one must estimate the biomass population changes using reasonable assumptions and a published model. In the absence of alternatives, it is reasonable to apply the Monod model to the present system with the simplification that ferrihydrite concentration is the liquid equivalent (millimoles ferrihydrite per liter), an approach used in other studies (Burnol et al., 2007). Then, to predict whether biomass remains constant here, one can examine two hypothetical extreme cases: (i) maximum cell growth without cell death, and (ii) cell death without cell growth. A test of the first case requires an estimate of the rate of G. sulfurreducens growing via a ferrihdyrite/acetate redox couple and via an AQDS/acetate redox couple, which have not been directly reported. However, parameters measured in experiments with other substrates can help estimate cell growth for this redox couple. Prima facie it is reasonable to assume that the cell yield remains constant on an energetic basis, i.e. the increase in cells per kJ released by a redox reaction remains constant as G. sulfurreducens switches from one electron acceptor to another while keeping acetate as the donor. This approach neglects cell maintenance energy requirements, so this approach overestimates yield. Brown et al. (2005) report the growth rate of G. sulfurreducens growing via a ferric citrate/acetate redox couple in a nutrient rich media. In the present study, G. sulfurreducens mediates ferrihydrite or AQDS reduction in a minimal artificial groundwater media, which is likely to be less efficient, so the assumption that G. sulfurreducens grows equally well here on an energetic basis leads to a maximum estimate of cell growth. Experiments presented in (Brown et al., 2005) use ferric citrate as the Fe(III) source and follow the reaction 2þ þ 8Fe3þ ðaqÞ þ CH3 COO /8FeðaqÞ þ 9H þ 2e3 ; DG0 ;
(A.1)
while experiments in the present study follow equations (5)e(7) in the main body of this paper. G. sulfurreducens grows via reaction A.1 in (Brown et al., 2005), but in the present study
1 ; DG0
FeOOH Ycell=c ¼ Ycell=kJ ðDG3 Þ;
(A.2)
(A.3)
and AQDS ¼ Ycell=kJ ðDG1 Þ: Ycell=c
(A.4)
Here, Y is a yield coefficient as in equation (2), where superscripts indicates the electron acceptor and subscripts indicate the units of Y, which are either cells per millimole acetate or cells per kJ. Plugging this into equation (2) gives 3þ
FeOOH=c Fe =c mmax ¼ mmax
DG3 ; DG0
(A.5)
and 3þ
AQDS=c Fe =c mmax ¼ mmax
DG1 : DG0
(A.6)
Brown et al. (2005) report that KcS ¼ 0:124 mM acetate and 1 ¼ 0:00184 mM1 hr . That study found that growth was linear with respect to ferric iron concentration, Fe3þ =c =KFe3þ but not separate values for and resolves the ratio mmax Fe3þ =c mmax or KFe3þ . This can be interpreted as a special case of MichaeliseMenten uptake kinetics where Fe3þ KFe3þ . Fe3þ =c =KFe3þ mmax
KFe3þ zKFeOOH zKAQDS ;
(A.7)
which is reasonable since half-saturation constants ðKs Þ are largely determined by the cell radius (Liu et al., 2003). Because Fe3þ in Brown et al. (2005) is 50 mM, which is much larger than the concentration of AQDS or FeOOH in the present study, and this half-saturation constant is larger still, one can further assume that all half-saturation constants are much larger than the concentration of AQDS and FeOOH and that growth is linear with respect to these two compounds. Acetate concentration is not limiting the concentration in these experiments (10 mM), because CH3 COO ¼ :987z1: Kc þ CH3 COO
(A.8)
Then, under the maximum growth with no cell death test case, the Monod equation (1) becomes: FeOOH=c dB mmax ðFeOOHÞB; ¼ KFeOOH dt
(A.9)
and AQDS=c dB mmax ðAQDSÞB: ¼ KAQDS dt
(A.10)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 4 9 e1 0 6 2
Using the assumptions and relationships developed in equations (A.5)e(A.8) one finds that 3þ
Fe =c dB mmax DG3 ðFeOOHÞB; ¼ KFe3þ DG0 dt
(A.11)
and 3þ
Fe =c dB mmax DG3 ðAQDSÞB: ¼ KFe3þ DG0 dt
(A.12)
Cell growth rate is maximum at the initial concentrate of FeOOH or AQDS, and fixing FeOOH and AQDS at the initial levels makes equations A.11 and A.12 ordinary differential equations with the following solutions: !# " Fe3þ =c mmax DG3 FeOOH t ; (A.13) BðtÞ=Bð0Þ ¼ exp KFe3þ DG0 and
"
BðtÞ=Bð0Þ ¼ exp
!# Fe3þ =c mmax DG3 AQDS t ; KFe3þ DG0
(A.14)
Then, plugging in the values for these experiments (w5 mM FeOOH, 150 mM AQDS), one finds that biomass growth is 1.0% for experiments without AQDS and 7.7% with AQDS over 72 h. For the other extreme case, cell death without cell growth, equation (1) leads to BðtÞ=Bð0Þ ¼ Bð0Þebt :
(A.15) e1
From b ¼ 0.00042 h , as in Brown et al. (2005), equation A.15 predicts a loss of 3.0% biomass over 72 h. Therefore, these two extreme hypothetical cases of maximum cell growth or maximum cell death suggest that biomass remains constant throughout all experiments presented here. The moderate case, including growth and death, is the sum of the extremes, suggesting þ4.4% change in experiments with AQDS and 2.0% change in experiments without AQDS. A second method to estimate biomass change in these experiments leads to similar conclusions. Instead of employing the Monod model, one can use the simple equation of linear growth BðtÞ ¼ Bð0Þ þ Ycell=kJ DG½Sð0Þ SðtÞ Bð0Þebt ;
(A.16)
where Ycell/kJ is as defined in equation A.2, DG is the kJ per mole electron acceptor, the substrate S(t) is either AQDS or FeOOH as measured in the experiments, and b is the decay coefficient. Growth estimates are higher using this approach, ranging from 1% to 40% for experiments without AQDS, and from 3% to 256% in experiments with AQDS. This approach is less robust than the Monod approach outlined above. But, to be conservative, experiments with the lowest initial biomass and AQDS will not be used for model fitting because the linear model predicts dramatic growth for these experiments.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 6 3 e1 0 7 0
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Investigating synergism during sequential inactivation of MS-2 phage and Bacillus subtilis spores with UV/H2O2 followed by free chlorine Min Cho a, Varun Gandhi a, Tae-Mun Hwang b, Sangho Lee b, Jae-Hong Kim a,* a
School of Civil and Environmental Engineering, Georgia Institute of Technology, 200 Bobby Dodd Way, Atlanta, GA 30332-0373, USA Environmental Research Division, Korea Institute of Construction Technology, 2311 Daehwa-Dong, Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 411-712, South Korea b
article info
abstract
Article history:
A sequential application of UV as a primary disinfectant with and without H2O2 addition
Received 6 May 2010
followed by free chlorine as secondary, residual disinfectant was performed to evaluate the
Received in revised form
synergistic inactivation of selected indicator microorganisms, MS-2 bacteriophage and
8 October 2010
Bacillus subtilis spores. No synergism was observed when the UV irradiation treatment was
Accepted 14 October 2010
followed by free chlorine, i.e., the overall level of inactivation was the same as the sum of
Available online 5 November 2010
the inactivation levels achieved by each disinfection step. With the addition of H2O2 in the primary UV disinfection step, however, enhanced microbial inactivation was observed. The
Keywords:
synergism was observed in two folds manners: (1) additional inactivation achieved by
MS-2 phage
hydroxyl radicals generated from the photolysis of H2O2 in the primary UV disinfection
Bacillus subtilis spores
step, and (2) damage to microorganisms in the primary step which facilitated the subse-
UV
quent chlorine inactivation. Addition of H2O2 in the primary disinfection step was also
H2O2
found to be beneficial for the degradation of selected model organic pollutants including
Advanced oxidation process (AOP)
bisphenol-A (endocrine disruptor), geosmin (taste and odor causing compound) and 2,4-D
Free chlorine
(herbicide). The results suggest that the efficiency of UV/free chlorine sequential disin-
Sequential disinfection
fection processes, which are widely employed in drinking water treatment, could be
Synergism
significantly enhanced by adding H2O2 in the primary step and hence converting the UV process to an advanced oxidation process. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Since the discovery that UV light efficiently inactivates Cryptosporidium parvum oocysts (Bolton et al., 1998; Bukhari et al., 1999; Clancy et al., 1998; Shin et al., 2001) and Giardia lamblia cysts (Craik et al., 2000; Linden et al., 2002) at relatively low doses, an increasing number of utilities across Europe and North America have installed UV processes for drinking water disinfection in the past decade. This trend is expected to continue, since the UV process is considered as the most cost
effective technology to control C. parvum oocysts and G. lamblia cysts (USEPA, 2006), and by the development of UV-based advanced oxidation processes (AOPs) that effectively treat taste and odor causing compounds (Rosenfeldt et al., 2005) and various organic pollutants (Stefan and Bolton, 1998). This process transpires via the generation of highly reactive hydroxyl radicals (OH) during the UV photolysis of hydrogen peroxide (H2O2) that is additionally added to the UV process. Since the UV irradiation treatment does not leave a residual, secondary application of free chlorine (or combined chlorine)
* Corresponding author. Tel.: þ1 404 894 2216; fax: þ1 404 385 7087. E-mail address:
[email protected] (J.-H. Kim). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.014
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as a residual disinfectant is often required for water distribution. Application of free chlorine is also instrumental in further inactivating viruses such as adenovirus (USEPA, 2006), norovirus (Shin and Sobsey, 2008), hepatitis A virus (Grabow et al., 1983), and Norwalk virus (Keswick et al., 1985) that are relatively resistant to the UV irradiation treatment (USEPA, 2006). The UV irradiation treatment followed by the addition of chlorine is one example of many sequential disinfection processes; different disinfectant combinations have been investigated and are currently practiced such as using ozone, chlorine dioxide, free chlorine, or UV as the primary disinfectant and free chlorine or monochloramine as the secondary disinfectant (Cho et al., 2003b, 2006; CoronaVasquez et al., 2002; Rennecker et al., 2000). In a sequential disinfection scheme, a strong primary disinfectant is first applied to achieve a portion of the target inactivation level followed by the secondary disinfectant to attain further inactivation and to provide residual disinfection for water distribution. When disinfectants are applied consecutively, it is often found that the overall inactivation level achieved is greater than the sum of the inactivation levels achieved when each disinfectant is applied independently (as separate single-step disinfection processes). For instance, the sequential application of ozone (or ozone/H2O2) followed by free chlorine (Cho et al., 2006) was shown to achieve a higher level of inactivation of Bacillus subtilis spores than the sum of the inactivation level achieved with individual ozone (or ozone/ H2O2) and free chlorine application. This enhanced inactivation is referred to as a synergism, which is beneficial since it leads to a reduction in the amount of disinfectant and reaction time as well as a potential decrease in the formation of disinfection by-products (Rennecker et al., 2000). However, there are few reports in the literature regarding the synergism involved in sequential disinfection processes employing UV or UV/H2O2 followed by free chlorine. While some literature suggested that synergism was not observed in the sequential disinfection processes employing UV alone in the primary step (i.e., MS-2 bacteriophage: Shang et al., 2007; B. subtilis spores: Cho et al., 2006), no literature is available for the sequential disinfection scheme with the UV irradiation as the primary step to which H2O2 is added. The objective of this study is, therefore, to evaluate a potential synergistic effect during the sequential application of the UV/H2O2 disinfection process followed by free chlorine and to compare quantitatively with the UV irradiation (without H2O2) followed by free chlorine. MS-2 bacteriophage and B. subtilis spores, which are frequently used as indicators for UV biodosimetry (USEPA, 2006), were employed as surrogates for human enteric viruses (Dawson et al., 2005; USEPA, 2006) and Bacillus anthracis (Cho et al., 2003b; Larson and Marin˜as, 2003), respectively.
2.
Materials and methods
2.1.
Preparation of MS-2 phages and B. subtilis spores
MS-2 phage (ATCC 15597-B1) stocks were prepared by the soft agar overlay (double-agar layer) method (Cho et al., 2005; Sjogren and Sierka, 1994) using a mutant strain of Escherichia
coli C3000 (ATCC 15597) as the host. The growth broth of E. coli C3000 contained 1.3% tryptone, 1% glucose, 1% MgSO4, 1% CaCl2, and 0.8% NaCl. The host was used in the exponential to early stationary phase for the plaque assay, and the host lawn medium was incubated overnight at 37 C. Suspensions of B. subtilis spores (ATCC 6633) were prepared following the procedure by Nakayama et al. (1996), except for minor modifications such as using a 1/10 diluted nutrient agar and providing an extended incubation period of 5e7 days (Cho et al., 2003a). In brief, a freeze-dried pellet of B. subtilis spores was rehydrated aseptically using a nutrient broth (Difco Co., USA) and incubated at 37 C for 18 h. The cells were then harvested from the broth by repeating centrifugation at 3500 g for 10 min and resuspension in 50 mL of 150 mM phosphate buffered saline (PBS) at pH 7.2 twice. Several 47mm sterile Petri dishes containing 1/10 diluted nutrient agar (0.8 g/L Nutrient Broth þ 15 g/L Bacto Agar, Difco Co., USA) were subsequently inoculated and incubated at 37 C for 5e7 days to induce sporulation. After incubation, B. subtilis spores were collected into 50 mL conical tubes by rinsing the agar with 150 mM PBS and cleaned by repeating centrifugation at 3500 g for 10 min and resuspension in PBS three times. The recovery of spores from each centrifugation and resuspension was over 99%. In order to inactivate any remaining vegetative cells, the stock suspension was heat treated at 80 C for 15 min before each experiment.
2.2.
Experimental procedure
Ultrapure water (>18 MU) prepared by a Milli-Q water purification system (Millipore, Billerica, MA) and analytical reagent grade chemicals (Aldrich Co., USA) were used to prepare all the experimental solutions. All glassware was cleaned with distilled water and further sterilized by autoclaving at 121 C for 15 min. The test suspension (10 mM PBS at pH 7.0) contained 3 106 pfu/mL MS-2 phages or 3 106 cfu/mL B. subtilis spores. The concentration of H2O2 was varied from 0 to 0.6 mM. For selected single-step disinfection experiments, test suspensions contained p-chlorobenzoic acid ( pCBA) as OH probe compound and 350 mg/L of 4,40 -dihydroxy-2,2-diphenylpropane (bisphenol-A), 2,4-dichlorophenoxyacetic acid (2,4-D) and geosmine as model organic contaminants. Primary disinfection with UV or UV/H2O2 was performed using a bench-scale collimated-beam UV reactor equipped with 15-W low-pressure UV lamps (Philips Co., Netherlands), emitting nearly monochromatic UV radiation at 253.7 nm (Bolton and Linden, 2001). This equipment introduced parallel UV light to experimental reactor through a 60 50 cm long collimating tube placed below the UV lamps. The experiments were conducted following the procedure described by Bolton and Linden (2003). Prior to the experiment, the UV lamps were turned on for 10 min. The experiments were initiated once a 80 45 mm sterile Petri dish containing 40 mL experimental suspension was placed normal to the incident UV light. The depth of suspension was approximately 1 cm and the UV intensity at 254 nm at surface of suspension was measured using a radiometer equipped with a UV 254 detector (UVX Radiometer, UVP Co., USA). The UV absorbance of experimental suspension containing phosphate buffer, target microorganisms, and/or H2O2 was measured using a UV/Vis
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spectrophotometer (Agilent 8453, Agilent Co., Germany) and the average light intensity (Bolton and Linden, 2003) in the suspension was determined to be 0.38 mW/cm2. Sample aliquots (1 mL) were withdrawn from the reactor at various reaction times. Temperature was controlled at 20 C throughout the experiments. Sequential disinfection was performed using the suspension obtained from the above UV or UV/H2O2 experiments. After the primary disinfection achieved 1 log inactivation, the sample suspension was immediately separated using a membrane (0.02 mm hollow-fiber membrane, H2L Co., South Korea) and washed and re-suspended in 10 mM PBS. The recovery of phages and spores from each filtration was over 99.9%. Secondary disinfection was carried out in a 50 mL batch reactor by adding various amounts of free chlorine (sodium hypochlorite, Junsei Co., Japan) from 0.1 to 3 mg/L as active free chlorine. At various reaction times, the residual free chlorine was instantaneously quenched with sodium thiosulfate (Na2S2O3) and samples were collected for viability assessment. All disinfection experiments were repeated three times.
2.3.
Analytical methods
For viability assessments, samples were serially diluted up to a 1/10,000 dilution ratio using 150 mM PBS at pH 7.1. Aliquots of dilutes (0.4 mL for MS-2 phages and 0.1 mL for B. subtilis spores) were inoculated onto three replicate 47-mm sterile Petri dishes containing nutrient agar (and host E. coli for MS-2 phage). Plaque forming unit for MS-2 phage and colony forming units for subtilis spores were counted after incubation at 37 C for 24 h. Selection of statistically meaningful plate counts was carried out according to Standard Methods (APHA et al., 1999). Concentrations of H2O2 and free chlorine were measured based on a titanium sulfate method and N,N-diethyl-p-phenylene diamine (DPD) colorimetric method, respectively, using a UV/ vis spectrophotometer (Agilent 8453, Agilent Co, Germany) (Cho et al., 2006). Concentrations of pCBA, bisphenol-A, and 2, 4-D were determined using a HewlettePackard 1100 HPLC system (Wilmington, DE) equipped with a C18 reverse-phase column (XTerra Rp-18 reverse-phase column) using
acetonitrile as the eluent. Geosmine concentrations were determined by an Agilent 6890 GC/MS equipped with a purge and trap and DB-5 column (30 mm 0.25 mm 0.25 mm) (Cho et al., 2003a; Cho and Yoon, 2008).
3.
Results and discussion
3.1.
Analysis of inactivation kinetics
Fig. 1 shows the decrease in the viability of MS-2 phages (Fig. 1a) and B. subtilis spores (Fig. 1b) versus IT (UV dose: i.e., the average light intensity at 254 nm, I, in mW/cm2, and exposure time, T, in s: Bolton and Linden, 2003) during the single-step disinfection with UV/H2O2. Fig. 2 shows the decrease in viability plotted versus CT (i.e., the product of time-averaged free chlorine concentration, C, and contact time) during free chlorine disinfection. These were performed either as the primary disinfectant in a single-step or as the secondary disinfectant after 1 log inactivation had been achieved by the primary UV or UV/H2O2 disinfection following the kinetics determined in Fig. 1. Note that the secondary disinfection data in Fig. 2 were normalized such that the first data point (i.e., 1 log after primary disinfection) was placed at the origin. Therefore, 1 log inactivation achieved by secondary disinfection with free chlorine would correspond to an overall 2 log inactivation by the sequential process (i.e., 1 log by primary disinfection with UV or UV/H2O2 and an additional 1 log by secondary disinfection by free chlorine). The inactivation curves in Figs. 1 and 2 were characterized by pseudo-first order decrease in viability with respect to IT (i.e., UV dose) or CT, respectively, with the presence of initial lag phase in some of the curves during which little inactivation occurred. The kinetics for UV and UV/H2O2 disinfection were analyzed using the following delayed Chick-Watson model (Rennecker et al., 2000): 8 > > > <
N 0 ¼ exp k IT ITlag N0 > > > :
if IT ITlag ; if IT > ITlag ;
(1)
Fig. 1 e Inactivation kinetics of (a) MS-2 phage and (b) B. subtilis spores during the single-step application of UV/H2O2 (Insets in (a) and (b) show changes in k and ITlag as a function of initial H2O2 concentration, respectively; pH 7, 20 C, [I]0 [ 0.38 mW/cm2).
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0
log(N/N0)
-1
-2 Synergistic Inactivation
Synergistic Inactivation
Cl2 only
-3
-4 0.0
UV followed by Cl2
Cl2 only
UV/H2O2 followed by Cl2
UV followed by Cl2
UV/H2O2 followed by Cl2 + t-BuOH
UV/H2O2 followed by Cl2
0.1
a
0.2
0.3
0.4
0
60
120
b
CT (mg-min/L)
180
240
300
360
CT (mg-min/L)
Fig. 2 e Inactivation kinetics of (a) MS-2 phage and (b) B. subtilis spores during the single-step application of free chlorine and sequential application of free chlorine after UV and UV/H2O2 (pH 7, 20 C, UV dose: 42 mJ/cm2, [H2O2]0 [ 0.6 mM, [HOCl]0 [ 0.04e4 mg/L). where N ¼ concentration (pfu/mL or cfu/mL) of viable microorganisms at time t, N0 ¼ initial concentration (pfu/mL or cfu/ mL) of viable microorganisms, I ¼ incident light intensity (mW/cm2), k ¼ inactivation rate constant (cm2/mJ), and ITlag ¼ x-axis intercept of the linear portion of inactivation curve. For free chlorine disinfection, IT, in the above equaRt tions, was replaced by CT ¼ 0 Cdt where C ¼ concentration of free chlorine as Cl2 (mg/L), k in cm2/mJ by k in L/(mg-min), and ITlag by CTlag Correlation coefficients for the linear regressions on all the data in the linear region were relatively high (R2 > 0.97). These kinetic parameters determined from Figs. 1 and 2 are summarized in Tables 1 and 2, respectively.
3.2.
for B. subtilis spores to UV light (Mamane-Gravetz et al., 2005; USEPA, 2006), although slight differences in absolute values of UV dose might have resulted from lot to lot variations in microorganisms and differences in test methods. Control test results suggested that addition of H2O2 up to 2 mM did not result in inactivation of either MS-2 phage or B. subtilis spores when UV was not applied. The kinetics of MS-2 phage and B. subtilis spores inactivation increased as the H2O2 concentration was increased (Fig. 1 and Table 1) under the UV irradiation treatment. For example, the UV dose required to achieve a 3 log inactivation at H2O2 concentration of 0.6 mM was 32 and 21 mJ/cm2 for MS-2 phage and B. subtilis spores, respectively. These values were approximately 43% and 24% of the UV dose required to achieve a 3 log inactivation of MS-2 phage and B. subtilis spores by the UV irradiation treatment only. This suggests that the addition of H2O2 was more effective in enhancing inactivation kinetics for MS-2 phage than B. subtilis spores. It is noteworthy that the pseudo-first order rate constant (k) gradually increased as H2O2 concentration was increased for MS-2 phage (Fig. 1a inset). In contrast, for B. subtilis spores, rate constant negligibly changed but the lag-phase factor (ITlag) gradually decreased as H2O2 concentration was increased (Fig. 1b inset). The kinetics were found to be enhanced mostly by OH produced from the photolysis of H2O2 by UV. When excess t-butanol (30 mM) was added to the reaction mixture
Single-step disinfection: UV or UV/H2O2
When the UV irradiation treatment was applied without H2O2, MS-2 phage and B. subtilis spores were inactivated following pseudo-first order kinetics with rate constants (k) of 0.053 and 0.129 cm2/mJ, respectively. The inactivation kinetics for B. subtilis spores was faster by approximately 2.5 times, but exhibited an initial lag phase with ITlag ¼ 4.7 mJ/cm2. The UV dose required to achieve a 3 log inactivation of MS-2 phage and B. subtilis spores by UV alone was 57 and 28 mJ/cm2, respectively. These results are consistent with earlier findings that the UV dose required to achieve 3 log inactivation was 49 mJ/cm2 for MS-2 phage and 24.5 mJ/cm2 for B. subtilis spores, corresponding to approximately 50% higher sensitivity
Table 1 e Lag-phase factor and inactivation rate constant during single-step application of UV/H2O2 (obtained from fitting data in Fig. 1). [H2O2]0 (mM) [t-butanol]0 (mM) MS-2 B. subtilis spores
a
ITlag kb ITlaga kb
a Lag phase: mJ/cm2. b Inactivation rate constant: cm2/mJ.
0
0.05
0.10
0.20
0.25
0.35
0.60
0.60
0
0
0
0
0
0
0
30
0.22 0.053 4.7 0.129
0.54 0.056 e e
0.01 0.060 3.98 0.141
0.82 0.069 e e
e e 2.30 0.146
0.68 0.080 e e
1.46 0.089 0.99 0.155
0.63 0.054 5.34 0.137
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Table 2 e Lag-phase factor and inactivation rate constant during the sequential application of free chlorine after UV or UV/H2O2 (obtained from fitting data in Fig. 2). Experimental conditions
Kinetics
Primary Secondary t-BuOH CTlaga disinfection disinfection (mM) MS-2 phage
B. subtilis spores
e UV UV/H2O2 UV/H2O2 e UV UV/H2O2 UV/H2O2
Cl2 Cl2 Cl2 Cl2 Cl2 Cl2 Cl2 Cl2
0 0 0 30 0 0 0 30
0.03 0.03 0.02 0.05 61 45 30 e
kb 8.986 9.276 13.002 10.213 0.011 0.011 0.013 e
a Lag phase: mg-min/L. b Inactivation rate constant: L/mg-min.
containing 0.6 mM H2O2 (i.e., the greatest amount of H2O2 examined), the kinetics of inactivation for both MS-2 phage and B. subtilis spores were significantly reduced and became the same as those obtained with the UV irradiation treatment alone. It is noteworthy that the kinetics was not slower than those obtained with UV alone, since the attenuation of the UV radiation by H2O2 was negligible (UV transmittance: near 97% at the highest H2O2 concentration of 0.6 mM). If UV absorption by H2O2 were significant, the inactivation by UV in UV/H2O2 system could have been inhibited to some degree. The role of OH on MS-2 phage inactivation was quantified separately from that of the UV irradiation treatment. The OH CTwas determined using pCBA as a surrogate using the method described by Elovitz and von Gunten (1999). Fig. 3 shows the level of MS-2 phage inactivation plotted versus CT of OH, which was determined by measuring the steady-state OH concentration using pCBA in the same experimental conditions of Fig. 1. Note that the first data point in the curve is located at 2 logs which represents the inactivation level achieved by the UV irradiation treatment only. The result suggests that the additional inactivation achieved with
Fig. 3 e Level of MS-2 phage inactivation versus H2O2 concentration during single-step UV/H2O2 disinfection (UV dose: 42 mJ/cm2).
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H2O2 addition was linearly correlated with the level of OH exposure (CT). For example, increasing the H2O2 concentration from 0.10 mM to 0.60 mM increases the OH CT by a factor of six (0.5 108 to 3.0 108 mg-min/L as OH) and inactivation by OH by a factor of four (from an additional 0.4 log to 1.6 log inactivation). This analysis confirms that OH plays a major role in the enhanced microbial inactivation in the UV/H2O2 system. Enhanced MS-2 phage inactivation might be related to the oxidation of the outer protein coat by OH (Dean et al., 1997; Fiers et al., 1976; Mamane et al., 2007). For B. subtilis spores, oxidation of the cell membrane or cell wall components by OH resulted in the disintegration of the cell and enhanced UV light penetration (Maness et al., 1999; Sunada et al., 2003). The OH CT determined in this study was different from that reported by Mamane et al. (2007) for MS-2 phage (e.g., for 1 log inactivation, OH CT of 0.34 108 mgmin/L was reported by Mamane et al. (2007), while 1.5 108 mg-min/L was measured in this study (Fig. 3), although reasons for such a difference are unclear.
3.3. Sequential disinfection: UV or UV/H2O2 followed by free chlorine Fig. 2 shows the inactivation kinetics of MS-2 phage (Fig. 2a) and B. subtilis spores (Fig. 2b) by the sequential application of UV with and without 0.6 mM H2O2 to achieve 1 log inactivation followed by Cl2. Although chlorine concentration was varied between 0.1 and 3.0 mg/L as Cl2 for each case, all the data points merged into a single inactivation curve when plotted versus CT. When UV alone was applied as the primary disinfectant and chlorine was subsequently applied (i.e., the kinetics were the same with and without preceding UV treatment), no synergistic effect was observed for both MS-2 phage (Shang et al., 2007) and B. subtilis spores (Cho et al., 2006). In contrast, significant synergistic effects were observed when free chlorine was applied after UV/H2O2. For example, the CT required to achieve 2 log inactivation by free chlorine after UV/H2O2 pretreatment was reduced by 32% for MS-2 phage and 18% for B. subtilis spores, when compared to the CT required by single-step inactivation by free chlorine. The observed synergism for MS-2 phage consisted of a decrease in the lag-phase factor (from 0.03 to 0.02 mg-min/L) as well as an increase in the inactivation rate constant (from 8.986 to 13.002 L/mg-min from a linear portion of the curve). For B. subtilis, post-shoulder rate constant did not change, but lag-phase factor decreased from 61 to 30 mg/L-min (Table 2). The synergism observed in the sequential disinfection process with UV/H2O2 followed by free chlorine might be related to the disruption of cell membrane and/or cell wall components by OH during the primary disinfection which facilitates the reactive diffusion of free chlorine during the secondary disinfection step (Arana et al., 1999; Cho et al., 2006). Similar synergistic effects have been previously reported when chemical disinfectants are subsequently applied (Cho et al., 2006). Table 3 summarizes synergistic effects quantified using Percent Synergistic Effect previously defined as “additional log inactivation achieved via sequential disinfectant application compared to individual application” (Cho et al., 2006). Percent synergistic effects ranged from 40 to 250% depending on the microorganisms (B. subtilis spores
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Table 3 e Comparison of Percent Synergistic Effect in sequential disinfection with previous studies. Sequential disinfection Percent Synergistic Effect (%)a Primary Secondary Bacillus subtilis spores
O3 ClO2 UV UV/H2O2
HOCl HOCl HOCl HOCl
75%b,c 145%c 0c 65% (this study)
MS-2 phage
UV/H2O2
HOCl
75% (this study)
Cryptosporidium parvum oocyst
O3 O3 ClO2 ClO2
HOCl Chloramine HOCl Chloramine
100%d,e,f, 250%g 100%h, 250%d 100%i 40%i
a Additional log inactivation obtained via sequential disinfectant application (compared to 1 log inactivation by primary disinfectant and 1 log inactivation by secondary disinfectant when each was applied independently) (Cho et al., 2006). b (Cho et al., 2003b). c (Cho et al., 2006). d (Rennecker et al., 2000). e (Driedger et al., 2000). f (Corona-Vasquez et al., 2002). g (Li et al., 2001). h (Biswas et al., 2005). i (Corona-Vasquez et al., 2002).
and C. parvum oocyst in this survey, no data available for MS2 phage during chemical sequential disinfection) and the sequence of disinfectant application. Percent synergistic effects for UV/H2O2-followed-by-HOCl process for MS-2 phage and B. subtilis spores were 75% and 65%, respectively. Also evident in Table 3 is the absence of synergism when the UV irradiation treatment was followed by chemical disinfection. Inactivation by UV has been primarily linked to damages in DNA and RNA without damage to cell wall/ membrane components (Shin et al., 2001; USEPA, 2006). Therefore, synergism is not expected with subsequent application of free chlorine; the inactivation mechanism involving reactive diffusion across the cell wall/membrane (Young and Setlow, 2003).
Effect of the OH on synergism was further quantitatively analyzed in Fig. 4. Sequential disinfection was first performed to achieve 1 log inactivation by primary disinfection with UV and additional 1 log inactivation by secondary application of free chlorine, i.e., overall 2 log inactivation for both microorganisms (i.e., the first bar in Fig. 4 (a) and (b)). Addition of 0.6 mM of H2O2 during the primary UV disinfection step, with other conditions fixed, achieved overall inactivation of 3.5 log for MS-2 phage and 3.3 log for B. subtilis spores. Approximately 0.9 log additional inactivation for MS-2 phage and 0.8 log for B. subtilis spores were achieved during the primary disinfection step due to the additional contribution of OH as discussed above. Furthermore, approximately 0.6 log inactivation for MS-2 phage and 0.5 log for B. subtilis spores were additionally achieved during the secondary chlorine disinfection step. Collectively, the synergism achieved by adding H2O2 occurred in both disinfection steps and summed up to 1.5 log (96.8%) and 1.3 log (95.0%) inactivations that were additionally achieved for MS-2 phage and B. subtilis spores, respectively.
3.4.
Degradation of selected organic contaminants
Addition of H2O2 in the UV disinfection process, whether followed by secondary free chlorination or not, could result in additional benefit with respect to destruction of some organic contaminants. Chemicals such as bisphenol-A, geosmin and 2,4-D (each 350 mg/L) were all found to be negligibly degraded by the individual application of 57 mJ/cm2 (0.38 mW/cm2 for 150 s) of UV or 0.6 mM of H2O2. When 0.6 mM H2O2 was added under 0.38 mW/cm2 of UV irradiation, 73%, 65% and 37% of bisphenol-A, geosmin and 2,4-D were degraded, respectively, within 2.5 min (i.e., hence UV dose ¼ 57 mJ/cm2). These resulted from relatively high rate constants between these chemicals and OH (k(OH þ bisphenol-A) ¼ 1010 M1 s1, k (OH þ geosmin) ¼ 8 109 M1 s1 and k(OH þ 2,4D) ¼ 3 109 M1 s1 (Buxton et al., 1988)). Using these rate constants and steady-state OH concentration (i.e., independently measured using pCBA) would predict 75%, 67%, and 34% degradation of bisphenol-A, geosmin, and 2,4-D, respectively, which are consistent with the above observations.
Fig. 4 e Schematic illustration of synergism observed with the sequential application of UV/H2O2 followed by free chlorine (right bar) compared to UV followed by free chlorine (left bar) for inactivation of (a) MS-2 phage and (b) B. subtilis spores (pH 7, 20 C, UV dose: 19 mJ/cm2, [H2O2]0 [ 0.6 mM, [Cl2]0 [ 0.15 mg/L).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 6 3 e1 0 7 0
Similar findings have been reported for destruction of endocrine disrupting compounds such as bisphenol-A, ethinyl estradiol and estradiol (Rosenfeldt and Linden, 2004) and taste and odor causing compounds such as methylisoborneol (MIB) and geosmin (Rosenfeldt et al., 2005), amongst others.
4.
Conclusion
The results from this study collective suggest that it could be beneficial to add H2O2 in UV disinfection process in particular when free chlorine is subsequently added as the residual disinfectant. Synergistic effects with respect to microorganism inactivation would be two folds. First, enhanced inactivation of MS-2 phage and B. subtilis spores was observed during the primary UV disinfection step due to the additional inactivation achieved by OH that is produced by H2O2 photolysis. Second, additional level of inactivation was achieved during the secondary disinfection with chlorine. Alternatively, secondary disinfection could benefit from reduced chlorine dosage and accompanying reduction in disinfection by-product formation, while a care must be taken not to induce chlorine decay due to residual H2O2. Overall, the synergism was significant, for example, as much as 1.5 log and 1.3 log inactivation, in addition to the 2 logs expected without synergism, for MS-2 phage and B. subtilis spores for the case discussed above. Finally, the addition of H2O2 in UV process could result in organic contaminant degradation, which otherwise would not be achieved by UV nor free chlorine. These synergistic effects and contaminant degradations resulted from advanced oxidation by OH. It should be noted that all the experiments were performed in organic-free water (except when model contaminants were added) in this study and therefore further studies are required to accurately assess and quantify the synergistic effects in natural waters when UV/H2O2 process is applied. In natural waters, it is expected that more OH (hence more UV irradiation and H2O2) would be required to bring about the similar level of synergistic effects due to UV light absorption and OH scavenging by organic matter and carbonate species.
Acknowledgements The study was supported by the Eco-Technopia 21 project from the Ministry of Environment in Korea and the Korea Research Foundation Grant funded by the Korean Government (KRF-2008-357-D00142).
references
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Synthetic musk emissions from wastewater aeration basins Nabin Upadhyay a, Qinyue Sun b, Jonathan O. Allen c, Paul Westerhoff d, Pierre Herckes a,* a
Department of Chemistry and Biochemistry, Arizona State University, PO Box 871604, Tempe, AZ 85287-1604, USA School of Mechanical, Aerospace, Chemical and Materials Engineering, Arizona State University, PO Box 871604, Tempe, AZ 85287-1604, USA c Allen Analytics LLC, 3444 N. Country Club Rd., Suite 100, Tucson AZ 85716-1200, USA d School of Sustainable Engineering and The Built Environment, Arizona State University, PO Box 5306, Tempe, AZ 85287- 5306, USA b
article info
abstract
Article history:
Wastewater aeration basins at publicly owned treatment works (POTWs) can be emission
Received 12 May 2010
sources for gaseous or aerosolized sewage material. In the present study, particle and gas
Received in revised form
phase emissions of synthetic musks from covered and uncovered aeration basins were
22 September 2010
measured. Galaxolide (HHCB), tonalide (AHTN), and celestolide (ADBI) were the most
Accepted 18 October 2010
abundant, ranging from 6704 to 344,306 ng m3, 45e3816 ng m3, and 2e148 ng m3 in the
Available online 26 October 2010
gas phase with particle phase concentrations 3 orders of magnitude lower. The musk species were not significantly removed from the exhaust air by an odor control system,
Keywords:
yielding substantial daily emission fluxes (~200 g d1 for HHCB) into the atmosphere.
Synthetic musks
However, simple dispersion modeling showed that the treatment plants are unlikely to be
Wastewater
a major contributor to ambient air concentrations of these species. Emission of synthetic
Publicly owned treatment works
musk species during wastewater treatment is a substantial fate process; more than 14% of
Aeration basin
the influent HHCB is emitted to the atmosphere in a POTW as opposed to the <1% predicted
Odor control
by an octanolewater partition coefficient and fugacity-based US EPA fate model. The
Atmospheric emissions
substantial atmospheric emission of these compounds is most likely due to active stripping that occurs in the aeration basins by bubbling air through the sludge. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Synthetic musksdsubstitutes for natural musk fragrancesdare a class of compounds widely used in personal care products (PCPs) (Rimkus, 1999; Roosens et al., 2007). Due to their toxicity to microorganisms and their bioaccumulation potential, worldwide production of synthetic nitro-musks, including musk xylene (MX) and musk ketone (MK), is decreasing, while the production of polycyclic musks, including galaxolide (HHCB) and tonalide (AHTN) is still prevalent. HHCB and AHTN alone comprise 90e95% of the total market volume of musk species (HERA, 2004; Reiner et al., 2007; Rimkus, 1999). An estimated 6000 metric tons of
polycyclic and nitro musk compounds were produced globally in 1999 (Salvito, 2006). There is increasing concern in the scientific community about the possible toxicity of polycyclic musks to terrestrial life, including humans, which has prompted more research on this group of emerging contaminants. Limited studies exist on the exposureeresponse relationships of synthetic musks; however, some findings have indicated their accumulation in aquatic biota, including fish and mussels, and in human breast milk, as well as their endocrine disruption potential (Bitsch et al., 2002; Luckenbach and Epel, 2005; Schreurs et al., 2004). HHCB, AHTN, and ADBI (celestolide) have been detected on the order of a few ng m3 down to pg m3 levels even in
* Corresponding author. Tel.: þ1 480 965 4497; fax: þ1 480 965 2747. E-mail address:
[email protected] (P. Herckes). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.024
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remote and pristine environments, namely in the ambient air in the Great Lakes area (Peck and Hornbuckle, 2004), over the North Sea, and in the arctic (Xie et al., 2007). The major pathway of PCPs into the environment is through their use and consequently through wastewater as they wash off the skin as well as release from soaps, detergents, and other consumer products (Kolpin et al., 2002; Ternes, 1998). Numerous studies have reported HHCB and AHTN in wastewater (liquid and sludge phases). Publicly owned treatment works (POTWs) collect and treat sewage from residential, commercial and industrial sources (Buerge et al., 2003; Horii et al., 2007; Reiner et al., 2007). Discharge of treated wastewater is responsible for musk compounds in surface waters and oceans (Buerge et al., 2003; Rimkus, 1999; Xie et al., 2007). The concentrations of polycyclic musks in wastewaters are on the order of hundreds to thousands of ng L1 (Rimkus, 1999; Simonich et al., 2002). Since these compounds are semivolatile and hydrophobic in nature, they partition into the gas phase or biosolids. Given the high concentrations of HHCB and AHTN in aeration basins, where air is bubbled through the wastewater, a significant potential exists for their volatilization and aerosolization. Most research on PCP fate and transport through POTWs focuses on the liquid phase, namely biosorption or biodegradation, whereas there is little information on atmospheric emissions of artificial musk species from POTWs. The objectives of this study are (i) to determine the concentrations of synthetic musk compounds in the ambient air and wastewater samples at covered and uncovered POTWs; (ii) to assess the contribution of POTWs to environmental musk concentrations, especially in the gas phase; and (iii) to evaluate the effectiveness of an odor control system at a POTW intended to remove musk compounds in the exhaust air. Samples collected at two POTWs and at a suburban site in the Phoenix (AZ) metropolitan area were analyzed by gas chromatography/mass spectrometry (GC/MS). The findings of this study provide new insights into the volatilization of musk species in sewage treatment and the role of POTWs as atmospheric emission sources for these species.
2.
Experimental work
2.1.
Reagents
The dichloromethane (DCM) used as solvent was Fisher Optima Grade (Fair Lawn, NJ). HHCB (522 mg mL1 solution in diethyl phthalate) and MX and MK (100 mg mL1 solutions in methanol) were purchased from Sigma Aldrich (St. Louis, MO). AHTN and ADBI were purchased in the powder form from Toronto Research Chemicals Inc. (ON, Canada); deuterated polycyclic aromatic hydrocarbons (d10-acenaphthene, d10phenanthrene and d10-fluoranthene), used as internal standards, were purchased from SigmaeAldrich (St Louis, MO). d10-acenaphthene and d10-phenanthrene (each 2000 mg mL1 in methanol) were used as purchased while the powder of d10fluoranthene was dissolved in DCM (4570 mg mL1 in DCM). Other reagents included NaCl (>99%) from Fisher Scientific (Fairlawn, NJ), ACS Reagent Grade Na2SO4 (>99%) from SigmaeAldrich (St. Louis, MO), and Pesticide Grade Glass Wool from Supelco (Bellefonte, PA).
2.2.
Sample collection
Particle and gas phase samples were collected at two POTWs in the Phoenix (AZ) metropolitan area using a commercial semivolatile polyurethane foam (PUF) sampler (TE-1000 PUF, Tisch Environmental Inc., Cleves, OH). Plant A has a design treatment capacity of 68 million liters per day (MLD) of wastewater and includes the following covered processes: headworks, primary sedimentation, aerated activated sludge treatment, secondary sedimentation, tertiary filtration, and disinfection. However, these processes are not closed to air inflow from outside. All of these processes are equipped with ventilation systems to collect and treat off-gases from the facility. Off-gas is passed through an odor control unit (OCU) that consists of packed beds of activated carbon. Air samples at Plant A were collected via an aluminum inlet pipe (4-inch diameter) with one end inserted through a hole in the exhaust side wall of the aeration basin and the other end connected to the inlet of the PUF sampler. Similarly, one end of the aluminum duct faced downward at the OCU vent while the other end was secured to the inlet of the sampler. Plant B has a design capacity of 680 MLD of wastewater, is an uncovered facility (i.e., no odor control), and employs primary sedimentation, aerated activated sludge treatment, secondary treatment, and disinfection. Ambient samples from Plant B were collected at the aeration basin as well as at a perimeter site 200 m away from the aeration basin. Additional urban ambient samples were collected from the rooftop of the School of Life Sciences at the Tempe campus of Arizona State University (ASU). Sampling was conducted during December 2008eFebruary 2009 at Plant A, May 2009 at Plant B, and April 2009 at ASU. The sampled air volume ranged from 133 to 387 m3. Meteorological data, which included speed and direction of wind, were obtained from the meteorological station at Sky Harbor International Airport (http://www. wunderground.com/US/AZ/Phoenix.html). Prevalent wind directions during sampling were ESE, SE, ENE at Plant A; SE, W, SW at Plant B; and SE, ENE, WSW at ASU with a daily average wind speed of 1.3e3.1 m s1. During each sampling, the PUF sampler was operated at a flow rate of 24 L min1 for 9e24 h. Total suspended particulate matter (TSP) was collected on a 4-inch diameter quartz fiber filter (QFF) that was followed by a polyurethane foam plug (PUFP, 3-inch diameter 4-inch height) to trap gas phase species. QFFs were wrapped in aluminum foil and fired at 550 C overnight before use. PUFPs were cleaned prior to use with Alconox and Milli-Q water three times and air-dried under a hood before sonication (20 min) in DCM three times and air-drying. The cleaned PUFPs were stored in amber glass bottles until sampling and extraction. Representative grab samples were collected from the aeration basins close to the sampling inlets during air sampling. Samples were collected in polypropylene bottles cleaned with deionized water (>18 MU-cm) and were stored in the dark at 4 C prior to analysis.
2.3.
Sample preparation
An overview of sample preparation for musk analysis is shown in supporting information (SI Fig. S1). For the particle phase
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musk compounds, one-half of a QFF was spiked with the set of internal standards (10 mL each) and extracted 3 times in 25 mL of DCM under sonication. For the gas phase musk samples, the PUFPs were spiked with the internal standards (10 mL each) and extracted 3 times in 50 mL of DCM using compressionerelease cycles. The combined extracts for each phase were filtered through a 0.7 mm pore size Whatman glass fiber filter (GFF), dried under nitrogen, and concentrated to 250 mL separately. Filter blanks for PUFF and PUFP were subject to same extraction and analytical procedures as the samples. Wastewater samples (two from each plant) were first filtered through GFFs. A 250 mL filtrate aliquot was spiked with the internal standards and the salinity increased with 10 g of NaCl. The aqueous phase was then extracted 3 times in 50 mL of DCM. The combined DCM extracts were dried over anhydrous Na2SO4 and concentrated to 250 mL prior to analysis by GC/MS.
2.4.
GC/MS analysis
An Agilent 6890 gas chromatograph coupled to an Agilent 5973 inert mass selective detector with electron impact ionization was used to determine the musk species. Separation was accomplished using an HP 5MS capillary column (30 m
250 mm 0.25 mm, 5% phenyl-methyl-siloxane film). Injections of 1 mL aliquots were performed in splitless mode, and helium (ultra-high purity) was used as a carrier gas. The GC temperature profile consisted of an initial hold time of 10 min at 65 C followed by a temperature gradient of 10 C min1 to a final temperature of 300 C, which was held constant for 20 min. Authentic standards were used for identification and quantification of the target species.
3.
Results and discussion
The synthetic musk compounds under investigation as well as their physicochemical properties are shown in Table 1. Watereoctanol partition coefficients (KOW), water solubility (SW), and Henry’s constants (HC) for these compounds are similar to those for hydrophobic semivolatile organic compounds (Balk and Ford, 1999; Paasivirta et al., 2002; Tas et al., 1997). The observed bioconcentration factors of MK and MX correlate well with their KOW values, whereas those for HHCB and AHTN are lower than predicted from KOW (Rimkus, 1999; Rimkus et al., 1997).
Table 1 e Synthetic musks and their properties. CAS No.
MW
Ions quantified
Retention time (min)
Log KOW
SW, (mg L1)
HC, (Pa m3 mol1)
Galaxolide, HHCB
1222-05-5
258.4
243, 213
25.03
5.9a
1.75
11.3a
Tonalide, AHTN
1506-02-1
258.4
243, 258
25.15
5.7a
1.25
12.5a
Celestolide, ADBI
13171-00-1
244.3
229, 244
23.55
6.6b
0.015
Musk xylene, MX
81-15-2
297.2
282, 229
25.12
4.9c
0.49
0.018c
Musk ketone, MK
81-14-1
294.3
279, 294
26.33
4.3c
1.9
0.0061c
Compound
a Balk and Ford, 1999. b Paasivirta et al., 2002. c Tas et al., 1997.
1801b
Structure
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3.1.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 1 e1 0 7 8
Occurrence of musk species
Table 2 presents the observed concentrations of the target musk species. In the present study, MX and MK were not detected in any samples, so they are not included in any tables or figures. Considering the sampling times and analytical protocols used, atmospheric (gas and particle phases) concentrations of these species would be substantially below 1 ng m3. HHCB, AHTN, and ADBI were detected in atmospheric samples. Trace amounts of HHCB were detected in field blanks; however, the equivalent air concentrations (0.12 ng m3 in gas and 3.2 ng m3 in particle phase) were orders of magnitude lower than the typical sample concentrations. Detection limits (DLs) for the present work were defined as the smallest signal of the analyte that can be distinguished from background noise by the GC/MS. DLs for HHCB, AHTN, and ADBI were 0.6, 0.03, and 0.03 ng m3 (air volume ¼ 350 m3), respectively. Gas-phase HHCB and AHTN ranged from 74,585 to 344,306 ng m3 and 374 to 3816 ng m3 at Plant A and 6704 to 17223 ng m3 and 45 to 192 ng m3 at Plant B, respectively. Gasphase ADBI in our POTW samples ranged from 2 to 148 ng m3, which are several orders of magnitude higher than reported in urban or background samples in the published literature (Peck and Hornbuckle, 2004; Xie et al., 2007). Particle phase concentrations were much lower, with HHCB and AHTN ranging from 11 to 146 ng m3 and 2 to 26 ng m3 at Plant A and 73 to 110 ng m3 and 15 to 23 ng m3 at Plant B, respectively. It is noteworthy that the gas and particle phase HHCB and AHTN showed a steep decline, e.g., at Plant B, by 1e2 orders of magnitude between the over-the-aeration basin and the off-site location (Table 2). Short atmospheric lifetime on the order of 5.3 h (for HHCB) (Aschmann et al., 2001), quick diffusion, and sampling on different days at the aeration basin and off-site
location might have caused these large discrepancies in concentrations between the emission source and off-site location. However, high concentrations of gas phase HHCB and AHTN close to the plant (Plant B) and background ASU (Table 2) could not be explained. Atmospheric concentrations of musk species during our study were highly variable with one sample (5-Nov-08, Plant A) having exceptionally high concentrations of musk compounds. This is likely reflective of changes in wastewater contents. In the present study, daily liquid samples were not collected. However, Reiner and coworkers (Reiner et al., 2007) report HHCB and AHTN in POTWs vary by a factor of 6 in wastewater samples collected within 5 consecutive days. In all cases, the musk species were partitioned nearly exclusively into the gas phase. The percentage concentrations of gas-phase HHCB and AHTN (cg/(cgþcp) 100%, where cp and cg are the particle and gas phase concentrations, respectively) at both plants and off-site at Plant B were >99% and 95.5%, respectively. Urban background gas-phase concentrations of HHCB and AHTN ranged from 213 to 238 ng m3 and 1 to 2 ng m3, respectively, while particle-phase concentrations of HHCB and AHTN were both <0.06 ng m3. The reported concentrations of airborne (gas þ particle) HHCB and AHTN in the literature are on the order of 1e5 ng m3 in urban areas and an order of magnitude lower at pristine background sites (Peck and Hornbuckle, 2004, 2006). Compared to the concentrations in urban air in Milwaukee, WI (Peck and Hornbuckle, 2004), HHCB at the urban (ASU) site in this study is 1e2 orders of magnitude higher whereas AHTN has similar concentrations (Fig. 1). Airborne HHCB and AHTN at the aeration basins are, respectively, 2e3 and 1e2 orders of magnitude higher than those in a cosmetic plant environment (Chen et al., 2007)dthe only study that has investigated these species at a site labeled as major musk emission source. The cited studies have reported MX and MK on the order of tens of
Table 2 e TSP and airborne concentrations of the musks in the POTWs and at a background site.a Site
Date
TSP (mg m3)
Gas phase 3
Particle phase
HHCB (ng m ) AHTN (ng m ) ADBI (ng m ) HHCB (ng m3) AHTN (ng m3) Plant A Aeration chamber
Odor control unit
Plant B Aeration chamber
Off-site
ASU
3
3
29-Oct-08 1-Nov-08 5-Nov-08 15-Jan-09 16-Jan-09 18-Jan-09
71.0 53.0 32.9 NA 8.06 5.86
74585 112455 344306 79047 95656 91044
1025 1395 3816 496 638 374
17 31 148 17 27 14
11 13 146 110 99 73
2 3 26 23 18 15
21-May-09 23-May-09 25-May-09 29-May-09 30-May-09 31-May-09
23.0 29.0 20.3 17.4 18.2 45.4
14610 17223 6704 282 429 674
80 192 45 2 3 5
3 2 2 ND ND ND
3.14 1.72 3.90 0.20 0.19 0.12
0.7 0.5 0.9 0.063 0.090 0.06
28-Apr-09 29-Apr-09 30-Apr-09
NA NA NA
236 213 238
2 2 1
ND ND ND
0.05
0.02 ND ND
a Equivalent field blank concentrations of HHCB in gas phase ¼ 0.12 ng m3 and in particle phase ¼ 3.2 ng m3 (air volume ¼ 350 m3); NA, not available; ND, not detected; FB, field blank.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 1 e1 0 7 8
pg m3 in the ambient air, consistent with the non-detection in our study.
Sewage concentrations compared to other studies
a
1000000 100000 10000 1000 100 10 1 0.1 0.01
In aeration basins, air is bubbled through wastewater using submerged diffusers to deliver oxygen to aerobic bacteria that
Se a th
N or
R
ur a
lG
er m
an y
SA ,U
(d )
(d )
(c
)
(c ) SA
ke
U
rb
La
an
ga n
M ic hi
au ke e,
fa
an ce gr fra
gr O
ut s
id e
fra
U
ct o
ry ct o fa e
an c
U AS In si de
ry
(b )
) a
(a ri z on
B
,A
la nt at P
si te ffO
b
(a
)
(a ) B an t
A Pl
an t
1000
(b )
HHCB AHTN ADBI
Pl
Concentration (ng m )
-3
3.3. Emission fluxes from wastewater treatment operations
Gas phase
(a )
-3
Concentration (ng m )
An ample amount of data is available on the liquid and sludgephase musk concentrations in POTWs in many countries. To examine if Arizona POTWs are particular compared with similar other studies, we also measured musk concentrations in wastewater samples from aeration basins (Table 2). Measured liquid-phase concentrations of HHCB were 13600 and 25000 ng L1 at Plant A which, on average, are about two times the HHCB concentrations (12200 and 7800 ng L1) at Plant B. These concentrations are much higher than those found in the influents of a rural area of Kentucky and an urban area of Georgia (<1000 ng L1) (Horii et al., 2007), but are comparable to the concentrations reported for POTWs in New York (1780e12,700 ng L1) (Reiner et al., 2007) and in Europe (16600 10400 ng L1) (Simonich et al., 2002). In contrast to the AHTN reported in many POTWs influents in Europe (42e250 ng L1) (Berset et al., 2004; Ricking et al., 2003), it was not detected in the wastewater samples in the aeration basin in this study. Based on KOW and SW (Table 1), the percentage of
M il w
3.2.
HHCB and AHTN removed by sorption, and subsequent removal of solids by sedimentation/filtration, should be of the same order of magnitude for both. This suggests, therefore, that non-detection of AHTN is due to its concentration below our detection limit (42 ng L1) in the wastewater. Note that the concentrations reported are only the dissolved fraction and do not cover the total mass of musks in wastewater samples. Although we have measured one of the highest levels of HHCB (25000 ng L1) in wastewater, the concentrations seem comparable to other treatment plants in the world. Given their hydrophobic nature and volatility, water to air partitioning of synthetic musks, especially when the water is vigorously mixed with air, can lead to a significant concentration of airborne musks by POTWs. In the next section, we present atmospheric emission estimate of these compounds at a covered aeration basin with regulated air supply and defined cross-section of exhaust vent.
Particle phase HHCB AHTN
100 10 1 0.1 0.01
Pl
tA an
) (a Pl
tB an
O
te si ff-
) (a P at
) ) ) ) (c (c (d (d y a A A n e S S a S c i m ,U ,U fa rth er Ar ee an o e e , G k g c c i N U h al au an an ic AS ur gr gr ilw M R a M rf a fr ke n e e La id id ba r s s t U In u O
B nt la
) (a
na zo
) (a
y or ct fa
) (b
ry to
) (b
Fig. 1 e General occurrence of musk compounds in gas phase and particle phase in the atmosphere. References: aThis study; b Chen et al., 2007; cPeck and Hornbuckle, 2004; dXie et al., 2007.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 1 e1 0 7 8
degrade organics and oxidize nitrogen species. Volatile organics, many of which cause foul odors, are stripped from the water during aeration (Hwang et al., 1994; Krasner et al., 2009). Emission rates were estimated for Plant A using the amount of air supplied to the aeration basin and the measured concentrations of musk species (Fig. 2, details in SI Table S1). Assumptions included that no air leakage occurs in and between the aeration basin and OCU as well as that the rate of air supplied to the aeration basin is the same as the rate venting out from the OCU. Based on the vent diameter (0.762 m), number of vents (n ¼ 4) and the exhaust air speed (12 m s1), the rate of air supplied to the aeration basin was estimated to be 21.9 m3 s1. The overall emission flux was calculated using Eq. (1): 24 60 60 E ¼ cg A v 109
(1)
where E is the emission flux (g d1), A is the cross-sectional area of the vent (m2), v is the speed of exit air (m s1), and cg is the gas phase concentration (ng m3) of a musk compound. Emission fluxes of musk compounds were calculated based on their air concentrations inside the aeration basin. Gasphase emission of HHCB, on average, from the aeration basin is dominant (177 g d1), followed by AHTN (2.3 g d1) and ADBI (0.05 g d1). Note that the November 5 sample is not included in this average because concentrations of musks in this sample are substantially (factors of 3e9) higher than in the other two samples (see SI Table S1). Wastewater aeration emissions of synthetic musks are substantial, but are they the major source of musk species in the atmosphere? To test this, the amount of HHCB and AHTN observed at the urban site owing to Plant A was estimated using a Gaussian plume model (Pasquill, 1971). To calculate an upper limit concentration under the assumption that the site is directly downwind of the emission source, the
Gas phase 3
200
2
100
1
Aeration basin Odor control unit
-1
Emission (g d )
a 300
0 AHTN
0 HHCB
b
ADBI
ADBI
Particle phase Aeration basin Odor control unit
-1
Emission (g d )
0.30
AHTN
0.20 0.10 0.00 HHCB
AHTN Musk compound
Fig. 2 e Emission flux of gas and particle phase musk compounds from a covered POTW.
atmospheric concentration of a species due to the POTW at a receptor site can be estimated using a simplified Gaussian plume model: C¼
Qa pusy sz
(2)
where C is the gas phase concentration (ng m3) of the species at a receptor site due to an emission source, Qa is the steadystate emission (ng s1) of the species, u is the average wind speed (m s1), and sy and sz are the dispersion coefficients (m) in the lateral and vertical directions, respectively, and were estimated from the PasquilleGifford curves (Martin, 1976). sy and sz depend on the atmospheric stability (commonly categorized as very unstable, unstable, slightly unstable, neutral, slightly stable, and stable) and the distance downwind of emission source (Ragland, 1976). In the present case, Plants A and B were located about 8 and 40 km away from ASU, respectively. Assuming worst-case scenarios for various atmospheric stability conditions (unstable to stable and wind speed 0e5 m s1), the model predicted that Plant A’s contribution to gas phase HHCB and AHTN at ASU was not more than 10% of the measured value in any case. The emission rates for gas phase musks and atmospheric conditions used in Eq. (2) are given in SI Tables 1 and 2, respectively. These results imply that other musk sources must be substantial and that the aeration basin emissions are certainly not the exclusive source of urban musk concentrations. Other potential emission sources include the volatilization of these species from the usage of products containing synthetic musks and from wastewater transport and disposal processes, including biosolid dispersal.
3.4.
Effects of odor control unit on musk emissions
Activated carbon beds are used to control through sorption processes and surface chemistry the malodorous compounds in water and wastewater (Hwang et al., 1994; Westerhoff et al., 2005). Fig. 3 and Table 2 present the gas (and particle) phase concentrations of musk species before and after the odor treatment at Plant A. It is important to note that these observations are based on a limited number of samples with a time delay between the pre and post OCU sampling. The observations suggest a gas-phase AHTN decrease by more than 50% in the exhaust air and were significantly lower than in the aeration basin. On the other hand, the higher levels of particle-phase HHCB and AHTN in the exhaust air from the OCU than in the aeration basin resulted likely from the condensation of the gas-phase musks on existing particles in the OCU or during transport. In fact, the exhaust air (above the aeration basin) in the winter time is several degrees warmer (w30 C) than outside ambient air (10e20 C) and exhaust air is cooling rapidly on its way to and through the OCU favoring condensation of semivolatile species on existing particles. Highly variable concentrations of musk species, especially in the aeration basin, and sampling at aeration basin and OCU on different days could explain the discrepancy. Parallel sampling between the two locations and a larger sample size would provide better insights into the effectiveness of OCU and the atmospheric emissions of musk species.
-3
Concentration (ng m )
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 1 e1 0 7 8
a
Gas phase
400000
4000
300000
3000
200000
2000
100000
1000
0
0
-3
Concentration (ng m )
HHCB 150
Aeration basin Odor control unit
b
AHTN
Particle phase
100
50
0 AHTN Musk compound
Fig. 3 e Concentrations of musk compounds in the aeration basin and exhaust of an odor control unit in a covered POTW.
3.5. Volatilization as fate of musk species during sewage treatment Given the high volatilization rates of the musk species, volatilization of synthetic musks could be a substantial fate during treatment. The fraction of musk species lost by volatilization to the atmosphere (Fair) from the covered POTW (Plant A) was calculated using: Fair ¼
E 100% cw Q w
results are unclear. The Henry constant used by EPI (HC ¼ 31 Pa m3 mol1) is 3 times higher than the measured HC value (11.3 Pa m3 mol1) (Balk and Ford, 1999); hence EPI should tend to overestimate volatilization. One issue might be static considerations in the fate model as opposed to the active “stripping” that occurs in the aeration basins by bubbling air through the sludge. Further investigation is warranted to reconcile model and observations.
ADBI Aeration basin Odor control unit
HHCB
1077
(3)
where E is the emission flux (g d1) of the species in the gas phase (Table S1), cw is the average liquid-phase concentration of the species (g L1), and Qw is the sewage treatment capacity (L d1) of the plant. Fair for HHCB was estimated for the design treatment capacity Qw (6.8 107 L d1) of the plant with an average cw of 1.900 105 g L1. Using the estimated daily gas phase emission rate E from the previous section, the fraction of HHCB emitted to the air from the OCU (using Eq. (3)) was found to be 14 1%. This is a rough emission estimate because the plant might not be working at design capacity or the musk load could be higher when the sewage flow is low during dry season. The environmental fate of synthetic musk compounds can also be estimated using the Estimation Program Interface (EPI) Suite (v 4.00) (EPA, 2009). EPI is a Microsoft WindowsTM-based mathematical model that uses physicochemical properties of chemical species to derive their partitioning into air, water, and sludge phases. EPI estimates 93% removal of HHCB in POTWs, mostly (92%) through adsorption to sludge and a minor portion (>1%) by bacterial activity with less than 1% emission into air. This is in sharp contrast to our observations of more than 14% of HHCB emitted to the air in a POTW. The reasons for the diverging
4.
Conclusions
This study reveals that POTWs contribute to the environmental occurrence of musk species not only through wastewater and biosolids, but also through direct atmospheric emissions from aeration basins. Commonly used odor control systems do not appear to be efficient in the removal of synthetic musks. Results of this study suggest that atmospheric emissions are a substantial fate for these species in the sewage treatment process, although this is not reflected in a common fate model. While emissions by POTWs are substantial, they are by no means the only or even the dominant sources of synthetic musks in an urban atmosphere. Further work is warranted to improve our understanding of emission sources and strengths of atmospheric concentrations of these compounds.
Acknowledgements This project was funded by Science Foundation Arizona CAA 0284-08. The authors would like to thank the staff of the two unnamed utilities in the Phoenix (Arizona) Metro area for their cooperation. We are grateful to Prof. Matt Fraser and Andrea Clements for assistance with sample analysis and access to facilities and to Prof. Jeff Collett at Colorado State University for the loan of the sampler.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.10.024.
references
Aschmann, S.M., Arey, J., Atkinson, R., Simonich, S.L., 2001. Atmospheric lifetimes and fates of selected fragrance materials and volatile model compounds. Environmental Science & Technology 35, 3595e3600. Balk, F., Ford, R.A., 1999. Environmental risk assessment for the polycyclic musks AHTN and HHCB in the EU - I. Fate and exposure assessment. Toxicology Letters 111, 57e79. Berset, J.D., Kupper, T., Etter, R., Tarradellas, J., 2004. Considerations about the enantioselective transformation of polycyclic musks in wastewater, treated wastewater and sewage sludge and analysis of their fate in a sequencing batch reactor plant. Chemosphere 57, 987e996.
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Bitsch, N., Dudas, C., Korner, W., Failing, K., Biselli, S., Rimkus, G., Brunn, H., 2002. Estrogenic activity of musk fragrances detected by the E-screen assay using human MCF-7 cells. Archives of Environmental Contamination and Toxicology 43, 257e264. Buerge, I.J., Buser, H.R., Muller, M.D., Poiger, T., 2003. Behavior of the polycyclic musks HHCB and AHTN in lakes, two potential anthropogenic markers for domestic wastewater in surface waters. Environmental Science & Technology 37, 5636e5644. Chen, D.H., Zeng, X.Y., Sheng, Y.Q., Bi, X.H., Gui, H.Y., Sheng, G.Y., Fu, J.M., 2007. The concentrations and distribution of polycyclic musks in a typical cosmetic plant. Chemosphere 66, 252e258. EPA, 2009. Estimation Program Interface Suite for Microsoft Windows, V. 4.00. United States Environmental Protection Agency, Washington, DC, USA. HERA, 2004. Human & Environmental Risk Assessment on Ingredients of Household Cleaning Products: Polycyclic Musks AHTN (CAS 1506-02-1) and HHCB (CAS 1222-05-05) Environmental Section Ver. 2.0. Available online at: http:// www.heraproject.com/RiskAssessment.cfm. Horii, Y., Reiner, J.L., Loganathan, B.G., Kumar, K.S., Sajwan, K., Kannan, K., 2007. Occurrence and fate of polycyclic musks in wastewater treatment plants in Kentucky and Georgia, USA. Chemosphere 68, 2011e2020. http://www.wunderground. com/US/AZ/Phoenix.html (accessed October 7). Hwang, Y., Matsuo, T., Hanaki, K., Suzuki, N., 1994. Removal of Odorous compounds in Waste-water by using activated carbon, Ozonation and aerated Biofilter. Water Research 28, 2309e2319. Kolpin, D.W., Furlong, E.T., Meyer, M.T., Thurman, E.M., Zaugg, S. D., Barber, L.B., Buxton, H.T., 2002. Pharmaceuticals, hormones, and other organic wastewater contaminants in US streams, 1999-2000: a national reconnaissance. Environmental Science & Technology 36, 1202e1211. Krasner, S.W., Westerhoff, P., Chen, B.Y., Rittmann, B.E., Amy, G., 2009. Occurrence of disinfection Byproducts in United States wastewater treatment plant effluents. Environmental Science & Technology 43, 8320e8325. Luckenbach, T., Epel, D., 2005. Nitromusk and polycyclic musk compounds as long-term inhibitors of cellular xenobiotic defense systems mediated by multidrug transporters. Environmental Health Perspectives 113, 17e24. Martin, D.O., 1976. Comment on change of concentration standard Deviations with distance. Journal of the Air Pollution Control Association 26, 145e146. Paasivirta, J., Sinkkonen, S., Rantalainen, A.L., Broman, D., Zebuhr, Y., 2002. Temperature dependent properties of environmentally important synthetic musks. Environmental Science and Pollution Research 9, 345e355. Pasquill, F., 1971. Atmospheric dispersion of Pollution. Quarterly Journal of the Royal Meteorological Society 97, 369e395.
Peck, A.M., Hornbuckle, K.C., 2004. Synthetic musk fragrances in Lake Michigan. Environmental Science & Technology 38, 367e372. Peck, A.M., Hornbuckle, K.C., 2006. Synthetic musk fragrances in urban and rural air of Iowa and the Great Lakes. Atmospheric Environment 40, 6101e6111. Ragland, K.W., 1976. Worst-case ambient air concentrations from point sources using the Gaussian Plume model. Atmospheric Environment 10, 371e374. Reiner, J.L., Berset, J.D., Kannan, K., 2007. Mass flow of polycyclic musks in two wastewater treatment plants. Archives of Environmental Contamination and Toxicology 52, 451e457. Ricking, M., Schwarzbauer, J., Hellou, J., Svenson, A., Zitko, V., 2003. Polycyclic aromatic musk compounds in sewage treatment plant effluents of Canada and Sweden - first results. Marine Pollution Bulletin 46, 410e417. Rimkus, G.G., 1999. Polycyclic musk fragrances in the aquatic environment. Toxicology Letters 111, 37e56. Rimkus, G.G., Butte, W., Geyer, H.J., 1997. Critical considerations on the analysis and bioaccumulation of musk xylene and other synthetic nitro musks in fish. Chemosphere 35, 1497e1507. Roosens, L., Covaci, A., Neels, H., 2007. Concentrations of synthetic musk compounds in personal care and sanitation products and human exposure profiles through dermal application. Chemosphere 69, 1540e1547. Salvito, D., 2006. Correspondence. Marine Pollution Bulletin 52 (10), 1316. Schreurs, R., Legler, J., Artola-Garicano, E., Sinnige, T.L., Lanser, P. H., Seinen, W., van der Burg, B., 2004. In vitro and in vivo antiestrogenic effects of polycyclic musks in zebrafish. Environmental Science & Technology 38, 997e1002. Simonich, S.L., Federle, T.W., Eckhoff, W.S., Rottiers, A., Webb, S., Sabaliunas, D., De Wolf, W., 2002. Removal of fragrance materials during US and European wastewater treatment. Environmental Science & Technology 36, 2839e2847. Tas, J.W., Balk, F., Ford, R.A., vandePlassche, E.J., 1997. Environmental risk assessment of musk ketone and musk xylene in the Netherlands in accordance with the EU-TGD. Chemosphere 35, 2973e3002. Ternes, T.A., 1998. Occurrence of drugs in German sewage treatment plants and rivers. Water Research 32, 3245e3260. Westerhoff, P., Yoon, Y., Snyder, S., Wert, E., 2005. Fate of endocrine-disruptor, pharmaceutical, and personal care product chemicals during simulated drinking water treatment processes. Environmental Science & Technology 39, 6649e6663. Xie, Z.Y., Ebinghaus, R., Temme, C., Heemken, O., Ruck, W.G., 2007. Air-sea exchange fluxes of synthetic polycyclic musks in the North Sea and the Arctic. Environmental Science & Technology 41, 5654e5659.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 9 e1 0 8 6
Available at www.sciencedirect.com
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Biochemical oxygen demand and nutrient processing in a novel multi-stage raw municipal wastewater and acid mine drainage passive co-treatment system W.H. Strosnider a,*, B.K. Winfrey b, R.W. Nairn c,1 a
Saint Francis University, Environmental Engineering Department, 117 Evergreen Drive, Loretto, PA 15940, USA Department of Environmental Science and Technology, University of Maryland, 1426 Animal Sciences Building, College Park, MD 20742, USA c Center for Restoration of Ecosystems and Watersheds, University of Oklahoma, School of Civil Engineering and Environmental Science, 334 Carson Engineering Center, 202 W. Boyd St., Norman, OK 73019, USA b
article info
abstract
Article history:
A laboratory-scale, four-stage continuous flow reactor system was constructed to test the
Received 9 April 2010
viability of high-strength acid mine drainage (AMD) and municipal wastewater (MWW)
Received in revised form
passive co-treatment. The synthetic AMD had pH 2.60 and 1860 mg/L acidity as CaCO3
13 October 2010
equivalent with 46, 0.25, 2, 290, 55, 1.2 and 390 mg/L of Al, As, Cd, Fe, Mn, Pb and Zn,
Accepted 19 October 2010
respectively. The AMD was introduced to the system at a 1:2 ratio with raw MWW from the
Available online 28 October 2010
City of Norman, Oklahoma USA containing 265 94 mg/L BOD5, 11.5 5.3 mg/L PO3 4 , and 3 þ 20.8 1.8 mg/L NHþ 4 eN. During the 135 d experiment, PO4 and NH4 eN were decreased to
Keywords:
<0.75 and 7.4 1.8 mg/L, respectively. BOD5 was generally decreased to below detection
Phosphate
limits. Nitrification increased NO 3 to 4.9 3.5 mg/L NO3 eN, however relatively little
Nitrate
denitrification occurred. Results suggest that the nitrogen processing community may
Ammonium
require an extended period to mature and reach full efficiency. Overall, results indicate
Sewage
that passive AMD and MWW co-treatment is a viable ecological engineering approach for the developed and developing world that can be optimized and applied to improve water quality with minimal use of fossil fuels and refined materials. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Acid mine drainage (AMD) and municipal wastewater (MWW) are common environmental liabilities whose sustainable treatment is central to maintaining global water resource quality and conserving energy resources. Untreated AMD degrades water resources in coal and metal mining regions globally (Wolkersdorfer and Bowell, 2004a, 2004b, 2004c). Discharges of untreated MWW degrade water resources in
many developing nations (Gadgil, 1998; Nelson et al., 2001). In developed nations, where MWW is generally addressed actively (e.g., mechanical clarification, activated sludge, rotating biological contactors, etc.), treatment usually consumes substantial fiscal, material and energy resources (Muga and Mihelcic, 2008). Conventional active MWW and AMD treatment methods are commonly energy-intensive with higher operational and maintenance costs when compared to passive treatment approaches (Nelson et al.,
* Corresponding author. E-mail addresses:
[email protected] (W.H. Strosnider),
[email protected] (B.K. Winfrey),
[email protected] (R.W. Nairn). 1 Tel.: þ405 325 3354. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.026
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2001; Younger et al., 2002; Mannino et al., 2008; Muga and Mihelcic, 2008). Passive methods (e.g., treatment wetlands, soil filters, trickling filters, etc.) generally require larger land areas and higher construction costs when compared to active methods. Passive treatment can be considered an application of ecological engineering, which entails “the design of sustainable ecosystems that integrate human society with its natural environment for the benefit of both” (Mitsch, 1996). Renewable energy and natural unprocessed material flows must outweigh those of fossil fuel and refined material in ecological engineering applications (Mitsch and Jorgensen, 2004). Conventional MWW treatment generally consumes considerable economic, energy and material resources. Mechanical aeration, sludge scraping, clarifier skimming, sludge/effluent pumping, ultraviolet disinfection and other conventional MWW treatment practices require substantial energy, often supplied by non-renewable resources (Metcalf and Eddy Inc., 1991; Mannino et al., 2008; Muga and Mihelcic, 2008). Excess P and/or suspended solids are commonly removed from MWW by alum or ferric iron salt dosing (Metcalf and Eddy Inc., 1991; Omoike and Vanloon, 1999). Alum or ferric iron salt dosing can be relatively expensive and coagulant/flocculant demand has increased over recent decades (Ouellette, 1996; Hoffman, 2004; Kirschner, 2006) due in part to stricter P discharge standards (Jarvis, 2000; Hoffman, 2004). The passive co-treatment of AMD and MWW is a nascent application of ecological engineering that blends aspects of passive AMD treatment and conventional active MWW treatment. The passive treatment of AMD often requires suitable organic substrate electron donors for dissolved oxygen (DO) stripping, bacterial sulfate reduction (BSR) and the bacterially-mediated reduction of metals, such as Fe. Conventional active MWW treatment can require electron acceptors for bacterial oxidation of carbon substrate, chemicals for pathogen removal, and physical or chemical filtration or flocculation for solids or P removal. In theory, the requirements of both AMD and MWW treatment can be met within the same system, as each effluent possesses properties and constituents that can be passively utilized by the other. Effective MWW treatment is required to safeguard receiving water bodies from eutrophication and subsequent environmental degradation. Generally, MWW treatment must address suspended solids, P, N and oxygen demand concentrations. Suspended solids can be removed by biodegradation, flocculation, settling or filtration (Metcalf and Eddy Inc., 1991). P and suspended solids concentrations can be decreased by flocculation with free Al(III) and Fe(III) ions (Omoike and Vanloon, 1999; Parsons and Smith, 2008). Adler and Sibrell (2003) and Wei et al. (2008) demonstrated that soluble phosphorus will sorb to pre-existing AMD floc. AMD has been used to create coagulants for MWW and drinking water treatment (Menezes et al., 2010). MWW nitrogen processing generally requires sequential nitrification and denitrification (Metcalf and Eddy Inc., 1991). Bacterial populations central to AMD treatment, such as aerobic heterotrophs, iron reducing bacteria (IRB), and sulfate reducing bacteria (SRB) as well as their associated supporting communities require sufficient nutrients for optimum operation (Neculita et al., 2007). Often,
BSR substrate is supplemented with N and/or P to encourage greater SRB activity (Kaksonen and Puhakka, 2007). Oxygen demand is a function of the concentration of biodegradable organic matter, nutrients and readily oxidized constituents. Oxygen demand can be lowered by bacterial respiration or reaction of labile organic matter and nutrients. For example, aerobic heterotrophs, SRB, IRB and denitrifying bacteria utilize short-chain labile organic carbon thus lowering oxygen demand. Although Roetman (1932) first suggested mixing AMD with MWW for pathogen removal, and despite the amount of aforementioned peripheral research, only two systems have been encountered in the literature that were intentionally constructed to simultaneously treat these effluents. Johnson and Younger (2006) built a field-scale single-stage constructed wetland treatment system that successfully improved the water quality of weak secondary MWW effluent (w14 mg/L BOD5) and relatively weak (net-alkaline with w3 mg/L Fe) AMD. The system described in this manuscript was intended as a proof of concept and is the first attempt to simultaneously co-treat high-strength AMD and MWW. Strosnider et al. (in press) and Winfrey et al. (2010) documented highly effective metals and fecal indicator bacteria processing within the same system as described in this manuscript. Strosnider et al. (in press) documented dissolved Al, As, Cd, Fe, Mn, Pb and Zn concentrations consistently decreased by 99.8, 87.8, 97.7, 99.8, 13.9, 87.9 and 73.4%, respectively, pH increase to 6.79, and net acidic influent conversion to net-alkaline effluent. Winfrey et al. (2010) noted a 100% reduction in total coliforms, fecal coliforms, Escherichia coli, and fecal streptococci. To fully ascertain the promise of co-treatment, the objectives of this study were to determine the processing efficiencies of key MWW components (BOD5, nitrogen and phosphorus) in the high-strength AMD and raw MWW co-treatment system.
2.
Methods
2.1.
Experimental design
The experimental setup involved four serial unit processes in quadruplicate (Fig. 1). The first unit processes were primary clarifiers in basins for MWW and AMD mixing to raise AMD pH to that less inhibitory to BSR, metals complexation with organic ligands, Fe and Al flocculation with P and suspended solids, Fe(III) and SO4 reduction, DO stripping and BOD processing via heterotrophic bacterial activity and solids settling. The second and third unit processes together emulated a reducing and alkalinity producing system (RAPS), which are common unit processes in AMD treatment. The upper column sections of the RAPS emulations columns, which were filled with inert biomedia, were designed to encourage further DO stripping, BOD5 processing, Fe(III) reduction and BSR. The bottom of the columns was filled with limestone for abiotic alkalinity generation via calcite dissolution and to encourage further BSR. The final unit processes were aerobic wetland mesocosms for further BOD processing via the activity of aerobic heterotrophic and denitrifying bacteria, sequential Fe then Mn oxidation and precipitation, as well as the removal of
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 9 e1 0 8 6
Fig. 1 e Conceptual experimental layout. C1e4 represent the clarifiers, K1e4 the Kaldnes, L1e4 the limestone and W1e4 the wetlands. Black dots indicate sampling points.
remaining As, Cd, Pb and Zn via sorption to Fe oxyhydroxides. Each unit process was connected to the next via clear vinyl tubing and sampled at its outflow. The primary clarifier unit process was sized for a relatively high retention time of 32 h for more complete mixing through passive diffusion, and to encourage bacterial activity and thorough solids settling of the light flocculant created. Retention times of 1.5e2.5 h are typical for MWW primary clarification systems (Metcalf and Eddy Inc., 1991). However, retention times of around 6 h or greater commonly exist where further sedimentation or biological activity is desired (Gernaey et al., 2001). Fe oxyhydroxide particles are relatively low in density and small in particle size, thus also necessitating greater residence times than those typically noted in MWW clarification (Younger et al., 2002). Four-cm deep single transverse baffles and 2.5-cm radius semi-circular weirs served as the physical structures in the low density polyethylene (LDPE) basins that comprised this unit process. Sludge was wasted from the bottom of the clarifiers under gravity flow with a barbed high density polyethylene (HDPE) T-connector attached to an HDPE valve and clear vinyl tubing. The RAPS emulation columns were 91.5 cm in height and 12.5 cm in diameter. The bottom 38 cm of the columns were filled with high quality (>90% CaCO3) limestone washed of all fines and separated by sieve analysis adapted from ASTM
1081
D422 with the fraction passing a 2.54-cm sieve yet retained by a 1.27-cm sieve. The remaining top 53.5 cm of the columns were packed with Kaldnes K3 biofilm media to provide bacterial biofilm attachment surface. Kaldnes K3 media are polyethylene high surface area (500 m2/m3) components that are typically used in moving bed biofilm wastewater and drinking water treatment (Saliling et al., 2007). Following Pruden et al. (2007) findings of the importance of inoculation to sulfate reducing bioreactor performance, the Kaldnes zone was inoculated with 100 mL of RAPS substrate from two mature passive coal mine AMD treatment systems in Pittsburg and Latimer Counties, OK. Each column was wrapped in aluminum foil to emulate the lightless conditions in RAPS substrate. Residence times were 42 and 18 h for the Kaldnes and limestone stages, respectively. The aerobic constructed treatment wetland mesocosms were two shallow LDPE storage containers. Each wetland was bisected longitudinally with plastic to create the necessary four treatment trains. Wetland soil was collected from an existing constructed mitigation wetland at the Midwest City, Oklahoma MWW Treatment Plant. The surface flow mesocosms were planted with Hydrocotyle ranunculoides and Nasturtium officinale. The wetlands were placed under timed grow-lights on a 12 h/ d cycle. Wetland residence time was 67 h. Raw MWW collected after grit screening at the Norman, OK MWW treatment plant and synthetic AMD approximating that found at Cerro Rico de Potosı´, Bolivia were introduced to the system at a 2:1 ratio (MWW:AMD) with peristaltic pumps at a combined flow rate of (3.8 L/d). Each treatment train continuously handled influent for 135 d. The system was gravity flow from the first (clarifier) to the last (wetland) unit processes. MWW was collected weekly, homogenized during pumping and refrigerated at 4 C before introduction to the system. AMD was prepared weekly and stored at room temperature (20 C) until use. All unit processes were maintained at room temperature throughout the experiment. Sludge was wasted from the clarifiers in varying amounts at irregular intervals to average 0.69% of the combined inflow over the duration of the experiment as to not allow sludge buildup to reduce retention time.
2.2.
Data collection
An Accumet AR60 multimeter was used to determine temperature and DO concentrations. BOD5 was determined using the 5day BOD test following standard methods (APHA, 1998). Samples 3 for anions (Cl, F, NO 2 , NO3 , and PO4 ) were immediately filtered through Dionex OnGuard II H cartridges and 0.2-mm nylon filters then quantified with a MetrOhm 761 compact ion chromatograph unit following EPA method 300. NHþ 4 samples were immediately processed using the Hach high-range Test ’N Tube salicylate method (Hach, 2002). Following Sharp et al. (1995), water samples for dissolved organic carbon (DOC) and dissolved total nitrogen (DTN) were immediately filtered through 0.45-mm nylon filters and stored at <0 C in 40-mL amber glass EPA vials with polypropylene caps and Teflon septa until quantification with an Analytik Jena multi N/C 2100. Biological activity reaction tests (BART) by Droycon Bioconcepts Inc. (DBI) and the MPN technique were applied to estimate bacterial populations. BART tests were conducted to determine estimates of fermentative, nitrifying and denitrifying bacteria
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concentrations. BART tubes were incubated at 22 C and read every 12 or 24 h. DBI QuickPop software was used to estimate populations. The most probable number (MPN) technique for enumeration of fermentative bacteria followed Pepper et al. (1995) using test tubes with Durham tubes containing phenol red broth with lactose from Becton Dickinson Diagnostic Systems. Triplicate test tubes were inoculated in an anaerobic nitrogen-filled chamber for a dilution range of 101e108 and incubated for 24 h at 37 C. MPN results were interpreted using guidance and tables from Woomer (1994). Percent coverage of each wetland plant species was estimated at days 29, 75, 94, 99, 112, 130 and 133 using digital photography. Sampling generally occurred bi-monthly during this standard operational run for a total of ten sampling events. BOD5 and anion samples were taken throughout the entire experiment. NHþ 4 samples were taken for the last five sampling events. DOC and DTN samples were only taken during the final two sampling events. Water samples for MPN tests were taken from the columns on the final sampling event (day 133). BART tests were initiated on days 85, 112, 123, and 130.
(289 19.8 mg/L as CaCO3 equivalent), BOD5 (265 94 mg/L), Cl (69 3.8 mg/L) and SO2 4 (70 16 mg/L) concentrations place the MWW used between the “medium” and “strong” designations of MWW established by Metcalf and Eddy Inc. (1991). The mean MWW DOC and DTN were 42 and 30 mg/L, respectively, and DO was 0.98 mg/L. Although the AMD influent characteristics were consistent, some MWW influent parameters, such as BOD5 and PO3 4 (11.5 5.3 mg/L), varied somewhat throughout the experiment. This variation is to be expected and is typical of loadings experienced by conventional wastewater treatment plants (Metcalf and Eddy Inc., 1991). However, MWW alkalinity, Cl, F (1.0 0.09 mg/L), NHþ 4 eN (20.8 1.8 mg/L), NO2 eN (<0.08 mg/L), and NO3 eN (<0.11 mg/L) were relatively consistent.
3.2.
Phosphorus
3.
Results and discussion
PO3 4 was significantly decreased from the theoretical influent mix 7.7 3.5 mg/L to <0.75 mg/L by the clarifier outflow, producing a removal rate of 0.69 g m2 d1 (5.6 g m3 d1). Flocculation with Al(III) and Fe(III) (e.g., Omoike and Vanloon, 1999; Parsons and Smith, 2008) or sorption to pre-existing AMD floc (e.g., Adler and Sibrell, 2003; Wei et al., 2008) were the likely primary removal mechanisms. Stoichiometrically, more than enough Fe and Al were removed from solution in removal. The the clarifier to account for the observed PO3 4 removal indicates that observed thorough and rapid PO3 4 higher removal rates may be possible in an optimized system. as The multi-stage co-treatment system removed PO3 4 well as conventional MWW treatment plants and better than conventional treatment wetlands and the single-stage cotreatment system described by Johnson and Younger (2006). The extent and rate of PO3 4 removal noted in the clarifier is similar to that observed in conventional MWW treatment plants that incur significant costs via additional intensive treatment steps or flocculant dosing (Metcalf and Eddy Inc., 1991; Parsons and Smith, 2008). Assuming that influent P was primarily in the form of PO3 4 , the removal rate in the clarifier was greater than 90% of free water surface (FWS) wetlands, the majority of which received tertiary or secondary concentrations (Kadlec and MWW influent with lower PO3 4 Wallace, 2009). The Johnson and Younger (2006) single-stage yet co-treatment system treated MWW with 39% less PO3 4 removed 10e50%, a much lower removal efficiency and rate than was observed in the multi-stage co-treatment system clarifier. Results indicate that PO3 4 removal is enhanced by cotreatment with higher strength AMD that contains greater concentrations of Al and Fe.
3.1.
Influent characteristics
3.3.
2.3.
Data analysis
Because direct sampling of the clarifier influent mix was impossible due to the experimental design, the theoretical influent mix (TMix) chemical composition was calculated using the ratio of AMD to MWW. To account for dilution, TMix concentrations were used to calculate processing efficiencies and rates. To facilitate the calculation of means and application of statistical tests, values below detection limits were assigned one half the value of the detection limit (Miller and Miller, 1986). Prior to means or median testing, all data sets were tested for normality with the AndersoneDarling test and similarity of variance. Due to the prevalence of normality and unequal variances, Welch’s ttest was applied for all DTN and DOC comparisons. The nonparametric KruskaleWallis Multiple Comparisons test was used to determine statistical difference between the medians of the remaining data sets due to the prevalence of unequal variances and non-normality. Because estimated plant coverage and NO 3 were not measured on the same days, a second order polynomial mathematical model of percent estimated coverage was created for each wetland. All statistical testing was completed with Minitab version 15. All comparisons of measures of central tendency hereafter mentioned were backed by the statistical analyses to achieve 95% confidence.
The high-strength synthetic AMD was similar in composition to that generated in the base/precious metal mining district of Cerro Rico de Potosı´, Bolivia where untreated high-strength AMD and raw MWW pollute the headwaters of the Rio Pilcomayo (Strosnider and Nairn, 2010). The AMD had pH 2.6, 7.69 mg/L DO, 0.41 mg/L NO 3 eN, 0.29 mg/L DTN, 0.0 mg/L DOC, and 1860 mg/L acidity as CaCO3 equivalent with 46, 0.25, 2, 290, 55, 1.2 and 390 mg/L of dissolved Al, As, Cd, Fe, Mn, Pb and Zn, respectively (Strosnider et al., in press). The mean alkalinity
Nitrogen
The multi-stage co-treatment system demonstrated promising nitrification rates yet underperformed with regards concentrations were statistically to denitrification. NO 3 unchanged until the wetland where they significantly increased to 4.94 3.49 mg/L NO 3 eN due to nitrification, for an average rate of 0.036 g N m2 d1. Nitrification requires oxygen and did not occur in the first three unit processes where DO was 1.68 0.65, 0.71 0.10, and 0.72 0.12 mg/L in the clarifier, Kaldnes and limestone, respectively. For the final
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 9 e1 0 8 6
five sampling events, mean wetland NHþ 4 eN removal (7.76 mg/L) compared well with NO 3 eN increase (6.95 mg/L), indicating that minimal denitrification occurred for this period on average. However, the DTN data taken for the final two sampling events indicated that N removal, and hence denitrification, eventually occurred. DTN was not significantly changed until it decreased within the wetlands from 18 to 12 mg/L N. BART results indicated that nitrifying bacteria were not aggressive until the wetlands as well, where 103 cfu/mL were detected. Denitrifying bacteria concentrations were 101e106 cfu/mL throughout the first three unit processes, however there was little NO 3 available. Denitrifying bacteria ranged from 102 to 105 cfu/mL in the wetlands. Conventional MWW treatment plants and passive treatment wetlands generally process N better than the multistage co-treatment system. Conventional MWW treatment plants commonly nitrify and denitrify more effectively with less retention time than the multi-stage co-treatment system (e.g., Koivunen et al., 2003; Tandukar et al., 2007). FWS and horizontal sub-surface flow (HSSF) treatment wetlands have been found to process TN and NO 3 effectively (Kadlec and Wallace, 2009), unlike the multi-stage co-treatment system. The wetland unit process nitrification rate was an order of magnitude less than the mean FWS and HSSF wetland rates (Kadlec and Wallace, 2009) indicating that nitrification may have been substantially inhibited. Because AMD generally limits bacterial activity (Niyogi et al., 2003), microbial nitrification and denitrification as well as nitrogen assimilation could be relatively decreased in the system. Although pH was circumneutral and the other AMD metals were decreased to below concentrations of concern, Zn was orders of magnitude above typical background concentrations from the wetland inflow to outflow (Strosnider et al., in press). Wetland denitrification was likely primarily limited by DO concentrations which averaged 4.4 mg/L at the wetland outflow and/or the lack of labile carbon substrate, evidenced by BOD5 being generally driven to below detection limits by the wetland outflow. Johnson and Younger (2006) documented nitrification and denitrification within the single-stage co-treatment wetland where Zn concentrations were near background levels, another indication that Zn was a possible inhibiting factor for nitrification and/or denitrification. Nitrification and denitrification has also been documented in constructed wetlands treating Fe-rich AMD with elevated NHþ 4 (Demin and Dudeney, 2003). However, the multi-stage co-treatment system did not appear to have reached N treatment equilibrium and this may have been due to the dynamic nature of the wetland plant community and substrate influencing the microbial community. The wetlands were initially dominated by N. officinale (60e85% at day 29) which quickly became stressed, exhibited chlorosis, and died off by day 75 to remain <4% coverage throughout the remainder of the experiment. Hydrocotyle verticillata, which displayed no stress throughout the experiment, replaced the N. officinale to increase from 10e35% at day 29 to 72e95% by day 133. The second order polynomial model created for estimated total plant coverage of each wetland from days 75 to 133 had R2 values of 0.98, 0.89, 0.91 and 0.95, for wetlands 1e4, respectively, demonstrating a tight fit to the data. Few wetland plant species are tolerant of highly elevated metals
1083
concentrations (Younger et al., 2002), and it appears N. officinale was not. However, H. verticillata was tolerant of the highly elevated Zn (64.7e34.3 mg/L) and Fe (45.1e0.18 mg/L) present from the wetland inflow to outflow (Strosnider et al., in press). The nitrification rate increased over the duration of the 2 1 d at day 20 to 0.091 g experiment from 0.012 g NO 3 eN m 2 1 d at day 133, indicating that the nitrifying NO3 eN m community was maturing throughout the experiment and likely not reached maximum efficiency (Figs. 2 and 3). Wetland outflow NO 3 eN concentrations were positively correlated with time after experiment initiation (Pearson’s correlation coefficient 0.859, p < 0.001) as well as modelled estimated plant coverage (Pearson’s correlation coefficient 0.636, p ¼ 0.001). Nitrification can be enhanced by aquatic macrophyte oxygen transfer to substrate (Kadlec and Wallace, 2009), which would have increased over time in the wetland due to the increasing vitality of the plant community. In an FWS wetland treating NHþ 4 and Fe-rich AMD, Demin and Dudeney (2003) noted no nitrification for the first three years and that six years were necessary for optimization. Demin and Dudeney (2003) posit that the buildup of Fe oxyhydroxide floc created more suitable nitrifying community substrate. No Fe oxyhydroxide floc was present in the multistage co-treatment wetland at experiment initiation. However, approximately 0.3 cm of Fe oxyhydroxide floc was noted to cover the multi-stage co-treatment wetland substrate by the conclusion of the experiment, which following Demin and Dudeney (2003) would have been a more suitable setting for nitrifying bacteria. The lag in nitrification performance may also be attributed to the time necessary for natural selection and horizontal gene transfer (e.g., BakerAustin et al., 2006) to shape an efficient nitrifying community suited to the unique setting and stresses of the wetland. Regardless, the findings of Demin and Dudeney (2003) buttress the supposition that the multi-stage co-treatment N removal performance had not reached optimum efficiency by the termination of the experiment.
3.4.
Biochemical oxygen demand
The system demonstrated consistently efficient BOD5 processing (Table 1). Although BOD5 could not be tracked throughout the entire system due to highly elevated concentrations of chemical oxygen demand unrelated to the MWW, DOC can be a suitable proxy for tracking BOD5 (Khan et al., 1998; Servais et al., 1999). DOC was dramatically and significantly decreased in the clarifier (mean 28e5.1 mg/L C). A further significant DOC decrease was noted from the wetland influent to effluent (mean 6.2e2.5 mg/L C). Sedimentation aided by flocculation with Al(III) or Fe(III) likely decreased BOD5 and DOC in the clarifiers (e.g., Metcalf and Eddy Inc., 1991; Omoike and Vanloon, 1999). The activity of various heterotrophic microbes (i.e., SRB, denitrifiers, fermenters, IRB, and aerobic heterotrophs) likely served to decrease BOD5 and DOC throughout the system. BART results indicated that gram-negative fermenting bacteria were present in concentrations of 102e103 cfu/mL in the limestone and wetlands. MPN results were lower, with 101.83 and 101.76 cells/mL of fermentative bacteria present in the Kaldnes and limestone unit processes, respectively. Substantial SRB and IRB
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populations were also present throughout the system (Strosnider et al., in press). The aforementioned mechanisms resulted in BOD5 processing to below detection limits and an overall systemic removal rate of 1.14 g m2 d1 or 22.3 g m3 d1. However, BOD5 exertion can be decreased by elevated concentrations of heavy metals. Mittal and Ratra (2000) noted that Zn in concentrations of 20 and 50 mg/L decreased BOD5 exertion by 28.7 and 37.5%, respectively. Because the wetland outflow BOD5 was below or near detection limits, the mean 34.3 mg/L Zn present did not substantially impact overall removal performance. It appears as though BOD5 was successfully processed despite concentrations of Al in the clarifiers and Kaldnes as well as Zn throughout the system that Mittal and Ratra (2000) found to be inhibitory to BOD5 exertion. The multi-stage co-treatment system processed BOD5 as well or better than conventional municipal wastewater treatment plants (MWWTPs), treatment wetlands and the single-stage wetland co-treatment wetland. BOD5 processing was more complete than is often documented in conventional MWWTPs, which commonly produce effluent with BOD5 of 5e30 mg/L (e.g., Koivunen et al., 2003; Tandukar et al., 2007; Jamwal et al., 2009). However, conventional MWWTPs often have much lower residence times (Metcalf and Eddy Inc., 1991). The BOD5 processing of the multi-stage co-treatment system also outperformed the three primary types of MWW treatment wetlands: FWS, HSSF, and vertical flow (VF). Compared to the annual mean treatment performance of 136 FWS wetlands, the system produced effluent with BOD5 less than the vast majority treating tertiary MWW (influent BOD5 < 30 mg/L) and all FWS treating primary and secondary MWW (Kadlec and Wallace, 2009). Compared to the annual mean treatment performance of 202 HSSF wetlands, the system produced effluent with BOD5 less than the vast majority treating tertiary MWW and all HSSF wetlands treating primary and secondary MWW (Kadlec and Wallace, 2009). The system produced effluent with BOD5 less than the annual mean treatment performance of the vast majority of 62 VF wetlands, (Kadlec and Wallace, 2009). The system drove BOD5 to below typical background concentrations in natural 21
90
98
18
wetlands and treatment wetlands (Kadlec and Wallace, 2009). Johnson and Younger (2006) reported 20e75% removal in a single-stage co-treatment wetland with a mean residence time of 14 h receiving secondary MWW with only w14 mg/L BOD5. Although the residence time of the multi-stage co-treatment system was 10 times that of the Johnson and Younger (2006) system, the multi-stage system demonstrated much more complete BOD5 removal while handling MWW with a BOD5 concentration approximately 19 times greater.
3.5.
Sustainability
Full-scale AMD and MWW passive co-treatment systems may result in fossil fuel, refined material, and cost savings. The Kaldnes biomedia used in this experiment could be substituted with any high surface area naturally occurring material, such the nonreactive river rock used in conventional MWW treatment trickling filter systems (e.g., Metcalf and Eddy Inc., 1991). Without the use of plastic in the Kaldnes stage, field-scale co-treatment systems with the same unit processes could be constructed primarily with
111 118
15 NH4+-N mg/L
Fig. 3 e Mean NOL 3 eN concentrations from the TMix to the system outflow for each sampling period. Bars are shaded according to the d elapsed from experiment initiation to the sampling event. Error bars represent one standard deviation above and below the mean.
133
Table 1 e BOD5 concentrations in the influent MWW, TMix, and each treatment train’s wetland outflow (W1e4).
12 9
Day
MWW
TMix
W1
W2
W3
W4
1.92 <1.24 2.30 2.06 <1.35 <1.16 <1.04 2.22 1.31 <1.47
1.50 <1.24 2.40 3.10 <1.35 <1.16 <1.04 <0.98 1.27 <1.47
3.24 1.63 2.50 2.11 <1.35 <1.16 <1.04 1.15 1.47 <1.47
mg/L
6 3 0 TMix
Clarifier
NHD 4 eN
Kaldnes
Limestone
Wetland
Fig. 2 e Mean concentrations from the TMix to the system outflow for the last five sampling periods. Bars are shaded according to the d elapsed from experiment initiation to the sampling event. Error bars represent one standard deviation above and below the mean.
20 34 55 69 83 90 98 111 118 133
472 296 236 148 227 251 256 320 302 141
311 195 156 97 150 166 169 211 199 93
3.49 1.24 2.10 <1.42 <1.35 <1.16 <1.04 2.38 <1.03 <1.47
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 7 9 e1 0 8 6
unrefined, low embodied energy (emergy) materials (e.g., earthen berms, limestone, concrete) (Odum, 1996). Aside from low emergy construction, these systems could be operated using gravity flow without ongoing purchased energy inputs, as was demonstrated in this manuscript. A field-scale passive co-treatment system could use commonly engineered structures, such as ponds, aerobic wetlands, clarifiers, and vertical flow bioreactors, which could decrease engineering costs. Significant cost savings would result from eliminating the need to purchase and transport organic substrate, often a major cost of passive AMD treatment. Cost savings would also result by using AMD as a coagulant/flocculant and disinfectant for MWW treatment. In addition, the use of MWW as a substrate consumes an item with negative societal value in the place of valuable organic substrate, such as the compost or refined low molecular weight carbon sources (e.g., methanol) often used in AMD treatment (Younger et al., 2002). Raw MWW and high-strength AMD are often not independently addressed with passive methods because of the limitations of conventional passive treatment technologies and/or the lack of locally available suitable carbon substrate (Younger et al., 2002; Kadlec and Wallace, 2009). No instance of active co-treatment of AMD and MWW was encountered in the literature. The efficiency and rate of which both waste streams can be passively treated within the same system will determine the extent of energy, material and cost savings from reduced system footprint.
4.
Conclusions
These results, coupled with the metal removal performance and fecal indicator bacteria sterilization noted in Strosnider et al. (in press) and Winfrey et al. (2010), indicate that multistage passive co-treatment is capable of highly efficient processing of both AMD and MWW constituents. The lack of denitrification may be addressed by an effluent return loop to increase denitrification similarly applied in conventional MWW treatment for enhanced denitrification (e.g., Metcalf and Eddy Inc., 1991). The initial anaerobic unit processes should be suitable to denitrify recycled nitrate-rich effluent. Because the true nitrification potential was not yet observed, longer-term experiments to determine the maximum steady-state nitrification rate may be necessary. There is also the possibility of combining aspects of co-treatment with conventional MWW treatment, such as the use of AMD as a flocculant-source for PO3 4 removal, to create more efficient hybrid facilities which, along with passive co-treatment systems such as the one described in this study, present considerable energy, material and cost savings opportunities. As high-strength AMD and MWW passive co-treatment has proven a viable approach backed by theory and experimental results, field pilot studies are a logical next step.
Acknowledgments Drs. Keith Strevett and David Sabatini of the University of Oklahoma School of Civil Engineering and Environmental
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Science provided important experimental design guidance. Special thanks are extended to Beatriz Santamaria, Ronald Conlon, Alex Brewer, Jonathan Clifton, Darcy Lutes, Julie LaBar, Srividhya Viswanathan and other University of Oklahoma Center for Restoration of Ecosystems and Watersheds personnel for laboratory assistance. This study would have been impossible without the support provided by Darrell Schwartz and other City of Norman Municipal Wastewater Treatment Plant staff. Funding was provided by U.S. EPA Agreement FY04 104(b)(3) X7-97682001-0, USGS Agreement 04HQAG0131 and the University of Oklahoma Center for Restoration of Ecosystems and Watersheds.
references
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Menezes, J.C.S.S., Silva, R.A., Arce, I.S., Schneider, I.A.H., 2010. Production of a poly-alumino-iron sulphate coagulant by chemical precipitation of a coal mining acid drainage. Minerals Engineering 23 (3), 249e251. Metcalf and Eddy, Inc., 1991. Wastewater Engineering: Treatment, Disposal and Reuse, third ed. McGraw Hill. Miller, J.C., Miller, J.N., 1986. Statistics for Analytical Chemistry. Ellis Horwood Ltd, UK. Mitsch, W.J., 1996. Ecological engineering: a new paradigm for engineers and ecologists. In: Schulze, P.C. (Ed.), Engineering within Ecological Constraints. National Academy Press, Washington, DC. Mitsch, W.J., Jorgensen, S.E., 2004. Ecological Engineering and Ecosystem Restoration. John Wiley & Sons, Inc., New York, p. 411. Mittal, S.K., Ratra, R.K., 2000. Toxic effect of metal ions on biochemical oxygen demand. Water Resources 34 (1), 147e152. Muga, H.E., Mihelcic, J.R., 2008. Sustainability of wastewater treatment technologies. Journal of Environmental Management 88, 437e447. Neculita, C., Zagury, G.J., Bussie´re, B., 2007. Passive treatment of acid mine drainage in bioreactors using sulfate-reducing bacteria: critical review and research needs. Journal of Environmental Quality 36, 1e16. Nelson, M., Odum, H.T., Brown, M.T., Alling, A., 2001. “Living off the land”: resource efficiency of wetland wastewater treatment. Advances in Space Research 27 (9), 1547e1556. Niyogi, D.K., Lewis, W.M., McKnight, D.M., 2003. Direct and indirect effects of mine drainage on bacterial processes in mountain streams. Journal of the North American Benthological Society 22 (2), 276e291. Odum, H.T., 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wiley & Sons, Inc. Omoike, A.I., Vanloon, G.W., 1999. Removal of phosphorus and organic matter removal by alum during wastewater treatment. Water Research 33 (17), 3617e3627. Ouellette, J., 1996. Coagulants and flocculants rise. Chemical Marketing Reporter 250 (15), SR18. Parsons, S.A., Smith, J.A., 2008. Phosphorus removal and recovery from municipal wastewaters. Elements 4, 109e112. Pepper, I.L., Gerba, C.P., Brendecke, J.W., 1995. Environmental Microbiology: A Laboratory Manual. Academic Press. Pruden, A., Messner, N., Pereyra, L., Hanson, R.E., Hiibel, S.R., Reardon, K.F., 2007. The effect of inoculums on the performance of sulfate-reducing columns treating heavy metal contaminated water. Water Research 41, 904e914. Roetman, E.T., 1932. The Sterilization of Sewage by Acid Mine Water. Civil Engineering Masters thesis, West Virginia University.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 8 7 e1 0 9 4
Available at www.sciencedirect.com
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Efficiency of water disinfectants against Legionella pneumophila and Acanthamoeba Mathieu Dupuy a, Ste´phane Mazoua a, Florence Berne a, Charles Bodet a, Nathalie Garrec b, Pascaline Herbelin c, Florence Me´nard-Szczebara d, Sandrine Oberti d, Marie-He´le`ne Rodier a, Sylvie Soreau c, France Wallet e, Yann He´chard a,* a
Universite´ de Poitiers, Laboratoire de Chimie et Microbiologie de l’Eau, CNRS UMR 6008, 40 avenue du recteur Pineau, 86022 Poitiers Cedex, France b CAE, Veolia Environnement, 1 place de Turenne, 94410 Saint-Maurice Cedex, France c EDF, Division Recherche et De´veloppement, 6 Quai Watier, 78401 Chatou, France d Anjou Recherche, Veolia Environnement, Chemin de la digue BP76, 78603 Maisons Laffitte Cedex, France e EDF, Service des Etudes Me´dicales, 22-28 rue Joubert, 75009 Paris, France
article info
abstract
Article history:
Free-living amoebae might be pathogenic by themselves and be a reservoir for bacterial
Received 8 June 2010
pathogens, such as Legionella pneumophila. Not only could amoebae protect intra-cellular
Received in revised form
Legionella but Legionella grown within amoebae could undergo physiological modifications
18 October 2010
and become more resistant and more virulent. Therefore, it is important to study the
Accepted 19 October 2010
efficiency of treatments on amoebae and Legionella grown within these amoebae to
Available online 28 October 2010
improve their application and to limit their impact on the environment. With this aim, we compared various water disinfectants against trophozoites of three
Keywords:
Acanthamoeba strains and L. pneumophila alone or in co-culture. Three oxidizing disinfectants
Chlorine
(chlorine, monochloramine, and chorine dioxide) were assessed. All the samples were
Amoebae
treated with disinfectants for 1 h and the disinfectant concentration was followed to
Bacteria
calculate disinfectant exposure (Ct). We noticed that there were significant differences of
Oxidant
susceptibility among the Acanthamoeba strains. However no difference was observed
Biocide
between infected and non-infected amoebae. Also, the comparison between the three disinfectants indicates that monochloramine was efficient at the same level towards free or co-cultured L. pneumophila while chlorine and chlorine dioxide were less efficient on cocultured L. pneumophila. It suggests that these disinfectants should have different modes of action. Finally, our results provide for the first time disinfectant exposure values for Acanthamoeba treatments that might be used as references for disinfection of water systems. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Legionella pneumophila is a waterborne pathogenic bacterium responsible for severe pneumonia called Legionnaires’ disease
(Fields et al., 2002; Steinert et al., 2002). L. pneumophila can be found at high levels in man-made water systems such as air conditioning, cooling towers and spas (Borella et al., 2005). These systems are mainly implicated in outbreaks as they
* Corresponding author. Tel.: þ33 5 49 45 40 07; fax: þ33 5 49 45 35 03. E-mail address:
[email protected] (Y. He´chard). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.025
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might produce infected water droplets, which are inhaled by people. In the environment, L. pneumophila is ubiquitously found in fresh water and could survive within biofilms and free-living amoebae (Borella et al., 2005; Taylor et al., 2009; Temmerman et al., 2006). Several protozoa, such as freeliving amoebae, feed on biofilms leading to L. pneumophila phagocytosis. However, L. pneumophila has the ability to resist phagocytosis and multiply within amoebae, via a well-known mechanism (Molmeret et al., 2004). A similar mechanism is used by these bacteria to resist phagocytosis by macrophages, leading to lung infection (Molmeret et al., 2004). Protozoa, such as amoebae, are proposed to provide an intra-cellular environment for L. pneumophila multiplication in water systems. It may also be expected that bacteria will multiply freely under specific conditions (Borella et al., 2005; Taylor et al., 2009). In the amoebae, intra-cellular Legionella are i) protected from adverse conditions or disinfectant treatments (Bichai et al., 2008; Thomas et al., 2004), ii) highly virulent as they adapt to intra-cellular life (Molmeret et al., 2005) and iii) less sensitive to disinfectants because of phenotypic modification (Garduno et al., 2002). Various disinfectants (e.g. chlorine, monochloramine, .) and physical treatments (e.g. heat, UV, .) are used in water systems to control Legionella growth (Kim et al., 2002). Several disinfection studies have been performed on Legionella (Campos et al., 2003; Kim et al., 2002). In case of treatment failure, L. pneumophila might be able to recolonize water systems. It has been hypothesized that this recolonization is made possible because Legionella is protected in the biofilm or in amoebae (Barker et al., 1992; Donlan et al., 2005; Murga et al., 2001). Few studies have examined the impact of these treatments on L. pneumophila grown in co-culture with amoebae (Garcia et al., 2007; Storey et al., 2004). In addition, only few studies have reported the impact of chlorine on Acanthamoeba (Critchley and Bentham, 2009; Cursons et al., 1980; Kuchta et al., 1993) but no Ct values were calculated and monochloramine was poorly studied. In our study, we compared the efficiency of three disinfectants, commonly used in water treatments, on three strains of Acanthamoeba and one strain of L. pneumophila alone or in co-culture.
2.
Materials and methods
2.1.
Amoebae isolation and culture
(ampicillin 200 mg/mL and streptomycin 200 mg/mL). All isolates adapted to growth in axenic medium were subcultured in the 1034 medium without antibiotic at 25 C.
2.2.
L. pneumophila Lens was kindly provided by the National Reference Center of Legionella, Lyon, France (Cazalet et al., 2004). The bacteria were grown on buffered charcoal yeast extract (BCYE) agar plates at 37 C for 4 days before co-culture experiments.
2.3.
Co-culture of L. pneumophila and Acanthamoeba
Axenic Acanthamoeba were grown at 25 C for 3 days in 25-cm2 tissue culture flasks (NUNC) containing 5 mL of 1034 medium. Adherent trophozoites were washed once with amoeba buffer (2.5 mM KH2PO4, 4 mM MgSO4, 0.5 mM CaCl2, 2.5 mM, Na2HPO4, 0.05 mM (NH4)2FeII(SO4)2) and suspended in this buffer. The cells were then pelleted by centrifugation (500g, 15 min) and resuspended at a concentration of 106 cells/mL in amoeba buffer supplemented with 10% 1034 medium. L. pneumophila Lens were harvested from BCYE plate and diluted in amoeba buffer at a concentration of 108 cells/mL. An aliquot of this sample was added to the trophozoites’ suspension to achieve a multiplicity of infection (MOI) of 0.1, leading to a final concentration of amoebae and L. pneumophila Lens of 105e106 cells/mL and 104e105 cells/mL, respectively. All the samples were incubated at 30 C for 24e48 h. Co-cultures contained both infected and non-infected trophozoites, as well as free L. pneumophila. The samples were centrifuged (500g, 15 min), washed twice in phosphate buffer (50 mM, pH 8) and adjusted to 106 amoebae/mL before disinfection treatments.
2.4.
Amoeba identification
DNA was extracted from amoeba cells using the NucleoSpin Tissue kit (Macherey Nagel). An 18S rDNA PCR was performed with primers Ami6F1 and Ami9R, as described previously (Thomas et al., 2006). The amplicons (w850 bp) were sequenced with each primer using the BigDye Terminator v3.1 cycle sequencing kit (Applied Biosystems) and analyzed using the 3130 Genetic Analyzer (Applied Biosystems).
2.5. With the aim to isolate environmental strains, water samples from different sources were collected by Anjou Recherche (Maisons-Laffitte, France) and Electricite´ De France (EDF Chatou, France). A drop of water was placed onto a nonnutrient agar plate (made only with water and agar 15 g/L) seeded with a lawn of Escherichia coli XL1 Blue (Stratagene). This medium is referred to as NNA-Eco. Plates were incubated at various temperatures and examined daily for 7e14 days. After amoebae growth (indicated by zone of lysis), an isolate was transferred onto a fresh NNA-Eco plate. The amoebae grown on NNA-Eco were then transferred to axenic broth 1034 medium (peptone 10 g/L, yeast extract 10 g/L, ribonucleic acid 1 g/L, folic acid 115 mg/L, hemin 1 mg/L, KH2PO4 0.36 g/L, Na2HPO4 0.5 g/L, pH 6.5) containing antibiotics
L. pneumophila culture
Disinfection treatments
Three different disinfectants, commonly used by industries to disinfect their networks and circuits, were used: chlorine at 30 C (used in cooling towers) or at 50 C (used in hot water systems), chlorine dioxide and monochloramine. The initial concentrations were as follows: chlorine between 2 and 3 mg Cl2/L (to provide approximately 1 mg Cl2/L residual after 1 h), chlorine dioxide 0.4 mg/L and monochloramine 0.8 mg Cl2/L. All the solutions were prepared from reagentgrade chemicals and deionized water. Stock solutions were stored at 4 C. All the glassware was cleaned with chlorine (100 mg/L) for at least 1 h and carefully rinsed with deionized water. Chlorine solution was freshly prepared by dilution of sodium hypochlorite (13%, ACROS Organics). Chlorine
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concentration was measured by the 4500-Cl G DPD method (APHA, 2005) before and during treatments in order to determine the residual concentration. Stock solution of monochloramine was obtained by adding free chlorine in a solution of ammonium chloride under agitation, with a chlorine to nitrogen molar ratio of 0.5 and at a pH of 8.5. The final concentration of the stock solution of monochloramine was 2 mM (or about 140 mg Cl2/L). A stock solution of chlorine dioxide was prepared by slowly adding sulfuric acid to a sodium chlorite solution and then by collecting the gaseous chlorine dioxide produced in ultrapure water according to the 4500-ClO2 B method (APHA, 2005). The concentration of the stock solution was about 400 mg/L. The DPD method was also used to measure the residual concentration of monochloramine and chlorine dioxide. Treatment with chlorine, monochloramine and chlorine dioxide was stopped by addition of 10 mL of sterile sodium thiosulfate (0.1 M). Before each treatment, 1 mL of microbial cells’ suspension was transferred into 100 mL of sterile phosphate buffer (50 mM, pH 8), leading to a concentration of 104 amoebae/mL and 104e105 L. pneumophila/mL. The sample was incubated at 30 C (and 50 C for chlorine only) under agitation and disinfectant (between 0.1 and 0.5 mL at room temperature) was added. The concentration of disinfectant (Supplementary figures) and the survival of Acanthamoeba and L. pneumophila were followed after 0, 2, 15, 30 and 60 min of treatment. For each experiment, a disinfectant consumption test without microorganism was conducted in 100 mL of phosphate buffer under the same conditions of temperature in order to evaluate the stability of the biocide. The disinfectant exposure was quantified by Ct (concentration time, in mg min/L), which corresponds to the geometric area under the disinfectant decay curve. Microbial inactivation (loss of cultivability) was recorded as a function of Ct, to evaluate the effectiveness of the disinfectants. Ct tables have been developed for some waterborne pathogens to indicate conditions necessary for a 2-log (Ct99%) or 3-log (Ct99.9%) inactivation (King et al., 1988; Rose et al., 2005). We have considered that treatments were efficient when a 3-log reduction was reached, as this value is mainly used in the literature.
2.6.
3.
Results
3.1. Isolation and identification of Acanthamoeba strains Water samples from cooling towers were used to isolate amoebae on plates. Among the primary isolates, several amoebae, whose morphology was similar to that of Acanthamoeba strains, were selected. Three of these strains were axenized in the 1034 medium. In order to confirm the genus of these amoebae at the molecular level, their 18S rDNA was sequenced. The comparison of these sequences to data banks (BLAST nr) unambiguously confirmed that they belong to the Acanthamoeba genus. Moreover, these strains were different from each other since their sequences displayed differences. These sequences were deposited at GenBank (GU936482, GU936483, GU936484). The strains were named Acanthamoeba V1, S2 and M3 respectively.
3.2.
Acanthamoeba infection by L. pneumophila
The ability of L. pneumophila to infect each amoeba strain was tested. Amoebae (105 cells/mL) were mixed with L. pneumophila (104 cells/mL) to have a MOI of 0.1. The growth of L. pneumophila was followed by CFU measurement each day. In co-culture with Acanthamoeba V1 or Acanthamoeba M3, L. pneumophila grew rapidly and at the same rate than in ATCC collection strains (Fig. 1). The population was amplified by more than 2 log within 48 h. Legionella grew slower, ending to 2-log amplification in 72 h with Acanthamoeba S2. These results show that Legionella was able to infect all these strains, although the infection rate might be different between the Acanthamoeba strains.
Chlorine treatment at 30 C and 50 C
3.3.
The aim of this study was to compare disinfection treatments against L. pneumophila and Acanthamoeba, either alone or in
Acanthamoeba V1 A. castellanii ATCC 30254
Survival of Acanthamoeba and L. pneumophila
10 8
A. castellanii ATCC 50739 Acanthamoeba S2
10
CFU/mL
The survival of Acanthamoeba and L. pneumophila was determined by the most probable number (MPN) procedure (Beattie et al., 2003). For L. pneumophila counts, co-cultures were centrifuged (14,000g, 5 min) and vortexed for 1 min to release intra-trophozoite bacteria, as previously described (Wintermeyer et al., 1995). Then, 1, 0.1, 0.01 and 0.001 mL of each sample was inoculated onto NNA-Eco plates for amoebae or onto BCYE plates for L. pneumophila. Each inoculation was done in quintuplicates. Plates were incubated for 15 days at 25 C for amoeba or 7 days at 37 C for L. pneumophila. Each plate was examined for the absence or presence of microbial growth and the results were reported using an MPN table (Beattie et al., 2003). The limits of detection were 1.8 102 NPP/ L and 1.6 106 NPP/L, leading to a maximum amplitude of 3.94 log.
Acanthamoeba M3
7
10 6 10 5 10 4 10 3 0
24
48
72
Time (h) Fig. 1 e L. pneumophila growth in co-culture with various Acanthamoeba strains.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 8 7 e1 0 9 4
-2
-3
-4 0
20
40
60
80
100
Ct (mg.min/L)
B
0
-1
0
-1
log (N/N0)
0
-1
log (N/N0)
A
A
log (N/N0)
co-culture. The first step was to assess chlorine efficiency on these microorganisms at 30 C and 50 C. Cultures or cocultures were treated with an initial chlorine concentration (between 2 and 3 Cl2/L) chosen to have a residual free chlorine concentration of approximately 1 mg Cl2/L at the end of the treatment. The consumption of biocides during the experiments was followed in order to calculate the Ct and is presented in Figs. S1 and S2 (Supplementary information). The consumption of chlorine was lower with Legionella than with Acanthamoeba (Fig. S2). The results with Acanthamoeba trophozoites show that chlorine was efficient to inactivate, by 3 log at least, all the strains (Fig. 2). The efficiency seems to be slightly higher at 50 C than at 30 C (Fig. 2). Inactivation values for Acanthamoeba are compared in Fig. 6. Statistical analyses (performed using unpaired two-tailed Student’s t-test) show that, for a given Ct, there were significant differences of sensitivity between strains and that Acanthamoeba M3 was the more sensitive strain
-2
-2
-3
-3
-4 0
20
40
60
80
100
Ct (mg.min/L) -4 0
20
40
60
80
100
Ct (mg.min/L)
B
0
log (N/N0)
-1
-2
-3
-4 0
20
40
60
80
100
Ct (mg.min/L) Fig. 2 e Reduction of Acanthamoeba trophozoites cultivability after chlorine treatment at 30 C (A) or 50 C (B). Acanthamoeba V1 infected (B) or not (C) with L. pneumophila, Acanthamoeba S2 infected (,) or not (-) with L. pneumophila, Acanthamoeba M3 infected (6) or not (:) with L. pneumophila was treated with the disinfectant for 60 min and the survival was counted by the MPN method. Bars represent standard errors of the means of three independent experiments.
Fig. 3 e Reduction of L. pneumophila cultivability after chlorine treatment at 30 C (A) or 50 C (B). L. pneumophila alone (A) or in co-culture with Acanthamoeba V1 (B), Acanthamoeba S2 (,), or Acanthamoeba M3 (6) was treated with the disinfectant for 60 min and the survival was counted by the MPN method. Bars represent standard errors of the means of three independent experiments.
(Fig. 6A and B). Besides, there was no significant difference (P > 0.005) of sensitivity between infected or non-infected Acanthamoeba. Chlorine treatment was also highly efficient on Legionella and treatment at 50 C seems to be even more efficient (Fig. 3). Inactivation values for Legionella are compared in Fig. 7. The comparison of inactivation at 30 C clearly shows that cocultured L. pneumophila were significantly less sensitive (P < 0.005 or P < 0.001) than free L. pneumophila (Fig. 7A). This was not seen at 50 C (Fig. 7B) because the detection threshold was rapidly reached. Also, there was no difference (P > 0.005) between L. pneumophila co-cultivated with the different amoebae strains.
3.4.
Monochloramine and chlorine dioxide treatment
Monochloramine (NH2Cl, 0.8 mg Cl2/L initial concentration) and chlorine dioxide (ClO2, 0.4 mg/L initial concentration)
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-2
-3
-4 0
10
20
30
40
50
6
8
10
Ct (mg.min/L)
B
0
-1
0
-1
log (N/N0)
0
-1
log (N/N0)
A
A
log (N/N0)
were used for amoeba and L. pneumophila treatment. The consumption of biocides during the experiments was followed in order to calculate the Ct and is presented in Figs. S3 and S4 (Supplementary information). Monochloramine concentration was more stable than those of chlorine or chlorine dioxide. Chlorine dioxide was highly efficient on Acanthamoeba M3 only (Figs. 4B and 6C). Chlorine dioxide displayed an inactivation pattern similar to that of chlorine. Monochloramine was highly efficient on Acanthamoeba M3 and V1 but Acanthamoeba S2 was significantly less sensitive (Figs. 4A and 6D). As for chlorine, there was no significant difference (P > 0.005 using unpaired two-tailed Student’s t-test) of sensitivity between infected and non-infected amoebae towards these two disinfectants (Fig. 6). The results with L. pneumophila show that monochloramine and chlorine dioxide were efficient (Fig. 5). With chlorine
-2
-3
-2
-4
-3
0
2
4
Ct (mg.min/L) -4 0
10
20
30
40
50
Ct (mg.min/L)
B
0
Fig. 5 e Reduction of L. pneumophila cultivability after monochloramine (A) or chlorine dioxide (B) treatments. L. pneumophila alone (A) or in co-culture with Acanthamoeba V1 (B), Acanthamoeba S2 (,), or Acanthamoeba M3 (6) was treated with the disinfectant for 60 min and the survival was counted by the MPN method. Bars represent standards errors of the means of three independent experiments.
lo g ( N / N 0)
-1
-3
dioxide, co-cultured bacteria were less sensitive (P < 0.001) than free bacteria (Fig. 7C). On the contrary, there was no significant difference (P > 0.005) between free and co-cultured bacteria treated with monochloramine (Fig. 7D).
-4
4.
-2
0
2
4
6
8
Discussion
10
Ct (mg.min/L) Fig. 4 e Reduction of Acanthamoeba trophozoites cultivability after monochloramine (A) or chlorine dioxide (B) treatments. Acanthamoeba V1 infected (B) or not (C) with L. pneumophila, Acanthamoeba S2 infected (,) or not (-) with L. pneumophila, Acanthamoeba M3 infected (6) or not (:) with L. pneumophila was treated with the disinfectant for 60 min and the survival was counted by the MPN method. Bars represent standard errors of the means of three independent experiments.
In order to improve the efficiency of water treatment, we compared three disinfectants in controlled conditions. The treatment doses were chosen to be realistic and representative of actual practices. The results allow estimating both efficiency of the disinfectants and sensitivity of three Acanthamoeba strains and L. pneumophila alone or in co-culture. No study had compared the sensitivity of Acanthamoeba towards these three common disinfectants. Also, for water treatment, it is important to define Ct values to inactivate a given microorganism. We thus decided to calculate these Ct values and represent them in the function of inactivation.
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0
B
-3
cc
A ca
V1
S2
cc
cc 3
cc V1
cc A ca
3 M
S2 A ca
M A ca
V1 A
ca A
ca
S2
cc 3 M
ca A
A
ca
V1
S2 A
ca
M ca
3
-5 cc
-5 cc
-4
cc
-3
-4
A
*
V1
** **
** **
-3
**
-2
S2
-2
log (N/N0)
-1
3
log (N/N0)
-1
0
A ca
D
A ca
0
A ca
A ca
V1
M
3
cc
cc ca A
A
A
ca
ca
M
S2
3
V1
S2 A
A
ca
ca
M ca A
C
M
-5
*
V1
-5
*
A ca
-4
cc
-4
S2
** *
A ca
*
-3
-2
A ca
-2
log (N/N0)
-1
3
log (N/N0)
-1
0
A ca
A
Fig. 6 e Comparison of Acanthamoeba inactivation after treatments. (A) Chlorine at 30 C, Ct [ 5 mg min/L, (B) chlorine at 50 C, Ct [ 5 mg min/L, (C) chlorine dioxide at 30 C, Ct [ 5 mg min/L, and (D) monochloramine at 30 C, Ct [ 2 mg min/L. Acanthamoeba M3, S2 and V1 were grown alone or in co-culture (cc). Values are the average calculated from three independent experiments ± standard deviation. Statistical analyses were performed by unpaired two-tailed Student’s t-test (*P < 0.005; **P < 0.001).
Regarding Acanthamoeba, we show that the efficiency of the treatments clearly depends on the target strain. Similar results have been reported recently with other treatments (Coulon et al., 2010). Acanthamoeba M3 was the most sensitive strain to chlorine and chlorine dioxide but not to monochloramine. Chlorine and chlorine dioxide displayed a different inactivation pattern than monochloramine; it could be hypothesized that monochloramine should have a different mode of action as compared to the two other disinfectants. It was previously reported that L. pneumophilainfected Acanthamoeba polyphaga exhibited higher resistance to chlorine than uninfected amoeba (Garcia et al., 2007). In contrast, no significant differences of sensitivity between infected or non-infected amoebae were observed for all treatments in this study. This suggests that protection of amoeba against disinfectant conferred by L. pneumophila may be limited to specific conditions. Regarding L. pneumophila, almost all the treatments were efficient, i.e. leading to at least a 3-log reduction of the bacterial population in our conditions. However their efficiency, estimated by the log of inactivation (Figs. 6 and 7), could be different. These results are in agreement with many studies on Legionella sensitivity to oxidizing disinfectants (Kim et al., 2002). Besides temperature seems to influence the disinfectant efficiency. Indeed, chlorine was more efficient at 50 C than at 30 C, at least on co-cultured
Legionella. Similarly, chlorine was shown to be more efficient at 43 C than at 25 C on these bacteria, although chlorine decay was faster at the higher temperature (Muraca et al., 1987). Our results clearly indicate that chlorine and chlorine dioxide were more efficient on free L. pneumophila as compared to co-cultured L. pneumophila. The difference between free and co-cultured bacteria is in agreement with the study of Garcia et al. (2007), which reported a higher resistance to chlorine of L. pneumophila when internalized within A. polyphaga. Also, Barker et al. have reported that L. pneumophila grown in Acanthamoeba were less sensitive than free bacteria to disinfectants (polyhexamethylene biguanide, benzisothiazolone and 5-chloro-N-methylisothiazolone) and antibiotics (Barker et al., 1992, 1995). It was proposed that L. pneumophila grown within amoebae were less sensitive because these bacteria undergo phenotypic modifications upon intra-cellular growth. Interestingly, monochloramine displays a different behavior, as compared to the other treatments. Indeed, there was no difference of efficiency against the intra-cellular and free L. pneumophila with monochloramine, while other treatments were more efficient on free L. pneumophila. This behavior has not been reported before. It suggests that monochloramine has a different mode of action as compared to chlorine and chlorine dioxide.
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-1
-1
V1
0 -1
-2 -3
**
-4
**
**
log (N/N0)
-2 -3 -4
V1
S2 Lp n
cc
A ca
A ca cc
cc
Lp n
Lp n
cc
A ca
A ca
V1
S2 A ca cc
Lp n
cc
A ca
M
3
Lp n Lp n
Lp n
-5
-5
Lp n
log (N/N0)
cc
A ca cc Lp n
D
A ca
V1
Lp n
Lp n
cc
cc
A ca
A ca
M 3 A ca cc
Lp n
0
cc
-5
Lp n
-5
Lp n
-4
S2
-4
S2
-3
A ca
*
M 3
**
-2
Lp n
**
-3
log (N/N0)
-2
Lp n
log (N/N0)
-1
C
0
B
0
M 3
A
Fig. 7 e Comparison of L. pneumophila inactivation after treatments. (A) Chlorine at 30 C, Ct [ 5 mg min/L, (B) chlorine at 50 C, Ct [ 3 mg min/L, (C) chlorine dioxide at 30 C, Ct [ 0.6 mg min/L, and (D) monochloramine at 30 C, Ct [ 2 mg min/L. L. pneumophila were grown alone or in co-culture (cc) with Acanthamoeba M3, S2 or V1. Values are the average calculated from three independent experiments ± standard deviation. Statistical analyses were performed by unpaired two-tailed Student’s t-test (*P < 0.005; **P < 0.001).
5.
Conclusion
Our work has compared for the first time the efficiency of chlorine, chlorine dioxide and monochloramine on Acanthamoeba and L. pneumophila. All these disinfectants, at concentrations similar to those used in water systems were efficient in the conditions set up in this study. However, their efficiency may vary with the Acanthamoeba strain. Interestingly, chlorine and chlorine dioxide were less efficient when L. pneumophila was co-cultivated with Acanthamoeba while monochloramine did not display the same selectivity. The inactivation pattern of Acanthamoeba by monochloramine was also different from the two other disinfectants. It suggests that monochloramine would have a different mode of action. This hypothesis will be tested in further experiments with Acanthamoeba. With the dual purpose of protection of health and environment, our results might help to adapt treatment strategies against amoebae and L. pneumophila.
Acknowledgment We are grateful to Pierre Pernin (University of Lyon, France), who was involved in amoebae isolation and morphological
characterization. C.B. was supported by a grant from the CNRS.
Appendix. Supplementary data Supplementary data related to this article can be found, in the online version, at doi:10.1016/j.watres.2010.10.025.
references
APHA, 2005. Standard Methods for the Examination of Water and Wastewater, twenty first ed. Washington, DC, USA. Barker, J., Brown, M.R., Collier, P.J., Farrell, I., Gilbert, P., 1992. Relationship between Legionella pneumophila and Acanthamoeba polyphaga: physiological status and susceptibility to chemical inactivation. Applied and Environmental Microbiology 58 (8), 2420e2425. Barker, J., Scaife, H., Brown, M.R., 1995. Intraphagocytic growth induces an antibiotic-resistant phenotype of Legionella pneumophila. Antimicrobial Agents and Chemotherapy 39 (12), 2684e2688. Beattie, T.K., Seal, D.V., Tomlinson, A., McFadyen, A.K., Grimason, A.M., 2003. Determination of amoebicidal activities of multipurpose contact lens solutions by using a most
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probable number enumeration technique. Journal of Clinical Microbiology 41 (7), 2992e3000. Bichai, F., Payment, P., Barbeau, B., 2008. Protection of waterborne pathogens by higher organisms in drinking water: a review. Canadian Journal of Microbiology 54 (7), 509e524. Borella, P., Guerrieri, E., Marchesi, I., Bondi, M., Messi, P., 2005. Water ecology of Legionella and protozoan: environmental and public health perspectives. Biotechnology Annual Review 11, 355e380. Campos, C., Loret, J.F., Cooper, A.J., Kelly, R.F., 2003. Disinfection of domestic water systems for Legionella pneumophila. Journal of Water Supply: Research and Technology e AQUA 52, 341e354. Cazalet, C., Rusniok, C., Bruggemann, H., Zidane, N., Magnier, A., Ma, L., Tichit, M., Jarraud, S., Bouchier, C., Vandenesch, F., Kunst, F., Etienne, J., Glaser, P., Buchrieser, C., 2004. Evidence in the Legionella pneumophila genome for exploitation of host cell functions and high genome plasticity. Nature Genetics 36 (11), 1165e1173. Coulon, C., Collignon, A., McDonnell, G., Thomas, V., 2010. Resistance of Acanthamoeba cysts to disinfection treatments used in health care settings. Journal of Clinical Microbiology 48 (8), 2689e2697. Critchley, M., Bentham, R., 2009. The efficacy of biocides and other chemical additives in cooling water systems in the control of amoebae. Journal of Applied Microbiology 106 (3), 784e789. Cursons, R.T., Brown, T.J., Keys, E.A., 1980. Effect of disinfectants on pathogenic free-living amoebae: in axenic conditions. Applied and Environmental Microbiology 40 (1), 62e66. Donlan, R.M., Forster, T., Murga, R., Brown, E., Lucas, C., Carpenter, J., Fields, B., 2005. Legionella pneumophila associated with the protozoan Hartmannella vermiformis in a model multispecies biofilm has reduced susceptibility to disinfectants. Biofouling 21 (1), 1e7. Fields, B.S., Benson, R.F., Besser, R.E., 2002. Legionella and Legionnaires’ disease: 25 years of investigation. Clinical Microbiology Reviews 15 (3), 506e526. Garcia, M.T., Jones, S., Pelaz, C., Millar, R.D., Abu Kwaik, Y., 2007. Acanthamoeba polyphaga resuscitates viable non-culturable Legionella pneumophila after disinfection. Environmental Microbiology 9 (5), 1267e1277. Garduno, R.A., Garduno, E., Hiltz, M., Hoffman, P.S., 2002. Intracellular growth of Legionella pneumophila gives rise to a differentiated form dissimilar to stationary-phase forms. Infection and Immunity 70 (11), 6273e6283. Kim, B.R., Anderson, J.E., Mueller, S.A., Gaines, W.A., Kendall, A.M., 2002. Literature review e efficacy of various disinfectants against Legionella in water systems. Water Research 36 (18), 4433e4444. King, C.H., Shotts Jr., E.B., Wooley, R.E., Porter, K.G., 1988. Survival of coliforms and bacterial pathogens within protozoa during
chlorination. Applied and Environmental Microbiology 54 (12), 3023e3033. Kuchta, J.M., Navratil, J.S., Shepherd, M.E., Wadowsky, R.M., Dowling, J.N., States, S.J., Yee, R.B., 1993. Impact of chlorine and heat on the survival of Hartmannella vermiformis and subsequent growth of Legionella pneumophila. Applied and Environmental Microbiology 59 (12), 4096e4100. Molmeret, M., Bitar, D.M., Han, L., Kwaik, Y.A., 2004. Cell biology of the intracellular infection by Legionella pneumophila. Microbes Infection 6 (1), 129e139. Molmeret, M., Horn, M., Wagner, M., Santic, M., Abu Kwaik, Y., 2005. Amoebae as training grounds for intracellular bacterial pathogens. Applied and Environmental Microbiology 71 (1), 20e28. Muraca, P., Stout, J.E., Yu, V.L., 1987. Comparative assessment of chlorine, heat, ozone, and UV light for killing Legionella pneumophila within a model plumbing system. Applied and Environmental Microbiology 53 (2), 447e453. Murga, R., Forster, T.S., Brown, E., Pruckler, J.M., Fields, B.S., Donlan, R.M., 2001. Role of biofilms in the survival of Legionella pneumophila in a model potable-water system. Microbiology 147, 3121e3126. Rose, L.J., Rice, E.W., Jensen, B., Murga, R., Peterson, A., Donlan, R.M., Arduino, M.J., 2005. Chlorine inactivation of bacterial bioterrorism agents. Applied and Environmental Microbiology 71 (1), 566e568. Steinert, M., Hentschel, U., Hacker, J., 2002. Legionella pneumophila: an aquatic microbe goes astray. FEMS Microbiology Reviews 26 (2), 149e162. Storey, M.V., Winiecka-Krusnell, J., Ashbolt, N.J., Stenstrom, T.A., 2004. The efficacy of heat and chlorine treatment against thermotolerant Acanthamoebae and Legionellae. Scand Journal of Infectious Diseases 36 (9), 656e662. Taylor, M., Ross, K., Bentham, R., 2009. Legionella, protozoa, and biofilms: interactions within complex microbial systems. Microbial Ecology 58 (3), 538e547. Temmerman, R., Vervaeren, H., Noseda, B., Boon, N., Verstraete, W., 2006. Necrotrophic growth of Legionella pneumophila. Applied and Environmental Microbiology 72 (6), 4323e4328. Thomas, V., Bouchez, T., Nicolas, V., Robert, S., Loret, J.F., Levi, Y., 2004. Amoebae in domestic water systems: resistance to disinfection treatments and implication in Legionella persistence. Journal of Applied Microbiology 97 (5), 950e963. Thomas, V., Herrera-Rimann, K., Blanc, D.S., Greub, G., 2006. Biodiversity of amoebae and amoeba-resisting bacteria in a hospital water network. Applied and Environmental Microbiology 72 (4), 2428e2438. Wintermeyer, E., Ludwig, B., Steinert, M., Schmidt, B., Fischer, G., Hacker, J., 1995. Influence of site specifically altered Mip proteins on intracellular survival of Legionella pneumophila in eukaryotic cells. Infection and Immunity 63 (12), 4576e4583.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 9 5 e1 1 0 4
Available at www.sciencedirect.com
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Direct photodegradation of carbamazepine followed by micellar electrokinetic chromatography and mass spectrometry Vaˆnia Calisto a, M. Rosa´rio M. Domingues b, Guillaume L. Erny a, Valdemar I. Esteves a,* a
Department of Chemistry and CESAM (Centre for Environmental and Marine Studies), University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal b Department of Chemistry, Mass Spectrometry Centre, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal
article info
abstract
Article history:
Carbamazepine, a widely consumed psychotropic pharmaceutical, is one of the most
Received 2 July 2010
commonly detected drugs in the environment. To better assess the environmental persis-
Received in revised form
tence of carbamazepine in aqueous matrices, the effect of pH and dissolved oxygen on the
6 September 2010
direct photodegradation rate of this pharmaceutical was evaluated in this study, using
Accepted 30 October 2010
simulated solar irradiation. In order to follow the degradation and the emergence of photo-
Available online 5 November 2010
products, a micellar electrokinetic chromatography based method was developed, consisting on the use of a dynamically coated capillary column. The developed methodology showed
Keywords:
good repeatability and efficiency in the separation of carbamazepine and photoirradiation
Pharmaceuticals
products. Also, seven photodegradation products were identified by electrospray mass
Environment
spectrometry (ESI-MS), including the known carcinogenic acridine that was produced under
Persistence
all the pH and oxygenation levels studied and one newly identified photoproduct.
Capillary electrophoresis
This paper gives new insights into the role of dissolved oxygen on the photodegradation
Quantum yield
rate of carbamazepine. The results indicate that acidic pH, combined with the absence of
Oxygen
dissolved oxygen in the aqueous matrix, results in very high direct photodegradation rates. At basic pH, dissolved oxygen does not interfere with the process and very low rates were observed. At environmentally relevant conditions, carbamazepine was shown to persist in the environment from 4.5 to 25 days. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The study of the environmental impact of pharmaceuticals has been considered one of the most relevant growing fields of research (Halling-Sorensen et al., 1998; Petrovic and Barcelo´, 2007; Glassmeyer et al., 2008; Calisto and Esteves, 2009). The emergence of this concern, especially in the last decade, is justified by the large number of published studies reporting the widespread occurrence of these compounds (Glassmeyer et al., 2008), their persistence in ecosystems as well as their
ability to interfere with non-target organism at extremely low concentrations (Brooks et al., 2003a, 2003b; Brain et al., 2004; Johnson et al., 2005; Gust et al., 2009). Carbamazepine (CBZ) is an anti-epileptic drug frequently used in the treatment of seizures (Brunton et al., 2008). It is estimated that 1014 tons per year of carbamazepine are consumed worldwide (Zhang et al., 2008). Among the large number of pharmacologically active compounds, this drug should be made object of special attention as it is one of the most frequently detected pharmaceuticals in environmental
* Corresponding author. Tel.: þ351 234401408. E-mail address:
[email protected] (V.I. Esteves). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.037
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matrices (Petrovic and Barcelo´, 2007; Zhang et al., 2008). It has been found in surface (Ternes, 1998; Metcalfe et al., 2003; Miao and Metcalfe, 2003; Tixier et al., 2003; Hummel et al., 2006; Madureira et al., 2009) and ground waters (Focazio et al., 2008), waste water treatment plants (WWTP) influents and effluents (Ternes, 1998; Andreozzi et al., 2003; Miao and Metcalfe, 2003; Bendz et al., 2005; Hummel et al., 2006; Bahlmann et al., 2009) and even in drinking water (Heberer et al., 2002). These occurrences are a consequence of the already reported inadequacy of the treatment methods applied in the WWTPs. Several studies showed that the removal efficiency in WWTP is typically below 10% (Zhang et al., 2008). Moreover, the carbamazepine concentration found in WWTP effluents is frequently higher than that found in the influents. This fact may result from a phenomenon of deconjugation of the carbamazepine conjugated forms generated during metabolisation, contributing to the increase of the environmental concentration of this drug (Ternes, 1998; Clara et al., 2004; Bendz et al., 2005; Joss et al., 2005). Apart from the large number of occurrences in aquatic environments, this compound constitutes an additional source of concern as it has been shown that carbamazepine is highly resistant to biodegradation (Andreozzi et al., 2002, 2003; Tixier et al., 2003; Lam and Mabury, 2005). Also, as its solidwater distribution coefficient is extremely low, carbamazepine is expected to be present in the water phase in significant amounts (Ternes et al., 2004). Taking into account its preference to remain in the aqueous phase, direct or indirect photochemical degradation could be the most relevant processes that will determine the persistence of this kind of compound in surface waters, particularly exposed to sunlight (Boreen et al., 2003; Arnold and McNeill, 2007). However, reported studies indicate that carbamazepine is considerably resistant to photodegradation when compared with other pharmaceuticals (Andreozzi et al., 2003; Lam and Mabury, 2005). Moreover, some of the photodegradation products already identified in literature are known to produce more adverse effects than carbamazepine itself. This is the case of acridine, one of the most frequently identified photoproducts, that is considered a mutagenic and carcinogenic compound (Chiron et al., 2006; Kosjek et al., 2009). This manuscript aims to give new insights into the understanding of direct photodegradation of carbamazepine in water. Given that very limited data is available about the effect of the pH and dissolved oxygen on the behavior of this contaminant (environmental half-life and photoproducts produced), we hereby present some work that intends to better clarify this issue. A new micellar electrokinetic chromatography (MEKC) based method (developed to follow the photodegradation of carbamazepine and the emergence of photodegradation products) is also presented.
SigmaeAldrich), sodium chloride, ethylvanillin (99%, Aldrich), sodium tetraborate (Riedel-de Hae¨n), sodium hydroxide (Fluka), sulfuric acid (Fluka, 95e97%) and acetonitrile (HPLC gradient grade, VWR, Prolabo). Ultra-pure water, used in the preparation of standard solutions, running buffer and irradiation samples, was obtained using a Milli-Q Millipore system (Milli-Q plus 185).
2.2.
Irradiation experiments
2.2.1.
Irradiation Apparatus
The irradiation experiments were performed with the Solarbox 1500 (Co.fo.me.gra, Italy) equipped with a 1500 W arc xenon lamp and special outdoor UV filters that restrict the transmission of light with wavelengths below 290 nm. The uniformity of the irradiation was provided by a parabolic reflection chamber, whereas the temperature of the irradiation chamber was maintained by an air cooled system. During the irradiation, the irradiance was kept constant at 55 W m2 (290e400 nm); a multimeter (Co.fo.me.gra, Italy) equipped with a black standard temperature sensor and a UV 290e400 nm large band sensor was used to monitor the irradiance levels and temperature. Carbamazepine stock solutions were irradiated in triplicate using 25 mL quartz tubes with a diameter of 1.5 cm. Each set of experiments was accompanied by dark controls inside the irradiation chamber, also in triplicate, foiled several times with aluminum paper. The quartz tubes were suspended inside the chamber using a home-made metallic holder which guaranteed that samples were homogeneously irradiated.
2.2.2.
2.2.3.
Materials and methods
2.1.
Chemicals
All chemicals used were of analytical grade: carbamazepine (99%, Sigma), sodium dodecyl sulphate (99%, for electrophoresis, SigmaeAldrich), hexadimethrine bromide (polybrene,
Study of the effect of pH and dissolved oxygen
To study the photodegradation behavior of carbamazepine under different conditions, experiments were conducted at four different pH: 2.9, 4.0, 5.8 and 9.0. The pH of the stock solutions was adjusted using 1 M sodium hydroxide or formic acid prior to irradiation. Also, the effect of the dissolved oxygen in water was evaluated. Accordingly, each experiment was repeated after sparging the solutions with nitrogen or oxygen in order to remove or saturate the solutions with oxygen, respectively. The samples were sparged with the referred gases during approximately 1 min per mL of solution. During the sampling procedure, the quartz tubes were kept under oxygen or nitrogen atmosphere depending on the sample under study.
2.3.
2.
Sampling
Irradiation experiments were carried out up to 24 h. Sampling consisted on the collection of 2 mL aliquots of all the irradiated samples at specific time intervals. The aliquots were stored at 4 C and analyzed by MEKC within the next 3 days.
Capillary electrophoresis
To follow the degradation of carbamazepine and the appearance of its photodegradation products, a micellar electrokinetic chromatography (MEKC) based methodology was developed. The Capillary Electrophoresis (CE) analyses were performed using a commercial instrument (Beckman P/ACE MDQ (Fullerton, CA, USA)), equipped with a photodiode array
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 9 5 e1 1 0 4
UVevis detection system. Approximately 1 mm of the capillary external coating was removed by burning the extremities prior to conditioning; the capillary extremities were then polished to increase the efficiency of the method and decrease of baseline noise.
2.3.1.
Capillary column conditioning and coating
A fused-silica capillary with a total length of 50 cm (40 cm to detector) and 75 mm of internal diameter was used. The new capillaries were first conditioned with 1 M NaOH for 30 min followed by ultra-pure water for 15 min. Subsequently, to proceed to the capillary coating, it was flushed with hexadimethrine bromide (polybrene) 0.5% (w/v) in 0.5 M NaCl for 20 min, as described in Pranaityte´ and Padarauskas (2006). Then, the capillary was washed with ultra-pure water for 2 min, followed by a 20 min flushing with running buffer. The capillary was washed with running buffer for 20 min at the beginning of each working day and with ultra-pure water for 5 min at the end of the day. When not in use the capillary extremities were left immersed in ultra-pure water to ensure the stability of the capillary coating. All the capillary conditioning and coating steps were performed at a pressure of 20 psi.
2.3.2.
Standard solutions and running buffer
Carbamazepine stock solutions were prepared with ultra-pure water with a final concentration of 9.5 mg L1 (approximately half of its solubility in water) and were stored in dark glass bottles at 4 C, for no more than 1 month. A stock solution of ethylvanillin was used as internal standard (IS). This solution was prepared by dissolving ethylvanillin in acetonitrile (approximately 10% of the total volume) and further diluting it with ultra-pure water to a final concentration of 167 mg L1. The solution was stored at 4 C under N2 atmosphere. For the CE calibration curve, six standard solutions, with concentrations ranging from 0.5 to 8 mg L1, were prepared by diluting the stock solution. Standard solutions also contain IS (final concentration of 3.34 mg L1), SDS and borax with the same concentrations of the running buffer. Standard solutions were analyzed in quadruplicate. The running buffer consisted on 25 mM borax and 50 mM SDS, pH 9.2, freshly prepared every 2 days and stored at 4 C. All the solutions were filtered through a 0.22 mm membrane filter.
2.3.3.
Sample preparation for CE analysis
CE samples were prepared by adding SDS, borax and IS to the irradiated samples in order to obtain a final concentration of 50 mM, 25 mM and 3.34 mg L1, respectively.
2.3.4.
Separation conditions
Samples were injected for 4 s at 0.5 psi. Electrophoretic separations were performed in direct polarity with a positive power supply of 20 kV for 13 min. The temperature was maintained at 25 C. The resulting current was of approximately 110 mA. Detection of carbamazepine and carbamazepine photodegradation products were monitored at 210 nm. Running buffer vials were changed every 3 runs.
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2.4. Identification of photodegradation products by mass spectrometry The identification of carbamazepine photodegradation products was performed by electrospray mass spectrometry (ESIMS), using N2-sparged samples collected after 30 min of irradiation at pH 2.9, and not sparged samples collected after 24 h of irradiation at pH 5.8. Positive-ion ESI-MS and ESI-MS2 were carried out on a Micromass (Manchester, UK) Q-TOF2 hybrid tandem mass spectrometer. For ESI analysis, irradiated samples were diluted in methanol (0.1% formic acid v/v). Samples were introduced at a flow rate of 10 mL min1 into the ESI source. In the MS and MS2 experiments, the time-of-flight (TOF) mass resolution was set to approximately 9000. The cone voltage was 35 V, and the capillary voltage was 3 kV. The source temperature was 80 C and the desolvation temperature was 150 C. MS2 spectra were obtained using argon as the collision gas with the collision energy set between 18 and 40 eV. The data was processed using MassLynx software (version 4.0).
3.
Results and discussion
3.1.
Optimization of MEKC method
The performance of the adopted methodology was evaluated comparing the results obtained using uncoated and coated capillary columns. Initially, irradiated samples were analyzed with an uncoated capillary after being conditioned with 1 M NaOH for 30 min followed by ultra-pure water for 10 min. In Table 1, results of repeated injections of a carbamazepine standard solution (mean peak area, migration time and relative standard deviation) are shown. The results show that the method provides good repeatability of the evaluated parameters when using standard solutions. However, resolution of the peaks due to carbamazepine and to its photodegradation products was unsatisfactory in the samples irradiated for long periods of time. Also, when analyzing these samples, the repeatability of peak area was strongly affected, showing the lack of efficiency of the methodology under these conditions. Taking into account the obtained results, a dynamically coated capillary was used. As it was previously reported, dynamically coated capillaries have the advantage of improving the reproducibility (Vanhoenacker et al., 2004; Pranaityte´ and Padarauskas, 2006) by decreasing the interaction between the analytes and the capillary inner wall, avoiding variations due to possible modifications in the chemical structure of the bare silica capillary surface between repeated injections. Moreover, this type of coating has already been reported as a promising tool to improve separation efficiency (Erny et al., 2009). This procedure is easy to implement and, on the whole, consists on coating the capillary surface with a buffer containing a multiple charged polycation (polybren) and then flushing the positively charged surface with an anionic surfactant (SDS). The first layer of SDS interacts with the cationic surface of a polybren and a second layer of SDS is formed by establishing hydrophobic interactions with
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Table 1 e Comparison of the CE methodology repeatability considering the usage of a coated and an uncoated capillary: mean and relative standard deviations (R.S.D.) of the ratio between carbamazepine (CBZ) and internal standard (IS) peak areas and CBZ migration time (n [ 4). Composition of the standard solution
Uncoated Capillary CBZ stock IS Coated Capillary CBZ stock IS 50% CBZ stock þ 50% running buffer IS 90% CBZ stock þ 10% running buffer IS
Ratio between CBZ and IS peak areas Mean (a. u.)
R.S.D. %
Mean (min)
R.S.D. %
9.36
1.61
10.67
0.04
9.29 4.26 7.13
4.08 0.44 0.78
10.36 9.95 10.29
0.10 0.25 0.35
the first one, resulting in a highly negatively charged surface. The chemical modifications of the capillary surface during the procedure are illustrated in the Fig. 1. After coating, the performance of the new methodology was evaluated (results presented in Table 1). Similarly to the previous case, the method proved to have good repeatability, however, in this case, it was possible to separate carbamazepine from its photoirradiation products. The comparison of the electrochromatograms of a sample irradiated for 24 h, obtained with a coated capillary (A) and an uncoated capillary (B) is shown in Fig. 2. The hypothesis of co-migration of carbamazepine and photodegradation products in the latter case, as well as the ability of the coated capillary to perform the separation efficiently, is clearly demonstrated. Furthermore, the addition of running buffer to the irradiated samples, prior to MEKC analysis, substantially improved the repeatability of the ratio between carbamazepine and IS peak areas. The addition of 10% of the running buffer was chosen (instead of 50%) to avoid a substantial dilution of the samples. The method turned out to be repeatable between successive runs without the need to recoat the capillary. It was also possible to use an efficient and faster procedure of washing between runs (2 min) when compared to traditional MEKC methodologies, increasing even more the speed of analysis that characterize CE related methods. The good stability of the coating for several weeks was also verified, for which it is indispensable the storage of the capillary filled with water and with its extremities immersed in ultra-pure water.
3.2.
CBZ migration time
Calibration curve for MEKC
A linear calibration curve was obtained, by means of a leastsquares linear regression, using six standard solutions with concentrations ranging from 0.5 to 8.0 mg L1. The linear regression was based on the mean ratio between the peak area
of carbamazepine and the peak area of the IS that resulted from four replicate injections of each standard solution as a function of the standard solution concentration. The equation of the regression curve is given by y ¼ (0.923 0.002)x e (0.075 0.009). The correlation coefficient takes the value of 1.000, confirming the excellent linear response of the adopted methodology in the studied range of concentrations. Additionally, the limit of detection (LOD) and of quantification (LOQ) were determined according to 3sx/y/b and 10sx/y/b, respectively, where b is the slope and sx/y is the residual standard deviation of the determined linear regression (J.N. Miller and Miller, 2005). Accordingly, the LOD and LOQ values are 0.040 and 0.134 mg L1, respectively.
3.3. Carbamazepine photodegradation under simulated solar irradiation The direct photodegradation rate of carbamazepine was evaluated in aqueous solutions with a concentration of 9.5 mg L1 and pH values of 2.9, 4.0, 5.8 and 9.0. To better understand the role of oxygen and oxygen reactive species in the photodegradation process of this pharmaceutical, the carbamazepine solutions were irradiated in an oxygen saturated medium (solutions sparged with O2 prior to irradiation) and in a nitrogen-deoxygenated medium (solutions sparged with N2). In Fig. 3, the percentages of carbamazepine photodegradation as a function of irradiation time for samples not sparged (a), sparged with O2 (b) and sparged with N2 (c) are shown. It is clearly evident that higher percentages of photodegradation were attained at pH 2.9 for all the irradiated solutions. Moreover, it is also clear that the photodegradation process strongly depends on the amount of dissolved oxygen present in test solutions. For example, at pH 2.9 the oxygenated solutions reached approximately 85% of degradation after 24 h of irradiation, while in the nitrogen-deoxygenated solution the same value was observed in a period of 1 h.
Fig. 1 e Schematic representation of the capillary coating used in the developed methodology.
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Fig. 2 e Electrochromatograms of samples resulting from the irradiation of carbamazepine for 24 h, obtained using a coated capillary (A) and an uncoated capillary (B). Peak identification: 1 e Ethylvaniline (internal standard); 2 e Carbamazepine; 3 and 4 e photodegradation products. Experimental conditions: capillary 0.5 m length (0.4 to detector), 75 mm internal diameter, applied voltage 20 kV, capillary temperature 25 C, running buffer 50 mM SDS and 25 mM borax, detection at 210 nm.
However, this dependence is only observed at low pH; significant differences in the photodegradation extent tend to disappear at high pH values. Very low photodegradation was observed at the highest pH studied (9.0) in all oxygenation levels. To allow a better comparison of results obtained under distinct experimental conditions, the determination of kinetics parameters was performed by fitting a first order kinetics to each set of results. Considering the Naperian logarithm of the ratio between the concentration of carbamazepine at a give irradiation time (Ct) and the initial concentration of carbamazepine (C0) as a function of the irradiation time, linear regressions were obtained. Correlation values (r) for solutions sparged with O2 and not sparged range from 0.97 to 0.99; solutions sparged with N2 have lower correlation values (between 0.89 and 0.98). The satisfactory linearity of the regressions allowed the determination of the apparent pseudo-first order photodegradation
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rate constants (k) and half-life times (t1/2). The results are presented in Table 2. The most relevant result was obtained for degradation in deoxygenated solutions at pH 2.9 in which the photodegradation rate of carbamazepine was increased by a factor of 6 and 17 when compared with non-sparged and O2-sparged test solutions, respectively. A considerable increase on the photodegradation rate (by a factor of 6) was also observed at pH 4.0 in nitrogen-deoxygenated solutions. The presence of oxygen undoubtedly inhibits the degradation process; the higher the concentration of dissolved oxygen, the lower the photodegradation rate. However, and as it was stated before, the effect of the oxygenation level of the medium is negligible at pH 9.0. The described results point out that reactive oxygen species (such as singlet oxygen) do not have a significant role on the degradation pathway of carbamazepine, seeing that the presence of molecular oxygen did not increase the rate of the degradation process. In addition, and considering the fact that at low pH and in the presence of oxygen, significantly lower degradation rates were observed and that triplet excited states are efficiently quenched by oxygen, it is predictable that one of the most significant photodegradation pathways of carbamazepine arises from its triplet excited states that were quickly deactivated in highly oxygenated solutions. One tentative hypothesis to explain the observed phenomenon is the possible formation of hydrated electrons. Hydrated electrons, unitary negative charges chemically unbounded to any particular atom, are considered the most elementary and reactive nucleophiles (Anbar and Hart, 1964). Hydrated electrons have been shown to be photoproduced by a wide variety of aromatic compounds, and quickly react with organic compounds that have electronegative atoms (Anbar and Hart, 1964; Joschek and Grossweiner, 1966; Zepp et al., 1987). One known compound that photoproduces hydrated electrons is acridine (Joschek and Grossweiner, 1966; Kellmann and Tfibel, 1980, 1982) which is, curiously, one of the most common direct photodegradation product of carbamazepine (Chiron et al., 2006) and was identified in this study (see section 3.5). The possible production of hydrated electrons during the irradiation experiments, and its reactivity towards carbamazepine, is a possibility that would also explain the degradation decrease in highly oxygenated mediums, taking into account that hydrated electrons are effectively scavenged by molecular oxygen (Zepp et al., 1987).
Fig. 3 e Photodegradation of carbamazepine as a function of the irradiation time under different pH: > - pH 2.9; , e pH 4.0; B e pH 5.8 and Δ - pH 9.0. a) Not sparged; b) sparged with O2; c) sparged with N2. Each point corresponds to the mean percentage of photodegradation of three sample replicates. Relative standard deviations are below 10%. Dotted lines are shown only for clarity purposes.
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Table 2 e Correlation coefficient (r), half-life time (t1/2) and apparent photodegradation rate (k) of carbamazepine obtained by fitting a first order kinetics model to the photodegradation data, considering different pH and dissolved oxygen conditions. n represents the number of points used in the linear regression and s represents the standard deviation. Halflife times converted to units equivalent to summer sunny days (SSD) and average quantum yields (fave) of carbamazepine photolysis are also presented (discussed in section 3.4).
Not Sparged
Sparged with O2
Sparged with N2
pH
r
n
t1/2 s/(h)
k s/(h1)
t1/2 s/(SSDa)
4ave
2.9 4.0 5.8 9.0 2.9 4.0 5.8 9.0 2.9 4.0 5.8 9.0
0.994 0.986 0.992 0.987 0.996 0.971 0.975 0.993 0.925 0.980 0.888 0.924
4 5 7 5 5 6 5 6 4 6 5 5
2.9 0.2 63 6 17 1 95 9 8.6 0.5 58 7 36 5 67 4 0.5 0.1 12 1 66 20 63 15
0.24 0.02 0.011 0.001 0.040 0.002 0.007 0.001 0.081 0.004 0.012 0.001 0.019 0.003 0.010 0.001 1.3 0.3 0.060 0.006 0.011 0.003 0.011 0.003
e e 4.5 0.3 25 2 e e 91 18 1 e e 17 5 17 4
6.4 105 2.9 106 1.1 105 2 106 2.1 105 3.2 106 5.1 106 2.7 106 3.5 104 1.6 105 2.9 106 2.9 106
a 1SSD e unit equivalent to 1 summer sunny day (clear sky) at 45 N latitude. Results are shown only for experiences performed at relevant pH values for surface waters (5.8 and 9.0).
Another reasonable explanation (but again, tentative), arises from the pH dependence of these results suggesting that different forms of carbamazepine could be implicated. Several cases of pharmaceuticals that have different direct degradation rates which vary with the predominant form in solution (cationic or anionic forms) have been reported (Arnold and McNeill, 2007). It is also reported in the literature that the quantum yield of a degradation process could be pH dependent when different anionic or cationic forms are present (Arnold and McNeill, 2007). In the particular case of carbamazepine, it has a pKa of 13.9 (Jones et al., 2002) related to the deprotonation of the NH2 group and a pKa of 2.3 (Nghiem et al., 2005) related to the protonation of the amino groups. This means that at environmentally relevant pH, carbamazepine should be present in its neutral form. However, at the tested pH 2.9, a protonated form of carbamazepine could exist in equilibrium with the neutral form at a concentration high enough to interfere with the photodegradation rate. Nevertheless, taking into account the observed pH dependence,the existence of a protonated form of carbamazepine at very low pH might not be enough to explain the obtained results, suggesting that a different pH dependence source should be operating. To fully elucidate this point further research is needed, including the mechanistic study of the photodegradation processes of carbamazepine at different pH.
3.4. Determination of the apparent quantum yield of carbamazepine The quantum yield (4) of carbamazepine photolysis can be defined as the ratio between the carbamazepine photodegradation rate and the rate of light absorption. When considering the environmental relevance of photodegradation processes, this parameter is of crucial importance to assess the persistence of a contaminant. Its determination also allows a valid comparison between other studies reported in the literature.
The carbamazepine’s photodegradation quantum yield was determined considering an overall average over the lamp emission wavelength range (290e800 nm) and respective emission intensities. The approach followed was adapted from Chiron et al. (2006). Accordingly, the carbamazepine average quantum yield (4ave) is given by (equation (1)): fave ¼ P
C k 0 ; I0li 1 103li bC0
(1)
where k is the apparent first order rate constant (s1), C0 is the initial concentration of carbamazepine in solution (mol L1), I0li is the lamp emission intensity at the wavelength li (Ein L1 s1), 3 is the molar absorbivity of carbamazepine at li (L mol1 cm1) and b is the path length inside the photoreactor (cm) (diameter of the cylindrical photoreactor, 1.5 cm). The calculation was made considering a volume of 25 mL of irradiated solution and a solution exposure area of 53 cm2. Emission lamp spectrum and absorption spectrum of carbamazepine are shown in the Supporting Information. The values obtained for all the experimental conditions are presented in Table 2 and vary between 2 106 and 3.5 104, being consistent with previously published literature data (Chiron et al., 2006; Lam and Mabury, 2005; Andreozzi et al., 2003).
3.5.
Environmental relevance of the results
The results presented in Table 2 (half-life times and apparent pseudo-first order rate constants) are related to specific experimental conditions: samples were irradiated with irradiance of 55 W m2 in the range 290e400 nm (or 550 W m2 in the range 290e800 nm, according to the manufacturer specifications). As the adopted lamp aims to simulate sunlight, it is reasonable to convert the obtained results to outdoor half-life times. Vione et al. (2006) and Minero et al. (2007) had shown that on a cloudless summer day (45 N latitude) the sunlight has an irradiance of 22 W m2 (290e400 nm) and the total energy reaching the ground is 7.5 105 J m2. These measurements were made using the same model of multimeter from
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 0 9 5 e1 1 0 4
Fig. 4 e Proposed structures for the photodegradation products of carbamazepine identified by ESI(D)MS and MS2.
1101
Co.fo.me.gra. Therefore, it is possible to convert the half-life times of carbamazepine in units equivalent to summer sunny days: the total energy reaching the ground during 24 h under the referred conditions are equivalent to 3.8 h of irradiance under the conditions adopted in this study. Thus, results obtained at pH values considered environmentally relevant to surface waters (5.8 and 9.0) were converted to equivalents to summer sunny days (SSD) and presented in Table 2. Note that this conversion takes into account the day/night cycle, as explained by Vione et al. (2006). With this approach it is possible to estimate the real behavior of carbamazepine in the environment. According to the obtained set of results, this pharmaceutical can persist in the environment between 4.5 and 25 summer cloudless days, depending on the surface water pH and its level of aeration. These results are of great significance to assess the persistent of this pharmaceutical for example in atmosphere open aeration tanks, commonly found in urban Wastewater Treatment Plants, where pH values usually oscillate between 6 and 9. Taking into account the presented conclusions it could be reasonably expected that carbamazepine would take 1e4
Table 3 e Mass spectrometry data for the identification of carbamazepine photodegradation products: molecular weight (Mw), ions detected in ESI(D)MS and fragment ions detected in ESI(D)MS2. For each case, relative abundance of the relevant fragment ions and respective losses are also presented. The identification of the photoproducts using roman numbers I to VII is in accordance with Fig. 4. Compound
Mw
ESI(þ)MS m/z þ
CBZ
236
237 [M þ H]
I
179
180 [M þ H]þ
II
195
196 [M þ H]þ
III
207
208 [M þ H]þ
IV
209
210 [M þ H]þ
V
225
226 [M þ H]þ
VI
270
293 [M þ Na]þ
VII
450
473 [M þ Na]þ
ESI(þ)MS2 m/z (relative abundance %, loss)
Samples
220 (2, -NH3) 194 (100, -NHCO) 179 (2, -NHCO and -NH) 165 (1, -NHCO, -NH and -CH2) 152 (80, -H2CN) 128 (10) 195 (25, -H) 180 (3, -NH2) 168 (15, -CO) 167 (100, -HCO) 180 (100, -CO) 179 (25, - HCO) 178 (20, -H2CO) 152 (10, -H2CN and -CO) 182 (85, -CO) 180 (100, -H2CO) 208 (90, -H2O) 180 (93, -H2O and CO) 182 (100, -CHOHCH2) 276 (3, -NH3) 275 (8, -H2O) 250 (28, -NHCO) 248 (25, - COHNH2) 232 (100, -NHCO and -H2O) 456 (10, -NH3) 439 (28,-NH3 and -NH3) 428 (48, -COHNH2) 413 (60, - COHNH2 and -NH) 411 (100, - COHNH2 and -NH3) 383 (15,- COHNH2 and -COHNH2)
e
A and B B
B >> A
B >> A B
A and B
B >> A
*Sample A: CBZ solution irradiated during 24 h, 55 W m2 (290e400 nm), in ultra-pure water. **Sample B: CBZ solution irradiated during 30 min, 55 W m2 (290e400 nm), in ultra-pure water sparged with N2, pH 2.9 (pH adjusted with formic acid).
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weeks of sunny weather to be eliminated from surface waters by photodegradation processes.
3.6. Identification of photodegradation products by mass spectrometry Various photodegradation products of carbamazepine were identified by mass spectrometry (compounds I-VII, Fig. 4). The samples were analyzed by ESI(þ)MS without a prior separation process, using not sparged samples collected after 24 h of irradiation at pH 5.8 (sample A) and N2-sparged samples collected after 30 min of irradiation at pH 2.9 (sample B). The mass spectra of the selected samples were compared with a carbamazepine solution not exposed to simulated solar radiation and the ions [M þ H]þ and/or [M þ Na]þ of possible photodegradation products were identified in both samples. Subsequently, the structure of the photoproducts was tentatively assigned to each ion based on the fragmentation pathway observed in the ESI-MS2 spectra (results reported in Table 3). As far as photodegradation product I (MW 179, [M þ H]þ ¼ 180) is concerned, ESI-MS and ESI-MS2 spectra patterns are consistent with acridine, one of the most common carbamazepine photoproducts, known to have mutagenic and carcinogenic activities. The [M þ H]þ ion of photoproduct II (Mw 195) at m/z 196 could correspond to acridone or 9-hydroxyacridine. Nevertheless, this product was identified as acridone seeing that its ESI-MS2 spectrum is characterized by a base peak at m/z 167 that could be assigned to the loss of a CO group (28 Da) and it is not possible to observe the loss of a water molecule (H2O, 18 Da) characteristic of hydroxylated products. The ESI-MS2 spectra of the photodegradation product III (Mw 207, [M þ H]þ ¼ 208) allow identifying it as acridine-9-carbaldehyde. The increase of the molecular weight by 28 units in comparison to acridine and the base peak at m/z 180 is fully consistent with an acridine moiety containing a carbonyl group. Other alternatives for photodegradation products with the same molecular weight were not supported by the ESI-MS2 spectra. Photoproduct IV (Mw 209, [M þ H]þ ¼ 210) is structurally similar to photoproduct III. The increase of the molecular weight by two units corresponds to two hydrogen atoms. Photoproduct V corresponds to a hydroxylation of compound IV; the ESI-MS2 spectrum pattern is similar to the one found for compound IV with an extra ion at m/z 208 that arises from the loss of a water molecule, which reinforces the presence of the OH group on the proposed structure. Compound VI was attributed to one of the possible dihydroxicarbamazepine isomers. The ESI-MS2 spectrum of VII (Mw 450, [M þ Na]þ ¼ 473) is characterized by the loss of one and two NH3 groups (m/z 456 and 439), and also the loss of one and two COHNH2 groups (m/z 428 and 383); the ion attributed to the base peak corresponds to a combined loss of one group NH3 and one group COHNH2 (at m/ z 411). Some examples of ESI-MS and ESI-MS2 spectra are shown in the Supporting Information. The identified carbamazepine photodegradation products I-III and V-VII are in accordance with previously published studies (Chiron et al., 2006; Kosjek et al., 2009). However, to the best of our knowledge, photoproduct IV has not been identified
until now. It is also important to highlight that the two distinct analyzed experimental conditions differed on the presence of products II and V that were not detected in sample A but appeared in sample B. In addition, experimental conditions applied to sample B resulted on the appearance of products III, IV and VII in more significant quantities than in sample A.
4.
Conclusions
The present study has shown that the direct photodegradation rate of carbamazepine is pH dependent. Lower pH results in increased rates, while higher pH is compatible with very slow degradation processes. Moreover, especially at acidic pH, it was clearly shown that the oxygenation level of waters also has a noteworthy influence on the process: the presence of oxygen is responsible for a significant decrease on the photodegradation rate of carbamazepine. Despite the fact that the extremely high photodegradation rates were obtained at pH values that are not significant in an environmental context, this study highlighted new aspects of the photodegradation process of this drug that have not been explored until now, particularly the role played by oxygen on the photodegradation. Considering the studied environmentally relevant pH conditions (5.8 and 9.0), the photodegradation rate of carbamazepine is relatively slow. The elimination of this pharmaceutical by photodegradation processes could take from 4.5 to 25 sunny summer days. These results clearly consolidate previous knowledge that this pharmaceutical is being potentially accumulated in the environment and are fully consistent with the high number of occurrences of this compound in several environmental matrices. Additionally, seven photodegradation products were identified, including acridine (known due to its mutagenic and carcinogenic activities) and one newly identified compound. The described aspects of carbamazepine degradation constitute a helpful tool to develop further investigation in what concerns the environmental persistence and relevant elimination methodologies of this widely spread contaminant.
Acknowledgments Vaˆnia Calisto and Guillaume L. Erny thank FCT (Fundac¸a˜o para a Cieˆncia e Tecnologia e Portugal) for a PhD grant (SFRH/BD/ 38075/2007) and a postdoctoral grant (SFRH/BPD/30548/2006), respectively. The authors also acknowledge FCT for financially supporting QOPNA and RNEM. The authors also appreciate the contribution of the editor and reviewers whose helpful comments improved the quality of this manuscript.
Appendix. Supplementary data The supplementary data associated with this article can be found in the on-line version at doi:10.1016/j.watres.2010.10.037.
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references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 0 5 e1 1 1 4
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Investigation on the removal of natural and synthetic estrogens using biofilms in continuous flow biofilm reactors and batch experiments analysed by gas chromatography/ mass spectrometry Christina Pieper*, Wolfgang Rotard* Berlin Institute of Technology, Department of Environmental Engineering, Chair of Environmental Chemistry, KF 3, Strasse des 17. Juni 135, 10623 Berlin, Germany
article info
abstract
Article history:
The degradation of the natural estrogen 17b-estradiol and the synthetic steroid hormone
Received 4 June 2010
17a-ethinylestradiol, two estrogens already detected in surface waters at low concentra-
Received in revised form
tion levels, was investigated using continuous flow biofilm reactors and batch experiments.
14 October 2010
Biofilms in continuous flow experiments were created by natural organisms from river
Accepted 30 October 2010
systems of the national park Unteres Odertal, Germany, whereas batch experiments were
Available online 5 November 2010
performed with isolated bacterial strains derived from biofilms. The analytical method, including solid phase extraction, silylation of analytes and measurement with GC/MS, was
Keywords:
optimised for the target compounds 17b-estradiol, 17a-ethinylestradiol and the possible
Estradiol
metabolites estrone and estriol. The performance characteristics of the analytical method,
Estrone
namely recovery, standard deviations, method detection limits (MDL) and method quan-
Ethinylestradiol
tification limits (MQL), were evaluated for accurate interpretation of degradation experi-
Microbial degradation
ments. Continuous flow biofilm reactors were operated with two different nutrient media
Naturally derived biofilms
under dosage of estradiol and ethinylestradiol. Both estrogens were rapidly degraded
Bioreactor
within several hours; the metabolite estrone (from estradiol as well as from ethinylestradiol) was detected in significant amounts and was further decomposed. In additional batch experiments using isolated bacterial strains from the natural biofilms to decompose estradiol and ethinylestradiol, different metabolisms of isolates were explored. Five of the 15 isolated bacterial strains tested degraded estradiol and ethinylestradiol with different degradation rates. The results suggest that biofilms from national park Unteres Odertal possess a high capability to aerobically decompose natural and also synthetic estrogens so that these microorganisms could provide enhanced removal of pollutants in municipal water treatment plants. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The use of steroidal estrogen hormones is widespread, for example estradiol in livestock breeding and ethinylestradiol as
oral contraceptive medication. The natural steroid 17b-estradiol (E2) is rapidly oxidized to the main intermediate estrone (E1) by activated sludge organisms from sewage treatment plants (STP) and can be further metabolised to estriol (E3) (Ternes et al., 1999;
* Corresponding authors. Tel.: þ49 30 314 21978; fax: þ49 30 314 29319. E-mail address:
[email protected] (W. Rotard). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.034
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Lee and Liu, 2002). Nevertheless, estrogen contamination has been observed at nanograms per litre concentration levels in sewage influents and effluents and surface water resources in several studies and the actual values mainly depending on the amount of estrogens in both form conjugated and unconjugated excreted in urine and faeces (Ternes et al., 1999; Baronti et al., 2000; Heberer, 2002). Investigations of German STP influents and effluents confirmed that conjugated steroids contributed up to 50% of the total steroid concentration (Adler et al., 2001). Although the concentrations detected are very low, they may still be important for the aquatic environment and adversely affect the reproductive biology of aquatic vertebrates and other species by disrupting the normal function of their endocrine systems and by provoking feminization, for example (Purdom et al., 1994; Irwin et al., 2001). The possible ecological hazard posed by steroidal estrogens is not clearly defined, but their frequent use has increased environmental contamination and bioaccumulation risk as shown by various studies (Ternes et al., 2002; Kolodziej et al., 2004). Because of the mentioned problems, removal rates during STP have been investigated in several studies (Auriol et al., 2006). Field data suggest that the activated sludge treatment process can consistently remove over 85% of estradiol, estriol, and 17-ethinylestradiol, while the removal performance for estrone appears to be lower and more variable (Johnson and Sumpter, 2001). Suzuki and Maruyama (2006) ascribe the adsorption process onto activated sludge as being helpful for decomposition of estrogens. An approximate removal rate of 65% for E2 was observed in STP while the E1 concentration increased during the treatment (Carballa et al., 2004). It has generally been observed that primary treatment of municipal and industrial wastewaters alone results in no or only limited removal of estrogens from sewage, while secondary treatment involving activated sludge reduces significantly all estrogens concentrations (Auriol et al., 2006). In other studies, batch configurations using activated sludge were applied to investigate the microbial degradation of estrogens. Ternes et al. (1999) did not observe a significant reduction of EE2 concentrations in aerobic batch experiments containing diluted slurry of activated sludge from an STP near Frankfurt (Germany). Lee and Liu (2002) showed that E2 and E1 are not persistent and can be easily degraded by sewage bacteria. Their proposed biodegradation of E2 appeared to initiate at the hydroxy group on the C-17 (ring D) of the molecule, leading to the formation of the major metabolite E1. These results were verified by Shi et al. (2004), who detected removal of E2, E1 and E3 by activated sludge and night soil-composting microorganisms, while EE2 was not removed. On the other hand, Hashimoto and Murakami (2009) showed the degradability of EE2 within 24 h by activated sludge of selected wastewater treatment plants (Japan) using batch experiments. Research is required in order to identify and optimise the process to maximise the estrogen removal. A systematic biodegradability study needs to be conducted using mixed microbial cultures to understand estrogen removal mechanisms and pathways in an engineered system (Khanal et al., 2006). This study aimed to investigate the microbial degradation potential for biofilms derived from water systems of the national park Unteres Odertal, Germany, relating to 17b-estradiol and 17a-ethinylestradiol. In previous work, the same sourced biofilms were studied for the degradation of phenazone-type drugs
(Pieper et al., 2010). The polder areas of the national park are intensely flooded by the Odra River during winter causing exposure of local microorganisms to river pollutants. This particular event enables adsorption of contaminants onto surfaces or metabolism by local microorganisms living in biofilms of river systems of the national park Unteres Odertal. Therefore, it is plausible that these natural microbial communities in biofilms offer what are hardly explored potentials for the degradation of persistent organic compounds. So far no studies exist which examine the degradability of natural and synthetic estrogens by river-derived biofilms. In the present study, the suitability of the sample clean-up procedure, derivatisation and determination step by GC/MS had first to be optimised and confirmed for target analytes E2 and EE2 and metabolites E1 and E3. Subsequently, the behaviour of target analytes exposed to selected biofilms was investigated using laboratory scale continuous flow biofilm reactors. Additional batch tests with isolated bacterial strains derived from biofilms were performed to investigate the degradation potential of selected isolated organisms obtained from national park Unteres Odertal.
2.
Experimental
2.1.
Standard compounds and chemicals
All analytical standards were of high purity grade (97%). The analytes 17b-estradiol, 17a-ethinylestradiol and metabolites estrone and estriol were purchased from Dr. Ehrenstorfer (Augsburg, Germany). The isotopically labelled compounds for internal quantification, 17b-Estradiol-16, 16, 17-D3 (D3-E2) and 17a-Ethinylestradiol-2, 4, 16, 16-D4 (D4-EE2), were also obtained from Dr. Ehrenstorfer (Augsburg, Germany). Stock solutions of 1 mg mL1 and dilutions were prepared in methanol and stored at 4 C in the dark until use. Derivatisation reagents N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) and N, O-bis(trimethylsilyl)trifluoroacetamide þ 1% trimethylchlorsilane (BSTFA þ TMCS) were from MachereyeNagel (Dueren, Germany). Acetic anhydride and pyridine were supplied by Roth (Karlsruhe, Germany) and dried using molecular sieve. The solvents methanol, cyclohexane and ethyl acetate of GC/MS grade were also purchased from Roth (Karlsruhe, Germany). Chemicals for growing solution leptothrix strains media 2 (LSM2, (Atlas, 1997)) were purchased from Roth GmbH (Karlsruhe, Germany) and VWR (Dresden, Germany).
2.2.
Sample clean-up, derivatisation and analysis
Samples from the continuous flow bioreactors and batch experiments using isolated bacterial strains were stored at 21 C and brought up to room temperature prior to preparation and analysis. Before extraction of starting analytes (E2 and EE2) and metabolites formed (E1 and E3), each sample was spiked with 250 ng of the internal standards D3-E2 and D4-EE2. Extraction of 50 mL sample volumes was performed using solid phase extraction (SPE) cartridges (Isolute ENV þ sorbent 100 mg, Biotage, Sweden) pre-conditioned and equilibrated with methanol and distilled water (5 mL each). The sorbent was washed with 5 mL of the water/methanol (3/1 v/v)
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mixture to eliminate matrix compounds. The elution step was carried out using 5 mL of a methanol/ethyl acetate mixture (9/1 v/v). The eluent solutions were then evaporated to dryness under a gentle nitrogen stream at 40 C, quantitatively transferred to vials using 200 mL and 100 mL methanol in sequence, evaporated to dryness again and derivatised using 15 mL MSTFA combined with 35 mL pyridine. The incubation was effected at 60 C within 60 min. After cooling, the derivatisation reagents were evaporated and the silylated sample extracts were dissolved in 50 mL cyclohexane and subsequently analysed by gas chromatography/mass spectrometry (GC/MS). A 2 mL aliquot was injected into the gas chromatograph (GC, Agilent 5890, Waldbronn, Germany) combined with mass spectrometer (MS, Agilent 5972, Waldbronn, Germany). The split/splitless inlet was operated isothermal in splitless mode at 300 C. The derivatised analytes were separated across a DB-5ms column (length 30 m, inner diameter 0.25 mm, film thickness 0.25 mm; J & W scientific) combined with a deactivated fused silica pre column (length 10 m, inner diameter 0.25 mm; J & W scientific) under a temperature program that began at 70 C, held for 1 min, then increased at 30 C min1 to 240 C, held isothermal for 1 min, then increased at 1.5 C min1 to 270 C, and continued increasing at 30 Ce300 C with a 1-min hold. The carrier gas (helium, pureness 5.0) had a linear velocity of 37 cm s1. The Transferline was heated to 300 C, while the source temperature was 180 C. For improved sensitivity, selected ion monitoring was used for quantification. Derivatised analytes were identified by retention times and ratios between molecular ions and three fragment ions (Table 1), which were obtained by selected ion monitoring (SIM mode). Internal quantification of results for E2, E1 and E3 was based on the response obtained for D3-E2 while the internal quantification of EE2 was based on D4-EE2. This procedure allows correction of possible concentration changes due to the sample preparation step and slight volume deviations to avoid misinterpretation of the degradation study. Covering the relevant concentration levels of the degradation studies, calibration solutions for each compound were prepared at 12 different concentration levels in the range 0.05 mg L1 to 90 mg L1. Compounds for internal quantification (D3-E2 and D4-EE2) were added to all calibration solutions analogue to samples at a concentration of 5 mg L1 each. For each analyte recovery, standard deviation and coefficient of variation were determined for the different nutrient media matrices LSM2 diluted 1:100 and LSM2 diluted 1:100 without beef and yeast extract used in batch experiments. To
this end, the solutions were spiked with the respective compounds to give a concentration of 10 mg L1 prior to sample preparation. Ten samples were extracted for each matrix and analysed according to the procedure described above. In addition, method detection limits (MDL) and method quantification limits (MQL) were calculated using the calibration graph method according to the German standard method DIN 32645 (2008) using spiked blank matrix samples (LSM2 diluted 1:100 and LSM2 diluted 1:100 without beef and yeast extract) at concentration levels in the range 0.001 mg L1 to 0.5 mg L1 and extracted and analysed with the optimised method (see above).
2.3.
Biodegradation experiments
Two different series of experiments were carried out to investigate the degradation of E2 and EE2 by microorganisms derived from national park Unteres Odertal. Using continuous flow biofilm reactors with two different nutrient conditions, the degradation potential of biofilms was first investigated. In the second study, isolated biofilm derived bacterial strains were investigated with respect to their metabolism of E2 and EE2 in batch experiments under limited nutrient conditions.
2.3.1.
Lab-scale continuous flow biofilm reactor
The degradation potential of microorganism communities in biofilms was investigated using two biofilm reactors. Each bioreactor consisted of a glass column filled with biofilms grown on glass tubes. The construction schema is shown in Fig. 1. Operation of continuous flow biofilm reactors were the same as previously described by Pieper et al. (2010). Each bioreactor consisted of a glass column (height 74 cm, inner diameter 10 cm) with a total inner volume of about 5 L, filled with glass tubes populated with biofilms. Biofilms grew on tubes exposed for about one month to river water of the national park Unteres Odertal. Five ports along the glassware columns enabled sampling at starting concentrations and different degradation stages. The growing medium and compounds under investigation (i.e. E2 and EE2) were continuously pumped into the reactors by different peristaltic pumps to permit adjustment of the respective concentrations. Starting concentrations for each compound were 100 mg L1. Since the occurrence of iron precipitating bacteria in these biofilms had been found in previous studies, we selected a cultivation medium which contains dissolved iron. Two different nutrient compositions were tested for each compound (E2 and EE2),
Table 1 e SIM conditions for GC/MS measurements, identification and quantification. Analytes (trimethylsilyl-derivatives)
Retention time [min]
Ion 1a (m/z)
Ion 2 (m/z)
Ion 3 (m/z)
13.52 12.98 15.48 17.19 13.49 15.43
416 342 440 504 419 444
401 327 425 386 404 429
325 314 285 345 328 287
Estradiol Estrone Ethinylestradiol Estriol d3-Estradiol d4-Ethinylestradiol a All Ion 1 are the molecule ions [M]þ
Ion 4 (m/z)
257 311
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temperature. For degradation studies, the nutrient media was spiked with 100 mg L1 each of E2 and EE2 so that each bacterial strain was cultivated in the presence of both compounds. Overall cultivation time was three weeks, samples of 50 mL being taken after 1, 2 and 3 weeks and stored at 21 C until clean-up and analysis (see 2.2). All organisms were cultivated in duplicate. A sterile negative control was applied and sampled and analysed in the same way to confirm the microbial formation of metabolites occurred only in incubated samples.
3.
Fig. 1 e Schematic representation of the biofilm reactors operated in continuous flow-mode.
Results and discussion
The present study investigated the biodegradability of 17b-estradiol and 17a-ethinylestradiol and the formation of metabolites estrone and estriol by bacteria communities and isolates derived from national park Unteres Odertal. Two different experimental setups, i.e. lab-scale continuous flow biofilm reactors and batch experiments in Erlenmeyer flasks, were used to exploit the degradation pathways. In order to ensure reliable interpretation of the degradation studies, sample clean-up and preparation procedure had to be optimised and validated initially.
3.1. Sample preparation and performance data of chemical analysis using in the first case the nutrient media LSM2 in 1:100 diluted form, and in the second case the same nutrient media LSM2 1:100 but without meat and yeast extract in order to limit nutrient supply even more, thus supporting the metabolism of the starting compounds. The flow rates were adjusted to provide a total hydraulic retention time of about 32 h. All reactors were operated without sampling under aerobic conditions giving the nutrient and estrogen solutions for one week to get a dynamic balance of substances. Then samples were taken every second day at the five different sampling ports of the reactors, so that the concentrations of the starting compounds and possible metabolites could be determined after exposure to the biofilm for 0, 8, 16, 24 and 32 h. Using the first nutrient media, the reactors were sampled for over ten weeks, while the reactor operation using the limited nutrient conditions lasted for over six weeks. 50 mL of sample volumes were taken and extracted, derivatised and analysed using the optimised method described in 2.2.
2.3.2.
The analysis of starting compounds E2 and EE2 and their metabolites E1 and E3 included solid phase extraction, derivatisation to increase analyte volatility and thermal stability, and finally measurement with GCeMS avoiding successfully the occurrence of signal suppression by matrix compounds if LC-MS/MS would be used (Lin et al., 2007). Three derivatisation methods were tested to compare the reaction products concerning their response in GC/MS: silylation with BSTFA þ TMCS and with MSTFA plus pyridine, and acetylation with acetic anhydride and pyridine. Zhang and Zuo (2005)
Biofilm derived isolates in batch experiments
Batch measurements were made with various isolated bacteria (isolated by working group of Prof. Szewzyk, TU Berlin) derived from biofilms of national park Unteres Odertal. The bacterial biofilm community has been investigated applying traditional cultivation techniques in combination with molecular methods. More than 200 different ion-precipitating bacteria were isolated and phylogenetically characterised (Schmidt, 2008). The same bacterial strains were chosen as investigated by Pieper et al. (2010) in previous work on the degradation of selected pharmaceutical compounds. 15 isolated bacterial strains were selected and cultivated in Erlenmeyer flasks using 200 mL of the 1:100 diluted nutrient media under aerobic conditions and incubated on a mechanical shaker at room
Fig. 2 e Products of derivatisation methods using MSTFA, BSTFA with TMCS and acetic anhydride. 20 ng of each derivatised substance (E2, EE2, E1 and E3) were analysed with GC/MS.
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Table 2 e Recovery (R), standard deviation (SD), coefficient of variation (CV) and method detection limit (MDL) and method quantification limit (MQL) according to DIN 32645 for selected analytes in different matrices, analytes were extracted and quantified with described method. For details see text. Analyte
E2 EE2 E1 E3
nutrient media diluted 1 to 100 1
nutrient media diluted 1 to 100 without meat and yeast
R (%)
SD (%)
CV (%)
MDL (ng L )
MQL (ng L1)
R (%)
SD (%)
CV (%)
MDL (ng L1)
MQL (ng L1)
97.7 106.1 98.6 65.6
0.7 5.9 21.7 13.1
0.7 5.6 22.0 20.0
28 78 135 251
81 230 805 1086
91.5 115.9 107.9 57.0
2.6 9.4 9.7 18.4
2.8 8.1 9.0 32.4
16 63 122 70
47 186 414 204
reported on solutions for simultaneous determination of estrone and 17a-ethinylestradiol silylated with BSTFA, because the trimethylsilyl (TMS) derivatives of EE2 partially convert into corresponding TMS derivative of estrone (E1) (Shareef et al., 2004). In Fig. 2, the results of the three tested derivatisation methods are presented comparing peak areas of the GC/MS-signals (total ions) for the respective substances. Acetylation gave too small a yield for E2, EE2 and E3. The three hydroxyl groups of E3, the two of E2 and the single hydroxyl group of E1 were acetylated completely so that only one derivatisation product was generated from each steroid hormone. Acetylating EE2 was difficult because only one of the two hydroxyl groups was acetylated which resulted in low GC/MS response. Using silylation, every hydroxyl group was
derivatised, even both of EE2. Silylation using MSTFA gave similar results to BSTFA þ TMCS; the product of E3 gave higher response using MSTFA and the product of E1 offered higher values using BSTFA þ TMCS. We finally chose MSTFA as derivatisation reagent because of the high response of silylated E3, since E3 is taken to be formed in lowest amounts by biodegradation in the following studies and hence maximum detection sensitivity is required. Conversion of EE2 derivatives to E1-derivatives resulting in silylation with MSTFA and pyridine as organic solvent was excluded by checking the derivatisation of single EE2 with chosen reagents. The capillary column DB5-ms using conditions described in 2.2 satisfied the aim of chromatographic separating of the various analytes.
Fig. 3 e Concentrations of natural and synthetic estrogens and metabolites on different sampling days and over the whole period of experiment. Degradation experiments with continuous flow biofilm reactors (nutrient media diluted 1:100) spiked with different estrogens at 100 mg LL1 and a hydraulic retention time of 32 h (a) E2, sampling day 29; (b) E2, mean values of period of experiment; (c) EE2, sampling day 26; (d) EE2, mean values of period of experiment.
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For extraction of each analyte and metabolites from an aqueous sample, good results were achieved using SPE cartridges with Isolute ENV þ sorbent. Extraction recoveries (R), standard deviations (SD) and coefficients of variation (CV) of the target compounds E2 and EE2 and the metabolites E1 and E3 were determined for the two different nutrient media (described in 2.3.1). Method detection limits (MDL) and method quantification limits (MQL) were determined according to the German standard method DIN 32645 (2008). In Table 2, performance characteristics of the optimised clean-up, derivatisation and GC/MS method are given for the determination of selected analytes and metabolites in the two different nutrient media. Recoveries, standard deviations and coefficients of variation determined in both media show reasonably good values for the intended experiments, except for E3, which possibly gives lower recovery values in the extraction process because of its high polarity. The MQL values for E3 and E1 are considered as not really applicable using the nutrient media LSM2 1:100 for the present investigation. The nutrient media diluted 1:100 was used to investigate the microbial degradation of E2 and EE2 with continuous flow biofilm reactor and batch experiments with isolated bacteria. Using starting concentrations of 100 mg L1, at least 1% E3 must be formed to get
a detectable quantity. This is an acceptable and realistic range for formation, therefore these MQL values are acceptable for the intended purpose. The same nutrient media was limited even more by omitting meat and yeast extract in order to examine the formation of metabolites E1 and E3 in continuous flow biofilm reactors by upgrading the MQL. Clearly, this nutrient-reduced media does not interfere in the determination of E3, reflected in decreased MDL and MQL values. All calibration graphs were linear over the investigated concentration levels starting at MQL of each substance to 90 mg L1 expressed by correlation coefficients (E2: 0.9927; EE2: 0.9973; E1: 0.9966; E3: 0.9852). In conclusion, the validation data show that the experimental conditions used in this study are well suited to the determination of the selected analytes and metabolites.
3.2. Removal of 17b-estradiol and 17a-ethinylestradiol by biofilms in bioreactors Different nutrient conditions of the biofilm reactor were tested to get more information about degradation possibilities and pathways for studied bacteria organism communities. The results are presented as measured concentrations in mg L1.
Fig. 4 e Concentrations of natural and synthetic estrogens and metabolites on different sampling days and during the whole period of experiment. Degradation experiments with continuous flow biofilm reactors (nutrient media diluted 1:100 except meat and yeast extract) spiked with different estrogens at 100 mg LL1 using a hydraulic retention times (hrt) of 32 h (a) E2, sampling day 27; (b) E2, mean values of period of experiment; (c) EE2, sampling day 20; (d) EE2, mean values of period of experiment.
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Fig. 5 e Results of the sterile negative control tested in batch experiments over 18 days (a) negative control I; (b) negative control II.
The different removal rates of E2 and EE2 and the formation rate of E1, resulting of the first operation mode including the application of the nutrient media diluted 1:100 (see 2.3.1) with a hydraulic retention time of 32 h, are shown in Fig. 3. The results given in Fig. 3a) and c) show the concentration rates of E2 and E1 at sampling day 29, and of EE2 and E1 at sampling day 26, respectively. The results shown in Fig. 3b) and d) reflect mean values calculated among the whole sampling period of ten weeks. E2 is almost completely removed within 8 h. The metabolite E1 is formed rapidly within a short time but not significantly further decomposed. The occurrence of E1 at the inlet of the reactor is probably a result of a certain degree of back mixing from the reactor solution and a biofilm grown in the inlet line. By sampling the sterile filtered spiking solutions of E2 and EE2, the sterile formation of E1 from E2 or E2 was excluded and the activity of grown biofilm in the inlet could be confirmed. Comparing the removal rates of one sampling day to mean values of the whole period of experiment, no change was observed either in formation of the metabolite E1 or in decomposition rate of E2, which was almost removed within 8 h. Using a second biofilm reactor in the same way with analogous nutrient conditions, additional experiments with EE2 investigated the possibility of its removal. Fig. 3c) gives the results of one sampling day, while Fig. 3d) shows the mean
values of EE2 and E1 concentrations calculated over the whole sampling period of ten weeks. Obviously, the concentration of EE2 was eliminated to 67% after 8 h. During the further course of incubation in the reactor, the compound EE2 was removed to 80% within 32 h. The formation of E1 from EE2 is somewhat different to its formation from E2; it was also formed in small amounts but seemed to be decreased after 24 h. The tested biofilm organisms potentially decomposed E1, but intermediates of E1 formation were not detected and a degradation rate is rather ambiguous. The calculated mean values confirm the removal rates of EE2 and the formation of E1. The metabolite E3 was not detected within these experiments because of a lack of its formation or because of its faster degradation. Fig. 4 shows the results of degradation experiments for the two estrogens E2 and EE2 using continuous flow biofilm reactors with more limited nutrient conditions (LSM2 1:100 yet without meat and yeast extract, see 2.3.1) to get better information on formation and decomposition of metabolites. The removal rates of E2 (Fig. 4a)) and EE2 (Fig. 4c)) are similar to those of the first operation mode offering more nutrients; E2 is eliminated completely within a few hours and EE2 is removed to 73% after 32 h. The metabolite E1 is formed from E2 as well as from EE2. Using these limited nutrient conditions, there can be observed a tendency of a further degradation of the metabolite
Fig. 6 e Degradation of E2 and EE2 by isolated bacteria strain A_243 derived from biofilms of Unteres Odertal tested in batch experiments over 18 days (a) A_243 I; (b) A_243 II.
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Fig. 7 e Degradation of E2 and EE2 by isolated bacteria strain B_207 derived from biofilms of Unteres Odertal tested in batch experiments over 18 days (a) B_207 I; (b) B_207 II.
E1 in both cases. Regarding the mean values calculated over the whole period of experiment (six weeks), the removal rates of 100% E2 and 70% EE2 could be confirmed (Fig. 4b) and c)). The results presented here using continuous flow biofilm reactors show that the biofilm organisms of national park Unteres Odertal are definitely able to metabolise selected steroid hormones, here 17b-estradiol and 17a-ethinylestradiol, within a few hours. The metabolite estrone was formed from E2 as well as from EE2 in similar concentration levels. Using these biofilm reactors, E1 was not confirmed to be the main metabolite of E2 or of EE2; the possibility of formation and decomposition of E1 within the first 8 h could not have been excluded because of the reactor construction. The metabolite estriol was not detected at all. The results demonstrate a particular degradation potential for organisms from river systems of national park Unteres Odertal which has not previously been shown. After termination of the presented experiments, the biofilms of both reactors were additionally extracted and analysed by GCeMS concerning levels of E2 and EE2 to prove the microbial removal of substances by excluding sorption effects on surfaces of biofilms. No concentrations of E2 or EE2 were detected in the biofilms. Of course there might have been taken place some sorption effects on surfaces of biofilms before the degradation of substances by biofilm organisms, because sorption effects are important factors at the first
stages of microbial removal. However, these effects do not have an impact of conclusions of the studies.
3.3.
Degradation of estrogens by isolated bacterial strains
Using several selected biofilm-derived isolated bacterial strains from the national park Unteres Odertal, the degradation of E2 and EE2 was investigated by performing batch experiments (see 2.3.2). The initial E2 and EE2 concentrations were analogous to conditions in continuous flow biofilm reactors. While Fig. 5 shows the results of a negative control to confirm the test conditions, significant results demonstrating different degradation rates are shown in Figs. 6e9. In Fig. 5, the concentrations of E2 and EE2 are shown over the incubation time of three weeks in the negative controls. The stability of the selected compounds is demonstrated in sterile experimental conditions; therefore degradation in inoculated samples can be attributed to microbial degradation. In the negative controls, small amounts of E1 were formed during sterilisation by autoclave. Consequently, the formation of E1 by bacterial strains was only confirmed by significant higher amounts than in negative controls. Fig. 6 shows the results of batch experiments with bacterial strain A 243 which metabolised the steroid hormone E2 within about 12 days, while the concentration level of the starting compound EE2 remained constant. Both test batches of
Fig. 8 e Degradation of E2 and EE2 by isolated bacteria strain A_223 derived from biofilms of Unteres Odertal tested in batch experiments over 18 days (a) A_223 I; (b) A_223 II.
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Fig. 9 e Degradation of E2 and EE2 by isolated bacteria strain A_288 derived from biofilms of Unteres Odertal tested in batch experiments over 18 days (a) A_288 I; (b) A_288 II.
bacterial strain A 243 formed E1 in large amounts and decomposed this product further within 18 days. The isolates B 207 (Fig. 7) degraded both E2 and EE2 similarly within 18 days, but the metabolite E1 was formed only at marginal concentration levels. Obviously, the bacterial strain B 207 was able to degrade E1, which might be analogue to the property of EE2-degradation. Metabolism of A 223 (Fig. 8) differed from the degradation pathways shown by incubation of A 243 and B 207. While the removal rate of EE2 was 60% after three weeks in the first sample (Fig. 8a)) and 94% in the second sample (Fig. 8b)), E2 seemed to be hardly metabolised. E1 was only formed in small amounts or degraded faster. In no case was E3 detected. Fig. 9 presents the results of isolate A 288 which showed a completely different metabolism, evidenced by the fastest decomposition of starting compounds E2 and EE2 which were already completely degraded at the first sampling after 6 days. Additionally, neither metabolite E1 nor E3 were detected within the run times of the experiment. This bacterial strain typifies a special nature. It seems to be able to metabolise the steroid hormones E2 and EE2 naturally without adaptation, in contrast to the other tested strains which are also able to metabolise the compounds but require a significant adaptation period. Several studies have reported degradation of natural and synthetic estrogens. Shi et al. (2004) investigated the metabolism of E2, EE2, E1 and E3 by activated sludge and night soilcomposting microorganisms and proved the degradation of the natural hormones E2, E1 and E3 but not the synthetic steroid EE2. This is different to the selected isolates presented here. Vader et al. (2000) observed improved degradation of estrogens within several hours by activated sludge with significant nitrifier capacity, underlining the different conditions required by different microorganisms. In conclusion, five of the 15 tested isolates neither metabolised E2 nor EE2, two of them only decomposed E2 but not EE2. Three tested isolates degraded both substances, and the results of five tested isolates were not clear because of different results in twice incubated samples. The presented results show that the metabolism of E2 and EE2 is different. Microorganisms certainly require different nutrient conditions, but some organisms may not be able to decompose steroids by nature. In comparison to relatively fast degradation rates obtained with the continuous flow biofilm reactor (see 3.2), the degradation using isolates requires several days.
This is primarily a result of the fact that in batch experiments bacteria strains had to grow first after inoculation, whereas continuous flow reactor experiments started with large functioning biofilms.
4.
Conclusions
The present experiments using continuous flow biofilm reactors and batch experiments with isolated bacterial strains demonstrate the degradation potential of the natural steroid hormone 17b-estradiol and the synthetic compound 17a-ethinylestradiol by organism communities and isolates both derived from national park Unteres Odertal. Biofilms are shown to offer high capability to remove E2 and EE2 rapidly under the given experimental conditions, but the formation of the metabolite E1 did not take place in large amounts. Significant sorption effects were not determined using these biofilm reactors and could be excluded by extraction and analysis of biofilms after degradation experiments, respectively. Performing batch experiments with isolated biofilm derived bacteria, the different possibilities and potentials of degradation pathways became evident. In summary of batch experiments it appears that some of the selected isolates naturally derived from national park Unteres Odertal have the capabilities to metabolise E2 as well as EE2, partially decomposed to E1. During wastewater treatment using sewage treatment plants, microbial activity plays a large role in elimination of environmental pollutants, even though a large number of persistent organic contaminants are not eliminated during wastewater treatment. There is incomplete EE2 and E1 biodegradation or removal during wastewater treatment and an occurrence of E1 in surface water (Zuehlke et al., 2005). Furthermore, increasing concern about the fate of 17a-ethinylestradiol in the environment stimulates the search for alternative sewage treatment methods. Additional investigations concerning the efficiency of selected isolates are required to specify their degradation potential. Moreover, there is a definite need for further research on the decomposition of E2 and EE2 by organisms of national park Unteres Odertal to identify the degradation pathways and metabolites using for example LCeQeTOFeMS/MS and LCeIoneTRAPeMSn which are powerful tools to examine complex samples because they
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provide important information on the molecular masses and the presence of certain functional groups of the analytes.
Acknowledgements The present research has been conducted as part of the project “Entwicklung eines Indikatorsystems fu¨r Verockerungsprozesse” kindly funded by the German Federal Ministry of Education and Research (project number 02WU0715). The authors would like to thank the project partners especially the working group of Prof. Szewzyk for isolating bacterial strains and Mr. D. Sauter for his engagement in performing his project work. Thanks go also to Mrs. D. Risse and Mr. S. Klemer for their assistance in handling the biofilm reactors and to Mr. R. Hatton for his linguistic improvements of the manuscript.
references
Adler, P., Steger-Hartmann, T., Kalbfus, W., 2001. Distribution of natural and synthetic estrogenic steroid hormones in water samples from southern and middle Germany. Acta Hydrochimica et Hydrobiologica 29 (4), 227e241. Atlas, R.M., 1997. Handbook of Microbiological Media, second ed. CRC Press, Inc., Boca Raton, Florida. Auriol, M., Filali-Meknassi, Y., Tyagi, R.D., Adams, C.D., Surampalli, R.Y., 2006. Endocrine disrupting compounds removal from wastewater, a new challenge. Process Biochemistry (Amsterdam, Netherlands) 41 (3), 525e539. Baronti, C., Curini, R., D’Ascenzo, G., Di Corcia, A., Gentili, A., Samperi, R., 2000. Monitoring natural and synthetic estrogens at activated sludge sewage treatment plants and in a receiving river water. Environmental Science and Technology 34 (24), 5059e5066. Carballa, M., Omil, F., Lema, J.M., Llompart, M., Garcia-Jares, C., Rodriguez, I., Gomez, M., Ternes, T., 2004. Behavior of pharmaceuticals, cosmetics and hormones in a sewage treatment plant. Water Research 38 (12), 2918e2926. DIN 32645, 2008. Chemical Analysisedecision Limit, Detection Limit and Determination Limiteestimation in Case of Repeatability; Terms, Methods, Evaluation. Arbeitsausschuss Chemische Terminologie (AChT) im DIN Deutsches Institut fur Normung e.V, Berlin. Hashimoto, T., Murakami, T., 2009. Removal and degradation characteristics of natural and synthetic estrogens by activated sludge in batch experiments. Water Research 43 (3), 573e582. Heberer, T., 2002. Occurrence, fate, and removal of pharmaceutical residues in the aquatic environment: a review of recent research data. Toxicology Letters 131 (1e2), 5e17. Irwin, L.K., Gray, S., Oberdorster, E., 2001. Vitellogenin induction in painted turtle, Chrysemys picta, as a biomarker of exposure to environmental levels of estradiol. Aquatic Toxicology 55 (1e2), 49e60. Johnson, A.C., Sumpter, J.P., 2001. Removal of endocrinedisrupting chemicals in activated sludge treatment works. Environmental Science and Technology 35 (24), 4697e4703. Khanal, S.K., Xie, B., Thompson, M.L., Sung, S., Ong, S.-K., Van Leeuwen, J., 2006. Fate, transport, and biodegradation of
natural estrogens in the environment and engineered systems. Environmental Science & Technology 40 (21), 6537e6546. Kolodziej, E.P., Harter, T., Sedlak, D.L., 2004. Dairy wastewater, aquaculture, and spawning fish as sources of steroid hormones in the aquatic environment. Environmental Science and Technology 38 (23), 6377e6384. Lee, H.B., Liu, D., 2002. Degradation of 17b-estradiol and its metabolites by sewage bacteria. Water, Air, and Soil Pollution 134 (1e4), 353e368. Lin, Y.-H., Chen, C.-Y., Wang, G.-S., 2007. Analysis of steroid estrogens in water using liquid chromatography/tandem mass spectrometry with chemical derivatizations. Rapid Communications in Mass Spectrometry 21 (13), 1973e1983. Pieper, C., Risse, D., Schmidt, B., Braun, B., Szewzyk, U., Rotard, W., 2010. Investigation of the microbial degradation of phenazone-type drugs and their metabolites by natural biofilms derived from river water using liquid chromatography/tandem mass spectrometry (LC-MS/MS). Water Research 44 (15), 4559e4569. Purdom, C.E., Hardiman, P.A., Bye, V.J., Eno, N.C., Tyler, C.R., Sumpter, J.P., 1994. Estrogenic effects of effluents from sewage treatment works. Chemistry and Ecology 8 (4), 275e285. Schmidt, B., 2008. Physiology and Phylogenity of Iron Oxidising Bacteria Isolated on Pollutants containing Media. (Pysiology und Phylogenie Eisenoxidierender Bakterienisolate auf schadstoffhaltigen Medien). Environmental Microbiology. TU Berlin, Berlin (Diploma). Shareef, A., Parnis, C.J., Angove, M.J., Wells, J.D., Johnson, B.B., 2004. Suitability of N, O-bis(trimethylsilyl)trifluoroacetamide and N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide as derivatization reagents for the determination of the estrogens estrone and 17a-ethinylestradiol by gas chromatography-mass spectrometry. Journal of Chromatography, A 1026 (1e2), 295e300. Shi, J.H., Suzuki, Y., Nakai, S., Hosomi, M., 2004. Microbial degradation of estrogen using activated sludge and night soilcomposting microorganisms. Water Science and Technology 50 (8), 153e159. Suzuki, Y., Maruyama, T., 2006. Fate of natural estrogens in batch mixing experiments using municipal sewage and activated sludge. Water Research 40 (5), 1061e1069. Ternes, T.A., Andersen, H., Gilberg, D., Bonerz, M., 2002. Determination of estrogens in sludge and sediments by liquid extraction and GC/MS/MS. Analytical Chemistry 74 (14), 3498e3504. Ternes, T.A., Kreckel, P., Mueller, J., 1999. Behavior and occurrence of estrogens in municipal sewage treatment plants-II, aerobic batch experiments with activated sludge. Science of the Total Environment 225 (1,2), 91e99. Vader, J.S., Van Ginkel, C.G., Sperling, F.M.G.M., De Jong, J., De Boer, W., De Graaf, J.S., Van der Most, M., Stokman, P.G.W., 2000. Degradation of ethinyl estradiol by nitrifying activated sludge. Chemosphere 41 (8), 1239e1243. Zhang, K., Zuo, Y., 2005. Pitfalls and solution for simultaneous determination of estrone and 17a-ethinylestradiol by gas chromatography-mass spectrometry after derivatization with N, O-bis(trimethylsilyl)trifluoroacetamide. Analytica Chimica Acta 554 (1e2), 190e196. Zuehlke, S., Duennbier, U., Heberer, T., 2005. Determination of estrogenic steroids in surface water and wastewater by liquid chromatography - electrospray tandem mass spectrometry. Journal of Separation Science 28 (1), 52e58.
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Correlation of bacterial communities supported by different organic materials with sulfate reduction in metal-rich landfill leachate Jana Schmidtova, Susan A. Baldwin* Chemical and Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver, B.C. V6T 1Z3, Canada
article info
abstract
Article history:
Several different organic materials, typical of those used in passive treatment systems for
Received 24 June 2010
mine influenced water, were tested for their ability to support sulfate-reducing bacteria
Received in revised form
and sulfate reduction in an anaerobic biological reactor (ABR). The quantity of sulfate-
29 October 2010
reducing bacteria (SRB) in each organic material, as determined using quantitative poly-
Accepted 31 October 2010
merase chain reaction (q-PCR) of the dissimilatory sulfite reductase (dsr) gene, correlated
Available online 18 November 2010
with the initial C/N ratio of each material. Potential sulfate reduction rates measured in the laboratory ranked silage > compost ¼ molasses/hay > cattails > pulp mill biosolids and
Keywords:
correlated with the q-PCR estimates of SRB in the submerged materials. A comparison of
Mine influenced water
bacterial communities using 16S rRNA gene clone library sequencing revealed similar
Passive treatment
distribution of clones among the phyla Bacteroidetes, Firmicutes and Proteobacteria for silage,
Natural organic substrate
compost and molasses/hay after 174 days of exposure in the seepage water. Silage, the
Sulfate-reducing bacteria
most successful material tested, contained more d-Proteobacteria-related sequences than
Dissimilatory sulfite reductase
the other materials and Spirochaetes-related clones were more abundant in silage than in
16S rRNA gene clone library
compost or molasses/hay. According to sequenced dsr clones, the SRB community in silage
Microbial community
differed from that for compost and molasses/hay, with fewer Desulfovibrio- and more Desulfomicrobium-related sequences in the silage. Pulp mill biosolids used in the ABR since 2004 contained an overall bacterial community that was more diverse than those for the freshly submerged organics, but only Desulfovibrio desulfuricans-related sequences were found in the dsr library. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Mine influenced water is the most common pollution problem related to mining. During and after mining, rocks with sulfide minerals are exposed to air and water causing their oxidization. This generates seepage containing high concentrations of sulfate and dissolved metals (INAP, 2009). In addition, legacy technologies, such as roasting, generated large quantities of dust containing metal (e.g. arsenic), which was landfilled
since it has no economic value. Seepage from such mine wastes poses a threat to aquatic life in receiving environments and treatment to remove the toxic components is required. The conventional approach to treating mine effluents uses chemical reagents for neutralization and precipitation; the highdensity sludge (HDS) process with lime addition being the current industry standard (INAP, 2009). However, the HDS process may not be economically feasible at remote and inaccessible sites or where long-term treatment is necessary
* Corresponding author. Tel.: þ1 604 822 1973; fax: þ1 604 822 6003. E-mail address:
[email protected] (S.A. Baldwin). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.038
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(Johnson and Hallberg, 2005a). As well, safe storage of the metal-laden sludge is a major challenge. Alternatively, passive bioremediation systems, such as constructed anaerobic wetlands and permeable reactive barriers, are less expensive since they have low construction and operating costs. They rely on natural processes such as sulfate reduction by sulfatereducing bacteria (SRB) to raise the pH and generate sulfide, which causes metals to precipitate (Eccles, 1999). However, although bioremediation can remove metals down to very low concentrations, it is not always reliable or sustainable as some passive treatment systems operate only for very short periods of time before failing (Neculita et al., 2007). This is because the mechanisms responsible for metal removal have not been well characterized. Bacteria are deemed to play an important role in treatment but their presence and activity are measured rarely (Dann et al., 2009). Therefore, knowledge about the dynamics and diversity of microbial consortia and the rates of organic material degradation are areas that need to be addressed so as to assess the sustainability of these systems. In passive treatment, a complex organic material provides nutrients for bacteria that are responsible for sulfate reduction and metal removal, such as SRB. To minimize costs, waste organics close to the mine site are used, such as wood chips and sawdust (Johnson and Hallberg, 2005b) (Blumenstein and Gusek, 2008), manure (Cocos et al., 2002; Gibert et al., 2004; Zagury et al., 2006), agricultural byproducts such as hay and alfalfa (Bechard et al., 1994), composted food waste or natural vegetation (Kelman Wieder, 1993) (Smith and Kalin, 1991), or industrial wastes such as pulp mill biosolids (Duncan et al., 2004) (Hulshof et al., 2006). The consensus of most laboratory- and pilot-scale studies to date is that a mixture of different materials is more effective than one single type (Waybrant et al., 1998) (Brown, 2007) (Zagury et al., 2006). However, it is not known why some complex organic materials are better at supporting sulfate reduction than others. Because SRB metabolize only certain low molecular weight carbon compounds that are generally not present in complex organic wastes, they rely on other microbes, including hydrolytic, acidogenic and acetogenic bacteria, to supply the electron donors. Therefore, often the bottleneck and rate limiting step of sulfate reduction in passive anaerobic bioreactors (ABRs) for mine drainage treatment is the decomposition of complex organics (Waybrant et al., 1998; Castro et al., 1999; Gibert et al., 2004). In this study, several different organic materials, typical of those used in mine passive treatment systems, were suspended in the plume of metal rich (As, Zn, Cd) effluent flowing through a constructed anaerobic biological reactor (ABR) in Trail, British Columbia, Canada. The goal was to find out if the organic materials differ in their ability to support sulfatereducing bacteria and sulfate reduction and to see if there is a correlation with the overall bacterial communities supported by the materials. The microbial community structures of total bacteria and SRB were assessed using 16S rRNA and dsrA genes as molecular markers, respectively. Another goal of our project was to demonstrate the usefulness of quantitative polymerase chain reaction (q-PCR) for the dsrA gene for monitoring of SRB in the field and as a proxy for in situ sulfate reduction. Characteristics of each organic material were measured so as to explore any relationships between the type of organic material, potential sulfate reduction rate and the bacterial community.
2.
Materials and methods
2.1.
Characteristics of the study site
A treatment system near Trail, British Columbia, Canada was built by Nature Works Remediation Corporation (http://natureworks.net/) to treat leachate from a historic landfill in the proximity of the Teck zinc and lead smelter. The first two steps in a series of constructed wetlands are sub-surface, vertical flow cells called anaerobic biological reactors (ABRs) filled with a pulp mill biosolids mix (60% kraft pulp mill biosolids, 35% sand and 5% cow manure) and limestone. At the time of this study, the constructed wetland had been treating the landfill seepage, which contained arsenic, zinc, cadmium and sulfate in elevated concentrations, for 5 years. During the study period, total and dissolved As entering the first anaerobic cell varied from 9.8 to 48 mg L-1 and 0.66 to 27 mg L-1, respectively. The highest influent concentrations were in late April and, assuming a one-month hydraulic retention time for the first ABR, 27% of total and 51% of dissolved As were removed. Similarly, 49% and 31% of total and dissolved Zn, respectively, were retained in the first ABR for the same period. Zinc concentrations in the influent varied between 45 and 83 mg L-1 over the whole study period. Cadmium concentrations also decreased, but dissolved Fe increased due to the reducing environment of the ABRs. Sulfate, which varied between 520 and 1300 mg L-1 in the influent, remained unchanged through the treatment system or appeared to increase. Temperature of the pore water inside the ABR piezometer where the materials were suspended was around 6.5 C, 17 C and 8 C in April, July and October, respectively. Typically, some dissolved oxygen was present in the spring months (i.e. 1.5 mg L-1 in April) and decreased to less than 0.3 mg L-1 in the summer and fall months accompanied by negative redox potential of 172 to 180 mV. The pH of the piezometer water varied between 5.9 and 6.8 during the study period.
2.2.
Experimental set-up
Each of the ABRs contained a piezometer in the centre. Organic materials tested in this study were suspended within the aqueous plume flowing through the first ABR approximately 2 m below the water surface in the piezometer. The following materials were each sealed in duplicate screen mesh bags (5 7 cm, ca. 1 mm mesh size): 13 g of alfalfa silage (Poundmaker Agriventures, Lanigan, SK); 7 g of a mixture of fresh and partially decomposed cattails (taken from the Typha latifolia pond); 7 g of vegetable and woody debris compost (University of British Columbia, Vancouver, BC); 8 g of a dried sugar beet pulp molasses (sweet 45, Westway Feed Products, Tomball, TX) and hay (mixed crop of alfalfa and orchid grass 65:35) mixture (2:1 w/w); 12 g of kraft pulp mill biosolids (Celgar Pulp and Paper Mill, Castlegar, British Columbia). All materials were obtained fresh from source, homogenized and cut to ca. 0.5 cm3 pieces. All 10 bags were submerged on May 3, 2006 (time ¼ 0). The first set was removed on August 19, 2006 (time ¼ 109 days) and the second set was taken out on October 23, 2006 (time ¼ 174 days). All bags were immediately placed on ice and kept frozen until chemical analysis. In addition, a sample from the anaerobic
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 1 5 e1 1 2 8
bioreactor was taken with a PVC corer (0.5 m in length, 10 cm in diameter) from the upper-layer of the bioreactor and kept frozen until DNA extraction.
2.2.1.
Chemical analysis of organic materials
The following parameters were measured before and after the in situ exposure. Dissolved organic carbon (DOC) was measured using TOC-Vcph analyzer (Shimadzu, Columbia, MD). 4 g of wet material from each mesh bag was washed with 30 mL of deionized water in a 50 mL Falcon tube shaken at 250 rpm for 2 h, followed by centrifugation at 8000 rpm for 10 min and analysis of the syringe-filtered (0.22 mm) supernatant. The washed pellet was dried at 60 C overnight and used for particulate organic carbon/particulate nitrogen (POC/PN) and easily degradable material (EDM) analysis. POC and PN were measured using the method by Verardo et al. (1990). EDM was analyzed by modified gravimetric forage fibre analysis as described in Prasad et al. (1999). In order to estimate the experimental error associated with the chemical analyses, some samples were analyzed in triplicate. For the DOC measurements the standard deviations were less than 5% of the average values. For the EDM measurements, the standard deviations were less than 6%, and for POC and PN, they were less than 2.5% and 4%, respectively, of the average values.
2.3.
DNA extraction
Genomic DNA was extracted from previously homogenized and thawed materials removed from their mesh bags using the MoBio PowerSoil DNA extraction kit (MoBio Laboratories, Solana Beach, CA) according to the manufacturer’s instructions with the following modifications: alternative protocol for maximum yields was used; the spin column was rinsed twice with 300 mL of solution C4; and finally DNA was eluted in 100 mL of 10 mM Tris. Total nucleic acid concentration and purity were measured spectrophotometrically with NanoDrop ND-1000 UVeVis Spectrophotometer (NanoDrop Technologies, Wilmington, DE) at 260 and 280 nm.
2.4.
PCR and quantitative PCR (q-PCR)
DNA was extracted from the organics contained in the mesh bags at time 0, and after 109 and 174 days of exposure in the ABR water. The DNA was subjected to PCR for small subunit (SSU) ribosomal DNA gene fragments targeting different SRB groups and q-PCR for targeting the dsr gene. Based on the multiple alignments of dsrA genes from both cultured SRB and environmental sequences found in GenBank, conserved regions of the gene were selected as primers for SRB quantification. The forward primer DSR1F0 (50 -ACSCACTGGAAGCACGGC-30 ) was modified from previously published primer DSR1F (Wagner et al., 1998). A degenerate reverse primer DSR210R (50 -CGGTGGMRCCRTGCATRTT-30 ) was designed to match the majority of dsr sequences currently available and yield a target product of ca. 200 bp (Schmidtova et al., 2009). The primers were tested by amplification of several pure SRB strains: Desulfobacterium autotrophicum (DSM 3382), Desulfobacter curvatus (DSM 3379), Desulfosarcina variabilis
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(DSM 2060), and Desulfovibrio desulfuricans subsp. desulfuricans (DSM 1926). Total eubacterial primers used were 27F (Lane, 1991) and degenerate 519R (50 -GNTTTACCGCGGCKGCTG-30 ). q-PCR of SRB was performed on the ABI PRISM 7000 (Applied Biosystems) real-time thermocycler. The reaction mixture (12 mL) contained iTaq SYBR Green Supermix with ROX (Biorad), each primer at a final concentration 300 nM, nanopure water and template DNA. MicroAmp 96-well reaction plates (Applied Biosystems) were used. The amplification conditions were as follows: 2 min at 50 C; 10 min at 95 C; and 40 cycles of 15 s at 94 C followed by 1 min at 60 C. Each sample was amplified in triplicate. For some samples the reaction was repeated, yielding up to 6 analytical replicates. The external standard curve for dsr quantification was constructed with genomic DNA extracted from a pure culture of Desulfobacterium autotrophicum (DSM 3382). The detection limit was 100 dsr copies per reaction, the efficiency (E ¼ 10(1/ slope) , where 2 indicates an exact doubling per cycle) was 1.84 and R2 ¼ 0.96. Concentrations of the samples were extrapolated from the standard curve using ABI Prism 7000 SDS Software (Version 1.0, Applied Biosystems). q-PCR of total eubacteria was performed on the Miniopticon system (Biorad, Hercules, CA). The reaction mixture (25 mL) contained iTaq SYBR Green Supermix (Biorad), each primer at a final concentration 300 nM, nanopure water and template DNA. The amplification conditions were as follows: 2 min at 50 C; 10 min at 95 C; and 40 cycles of 15 s at 94 C followed by 1 min at 60 C. The external standard curve was constructed as described by Zaikova et al. (2009). The gene copy number was diluted from 100 to 108 copies.
2.5.
Clone library construction
Clone libraries of 16S rRNA and dsr genes were constructed from 4 of the organic materials: silage, compost, molasses and hay taken from the reactor after 174 d exposure, and pulp mill biosolids taken directly from the ABR. PCR amplification of 16S rRNA genes was carried out on an iCycler (Biorad) using universal bacterial primers 27F and 1492R (Lane, 1991). Taq DNA polymerase (Invitrogen) was used and the following reaction conditions were applied: 1 cycle at 94 C for 3 min; 35 cycles at 94 C for 40 s, 55 C for 1.5 min, 72 C for 2 min; 1 cycle at 72 C for 10 min. PCR amplification of dsr genes was carried out using primers DSR1F and DSR4R (Wagner et al., 1998). The same conditions applied except for the melting temperature, which was 60 C in this case. Products were further purified using the QIAquick PCR purification kit (Qiagen) according to the manufacturer’s instructions. Purified PCR products were ligated into the pCR2.1-TOPO vector as described in the protocol of TOPO TA Cloning kit (Invitrogen, Carlsbad, CA). Ligation reaction mixtures were transformed into One Shot TOP10 competent Escherichia coli cells (Invitrogen). Transformants were selected by blue and white screening. 288 white colonies were randomly selected from each sample for the SSU rRNA library and 196 white colonies were randomly selected from each sample for the dsr library, and stored in a glycerol stock solution in 96-well culture plates at 80 C. Plasmid inserts from several colonies stored in glycerol stock were checked by direct PCR using standard M13F and M13R primers and confirmed with agarose electrophoresis.
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2.6.
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Sequencing and phylogenetic analysis
Clones were purified with QIAquick PCR purification kit and sequenced bidirectionally by the Michael Smith Genome Sciences Centre (Vancouver, Canada, www.bcgsc.ca) using M13F and M13R primers. Assembled and trimmed sequences of 16S rRNA inserts (Sequencher; Gene Codes, Ann Arbor, MI) were imported and aligned with the ARB phylogeny computer program (Ludwig et al., 2004). All sequences were checked for chimeras with the Ribosomal Database Project II chimera check program (Cole et al., 2003). Sequences with higher than 97% similarity were combined into single OTUs using DNADIST from PHYLIP version 3.68 (Felsenstein, 2005) and DOTUR (Schloss and Handelsman, 2005). The closest phylogenetic neighbors were found using BLAST searches on the NCBI database. The phylogenetic trees and the bootstrap analyses (100 replicates) were constructed with the PhyML software package (Guindon and Gascuel, 2003) by using the maximum likelihood method. Good’s coverage was calculated by using the following formula: C ¼ (1 (n1/N )) 100, where n1 is the number of clones that occurred only once in the clone library and N is the total number of clones analyzed. Chao1 was calculated using DOTUR. The final phylogenetic trees were constructed with selected closest relatives and additional cultured species. Comparison of libraries and cluster diagram was constructed with UniFrac (Lozupone et al., 2006).
2.7.
Sulfate reduction rate
Potential sulfate reduction rate (SRR) in the ABR pore water by amending with each of the five organic materials was determined in laboratory-scale batch reactors, as this could not be measured in the field. Duplicate 150 mL glass bottles containing: 10 g of silage or 15 g of pulp mill biosolids or 5 g of molasses þ 2.5 g of hay or 6 g of fresh and partially decomposed cattails, plus a control bottle without any material were set up. 140 mL of N2-purged water taken from within the ABR piezometer, 5 mL of mixed culture laboratory SRB inoculum previously enriched in Postgate B medium from sulfate-rich sediment (Lac DuBois (Brown, 2007)), and 1 mL of sodium thioglycolic acid were added and pH was adjusted to pH 7.5e8. The bottles were kept in the dark at room temperature and sulfate was measured using the barium chloride precipitation method (Clesceri
et al., 1998) at times 0, 4, 8, 12, 15, 22 days. The SRR was determined from the slope of the linear portion of the sulfate concentration versus time plots and reported as the average from duplicate reactors.
3.
Results
The focus of our study was to compare different types of organic material in terms of their ability to support SRB under the same in situ conditions using the aqueous environment of the first Trail ABR. This provided a more realistic continuous flow system compared with static tests in the laboratory.
3.1.
Chemical characteristics of the organic materials
Organic materials vary in terms of their biodegradability depending on their content of dissolved organic compounds, labile and recalcitrant solid portions as well as presence of inhibiting substances. Several tests were performed to compare the organic materials and to monitor their utilization over time (Table 1). As expected, the molasses and hay mixture contained the highest amount of dissolved organic carbon (DOC) initially as well as the most easily degradable material (EDM). The molasses used in this experiment contained a minimum of 45% of soluble sucrose, as defined by the manufacturer. Significant DOC levels were found also in compost and silage. The lowest initial EDM of 20.8%, indicating the highest amount of complex cellulosic and lignin compounds, was found in the compost. The C/N ratios in the starting materials ranged from 25.4 (cattails and pulp mill biosolids) to 52.5 (silage). The DOC disappeared quickly for all materials, as expected. The silage contained the highest amount of soluble carbon after 109 days, but, like all the other materials, little DOC remained after 174 days. The EDM of the insoluble fraction was consumed more slowly. For the biosolids and compost, the decrease was linear (R2 > 0.99), whereas for the molasses and hay mixture, with the highest initial EDM, the decrease was more rapid in the first 109 days. Interestingly, silage EDM was consumed more rapidly in the later period from 109 to 174 days. The smallest change in EDM occurred in cattails with almost no decomposition after 109 days. The amount of particulate organic carbon (POC) and nitrogen (PN) remained relatively unchanged with
Table 1 e Carbon characteristics of organic substrates before and after treatment. DOC (mg g1)a Substrate/time (days) Pulp mill biosolids Silage Cattails Vegetable compost Molasses and hay
0 19.3 86.4 16.3 111.1 725.6
109 10.3 23.4 12.7 7.8 16.8
174 7.9 6.5 3.0 2.2 4.6
POC (% w/w) 0 41.8 42.7 24.3 45.0 42.0
109 50.1 37.1 32.4 44.6 44.7
174 45.4 45.1 23.3 45.6 45.4
PN (% w/w) 0 1.9 0.9 1.1 1.3 1.6
109 2.0 2.0 2.1 1.0 1.3
174 1.9 1.2 1.3 0.9 0.7
EDM (% w/w) 0 36.8 45.8 37.6 20.8 47.7
109 28.8 40.7 32.0 15.8 26.1
C/N (molar) 174 25.5 29.9 31.8 13.6 24.0
0 25.4 52.5 25.4 41.2 31.1
109 29.3 21.6 18.2 50.9 38.8
174 28.4 44.3 21.5 60.9 71.9
DOC stands for dissolved organic carbon; POC stands for particulate organic carbon; PN stands for particulate nitrogen; EDM stands for easily degradable material; C/N stands for carbon/nitrogen ratio. a Measured on leachates as described in Materials and methods.
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the exception of molasses, where the PN dropped from 1.6% to 0.7%. The C/N of both plant-derived materials (cattails and silage) decreased by ca. 15%. The remaining materials’ C/N ratios increased by 11% (pulp biosolids), 48% (compost) and 131% (molasses and hay).
3.2.
Quantification of SRB and total bacteria
Although the biosolids underwent some degradation, as shown by the decrease in EDM, no SRB were detected using our q-PCR primers for the dsr gene at any time. Therefore, biosolids were excluded from any further analysis. For the other materials, we did not detect any SRB initially before submerging them in the ABR water. However, we found that other bacteria were present at the start of the experiment in amounts from 2.5 106 copies g1 dry weight in the compost to 5.5 108 copies g1 dry weight in silage (Fig. 1(a)). After exposure, the amount of
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bacteria increased in all materials. Significant numbers of SRB (3.2e7.1 106 copies g1 dry weight) were found in all four materials after 109 days of incubation in the ABR, and this amount further increased by more than 10-fold during the remaining exposure time (Fig. 1(b)). At the end of the incubation period, silage contained the most SRB (1.1 109 dsr copies g1 dry weight). The highest amount of total bacteria (9.3 109 copies g1 dry weight) after the incubation period was found also in silage. Assuming that there is only one dsr copy per sulfate-reducing bacterium (Kondo et al., 2004) the fraction of SRB in the total bacteria population can be estimated. In August, less than 1% of the total bacteria population comprised SRB. Whereas, by October, SRB as a fraction of the total community had increased to 11.7 1.7% in silage, followed by compost (8.2 2.0%), and molasses and hay mix (7.1 1.6%) (Fig. 1(c)). The cattails contained the lowest amount and fraction of SRB. To compare the above results with the microbes supported on the biosolids inside the ABR, a core taken from within the actual wetland was analyzed as well. We found an average value for total bacteria and SRB of 6.06 108 copies of 16S rRNA g1 dry weight and 6.46 106 dsr copies g1 dry weight, respectively. According to these results, SRB comprised only 1% of the total bacterial community in the ABR biosolids sample.
3.3.
Sulfate reduction rates
Potential sulfate reduction rates were determined during a 22 day batch reactor study. For most organic materials there was a lag period of about 4 days, after which the sulfate concentration decreased linearly with respect to time until day 15 or 22, depending on the material. No lag period was observed when the molasses and hay mixture was used for sulfate reduction. Potential sulfate reduction rates were determined from the linear portions of the concentration versus time plots (Fig. 2). The reactors with silage as a carbon source reduced 1 d1. Approximately four times less 550 3 nmol SO2 4 mL sulfate was reduced in reactors with the molasses/hay mL1 d1) and compost mixture (142 18 nmol SO2 4 1 1 mL d ). The cattails achieved the next (133 59 nmol SO2 4 1 1 d . lowest sulfate reduction rate of 56 16 nmol SO2 4 mL Concentration versus time data for biosolids were a poor fit for a linear regression (R2 < 0.9) and therefore we assumed that there was no significant reduction of sulfate over the duration of the experiment for this material. Since cattails and biosolids performed poorly in the SRR experiments and supported only low numbers of SRB when they were submerged in the ABR, they were excluded in the phylogenetic analysis, the purpose of which was to characterize microbial community structure in organics successful at supporting sulfate reduction. Fig. 1 e (a) Total bacteria, (b) sulfate-reducing bacteria numbers per gram dry weight of solids and (c) the ratio of sulfate-reducing bacteria to total bacteria at three time points during exposure of the organics to ABR water in situ. The same measurements in the core removed from the ABR are shown as asterisks to the right of the plots (*). Error bars represent standard deviation of 3 or more analytical replicates.
3.4.
Bacterial community structure
To explore the bacterial communities associated with degradation of those materials most successful at supporting SRB and sulfate reduction, 16S rRNA clone libraries were sequenced and a phylogenetic analysis performed. Samples of silage, compost, and molasses with hay after 174 d in situ exposure as well as the core taken directly from the ABR (ABR pulp mill biosolids
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TR27_R_4, in the silage, compost and molasses/hay libraries (Fig. 4). The second most abundant phylum was Firmicutes with Clostridia related clones being prominent (25%), followed by another cluster of OTUs (TR29_R_230 related) unique to our library with closely related neighbors from the phyla Fibrobacteres and candidate division TG3. Of the Proteobacteria, most sequences fell within the d-Proteobacteria class, except in the ABR biosolids, and Spirochaetes rel. clones were present mostly in the silage and ABR biosolids libraries. All OTUs and their closest relatives, together with the frequency in each library are presented in the Supplementary material (Table S1) as are phylogenetic trees constructed with representative OTUs and a selection of closely related environmental and cultured clones (Fig. S1(a)e(c)). The calculated distances between clone libraries of different materials are outlined in a cluster diagram produced with the software program UniFrac (Lozupone et al., 2006) (Fig. S2). Overall, the sequences found in our libraries were most closely related (>97%) to clones from anaerobic digesters (Riviere et al., 2009), contaminated environments (Brodie et al., 2006, Grabowski et al., 2005) and iron-reducing enrichments (Holmes et al., 2007) (Figs. S1(a)e(c), Table S1). The bacterial diversity associated with different materials is described below.
Sulfate concentration (mg/L)
1200 1000 800 600 400 Silage Molasses Compost
200 0
0
5
10 Time (days)
15
20
Sulfate concentration (mg/L)
750 700 650 600
3.4.1.
550 Biosolids Typha leaf litter 500
4
6
8
10
12
14
16
18
20
22
Time (days)
Fig. 2 e Sulfate concentrations (mg L-1) versus time (d) measured in the laboratory bioreactors incubated with organic materials and ABR water. Linear regressions were performed with Microsoft Excel 2008. The error bars represent the standard deviation of triplicate measurements of sulfate.
sample) were analyzed. A total of 816 clones were chosen and sequenced for 16S rRNA analysis. To avoid microdiversity, clones that were 97% identical were grouped into operational taxonomic units (OTUs), yielding 366 OTUs. Good’s coverage estimating the fraction of the total bacterial community that has been targeted suggests that the ABR pulp mill biosolids contained the most diverse bacterial community of which ca. 51% was covered by the clone library. The remaining samples’ coverage values were between 64 and 68% (Table 2). Over 50% of all 16S rRNA clones belonged to the phylum Bacteroidetes (52%) (Fig. 3) with a significant cluster of one OTU,
Silage
The dominant Bacteroidetes OTU TR27_R_4 related cluster, represented by 130 out of 211 clones (61.6%) was most closely related (96% identity) to an unclassified environmental sequence GW-32 obtained from a household biogas digester (Fig. S1(a), Table S1). Other close relatives, although with lower identity (94%), were classified taxonomically as Cytophaga sp. related and they were found in a “paper pulp column” (Fig. S1 (a)). Within the same cluster, eleven clones in a TR27_R_1 related group were unique to silage and similar (98%) to an unclassified sequence from the Cholet municipal wastewater anaerobic sludge treatment digester in France (Riviere et al., 2009). The closest cultured species, although very distantly related, to the TR27_R_4 and TR27_R_1 groups include a Cytophaga sp. strain AN-BI4 (AM157648, 91% identity) from the interface between Bannock Basin hypersaline brine and seawater in the Mediterranean (Daffonchio et al., 2006) and Prolixibacter bellariivorans (89%) (Holmes et al., 2007). Three small clusters from our library, one of which was unique to silage, were similar to sequences from a biodegraded oil reservoir (Grabowski et al., 2005), a chromium contaminated landfill and a uranium contaminated aquifer. All of these were grouped within Prolixibacter rel. sequences. Another group of TR29_R_302 rel. clones classified under the WCHB1-32 environmental group, named after a clone from a hydrocarbon and chlorinated solvent contaminated aquifer, was placed
Table 2 e Parameters of SSU rRNA clone libraries.
Number of clones Number of OTUs Good’s coverage (%) Chao1a
Silage
Compost
Molasses þ hay
ABR biosolids
211 104 64 243 (172e379)
179 86 64 244 (163e409)
246 97 68 361 (226e634)
180 121 51 276 (205e406)
a Values in brackets represent 95% confidence interval.
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Fig. 3 e Overall distribution of clones among phyla and Proteobacteria classes for the three materials subjected to SSU rDNA analysis. Pie chart wedges represent percentage of sequences in each group.
putatively in the Cytophagales division (Dojka et al., 1998). Taken together, the Prolixibacter, WCHB1-32 and TR27_R_4 clusters were the most prominent of all Bacteroidetes sequences found in silage. Most other Bacteroidetes OTUs were distributed among unclassified environmental groups frequently related to sequences from iron-reducing enrichments, hydrocarbon polluted environments and anaerobic digesters. The Firmicutes; Clostridia related (Fig. 4) clones were distributed across many different families (Fig. S1(b)) with only a few small clusters of note closely related to clones from anaerobic digesters (Godon et al., 1997), cultured species and clones from the gut-related Ruminococcaceae family, clones from uranium-contaminated soil (Brodie et al., 2006) and the cultured species Sporobacter termitidis. The d-Proteobacteria, were represented with 13 clones containing putative SRB related to Desulfovibrio, Desulfomicrobium and Desulfobacter sp. Eleven silage clones were Spirochaetes related (5.2%) five of which were affiliated with a clone from a SRB fluidized-bed reactor treating ARD and fed by ethanol (Kaksonen et al., 2004).
3.4.2.
Compost
The composition of main phyla in this library was similar to silage with Bacteroidetes and Firmicutes dominant and comprising 55% and 20% of all clones, respectively. Some Bacteroidetes clones were unique to the compost library (red OTUs in Fig. S1(a)), but in general these clustered together with clones from the other libraries. One noticeable difference was the presence of more Sphingobacteriales in the compost library compared with the others (Fig. 4). The distribution of Proteobacteria in the compost library differed from that in silage in
that there were fewer d-Proteobacteria. Nevertheless, these fell within the same families of SRB as the silage clones. Far fewer Spirochaetes and the presence of a large cluster of 20 Fibrobacteres related clones (Hongoh et al., 2006) that formed 10.6% of all compost sequences were the features that most distinguished the compost clone library from that for silage.
3.4.3.
Molasses and hay
Unique to the molasses and hay library was a small cluster of Paludibacter related clones (Fig. S1(a)), closely related to the propionate producing fermenter Paludibacter propionicigenes (Ueki et al., 2006). As in the other two libraries, only a few Proteobacteria were found and most of them were SRB related d-Proteobacteria. Also unique to the molasses and hay clone library were sequences related to clones within the phyla Cyanobacteria and Chlorobi from an acid-impacted lake and contaminated sediment (Percent et al., 2008) (Abulencia et al., 2006) suggesting that some light penetrated to the depths where these organics were submerged. Together with the compost 16S rRNA library, this library also contained a significant amount of the novel OTU TR29_R_230 Fibrobacteres related sequences (7.7%).
3.4.4.
ABR pulp mill biosolids
The ABR microbial community was very different from the others (Fig. S2). Bacteroidetes related sequences were not dominant (Fig. 3), and although some TR27_R_4 related clones were detected, a different cluster of unclassified Bacteriodales (TR8_R_676 related) was prevalent in the ABR biosolids (Fig. 4). This group of 20 clones is not shown on the phylogenetic tree in Fig. S1(a) since there were no closely related sequences in the database and these OTUs could not be classified in any family or existing environmental group. Instead, Clostridia
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Fig. 4 e Dotplot representation of bacterial diversity in different carbon materials based on phylogenetic proximity to relevant reference groups and environmental sequences. The area of closed circles determines the percentage of clones falling within certain group.
dominated the ABR biosolids library comprising 40% of all clones. Also in contrast to the other libraries, Firmicutes were more diverse containing also sequences related to Bacillales (6 sequences) as well as Mollicutes (7 sequences) orders. Like the TR8_R_676 cluster, these sequences could not be classified into any families or environmental groups due to their low homology to the existing database sequences. As far as the
Proteobacteria were concerned, ABR biosolids contained more a-Proteobacteria than any of the other libraries (Fig. S1(c)). No d-Proteobacteria were recovered in the clone library probably due to the low numbers of SRB in the biosolids sample as revealed in the dsr q-PCR analysis. Like silage, ABR biosolids contained a cluster of Spirochaetes and some TR29_R_230 Fibrobacteres related clones were present (Fig. 4).
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3.5.
Phylogenetic analysis of SRB
Microbes involved in hydrolysis and acidogenic processes associated with carbon compound decomposition dominate the 16S rRNA libraries. Although the d-Proteobacteria sequences that we found confirmed the presence of SRB, they formed a very small fraction of the whole community and thus the 16S rDNA library was not adequate to survey the diversity of SRB in our samples. Therefore, clone libraries of the dsr gene, specific for SRB, were constructed. A total of 78, 68, 74, and 121 clones were sequenced from silage, compost, molasses and hay, and ABR pulp mill biosolids, respectively. These sequences were grouped into 39 OTUs based on 97% sequence similarity. The distribution and phylogenetic relatives of sequenced clones are shown in Fig. S3(a)e(c) and Table S2. The majority of SRB in the samples are affiliated with Desulfovibrio, Desulfonema, Desulfomicrobium and Desulfotomaculum sp. (Fig. 5). An additional six SRB families were detected, although with fewer dsr clones. Compost and molasses/hay were most similar in the SRB related families detected and they contained mostly Desulfovibrio followed by Desulfonema related dsr sequences. In contrast, Desulfovibrio related clones made up only a small portion of the silage dsr library, which contained an equal amount of Desulfonema and Desulfomicrobium related dsr clones as the most represented SRB families. The fresh organics supported a diverse community of SRB, whereas only Desulfovibrio related dsr clones were found in the ABR biosolids sample. Over 92% of all sequences in the biosolids library, represented by clones TR8A_213 and TR8A_214, were closely related (98%) to a single SRB Desulfovibrio sp. related sequence (NTUA-1A-DSR3) from an up-flow fixed-bed reactor fed with lactate (Remoundaki et al., 2008) (Fig. S3(b)). Interestingly, this environmental clone appeared solely at the bottom of the reactor, where conditions were
molasses/hay
compost
silage
Desulfonema Desulfomicrobium Desulfotomaculum Desulfobulbus Desulfobacter Desulfobacterium Desulfatibacillus Desulforhopalus Desulfoarculuo Desulfovibrio
ABR biosolids
Fig. 5 e Cluster diagram of sulfate-reducing bacteria families identified using dsr clone library analysis in the organic materials. Percentage of clones in each family is proportional to green intensity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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highly reducing. The cultured species most closely related to TR_8A_213 and TR_8A_214 is Desulfovibrio desulfuricans strain F28-1 (97% identity). A cluster of clones also closely related to this strain was prevalent in heavy metal contaminated sediments in a mining area of Portugal suggesting that it may have a particular resistance to high concentrations of Fe, Cu and Zn (Martins et al., 2009). The Desulfovibrio sp. in the compost and molasses/hay dsr libraries (with OTU TR29_99 containing the most clones) were related to another clone NTUA-5A-DSR22 recovered from the same SRB fixed-bed reactor and a different cultured Desulfovibrio species; D. aminophilus (Fig. S3(b)). This SRB species is capable of growing on amino acids as electron donors. Desulfonema related sequences represented 24% of all dsr clones in silage, 14.7% clones in compost, and 20.3% in molasses and hay (Fig. S3(c) and Fig. 5). The only SRB family that was more prevalent in the silage dsr library than the others was Desulfomicrobium, with OTU TR27_51 represented by the most clones.
4.
Discussion
4.1.
Overall performance of the different materials
According to the SRRs measured in the laboratory bioreactors, the materials ranked silage > molasses/hay ¼ compost > cattails > pulp mill biosolids. In terms of estimated percent SRB of the total bacterial community in the submerged samples after 174 days (Fig. 1) the ranking was the same. This indicates that q-PCR of the dsr gene is a good proxy for estimating the importance of sulfate reduction. To understand why these materials ranked as they did, first we explored whether there were any correlations between SRR or SRB numbers and bulk properties of the materials. Factors that we expected to influence the growth of SRB included the availability and type of low molecular weight carbon compounds that would be present in the DOC fraction, the amount and decomposition rate of the more labile of the insoluble components and the overall carbon content. Overall, we did find that the materials with high initial DOC (silage, compost, and molasses/hay) were most successful at supporting SRB. But there was no statistical trend since although the molasses/ hay mixture had more than eight times as much DOC than silage; the SRR using the latter was almost four times greater. The dissolved organic carbon fraction contained many different chemical compounds that differed between silage and the molasses and hay mixture. Silage contained more short chain fatty acids than molasses and hay (data not shown). Silage is produced through lactic acid fermentation of agricultural plants as a way of preserving and storing livestock feed. The soluble portion of silage contains lactate, which is a carbon source preferred by many SRB (Li, 2009). In a continuous flow system, we would expect the initial soluble organic carbon fraction to be washed out and therefore available for microbes to use only in the early stages after initial exposure of the organics to the influent water. It is probable that microniches within the material retained some of the DOC for periods much longer than the mean residence time. However, to sustain SRB growth over the long period, low molecular weight carbon sources would need to be produced from
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 1 5 e1 1 2 8
16 silage
14 compost mix
12
SRB (%)
hydrolysis and fermentation of the insoluble portion of the organics, presumably starting with the easily degradable materials. In fact others suggested a negative correlation of sulfate reduction with lignin content, this being the most recalcitrant component (Gibert et al., 2004). As well, in our study, the organic materials with the most EDM initially, molasses/hay and silage, were the best at supporting SRB but there was an exception in the case of compost. Compost contained the least amount of EDM, but still outperformed cattails and biosolids for supporting SRB. Interestingly, the EDM of cattails, which was higher than that in compost, degraded very slowly over the duration of the experiment. All materials still contained over half the initial amount of EDM after 179 days and the rates of EDM decrease over time differed between materials suggesting that the chemical composition of each organics’ EDM varies. In another study comparing organic materials for sulfate reduction, Zagury et al. (2006) also found that leaf compost, containing low DOC, did not support sulfate reduction in their laboratory bioreactors. However, leaf compost was one of the main ingredients in a mixture, containing also poultry manure and wood chips, that was the most reactive in terms of SRR in another study (Cocos et al., 2002). The poultry manure was very high in DOC and easily accessible substances. Despite their testing of several organic materials Zagury et al. (2006) concluded that initial characteristics of the organic materials could not predict sulfate reduction rates. We make the same observation here in that, although in general greater amounts of DOC and EAM lead to higher SRRs, there is no positive statistical correlation. The other bulk material property that we measured was C/ N ratio. Several authors claim that low C/N ratio (∼10) improves sulfate reduction and metal precipitation (Bechard et al., 1994; Prasad et al., 1999). Other studies found no correlation of SRR with C/N ratio (Gibert et al., 2004). In our study, we found a strong positive correlation (r2 ¼ 0.89) between the initial C/N ratio and SRB fraction of the bacterial community (Fig. 6(a)). This finding, although with less statistical confidence, was further extended to the correlation of C/N ratio with actual sulfate reduction rate measured during the laboratory experiments (Fig. 6(b)). Zagury et al. (2006) also found that the materials with C/N close to the theoretical value (∼6.6) did not promote sulfate reduction. On the other hand, maple wood chips with C/N of 567 performed the best in their study. It is important to note that different C/N ratios were reported for the same material in different studies. The C/N ratio of 70, as opposed to 25.4 in this study, was reported for cattails in Florida wetlands (Corstanje et al., 2006). Also, higher C/N ratios of pulp mill biosolids (up to 100) were measured at an Ontario paper mill (Price and Voroney, 2007). Therefore, the C/N ratio largely depends on the nature, location, and decomposition state of each material and is not uniform for a particular material. Usually C/N ratios are recommended to ensure that neither N nor C is limiting. But, in the Trail ABR, there are high concentrations of nitrate and ammonium in the influent water, therefore nitrogen, as a nutrient source for the bacteria, may not be limiting. This may explain why we found a correlation between SRR and increasing C content in our materials. However, in other environments where the organic material is the only source of nitrogen, a lower C/N ratio may be better for sulfate reduction. Overall, the C/N ratio was the only
molasses/hay
10 8 cattails
6 4
y = 0.31x - 4.52 R² = 0.89
2 0 20
30
40
50
60
C/N 600 silage
SRR (nmol cm-3 d-1)
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500 400
compost mix molasses/hay
300
cattails biosolids
200 100
y = 16.64x - 401.04 R² = 0.83
0 20
30
40
50
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C/N Fig. 6 e Correlation between (a) SRB amount in submerged organic sample and (b) laboratory determined SRR and initial C/N ratio of carbon materials. The error bars represent standard deviations from multiple measurements.
parameter that could be correlated with sulfate reduction and should be considered when choosing an organic substrate taking into account also the amount of nitrate or ammonium in the influent water. Monitoring of organic material properties and SRB over longer periods of time is needed to see how SRR changes once the EDM runs out and to measure degradation rates of the more recalcitrant components: hemicellulose, cellulose and lignin. If the EDM decomposition rates measured in this study are assumed to be linear, which is true for only some of them, then EDM in the three materials most successful at supporting SRB would run out in 1e3 years.
4.2. Variations in bacterial population among different materials One of the main questions of our work was: Do the microbial communities associated with organic matter degradation and sulfate reduction vary when different organic materials are used in an anaerobic bioreactor for metal removal? According to the Good’s coverage values our clone libraries do not reflect the total bacterial diversity in the samples and the more rare phylotypes cannot be characterized. This is due
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 1 5 e1 1 2 8
to the very high diversity within organic-rich anaerobic communities as discovered in other studies (Riviere et al., 2009, Chouari et al., 2005). Even with high throughput sequencing technologies, accurate estimations of diversity are still elusive. For example, although one study obtained 139,819 sequences from a soil sample through pyrosequencing, they still estimated only 34e48% coverage of the actual diversity (Roesch et al., 2007). Nevertheless, our analysis revealed some interesting clusters of OTUs for some of the more prevalent organisms and permitted a comparison of the presence of these in the different organic materials. The three organic materials most successful at supporting sulfate reduction contained similar bacterial communities associated with hydrolysis and fermentation. The most predominant cluster, TR27_R_4, was only distantly related to any known cultured organisms. These were Cytophaga related, which are gram-negative gliding soil bacteria known to degrade recalcitrant cellulose compounds and produce a range of fatty acids as byproducts. Similarly, the Firmicutes phylum was dominated by Clostridia rel. clones, which clustered within families or environmental groups of organisms also known to degrade recalcitrant carbohydrates and to produce fermentation byproducts such as organic acids (Fig. S1). Some of the clones in our libraries were related to microbes that degrade proteinaceous material and amino acids. Taken together this suggests that a wide variety of low molecular weight carbon compounds were produced in the organic materials over the duration of the experiment. Although the three libraries were similar at the phylum (Fig. 3) and order (Fig. 4) levels, close inspection of the phylogenetic trees revealed more specialization and diversity at the OTU level. The same observation of specialization at the family and genus levels was made in the community analyses of multiple municipal anaerobic digesters (Riviere et al., 2009). Presence of Spirochaetes related groups in silage might be related to the unique compositional properties of that material. The silage that we used came from alfalfa, which is known to be high in protein content when compared to molasses, hay or wood. High protein containing organics were found to perform better at supporting sulfate reduction (Coetser et al., 2006) and it may be that this protein content results in protein hydrolyzing and amino acid fermenting organisms that are found in the silage community and not in the others. Plus silage includes lactate and other low molecular weight organic acids. In other studies, Spirochaetes related organisms were shown to oxidize propionate and butyrate and were syntrophic with sulfate reducers (Roest et al., 2005). Many of the silage Spirochaetes OTUs in our study were related to a clone, SR_FBR_E2, from a laboratory SRB reactor treating simulated acid mine drainage (Kaksonen et al., 2004), thus this group may play an important role in supporting SRB in mine drainage treatment systems. At the phylum level, the compost and molasses/hay microbial communities were most similar to each other (Fig. S2). Their bacterial community structure could be distinguished from that for silage by the presence of the Fibrobacteres related cluster TR29_R_230. Species of the Fibrobacteres phylum are known cellulose degraders and perhaps their presence in compost and molasses/hay suggests that cellulose degradation was a more important process in those organics in contrast to silage.
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The fresh organics’ phylogenies were more similar to each other than to that of the ABR biosolids with the main difference being the greater proportion of Bacteroidetes clones in the fresh organics. Species from this phylum are mostly found in soils with high carbon mineralization rates, which could explain why they were found in high frequency in the fresh materials (Fierer et al., 2007). In contrast, the largest fraction of the ABR biosolids clones was assigned to the Firmicutes phylum. We hypothesize that Firmicutes related organisms are more prevalent when cellulosic materials are degrading and that Bacteroidetes related organisms are dominant in the earlier stages of organic matter degradation when there is more soluble and easily degradable material. However, clearly both hydrolysis and acidogenesis were taking place in the fresh organics and in the ABR biosolids. We suggest that the main reason for the difference in bacterial community structure between the ABR biosolids sample and the other fresh organics was the difference in their duration of submersion in the ABR. Another study of the microbial community in an anaerobic mine passive treatment system found increasing diversity over time as the system aged, but with decreasing variability (Dann et al., 2009).
4.3. Correlation of the SRB community structure with overall bacterial community, SRB numbers and SRRs The two organic samples, compost and molasses/hay mixture, were most similar to each other in all of the analyses in this study: the SRR, estimated numbers of SRB using q-PCR of dsr, overall distribution of phlya (Fig. 3) and SRB families in the clone libraries (Fig. 5). Silage, which obtained the highest SRR and supported the most SRB, could be distinguished from compost and molasses/hay with respect to bacterial, especially with respect to SRB, phylogeny. This suggests that despite the close similarities, there is a correlation between bacterial community structure and the ability of organic materials to support sulfate reducers. This was seen most clearly in the nature of the inferred SRB supported by the different materials. The dsr clone library revealed high diversity of SRB in the fresh organics versus dominance of Desulfovibrio desulfuricans related clones in the ABR biosolids. It is well known that a consortium of SRB results in a higher SRR than that obtained when culturing one species. If the nature of biosolids is such that only one strain of SRB is able to exist in that organic material then this may explain why we obtained only low SRRs in our laboratory bioreactors with biosolids. Silage had some unique features to its dsr clone library, which may explain why it achieved the highest sulfate reduction rate. Desulfomicrobium were more highly represented in silage than in any of the other organics, and far viewer Desulfovibrio clones were found in the silage library than in the rest. It might be that the Desulfonema and Desulfomicrobium related clones that we found had a preference for the carbon sources provided in silage, whereas Desulfovibrio related clones thrived better on the chemical compounds released in degradation of the other organics. In addition to comprising more readily available low molecular weight electron donors for SRB, silage may also support hydrolytic and fermentative microbial communities that are also providing preferred carbon sources for sulfate reducers. For example, when comparing the distribution of clones among
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phyla for all the fresh organics, the one feature that differentiated silage from compost and molasses/hay was the presence of more Spirochaetes-related clones in silage. Therefore it is worthwhile investigating the role that these organisms might play in passive treatment systems for sulfate reduction especially since other studies have shown a synergistic relationship between Spirochaetes sp. and SRB (Roest et al., 2005).
5.
Conclusions
1. Five materials tested for their suitability for use in passive bioremediation processes for sulfate reduction were ranked silage > compost ¼ molasses/hay > cattails > pulp mill biosolids. 2. Of all of the bulk material properties measured the only correlation that we found was an increase of SRR and SRB numbers with C/N ratio. 3. Quantitative polymerase chain reaction of the dsr gene was found to be a good proxy for measuring potential sulfate reduction rates in the field. 4. The overall bacterial community structure of different organic materials in the same environment is very similar at the phylum level with more diversity at the OTU level. 5. There was a correlation between SRR, estimated numbers of SRB and the bacterial community structures for the materials tested. 6. The material, silage, that achieved the highest SRR and supported the most SRB, had a SRB community structure that was different from those for other materials also supporting sulfate reducers in the same environment.
Acknowledgements Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada through a Discovery Grant (RGPIN 184333-04) and Genome British Columbia through an Applied Genomics Innovation Program grant (108ROC) to Susan A. Baldwin. NatureWorks personnel, in particular Al Mattes and Jim Hall, are thanked for allowing access to the site and for providing assistance during the sampling. Dr. Steven Hallam and Jinshu Yang, of the Department of Microbiology and Immunology at the University of British Columbia are acknowledged for their assistance in preparation of the clone libraries.
Appendix. Supplementary material Supplementary material related to this article can be found online at doi:10.1016/j.watres.2010.10.038.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Biodiversity and population dynamics of microorganisms in a full-scale membrane bioreactor for municipal wastewater treatment Cai-Yun Wan a,1, Heleen De Wever b,1, Ludo Diels b, Chris Thoeye c, Jun-Bin Liang a, Li-Nan Huang a,b,* a
Key Laboratory of Gene Engineering of the Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, PR China b Unit of Separation and Conversion Technology, Flemish Institute for Technological Research (VITO), B-2400 Mol, Belgium c Research and Product Development Department, Aquafin NV, B-2630 Aartselaar, Belgium
article info
abstract
Article history:
The total, ammonia-oxidizing, and denitrifying Bacteria in a full-scale membrane biore-
Received 5 June 2010
actor (MBR) were evaluated monthly for over one year. Microbial communities were
Received in revised form
analyzed by denaturing gradient gel electrophoresis (DGGE) and clone library analysis of
30 September 2010
the 16S rRNA and ammonia monooxygenase (amoA) and nitrous oxide reductase (nosZ )
Accepted 4 November 2010
genes. The community fingerprints obtained were compared to those from a conventional
Available online 12 November 2010
activated sludge (CAS) process running in parallel treating the same domestic wastewater. Distinct DGGE profiles for all three molecular markers were observed between the two
Keywords:
treatment systems, indicating the selection of specific bacterial populations by the con-
Membrane bioreactor
trasting environmental and operational conditions. Comparative 16S rRNA sequencing
Bacteria
indicated a diverse bacterial community in the MBR, with phylotypes from the a- and
Ammonia-oxidizing and denitrify-
b-Proteobacteria and Bacteroidetes dominating the gene library. The vast majority of
ing bacteria
sequences retrieved were not closely related to classified organisms or displayed relatively
Molecular diversity
low levels of similarity with any known 16S rRNA gene sequences and thus represent
Population dynamics
organisms that constitute new taxa. Similarly, the majority of the recovered nosZ sequences were novel and only moderately related to known denitrifiers from the a- and bProteobacteria. In contrast, analysis of the amoA gene showed a remarkably simple ammonia-oxidizing community with the detected members almost exclusively affiliated with the Nitrosomonas oligotropha lineage. Major shifts in total bacteria and denitrifying community were detected and these were associated with change in the external carbon added for denitrification enhancement. In spite of this, the MBR was able to maintain a stable process performance during that period. These results significantly expand our knowledge of the biodiversity and population dynamics of microorganisms in MBRs for wastewater treatment. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Key Laboratory of Gene Engineering of the Ministry of Education, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, PR China. Tel./fax: þ86 20 8411 2399. E-mail address:
[email protected] (L.-N. Huang). 1 These authors contributed equally to this work. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.008
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1.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 2 9 e1 1 3 8
Introduction
Submerged membrane bioreactors (MBR) combine efficient biological treatment with membrane separation and are now widely accepted as an advanced technology for obtaining high-quality effluent. This process offers several important advantages over conventional wastewater treatment systems, including high biodegradation capacity and efficiency, excellent permeate quality and low sludge production. As more stringent effluent standards are expected and the costs of membrane and membrane process continue to fall, the applications of MBR in municipal and industrial wastewater treatment are becoming increasingly widespread around the globe (Judd, 2007). The bacterial communities present in active biomass in activated sludge mixed liquor represent the core component of every MBR for biological carbon and nutrient removal. While bacteria in wastewater treatment plants (WWTP) have been intensively studied by culture-dependent methods (Ueda and Earle, 1972) and nucleic acid-based molecular approaches (Bond et al., 1995; Nogueira et al., 2002; Wagner and Loy, 2002), surprisingly little research has been conducted to explore the influence of membrane separation and operating conditions on the overall microbial community structure and diversity of the MBR. Consequently, much of what is known or assumed concerning biological processes in MBRs has primarily come from investigations of conventional activated sludge (CAS) systems, regardless of the fact that significant differences in operating conditions exist between the two treatment processes. Importantly, MBRs are typically operated at high sludge concentrations and low food-to-microorganism (F/M) ratios. Consequently, since energy supply is limited, the microorganisms would preferentially use the carbon sources to satisfy their maintenance energy demands as opposed to biomass growth (Muller et al., 1995; Low and Chase, 1999). In addition, other contrasting operational aspects of MBRs, including longer sludge retention time (SRT), shorter hydraulic retention time (HRT) and shear forces, would also have an impact on the activated sludge communities. To date, denaturing gradient gel electrophoresis (DGGE)-based community structure analysis has indicated that the bacterial communities in pilot-scale MBRs fed with raw sewage were distinct from that in the CAS process (Luxmy et al., 2000), while another investigation (through fluorescent in situ hybridization, FISH) has revealed minor differences in the nitrifying community composition between parallel-running MBR and CAS pilot systems (Manser et al., 2005). In addition, pilot studies monitoring long-term community structure changes have demonstrated diverse and dynamic bacterial populations in MBRs for graywater (Stamper et al., 2003) and municipal wastewater (Miura et al., 2007) treatment even during periods of stable operation. More recently, Huang et al. (2008) have revealed by 16S rRNA clone library analysis that novel members of the Bacteria domain are ecologically significant in laboratory-scale municipal wastewater treatment MBRs operated under different conditions. These pioneering works highlight the need for exploring the microbial community composition and diversity in these relatively new biological wastewater treatment systems. To our knowledge,
there have been no molecular microbial diversity surveys of full-scale MBR systems for municipal wastewater treatment, and seasonal variations of these diverse communities in relation to process performance remain little known. We hypothesized that the contrasting operational and environmental conditions of full-scale MBRs as opposed to CAS systems will have great impact on the physiological state and bacterial community structure and population dynamics of mixed liquor. Our study site (in a European country) represents a unique and ideal WWTP at which to examine and compare the microbial community composition, since there is an MBR and a CAS system running in parallel treating the same municipal wastewater and the MBR was originally inoculated with activated sludge from the CAS process. We used DGGE fingerprinting technique to examine how the structure of bacterial populations varied seasonally in both environments. Clone library analysis of phylogenetic and functional markers provided the first detailed molecular look at the composition and diversity of total community and ammonia- oxidizing and denitrifying Bacteria in a full-scale municipal wastewater treatment MBR. Additionally, we examined the impact of changes in MBR operating conditions on the key microbial groups when external carbon sources for the stimulation of denitrification were switched from one to another during the study period. Significant shifts in total bacteria and denitrifying community were observed with some of the major shifting bands in the 16S rRNA gene DGGE profiles corresponding to microorganisms capable of denitrification.
2.
Materials and methods
2.1.
Full-scale MBR
The submerged MBR (6520 m3/d in capacity) was constructed in 2003 as an extension of the existing CAS system to comply with a more stringent effluent regulation and an increase in load. It consists of a denitrification compartment where pretreated municipal wastewater is introduced, a nitrification compartment, and a filtration compartment where activated sludge is retained by submerged hollow-fiber microfiltration membrane modules (Zenon, ZeeWeed, total membrane surface area 10,160 m2 and nominal pore diameter 0.03 mm). The compartments are well-mixed either by continuous stirring (denitrification) or aeration (nitrification and filtration). The MBR is operated at a constant flow of 230 m3/h, with the remaining flow (ranging between 0 and 1440 m3/h, with an average value of 418 m3/h) being treated in the parallel CAS system. Detailed set-up and operational information of the full-scale MBR was given elsewhere (Fenu et al., 2010). Initially, acetate was added to the denitrification compartment to enhance denitrifying activity. This carbon source was then changed to butyrate on December 5 2006. Subsequent switches of carbon source between butyrate and acetate occurred on April 4 and in the end of May 2007 (Fig. 1). In contrast to the MBR, the CAS process has no denitrification zones. Pretreated wastewater is introduced directly to the aeration compartments. Separation of treated water from
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A S O
N D J
F M A
M J J
Time (m) J
A S O N
D
J
F M
A M J
J
MAR 2007 FEB 2007 APR 2007 JUN 2007 MAY 2007 SEP 2006 AUG 2006 NOV 2006 DEC 2006 OCT 2006 JUL 2006 JAN 2007 JUL 2007 MAY 2007 APR 2007 JAN 2007 FEB 2007 MAR 2007 DEC 2006 JUL 2007 JUN 2007 NOV 2006 OCT 2006 SEP 2006 AUG 2006 JUL 2006
Marker
J
Marker
Marker
Time (m)
MBR
CAS
Similarity (%)
CAS
MBR
75
80
85
90
95
100
Fig. 1 e Comparison and Pearson correlation analysis of 16S rRNA gene DGGE fingerprint profiles from the MBR and CAS over the course of 13 months (July 2006 through July 2007). Clustering is based on the unweighted pair group method using arithmetic average (UPGMA) algorithm. Arrows indicate the time points when switches of carbon source occurred: from acetate to butyrate on December 5 2006 (one week before the December 2006 sludge sampling), back to acetate on April 4 2007 (three weeks before the April 2007 sampling), and to butyrate again in the end of May (one week after the May 2007 sampling).
the biomass is accomplished by secondary sedimentation. Consequently, sludge concentration (mixed liquor suspended solid, MLSS) in the bioreactors is maintained at a much lower level than that in the MBR (2.49 g/l versus 9.08 g/l, see Table S1 in the Supplementary Materials). Average sludge load (or F/M) during the study period was 0.18 and 0.025 kg BOD/kg MLSS.d for the CAS and MBR systems, respectively.
2.2.
Analytical procedures
All samples were kept in cooler boxes during sampling and transportation to the laboratory (within 1 h). 24-h composite samples of feed wastewater and effluents (membrane permeate and CAS effluent) were collected monthly for over one year (July 2006-July 2007), taking into account the HRT of both treatment systems. Samples were analyzed for a full-set of conventional performance parameters (Supplementary Materials text). Mixed liquor was sampled (in triplicate) directly from the aeration compartments of the two treatment systems. To indicate biological activity, specific oxygen uptake rate (SOUR) and specific nitrification rate (SNR) were determined as described previously (Huang et al., 2008). Additional sludge samples were taken from the denitrification compartment of the MBR for the measurement of specific denitrification rate (SDNR) (Supplementary Materials text) and molecular microbial analysis of the denitrifying community (as described below).
2.3.
DNA Extraction and DGGE analysis
Aliquots (1e3 mL) of mixed liquor samples were centrifuged for 10 min at 12 000 g, 4 C. The cell pellets were rinsed twice with sodium phosphate buffer (120 mM, pH 8.0) and total community genomic DNA was extracted using an UltraClean Soil DNA kit (MoBio, Solana Beach, Calif.). The 16S rRNA gene,
and ammonia monooxygenase and nitrous oxide reductase genes (amoA and nosZ, responsible for the key steps in the ammonia oxidation and denitrification pathways, respectively) were chosen as molecular markers for community fingerprint analysis of total bacteria and ammonia-oxidizing and denitrifying communities, respectively. For total bacterial community, a 496-bp 16S rRNA gene fragment was amplified with primer set GC-63F/518R as described previously (Huang et al., 2008), and DGGE of the PCR products was performed using an INGENYphorU-2 apparatus (INGENY International BV, The Netherlands). Fragments of amoA (about 490 bp) were amplified with primers amoA-1F-Clamp and amoA-2R-TC (Supplementary Materials text) and resolved by DGGE following the procedures of Nicolaisen and Ramsing (2002) with some modifications (Supplementary Materials text). The nosZ fragments (414 bp in length) were obtained using primers nosZeF and nosZ1622R-GC (Throback et al., 2004) and DGGE was performed as detailed in the Supplementary Materials text. DGGE profiles were analyzed using Bionumerics 4.0 software (Applied Maths). Similarity matrices between tracks were calculated from the intensity data with band-independent, whole-densitometric curve-based Pearson correlation coefficients and then subjected to UPGMA (unweighted pair group method using arithmetic average) clustering.
2.4.
Clone library, rarefaction, and statistical analysis
Clone libraries of 16S rRNA, amoA and nosZ genes were constructed for DNA extracts from MBR sludges obtained in November 2006. Nearly complete 16S rRNA gene fragments were amplified in triplicate with primers 27F and 1492R (Dojka et al., 1998) as described previously (Huang et al., 2004). The amplicons were pooled, purified with a QIAquick PCR purification kit (Qiagen), and cloned into pMD18-T vector by TA
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Table 1 e Bioreactor performance of the full-scale MBR and CAS process.a COD Influent MBR effluent CAS effluent
220 90 24 6 31 7
TOC 54 20 ND ND
DOC 18 5 8.6 2.1 9.2 1.7
NHþ 4 eN
TeN 26 8 7.7 1.9 b 14 6
19 8 0.2 0.2 2.9 2.3
NO 3 eN 0.9 0.6 4.8 2.4 8.5 7.0
b
NO 2 eN 0.1 0.1 0.1 0.2 0.3 0.2
b b
TeP 3.9 1.6 0.3 0.2 0.5 0.2
b
a Concentrations (mean standard deviation) in mg/l. Data are based on the monthly samples collected during the whole study period (n ¼ 13). b Some values are below detection limit and thus not included in the statistical analysis. ND, not determined.
Cloning Kit (Takara). Randomly selected clones from the library were determined by colony PCR (with primer combinations 27F/M13-47 and 27F/RV-M) for their rRNA gene inserts orientation and then sequenced forwardly using primers M1347 or RV-M on Applied Biosystems 3730xl capillary sequencers. Chimeric sequences were identified as described (Huang et al., 2004) and excluded from subsequent analysis. For representatives of the operational taxonomic units (OTU, defined as groups in which sequences differed by 3%) that comprised two or more clones, nearly complete 16S rRNA gene sequences were obtained by a second sequencing run starting from the opposite side of the vector with the corresponding vector-primers. Rarefaction, richness (nonparametric richness estimators ACE and Chao 1) and diversity statistics (the Shannon diversity index) were calculated using DOTUR software (Schloss and Handelsman, 2005) and phylogenetic analyses were performed using the ARB software package (Ludwig et al., 2004) as described previously (Huang et al., 2008). Neighbor-joining (NJ) trees that differed in the reference sequences, the set of alignment positions and the outgroup sequences used, were generated and compared. For functional gene cloning, non-GC-clamp primers were used to obtain the amoA and nosZ fragments. PCR conditions were the same as those used for DGGE analysis. Randomly selected clones from each library were screened by restriction fragment length polymorphism (RFLP) analysis (Supplementary Materials text). Then one to three representatives of each unique RFLP type were fully sequenced using vector specific primers. OTUs were defined at the 95% sequence similarity threshold using DOTUR (Schloss and Handelsman, 2005). Statistical and rarefaction analysis of the amoA and nosZ sequences were conducted as for the 16S rRNA sequences. Phylogenetic trees were calculated using ARB software (Ludwig et al., 2004). To compare microbial community composition before and after the carbon source change, additional clone libraries of 16S rRNA and nosZ genes were established for MBR sludge sampled in February 2007. The 16S rRNA, amoA and nosZ gene sequences obtained in this study have been deposited in the EMBL/GenBank/DDBJ database under accession numbers FN827166-FN827320 and FN868486-FN868537, respectively.
3.
Results
3.1.
Biological activities and performance
Both treatment systems were able to maintain a good and stable carbon (COD) removal over the course of 13 months (Table 1, Fig. S1). Complete nitrification was consistently
achieved in the MBR as it was operated at a low food (ammonium) to microorganisms’ ratio. In contrast, although ammonium concentrations in the CAS effluent were mostly below 3.0 mg/l, ammonium removal was relatively low (29e77%) from December 2006 through March 2007 (Fig. S1) likely due to the relatively low temperature and SNR during that period. Statistical analysis revealed a correlation between ammonium removal and bioreactor temperature (Pearson’s correlation coefficient r ¼ 0.702, P < 0.001) and SNR (r ¼ 0.560, P < 0.05) for the CAS system. Although average SOUR value was relatively higher for the CAS process (Table S1), total community activity (as indicated by the SOUR values) was not significantly different between the two treatment systems (P ¼ 0.269, paired t tests). In contrast, the ammonia-oxidizing Bacteria (AOB) community was more active in the CAS reactor as evidenced by the higher SNR values (Table S1 and P ¼ 0.004, paired t tests). In spite of these, the MBR process constantly provided a better treatment in terms of treated water quality and carbon and nutrient removal from sewage (Table 1, Fig. S1) probably due to the higher biomass concentration and membrane rejection of suspended solids and organic compounds. An additional denitrification zone and the constant flow would also have contributed to the increased process efficiency. The substantial fluctuations in total and denitrifying bacteria associated with changes in carbon source (as described below) did not significantly affect the overall bioreactor performance of the MBR.
3.2. Population dynamics of total bacteria and N-cycling communities: MBR versus CAS PCR-based DGGE fingerprints were used to compare microbial community composition between the MBR and CAS, and to follow temporal fluctuations in bacterial populations in each treatment system. Profoundly different pattern types by DGGE analysis of the 16S rRNA gene were distinguished between the two bacterial communities, and this was confirmed by the statistical analysis which formed two separate major clusters according to the environment (Fig. 1). While the MBR 16S rRNA fingerprints were dominated by a limited number of bands, the CAS fingerprints displayed complex banding patterns and thus indicated a more diverse bacterial community. For DGGE analysis of N-cycling groups, the amoA gene profiles revealed a simple AOB community in the MBR and CAS, while fingerprints of the nosZ gene exhibited complex banding patterns for the denitrifying communities in both systems (Fig. S2 and Fig. 2). Similar to those of the 16S rRNA gene, DGGE profiles of both functional markers formed system-specific clusters in the respective UPGMA dendrograms. These results indicated that the membrane separation process and different
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operating conditions had led to the selection of different total bacterial and N-cycling communities in the MBR. Temporally, the MBR bacterial community showed considerable stability before December 12 2006 (Fig. 1). However, a clear shift in community composition was evident since the samples of January 2007 which were taken one and a half months after the carbon source was changed from acetate to butyrate. Thereafter, the MBR populations gradually evolved to another fluctuating community with the DGGE fingerprints forming a separate cluster in the UPGMA dendrogram. Fluctuations were mainly due to shifts of the dominant bands and variations in relative abundance of common bands among samples from that period. Matching of clones from the 16S rRNA gene library back to the community band patterns (by running GC-63F/518R-amplicons of randomly selected clones together with amplicons of the original total community genomic DNA on the same DGGE gels) revealed that some of the important shifting bands corresponded to microorganisms capable of denitrification (Table S2). Highly similar results were obtained by DGGE analysis of the denitrification coding gene (Fig. 2). The nosZ fingerprints of samples collected one and a half months after the first carbon source change formed a separate and fluctuating clade in the clustering analysis. In contrast, little variations were found in AOB populations between the sludges taken before and after the carbon source change (Fig. S2). Generally, the clustering of amoA fingerprints more followed the time course, reflecting a gradual evolution of this functional group over time in the MBR. DGGE analysis revealed fluctuations over time in community composition of total bacteria and N-cycling groups in the CAS process (Figs. 1 and 2 and Fig. S2). Fingerprints of 16S rRNA gene from December 2006 to March 2007 were highly similar, and these communities formed a tight cluster separating from the prior and subsequent samples in the UPGMA dendrogram. This trend was even more profound in DGGE analysis of the nosZ gene. The amoA fingerprint analysis also showed that AOB communities from the cold months differed considerably from
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those of other seasons. These findings suggested that factors related to seasonal forces had an influence on the CAS bacterial populations. In addition, samples collected in July 2006 clustered apart from those of July 2007 in all three UPGMA dendrograms, suggesting no seasonal reproducibility in temporal evolution of microbial communities.
3.3.
Microbial diversity in the full-scale MBR
To investigate the biodiversity of total bacterial community and N-cycling groups in the MBR, clone libraries of the 16S rRNA and amoA and nosZ genes were constructed for sludge samples collected in November 2006. Comparative sequence analysis of 146 randomly selected clones from the 16S rRNA gene library revealed 99 unique OTUs distributed among at least 14 phylogenetic divisions (Table 2 and Fig. 3). The minimum numbers of bacterial species in the MBR were predicted at approximately 300 according to both nonparametric estimators (Chao 1 and ACE) (Table 2). The vast majority of the OTUs comprised a single clone, indicating a diverse bacterial community. This was further supported by the steep rarefaction curve (Fig. S3). Clone distribution within the library was the following: b-Proteobacteria (27%), Bacteroidetes (25%), and a-Proteobacteria (14%). The remaining phylogenetic groups, such as the d-Proteobacteria, Actinobacteria, Acidobacteria, Chorobi, and Firmicutes, made up a total of 34% of the bacterial library. Only seven OTUs were identified as already known species, and five OTUs affiliated with not yet cultured microorganisms in public databases with 97% sequence similarity (Table 2). The remaining 87 OTUs (90% of the total clones) showed <97% similarity with the most closely related bacterial 16S rRNA gene sequences and thus represent novel phylotypes not described in previous analyses of activated sludge communities. Of these, some displayed very low levels of identity to any known 16S rRNA sequences and thus could not be affiliated (Fig. 3).
Fig. 2 e Comparison and Pearson correlation analysis of nosZ gene DGGE fingerprints from the MBR and CAS over the course of 13 months (July 2006 through July 2007). Clustering is based on the unweighted pair group method using arithmetic average (UPGMA) algorithm. Arrows indicate the time points when switches of carbon source occurred: from acetate to butyrate on December 5 2006 (one week before the December 2006 sludge sampling), back to acetate on April 4 2007 (three weeks before the April 2007 sampling), and to butyrate again in the end of May (one week after the May 2007 sampling).
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Table 2 e Diversity, predicted richness and novelty of the environmental clone sequences retrieved from the MBR.a No. of clones analyzed
No. of OTUs
146
99
47
amoA
80
13
93.8
nosZ
78
22
89.7
16S rRNA
% Coverage
Chao 1 valueb
ACE valueb
Shannon index
OTUs 97% cultivatedc
OTUs 97% not yet cultivatedc
Novel OTUs (<97%)d
303 (203, 497) 16 (13, 30) 29 (23, 56)
310 (214, 487) 17 (14, 32) 28 (24, 47)
4.38
7 (7.1, 6.8) 2 (15.4, 6.3) 0
5 (5.1, 3.4) 6 (46.2, 75.0) 0
87 (87.9, 89.7) 5 (38.5, 18.8) 22 (100, 100)
2.35 2.70
a Data are based on the November 2006 sludge clone libraries. Operational taxonomic units (OTU) were defined by a 3% and 5% difference in the nucleic acid sequence alignment for the 16S rRNA and the amoA and nosZ genes, respectively. Accordingly, Good’s coverage estimates were calculated using a 3% and 5% cutoff for the 16S rRNA and functional genes, respectively. b Numbers in parentheses are lower and upper 95% confidence intervals for the nonparametric richness estimators Chao1 and ACE. c Sharing 97% (95% for the amoA and nosZ genes) sequence similarity to the closest sequence with a cultivated representative. d Sharing 97% (95% for the amoA and nosZ genes) sequence similarity to the closest sequence without a cultivated representative.
Screening and comparative sequencing of 80 amoA clones resulted in a total of 13 OTUs (Table 2). Phylogenetic analysis revealed a low diversity of AOB in the MBR (Fig. S4). All retrieved amoA sequences belonged to the b-Proteobacteria AOB and, with only one exception, affiliated with the
Nitrosomonas oligotropha-like cluster. The vast majority of these sequences (53% of the total OTUs and 72% of the clone library) formed two individual clades separating apart from well-described nitrifier species but with amoA clones previously recovered from other activated sludge systems as their
Fig. 3 e Evolutionary distance dendrogram constructed using the neighbor-joining method and showing the phylogenetic affiliation of the 16S rRNA gene sequences retrieved from the MBR (activated sludge collected in November 2006). A region from position 28 to position 927 (Escherichia coli numbering) was included in the phylogenetic analysis. The number of OTUs and percentage of total clones (numbers shown to the left of the clades) belonging to the corresponding division in the clone library are indicated in parentheses. The scale bar represents the substitution per nucleotide position. The tree was calculated using the ARB software package. Members of the Archaea domain were used as outgroups (not shown).
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Flavobacterium Terrimonas
Ferribacterium Zoogloea
Niastella Unclassified
Flavobacterium
Haliscomenobacter
Rhodoferax
Unclassified
Unclassified Aquabacterium
Rhodoferax
Ferribacterium
Unclassified
Acidovorax Thiobacter Diaphorobacter Propionivibrio Zoogloea
Comamonas Derxia Dechloromonas
-Proteobacteria November 2006
Curvibacter
Aquabacterium Hydrogenophaga
Terrimonas
-Proteobacteria February 2007
Bacteroidetes November 2006
Bacteroidetes February 2007
Fig. 4 e Distribution of genera within the dominant groups (b-Proteobacteria and Bacteroidetes) in the November 2006 and February 2007 MBR 16S rRNA clone libraries. The total number of clones used for each gene library analysis was 146 and 113, respectively. Activated sludge samples were obtained approximately three weeks before and 11 weeks after the first carbon source change (from acetate to butyrate on December 5 2006). Bacterial 16S rRNA gene sequences are assigned to genera by using the Ribosomal Database Project (RDP) Classifier. Percentages are based on the number of clones of bProteobacteria and Bacteroidetes in each library.
closest relatives. No Nitrosospira-related sequences were detected in the amoA library. Sequence analysis of 78 randomly selected clones from the nosZ library revealed a total of 22 OTUs (Table 2). Of these, 20 sequences exhibited <90% similarity with their closest neighbors in public databases and were not closely related to any cultured denitrifiers (71e84% similarity) (Fig. S5). In marked contrast to the 16S rRNA and amoA gene sequences, the vast majority of the nosZ sequences (20 OTUs and 93% of the gene library) do not have their database matches from activated sludge processes; instead, they are most closely related to environmental clones recovered from soils and sediment. Phylogenetic analysis grouped all nosZ sequences into two major clusters whose cultured members are a- and b-Proteobacteria, although in some cases they formed subclades clearly separated from other known bacterial species (Fig. S5). Additional clone libraries of the 16S rRNA and nosZ gene were generated for MBR sludge collected in February 2007 when stabilization of microbial community after the disturbance (change in external carbon) was evidenced by DGGE analysis (Figs. 1 and 2). The data were then compared with those from the November 2006 sample. Although comparative 16S rRNA sequencing revealed that the b-Proteobacteria, Bacteroidetes and a-Proteobacteria remained the predominant groups within the February 2007 community, relative abundance of clones in the gene library increased by 44 and 40% for the first two divisions and decreased by 42% for the latter (Fig. S6). Large differences between the two 16S rRNA gene libraries were also detected at finer phylogenetic levels of resolution. Of the 13 b-Proteobacteria-affiliated genera identified, only four were found common in both communities and shifts in relative abundance of these genera were observed, particularly for Zoogloea, Rhodoferax, and the unclassified b-Proteobacteria that were only most closely related to environmental clones (Fig. 4). Similarly, significant shifts in abundance distribution of genera affiliated with the Bacteroidetes were found between the two clone libraries, with Flavobacterium clearly enriched and the unclassified Bacteroidetes less favored in the February 2007 community (Fig. 4). At the phylotype level, only 17 OTUs were detected in both bacterial communities, and they were disproportionately distributed in the respective clone libraries. Most notably, of
the three most abundant OTUs in either clone library, two were not detected or could only be occasionally recovered in the other library. Phylogenetic analysis identified in the evolutionary distance trees sample-specific clusters or clusters where clones were disproportionately distributed in the two 16S rRNA gene libraries (data not shown). In addition, rarefaction (Fig. S3) and diversity index analysis (data not shown) revealed significantly different relative species richness in the two MBR sludges, the February 2007 sample having lower diversity. Clone library analysis of the nosZ gene also indicated a significant deviation in denitrifying bacteria from the November 2006 community. The library constructed from the February 2007 sample was dominated (80%) by a diverse group of sequences clustered with cultured members from the b-Proteobacteria (data not shown). Of the 18 OTUs identified, 11 were not detected in the November 2006 sample. Significant shifts in some of the dominant OTUs were also observed between the two denitrifying communities. Rarefaction analysis indicated that the November 2006 nosZ library had a relative species richness significantly higher than that of the February 2007 library (Fig. S3), similar results being obtained by the nonparametric estimators of species richness, including Chao 1and ACE values (29 versus 18 and 28 versus 20, respectively).
4.
Discussion
In a previous pilot-scale study, Luxmy et al. (2000) used molecular fingerprinting technique to demonstrate that bacterial community in the MBR fed with raw sewage was different from that in the CAS process. In agreement with this, our DGGE analysis revealed process type-specific microbial communities, the full-scale MBR and CAS systems harboring distinct total bacterial communities and N-cycling groups with the corresponding community fingerprints separating apart in the UPGMA dendrograms independent of seasons (Figs. 1 and 2 and Fig. S2). Although these results need to be confirmed by further research at other full-scale plants, we believe that our findings are practically representative given that samples spanning a period of over one year were
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analyzed for both systems. Our study site is particularly interesting for such a comparison since the two processes are operated in parallel and treat the same wastewater. Importantly, the MBR was originally inoculated with activated sludge from the CAS plant. The differences in microbial communities of the two environments are likely due to the complete rejection of biomass by the membrane and the contrasting operating conditions applied (e.g., sludge age, F/M ratio, flow consistency) in the MBR which would greatly affect microbial community structure and physiological state of mixed liquor. The more complex 16S rRNA DGGE profiles observed for the CAS samples are most probably related to the bigger temporal fluctuations in substrate concentrations in this system since it is operated at a variable flow mode, thus allowing for diverse organisms with different growth kinetics to be transiently competitive. Compared with the CAS process, the MBR has an additional denitrification compartment and this may also have a significant influence on the activated sludge community. Finally, the impact of addition of readily biodegradable carbon substrates on the MBR microbial functional groups might be important since less variable substrate composition may lead to less complex microbial diversity (Hagman et al., 2008). Our comparative 16S rRNA sequence analysis has shown that, similar to the conventional treatment systems (Wagner and Loy, 2002), Proteobacteria- (b and a) and BacteroidetesOTUs were most frequently detected in the MBR community (Fig. 3). However, the vast majority of retrieved sequences represent putative phylotypes never obtained in culture or not described in previous investigations of activated sludge ecosystems. Similarly, novel bacterial sequences were abundantly recovered from lab-scale MBRs treating domestic wastewater and operated under different conditions (Huang et al., 2008). These results are consistent with molecular characterizations of microbial communities in wastewater treatment ecosystems which have revealed that previously unrecognized, as yet uncultured bacteria are responsible for most key processes in CAS and biofilm reactors (Wagner and Loy, 2002). In-depth molecular inventories of the relatively novel, microbially diverse MBR systems are needed, as this more comprehensive picture would help reveal factors influencing process efficiency and stability and understand how selection pressures imposed by membrane separation affect community structure and diversity within bioreactors. Ultimately, a thorough knowledge of the microbial community structure in the mixed liquor biomass will provide opportunities to elucidate the underlying mechanisms for biofilm development and maturation on the membrane surface which lead to severe irreversible fouling, a major obstacle for the wider application of MBRs for wastewater treatment. Ammonium is removed from wastewater via nitrification and denitrification. Previous investigations have shown that a wide variety of different b-proteobacterial ammonia oxidizers occur in nitrifying WWTPs, with members affiliated with the Nitrosomonas europaea/eutropha lineage, the Nitrosococcus mobilis lineage and the Nitrosomonas marina cluster were most frequently detected (reviewed by Wagner and Loy, 2002). However, Manser et al. (2005) reported more recently that bacteria related to the N. oligotropha lineage dominated the AOB populations in a pilot MBR plant treating domestic
wastewater. Similarly, our comparative amoA sequencing revealed that the AOB community in the full-scale MBR is simple with the detected OTUs almost exclusively affiliated with the N. oligotropha cluster (Fig. S4). Since members of the N. oligotropha lineage have relatively high substrate affinity (Koops and Pommerening-Roser, 2001), they may gain ecological advantages in MBRs for domestic wastewater treatment where the food (ammonia) to microorganisms (ammonium oxidizers) ratios are generally low. The presence of an AOB community low in species richness in the MBR might render its nitrification more susceptible to perturbation. Microbial population dynamics have been well-documented in traditional treatment systems (Lee et al., 2002; Wells et al., 2009). Although substantial fluctuations in bacterial community structure have been demonstrated previously in laboratory- or pilot-scale MBRs (Stamper et al., 2003; Miura et al., 2007; Huang et al., 2008), the bacterial population in the full-scale MBR was rather stable during the period before external carbon change (Figs. 1 and 2). Temporal fluctuations in influent wastewater characteristics and temperature in the bioreactors are generally thought to affect microbial community structure in WWTPs. The impact of feed strength should be less significant for MBRs since they are typically operated at lower F/M ratios. Likewise, the influence of invasion by exotic species via influent and extinction of bacteria from the indigenous community are less important since MBRs tend to maintain high biomass concentrations and limited sludge wasting by membrane rejection. In addition, the limited variations in bioreactor temperature at our study site would also favor a stable bacterial community in the MBR. Readily biodegradable carbon sources have been intermittently added to the anoxic zones of conventional treatment plants to enhance denitrification (Hallin and Pell, 1998; Hasselblad and Hallin, 1998). Although the effectiveness of this approach to increase the denitrification capacity is well documented, little is known about the impacts of external carbon on the microbial community structure and other biological processes of activated sludge. Our DGGE analysis revealed profound different banding patterns of the 16S rRNA and nosZ gene after the carbon source was changed in December 2006 from acetate to butyrate in the full-scale MBR, whereas similar shifts in microbial community profile were not detected in the parallel-running CAS process during the same period (Figs. 1 and 2). Clone library analysis further supported that total bacterial and denitrifying community composition differed significantly between MBR sludge obtained before and after the external carbon change, the later sample having lower 16S rRNA and nosZ gene diversity. Hagman et al. (2008) have recently reported substrate (external carbon source) preferences by involved microbial consortia, with acetate being used by more diverse bacterial populations which are among the dominant denitrifying groups typically present in activated sludge ecosystems. Similarly, Ginige et al. (2005) have demonstrated that the addition of acetate (to enhance denitrification) caused a dramatic shift in the microbial community structure, with denitrifiers from the families Comamonadaceae and Rhodocyclaceae in the b-Proteobacteria being rapidly enriched in the laboratory-scale bioreactor originally seeded with activated sludge from a full-scale plant (which had not been exposed to
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 2 9 e1 1 3 8
any form of external carbon source augmentation). Interestingly, our matching clone screening showed that 16S rRNA sequences corresponding to some of the major shifting bands in the total-community DGGE profiles of the MBR could be assigned to as yet uncultured Ferribacterium/Dechloromonasrelated bacteria and Zoogloea species in the family Rhodocyclaceae (Table S2), and members of genera Dechloromonas and Zoogloea have previously been found consistently present in a denitrifying fluidized bed bioreactor (Gentile et al., 2006). These indicate a possible impact of the change in carbon source on the denitrifier community in the full-scale bioreactor. The additional fluctuations in community profiles from April through July 2007 might reflect response of the mixed liquor bacterial populations to the subsequent switches of carbon source between butyrate and acetate. In spite of this, the overall process performance of the MBR remained relatively stable during the study period. Other studies have demonstrated that dynamic bacterial populations may still maintain stable performance in bioreactor treatment systems (Stamper et al., 2003; Fernandez et al., 1999). It is also possible that both microbial community structure and function were affected by the perturbation but function returned to its original state shortly, and our monthly sampling was not frequent enough to detect such changes. In a previous investigation of a pilot-scale denitrifying bioreactor, both function and community structure were disrupted by disturbances, but nitrogen removal recovered within a few days, indicating a high functional resilience (Gentile et al., 2006). It was suggested that flexible community structure and potentially the activity of minor members under nonperturbation conditions promotes system recovery. It remains unknown whether the adaptation and shift in the MBR microbial community influence other biological processes and stability of activated sludge at our study site.
5.
Conclusions
This represents the first report using cultivation-independent molecular approaches to elucidate the phylogenetic composition and diversity of the microbial communities in a fullscale MBR for municipal wastewater treatment. Our data have shown that total community bacteria and N-cycling groups in the MBR were constantly distinct from those in the parallelrunning CAS bioreactor, reflecting the contrasting environmental and operational conditions in the two treatment systems. The frequent detection of novel 16S rRNA and nosZ gene sequences in the clone libraries indicate the predominance and thus the potentially significant role of previously unrecognized bacteria in the carbon and nutrient removal processes. This study is an important step toward exploring the relationship between bacterial diversity and biogeochemical function within the MBR ecosystem.
Acknowledgements Li-Nan Huang was partially supported by the European Commission through a Marie Curie Fellowship (MIF1-CT-2005-
1137
021768). We thank G. Borgmans for technical supports and H. Paar for the DGGE analysis, and the two anonymous reviewers for providing thoughtful and constructive comments on the manuscript.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.11.008.
references
Bond, P.L., Hugenholtz, P., Keller, J., Blackall, L.L., 1995. Bacterial community structures of phosphate-removing and nonphosphate-removing activated sludges from sequencing batch reactors. Applied and Environmental Microbiology 61, 1910e1916. Dojka, M.A., Hugenholtz, P., Haack, S.K., Pace, N.R., 1998. Microbial diversity in a hydrocarbon- and chlorinated-solvent contaminated aquifer undergoing intrinsic bioremediation. Applied and Environmental Microbiology 64, 3869e3877. Fenu, A., Roels, J., Wambecq, T., De Gussem, K., Thoeye, C., De Gueldre, G., Van De Steene, B., 2010. Energy audit of a full scale MBR system. Desalination 262, 121e128. Fernandez, A., Huang, S.Y., Seston, S., Xing, J., Hickey, R., Criddle, C., Tiedje, J., 1999. How stable is stable? Function versus community composition. Applied and Environmental Microbiology 65, 3697e3704. Gentile, M., Yan, T., Tiquia, S.M., Fields, M.W., Nyman, J., Zhou, J., Criddle, C.S., 2006. Stability in a denitrifying fluidized bed reactor. Microbial Ecology 52, 311e321. Ginige, M.P., Keller, J., Blackall, L.L., 2005. Investigation of an acetate-fed denitrifying microbial community by stable isotope probing, full-cycle rRNA analysis, and fluorescent in situ hybridization-microautoradiography. Applied and Environmental Microbiology 71, 8683e8691. Hagman, M., Nielsen, J.L., Nielsen, P.H., Jansen, J.la C, 2008. Mixed carbon sources for nitrate reduction in activated sludgeidentification of bacteria and process activity studies. Water Research 42, 1539e1546. Hallin, S., Pell, M., 1998. Metabolic properties of denitrifying bacteria adapting to methanol and ethanol in activated sludge. Water Research 32, 13e18. Hasselblad, S., Hallin, S., 1998. Intermittent addition of external carbon to enhance denitrification in activated sludge. Water Science and Technology 37, 227e233. Huang, L.N., De Wever, H., Diels, L., 2008. Diverse and distinct bacterial communities induced biofilm fouling in membrane bioreactors operated under different conditions. Environmental Science and Technology 42, 8360e8366. Huang, L.N., Zhou, H., Zhu, S., Qu, L.H., 2004. Phylogenetic diversity of bacteria in the leachate of a full-scale recirculating landfill. FEMS Microbiology Ecology 50, 175e183. Judd, S., 2007. The status of membrane bioreactor technology. Trends in Biotechnology 26, 109e116. Koops, H.P., Pommerening-Roser, A., 2001. Distribution and ecophysiology of the nitrifying bacteria emphasizing cultured species. FEMS Microbiology Ecology 37, 1e9. Lee, N., Jansen, J., Aspegren, H., Dircks, K., Henze, M., Schleifer, K. H., Wagner, M., 2002. Population dynamics and in situ physiology of phosphorus-accumulating bacteria in wastewater treatment plants with enhanced biological phoshorus removal operated with and without nitrogen removal. Water Science and Technology 46, 163e170.
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Low, E.W., Chase, H.A., 1999. The effect of maintenance energy requirements on biomass production during wastewater treatment. Water Research 33, 847e853. Ludwig, W., Strunk, O., Westram, R., Richter, L., Meier, H., Yadhukumar Buchner, A., Lai, T., Steppi, S., Jobb, G., 2004. ARB: a software environment for sequence data. Nucleic Acids Research 32, 1363e1371. Luxmy, B.S., Nakajima, F., Yamamoto, K., 2000. Analysis of bacterial community in membrane separation bioreactors by fluorescent in situ hybridization (FISH) and denaturing gradient gel electrophoresis (DGGE) techniques. Water Science and Technology 41, 259e268. Manser, R., Gujer, W., Siegrist, H., 2005. Membrane bioreactor versus conventional activated sludge system: population dynamics of nitrifiers. Water Science and Technology 52, 417e425. Miura, Y., Hiraiwa, M.N., Ito, T., Itonaga, T., Watanabe, Y., Okabe, S., 2007. Bacterial community structures in MBRs treating municipal wastewater: relationship between community stability and reactor performance. Water Research 41, 627e637. Muller, E.B., Stouthamer, A.H., van Verseveld, H.W., Eikelboom, D. H., 1995. Aerobic domestic wastewater treatment in a pilot plant with complete sludge retention by cross-flow filtration. Water Research 29, 1179e1189. Nicolaisen, M.H., Ramsing, N.B., 2002. Denaturing gradient gel electrophoresis (DGGE) approaches to study the diversity of ammonia-oxidizing bacteria. Journal of Microbiological Methods 50, 189e203.
Nogueira, R., Melo, L.F., Purkhold, U., Wuertz, S., Wagner, M., 2002. Nitrifying and heterotrophic population dynamics in biofilm reactors: effects of hydraulic retention time and the presence of organic carbon. Water Research 36, 469e481. Schloss, P.D., Handelsman, J., 2005. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Applied and Environmental Microbiology 71, 1501e1506. Stamper, D.M., Walch, M., Jacobs, R.N., 2003. Bacterial population changes in a membrane bioreactor for graywater treatment monitored by denaturing gradient gel electrophoretic analysis of 16S rRNA gene fragments. Applied and Environmental Microbiology 69, 852e860. Throback, I.N., Enwall, K., Jarvis, A., Hallin, S., 2004. Reassessing PCR primers targeting nirS, nirK and nosZ genes for community surveys of denitrifying bacteria with DGGE. FEMS Microbiology Ecology 49, 401e417. Ueda, S., Earle, R.L., 1972. Microflora of activated sludge. Journal of General and Applied Microbiology 18, 239e248. Wagner, M., Loy, A., 2002. Bacterial community composition and function in sewage treatment systems. Current Opinion in Biotechnology 13, 218e227. Wells, G.F., Park, H.D., Yeung, C.H., Eggleston, B., Francis, C.A., Criddle, C.S., 2009. Ammonia-oxidizing communities in a highly aerated full-scale activated sludge bioreactor: betaproteobacterial dynamics and low relative abundance of Crenarchaea. Environmental Microbiology 11, 2310e2328.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 3 9 e1 1 4 6
Available at www.sciencedirect.com
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Concentration levels of urea in swimming pool water and reactivity of chlorine with urea Joseph De Laat*, Wentao Feng, Diab Adams Freyfer, Florence Dossier-Berne Laboratoire de Chimie et Microbiologie de l’Eau (UMR CNRS 6008), Universite´ de Poitiers, ENSIP 40, Avenue du Recteur Pineau, 86022 Poitiers Cedex, France
article info
abstract
Article history:
This study investigated the reactivity of chlorine with urea which is the main nitrogen
Received 17 August 2010
contaminant introduced into swimming pool water by bathers. In the first part of this
Received in revised form
study, analyses showed that the mean concentrations of urea and TOC determined from 50
2 November 2010
samples of municipal swimming pool were equal to 18.0 mM (s.d. 11.7) and 3.5 mg C L1 (s.d.
Accepted 4 November 2010
1.6), respectively. The mean value for the urea contribution to the TOC content was 6.3%
Available online 10 November 2010
(s.d. 3.3). The rate of decomposition of urea in swimming pool water measured during the closure time of the facility was very slow (decay at the rate of z1% per hour in the presence
Keywords:
of 1.6e1.8 mg L1 of free chlorine). In the second part of this work, experiments carried out
Chlorine demand
with phosphate buffered solutions of urea ([Urea]0 ¼ 1 mM; [Cl2]0/[Urea]0: 0.5e15 mol/mol;
By-products
pH 7.4 0.2; reaction time: 0e200 h) showed that long term chlorine demand of urea was
Chloramines
about 5 mol Cl2/mol of urea. Chlorination led to a complete mineralization of organic
Nitrate
carbon into CO2 for a chlorine dose of 3.5 mol/mol and to the formation of 0.7e0.8 mol
Total nitrogen
NO 3 /mol of urea for chlorine dose of 8e10 mol/mol. Experiments conducted with dilute solutions of urea ([Urea]0 ¼ 50 mM; pH z 7.3) confirmed that the degradation rate of urea by chlorine is very slow under conditions simulating real swimming pool water. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Chlorine gas, sodium or calcium hypochlorite and chlorinated isocyanurates are the most widely used reactants for the disinfection of public swimming pool water. Along with its disinfectant properties, chlorine reacts with the organic material introduced by the bathers into the pool water to form inorganic chloramines and numerous organohalogenated disinfection by-products (DBPs) (Judd and Black, 2000; Kim et al., 2002; Judd and Bullock, 2003; WHO, 2006; De Laat et al., 2009; Weaver et al., 2009). Substances introduced in the pool water by the bathers are urine, sweat, danders, skin particles, soap residues, cosmetics. It has been estimated that 25e77.5 mL of urine and 200e1000 mL of sweat may be
excreted per bather (Seux et al., 1985; Gunkel and Jessen, 1988; Erdinger et al., 1997). These body fluids contain many nitrogenous compounds and the nitrogen release into swimming pools has been estimated to 0.85 g N h1 per swimmer (Seux, 1988). Urea (H2NCONH2) is a final product of protein metabolism and is the main nitrogen compound introduced into swimming pools by the bathers (z0.8e1.5 g urea/bather) because urea is the predominant nitrogen compound in urine (z20 g L1, z84% of the total N content of urine) and in sweat (z1.5 g L1, z68% of the total N content of sweat) (WHO, 2006). Urea can also be released by the skin because the urea content of the horny layer is about 8 mg per cm2 of skin surface (Jacobi, 1971). Typical concentrations of urea in swimming pool water
* Corresponding author. E-mail address:
[email protected] (J. De Laat). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.005
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are in the range 0.5e3 mg L1 (10e50 mM) but concentrations up to 6 mg L1 (100 mM) have also been measured (Ja¨ggli, 1995; Abidi et al., 2001). The reactivity of chlorine with urea is not very well documented. De Laat et al. (1982) determined a chlorine demand of 3.8 mol of Cl2/mol of urea after a reaction time of 15 h at 20 C and pH ¼ 7.0 0.1; ([Urea]0 ¼ 0.1 mM; [Cl2]0 ¼ 2 mM). Besse et al. (1985) and Seux (1988) showed that chlorination of urea at a dose of 5 mol Cl2/mol of urea at pH ¼ 4.5 1.0 leads to a complete oxidation of organic carbon into CO2 and to the production of equivalent amounts of dichloramine and trichloramine: Chlorine
Chlorine
H2 NCONH2 ! Cl2 NCONCl2 ! CO2 þ NHCl2 þ NCl3
(1)
At neutral pH (pH z 7.5), Besse et al. (1985) and Seux (1988) obtained a complete mineralization of urea into CO2 and chloramines at a chlorine dose of 4 mol Cl2/mol of urea. Trace amounts of nitrite and nitrate were detected at high chlorine doses. By using membrane introduction mass spectrometry (MIMS), Li and Blatchley (2007) detected trichloramine in chlorinated aqueous solutions of urea at pH 7.5 ([Urea]0 ¼ 18 mM, [Chlorine]0/[Urea]0: 10 mol/mol). For a 96 h reaction time, the concentrations of NCl3 were roughly equal to 0.1 and 0.3 mg L1 as Cl2 (0.5e1.5 mM of NCl3) for applied chlorination doses of 1.6 and 9.5 mol of Cl2/mol of urea, respectively. Volatile chloramines, and in particular trichloramine, have been suggested to largely contribute to the typical smell and to the irritating properties of the atmospheres of indoor swimming pools (He´ry et al., 1995). Exposure of children to trichoramine may also adversely affect the lung epithelium permeability and may increase the risk of developing asthma (Bernard et al., 2003). As the reactivity of chlorine with urea is not well known and as urea is considered as one of the main precursor of chloramines during chlorination of swimming pool water, this work has been conducted in order to better understand the reactions of chlorine with urea. In the first part of this work, the concentrations of urea in water samples collected from various indoor municipal swimming pools have been determined in order to obtain more data about the concentration levels of urea and the stability of urea in swimming pool water. In the second part of this work, batch experiments have been performed with concentrated solutions of urea (1 mM) in order to examine the influence of chlorine doses and of pH on the chlorine demand of urea, the fate of organic carbon and organic nitrogen on the formation nitrogen by-products (chloramines and nitrate). The rates of urea degradation and of chlorine consumption under conditions simulating swimming pool water ([Urea]0 ¼ 50 mM; [Chlorine]0 ¼ 25e50 mM) have also been investigated.
2.
Material and methods
2.1.
Preparation of solutions
All reagents used in this work were analytical grade and purchased from Acros Organics. All solutions were prepared with purified water delivered by a Millipore system (Milli RX75/Synergy 185). Stock solutions of urea (10 mM) were
prepared daily. Stock solutions of free chlorine (100 mM, pH z 9) were prepared from sodium hypochlorite (z2 M, Acros). Glassware were soaked in a dilute chlorine bath (z2 mM) for at least 15 h and rinsed with purified water.
2.2.
Experimental conditions
Chlorination of urea ([Urea]0 ¼ 10 mM e 1 mM; [Cl2]0/ [Urea]0 ¼ 0.5e15 mol/mol) was carried out in 10 mM phosphate buffer and at 25.0 0.5 C. Experiments were carried in 1 L aluminium-foil-wrapped glass bottles. The reaction was initiated by introducing the appropriate volume of the stock solution of sodium hypochlorite to the solution of urea under vigorous stirring. The bottles were hermetically closed immediately after chlorine addition. After a stirring time of 3 min, the bottles were incubated in the dark at 25.0 0.5 C.
2.3.
Concentrations of urea in swimming pools
50 swimming pool water samples were collected from 17 indoor pools located at Poitiers or near Poitiers. All these pools use only chlorine gas or sodium hypochlorite to disinfect water (no use or cyanuric acid derivative). Samples were taken approximately 20e30 cm below the surface of each pool and collected in 125 mL polyethylene bottles. Immediately after sampling, 0.25 mL of sodium thiosulphate 25 mM was added to 125 mL-sample in order to quench chlorine residual, 1 mL of concentrated sulphuric acid was added to prevent decomposition of urea. Samples were stored at 4 C prior to analyses. Analyses (urea, TOC, potassium) were conducted within 48 h after collection. Under these conditions, preliminary studies showed that the concentrations of urea did not significantly vary over a storage time of 7 days in the dark at 4 C.
2.4.
Analytical methods
The concentrations of free chlorine ([HOCl] þ [ClO]) in stock solutions of sodium hypochlorite (pH z 9) were determined by iodometric titration with sodium thiosulphate 0.1 M (Prolabo, > 99.9%). For the experiments conducted with solutions containing 1 mM of urea, free chlorine and inorganic chloramines present in chlorinated solutions of urea were determined by the DPD/ FAS titrimetric method (APHA, 2005; relative standard deviation: 3%). Total chlorine (free chlorine þ combined chlorine) was also determined iodometrically with sodium thiosulphate (relative standard deviation: 3%). For the experiments conducted with dilute solutions of urea ([Urea]0 50 mM), free chlorine and total chlorine concentrations were determined by using the DPD colorimetric method (APHA, 2005). The quantification limit and the relative standard deviation were equal to 2 mM and 5e10%, respectively. Urea was determined colorimetrically using the diacetyl monoxime method (Cozzi, 2004). Urea reacts at 100 C with diacetyl monoxime and thiosemicarbazide in the presence of sulphuric acid, phosphoric acid and ferric chloride to form a coloured compound which absorbs at 520 nm. Calibration curves were obtained from standard solutions of urea ([Urea] ¼ 1e100 mM). Each analysis was performed in triplicate and the relative standard deviation was 5e10%. All swimming
Results and discussion
3.1. Concentrations and stability of urea in swimming pool waters 3.1.1.
Mean concentrations of urea and TOC
The concentrations of urea and TOC determined in 50 indoor swimming pool water samples have been reported in Supplementary content (Table S1 and Figs. S1eS4). French regulations for public swimming pools has set a minimum concentration of hypochlorous acid between 0.4 and 1.4 mg Cl2 L1 and a pH value between 6.9 and 7.7. As the pKa of HOCl is 7.5, the concentration of free chlorine in water must therefore be kept between 0.6 mg Cl2 L1 (pH ¼ 6.9) and 3.5 mg Cl2 L1 (pH ¼ 7.7). In addition, the concentration of combined chlorine must always be lower than 0.6 mg Cl2 L1. The concentrations of free chlorine and of combined chlorine measured in the present work ranged between 1.4 and 2.0 mg Cl2 L1 (20e30 mM) and between 0.21 and 1.0 mg Cl2 L1 (3e15 mM), respectively. The pH values ranged between 7.1 and 7.6. The concentrations of urea measured in the present work ranged between 0.14 and 3.7 mg L1 (2e62 mM) and were of the same order of magnitude than those published by Ja¨ggli (1995) and Abidi et al. (2001). The mean concentrations of urea and TOC were equal to 1.08 mg L1 (s.d. 0.7) (or 18.0 mM; s.d. 11.7) and 3.5 mg C L1 (s.d. 1.6) respectively (Table S2). About 80% of the values of urea concentrations ranged between 0.5 and 1.5 mg L1 (8e25 mM) and 12% of the values were higher than 2.0 mg L1 (Fig. S2). As far as TOC values are concerned, 80% of the measured values ranged between 1.5 and 4 mg C L1 (Fig. S3). The mean contribution of urea to the TOC content was 6.35% (Table S2) and for 65% of samples, the contribution of urea to TOC ranged between 3 and 9% (Fig. S4).
3.1.2. Change in urea concentration in a spa water during an opening day Fig. 1 presents the changes in the concentrations of urea, TOC, potassium and combined chlorine which have been
Bathers in the spa
3.
12
C oncentration (mg/L)
pool water samples were also fortified with 20 and 40 mM of urea. Recoveries of urea from fortified samples were over 90% with relative standard deviations of 10%. Total Organic Carbon (TOC) and Total Nitrogen (TN) in chlorinated solutions of urea or in pool water samples were determined with a Shimadzu TOC-VCH analyser equipped with the TNM-1 unit. Absorption spectra of solutions were measured with a SAFAS DES 190 double beam spectrophotometer. pH measurements were made with a pH-meter Radiometer PHM 240. Nitrate ions were analyzed by an ion-pairing HPLC method using a Zorbax Extend-C18 analytical column (250 mm 4.8 mm, 5 mm) and a UV detection at 214 nm. The mobile phase was a solution adjusted to pH 6.0 of 10 mM KH2PO4 and 2.5 mM tetrabutylammonium hydrogen sulphate. The flow rate was 0.8 ml min1 and the injection volume was 100 mL. Potassium in swimming pool water samples was determined with a Sherwood Model 410 Flame photometer and calibrated with standard solutions of potassium (0e5 mg Kþ L1) prepared with potassium chloride.
2.2
Concentration (mg/L
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 3 9 e1 1 4 6
2.5
1141
Number of bathers in the spa
10 8 6 4 2 0
2.1
AM PM Potassium
2.0 1.9 1.8 1.7 1.6 1.5
2.0 1.5
Urea TOC Combined chlorine
1.0 0.5 0.0 10:00 11:00 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 AM AM PM PM AM PM PM PM PM PM PM
Fig. 1 e Changes in the average number of bathers in the spa (top) and in the concentrations of potassium (middle), urea, TOC and combined chlorine (bottom) between 10:00 a.m. and 8:00 p.m. (opening hours of the aquatic centre to the bathers) on June 24, 2009.
measured in the spa water of an indoor swimming pool located at Poitiers. The total volume of water in the spa (5 m3) and in the balancing tank of the spa (3 m3) is equal to 8 m3, and each night, 5 m3 of water are renewed in order to decrease the concentration of combined chlorine in water. The concentration of free chlorine and the pH of the spa water were automatically regulated at 1.7 0.1 mg Cl2 L1 and 7.40 0.05, respectively. Samples were collected every hour during the opening hours of the pool to the public (June 24, 2009, from 10:00 a.m. to 8:00 p.m.). Potassium has been analyzed because it is a good indicator of anthropogenic contamination of swimming pool water (Erdinger et al., 1997). The data have been reported in Section S.1.2 of the Supplementary Content. As depicted the data in Fig. 1, the average number of bathers in the spa was about 1e4 persons between 10:00 a.m. and 3:00 p.m. and about 7e10 persons between 3:00 p.m. and 6:00 p.m.. Analyses showed an increase of the concentrations of urea (from 0.6 mg L1 to 1.8 mg L1 or from 10 mM to 30 mM), TOC (from 1.1 mg C L1 to 2.4 mg C L1), potassium (from 0.65 mg L1 to 2.1 mg L1) and of combined chlorine (from 0.18 mg Cl2 L1 to 0.56 mg Cl2 L1 or from 2.5 mM to 7.9 mM) between 10:00 a.m. and 8:00 p.m.. It should be noted that urea is present in water at the opening of the pool at 10:00 a.m. because all the water has not been renewed during the night. The contribution of urea to the TOC content increased from
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10% at 10:00 a.m. to 15% at 8:00 p.m.. Mass balances indicate that the amounts of urea, TOC and potassium introduced into the spa by the bathers between 10:00 a.m. and 8:00 p.m. were nearly equal to 11.1 0.6 g of urea, 12.4 0.5 g of TOC and 4.7 0.3 g of Kþ (Table S4). These calculations have been done assuming that the losses of urea and of TOC by chemical oxidation reactions could be neglected.
urea in the 25-meter pool would be roughly equal to 700 g of Cl2 per day. For this pool, this chlorine consumption by urea only represents about 25e40% of the daily consumption of chlorine which has been estimated as 2.40 0.5 kg of Cl2. A similar calculation cannot be done for the leasure pool because the chlorine consumption is unknown. These data are only indicative and must be confirmed by other studies.
3.1.3.
3.2.
Stability of urea in the presence of free chlorine
To examine the stability of urea in the presence of free chlorine, the concentrations of urea in a 25-meter swimming pool (Mean values: [Free chlorine] ¼ 1.58 0.05 mg Cl2 L1; pH ¼ 7.37 0.03; 27.0 0.2 C) and in a recreational pool ([Free chlorine] ¼ 1.77 0.07 mg Cl2 L1; pH ¼ 7.51 0.03; 30.0 0.2 C) have been determined during three consecutive days (June 2e4, 2010) at 10:00 p.m. and at 9:00 a.m. (Fig. 2). The mean values for free chlorine concentration, pH and temperature reported above represent the mean outputs of the two online analysers used for measurement and dosing control of free chlorine and pH. At the same time, the volumes of water given by the water counters were also noted in order to calculate the exact volumes of make-up water which have been added in the two pools between 10:00 p.m. and 9:00 a.m. (closing hours of the facility) and to take into account the decrease of the concentration of urea by dilution (Table S5). Our data showed that urea is very slowly degraded by free chlorine under conditions encountered in swimming pools ([Urea]: 1e2 mg L1 (16.7e33.4 mM); [Free chlorine]: 1.5e2.0 mg Cl2 L1 (21e28 mM); pH: 7.2e7.5; T: 25e30 C). The decrease of the concentration of urea resulting from chlorination reactions ranged from 3.6% to 15.4% (mean decrease: 11.2%; 6 values) after a reaction time of 11 h in the presence of 1.6e1.8 mg Cl2 L1 (Table S5 and Fig. 2). By assuming a mean concentration of urea of 25 mM in water, a total volume of water of 460 m3 for the 25-meter swimming pool, a mean decrease of the concentration of urea of 25% per day and a chlorine consumption of 3.5 mol of Cl2/mol of urea for dilute solutions of urea (see below), the chlorine consumption by
[Urea] (mg/L)
2.4 2.0
To investigate the influence of chlorine dose on the chlorine demands of urea and on the fates of the organic carbon and of the organic nitrogen, all the experiments were carried out with an initial concentration of urea of 1 mM. Lower concentrations of urea will then be used at the end of this study to examine the influence of the initial concentration of reactants on the reaction kinetics.
3.2.1.
[Urea] at 10:00 pm Recreational pool [Urea] at 9:00 am [Urea] at 9:00 am (corrected values) 25 meter pool
1.6 1.2 0.8 0.4 0.0
Night 1 Night 2 Night 3 Night 1 Night 2 Night 3 Fig. 2 e Changes in the concentration of urea in a 25-meter swimming pool and in a recreational pool between 10:00 p.m. and 9:00 a.m. (June 2e4, 2010). Corrected values for urea concentration correspond to the concentration of urea which would be obtained if no make-up water had been added during the night.
Formation of chloramines and chlorine demands
DPD/FAS analyses indicate that the total concentration of inorganic chloramines formed during chlorination of urea ([Urea]0 ¼ 1 mM; Chlorine doses: 2 and 8 mol Cl2/mol of urea) were very low and did not exceed 0.2 mol Cl2/mol of urea under our experimental conditions (Figs S7a and S7b). In addition, our data indicate that total chlorine is mainly in the form of free chlorine at reaction times higher than 2 h. As compared to the MIMS method, it is known that the DPD/FAS titration is not the best method for differentiation and quantification of free chlorine and inorganic chloramines in chlorinated solutions of nitrogenous compounds (Shang and Blatchley, 1999; Li and Blatchley, 2007). Since we do not have a MIMS analyser, UV absorption spectra of chlorinated solutions of urea were recorded in order to highlight the formation of chloramines or of other absorbing by-products such as nitrate. Fig. 3 presents the change of the UV absorption spectrum as a function of reaction time obtained for a chlorinated solution of urea (1 mM) treated at a chlorine dose of 2 mol of Cl2/mol of urea. The data obtained for this chlorine dose as well as for all chlorine doses less than 3 mol Cl2/mol of urea (data not presented) showed
1.2
Absorbance (1-cm cell)
2.8
Chlorination of urea in phosphate buffered water
0 min 14 min 30 min 45 min 75 min 105 min 170 min > 24 h
1 0.8 0.6 0.4 0.2 0 200
225
250
275
300
325
350
Wavelength (nm) Fig. 3 e Chlorination of urea at a chlorine dose of 2 mol/ mol. Change of the UV absorbance spectrum as a function of reaction time ([Urea]0 [ 1 mM; [Cl2]0 [ 2 mM; pH [ 7.4 ± 0.1).
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3.2.2.
Chlorine demands
As said above, titrations by the DPD/FAS and the iodometric methods and UV absorbance spectra indicate that free chorine was the unique oxidant present in the chlorinated solutions of urea (1 mM) at reaction times higher than 24 h. Therefore, the chlorine demands could be determined from iodometric titrations. Data depicted in Fig. 4 present the chlorine demand of urea as a function of the applied chlorine dose. Under our conditions ([Urea]0 ¼ 1 mM; pH ¼ 7.4 0.2; reaction times: 20e192 h; Table S6), the data show that the chlorine demand of urea increased with increasing chlorine dose and reached
Chlorine consumed (mol/mol)
6 5 4 3 Experiment 2
2
Experiment 3
1 0 0
2
4
6
8
10
Chlorine dose (mol/mol)
Fig. 4 e Chlorine demands of urea as a function of the applied chlorine dose ([Urea]0 [ 1 mM; [Cl2] £ 10 mM; pH [ 7.4 ± 0.2).
a plateau for chlorine doses of about 8e10 mol Cl2/mol of urea. The chlorine demand at the plateau was nearly equal to 5.0 mol Cl2/mol of urea. This value is higher than the value of 3.8 mol/mol previously determined by De Laat et al. (1982). The difference between the two values might be explained by the fact that the lowest concentrations of reactants used by De Laat et al. (1982) ([Urea]0 ¼ 0.1 mM; [Cl2]0 ¼ 1 mM; pH ¼ 7; reaction times: 15 h) led to an incomplete reaction and/or to lower nitrate yields.
3.2.3.
Effect of pH
The effect of pH on the consumption of chlorine by urea ([Urea]0 ¼ 1 mM) has been investigated for initial pH values ranging from 5.2 to 9.3 (final pH values : 4.0e8.5) and for a chlorine dose of 10 mol Cl2/mol of urea. Iodometric titration was used to control the decay of total chlorine species at various reaction times. The data in Table S7 showed that total chlorine consumptions over a reaction time of 24 h ranged between 3.7 and 5 mol Cl2/mol of urea. More interestingly, the data obtained from another series of experiments (pH values ranging between 5.3 and 10.2 and kept constant during the
Chlorine consumed (mol/mol)
that chlorination of urea led to a progressive disappearance of the absorption band centred at 290e295 nm with increasing reaction time. This absorption band is not specific because it corresponds both to the maximum for the hypochlorite ion (e292nm z 368 M1 cm1) and for dichloramine (e292nm z 275 M1 cm1) (De Laat and Berne, 2009). In addition, the percentage decrease of the UV absorbance at 290e295 nm for a given reaction time was found to be nearly identical to the percentage decrease in the concentration of total chlorine. The decrease of the absorption band at 290 nm was accompanied by an increase and then to a decrease of UV absorbance in the region 200e230 nm. The UV absorbance in the region 200e230 nm was at a maximum at a reaction time of about 30 min and decreased slowly with reaction time. This change in the UV spectra in the region 200e230 nm demonstrates the formation of instable intermediates such as inorganic chloramines and chloro-ureas. It is well known that chloramines (and in particular dichloramine and trichloramine) strongly absorb UV light in the region 200e230 nm (Li and Blatchley, 2009) and that they are instable in water and decompose into N2 (main by-product) and nitrate (minor byproduct) as final by-products (Jafvert and Valentine, 1992). Unfortunately, the UV spectra of chloro-ureas are unknown. For reaction times higher than 24 h, the total residual concentration of chlorine was null and the UV absorption spectra did not vary over several days. Under these conditions (complete depletions of free and combined chlorine), the absorbance at 200e230 nm can be attributed to the nitrate ion. By using a molar absorption coefficient of about 1850 100 M1 cm1 at 225 nm for the nitrate ion (Fig. S8), a concentration of z0.04 mM of nitrate could be estimated from the UV spectra of chlorinated solutions of urea at a chlorine dose of 2 mol Cl2/mole of urea. As it will be seen below, this production of nitrate will be confirmed by HPLC analyses. For chlorine doses higher than 4 mol Cl2/mol of urea, the UV absorbance in the region 200e230 nm increased rapidly during the first minutes of the reaction and the transient formation of chloramines could not be observed spectrophotometrically because of the very high absorbance due to the formation of nitrate (Fig. S9). For reaction times higher than 20 h (20e192 h), the UV-absorbance spectra of all the solutions were very stable, indicating that the reactions are terminated (Fig. S10). The UV spectra showed the presence of a residual concentration of free chlorine in samples treated at chlorine doses higher than 4 mol Cl2/mol of urea. Furthermore, the UV absorbance in the region 200e230 nm indicated an increase of the formation of nitrate with increasing chlorine doses. This will be confirmed by HPLC analyses.
6 5
pH = 4.5
pH = 6.2
pH = 8.3
pH = 10.2
pH = 7.8
4 3 2 1 0 0
15
30
45
60
75
90
Reaction time (min) Fig. 5 e Effect of pH on the total chlorine demands ([Urea]0 [ 1 mM; [Cl2] [ 10 mM; Total chlorine determined by iodometry).
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3.2.4. Effect of chlorine dose on urea, TOC and TN removals and on the production of nitrate Residual concentrations of urea, TOC and TN were determined after reaction times of 24, 168 or 192 h. Experimental data have been reported in Tables S6aeS6c and presented in Figs. 6 and 7. The results in Fig. 6 demonstrate that chlorination of urea led to an almost complete removal of urea and of TOC for a chlorine dose of about 3.5 mol Cl2/mol of urea and after a reaction time of 24 h. Most interestingly, Fig. 6 shows that a decrease in the concentration of TOC could be observed at the lowest chlorine dose studied. TOC and urea removals of about 30% and 60% were obtained for chlorine doses of 1 and 2 mol Cl2/mol of urea, respectively. These data indicate that the initial attack of chlorine on urea represents the rate limiting step of the overall rate of mineralization of organic carbon into carbon dioxide under our experimental conditions ([Urea]0 ¼ 1 mM; pH ¼ 7.4). From these data, an overall stoichiometry of 3 mol of chlorine per mole of urea can be proposed for the degradation of urea at chlorine doses less than 2 mol of Cl2/mol of urea: NH2 CONH2 þ 3HOCl/CO2 þ N2 þ 3HCl þ 2H2 O
(2)
Regarding the residual concentrations of total nitrogen (TN), Fig. 7 shows a decrease in the TN concentration when the
1.0 TOC at t = 24 h (Experiment 1)
0.8
TOC at t = 168 h (Experiment 2)
[C]/[C] 0
TOC at t = 192 h (Experiment 3)
0.6
Urea at t = 192 h (Experiment 3)
1.0 TN at t = 24 h (Experiment 1)
[N] / [N-Urea] 0
reaction) demonstrate that pH has an important effect on the initial rates of chlorine consumption (Fig. 5). As depicted in Fig. 5, the fastest chlorine consumption was observed at pH 7.5e8 and the reactions were relatively slow under acidic and alkaline pH values. These data can be attributed to the fact that the various forms of free chlorine (HOCl and ClO; pKa of HOCl z 7.5) have not the same reactivity toward urea and to an important effect of pH on the distribution and on the stability of the reaction intermediates (chloro-ureas, inorganic chloramines) in aqueous solutions, in the absence and in the presence of free chlorine. Additional research using more appropriate analytical methods for the quantification of chloro-ureas and of chloramines is needed in order to elucidate the reaction pathways and the effects of pH on the reaction kinetics.
TN at t = 168 h (Experiment 2)
0.8
TN at t = 192 h) (Experiment 3) N-nitrate at t = 192 h (Experiment 3)
0.6 0.4 0.2 0.0 0
2
4
6
8
10
12
0.2 0.0 0
2
4
6
8
Chlorine dose (mol/mol) Fig. 6 e Effect of the chlorine dose on the residual concentrations of TOC and urea ([Urea]0 [ 1.0 mM, [TOC]0 [ 12 mg N/L; [Chlorine]0 [ 10 mM, pH 7.4 ± 0.2; T [ 25.0 C).
10
16
Fig. 7 e Effect of the chlorine dose on the residual concentrations of TN and on the production of nitrate. ([Urea]0 [ 1.0 mM, [N]0 [ 28 mg N/L; [Chlorine]0 [ 10 mM, pH 7.4 ± 0.2; T [ 25.0 C).
chlorine dose increased from 0 mol/mol to 3.5 mol/mol and an increase in the residual concentration of TN for chlorine doses greater than 3.5 mol/mol. TN removals were equal to 85, 70 and 62% for applied chlorine doses of 4.1, 8.15 and 15.3 mol/ mol, respectively. HPLC analyses showed that chlorination of urea leads to the formation of nitrate and that the production of nitrate increased when the applied chlorine dose increased. Nitrate yields of 0.1, 0.27 and 0.76 mol/mol of urea were measured for chlorine doses of 3, 4 and 10 mol Cl2/mol of urea, respectively. As shown in Fig. 7, the total nitrogen present in chlorinated solutions of urea at doses greater than 4 mol/mol is only in the form of N-nitrate. To confirm these data, solutions of urea were chlorinated at pH values ranging from 5.7 to 9.1 ([Urea]0 ¼ 1 mM; [Cl2]0 ¼ 10 mM) (Table S8). Analyses performed after a reaction time of 192 h confirmed the complete oxidation of TOC into CO2 (TOC removal > 98.5%), the partial removal of TN (TN removal: 60e72%) and the production of 0.54e0.78 mol of nitrate/mol of urea. The data also indicate that the highest values of nitrate production (z0.75e0.80 mol/mol) and of chlorine demands (z5 mol Cl2/mol of urea) were obtained at neutral pH values. The formation of nitrate explains the high chlorine demands obtained in the present work. If the two nitrogen atoms of urea are converted into 2 mol of N-nitrate, the following overall reaction indicates that the theoretical chlorine demand would be 8 mol Cl2/mol of urea: NH2 CONH2 þ 8HOCl/CO2 þ 2HNO3 þ 8HCl þ H2 O
0.4
14
Chlorine dose (mol/mol)
(3)
From reactions (2) and (3), the theoretical chlorine demand of urea would range between 3 and 8 mol Cl2/mol of urea depending on the nitrate yield. For a yield of 0.8 mol of nitrate/ mol of urea, the theoretical chlorine demand would be 5 mol of Cl2/mol of urea. This value is consistent with the experimental value determined in the present work at neutral pH (Table S8). It should be emphasized that a significant amount of nitrate can also be formed during the chlorination of ammonia, especially at high chlorine:ammonia ratios (White, 1999). An additional experiment conducted with ammonia
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([Ammonia]0 ¼ 2 mM, [Cl2]0 ¼ 4e15 mM, pH ¼ 7.0; reaction time ¼ 24 h) confirmed that an increase of the chlorine:ammonia ratio from 2 mol mol1 to 7.5 mol mol1 led to an increase of the chlorine demand of ammonia (from 1.67 mol mol1 to 2.15 mol mol1) and of the nitrate production (from 0.05 mol of NO 3 /mol of ammonia to 0.23 mol of NO3 /mol of ammonia) (Table S9). The experimental values of chlorine consumptions were in good agreement with the theoretical demands of 1.5 and 4 mol Cl2/mol of ammonia corresponding to a complete conversion of N-ammonia into N2 and NO 3 , respectively. The mechanism of nitrate formation is not well known. However, it is generally assumed that nitrate ion is formed from an intermediate (NOH) during the decomposition of dichloramine (Jafvert and Valentine, 1992; White, 1999).
a
3.2.5. Effect of initial concentrations of reactants on reaction kinetics
b
[C ] / [C ]0
0.8 Total chlorine (Control) Free chlorine Total chlorine Urea
0.6 0.4 [Urea]0 = 50 µM [Chlorine]0 = 25 µM
0.2 0.0 0
24
48
72
96
Reaction time (h) 1.0
[C] / [C]0
0.8 0.6
Total chlorine (Control) Free chlorine Total chlorine Urea
0.4 [Urea]0 = 50 µM [Chlorine]0 = 50 µM
0.2 0.0 0
24
48
72
96
Reaction time (h)
c
1 [Urea]0 = 50 µM; [Chlorine]0 = 290 µM
0.8
[C] / [C]0
As expected, the rates of consumption of total chlorine markedly decreased when the initial concentrations of reactants decreased from 1000 mM to 10 mM ([Cl2]0/[Urea]0 ¼ 10 mol/ mol, Fig. S11). To evaluate the fates of chlorine and urea under conditions similar to those encountered in swimming pool water, a 10 mM phosphate buffered solution of urea (50 mM) was chlorinated during 4 days at two chlorine doses: 25 mM (1.8 mg Cl2 L1; 0.5 mol Cl2/mol of urea) and 50 mM (3.55 mg Cl2 L1; 1 mol Cl2/mol of urea) (Fig. 8a and b). Another experiment has also been conducted with an excess of chlorine relative to urea ([Cl2]0 ¼ 300 mM; 6 mol Cl2/mol) and a reaction time of 6 days (Fig. 8c). The concentrations of free and total chlorine were determined at various reaction times using the DPD colorimetric method. The initial and the final pH values were 7.35 and 7.25, respectively. Data presented in Fig. 8 exhibit a slow decay of the concentration of free chlorine with reaction time. A complete disappearance of urea was observed only at the highest chlorine dose tested (6 mol/mol). After the complete depletion of free chlorine, removals of urea of about 15 and 30% were obtained for chlorine doses of 0.5 and 1 mol/mol, respectively (Fig. 8a and b). The stoichiometry for the overall reaction between chlorine and urea calculated from the residual concentrations of urea and chlorine gave a value of about 3.2e4.8 mol of Cl2/mole of urea and could not be determined more exactly because of the imprecision on the values of the residual concentrations of urea. The DPD colorimetric analyses did not reveal the presence of chloramines in the solutions. The differences between the concentrations of free chlorine and of total chlorine were always less than or equal to 1e1.5 mM of Cl2. These findings are surprising because urea is generally regarded as a major precursor of chloramines in swimming pool water (Seux, 1988). These results might be explained by a positive interference of chlorination by-products of urea (chloro-ureas, trichloramine) on the determination of free chlorine or by the fact that the low reaction rates between chlorine and urea in very dilute aqueous solution does not allow the build-up of chloramines in solution. Our data are consistent with a recent study published by Li and Blatchley (2007). By using MIMS method for the quantification of chloramines, these authors detected trace amounts of trichloramine (z0.3 mg Cl2 L1 or z 1.5 mM of trichloramine) during chlorination of dilute aqueous solutions of urea
1.0
Total chlorine (Control) Free chlorine Total chlorine Urea
0.6 0.4 0.2 0 0
24
48
72
96
120
144
Reaction time (h) Fig. 8 e Normalized concentration-time profiles for urea ([Urea]0 [ 50 mM), free and total chlorine obtained for initial concentrations of chlorine equal to (a) 25 mM, (b) 50 mM and (c) 290 mM (pH [ 7.30 ± 0.05, 25.0 C).
([Urea]0 ¼ 18 mM; [Chlorine]0: 0e10 mol/mol; pH ¼ 7.5, reaction time: 96 h). In the same study, the DPD/FAS titrimetric method was found to overestimate the concentration of chloramines and was not able to differentiate between chloramines. The DPD colorimetric method is the method that is conventionally used to measure the concentrations of free chlorine and combined chlorine in swimming pool water disinfected by chlorine gas or sodium hypochlorite (DPD-1 and DPD-3 tests). If this method does not detect the formation of combined chlorine during the chlorination of urea in dilute aqueous solution, one can suggest that the combined chlorine
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which is measured by this method in pool water does not result from the chlorination of urea, but from the chlorination of other nitrogenous contaminants released by the bathers.
4.
Conclusion
This work showed that the concentrations of urea in swimming pool water ranged between 2 and 60 mM (mean value: 18 mM, 50 values) and that the contribution of urea to the organic carbon content can reach 15% (mean value 6.3%). The analyses of urea which have been done immediately after closing the facility to the public and just before the opening to the public indicated that urea is very slowly degraded in swimming pools by free chlorine ([Free chlorine]: 1.7e2.0 mg Cl2 L1; pH: 7.3e7.5). Laboratory experiments carried out with phosphate buffered solutions of urea ([Urea]0 ¼ 1 mM; pH 7) confirmed that chlorination of urea leads to a complete mineralization of the organic carbon into CO2. The data also demonstrate the productions of high yields of nitrate as end-product (z0.7e0.8 mol/mol). The production of nitrate, the chlorine demands of urea and the reaction rates were found to be dependent on pH and on chlorination doses. Experiments conducted with low concentrations of urea (50 mM) confirmed the slow reactivity of chlorine with urea. Additional studies using specific analytical methods for the quantification and the differentiation of intermediates (chloro-ureas, inorganic chloramines) are needed to determine the productions of inorganic chloramines under various chlorination conditions of urea solutions (phosphate and bicarbonate buffered solutions, real water samples), better understand the reaction pathways and the complex reaction kinetics (determination of absolute reaction rate constants, kinetic modelling) and to elucidate the contribution of urea to the formation of chloramines in swimming pool water.
Acknowledgements The authors thank the French Agency for Environmental an Occupational Health Safety (AFSSET) for funding a part of this research (Project no EST-2008-07).
Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.11.005.
references
Abidi, H., Gass, J.L., Grenier-Loustalot, M.F., 2001. Analyse quantitative de l’ure´e dans l’eau par HPLC-APCI-MS-MS et HPLC-ES-MS-MS. Actualite´ Chimique 4, 33e36. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, twenty-first ed. American Public Health Association, Washington, DC. Bernard, A., Carbonnelle, S., Michel, O., Higuet, S., de Burbure, C., Buchet, J.-P., Hermans, C., Dumont, X., Doyle, I., 2003. Lung
hyperpermeability and asthma prevalence in schoolchildren: unexpected associations with the attendance at indoor chlorinated swimming pools. Occup. Environ. Med. 60, 385e394. Besse, P., Alouini, Z., Seux, R., 17e19 juin 1985. Devenir de l’ure´e dans les eaux de piscines. In: Actes du colloque national Piscines et Sante´. ENSP Rennes, pp. 104e112. Cozzi, S., 2004. A new application of the diacetyl monoxime method to the automated determination of dissolved urea in seawater. Marine Biol. 145, 843e848. De Laat, J., Berne, F., Brunet, R., Hue, C., 2009. Sous-produits de chloration forme´s lors de la de´sinfection des eaux de piscines. Etude bibliographique. Journal Europe´en d’Hydrologie 40, 109e128. De Laat, J., Berne, F., 2009. La de´chloramination des eaux de piscines par irradiation UV. Etude bibliographique. Journal Europe´en d’Hydrologie 40, 129e149. De Laat, J., Merlet, N., Dore´, M., 1982. Chlorination of organic compounds: chlorine demand and reactivity in relationship to the trihalomethane formation. Incidence of ammoniacal nitrogen. Water Res. 16, 1437e1450. Erdinger, L., Kirsch, F., Sonntag, H.-G., 1997. Potassium as an indicator of anthropogenic contamination of swimming pool water. Zentralblatt fu¨r Hygiene und Umweltmedizin 200, 297e308. Gunkel, K., Jessen, H.-J., 1988. The problem of urea in bathing water. Zeitschrift fu¨r die Gesamte Hygiene 34, 248e250. He´ry, M., Hecht, G., Gerber, J.M., Gendre, J.C., Hubert, G., Rebuffaud, J., 1995. Exposure to chloramines in the atmosphere of indoor swimming pools. Ann. Occup. Hyg. 39, 427e439. Jacobi, O., 1971. Die inhaltsstoffe des normalen stratum corneum und callus menschlicher haut. Arch. Dermatol. Res. 240, 107e118. Jafvert, C.T., Valentine, R.L., 1992. Reaction scheme for the chlorination of ammoniacal water. Environ. Sci. Technol. 26, 577e586. Ja¨ggli, M., 1995. Aspetti della gestione dell’urea nell’acqua di piscina. Trav. Chim. Aliment. Hyg. 86 (1), 45e54. Judd, S.J., Black, G., 2000. Disinfection by-product formation in swimming pool waters: a simple mass balance. Water Res. 34, 1611e1619. Judd, S.J., Bullock, G., 2003. The fate of chlorine and organic materials in swimming pools. Chemosphere 51, 869e879. Kim, H., Shim, J., Lee, S., 2002. Formation of disinfection by-products in chlorinated swimming pool water. Chemosphere 46, 123e130. Li, J., Blatchley III, E.R., 2007. Volatile disinfection byproduct formation resulting from chlorination of organic-nitrogen precursors in swimming pools. Environ. Sci. Technol. 41, 6732e6739. Li, J., Blatchley III, E.R., 2009. UV photodegradation of inorganic chloramines. Environ. Sci. Technol. 43, 60e65. Seux, R., Weicherding, J., Besse, P., Alouini, Z., Cle´ment, M., 17e19 juin 1985. Etude qualitative et quantitative de la pollution apporte´e par les baigneurs. In: Actes du colloque national Piscines et Sante´. ENSP Rennes, pp. 92e103. Seux, R., 1988. Evolution de la pollution apporte´e par les baigneurs dans les eaux de piscines sous l’action du chlore. Journal Franc¸ais d’Hydrologie 19 (2), 151e168. Shang, C., Blatchley III, E.R., 1999. Differentiation and quantification of free chlorine and inorganic chloramines in aqueous solution by MIMS. Environ. Sci. Technol. 33, 2218e2223. Weaver, W.A., Li, J., Wen, Y., Johnston, J., Blatchley, M.R., Blatchley III, E.R., 2009. Volatile disinfection by-product analysis from chlorinated indoor swimming pools. Water Res. 43, 3308e3318. White, G.C., 1999. Handbook for Chlorination and Alternative Disinfectants. John Wiley ans Sons Inc., NY. WHO (World Health Organization), 2006. Guidelines for safe recreational water environments. In: Swimming Pools and Similar Environments, vol. 2 120.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 4 7 e1 1 5 6
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Transport of polycyclic aromatic hydrocarbons and pesticides during snowmelt within an urban watershed Torsten Meyer*, Ying Duan Lei, Frank Wania Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, Canada M1C 1A4
article info
abstract
Article history:
During snowmelt events in urban watersheds large amounts of organic contaminants are
Received 22 July 2010
mobilized, potentially affecting the quality of surface and groundwater resources. The
Received in revised form
transport of polycyclic aromatic hydrocarbons (PAHs) and two pesticides in the highly
28 October 2010
urbanized Highland Creek watershed within the city of Toronto, Canada, was investigated
Accepted 4 November 2010
by sampling river water during two snowmelt periods. The dissolved and the particulate
Available online 11 November 2010
fractions were separately extracted and analyzed. While during normal flow conditions levels of the sum of nine PAHs including phenanthrene, anthracene, fluoranthene, pyrene,
Keywords:
benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, and
Polycyclic aromatic hydrocarbons
benzo(ghi)perylene ranged between 18 and 45 ng/L, concentrations at the onset of melting
Pesticides
varied from 550 to 4500 ng/L. Considering enhanced stream discharge rates during snow-
Urban watershed
melt the contaminant flux in the river increased by three orders of magnitude. The
Snowmelt
intensity of the melt event largely determined the extent of the PAH concentration increase in the river. The relatively water soluble pesticides chlorothalonil and lindane (g-HCH) also tended to appear early during melting. Their enrichment in river water may be influenced by the thickness of the snow pack at the onset of melting, and the mode of melt water ablation from the snow pack to the stream, i.e. whether it occurs by overland or subsurface flow. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
In urban watersheds organic contaminants are exposed to a multitude of possible environmental pathways, because sources of both water and chemicals often vary rapidly with time and space. In northern cities, snow related processes add to the complexity of contaminant fate. Snow can efficiently scavenge organic chemicals from the atmosphere to the ground (Lei and Wania, 2004) where it can be stored in snow packs for many weeks and months. During spring snowmelt those substances are transferred to melt water receiving streams, often in short and concentrated pulses (Meyer and Wania, 2008). This transfer is particularly rapid in highly
urbanized areas where impervious surfaces predominate, and channels, roads and sewers provide for efficient drainage. The presence of organic contaminants in surface waters that are able to disrupt the endocrine system of aquatic organisms, is a particular concern. Such effects are known or suspected for several PAHs (Schultz and Sinks, 2002) as well as for pesticides such as lindane (Tiemann, 2008). The seasonal snowmelt period often occurs during time periods when the endocrine system of developing organisms is particularly vulnerable to contaminant shock loads (Hickie et al., 1995; Leiva-Presa and Jenssen, 2006). PAHs are ubiquitous in urban watersheds and their concentrations in urban streams correlate with the degree of
* Corresponding author. Tel.: þ1 416 287 7506; fax: þ1 416 287 7279. E-mail address:
[email protected] (T. Meyer). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.004
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urbanization (Bryant and Goodbred, 2009) and with population density (Ngabe et al., 2000). Highest concentrations of PAHs in soils are found near busy streets, followed by soils near residential streets, and open spaces (Wang et al., 2008), suggesting that most PAHs released from vehicles are deposited close to the place of emission. We are not aware of any efforts to thoroughly characterize urban organic contaminant dynamics during snowmelt, but several studies that have investigated the transport of PAHs in urban watersheds during storm water runoff (e.g. Stein et al., 2006; see Go¨bel et al., 2007; Kim and Young, 2009) may facilitate understanding of contaminant fate during snowmelt. The observed first flush, i.e. peak concentrations of PAHs in stream water during the initial storm period (Go¨bel et al., 2007), suggests an immediate transfer of particle-bound chemicals from impervious urban surfaces to surface water (Stein et al., 2006). Go¨bel et al. (2007) found that PAH concentrations in runoff water correlate with enhanced storm water intensity. A stronger momentum of runoff water sweeps along more particles and the chemicals associated with them. Pesticides are present in urban watersheds due to application to lawns, parks, and golf courses within the urban areas or due to riverine and atmospheric transport from agricultural areas outside the urban area (Hoffman et al., 2000). Some pesticides, such as lindane and chlorothalonil, are sufficiently persistent to undergo long-range atmospheric transport on a regional scale (Muir et al., 2004). In the only known study investigating snowmelt induced pesticide transport in an at least partially urbanized watershed (Barber et al., 2006), pesticides entered the streams primarily from an agricultural reach upstream of the urban area and from a wastewater treatment plant that processes water influenced by urban pesticide usage. Stream concentrations during snowmelt were similar to concentrations found during base flow conditions. However, samples were taken only once during melting and during the part of the melt period when the river did not experience peak flow conditions. Understanding chemical transport during snowmelt requires an understanding of the dynamics of melt water movement within the snow pack and from the snow pack to the stream. Organic chemicals are not uniformly released from a melting snow pack but often in the form of concentrated pulses (Scho¨ndorf and Herrmann, 1987; Meyer et al., 2009a, b). Water soluble organic substances are eluted in high concentrations at an early stage of the melt, whereas more hydrophobic chemicals attached to particles accumulate at the snow pack surface and are often released at the very end of melting (Scho¨ndorf and Herrmann, 1987; Meyer et al., 2009a, b). On the catchment scale peak concentrations of pesticides have been observed in melt water receiving streams (Que´merais et al., 1994; Pham et al., 1996; Lafrenie`re et al., 2006) implicating direct transfer of pollutant loads from snow packs to surface waters. When melt water ablation primarily occurs as overland flow organic contaminants are transferred directly and undiluted to streams and rivers. When soils are more permeable the melt water is diluted and buffered while flowing as sub-surface flow within upper soil layers (Meyer and Wania, 2008). Melt water infiltration was found to be inversely correlated to the presence of water and ice in soil (Zhao and Gray, 1999).
Urban watersheds exhibit surface features that potentially influence chemical transport during melting. In highly urbanized areas snow melts earlier and runs off more rapidly compared to parks and forested areas, because stronger longwave radiation combines with a decreased snow albedo (Bengtsson and Westerstro¨m, 1992). Additional shifts in the timing of chemical release from snow occur due to uneven snow cover depths around the watershed (Meyer and Wania, 2008). Impervious surfaces and a system of channels and canalized stream beds allow for fast evacuation of melt water (TRCA, 1999). Differences between storm water runoff and snowmelt water ablation include the timing of chemical transport and the amplification of concentrations. Chemicals scavenged from the atmosphere by rain are immediately transferred to terrestrial and aquatic ecosystems. In contrast to snowmelt processes, no further concentration of organic chemicals takes place. Also, melt water runoff is likely to occur more often as overland flow, simply because of the different temperature regime. Finally, the temporal transition of melt water origin from urbanized areas to green spaces does not apply to rain-induced runoff. By measuring time-resolved river water concentrations of PAHs and two pesticides (chlorothalonil, lindane) in early spring, this study sought to identify the characteristics of organic contaminant transport during snowmelt in an urban, temperate watershed, and contrast those processes with storm water induced chemical transport.
2.
Methods
2.1.
Site description
The study was conducted in the Highland Creek watershed in the eastern part of the city of Toronto, Canada, along the northern coastline of Lake Ontario. The watershed comprises a net of busy streets and Canada’s busiest highway (12-lane Highway 401) (Fig. 1). The catchment has an area of 102 km2, and the watercourse an approximate total length of 74 km (TRCA, 1999). Approximately 85% of the watershed is urbanized: industrial/commercial (16%), residential (36%), institutional (7%), utilities/transportation (6%), and roads (20%) (TRCA, 1999); the remainder includes parks, forested areas, cemeteries, and golf courses. The largest contiguous green space is located downstream and encompasses sampling sites A, B, and C (Fig. 1). Lower Highland Creek is subject to extended stream bank erosion during runoff conditions (TRCA, 1999).
2.2.
Sampling
River water was sampled during two snowmelt periods, in March 2007 and March 2008. The 2007 melt period lasted 12 days and comprised three major melt events, whereas the 2008 melt spanned almost five weeks and encompassed two major melt events (Fig. 2). The snow pack build-up preceding both melt periods was influenced only very little by meltefreeze cycles (Figs. S1 and S2). The snowmelt period in 2008 included several snowfall and rain events (Fig. 2). At the end of sampling in both years most of the snow around the watershed had
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 4 7 e1 1 5 6
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Fig. 1 e Highland Creek watershed with sampling locations and the gauging stations GS-E (East) and GS-W (West) (green colour on the map designates green space) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
melted. In 2007 river water was sampled up to five times at eight sampling locations (Fig. 1) during days of enhanced melting (Fig. 2). Additional samples, representing background concentrations, were taken at the end of November 2007. In 2008 samples were only taken at the furthest downstream location (site A in Fig. 1), however, at a higher temporal resolution (11 samples). For extraction and analysis of organic contaminants 4-L water samples were collected in polyethylene containers (USGS, 2006). Simultaneously, smaller samples were collected and analyzed for specific conductivity and particle concentration (suction filtration using glass fibre filters GF/F, approximate cut-off size: 0.7 mm). The samples were stored at temperatures just above 0 C until extraction in order to minimize degradation. Meteorological records on an hourly basis provided by the weather station of the University of Toronto Scarborough (near sampling site A), stream discharge records on a daily basis from two gauging stations operated by Environment Canada, (2010) (Figs. 1 and 2), snow pack depth records from Buttonville Airport located 2 km north of the watershed (Figs. S1 and S2), as well as personal notes were used to define the hydrological processes responsible for the observed phenomena.
2.3.
Analysis
All samples were filtered through GF/Fs (Whatman, Brentford, UK) to separate the particulate from the dissolved fraction. The chemicals in the dissolved phase were extracted with C18 SPE cartridges (Supelco, SigmaeAldrich, St. Louis, MO). The extract was eluted with 5 mL each of ethyl acetate, dichloromethane/ethyl acetate (1:1), and dichloromethane (Usenko
et al., 2005). The extract was further concentrated to 0.5 mL using N2, whereby ethyl acetate was assumed to be the only remaining solvent. The particulate fraction was extracted by ultrasonification (VWR Aquasonic) in acetone for 30 min (Hollender et al., 2003). After filtering through 0.45 mm nylon membrane syringe filters the solvent was exchanged with isooctane and concentrated to 0.5 mL using N2. When traces of water remained within the extracts, samples were dried with sodium sulphate. Samples that needed cleanup were passed through columns containing 6% deactivated and neutral alumina, and eluted with 45 mL hexane. Both dissolved and particulate fractions were analyzed using gas chromatographyemass spectrometry, with either electron impact ionization (PAHs), or negative chemical ionization (pesticides). Details of the instrumental method are provided in Supplementary data. The analytes discussed in this paper include the 12 PAHs naphthalene (NAP), acenaphthylene (ACL), acenaphthene (ACN), phenanthrene (PHE), anthracene (ANT), fluoranthene (FLU), pyrene (PYR), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), indeno(1,2,3-c,d)pyrene (IP), benzo(ghi)perylene (BghiP), and the pesticides lindane (g-HCH) and chlorothalonil. Other pesticides were detected only sporadically or not at all (see Supplementary data). Quantification was achieved with six external calibration standards. Mirex was added to each sample extract and standard solution for volume correction. Labeled surrogates were added prior to analysis to obtain recovery rates. Field blanks and laboratory procedural blanks were included and subjected to the same extraction and cleanup procedures as the samples. All chemicals in the blanks were either non-detectable or concentrations were
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average percentage fraction
25
20
15
10
5
0
NAP ACL ACN PHE ANT FLU PYR BbF BkF BaP IN BghiP polycyclic aromatic hydrocarbon
Fig. 3 e Average composition of PAHs in river water.
Fig. 2 e Temperature (green graph), river flow rates during the melt periods in 2007 and 2008 at the two gauging stations GS-E (red graph) and GS-W (blue graph), and precipitation events (pictograms). The sample numbers are placed above the related graph points (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
consistently low. Instrument detection limits (IDLs) are listed in Table S1. Estimated method detection limits (EDLs) were calculated according to EPA-method 8280A (U.S. EPA, 1996) and are also listed in Table S1. Recovery rates of the surrogates are listed in Table S2. All reported data were blank corrected using the averages of 8 procedural blanks and 2 field blanks, provided they exceeded the EDLs. Following the recommendations from Thompson et al. (1999) all data were recovery corrected using a method described in Von Holst et al. (2002).
3.
Results
3.1.
Polycyclic aromatic hydrocarbons
Medium-molecular-weight PAHs with 3 and 4 rings were dominant in the water samples. However, other PAHs were also present in notable amounts (Fig. 3). Nine PAHs with three to six rings (PHE, ANT, FLU, PYR, BbF, BkF, BaP, IP, and BghiP) P were largely associated with suspended particles ð PAHÞ 9 (Tables S3 and S4) and tended to be released in the form of first
flushes (Figs. 4 and 5), similar to what has been found for PAH transport during storm water events. PAHs with five and six rings were only found in the particulate phase. During high flow conditions the average percentage of NAP in the samples decreased, whereas the fraction comprising five- and six-ring PAHs tended to increase (Figs. S3 and S4). P PAH in the samples Whereas the bulk concentration of 9 taken in November 2007 ranged between 18 and 45 ng/L, concentrations at the onset of melting varied from 550 to 4500 ng/L. Considering that stream discharge rates were enhanced approximately tenfold during early melting, the contaminant flux in the river increased by three orders of magnitude. Those concentrations and contaminant fluxes are of the same order of magnitude as those measured in streams receiving runoff water at the onset of strong rain events (Kim and Young, 2009; Stein et al., 2006). PAH levels were different at the different sampling locations (Fig. 4; Table S6). Peak concentrations at sampling site A were more pronounced in 2008 than in 2007. On the other hand, concentrations of suspended particles during peak flow conditions were lower in 2008 (Figs. 4 and 5).
3.2.
Pesticides
Whereas river water concentrations of chlorothalonil tended to be higher at an early stage of the 2007 melt period, concentration profiles of lindane did not indicate any consistent enrichment (Fig. 6). During the snowmelt 2008 both lindane and chlorothalonil were enriched in early melt runoff (Fig. 7). Mean concentration of chlorothalonil in the river water was 0.23 ng L1 (max. concentrations: 1.1 ng L1) whereas that of lindane was 1.1 ng L1 (max. concentrations: 4.1 ng L1) (Table S5). Consistent with the PAH measurements, peak concentrations of both pesticides in the river were higher and more pronounced in 2008 than in 2007 (Figs. 6 and 7; Table S5). Whereas chlorothalonil was only present within the dissolved phase, lindane could also be found in the particulate phase in significant amounts (Table S5). The observed concentrations of lindane and chlorothalonil are of the same order of magnitude as those reported for surface water in other studies (Bhatt et al., 2009; Muir et al., 2004) (Figs. 6 and 7).
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 4 7 e1 1 5 6
P Fig. 4 e Bar plots with concentrations of PAH (left vertical axis) and concentrations of particles (right vertical axis) in 9 Highland Creek river water at different times during the snowmelt period (first five samples). Sample number six was taken after melting and represents background concentrations. NS refers to cases where no samples were taken.
solids, however varies widely during the melt period. Daub et al. (1994) observed a transfer of particulate phenanthrene and pyrene into the dissolved phase during snowmelt in concert with a decrease of the suspended solid concentration within the melt water. To aid in the interpretation of the measurements, we estimated the equilibrium phase distribution of the analytes chemicals between suspended solids and dissolved phase
Discussion
1800
4.1.
Phase distribution
1500
The fate of a chemical in surface water to a large extent depends on its partitioning between the dissolved and particulate phase. Particle-bound substances are often deposited near river mouths and estuaries (Menzie et al., 2002), whereas chemicals that are in the dissolved phase may be transported over larger distances or enter groundwater aquifers. The extent of hydrophobic organic chemical sorption to particles in river water depends on the concentration of the suspended solids, their organic matter content, and the equilibrium sorption coefficient of the chemical normalized to organic carbon (KOC). Temperature is likely to be of minor influence because river water temperature varies only little during snowmelt. The suspended solids concentration and possibly also the organic matter content of the
PAH concentration [ng/L]
4.
900
concentration of the sum of 9 PAHs
750
particle concentration
1200
600
900
450
600
300
300
150
0
particle concentration [mg/L]
Unlike the PAHs, pesticides display rather similar concentrations at the different sampling sites. The relative standard deviation (RSD) of the chemical concentrations at different sampling sites was used to express the extent of concentration heterogeneity around the watershed. Based on sampling events 1 and 2 taken during peak flow conditions in 2007 (Fig. 2), the RSDs for the sum of 9 PAHs is on average double that for the two pesticides (Table S6).
0 1
2
3
4
5
6
7
8
9
10
11
sample order
P Fig. 5 e Concentrations of PAH and concentration of 9 particulate matter in eleven river water samples during the snowmelt period 2008 at the furthest downstream sampling site A.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 4 7 e1 1 5 6
Fig. 6 e Bar plots with concentrations of lindane and chlorothalonil in Highland Creek at different times during the snowmelt period (first five samples).
references to color in this paragraph, the reader is referred to the web version of this article.). The measured phase distribution is in general agreement with the predictions of Fig. 8. For example, chlorothalonil could only be quantified in the dissolved phase, and the larger
0.4 lindane chlorothalonil
3
0.3
2
0.2
1
0.1
0
chlorothalonil concentration [ng/L]
4 lindane concentration [ng/L]
(Fig. 8). In particular, we have plotted the particle-sorbed percentage as a function of a chemical’s log KOC and the suspended solids concentration, assuming that the organic matter content of the particles is 10% (Youakim and Reiswig, 1984). The particle-associated fraction is indicated by different colours and the vertical yellow lines refer to the chemicals’ log KOC value at temperatures slightly above 0 C. The upper part of the plot represents suspended solids concentrations that occur during the peak melt period, whereas the lower part is more reflective of particle loads during normal flow conditions. Based on these very simple equilibrium calculations, we would expect five- and six-ring PAHs to be almost entirely particle-bound during the peak melt period, but have appreciable dissolved phase presence during low flow conditions. PAHs with three to four rings, on the other hand, would be largely dissolved in water with low solids concentrations, but become increasingly particle-bound at higher suspended solids loads. While lindane is expected to be mostly dissolved, it starts to sorb to particles at very high solids concentrations. Chlorothalonil is so water soluble that it will remain truly dissolved even in highly turbid waters. The set of analytes includes thus the full range of possible partitioning characteristics, from chlorothalonil which is always dissolved to IP which is always particle-sorbed (For interpretation of the
0 1
2
3
4
5
6
7
8
9
10
11
sample order
Fig. 7 e Concentrations of lindane and chlorothalonil in river water vs. sample order during the snowmelt period 2008 at the furthest downstream sampling site A.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 4 7 e1 1 5 6
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Fig. 8 e Calculated percentage of organic chemical sorption to solids suspended in river water. The displayed range of solids concentrations corresponds to the measured range of particle concentrations during and after snowmelt. The upper transparent rectangle refers to solids concentrations representative of times of enhanced melting. KOC values were estimated based on Mackay et al. (2006) and Lei et al. (2000), using the relationship KOC [ 0.41KOW (Karickhoff, 1981). LIN and CHL refer to lindane and chlorothalonil, respectively.
PAHs (BbF, BkF, BaP, IP, BghiP) are indeed largely particlebound. Complete agreement between calculated and measured partitioning can not be expected to occur because the applied organic matter content of suspended solids and the assumption of equilibrium partitioning may not be valid for Highland Creek water. Furthermore, the calculated log KOC values do not consider sorption to condensed organic matter such as black carbon.
4.2.
Polycyclic aromatic hydrocarbons
4.2.1.
PAH phase partitioning in stream water
In order to investigate the chemicals’ partitioning behavior, the concentration ratio between the particulate and dissolved phase CS/CW of PHE, ANT, FLU, PYR was calculated and related to the measured concentration of particles in each sample CPart. (Table S8). Whereas significant correlations between CS/CW and CPart. were found in Eastern Highland Creek (sampling sites AeC in 2007, site A in 2008) (Table S8), correlations were relatively weak or not present at all in Western and Upper Highland Creek (sites DeH), which receive particles mostly from impervious surfaces such as roads and areas which are commercially and industrially used. Also, stream beds are canalized to a large extent, which prevents erosion and mixing with soil particles. The sorptive capacity of the suspended particles in the Western and Upper Creek was obviously more heterogeneous compared to the Eastern Creek. The observed dependence of partitioning behavior on the particle load has implications for the fate of PAHs with intermediate hydrophobicity (PHE, ANT, FLU, PYR), which constitute a relatively large fraction of the overall PAH load.
Strong melt events not only transport large amounts of PAHs downstream, but also a larger fraction of chemicals with intermediate hydrophobicity transfers to the particulate phase, and may eventually settle and become trapped within sediments.
4.2.2.
Transport dynamics of PAHs during snowmelt
River flow rates correlate well with particle concentrations in P PAH (Table S9). the water, and with the concentrations of 9 Therefore, PAH transport in urban watersheds during snowmelt depends largely on runoff rates, which in turn is controlled by the intensity of melting. PAH concentrations were unevenly distributed around the watershed, reflecting deposition of particle-associated chemicals near busy streets, and mixing of particulate matter exhibiting different extent of pollution, along the river. The stronger relative presence of five-ring and six-ring PAHs during high flow conditions can be attributed to the increase in particle concentrations. The lower relative abundance of NAP during those times may be caused by stronger evaporation due to enhanced stream water turbulence (Figs. S3 and S4). Assuming that the measured concentrations represent daily average values, and by using the strong and significant correlation between flow rate and chemical concentration in 2008 (Table S9), the mass of the sum of nine PAHs that passed sampling site A during the entire melt period in 2008, amounted to approximately 4.5 kg. River flow at gauging station GS-E is usually much higher than at GS-W, because the former receives water from a larger drainage area. However, during the first snowmelt event in each of the two melt seasons, the flow rates recorded at GS-E and GS-W were similar (Fig. 2), suggesting that most of the enhanced stream flow at site A during that time can be
1154
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attributed to melt water originating from highly urbanized areas in the Western and Upper part of Highland Creek (sites DeH); relatively little water is contributed from the green space in the Eastern part (sites AeC, Fig. 1). First flush PAHs found at site A in both years may therefore had their origin on impervious surfaces, while associated with grit and other particulate matter typical for urban areas. Those areas are subject to melt water runoff first (Figs. 2 and 4). Subsequent melt events notably increased the difference in the flow rates at GS-E and GS-W (Fig. 2), consistent with a delayed arrival of melt water from snow packs in the Eastern green space. As a result of rapid snow ageing at the onset of a melt period, melt water flow within snow packs becomes much faster during subsequent melt events (Meyer and Wania, 2008), which causes the difference in melt water transport from urbanized and green areas within a watershed to decrease. Higher saturation of upper soil layers during later stages of melting may add to the rapidity of melt water transport from green spaces to the streams (see Section 1). Therefore, PAHs sampled at site A after the first flush presumably come from both urbanized areas and green spaces. The last major melt events are again characterized by peak concentrations of PAHs in the stream water (sample 4 in 2007, Fig. 4, samples 7e9 in 2008, Fig. 5). This late peak may be related to the release of particle-associated PAHs that had accumulated at the surfaces of snow packs and banked-up snow piles. Another explanation may be a high intensity of melting, implied by enhanced river flow rates at that time (Fig. 2). A stronger melt water flow simply drags along more particles and associated PAHs (For interpretation of the references to color in this paragraph, the reader is referred to the web version of this article.). Because site A was sampled in both years (Fig. 1) we can compare some of the characteristics of the two melt seasons. Despite lower PAH peak levels in 2007, concentrations of suspended particles were higher that year, suggesting that enhanced stream bank erosion may have led to the dilution of polluted particles along the river stretch upstream of site A (TRCA, 1999). As a net result, less polluted particles may have entered the stream. Stronger cohesiveness of soils may explain the presence of more polluted particulate matter at site A in 2008. During the winter months preceding the melt period in 2007, temperatures were mostly above the freezing point until the temperature dropped and a snow pack was accumulating without any notable melt event, which should have kept soils permeable and less cohesive. The winter prior to the melt period in 2008 on the other hand, was characterized by much higher snowfall rates, repeated snow pack growth’ and melt cycles, followed by refreezing in upper soil layers. Larger CS/CW to CPart. ratios in 2007 indicate that suspended particles sampled in the Eastern Creek in 2007 exhibited a stronger sorption capacity compared to 2008 (Table S8). Eroded particulate matter in the streams in 2007 may have contained a larger organic matter fraction leading to stronger sorption of PAHs. Measured concentration ratios between individual PAHs in various environmental media are commonly used to trace their origin. Such a source analysis indicates that car-related traffic and coal combustion are important sources of PAHs to the Highland Creek watershed (Table S10 and associated text).
4.3.
Pesticides
Winter time air concentrations of pesticides in Southern Ontario are relatively low (Gouin et al., 2008, Hayward et al., 2010). Similarly, pesticide levels in urban streams are low during winter (Hoffman et al., 2000). However, notable amounts of both lindane and chlorothalonil have been found in snow of high-elevation national parks in the United States, which was attributed to regional atmospheric transport (Mast et al., 2007). Usage of lindane in North America during the growing seasons that preceded both melt periods can largely be assigned to seed treatment in the United States (U.S. EPA, 2006a, b). Pesticide usage is restricted within the City of Toronto, but not in the surrounding areas (Gouin et al., 2008). Both substances are therefore believed to reach the watershed by atmospheric transport and deposition with or to snow. During the snowmelt period, transport from the snow packs to the streams may occur in the form of overland flow or sub-surface flow. Melt water sub-surface flow may also cause partial redissolution of pesticides that had been deposited to soils during the snow-free period. Relatively uniform distribution of the pesticide concentrations between the sampling sites implicates however direct transport from the melting snow packs and banked-up snow piles to the streams. The melt season in 2008 was characterized by stronger pesticide enrichment in stream water, compared to 2007 (Figs. 6 and 7). As discussed earlier, in 2008 soils may have been frozen to a larger extent, leading to decreased soil permeability and enhanced overland flow. Such flow regime provides for a direct and undiluted chemical transfer to streams. A stronger peak release in 2008 may also be explained by a relatively large snow pack thickness of approximately 20 cm prior to melting, compared to a thickness of roughly 8 cm in 2007 (Figs. S1 and S2). Thicker snow packs are expected to lead to a stronger enrichment of water soluble organic chemicals (Meyer et al., 2009b). Chlorothalonil experienced a second concentration peak at the onset of the last major melt event (sample 7, Fig. 7). This peak may be related to enrichment processes in snow that had fallen after the first snowmelt event. Consistent with Meyer et al. (2009a, b) this peak appears at the beginning of the melt event. However, it is not clear why a similar peak was not observed for lindane. Rain falling during sampling event 5 presumably diluted and blurred the potential chemical signal in the stream water.
5.
Conclusions
Watersheds that are exposed to seasonal snowmelt will be significantly impacted by a changing climate. Relatively small temperature changes can lead to a large temporal and spatial shift of the zero-degree isotherm (Macdonald, 2005), which governs the mode of precipitation and the aggregate state of water. Therefore, an understanding of snowmelt-related contaminant fate processes is important for a sustainable management of urban water resources in the North. A large fraction of the PAHs found in Highland Creek river water was associated with suspended particles. Strong melt events correlate with large particle concentrations in the river water
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 4 7 e1 1 5 6
and elevated concentrations of particle-bound PAHs. Such events also cause a larger fraction of PAHs with intermediate hydrophobicity to become associated with particulate matter. The PAH flux at the onset of melting exceeded the background chemical flux by three orders of magnitude. Concentrations and fluxes of PAHs resemble those measured in urban streams at the onset of strong stormwater events. The mass of the S9PAH that passed the furthest downstream sampling site over the entire snowmelt period in 2008 amounted to approximately 4.5 kilograms. This rapid “cleansing” of watershed surfaces from particle-associated PAHs affects the surface water quality further downstream. Notable amounts of polluted particles will settle in areas of calmed flow, such as near the river mouth where Highland Creek enters Lake Ontario. The very large difference of PAH concentrations between background and peak flow during snowmelt has implications for the assessment of surface water quality. In order to ascertain contaminant fluxes in urban streams, it is essential to collect surface water samples during peak flow conditions, and preferably within a time window of only a few hours. The pesticides lindane and chlorothalonil also tended to be released early during melting, but enrichment was much less than for the particle-bound PAHs. Because pesticides reach the Highland Creek snow pack via atmospheric transport from outside the watershed, concentration levels were more uniform in space. Because there are no pesticide applications during winter, concentrations of both substances were relatively low and likely reflect regional atmospheric background. The thickness of the snow pack, and the mode of melt water ablation from the snow pack to the stream, i.e. overland flow vs. sub-surface flow, may determine whether pesticide peak releases from the snow pack coincide with similar peak loads in river water.
Acknowledgements We gratefully acknowledge funding from the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS). Special thanks to Chubashini Shunthirasingham, Sung-Deuk Choi, Stephen J. Hayward, Trevor N. Brown, and Colleen Metcalfe for help with sampling. We are also thankful for Chubashini’s assistance related to the pesticide analysis.
Appendix. Supplementary data Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.11.004.
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Nitrogen removal from wastewater using membrane aerated microbial fuel cell techniques Chang-Ping Yu a,b, Zhihua Liang a, Atreyee Das a, Zhiqiang Hu a,* a b
Department of Civil and Environmental Engineering, University of Missouri, E2509 Lafferre Hall, Columbia, MO 65211, USA Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
article info
abstract
Article history:
Nitrogen removal mainly relies on sequential nitrification and denitrification in waste-
Received 26 August 2010
water treatment. Microbial fuel cells (MFCs) are innovative wastewater treatment tech-
Received in revised form
niques for pollution control and energy generation. In this study, bench-scale wastewater
22 October 2010
treatment systems using membrane-aerated MFC (MAMFC) and diffuser-aerated MFC
Accepted 4 November 2010
(DAMFC) techniques were constructed for simultaneous removal of carbonaceous and
Available online 20 November 2010
nitrogenous pollutants and electricity production from wastewater. During 210 days of continuous flow operation, when the dissolved oxygen (DO) in the cathodic compartment
Keywords:
was kept at 2 mg/L, both reactors demonstrated high COD removal (>99%) and high
Wastewater treatment
ammonia removal (>99%) but low nitrogen removal (<20%). When a lower DO (0.5 mg/L)
Nutrient removal
was maintained after day 121, both the MFC-based reactors still had excellent COD removal
Membrane aerated system
(>97%). However, the nitrogen removal of MAMFC (52%) was 2-fold higher than that of
Microbial fuel cell
DAMFC (24%), indicating an enhanced performance of denitrification after DO reduction in the cathodic compartment of the MAMFC. Meanwhile, terminal restriction fragment length polymorphism (T-RFLP) analysis of ammonia-oxidizing bacteria (AOB) population in the MAMFC indicated the diversity of AOB with equally important Nitrosospira and Nitrosomonas species present in the cathodic biofilm after DO reduction. The average voltage output in the MAMFC was significantly higher than that in DAMFC under both DO conditions. The results suggest that MAMFC systems have the potential for wastewater treatment with improved nitrogen removal and electricity production. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Decentralized or on-site wastewater treatment systems are widely used in suburbs, small towns, and rural areas. According to the U.S. Environmental Protection Agency (USEPA), approximately 25% of the U.S. population relies on such small wastewater treatment systems (USEPA, 2002, 2005). Due to low removal efficiency, high effluent concentrations of organic matter and nutrients from on-site wastewater treatment facilities are the substantial burden of receiving water bodies (USEPA, 2002).
Carbonaceous and nitrogenous compounds can be effectively removed in municipal wastewater treatment plants (WWTPs) using alternating aerobiceanoxic processes. Conventional nitrogen removal mainly relies on sequential nitrification and denitrification by autotrophic and heterotrophic microorganisms, respectively. Technologies for efficient nitrogen removal in small wastewater treatment systems, however, have been very limited because the sophisticated operational requirements from WWTPs are difficult to apply to decentralized systems. Furthermore, conventional nitrogen removal processes require a significant amount of readily
* Corresponding author. Tel.: þ1 (573) 884 0497; fax: þ1 (573) 882 4784. E-mail address:
[email protected] (Z. Hu). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.002
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biodegradable organic matter, oxygen, and thus high-energy consumption, resulting inlarge amounts of sludge. Efforts for simultaneous organic and nitrogen removal from wastewater continue to grow. In one study, an activated sludge wastewater treatment process with extended aeration and sludge recirculation coupled with ion-exchange, has been demonstrated to improve nitrogen and phosphorus removal (Safferman et al., 2004). Membrane bioreactors (MBR) have also been applied to improve effluent water quality from decentralized systems (Meuler et al., 2008). These advanced treatment techniques are more efficient than current ones used in decentralized systems. However, high-energy consumption and prohibitive capital costs prevent their large scale applications. Microbial fuel cells (MFCs) are an emerging innovative technique of wastewater treatment for pollutant removal and electricity production. In recent studies, simultaneous organic matter, nitrate removal, and power production were achieved in two-chamber MFC reactors (total volume from 0.25 to 1.3 L) where denitrification was accomplished by microorganisms in the cathode (Clauwaert et al., 2007; Lefebvre et al., 2008). A recently developed MFC with a reactor volume of 0.336 L was coupled with an external aerobic nitrification reactor to convert ammonia in the feed solution to nitrate before it was circulated to the MFC cathodic chamber for nitrogen removal (Virdis et al., 2008). However, the set-up of an additional external nitrifying bioreactor makes it difficult to use in the field. Meanwhile, recent wastewater research used a membrane aerated biofilm reactor (MABR) process to remove nitrogen via simultaneous nitrification and denitrification (Hu et al., 2009; Semmens et al., 2003). MABR provides a counter-diffusion system where oxygen supplied to the base of the biofilm (membrane tubing) diffuses out of the biofilm while the substrate from the bulk liquid phase, such as NHþ 4 and organic carbon, diffuses into the biofilm. With efficient oxygen transfer through a gas-permeable membrane and biofilm thickness control, MABR has the potential to provide a flexible control strategy for efficient nitrogen removal (Wang et al., 2009). The objective of this study was to evaluate the effectiveness of the combined use of the membrane aerated biofilm process and MFC process to achieve simultaneous nitrification, denitrification and organic carbon removal in a single two-chambered MFC system with relatively large volume (7.2 L). The results suggest that membrane-aerated MFC (MAMFC) process has the potential for wastewater treatment with improved nitrogen removal and electricity production.
2.
Materials and methods
2.1.
Reactor design and operation
Two identical lab-scale MFC-based bioreactors with an individual volume of 7.2 L were constructed with glass. Schematic details of the MFC-based bioreactors are available in the supplemental information. Briefly, the MFCs were divided from left to right into three chambers using plastic baffles: an influent anaerobic (anodic) compartment, an aerobic/anoxic (cathodic) compartment, and an internal clarifier. The effective volumes of the anodic, cathodic compartments, and the clarifier were 2.1, 3.3 and 1.8 L, respectively. An array of three
vertical openings with a diameter of 1 cm was made on the baffle between the anodic and cathodic compartments to allow wastewater to flow through the system. In the cathodic compartment, aeration was provided via a diffuser adjacent to the cathode in the diffuser-aerated MFC (DAMFC) while a gaspermeable membrane aerated module (details below) was provided in the MAMFC. The MFC-based bioreactors were designed to mimic a twochamber septic tank. Unlike traditional MFCs, no proton exchange membrane was used for economical considerations. Furthermore, both the anode and cathode were made of inexpensive carbon paper (effective area of 565 cm2 each, Toray Carbon, E-TEK, NJ, USA) without any modification. The anode and cathode were placed in each compartment of the MFC and connected with an external circuit containing a resistor of 1000 U. For the DAMFC, room air was provided via a diffuser adjacent to the cathode to provide oxygen as an electron acceptor. The cathode was covered with a layer of micro-fibrous non-woven fabric (pore size ¼ 10 mm, Kunin Group Inc., Hampton, NH) on both sides to support biofilm formation. The surface area of each non-woven fabric was 565 cm2. Additional mixing in the anodic and cathodic compartment was provided by a magnetic stirrer. In the MAMFC, instead of using the diffuser, a gas-permeable membrane aerated module (active length ¼ 7 m, outer diameter ¼ 2.0 mm, inner diameter ¼ 1.5 mm, Silastic medical grade tubing, Dow Corning) was introduced between the cathode and the nonwoven fabric. The open end of the membrane tubing served as the inlet of oxygen while the other end was blocked. Pure oxygen was provided to the interior of the membrane to enable bubble-less diffusion. By changing the rates of air or pure oxygen supply, the dissolved oxygen (DO) concentrations in the cathodic compartments were maintained at about 2 and 0.5 mg/ L in the MFCs for phase 1 (day 0e120) and 2 (day 121e210) studies, respectively. Based on DO measurements, the oxygen concentrations were evenly distributed within the cathodic compartments due to the fact of mixing by the magnetic stirrer. A synthetic wastewater mainly containing sodium acetate with a target chemical oxygen demand (COD) concentration of 550 mg/L, 30 mg/L NH4 N and 6 mg/L total P was used throughout the operational period. The synthetic wastewater also contained: 44 mg/L MgSO4, 15 mg/L CaCl2$2H2O, 2 mg/L FeCl2$4H2O, 3.4 mg/L MnSO4$H2O, 1.2 mg/L (NH4)6Mo7O24$4H2O, 0.8 mg/L$CuSO4, and 1.8 mg/L Zn(NO3)2$6H2O. The two MFCs were operated under identical hydraulic retention time (HRT ¼ 3.6 d or at a flow rate of 2 L/d) and long solids retention time (never wasted except a total of 150 mL settled sludge used for activity measurements). For both the MFCs, the anodic compartments were inoculated with digested (w200 mL) sludge from the Columbia Wastewater Treatment Plant (WWTP, Columbia, MO). A nitrifying enrichment culture (mainly consisting of Nitrosospira, Nitrobacter and Nitrospira collected in the lab) and activated sludge from the Columbia WWTP were used as inocula for the cathodic compartments.
2.2. Bacterial activities inferred from extant respirometry To determine autotrophic and heterotrophic activities of microorganisms in the MFC-based bioreactors, oxygen uptake
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Nitrifying bacterial community analysis
Nitrifying bacterial community structure was analyzed to better understand the mechanisms of nitrogen removal in the DAMFC and MAMFC. Biomass samples including biofilms growing on non-woven fabric in the cathodic compartment were collected from the two MFCs. We extracted DNA using the Ultra clean Soil DNA Isolation Kit (Carlsbad, CA), following the manufacturer’s protocol. A terminal restriction fragment length polymorphism (T-RFLP) method was applied to the biomass samples based on the 16S rRNA genes for ammoniaoxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) (Siripong and Rittmann, 2007). The T-RFLP results were analyzed using Peak Scanner software (version: 1.0), and fragment sizes were compared to the expected standard fragment sizes (Siripong and Rittmann, 2007).
2.4.
Real-time polymerase chain reaction (PCR) assays
To further evaluate the abundance and function of microorganisms in the MFCs, DNA extracted from anodes, mixed liquid, and non-woven fabric was used for real-time PCR assays to quantify total bacterial 16S rRNA gene and AOB 16S rRNA gene using the TaqMan probe method, and Methanosarcina and Methanosaeta 16S rRNA genes using the SYBR green method. Bacterial 16S rRNA gene used the primers/probe set 1055f, 1392r, and the TaqMan probe 16STaq1115 as described elsewhere (Harms et al., 2003). The real-time PCR assay for AOB 16S rRNA gene used two forward primers CTO 189A/B and CTO189C, one reverse primer RT1r and the TaqMan probe TMP1 (Hermansson and Lindgren, 2001). Real-time PCR was also used to quantify two genera of methanogens with a Methanosaeta-specific primer set Mb1b 586F and Sar 835R and a Methanosaeta-specific primer set MS1b 585F and Sae 835R (Conklin et al., 2006). All real-time PCR assays were performed in triplicate, and all PCR runs included standards and control reactions without template. The threshold cycle (CT) of each real-time PCR reaction was automatically determined by detecting the cycle at which the fluorescence exceeded the calculated threshold. Gene copies were initially calculated by comparison of threshold cycles obtained in each PCR run from known standard DNA concentrations. All real-time PCR assays were run on a 7500 Real-time PCR System (Applied Biosystem, CA, USA.) with 7500 SDS system software version 1.4 (Applied Biosystem, CA, USA).
2.6. Spatial distribution of COD, ammonia and nitrate in the MFCs To determine the concentrations of carbonaceous and nitrogenous pollutants in the MFCs, water samples were collected on day 200 at five different locations in the MFCs in series: (far left) in front of and behind the anode in the anodic chamber, in front of and behind the cathode in the cathodic chamber, and in the clarifier (far right).
2.7.
Analytical methods and data analysis
The concentrations of COD and nitrogen species in the reactor influent and effluent were measured on a weekly basis using Standard Methods (American Public Health Association, 1999). The voltage generated in the MFCs was recorded real-time at
Diffuser-aerated MFC Phase 2
Phase 1
700 Concentration (mg/L)
2.3.
SYTO 9 (stains all cells, live or dead), and a red fluorochrome propidium iodide (stains only bacteria with damaged membranes). We used a laser-scanning confocal microscope (Zeiss LSM 510 META NLO) for fluorescence imaging of the biofilms. The microscope was equipped with a C-Apochromat 40/1.2 water-immersion objective (working distance 280 mm), a femtosecond NIR laser (Coherent Chameleon XR), and a focus drive motor for depth imaging along the Z-axis.
600 500 400
Influent COD
300
Effluent COD
200 100 0 0
50
100
150
200
250
Days
Membrane-aerated MFC Phase 2
Phase 1
700 Concentration (mg/L)
rates due to ammonia oxidation and acetate oxidation were measured individually using a batch extant respirometric assay (Hu et al., 2002). To minimize perturbation to the biological processes and due to technical considerations, only the settled sludge in aerobic/anoxic compartments of the bioreactors was used for bacterial activity measurement.
600 500 400
Influent COD
300
Effluent COD
200 100
2.5.
Visualization of biofilms
0 0
To determine the biofilm structure and activity, biofilms formed on anodes in the two MFCs were visualized by staining with a LIVE/DEAD Baclight bacterial viability kit (Molecular Probes, Eugene, OR). Viable and dead cells were detected by differential staining with a mixture of a green fluorochrome,
50
100
150
200
250
Days
Fig. 1 e Influent and effluent COD concentrations of the DAMFC and MAMFC during the study period. The vertical line indicates the separation between Phase 1 and Phase 2.
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0.01 Hz using a data acquisition system (LabView V 8, National Instruments, Texas). Coulombic efficiency (hc) defined as the ratio of electrons used as current to the maximum electron production (Logan et al., 2006) was calculated as: hc ð%Þ ¼
MI FnqDCOD
(1)
where F is Faraday’s constant (98,485 C/mol of electrons), n is the number of mole of electrons produced per mole of substrate (n ¼ 4 in COD unit), q (L/sec) is the volumetric influent flow rate, ΔCOD is the COD difference in the influent and effluent, M is the molecular weight of the substrate (M ¼ 32 in COD unit), and I is the current (ampere).
3.
was observed. As shown in Fig. 1, at an average influent COD concentration of 545 43 mg/L, the effluent COD concentrations were 8 6 mg/L and 5 4 mg/L for the DAMFC and MAMFC, respectively, which corresponds to the COD removal rates of 99% for both MFCs. During Phase 1 operation, with an average influent ammonia concentration of 30 mg N/L, more than 99% of ammonia was oxidized with no nitrite accumulation in both the MFCs, demonstrating almost complete nitrification (Fig. 2). Based on the measured nitrogen species in the effluents, the total inorganic nitrogen (NHþ 4 N þ NO2 N þ NO3 N)
Results and discussion
3.1. Organic and nitrogen removal in the MAMFC and DAMFC The two MFCs were operated and monitored for 210 days, which was divided into two phases. During the first 120 days of continuous flow operation, the DO in the cathodic compartment in the DAMFC and MAMFC was maintained around 2 mg/L (Phase 1). After about two months of start-up, the stable performance of COD and ammonia removal from both the MFCs
Fig. 2 e Influent and effluent concentrations of nitrogen species in the DAMFC and MAMFC during the study period. The vertical line indicates the separation between Phase 1 and Phase 2.
Fig. 3 e Concentration profiles of COD, ammonia, and nitrate inside the MFCs. Vertical lines indicate the anode, the cathode, and the two baffles between the anodic/ cathodic chamber and between the cathodic chamber and the internal clarifier.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 5 7 e1 1 6 4
removal rates for DAMFC and MAMFC were 18% and 20%, respectively. There was no statistically significant difference of the water quality data between these two MFCs during Phase 1 operation (ANOVA, P > 0.05). To improve total nitrogen removal, the DO concentrations were reduced to 0.5 mg/L in the bulk liquid of the cathodic compartments for both the MFCs after day 121 (Phase 2). After about one month transition period, a set of relatively stable water quality data was obtained. More than 97% of influent COD was removed for both the MFCs (Fig. 1). For the DAMFC, after the reduction of DO, the average total inorganic nitrogen removal rate increased to 24% whereas the ammonia removal rate dropped to 86% (Fig. 2). For comparison, more than 96% of influent ammonia was oxidized and the total inorganic nitrogen removal rate increased to 52% in the MAMFC, indicating improved denitrification in the cathodic compartment. While both MFCs had improved capability in removing total inorganic nitrogen from wastewater after DO was reduced to 0.5 mg/L, the MAMFC exhibited higher ammonia and total inorganic nitrogen removal efficiencies than the DAMFC (ANOVA, P < 0.01).
3.2. Spatial distribution of COD, ammonia and nitrate in the MFCs As shown in Fig. 3, approximately 84% of influent COD was already removed in the anodic compartment and 13% of COD
1161
was oxidized in the cathodic compartment for both the MFCs. COD consumed in the cathodic chamber could be important carbon sources for the heterotrophic denitrifiers to remove nitrate. A small fraction (12e21%) of ammonia was removed in the anodic compartment and the majority (68e82%) of ammonia was oxidized in the cathodic compartment through nitrification. Nitrate was detected in the anodic compartment ranging from 2.3 to 7.3 mg N/L, which could be attributed to nitrate diffusion from the cathodic compartment.
3.3. Nitrifying bacteria activities and community structure To understand why the ammonia removal rates were similar between the DAMFC and the MAMFC in Phase 1 but different after reducing DO in the cathodic compartments in Phase 2, we measured the nitrifying bacteria activities, which were inferred from the specific oxygen uptake rate measurements by taking the settled sludge in the cathodic compartments from both the MFCs. However, the nitrifying activities of the settled sludge from both the MFCs were too low to be detected (data not shown). The nitrifying bacterial community structure was further identified in the two MFCs. By using T-RFLP analysis, we detected AOB including genera of Nitrosospira and Nitrosomonas in both the MFCs in Phase 1 and Phase 2 (Fig. 4). Since peak area in the same electropherogram can be used to
Fig. 4 e Electropherograms of the 16S rRNA gene T-RFLP specific to most ammonia-oxidizing bacteria in Betaproteobacteria in the DAMFC and MAMFC biofilms. Nitrosospira lineage (105e107 bp) and Nitrosomonas lineage (164e166 and 276 bp) are indicated.
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Nitrosomonas europaea outcompetes Nitrosospira at high oxygen concentrations (Schramm et al., 2000). In Phase 2, we observed the changes of relative abundance of AOB population in the MAMFC. The abundance of Nitrosospira was increased to be comparable to Nitrosomonas in the biofilm on non-woven fabric after reducing DO, confirming that Nitrosospira spp. may be more tolerant to low oxygen conditions (Dytczak et al., 2008). For comparison, however, the AOB population in the DAMFC did not shift after reducing DO and the dominant genus was still Nitrosomonas. Compared to the ammonia removal data (Fig. 2), the inability for the Nitrosospira spp. to grow could contribute to the relatively lower ammonia removal in the DAMFC in Phase 2. However, further investigation is needed to prove this assumption and to find out the causes for the unchanged AOB population at low DO concentrations. Nitrite-oxidizing bacteria such as Nitrospira and Nitrobacter were detected in both the MFCs in Phase 1 and Phase 2 without significant changes (data not shown). Additionally, the nitrifying bacteria populations in the suspended liquid had similar community structures to the biofilm on non-woven fabric (data not shown).
3.4. Fig. 5 e Voltage output of the DAMFC and MAMFC during the study period. The vertical line indicates the separation between Phase 1 and Phase 2. A resistor of 1 K U was used.
represent the relative microbial abundance (Yu et al., 2005), much higher abundance of Nitrosomonas than that of Nitrosospira in the biofilm on non-woven fabric of the DAMFC and MAMFC was observed in Phase 1 (Fig. 4), indicating that Nitrosomonas may compete better under higher DO conditions. This result is consistent with a previous report that
Voltage output in MFCs
For the DAMFC, the voltage increased rapidly after the MFC was continuously fed with a synthetic wastewater during the startup period, and the recorded voltage reached 0.2 V within 3 days (Fig. 5). However, for the MAMFC, a lag period was observed in the first 7 days and increased gradually to 0.2 V after day 15 (Fig. 5). This could be due to slow diffusion of the gas-permeable membrane which limits the electricity generation at the beginning. The average voltage after the two month start-up in Phase 1 was 0.24 0.07 V and 0.19 0.05 V for MAMFC and DAMFC, respectively. In Phase 2, the average voltage was 0.25 0.07 V and 0.12 0.03 V for the MAMFC and DAMFC, respectively. The
Fig. 6 e Fluorescence images of biofilms formed on anodes of the DAMFC (left panel) and MAMFC (right panel) after LIVE (green)/DEAD (red) staining using laser-scanning confocal microscopy. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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voltage generation in the MAMFC was significantly higher than that in DAMFC (ANOVA, P < 0.001) in both phases. The calculated Coulombic efficiencies based on the voltage output for both the MFCs were between 0.07 and 0.21%. This value is much lower than the values reported in previous studies using miniature MFCs (Hu, 2008; Liu and Logan, 2004). In the absence of a proton exchange membrane between the anodic and cathodic compartments, acetate could diffuse freely to the cathodic compartment while oxygen diffused freely to the anodic compartment. This counter-diffusion behavior along with other factors (e.g., electrode materials, over potential) contributed to the low Coulombic efficiency. According to Eq. (1), theoretically, factors such as reducing volumetric influent flow rate q and resistor R could increase the Coulombic efficiency. Although the objective of this research was to present a proofof-concept prototype for improved nitrogen removal, future efforts are needed to improve the electricity production and Coulombic efficiency in a scaled-up MFC system.
3.5.
Biofilm structure and viability
To determine the biofilm thickness and cell viability, fluorescent images of the biofilms developed on the anodes of both
the MFCs were captured (Fig. 6). Bacteria growing inside the biofilm appeared to be inactive (with red color) and those on the outside were more active (green color). The thickness of the biofilms on each anode was around 100e150 mm. The MFCs had much thicker biofilms on the anodes than previously reported cellulose-fed MFCs using a defined co-culture of Clostridium cellulolyticum and Geobacter sulfurreducens (25 mm) (Ren et al., 2008).
3.6.
Microbial population in the MFCs
We used real-time PCR to quantify the selected microbial population in the anodic and cathodic compartments and the results are shown in Table 1. Conversion of 16S rRNA gene copies to cells was based on the following rules: AOB: 1 copies/ cell; Total bacteria: 3.6 copies/cell (Harms et al., 2003); Methanosaeta: 2 copies/cell; and Methanosarcina: 3 copies/cell (http://ribosome.mmg.msu.edu/rrndb/index.php). For both Phase 1 and Phase 2, Methanosaeta was the dominant methanogens in the anodic compartment. The ratio of methanogens to bacteria is about 1e6% both in the anode biofilm and in the suspended liquid. Since methanogens compete with anoderespiring bacteria for acetate, their presence may also lead to
Table 1 e The abundance of important microbial species in the MFCs in Phase 1 and Phase 2. Phase 1 Membrane-aerated MFC Electrode (cells/cm2) Anodic compartment Methanosaeta (average standard deviation) Methanosarcina (average standard deviation) Total bacteria (average standard deviation) Cathodic compartment Total bacteria (average standard deviation) AOB (average standard deviation) AOB/Total bacteria (%)
Mixed liquid (cells/L)
Diffuser-aerated MFC Electrode (cells/cm2)
Mixed liquid (cells/L)
1.5 0.1 108
9 0.3 109
1.6 0.1 108
6 0.5 109
4 2 105
2.3 0.3 108
1.3 0.3 106
1.1 0.1 108
4 1 109
1.5 0.2 1011
3.4 0.3 109
1.2 0.5 1011
3 1 109
5 1 108
9 2 109
3 2 108
6.7 0.5 106 0.2%
2 1 107 4%
1.0 0.9 107 0.1%
5.4 0.2 107 18%
9.8 0.9 107
3.3 0.5 109
1.5 0.02 108
6 2 108
2 0.3 105
1.0 0.1 107
1.7 0.1 105
4 0.8 106
1.6 0.1 109
2.1 0.4 1011
2.7 0.7 109
6 2 1010
2.8 0.5 109
2.8 0.7 109
1.2 0.1 109
9 8 109
3.8 0.3 106 0.1%
7 2 106 0.3%
5.4 0.2 106 0.5%
2.8 0.9 106 0.03%
Phase 2 Anodic compartment Methanosaeta (average standard deviation) Methanosarcina (average standard deviation) Total Bacteria (average standard deviation) Cathodic compartment Total bacteria (average standard deviation) AOB (average standard deviation) AOB/Total bacteria (%)
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low Coulombic efficiency (Logan et al., 2006). For the cathodic compartment, AOB was about 0.1e0.5% of the total bacteria in the biofilm formed on the non-woven fabric throughout the study. During Phase 1, AOB accounted for 4e18% of total bacteria in the liquid while in Phase 2, only 0.03e0.3% of total bacteria were AOB. The difference was likely due to higher DO concentrations maintained in the cathodic compartment in Phase 1 than that in Phase 2. Effective removal of nutrients from on-site wastewater treatment systems continues to be a hot issue. Although the objective of this study was to provide a proof-of-concept design with improved nitrogen removal and was not for improved electricity production, more studies and further fine tuning are needed to improve Coulombic efficiency and the total nitrogen removal for wastewater treatment.
4.
Conclusions
In this study, we proposed a newly designed membrane aerated microbial fuel cell system that was able to produce electricity and remove COD and nitrogen simultaneously. Results from the continuous flow operation suggested that when the dissolved oxygen in the cathodic compartment was maintained at 0.5 mg/L, the MAMFC had excellent COD removal (>97%) and an improved nitrogen removal (52%), indicating that MAMFC systems have the potential for wastewater treatment with improved nitrogen removal and electricity production.
Acknowledgements We thank Mr. Daniel Beerman and Miss Elizabeth Dolan for help in measuring reactor effluent water quality. We also thank the support by the CAS/SAFEA International Partnership Program for Creative Research Teams (KZCX2-YWT08).
Appendix. Supplementary data Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.11.002.
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Occurrence, partition and removal of pharmaceuticals in sewage water and sludge during wastewater treatment Aleksandra Jelic a, Meritxell Gros c, Antoni Ginebreda a, Raquel Cespedes-Sa´nchez d,e, Francesc Ventura d, Mira Petrovic a,b,*, Damia Barcelo a,c a
Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain b Institucio´ Catalana de Recerca i Estudis Avanc¸ats (ICREA), Passeig Lluis Companys 23, 80010 Barcelona, Spain c Catalan Institute for Water Research (ICRA), H2O Building, Scientific and Technological Park of the University of Girona, 101-E-17003 Girona, Spain d AGBAR, Aigu¨es de Barcelona, Avinguda Diagonal, 211, 08018 Barcelona, Spain e CETaqua,Water Technology Center, Paseo de los Tilos,3, 08034 Barcelona, Spain
article info
abstract
Article history:
During 8 sampling campaigns carried out over a period of two years, 72 samples, including
Received 30 June 2010
influent and effluent wastewater, and sludge samples from three conventional wastewater
Received in revised form
treatment plants (WWTPs), were analyzed to assess the occurrence and fate of 43 pharma-
5 November 2010
ceutical compounds. The selected pharmaceuticals belong to different therapeutic classes,
Accepted 6 November 2010
i.e. non-steroidal anti-inflammatory drugs, lipid modifying agents (fibrates and statins),
Available online 13 November 2010
psychiatric drugs (benzodiazepine derivative drugs and antiepileptics), histamine H2receptor antagonists, antibacterials for systemic use, beta blocking agents, beta-agonists,
Keywords:
diuretics, angiotensin converting enzyme (ACE) inhibitors and anti-diabetics. The obtained
Pharmaceuticals
results showed the presence of 32 target compounds in wastewater influent and 29 in
Wastewater treatment
effluent, in concentrations ranging from low ng/L to a few mg/L (e.g. NSAIDs). The analysis of
Wastewater
sludge samples showed that 21 pharmaceuticals accumulated in sewage sludge from all
Sludge
three WWTPs in concentrations up to 100 ng/g. This indicates that even good removal rates
Removal rate
obtained in aqueous phase (i.e. comparison of influent and effluent wastewater concentrations) do not imply degradation to the same extent. For this reason, the overall removal was estimated as a sum of all the losses of a parent compound produces by different mechanisms of chemical and physical transformation, biodegradation and sorption to solid matter. The target compounds showed very different removal rates and no logical pattern in behaviour even if they belong to the same therapeutic groups. What is clear is that the elimination of most of the substances is incomplete and improvements of the wastewater treatment and subsequent treatments of the produced sludge are required to prevent the introduction of these micro-pollutants in the environment. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Department of Environmental Chemistry, IDAEA-CSIC, c/Jordi Girona 18-26, 08034 Barcelona, Spain. Tel.: þ34934006172; fax: þ34932045904. E-mail address:
[email protected] (M. Petrovic). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.010
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1.
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Introduction
Pharmaceuticals are a large and diverse group of compounds designed to prevent, cure and treat disease and improve health. They have long been used in significant quantities throughout the world. Their usage and consumption are increasing consistently due to the discoveries of new drugs, the expanding population and the inverting age structure in the general population, as well as due to expiration of patents with resulting availability of less expensive generics (Daughton, 2003). After intake, these pharmaceutically active compounds undergo metabolic processes in organism. Significant fractions of the parent compound are excreted in unmetabolized form or as metabolites (active or inactive) into raw sewage and wastewater treatment systems. Sewage treatment plant effluents are discharged to water bodies or reused for irrigation, and biosolids produced are reused in agriculture as soil amendment or disposed to landfill. Thus body metabolization and excretion followed by wastewater treatment is considered to be the primary pathway of pharmaceuticals to the environment. Disposal of drug leftovers to sewage and trash is another source of entry, but its relative significance is unknown with respect to the overall levels of pharmaceuticals in the environment (Ruhoya and Daughton, 2008). Continual improvements in analytical equipment and methodologies enable the determination of pharmaceuticals at lower and lower concentration levels in different environmental matrices. Pharmaceuticals and their metabolites in surface water and aquatic sediment were subject of numerous studies concerning pharmaceuticals in the environment (Bartelt-Hunt et al., 2009; Nilsen, 2007); Vazquez-Roig et al., 2010. Several studies investigated the occurrence and distribution of pharmaceuticals in soil irrigated with reclaimed water (Gielen et al., 2009; Kinney, 2006); Ternes et al., 2007 and soil that received biosolids from urban sewage treatment plants (Carbonell et al., 2009; Lapen et al., 2008). These studies indicated that the applied wastewater treatments are not efficient enough to remove these micro-pollutants from wastewater and sludge, and as a result they find their way into the environment. Once entered the environment, pharmaceutically active compounds can produce subtle effects on aquatic and terrestrial organisms, especially on the former since they are exposed to long-term continuous influx of wastewater effluents. Several studies investigated and reported on it (Cleuvers, 2004; Nentwig et al., 2004; Schnell et al., 2009). Therefore, the occurrence of pharmaceutical compounds and the extent to which they can be eliminated during wastewater treatment have become active subject matter of actual research. Conventional systems that use an activated sludge process are still widely employed for wastewater treatment, mostly because they produce effluents that meet required quality standards (suitable for disposal or recycling purposes), at reasonable operating and maintenance costs. However, this type of treatment has limited capability of removing pharmaceuticals from wastewater (KasprzykHordern et al., 2009); Wick et al., 2009. Most of the studies on the fate of pharmaceuticals in WWTPs focused only on the aqueous phase, and concentrations of the compounds in sludge were rarely determined mainly due to the demanding
efforts required in the analysis in this difficult matrix. Out of 117 publications studied by Miege et al (Mie`ge et al., 2009), only 15 reported the concentrations of pharmaceuticals in sludge and 1 in suspended solid, and none of these papers reported the removal obtained taking into account both aqueous and solid phases of WWTPs. Still, the screening of sewage sludge showed that these micro-pollutants are very present in this medium (Lillenberg et al., 2009,; Lindberg et al., 2010; McClellan and Halden, 2010; Radjenovic et al., 2009a). In this study we aimed to determine the contamination of wastewater and sludge with 43 pharmaceutical compounds in order to obtain more information on their fate during conventional wastewater treatment. The selected pharmaceuticals belong to different therapeutic groups (i.e. non-steroidal antiinflammatory drugs (NSAIDs), lipid modifying agents (fibrates and statins), psychiatric drugs (benzodiazepine derivative drugs and antiepileptics), histamine H2-receptor antagonists, antibacterials for systemic use, beta blocking agents, beta-agonists, diuretics, angiotensin converting enzyme (ACE) inhibitors and anti-diabetics). The samples were provided from three conventional full-scale activated sludge sewage treatment plants with anaerobic digestion of sludge, from the region of Catalonia (Spain). The preparation and analysis of the samples were performed using high performance liquid chromatography coupled to a hybrid triple quadrupole e linear ion trap mass spectrometer (HPLC-QLIT- MS/MS) according to the previously developed multi-residual methodologies for analysis of pharmaceuticals in wastewater and sludge samples (Gros et al., 2009; Jelic et al., 2009).
2.
Experimental part
2.1.
Chemicals
All the pharmaceutical standards for target compounds were of high purity grade (>90%). ibuprofen, naproxen, ketoprofen, diclofenac and gemfibrozil were supplied by Jescuder (Rubı´, Spain). acetaminophen, indomethacin, mefenamic acid, phenazone, bezifibrate, mevastatin, fenofibrate, pravastatin (as sodium salt), carbamazepine, famotidine, ranitidine (as hydrochloride), cimetidine (as hydrochloride), erythromycin (as hydrate), azithromycin (as dehydrate), roxitromycin, clarithromycin, josamycin, tylosin a, sulfamethazine, trimethoprim, chloramphenicol, atenolol, sotalol, metoprolol (as tartrate), timolol, pindolol, nadolol, salbutamol, clenbuterol (as hydrochloride), enalapril (as maleate), glibenclamide, furosemide, hydrochlorothiazide and metronidazole were purchased from SigmaeAldrich (Steinheim, Germany). Standard atorvastatin (as calcium salt) was provided by LGC Promochem (London, UK), while diazepam, lorazepam and butalbital were from Cerilliant (Texas, USA). The isotopically labelled compounds, used as internal standards, were sulfamethazine-d4, famotidine-13C3, rac-timolol-d5 maleate, clarithromycin-n-methyl-d3, atorvastatin-d5 sodium salt, fenofibrate-d6, metoprolol-d7, metronidazole hydroxyl-d2, pravastatin-d3, ketoprofen-13C,d3, indomethazine-d4, rac-naproxen-d3, mefenamic acid-d3, gemfibrozil-d6, bezafibrate-d4 and furosemide-d5 from Toronto Research Chemicals;
Table 1 e Characteristics of the studied wastewater treatment plants (WWTPs). A) Treatment characteristics HRT (h)
SRT (days)
Designed Treatment Capacity (m3/day)
Average Flow (m3/day)
Population Equivavalent
Sludge tretment
Disposal of sludge
Sludge production (t/year)
Dry matter (t/year)
Organic matter (%)
WWTP1
Biological þ Tertiary
26-40
10
47500
25000
74000
Composting
9000
1800
75
WWTP2
Biological
20
6
35000
26000
170000
8500
2000
65
WWTP3
Biological with P and N removal
40
16
25000
21000
400000
Disposal to soil; Agricultural usage Disposal to soil; Inceneration Controlled disposal to landfill
11400
2900
53
Anaerobic digestion Anaerobic digestion Drying
B) Wastewater and sludge characteristics Wastewater
Sludge
SSInfluent SSEffluent BOD5In BOD5Out CODIn CODOut NtIn NtOut PtIn PtOut Teffluent pHInfluent pHeffluent NtSludge Namoniuim Norganic P K pHSludge (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) C (%) (%) (%) (P2O5) (K2O) (%) (%) WWTP1 WWTP2 WWTP3
191 330 750
43 20 10
175 390 1130
19 14 6
393 700 1800
82 85 36
39 70 126
25 42 11
6 8 16
4 3 1
n.d. 20 6 22 5
7.7 n.d. 7.5
7.3 7.2 7.2
5.4 5.9 5.1
1.2 2.9 1.1
4.2 2.9 4.1
3 5 3.6
0.3 0.3 0.2
6.5 6.4 7.6
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Type of treatment
SPM-Suspended particulate matter. BOD5-Biochemical Oxygen Demand. COD-Chemical Oxygen Demand. Nt-Total Nitrogen. Pt-Total Phosphorus.
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diazepam-d5 and phenobarbital-d3 from Cerilliant (Texas, USA); atenolol-d7, carbamazepine-d10, ibuprofen-d3, clotrimazole-d5, enalapriled5, hydrochlorothiazide-d2, glyburide-d3, albuterold3, cimetidine-d3, antipyrine-d3, aceta-minophen-d4, diclofenac-d4, clofibric-d4 acid, hydrochlorothiazide-3,3-d2 from CDN Isotopes (Quebec, Canada); sotalol hydrochloride d6 from Dr. Ehrenstorfer (Augsburg, Germany) and erythromycin-13C,d3 (N-methyl-13C,d3) from Isotec (Ohio, USA). The solvents, HPLC grade methanol, acetonitrile, water (Lichrosolv) and formic acid 98% were provided by Merck (Darmstadt, Germany). Nitrogen used for drying from Air Liquide (Spain) was of 99.995% purity. The cartridges used for solid phase extraction were Oasis HLB (200 mg, 6 mL) from Waters Corporation (Milford, MA, USA). The syringe filters of 0.45 mm pore size were purchased from Pall Corp (USA). The individual standard solutions as well as isotopically labelled internal standard solutions were prepared on a weight basis in methanol. Furosemide and butalbital were obtained as solutions in acetonitrile, while lorazepam and diazepam were dissolved in methanol, at a concentration of 1 mg/mL. The solutions were stored at 20 C. Fresh stock solutions of antibiotics were prepared monthly due to their limited stability while stock solutions for the rest of substances was renewed every three months. A mixture of all pharmaceuticals was prepared by appropriate dilution of individual stock solutions in methanol-water (25:75, v/v) and it was renewed before each analytical run. A separate mixture of isotopically labelled internal standards, used for internal standard quantification, was prepared in methanol and further diluted in methanolwater (25:75, v/v) mixture.
2.2.
Sample collection
Samples (i.e. influent and effluent wastewater, and sewage sludge) were obtained from three full-scale wastewater treatment plants (WWTPs) in the region of Catalonia (Spain). Table 1 (A and B) summarizes some characteristics of the three investigated WWTPs (source: Annual report of Catalan Water Agency for 2008). WWTP1 and WWTP2 treat predominantly municipal wastewater, while the WWTP3 influent has an important industrial contribution. The wastewater treatment process in WWTP1 consists in pre-treatment, primary settling, and biological treatment (anoxic/aerobic) followed by secondary settling. The secondary effluent then passes through coagulation/flocculation and lamella settling, and after microfiltration and chlorination is discharged as tertiary effluent. The gravity thickened (primary sludge) and flotation thickened waste activated sludge are mixed and dewatered via centrifuge, and sent for composting. The WWTP1 is designed for 210000 equivalent inhabitants (eq.inh.) and to treat up to 47500 m3/day of wastewater. It is situated in the tourist coastal area where the amount and the quality of water entering the plant are significantly affected by the seasonal population growth. The wastewater flow in WWTP1 changes from 15000 m3/day, during the winter months, to 32000 m3/day during the summer months. During 2008, it treated (on average) 25000 m3/day for 74000 eq.inh. The WWTP2, which usually works with 80% of designed treatment capacity, treated around 26000 m3/day in 2008, serving a population equivalent of around 170000. The
treatment includes pre-treatment and primary clarifier, followed by activated sludge treatment and secondary clarifier. Sludge generated from primary and secondary clarifiers is thickened and blended and fed to anaerobic digester system, and dewatered via centrifuge. WWTP3 employs primary sedimentation, followed by a secondary biological treatment for nitrogen and phosphorus removal. The sludge mixture proceeding from the primary and secondary settlers is thickened by gravity, treated by anaerobic digestion and dewatered on belt filter press. The WWTP3 treated an average of 21000 m3/ day of urban and industrial wastewater in 2008, which is about 80% of the total treatment capacity of the plant. Schematic diagrams of the treatment processes are shown in Fig. 1. Wastewater and sludge samples were collected in eight sampling campaigns between July 2007 and March 2009, in campaign intervals of 3 months covering all the seasons of the year. The sampling was carried out at dry weather flow, according to the established sampling protocols and locations defined by Aguas de Barcelona, and following the occupational health and safety regulations. Composite influent and effluent wastewater samples (24-h) were collected using an ISCO automated sampler (GLS Compact Comosite Samplers) with an integrated 5 L amber glass bottle and cooling system that was providing temperatures below 4 C. The sampling program was set to collect 50 mL of wastewater every 30 min during 24 h. The effluent samples were taken according to the retention times estimated with the current data for each WWTPs and sampling campaign. The influent wastewater samples were collected in the pre-treatment building of WWTP1, in the influent homogenization ponds in WWTP2, while in WWTP3, three different intakes in the pumping well of the plant were taken proportionally to the flow and volume and mixed. The effluent wastewater samples were taken after the secondary treatment at WWTP2 and WWTP3, and after the tertiary one in WWTP1. The samples were kept at 4 C until extraction (within 48 h). Prior to extraction, the water was vacuum filtered through 1 mm glass fiber filters, followed by 0.45 mm nylon membrane filters (Teknokroma, Barcelona, Spain). The analyzed samples of sludge were collected at the final phase of the process, i.e. treated sewage sludge. Ten grab samples of equal volume were taken from the exit belt to make up the composite sample of sludge. Sludge samples were mixed and chilled to 4 C for transportation and freeze-dried (LioAlfa 6, Telstar) at 50 C and under 0.044 bar vacuum and stored at 20 C until the analysis.
2.3.
Sample preparation
Procedures for preparation of water and sludge samples for instrumental analysis were described in detail previously (Gros et al., 2009; Jelic et al., 2009). In brief, in the filtered-aliquots of wastewater (100 mL for influent and 200 mL for effluent) Na2EDTA was added to a concentration of 0.1vol%. Then the target compounds were separated by solid phase extraction (Oasis HLB cartridges, 6 cc, 200 mg; Waters Corp., Milford, MA) using a Baker vacuum system (J.T. Baker, Deventer, The Netherlands), and concentrated via elution with pure methanol. The 8 mL eluents were evaporated under a stream of nitrogen and reconstituted in 1 mL of methanol-water mixture (25:75). Prior to instrumental
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WWTP1 Influent
Anoxic/aerobic treatment
Primary clarifier
Screen Grease and grit chamber
Alum/polyelectrolyte Secondary clarifier Lamella clarifier Tertiary effluent Chloriantion/ Microfiltration
Coagulation/ flocculation Return sludge
1ry Sludge
3ry Sludge 2ry Sludge
Sludge Gravity Thickener
Sludge Flotation Thickener Sludge outlet
Deposit Mix
Centrifuge
Composting
WWTP2 Influent
Anoxic/aerobic treatment
Primary clarifier
Screen Grease and grit chamber
1ry Sludge
Return sludge
Sludge Gravity Thickener
2ry Sludge
Sludge Flotation Thickener Anaerobic digestion
Centrifuge
WWTP3 Influent
Primary clarifier
Screen Grease and grit chamber
Secondary effluent
Sludge outlet
Coagulant Anoxic/aerobic treatment
Secondary effluent
Homogeniz. tank 1ry Sludge
Return sludge 2ry Sludge
Sludge Gravity Thickener
Sludge outlet
Anaerobic digestion
Filter press
Fig. 1 e Schematic diagram of the studied wastewater treatment plants.
analysis, these samples were fortified by a mixture of internal standard to a final concentration of 20 ng/mL. Sludge samples were extracted using an accelerated solvent extraction (ASE) (Dionex ASE 200, Dionex; Sunnyvale, CA). The extractions were carried out using a methanol-water mixture (1:2) as extraction solvent, at 1500 psi and 100 C in 3 static cycles, each lasting 5 min. Finally, the cell was flushed with 100% cell volume of fresh solvent. Concentrated extracts were dissolved in water in order to reduce the content of methanol (<5 Vol%) and processed further as water samples. Instrumental analysis of all samples was done by HPLC-QLIT-MS/MS.
2.4.
Instrumental analysis
The analytical method used in this study was already developed by M. Gros et al (Gros et al., 2009). Samples were analyzed using high performance liquid chromatography (HPLC) coupled to tandem mass spectrometry (MS/MS). LC analysis was performed using Symbiosis Pico (SP104.002, Spark, Holland), equipped with an autosampler and connected in series with a 4000 QTRAP Hybrid Triple Quadrupole - Linear Ion Trap mass spectrometer equipped with a Turbo Ion Spray source (Applied Biosystems-Sciex, Foster City, CA, USA). Chromatographic separation was achieved with a Purospher Star RP-18 endcapped column (125 mm 2.0 mm, particle size 5 mm) preceded by a C18 guard column (4 mm 4 mm, particle size 5 mm), both supplied by Merck (Darmstadt, Germany). The mobile phases for the analysis in negative ionization (NI) mode were a mixture of acetonitrile-methanol (1:1, v/v) (i.e. eluent A), and HPLC grade water (i.e. eluent B). The analysis in positive ionization (PI) mode was performed using acetonitrile as eluent A and HPLC grade water with 0.1% formic acid as eluent B. The target compounds were scanned in
MRM, monitoring two transitions between the precursor ion and the most abundant fragment ions for each compound. Further information on the methodology and its performances can be found elsewhere (Gros et al., 2009; Jelic et al., 2009).
2.5.
Removal rate calculation
In this study we employed a mass balance approach in order to asses quantitatively the removal of the selected pharmaceuticals during wastewater treatment. Even when dealing with such a complex system, we can assume that the WWTP behaves as a black-box with only one entrance (i.e. influent water) and two outlets (i.e. effluent water and treated sludge) and operates at steady state over the studied period of two years. Then, from the measured concentrations and the operation parameters (i.e. flow rates of influent, V_ influent , and V_ effluent , and sludge production, P_ sludge ) could be written as follows: _ in m _ out R_ Overall ¼ m
(1)
_ in ¼ m _ influent m
(2)
_ out ¼ m _ effluent þ m _ sludge m
(3)
V_ influent ¼ V_ effluent ¼ V_ l
(4)
R_ Overall ¼ cinfluent V_ l ceffluent V_ l þ csludge P_ sludge
(5)
_ out , m _ influent , m _ effluent and m _ sludge are the mass flow _ in , m where m rate (in g/day) of inlet, outlet, influent liquid, effluent liquid and sludge, respectively. R_ Overall (g/day) is the mass load lost
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per unit of time due to the sum of all processes that can occur during wastewater treatment. Mass flow rates of pharmaceutical compounds in influent and effluent streams were calculated by multiplying the measured concentrations in a given stream by the appropriate flow rate of that stream. Thus, the concentration of each pharmaceutical in the daily influent and effluent samples (cinfluent or ceffluent , [g/m3]) was multiplied by the flow rate for that day (i.e.V_ l , [m3/day]) to give the mass of the pharmaceutical entering or leaving the plant that day (g/day) (i.e. daily mass load). Similarly, the concentration of pharmaceuticals in the treated sludge (c_ sludge , [ng/g d.w.]) was multiplied by the production rate of sludge (tons/ day) to determine the mass of pharmaceuticals removed with the sludge (g/day). From these data, both removal from aqueous-phase, RAqueous phase(%), and overall removal (i.e. mass loss), ROverall(%), of the target compounds were calculated according to the eqs. (3) and (4), respectively: RAqueous phase ð%Þ ¼ 100 cinfluent
ROverall ð%Þ ¼ 100
V_ l ceffluent V_ l cinfluent V_ l
R_ Overall
cinfluent V_ l
(6)
(7)
Taking into account that the sampling at the influent and the effluent was performed in accordance with retention time and flow rate of the plants, the removal rates were calculated from pair-wise data and then averaged. Fig. 4 shows average percentages of the detected pharmaceuticals that were discharged with effluent, sorbed to sludge, and removed during treatment (i.e. overall removal rate).
3.
Results and discussion
3.1. Occurrence of pharmaceuticals in wastewater and sludge Table 2 shows the frequencies of detection and the limits of quantification of the pharmaceutical compounds detected in wastewater and sludge from the studied WWTPs. Out of 43 analyzed pharmaceutical compounds, 32 were detected in influent, 29 in effluent and 21 in sludge samples. The analysis of wastewater and sludge showed huge variation in concentration levels from campaign to campaign of a given plant. This can be due to changes of the composition of influent waters in different seasons, weather conditions and operational conditions of the plant, as well as due to the amount of the drug that is used. But, the sampling protocol itself has great influence on concentration values obtained (Ort et al., 2010). The fact is that the substances arrive in a small number of wastewater packets to the influent of WWTP, in unpredictable amounts and time intervals, thus the influent loads, especially, are easily systematically underestimated. In order to minimize the effect of sampling, triplicate of composite samples from 8 sampling campaigns were analyzed. The results are shown in Fig. 2 as box plots displaying 25th, median and 75th percentiles as boxes, and minimum and maximum concentration values, as line and triangle, respectively, so the overall uncertainty (including
variability and reducible uncertainty) could be understood better. The uncertainty due to analysis is not shown in the Fig. 2, and it was less than 5%, 6% and 11% for wastewater influent, effluent and sludge, respectively (Gros et al., 2009; Jelic et al., 2009). According to the daily loads and population served by each plant, the amount of the selected pharmaceuticals disposed in these plants is estimated to be 5.6, 2.0 and 0.4 g/day/1000 equivalent inhabitants for WWTP1, WWTP2 and WWTP3, respectively (Fig. 3). The highest levels at the influent of all three WWTPs were observed for NSAIDs that were expected due to their high consumption. In addition, topical application of the NSAIDs results in greater discharge of these compounds in unmodified forms. This result is in fairly good agreement with previously reported studies (Gracia-Lor et al., 2010; Mie`ge et al., 2009). At the influent of the plants, this group accounts for ca. 65% of all the therapeutic groups analyzed, as can be seen in the Fig. 3. Naproxen, ketoprofen and diclofenac were detected in all the samples in average concentration ranges 4.2e7.2 mg/L, 1.1e2.3 mg/L and 0.4e1.5 mg/L, respectively. Ibuprofen and acetaminophen were not included in the discussion because they yielded to high concentrations which can be due to the strong matrix effect and/or to interactions that may produce false identification and thus incorrect concentration values. Lower but still significant levels of lipid modifying agents (including fibrates and statins) (7e12%), diuretics (8e10%), and beta-blockers (5e9%) were detected entering these WWTPs. Furosemide, bezafibrate, atenolol and carbamazepine were quantified in all influent samples from the three WWTPs in average concentrations ranging from 0.4 to 1.4 mg/L. The amount found in effluent or sludge depended on the removal efficiency of plant and/or the physicochemical properties of the compounds. As the influent concentrations can give us information about the consumption of pharmaceuticals, the effluent and the sludge concentrations are important from the environmental point of view, since the pharmaceuticals find their way to the environment through discharges of treated waters to rivers, or disposal of sludge to agricultural and forest land. In the effluent waters, NSAIDs were present in the highest percentage (35e44%), followed by the lipid modifying agents (8e29%) and psychiatric drugs (both antiepileptic and benzodiazepine derivative drugs) (17e30%) (Fig. 3). The highest concentrations in the effluents of all the WWTPs were found for naproxen, diclofenac and carbamazepine, and they ranged from 0.4 to 1 mg/L depending on the compound and the removal efficiency of the plant. In the treated effluent of WWTP2, ketoprofen, bezafibrate, atenolol and furosemide were detected in much higher average concentrations (0.7, 0.4, 0.4 and 0.9 mg/L, respectively) than in the other two plants. Analysis of sludge samples showed the presence of 21 out of 43 analyzed pharmaceuticals, covering a wide range of physicochemical properties, as in the case of wastewater effluent. Diuretics accounted for ca. 19%, antibacterials for 16e21% and lipid modifying agents 15e20% of all the pharmaceuticals analyzed (depending on the plant). Hydrochlorthiazide, furosemide, atorvastatine, clarithormycin, carbamazepine and diclofenac were ubiquitous in samples from all three WWTPs, in average concentrations from 30 to 60 ng/g. On the other hand, beta-blockers, beta-agonist and histamine H2-receptor antagonists were found in very low concentrations in sludge.
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Table 2 e Frequency of detection (%) and limits of quantification (LOQ) of pharmaceuticals detected in wastewater influent (WWI), effluent (WWE) and sewage sludge from the studied WWTPs during 8 sampling campaigns. Compounds
Ketoprofen (KTP) Naproxen (NPR) Diclofenac (DCL) Indomethacine (INM) Mefenamic acid (MFA) Bezafibrate (BZF) Fenofibrate (FNB) Gemfibrozil (GMB) Atorvastatin (ATR) Pravastatin (PRV) Mevastatin (MVS) Diazepam (DZP) Lorazepam (LRZ) Carbamazepine (CBZ) Clarithromycin (CLR) Cimetidine (CMTD) Ranitidine (RNTD) Famotidine (FMTD) Sulfamethazine (SLFM) Trimethoprim (TRM) Metronidazole (MTR) Chloramphenicol (CHLR) Atenolol (ATN) Sotalol (STL) Metoprolol (MTP) Timolol (TML) Nadolol (NDL) Salbutamol (SLB) Enalapril (ENL) Glibenclamide (GLB) Furosemide (FRS) Hydrochlorthiazide (HCRT)
Frequency of detection, %
LOQ (ng/L)
LOQ (ng/g)
WWI
WWE
Sludge
WWI
WWE
Sludge
100 100 92 65 54 100 42 38 100 73 12 54 81 100 73 100 92 19 58 100 62 12 100 65 35 42 100 69 96 85 100 0
54 88 100 58 77 100 0 58 77 65 8 54 85 100 85 69 88 8 65 96 62 46 100 58 62 65 69 58 46 65 96 0
0 0 100 0 83 100 79 50 96 0 0 88 79 100 83 88 92 96 33 88 0 0 88 54 0 0 54 0 0 92 83 100
13 21 4.0 3.0 16 4.0 0.5 3.0 4.0 25 2.0 3.0 7.0 2.0 5.0 0.6 3.0 1.0 2.0 1.0 6.0 2.0 9.0 5.0 2.0 0.3 0.8 0.3 5.0 5.0 4.0 13
7.0 3.0 4.0 2.0 5.0 0.4 0.5 1.0 2.0 9.0 2.0 1.2 4.0 2.0 4.0 0.4 2.0 0.7 1.0 0.4 0.7 0.6 9.0 2.0 2.0 0.3 0.2 0.2 0.7 4.0 2.0 6.0
1.3 0.9 2.0 1.0 0.4 0.4 2.5 1.7 2.5 2.4 4.5 4.1 5.1 0.2 7.1 0.2 0.3 0.1 0.8 0.6 5.6 0.2 0.7 0.4 1.2 0.9 0.3 0.3 0.4 3.5 1.0 0.5
The total loads of analyzed pharmaceuticals that leave the plants unmodified (including sludge and effluent water) were calculated to equal 1.1, 0.9 and 0.1 g/day/1000 equivalent inhabitants for WWTP1, WWTP2 and WWTP3, respectively, of which only 3e9% (depending on the plant) was retained by sludge. The amount of pharmaceutical compounds detected in this study exiting the plants is not of great concern if we compare it with the results from some other studies done in this field (Castiglioni et al., 2006;,Zorita et al., 2009).
3.2. Overall removal of pharmaceuticals during wastewater treatment The daily mass loads of target compounds in wastewater influent and effluent, and in sludge, in g/day, were calculated as explained previously, and these values were used for the estimation and the comparison of the aqueous phase removal and the overall removal rates. The mass loads of pharmaceuticals that were discharged with effluent, sorbed to sludge and removed during treatment were normalized on influent of a given plant and presented in Fig. 4. Considering the fact that pharmaceuticals are grouped by the therapeutical applications for which they are used and not on the basis of their physicochemical similarity, their removal during treatment is expected to be diverse. Here the term removal refers to the conversion of
a pharmaceutical to a compound different than the analyzed one (i.e. the parent compound). Thus, the overall removal refers to all the losses of a parent compound produced by different mechanisms of chemical and physical transformation, biodegradation and sorption to solid matter. High aqueous-phase removal rates for some compounds (i.e. lipid regulator fenofibrate and histamine H2-receptor antagonists famotidine) would suggest very good removal of these compounds during the wastewater treatment. But, as shown in Fig. 4, only a certain percent of the total mass input is really lost during the treatment (overall removal). The rest was accumulated in sludge or discharged with the effluent. Sorption of fenofibrate, atorvastatine, diazepam and clarithromycin contributed to the elimination from the aqueous phase with more than 20% related to the amount of these compounds at the influent. This finding clearly indicates the importance of the analysis of sludge when studying wastewater treatment performances. Since many of the analyzed compounds were found in the sludge samples, the overall removal rate was the parameter used to compare the removal performances of the studied treatment plants. In general, the removal rates varied strongly without evident correlation to the compound structure, as can be seen in the Fig. 4. The antihypertensive enalapril and NSAIDs ketoprofen and naproxen were removed in all the three cases with very
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Fig. 2 e Box plots of concentration ranges (Min (L), P 0.25, Median, P 0.75 and Max (:) of the pharmaceuticals detected in wastewater influent (IN), effluent (OUT) and sewage sludge from the studied wastewater treatment plants (WWTP1, WWTP2 and WWTP3) during 8 sampling campaigns (compound abbreviations are indicated in Table 2).
g/day/1000 Eq.Inh.
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Diuretics ß-Blockers and ß-Antagonists Antibiotics Histamine H2 antagonists Psychiatric drugs Lipid regulators Analgesics and Antiinflamatories Others
Fig. 3 e Daily mass loads (g/day per 1000 eq.inh.) of different therapeutic groups at the influent and effluent, and in the sludge from the studied WWTPs.
good removal efficiency (>80%) and they did not accumulate in sludge. Similar removal of these compounds from aqueous phase, under conventional treatment conditions, was observed in various studies on this topic (Lishman et al., 2006; Sim et al., 2010; Zorita et al., 2009). But then, the most analyzed anticonvulsant carbamazepine showed very low removal (<25%) regardless of the treatment applied. The results concerning its persistence and ubiquitous occurrence match with those from previous studies (Joss et al., 2005; Pe´rez and Barcelo´, 2007; Radjenovic et al., 2009b). No significant overall removal during the studied treatments (<30%) was observed for antibiotics trimethoprim and metronidazole, and benzodiazepine lorazepam. The incomplete removal of these compounds during conventional treatment has been reported by several studies (Bendz et al., 2005; Go¨bel et al., 2007; Kasprzyk-Hordern et al., 2009). A benzodiazepine diazepam and antimicrobial chloramphenicol were detected in concentrations close to their corresponding LOQs thus no reliable conclusion could be made on their behaviour. Cholesterol lowering statin drugs pravastatin and mevastatin, antibiotic sulfamethazine, beta-blockers metaprolol and timolol, beta-agonist salbutamol were not accumulated in sludge and they showed a variety of removal rates between 30 and 80%. Inconsistent overall removal was also observed for NSAIDs mefenamic acid, indometacine and diclofenac, histamine H2-receptor antagonists cimetidine, famotidine and ranitidine, and diuretic furosemide. Some previous studies have also reported quite variable removal efficiencies of these compounds. For example, Castiglioni et al. (Castiglioni et al., 2006) observed low or no removal for salbutamol, furosemide and bezafibrate, whereas Kasprzyk-Hordern et al. (KasprzykHordern et al., 2009) noted higher removal for these compounds (>70%). Regarding the removal of histamine H2receptor antagonists, the reported removals varied from rather low (Radjenovic et al., 2009b) to high rates (i.e. 86% in (Kasprzyk-Hordern et al., 2009). It is difficult to give a final conclusion on removal of majority of the studied
pharmaceuticals, but it seems that the removal was mainly influenced by wastewater characteristics, operational conditions and treatment technology used. Comparing to the other two plants, WWTP1 offers better removal for the majority of the analyzed compounds (Fig. 4). This activated sludge plant featured by a tertiary treatment in WWTP1 improves the removal of diclofenac to 60%, while in the other two plants removal is much lower (<24%). Low removals of diclofenac were already reported in some publications on this topic (Cirja et al., 2008; Kimura et al., 2007; Quintana et al., 2005) imputed its persistence to the presence of chlorine group in the molecule. Some studies on removal during wastewater treatment showed no influence of solid retention time on the removal of diclofenac (Clara et al., 2005; Kreuzinger et al., 2004; Lishman et al., 2006). Furosemide, pravastatin, and ranitidine that were eliminated with removal ca. 80% and 60% in WWTP1 and WWTP3, respectively, marked very low (ca. 30%) removal rates in WWTP2. Better performances of WWTP1 and WWTP3 may be due to longer both hydraulic and solid retention times. As a compound spends more time in reactors wherein bacteria growth is promoted, the biological transformation may occur to a greater extent (Reif et al., 2008).It has been proven that longer SRT, especially, improves the elimination of most of the pharmaceuticals during sewage treatment (Clara et al., 2005; Go¨bel et al., 2007); Sua´rez et al., 2005. The negative values of removal rates (omitted in the Fig. 4) refer to an increase in the concentration of an analyzed parent compound during treatment. This phenomenon of “negative removal” for some compounds was already reported in the literature (Gros et al., 2009; Joss et al., 2005); Wick et al., 2009. Hydrochlorothiazide was not detected in influent neither effluent water samples, but it was detected in sludge. This was not at all expected according to its low logP and the fact that >95% of the dose of this pharmaceutical is excreted unchanged (EMA, 2009). Lipid-regulating agent gemfibrozil was detected in higher concentration in the effluent than in the influent water samples. Similar was observed for macrolide clarythromycin,
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Liquid removal
Ketoprofen Naproxen Diclofenac Indomethacine Mefenamic acid Bezafibrate Fenofibrate Gemfibrozil Atorvastatin Pravastatin Mevastatin Diazepam Lorazepam Carbamazepine Clarithromycin Cimetidine Ranitidine Famotidine Sulfamethazine Trimethoprim Metronidazole Chloramphenicol Atenolol Sotalol Metoprolol Timolol Nadolol Salbutamol Hydrochlorthiazide Enalapril Glibenclamide Furosemide
Overall removal
Removed during treatment to sludge Discharged from WWTP (Liquid effluent) Removed during treatment Sorbed Sorbed to sludge Discharged from WWTP
Fig. 4 e Normalized mass loads of the selected pharmaceuticals entering the studied WWTPs (i.e. fraction discharged with effluent, sorbed to sludge, and removed during treatment (overall removal rate))
anti-diabetic glibenclamide, lipid regulators fenofibrate and atrorvastatin, as well as for carbamazepine in one of the plants, which yielded higher concentration levels at the exit of a plant (i.e. including effluent and sludge) than at its entrance. The explanation for this could be found in sampling protocols, as noted before; not only because they could be inadequate, but because of the nature of disposal of pharmaceuticals. Even though the analysis of effluent and sludge yields more certain results, because they come from stabilization processes, the sampling in general may result in underestimated and even negative removals. Furthermore, the negative removal can be explained by the formation of unmeasured products of human
metabolism and/or transformation products (e.g. glucuronide conjugate, methylates, glycinates etc.) that passing through the plant convert back to the parent compounds. This can be considered as a reasonable assumption since the metabolites and some derivates of the mentioned compounds are wellknown (e.g. hydroxy and epoxy-derivatives of carbamazepine; 4-trans-hydroxy and 3-cis-hydroxy derivatives of glibenclamide; ortho- and parahydroxylated derivatives of atorvastatine; gemfibrozil acyl glucuronide etc.) (Aviram et al., 1998; Miao et al., 2005); Shipkova and Wieland, 2005. Gobel et al. (Go¨bel et al., 2007) proposed gradual release of the macrolides (e.g. clarithormycin) from feces particles during biological treatment as an
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 6 5 e1 1 7 6
explanation for the possible negative removal rates for these antibiotics. During complex metabolic processes in human body and bio-chemical in wastewater treatment, various scenarios of transformation from parent compound to metabolite and derivatives and vice versa can occur. These metabolites can be just as active as their parent compounds. Therefore, the occurrence of metabolites and transformation products and pathways should be included in the future studies in order to obtain accurate information on removal of pharmaceuticals during treatment and to determine treatment plant capabilities.
4.
Conclusions
The study showed that, even though the WWTPs meet the regulatory requirements for wastewater treatment (Directive 91/271/EEC), they are only moderately effective in removing pharmaceutical compounds. Two plants that operated with longer SRT (i.e. WWTP1, with a tertiary treatment, and WWTP3) offered better removal of the majority of the analyzed pharmaceuticals. The calculated removal rates may have been underestimated due to at least three factors: removal efficiency was calculated from the mean concentration values; the metabolites and transformation products of pharmaceuticals and their amounts were not defined; and the time-proportional sampling may not be perfectly suitable for pharmaceutical analysis, especially on influent (Ort et al., 2010). Still, the fact is that 29 pharmaceuticals were detected in effluent wastewater and 21 in sludge samples. These pharmaceuticals cover a wide range of physical-chemical properties and biological activities. Although the chronic toxicity effects of such a mixture are unknown and thus the risk that it could pose to the environment could not be fully assessed, their presence must not be ignored. The results of this and similar studies are very useful for the estimations of (a) the magnitude of pharmaceuticals that reach the environment via either effluent or sludge and (b) the efficiency of the currently applied wastewater treatments, regarding the elimination of pharmaceuticals. More information on quality, quantity and toxicity of pharmaceuticals and their metabolites are definitely needed especially when attempting to reuse wastewater and dispose sludge to agricultural areas and landfills.
Acknowledgements The work was financially supported by the Spanish Ministry of Education and Science through the project SOSTAQUA (CEN 2007-1039) (led by Aguas de Barcelona and financed by the CDTI (Centre for the Development of Industrial Technology) in the framework of the Ingenio 2010 Programme under the CENIT call), project CEMAGUA (CGL2007-64551/HID) and project Consolider-Ingenio 2010 [CSD2009-00065], and by the Spanish Ministry of the Environment and Rural and Marine Affairs through the project MMAMRM 010/PC08/3-04. The authors deeply thank Joaquim Munte´ from the Catalan Water Agency (Age`ncia Catalana de l’Aigua) for providing useful information for the study. A.Jelic gratefully acknowledges the JAE Program (Junta para la Ampliacio´n de Estudios eJAE
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Predoc), co-financed by CSIC (Consejo Superior de Investigaciones Cientı´ficas) and European Social Funds, for a predoctoral grant. Merck (Darmstadt, Germany) is gratefully acknowledged for providing the HPLC columns.
references
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Pharmaceuticals, personal care products and anthropogenic waste indicators detected in streambed sediments of the lower Columbia River and selected tributaries, National Ground Water Association, Paper 4483, p. 15. Ort, C., Lawrence, M.G., Rieckermann, J.r., Joss, A., 2010. Sampling for Pharmaceuticals and Personal Care Products (PPCPs) and Illicit Drugs in Wastewater Systems: Are Your Conclusions Valid? A Critical Review. Environmental Science & Technology 44, 6024e6035. Pe´rez, S., Barcelo´, D., 2007. Application of advanced MS techniques to analysis and identification of human and microbial metabolites of pharmaceuticals in the aquatic environment. TrAC Trends in Analytical Chemistry 26, 494e514. Quintana, J.B., Weiss, S., Reemtsma, T., 2005. Pathways and metabolites of microbial degradation of selected acidic pharmaceutical and their occurrence in municipal wastewater treated by a membrane bioreactor. Water Res. 39, 2654. Radjenovicc, J., Jelic, A., Petrovic, M., Barceloo, D., 2009. Determination of pharmaceuticals in sewage sludge by pressurized liquid extraction (PLE) coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS). Analytical and Bioanalytical Chemistry, 1e11. Radjenovic, J., Petrovic, M., Barcelo´, D., 2009. Fate and distribution of pharmaceuticals in wastewater and sewage sludge of the conventional activated sludge (CAS) and advanced membrane bioreactor (MBR) treatment. Water Research 43, 831e841. Reif, R., Sua´rez, S., Omil, F., Lema, J.M., 2008. Fate of pharmaceuticals and cosmetic ingredients during the operation of a MBR treating sewage. Desalination 221, 511e517. Ruhoya, I.S.R., Daughton, C.G., 2008. Beyond the medicine cabinet: An analysis of where and why medications accumulate. Environment International 34, 1157e1169. Schnell, S., Bols, N.C., Barata, C., Porte, C., 2009. Single and combined toxicity of pharmaceuticals and personal care products (PPCPs) on the rainbow trout liver cell line RTL-W1. Aquatic Toxicology 93, 244e252. Shipkova, M., Wieland, E., 2005. Glucuronidation in therapeutic drug monitoring. Clinica Chimica Acta 358, 2e23. Sim, W.J., Lee, J.W., Oh, J.E., 2010. Occurrence and fate of pharmaceuticals in wastewater treatment plants and rivers in Korea. Environmental Pollution 158, 1938e1947. Sua´rez, S., Ramil, M., Omil, F., Lema, J.M., 2005. Removal of pharmaceutically active compounds in nitrifying-denitrifying plants Water Science and Technology 52, 9e14. Ternes, T.A., Bonerz, M., Herrmann, N., Teiser, B., Andersen, H.R., 2007. Irrigation of treated wastewater in Braunschweig, Germany: An option to remove pharmaceuticals and musk fragrances. Chemosphere 66, 894e904. Vazquez-Roig, P., Segarra, R., Blasco, C., Andreu, V., Pico´, Y., 2010. Determination of pharmaceuticals in soils and sediments by pressurized liquid extraction and liquid chromatography tandem mass spectrometry. Journal of Chromatography A 1217, 2471e2483. Wick, A., Fink, G., Joss, A., Siegrist, H., Ternes, T.A., 2009. Fate of beta blockers and psycho-active drugs in conventional wastewater treatment. Water Research 43, 1060e1074. Zorita, S., Ma˚rtensson, L., Mathiasson, L., 2009. Occurrence and removal of pharmaceuticals in a municipal sewage treatment system in the south of Sweden. Science of The Total Environment 407, 2760e2770.
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Displacement mechanism of binary competitive adsorption for aqueous divalent metal ions onto a novel IDA-chelating resin: Isotherm and kinetic modeling Lanjuan Li a, Fuqiang Liu a,b,*, Xiaosheng Jing a,c, Panpan Ling a, Aimin Li a,b a
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, PR China State Environmental Protection Engineering Center for Organic Chemical Industrial Waste Water Disposal Resource Reuse, Nanjing 210046, PR China c Shaanxi Institute of Environmental Science, Xi’an 710054, PR China b
article info
abstract
Article history:
Adsorptive properties for Cu (II), Pb (II) and Cd (II) onto an iminodiacetic acid (IDA) chelating
Received 27 June 2010
resin were systematically investigated at the optimal pH-value in both single and binary
Received in revised form
solutions using batch experiments. The Langmuir isotherm model and the pseudo second-
1 November 2010
order rate equation could explain respectively the isotherm and kinetic experimental data
Accepted 6 November 2010
for sole-component system with much satisfaction. The maximum adsorption capacity in
Available online 12 November 2010
single system for Cu (II), Pb (II) and Cd (II) was calculated to be 2.27 mmol/g, 1.27 mmol/g
Keywords:
(II) > Cd (II) at the fixed initial concentration, and for each metal the initial sorption rate
Iminodiacetic acid chelating resin
increased as the initial concentration increased. In addition, the modified Langmuir model
Metal ion
could describe the binary competitive adsorption behavior successfully, with which the
and 0.65 mmol/g individually. The initial adsorption rate followed the order as Cu (II) > Pb
Competitive adsorption
interaction coefficient was obtained to follow the order as Cu (II) < Pb (II) < Cd (II).
Displacement mechanism
Furthermore, in every case of the investigated three binary systems, the reduction in both
Selectivity
the uptake amounts and distribution coefficients testified the antagonistic competitive phenomena. Obviously, this novel IDA-chelating resin possessed of a good selectivity toward Cu (II) over Pb (II) and Cd (II) for the obtained highest separation factor values were up to 21.30 and 133.91 in the range of tested. This interaction mechanism between the favorable component and other metal ions could mainly contribute to the direct displacement impact which be herewith illustrated schematically. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
In comparison with such conventional methods as precipitation, chemical oxidation or reduction and membrane separation, adsorption is regarded as one of the most effective and attractive processes with several advantages associated with
no chemical sludge and high removal efficiency. A considerable amount of research has explored the synthesis and modification of various adsorbents in an effort to enhance the affinity to metal ions (Trochimczuk 2000; Jeon and Holl, 2003; Jal et al., 2004). Unlike those traditional adsorbents of functionalized clays (Celis et al., 2000; Bhattacharyya and Sen
* Corresponding author. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, P.R. China. Tel.: þ86 25 86087695; fax: þ86 25 85572627. E-mail address:
[email protected] (F. Liu). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.009
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Gupta, 2007), zeolite (Wingenfelder et al., 2005), fibers (Deng et al., 2003), bioadsorbent (Deng and Ting, 2005a, b; Lim et al., 2008; Basha et al.,2009), modified low-cost adsorbent (Bernal-Martinez et al., 2008; Gurgel and Gil, 2009) and ionexchange polymers (Dabrowski et al., 2004), with one or more reactive functional groups containing O, N, S and P donor atoms and extensive structural diversity, chelating polymers are capable of forming stable complexes with metal ions, and thus induce large adsorption capacity, high selectivity, excellent mechanical character as well as easy recovery (Pilsniak and Trochimczuk, 2007). The IDA-chelating resin has shown a particularly higher selectivity for those transition metals than alkali metals because the IDA group can provide electron pairs to form a stable coordination covalent bond with divalent metals and is consequently powerful in removing metal ion from multi-metal mixtures (Lin and Juang, 2007; Mumford et al., 2007). So far, lots of works have been focused on the application of various adsorbents for the metal removal from the aqueous media (Xiao and Thomas, 2005; Gurgel and Gil, 2009; Huang et al., 2009). However, only a few of manuscripts are dedicated to the competitive uptake from the binary or multiple aqueous solutions (Xiao and Thomas, 2004; Seo et al., 2008). As a matter of fact, it is more important to evaluate the simultaneous adsorption behavior and interactions involving two or more metal species since sole toxic metal species rarely exists in natural streams and waste effluents from mining, metallurgical, tannery, chemical manufacturing and batterymanufacturing in which those toxic and carcinogenic metals such as Cu (II), Pb (II) and Cd (II) are widely detected (Meena et al., 2005). As of much significance, involved in the combined impact of the different metal ions and various adsorbents, the conclusion of interactive effect between metal species is not quite consistent with each other (Kang et al., 2004; Lv et al., 2005; Petrus and Warchol, 2005; Sheng et al., 2007). So, it is necessary to extensively investigate the competitive binding of the heavy metal ions such as Cu (II), Pb (II) and Cd (II) onto an IDA-chelating resin thoroughly. And then the adsorption operation process could likely be ahead toward ultimate application of separating and recovering certain metal ion from industrial wastewater. The aim of the present work is to determine the competitive adsorption properties through isotherm and kinetic modeling for aqueous divalent metal ions involving Cu (II), Pb (II) and Cd (II) onto a novel IDA-chelating resin at an optimal pH-value and temperature. Furthermore, the interactive processes and displacement mechanisms are schematically illuminated.
Reagent Factory of Nanjing (Jiangsu Province, P.R. China). Distilled water was used for the synthesis of new resin and preparation of metal stock solutions. The pH-value of the tested solution was adjusted to the desire value by dilute sodium hydroxide or nitric acid solution. The metal ions in aqueous phase were measured using an atomic adsorption spectrophotometer (AAS) method on a spectrophotometer with wavelengths at 324.7 nm, 283.3 nm, and 228.8 nm, corresponding to copper, lead, and cadmium, respectively.
2.2.
Adsorption experiments
2.2.1.
Adsorption isotherms
Adsorption equilibrium isotherms for single and binary solutes of Cu (II), Pb (II) and Cd (II) ions were all performed at the optimum pH-value of 5 and temperature at 303 K. Certain dosage of 0.100 g of dry resin (30e40 mesh) was firstly weighed accurately and placed directly into 250 mL conical flask. Then, 100 mL of aqueous solutions of Cu (II), Pb (II) and Cd (II) with certain concentrations (C0, mmol/L) ranging from 0.5 mmol/L to 5.0 mmol/L were added in mono-component adsorption systems. For binary solutions, the target metal ions were varied over the range of 0.5e5 mmol/L with a certain gradient, while the concentration of the interferential metal species was 0.5 mmol/L, 1.0 mmol/L, 2.0 mmol/L and 5.0 mmol/L, respectively. The flasks were completely sealed and agitated in an incubator shaker at the pre-settled temperatures under the rotating rate at 120 rpm. The adsorption tests were conducted continuously till 24 h to attain the equilibrium. The concentration (Ce, mmol/L) of the residual aqueous phase was determined using an atomic adsorption spectrophotometer (AAS, THERMO, USA). The amount of metal adsorbed per unit mass of resin could be determined with Eq. (1), the distribution coefficients were calculated with Eq. (2) and the decreasing ratio could be obtained with Eq. (3). Qe ¼
VðC0 Ce Þ m
(1)
Kd ¼
1000VðC0 Ce Þ mCe
(2)
Dr ¼
Qe Qe0 100% Qe
(3)
2.
Materials and methods
where C0 and Ce are the initial and equilibrium metal concentration (mmol/L), m is the mass of resin (g), V is the volume of solution (L). Qe is the equilibrium adsorption capacity (mmol/g), Kd is the distribution coefficient (mL/g), Dr is the decreasing ratio induced by the competition of the secondary species, Qe0 (mmol/g) is the equilibrium adsorption capacity in binary system.
2.1.
Materials
2.2.2.
The novel IDA-chelating resin used here was synthesized with the reported procedure in our previous work in which its typical properties could be well found (Ling et al., 2010). All reagents including the cupric nitrate, lead nitrate, cadmium nitrate, methanol, dichloromethane, sodium carbonate, sodium hydroxide and nitric acid were purchased from First
Adsorption kinetics
Certain amounts of sole solution or binary mixture (molar ratio of 1:1) with the settled concentration of 0.5 mmol/L, 1.0 mmol/L and 2.0 mmol/L were introduced with chelating resin (particle size 30e40 mesh, solid concentration 1.0 g/L, total volume 1000 mL). The mixture solutions were also adjusted to the initial pH-value of 5 and mechanically stirred at the abovementioned temperature under 120 rpm. The samples (1 mL)
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were withdrawn at pre-settled time interval and the adsorption amounts were likewise calculated by Eq. (1).
2.2.3.
(II) on marine alga showed good agreement with both modified competitive Langmuir and modified JeS model (Sheng et al., 2007).The multi-component Freundlich adsorption model adequately predicted the multi-component adsorption equilibrium date of the removal of Cr(VI) and Ni (II) adsorption by dried activated sludge (Aksu et al., 2002). In the present paper, the adsorption data of binary system were simulated with three isotherm models, which were all derived from the Langmuir isotherm since the adsorption behavior of the investigated metal species in single system followed the Langmuir isotherm model. The three-dimensional isotherm surfaces were generated using the MATLAB, version 7.0, with a minimum value of the residual sum of squares (RSS), which could be defined as
Modeling of metal adsorption to resin
2.2.3.1. For adsorption equilibrium isotherms. The isotherms used in the present paper are shown as follows: bQ0 Ce 1 þ bCe
(4)
Freundlich : Qe ¼ Kf Ce1=n
(5)
Langmuir : Qe ¼
RedlichePeterson : Qe ¼
Krp Ce 1 þ arp Cbe
(6)
RSS ¼
where Ce is the equilibrium concentration (mmol/L), Qe is the amount of adsorbed material at equilibrium (mmol/g), b is the affinity parameter or Langmuir sorption constant (L/mmol), which reflects the free energy of sorption, and Q0 is the capacity parameter (mmol/g), Kf and n are the Freundlich constant isotherm parameters, Krp and arp are the RedlichePeterson constants, and b is basically in the range of zero to one. The separation factor, an index of selectivity, was associated with several factors such as pH-value, temperature and concentration. The separation factor value of Hg (II)/Cd (II) increased from 2.5 at natural pH to 6.5 in the case of pH of 2 due to the interaction mechanism of Hg (II) changed with the pHvalue (Atia et al., 2003). The adsorption of Cu (II) and Zn (II) on iminodiacetic resin Lewatit TP207 was temperature-responsive and the separation factor value decreased from 83.0 at 10 C to 30.0 at 80 C (Muraviev et al., 1995). The selectivity of competitive adsorption in Cu (II)/Pb (II) and Cd (II)/Pb (II) binary system was found concentration dependent (Lv et al., 2005). The separation factor here was defined as Eq. (7). a12 ¼
Qe1 Ce2 Ce1 Qe2
n X
Qe;fit Qe;exp
2
(8)
i¼1
The isotherm models for binary solute, the nonmodified Langmuir model (Eq. (9)), the extended Langmuir model (Eq. (10)) and the modified Langmuir model (Eq. (11)), used in this section could be expressed as follows respectively. Qe;i ¼
Qm;i Ki Ce;i 1 þ Ki Ce;i þ Kj Ce;j
Qe;i ¼
Qmax KEL;i Ce;i 1 þ KEL;i Ce;i þ KEL;j Ce;j
(10)
Qe;i ¼
Qm;i ; Ki Ce;i =hi 1 þ Ki Ce;i =hi þ Kj Ce;j =hj
(11)
(9)
where the terms Qm,i and Ki are the maximum capacity and Langmuir adsorption constants determined from the corresponding individual Langmuir isotherm, and Qmax and KEx,i are parameters calculated from the Eq. (10) with a lowest RSS, while hi and hj are correction parameters for the first and secondary metal respectively, which are estimated from binary adsorption data.
(7)
There are several multi-component isotherm models derived from the single system, for instance, the nonmodified Langmuir model, the modified Langmuir model, the extended Langmuir model, nonmodified ReP, modified ReP, SheindorfeRedicheSheintuon model and JaineSnoeyink model. The extended Langmuir model had been successfully applied to represent the adsorption data of Pb (II), Zn (II) and Cd (II) onto Indonesian peat (Balasubramanian et al., 2009) and the equilibrium of the ternary system of Cr (VI), Cu (II) and Cd (II) for a biosorbent Rhizopus arrhizus (Sag et al., 2002).The competitive SRS model fitted the Cd (II)/Ni (II)/Zn (II) ternary adsorption equilibrium data satisfactorily and adequately (Srivastava et al., 2008).The biosorption of Pb (II), Cu (II) and Cd
2.2.3.2. For adsorption kinetics. Three kinds of kinetic models, pseudo-first-order rate equation, pseudo-second-order rate equation and intraparticle diffusion models, were considered to interpret the experimental data. The non-linear form was considered a better way to obtain the kinetic parameters (Ho, 2006), so the kinetic data in this present paper were fitted to the non-linear form as follows: The pseudo first order kinetic model : logðQe Qt Þ ¼ log Qe
k1 t 2:303
(12)
Table 1 e Single isotherm models parameters for the adsorption of Cu (II), Pb (II) and Cd (II). Metal
Langmuir constants 0
Cu (II) Pb (II) Cd (II)
Freundlich constants 2
RedlichePeterson constants 2
Q
b
r
Kf
n
r
Krp
arp
b
r2
2.27 1.27 0.65
24.08 8.97 4.71
0.999 0.997 0.998
0.20 0.52 0.27
3.15 5.21 4.65
0.920 0.901 0.902
212.81 371.29 561.61
104.15 360.86 1124.78
0.88 0.83 0.83
0.998 0.994 0.996
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The pseudo second order equation : Qt ¼
k2 Qe2 t 1 þ k2 Qe t
The intraparticle diffusion model : Qt ¼ kint t1=2
(13)
(14)
where Qt is the adsorption capacity in time t (mmol/g), Qe is the adsorption capacity at equilibrium (mmol/g) and k1, k2, kint are the adsorption rate constant of pseudo-first-order (min1), pseudo-second-order (g mmol1 min1), intraparticle diffusion rate (mmol g1 min1/2), respectively, and h ¼ k2Q2e is defined as the initial adsorption rate constant.
Fig. 1 e The distribution coefficients Kd (mL/g) of metal ions in binary solute: (a) Cu (II)/Pb (II) binary system, (b) Cu (II)/Cd (II) binary system and (c) Pb (II)/Cd (II) binary system.
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3.
Results and discussion
3.1.
Adsorption equilibrium isotherms
3.1.1.
Sole solute
3.1.2.1. For Cu (II)/Pb (II) binary system. In Cu (II)/Pb (II) double
The uptake of each metal species increased with the initial metal concentration while the metal-removal efficiency decreased which was known as the loading effect, describing the extent to which the total number of sorption sites is occupied by the sorbate (Prasad et al., 2008). Parameters predicted with Langmuir (Eq. (4)), Freundlich (Eq. (5)) and RedlichePeterson (Eq. (6)) isotherm model are tabulated in Table 1. The Langmuir and RedlichePeterson model could predict the experimental data appropriately with a satisfactory correlation coefficient (r2 > 0.99). Quantitatively, the maximum adsorption capacity was 2.27 mmol/g for copper, versus 1.27 mmol/g for lead and nearly 0.65 mmol/g for cadmium, corresponding to the sequence of metal electro negativity (Ling et al., 2010) and log b (MEDTA), which was the index of the stability constant and well documented of the order Cu (18.83) > Pb (17.04) > Cd (16.54) (Abollino et al., 2000). These thermodynamic data suggested that the complex MEDTA (formed by the metal M and the chelating agent) of EDTA (Ethylene Diamine Tetraacetic Acid) with Cu (II) should be more stable than those with Pb (II) and Cd (II). According to our previous investigation (Ling et al., 2010), the adsorption process of metal ions onto IDA resin could be contributed to both ion-exchange and chlelation. In addition, the isotherm data could be well described with the Langmuir model and the maximum capacities calculated were all close to those actually determined. Thus, the chemical interaction was considered to be the dominant mechanism, as was consistent with the earlier manuscript (Liao and Shi, 2005). And the detailed properties of the resin were as follows: the content of nitrogen and oxygen was 5.00% and 20.58%, the specific area was 14.24 m2/g, the pore volume was 0.12 cm3/g and the average pore size was 34.53 nm. In addition, the cation exchange capacity was about 5.50 mmol/g, so the concentration of different functional group was calculated to be 2.75 mmol/g for iminodiacetic acid group, 0.47 mmol/g for amino acetic acid and 0.35 mmol/g for amino-group. The adsorption of metal ions toward both the synthesized resin and chelating reagent EDTA followed the same tendency, implying the dominant functional group attached to resin beads was iminodiacetic acid which had the same characteristic with the chelating reagent.
3.1.2.
Binary solutes
The competitive adsorption equilibrium isotherms for Cu (II)/ Pb (II), Pb (II)/Cd (II) and Cu (II)/Cd (II) binary solutes were obtained by fixing the initial concentration of interferential metal ions.
Table 2 e Separation factors for Cu (II)/Pb (II)and Cu (II)/Cd (II) on IDA resin at different concentrations. C02 Pb ¼ 2.0 Pb ¼ 5.0 Cd ¼ 2.0 Cd ¼ 5.0 C01 (mmol/L) (mmol/L) Cu ¼ 0.5 Cu ¼ 1.0
16.40 10.68
21.30 13.50
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65.88 37.66
133.91 74.08
component solution, the uptake amount of copper increased with the increasing initial concentration under the fixed concentration of lead as the background, and the trend was similar with that of the single solute. However, the capacity of copper decreased with the increase in the initial concentration of lead, obviously in the higher concentration. The maximum uptake toward copper reduced from 2.24 mmol/g in sole solution to 1.59 mmol/g with the coexistence of 5 mmol/L lead, and the decreasing ratio value, Dr, was about 30%. The antagonistic phenomena could also be found when lead was the target species. The maximum capacity of lead in single system was 1.23 mmol/g, while the value decreased to 0.58 mmol/g in the presence of 5 mmol/L of copper. In the comparison of Dr, the inhibitory extent by copper was around 53%, almost twice of that by lead. The lower adsorption yield in the binary system validated the antagonism between the two metal ions. The analogous trend could also be observed in the ternary metal solution of Cd (II)/Zn (II)/Ni (II) (Srivastava et al., 2008), Cu (II)/Pb (II)/Cd (II) (Sheng et al., 2007), as the sorption capacity of the primary metal decreased because of the coexistent metal ions in the system (Balasubramanian et al., 2009). One possible explanation for the antagonism phenomenon was the direct competitive effects on the active adsorption sites. The distribution coefficients Kd (mL/g) (Eq. (2)), a potential mobility index of a metal, also verified the interaction between the two species in solution because of the competition effect. Kd reduced significantly with the coexistence of copper or lead, whereas the diversity could be examined from Fig. 1. The reduction of distribution coefficients of Cd (II), Ni (II) and Zn (II) was also found in the competitive adsorption onto sludge-amended soil (Antoniadis et al., 2007). In addition, the trend was in good agreement with the former results (Prasad et al., 2008). The Kd value dropped significantly at the higher background metal concentrations, slightly at the low concentrations. At a lower metal concentration, each metal species was mainly adsorbed onto specific sites, and the adsorption sites in binary systems may be partially overlapped with the further increase in total metal concentration (Saha et al., 2002; Papini et al., 2004). The Kd value of copper or lead reduced obviously when the target ion concentration was fixed at 0.5 mmol/L and the concentration of interferential species increased from 0.5 mmol/L to 5 mmol/L. However, it was noteworthy the decrease degree of lead should be more obvious than that of copper, 82% and 99% versus 62% and 94%, which was consistent with the adsorption capacity.
3.1.2.2. For Cu (II)/Cd (II) binary system. In Cu (II)/Cd (II) system, although the presence of cadmium seemed to make no pronounced inhibitory effect on the adsorption of copper, the coexistence of copper putted significant influence upon the adsorption for cadmium. Obviously when 5.0 mmol/L of copper was introduced, the removal of cadmium was almost negligible with a decreasing ratio Dr of 89%. The Kd value of copper in single solute of 0.5 mmol/L was 126.79 mL/g, but was remarkably decreased to 69.36 mL/g and 18.22 mL/g in the presence of 0.5 mmol/L and 5 mmol/L of cadmium as the competitive ions, with a reduction of 45% and 86% respectively. However, the Kd of cadmium was dropped with a more obvious reduction of 69% and 99% at the same case.
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Table 3 e Multi-component isotherm models parameters for the adsorption of Cu (II), Pb (II) and Cd (II) at 303 K. Nonmodified Langmuir
Modified Langmuir
Cu (II)/Pb (II) RSS Parameters
0.964 e
0.619 hCu ¼ 0.88; hPb ¼ 1.30
0.907 Qmax ¼ 1.92; KEL,Cu ¼ 22.23; KEL,Pb ¼ 2.37
Cu (II)/Cd (II) RSS Parameters
1.728 e
0.966 hCu ¼ 0.97; hCd ¼ 2.05
1.123 Qmax ¼ 2.00; KEL,Cu ¼ 12.62; KEL,Cd ¼ 0.25
Pb (II)/Cd (II) RSS Parameters
0.702 e
0.284 hPb ¼ 1.45; hCd ¼ 3.19
0.280 Qmax ¼ 1.22; KEL,Cd ¼ 0.46; KEL,Pb ¼ 3.99
3.1.2.3. For Pb (II)/Cd (II) binary system. Similarly, a greater inhibitory effect was exerted by lead than cadmium. In Pb (II)/Cd (II)system, the uptake amount of lead slightly reduced to 1.14 mmol/g in the presence of 5 mmol/L cadmium with a Dr
Extended Langmuir
value of only 7%. While the capacity of cadmium was significantly affected by the coexistence of lead and the Dr was nearly 92%. The Kd value of lead in single solute of 0.5 mmol/L was 33.79 mL/g, but decreased by 91.36% and 94.73% in the
Fig. 2 e Three-dimensional isotherm surfaces simulated with the modified Langmuir isotherm model: (a) Cu (II)/Pb (II) binary system, (b) Pb (II)/Cd (II) binary system and (c) Cu (II)/Cd (II) binary system.
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Table 4 e Kinetic rate equation parameters for the adsorption of Cu (II), Pb (II) and Cd (II) on the chelating resin at initial concentration of 0.5 mmol/L,1.0 mmol/L and 2.0 mmol/L. Metals
C0 (mmol/L)
First-order
Second-order 2
Intraparticle diffusion 2
Qe
k1
r
Qe
k2
h
r
kint
r2
Cu (II)
0.5 1.0 2.0
0.23 0.46 1.05
0.03 0.01 0.01
0.937 0.926 0.976
0.55 1.00 1.64
0.09 0.03 0.01
0.017 0.026 0.039
0.996 0.991 0.990
0.06 0.05 0.01
0.879 0.876 0.908
Pb (II)
0.5 1.0 2.0
0.26 0.73 0.97
0.02 0.03 0.01
0.945 0.947 0.965
0.47 0.72 0.96
0.06 0.03 0.02
0.014 0.016 0.016
0.996 0.991 0.990
0.03 0.03 0.06
0.980 0.990 0.976
Cd (II)
0.5 1.0 2.0
0.31 0.30 0.58
0.11 0.03 0.04
0.932 0.987 0.978
0.35 0.45 0.65
0.11 0.03 0.04
0.011 0.012 0.015
0.997 0.999 0.991
0.09 0.08 0.04
0.987 0.934 0.954
presence of 0.5 mmol/L and 5.0 mmol/L of cadmium, respectively. Whereas, the Kd of cadmium dropped more at the coequal condition.
3.1.3. Selectivity of IDA resin toward Cu (II) over Pb (II) and Cd (II) The separation factor was predicted to be concentration and coexistence species dependent. At the fixed initial concentration of Cu (II), the separation factor doubled when the initial concentration of Cd (II) ranged from 2.0 mmol/L to 5.0 mmol/L, but when the concomitant was Pb (II), the separation factor was only 1.3 times (Table 2). When the initial concentration of secondary species (lead or cadmium) was
constant, the separation factor decreased at elevated concentration of Cu (II). The aCu Pb value was always lower than at the same ratio value, moreover, the value of aCu aCu Cd Cd achieved to 133.91 when the initial concentration molar ratio was 10 while the value of aCu Pb was 21.30. The difference may indicate the good selectivity of this resin toward Cu (II) over Cd (II) especially at higher ratio.
3.1.4.
Model of binary adsorption isotherms
The parameters of multi-component models are shown in Table 3, from which the modified Langmuir model could describe the experimental data well over the other two with a lowest RSS value. Adding no more extra parameters, the
Fig. 3 e Time profiles of Cu (II) and Pb (II) adsorption by the chelating resin under noncompetitive and competitive conditions at equal molar ratio (C0 [ 0.5 mmol/L, 1.0 mmol/L and 2.0 mmol/L).
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nonmodified Langmuir isotherm model could not explain the adsorption profiles of the metal ions in the binary system with a RSS value of 0.964.The use of parameters, Qmax and KEL,i which were irrespective of those derived from the individual isotherm improved the agreement of the extended Langmuir isotherm model. The value of KEL,i revealing the affinity of the chelating resin toward the adsorbates duplicated the order of single isotherm. For example, the KEL,i value for copper ions in Cu (II)/Cd (II) binary system was almost 50 times of the cadmium species while the ratio of the KEL,i between the preferred ions and the secondary species in Cu (II)/Pb (II) and Pb (II)/Cd (II) system was only 9.3 and 8.6 (Table 3). The higher the interaction coefficient, h, the smaller the inhibitory effect of the metal ions on the adsorption of the other species. The interaction coefficient followed the order of Cu (II) < Pb (II) < Cd (II), which did not agree with the phenomena observed in the biosorption of the three metal ions toward a marine alga (Sheng et al., 2007), where the sequence was Pb (II) < Cu (II) < Cd (II). But the conclusion kept coincidence with the affinity of metal species which was mentioned above in Section 3.1.1 associated with the characteristic of functional group and the metal ions. The adsorption of copper was depressed clearly by the presence of lead neither cadmium while the suppressive influence of lead toward cadmium was more obvious than copper, but the cadmium exerted more inhibitory effect on the adsorption of lead than copper. Fig. 2 displays the three-dimensional adsorption surfaces for the metal uptakes of each species against the concentration at equilibrium. It is obvious from Fig. 2(a) that the shape of the isotherm surface of copper component was affected slightly by varying the initial concentration of lead from 0 to 5.0 mmol/L, whereas the surface of lead could not be duplicated accurately. The adsorption surface of lead behaved concavely downward and even close to the XY plane at a higher initial concentration of copper ions. As illustrated in Fig. 2(b) and (c), similar uptake surfaces were observed in the other two binary systems. For instance, in Cu (II)/Cd (II) system where the copper component was favorably adsorbed over cadmium ions, the cadmium removal was more significantly influenced by the presence of the coexistent copper species.
3.2.
Adsorption kinetics
3.2.1.
For single metal system
The adsorption rate was rapid at the first 100 min after which the rate slowed down and the equilibrium was reached in 6 h contact time. The same result was observed for all the examined metal species at three different initial concentrations. The pseudo second-order rate equation could describe the single metal species system with extremely high correlation coefficients highlighting the chemisorption rate-controlling mechanism (Reddad et al., 2002) and the parameters are tabulated in Table 4. The initial sorption rate, h, followed the order Cu (II) > Pb (II) > Cd (II) at the fixed initial concentration which was in accordance with the affinity order observed in the equilibrium isotherm experiments. But the reverse affinity order of initial sorption rate and equilibrium capacity was obtained because the weakly adsorbed metal species, nickel, strongly reacted with some functional groups and not with all moieties of the
polysaccharide (Reddad et al., 2002). The fact might also reveal the iminodiacetic acid group was the dominant functional group of the resin beads. For certain metal species, the sorption rate constants, k2, increased with a decrease in the initial concentration owing to the numerous metal ions competing against limited sorption sites and higher initial concentration reducing the diffusion in the boundary layer (Liu et al., 2005). However, the initial sorption rate, h, increased with an increase in the initial metal concentration which was consistent with the adsorption of lead onto a biosorbent of sugar beet pulp (Reddad et al., 2002).
3.2.2.
Binary component
3.2.2.1. For Cu (II)/Pb (II) binary systems. Fig. 3 shows the competitive kinetic results in Cu (II)/Pb (II) binary system at equimolar levels with the initial concentration of 0.5 mmol/L, 1.0 mmol/L and 2.0 mmol/L. When the initial concentrations were 0.5 mmol/L and 1.0 mmol/L, for both two components, the uptake amount was increased as the time prolonged and then followed by a plateau just as in the single solute. But the inhibitory effect caused by the competition of the two species could still be observed evidently from Fig. 3. As to the uptake amount of certain metal species, comparing the profiles of the single and binary systems, the latter were always lower during the experimental processes. When the initial concentration was 2.0 mmol/L, the kinetic adsorption behavior of copper and
Fig. 4 e Schematic illustration for the displacement mechanism between Cu (II) and Pb (II).
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lead was found diverse completely. The uptake capacity of copper ions was increasing upon the contact time while the adsorbed lead reduced gradually after a maximum, and such similar displacement results were also well documented in earlier literature (Kang et al., 2004; Lv et al., 2005). The possible elucidative mechanism was that both the copper and lead ions were adsorbed at the initiative stage when the most functional groups were available but the ever adsorbed lead released from the active sites because of the higher affinity of resin toward copper over lead. The desorption behavior resulted a decreasing uptake amount of lead ions at the succedent stage. Liu et al. (Liu et al., 2008) verified the conclusion with a pre-loaded experiment and speculated the displacement mechanism through an adjacent attachment and repulsion effect. As depicted in Fig. 4, the case here was not the same as the above reference because the IDA resin used in the present paper produced no net electric repulsion during the adsorption process. The lead ions could be adsorbed early because of the available active sites in the resin beads at the beginning, and then the favorable species copper ions approached the binding sites which were occupied by the lead ions, finally the copper ions displaced the lead species directly due to the more stable complexes formed between the copper species and the functional groups. The displacement phenomenon conduced desorption of the unfavorable component.
3.2.2.2. For Cu (II)/Cd (II) and Pb (II)/Cd (II) binary systems. The similar adsorption results on the behavior of metal ions in
1185
Cu (II)/Cd (II) and Pb (II)/Cd (II) binary system were observed. For instance, as shown in Fig. 5, in Cu (II)/Cd (II) systems, copper exerted a greater inhibitory effect on cadmium and desorption of cadmium ions was revealed when the initial concentration was as high as 2.0 mmol/L. While in the Pb (II)/ Cd (II) component, lead was the favorable adsorbed species which replaced the previously adsorbed cadmium species (Fig. 6).
3.2.3.
Description of the binary adsorption kinetics
Only the time profiles of copper species in Cu (II)/Pb (II) and Cu (II)/Cd (II) competitive solution at the initial concentration of 0.5 mmol/L showed a good correlation with the pseudo second-order rate equation well (r2 > 0.99). The deviation of the other species might due to the competitive effect of the two adsorbates and the displacement mechanism conducted by the stronger affinity to copper ions over the other two species toward resin beads, which was agree with the conclusion that only the favorable species, lead, can follow the pseudo-second-order equation in earlier literature (Lv et al., 2005). In addition, the fact should also be noted when the initial concentration was 1.0 mmol/L and 2.0 mmol/L, the pseudosecond-order rate model was not applicable, even for the most preferentially copper component. The possible reason was the higher the initial concentration, the stronger competitive effect because of the more numerous metal ions in the system and the less active sites available. Comparing the kinetic rate
Fig. 5 e Time profiles of Cu (II) and Cd (II) adsorption by the chelating resin under noncompetitive and competitive conditions at equal molar ratio (C0 [ 0.5 mmol/L, 1.0 mmol/L and 2.0 mmol/L).
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Fig. 6 e Time profiles of Pb (II) and Cd (II) adsorption by the IDA-chelating resin under noncompetitive and competitive conditions at equal molar ratio (C0 [ 0.5 mmol/L, 1.0 mmol/L and 2.0 mmol/L). constant of pseudo second-order rate equation, Qe, k2 and h, in single and binary systems, the antagonism in the mixture could be substantiated once again. For instance, the adsorption rate constants, k2, for copper were 0.09, 0.0346, 0.0399 (g mmol1 min1) in mono-metal solution, Cu (II)/Pb (II) and Cu (II)/Cd (II) binary systems, with a reduction of 62% and 56%. The smaller k2 value for copper in Cu (II)/Pb (II) solutions elucidated the greater inhibitory effect on copper removal exerting by lead than cadmium which was agree with the conclusion from the isotherm section.
concentration because of the competitive influence. Desorption of the unfavorable species (lead or cadmium) in binary solute was observed in the kinetic section and the possible mechanism could be described as direct displacement effect. It will be potential to apply the chelating resin in selectively separating and concentrating copper from Cu/Pb or Cu/Cd solution. The direct displace mechanism of the adsorption process might enlighten that the interaction time should be a key factor associated with the recovery efficiency for the favorite species.
Acknowledgements 4.
Conclusion
Due to the complex interaction of metal species and the IDA functional group, the maximum uptake capacity of individual adsorption was 2.27 mmol/g, 1.27 mmol/g and 0.65 mmol/g for Cu (II), Pb (II) and Cd (II) respectively. The antagonism between the two species resulted in the modified Langmuir isotherm with an introduction of the interaction coefficient representing the data adequately. The interaction coefficient followed the order of Cu (II) < Pb (II) < Cd (II), implying copper was the most favorable species and the affinity toward cadmium was the weakest. The separation factor achieved 21.30 and 133.91, implying the good selectivity of IDA resin toward Cu (II) over Pb (II) and Cd (II). The initial sorption rate, h, followed the same order with the affinity obtained from the isotherm study, but the time profile of the binary system could not fit the kinetic equations except the adsorption of copper ions at the low initial
The authors gratefully acknowledge generous support provided by the State Key Program of National Natural Science (Grant No. 50938004), the Resources Key Subject of National High Technology Research & Development Project (863 Project, Grant Nos. 2009AA06Z315 and SQ2009AA06XK1482331) P.R. China, the National Natural Science Foundation of P.R. China (No. 50878103, No.51078178) and the Discipline Crossing Foundation of Nanjing University.
references
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Molecularly imprinted polymer microspheres enhanced biodegradation of bisphenol A by acclimated activated sludge Ya-ting Xie a, Hai-bin Li b, Ling Wang a, Qian Liu a, Yun Shi a, Hai-yan Zheng c, Meng Zhang a, Ya-ting Wu a, Bin Lu a,* a
MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan, Hubei 430030, China b Department of Public Health, Xinxiang Medical University, East Jin Sui Road, Xinxiang, Henan 453003, China c School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, # 13Hangkong Road, Wuhan, Hubei 430030, China
article info
abstract
Article history:
The impacts of bisphenol A imprinted polymeric microspheres (MIPMs) on the biodeg-
Received 24 August 2010
radation of bisphenlol A by acclimated activated sludge were studied. Due to the selective
Received in revised form
adsorption of MIPMs to bisphenol A (BPA) and its analogues, addition of MIPMs to activated
7 November 2010
sludge increased levels of BPA and its metabolites, which were also the substrates of
Accepted 8 November 2010
biodegradation. Higher substrates (BPA and its metabolites) level promoted biodegradation
Available online 18 November 2010
efficiencies of activated sludge via accelerating removal speed of BPA and its metabolites, increasing degradation rate and decreasing half-lives of biodegradation. The enhancement
Keywords:
of MIPMs in degradation efficiencies was more significant in environmental water con-
Bisphenol A
taining low-level of pollutants, and water containing interferences such as heavy metals and humic acid. Furthermore, MIPMs were more suitable than non-selective sorbents such
Biodegradation Molecularly
imprinted
polymeric
as active carbon to be used as enhancer for BPA biodegradation. MIPMs combined with
microspheres
activated sludge are simple, effective, environmental-friendly processes to biodegrade low-
Acclimated activated sludge
level pollutants in environmental water.
Enhancement
1.
Introduction
Bisphenol A (BPA) is a widely used intermediate in the production of polycarbonate plastics and epoxy resins. As one of the most ubiquitous environmental estrogen disruptors, BPA can cause adverse health effects on ecosystems and on human being by mimicking or interfering hormonal activities to affect growth, development, and reproduction. The health impacts of BPA may be cumulative and are irreversible, endangering the sustainable development of humans (Staples et al., 1998). Even trace BPA produces adverse effects on
ª 2010 Elsevier Ltd. All rights reserved.
aquatic life. Based on the capability of BPA to increase vitellogenin levels in male rainbow trout, the predicted no-effect concentration of BPA for aquatic life was 64 mg/L (Lahnsteiner et al., 2005). However, concentrations lower than predicted no-effect concentration can cause alterations in reproduction. When male and female brown trout (Salmo trutta f. fario) were exposed to BPA during the late pre spawning and spawning period, the ovulated time of female brown trout delayed for 2 weeks (1.75 mg/L), 3 weeks (2.4 mg/L) or even did not ovulate (5 mg/L). Male brown trout produced low quality semen at the beginning and in the middle of spawning, which resulted in
* Corresponding author. Tel.: þ86 27 83691809; fax: þ86 27 83657765. E-mail addresses:
[email protected] (Y.-t. Xie),
[email protected] (H.-b. Li),
[email protected] (L. Wang),
[email protected] (Q. Liu),
[email protected] (Y. Shi),
[email protected] (H.-y. Zheng),
[email protected] (M. Zhang),
[email protected] (Y.-t. Wu),
[email protected] (B. Lu). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.014
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a spawning delay for 4 weeks in late spawning period (Lahnsteiner et al., 2005). Furthermore, BPA is easily accumulated through food chain. To bream (Abramis brama) and flounder (Platichthysflesus) living in surface water containing less than 0.01e0.33 mg/L BPA, the levels of BPA in liver varied from 2 to 75 mg/kg dry weight, and from 1 to 11 mg/kg dry weight in the muscle (Kang et al., 2006). A daily exposure dose of 50 mg/kg body weight/day was stated to be safe for humans by the U.S. Food and Drug Administration and the U.S. Environmental Protection Agency in the 1980s. However, several recent studies have reported significant impacts on rats at doses below the predicted safe dose. For example, a reduction in the epididymal sperm motility and sperm count were observed in rats exposed to 0.2e20 mg/kg/day BPA for 45 days (Kang et al., 2006). Nowadays, BPA can be removed by physicochemical methods (e.g. H2O2 þ UV, UV þ O3, H2O2 þ O3, TiO2 photocatalysis) (Gogate and Pandit, 2004; Torres et al., 2007), adsorption methods (Choi et al., 2005; Lin et al., 2008), and biological degradation methods (Lobos et al., 1992; Spivack et al., 1994; Suzuki et al., 2004). Physicochemical methods are not cost-effective in dealing with large volume of low-level pollutants. Sometimes secondary pollutants that are not effectively eliminated by the same technique are developed, which may be more hazardous than the original pollutant (Gogate and Pandit, 2004; Torres et al., 2007). Adsorption methods need further treatment to completely degrade the adsorbed pollutants (Choi et al., 2005; Lin et al., 2008). Biological degradation methods are practical to degrade relatively low concentration of pollutants in large amount of water. BPA-degrading bacterium, isolated from activated sludge in sewage plants or natural river water, mineralized 60% of the total carbon of BPA to CO2, assimilated 20% into bacterial cells, and converted 20% to soluble organic compounds (Lobos et al., 1992; Spivack et al., 1994; Suzuki et al., 2004). Compared with BPA, very low acute toxicity was detected among all the BPA biodegradation products, and only 4-hydroxyacetophenone (HAP) had slight estrogenic activity (Ike et al., 2002). Biological degradation methods have the prospect to eliminate BPA by degrading them into less harmful intermediates or, ultimately, carbon dioxide and water. The initial concentration of target pollutant is one major factor that will affect biodegradation efficiency. The biodegradation rates of p-chlorobenzoate and chloroacetate increased significantly when the initial concentrations increased from 47 ng/L to 47 mg/L (Boethling and Alexander, 1979). Biodegradation mainly depends on enzymes produced by microorganisms to degrade target pollutants. The target pollutants are also the substrates of the enzyme reactions. There is usually a hyperbolic relationship between the biodegradation rate and the substrate concentration in enzyme-catalyzed reaction (Leskovac, 2003). According to MichaeliseMenten kinetics, certain amount of BPA is necessary to achieve good biodegradation efficiency. Low concentration of BPA is difficult to be degraded effectively in ordinary wastewater treatment plants. For example, BPA (15e5400 mg/ L) in landfill leachates can be degraded to 0.5e5.1 mg/L (Yamada et al., 1999). But 0.08e4.98 mg/L BPA can only be degraded to 0.01e1.08 mg/L in wastewater treatment plants of Canada with removal efficiencies of 37e94%. After treatment,
0.542 and 3.01 mg/L BPA in wastewater were reduced to 0.162 and 0.258 mg/L respectively in a wastewater treatment plant in Germany. The removal efficiency was 70% and 91% respectively (Zhao et al., 2008). BPA (mean concentration 933.2 ng/L) in the influent can be reduced to 81.4 ng/L (mean concentration) in the effluents with mean removal efficacy about 90% in three sewage treatment plants of China (Zhou et al., 2010). Besides, the degradation of low concentration of BPA usually required a long incubation time and sometimes had a lag phase (Klecka et al., 2001; Ying and Kookana, 2003). For example, biodegradation half-lives for BPA were 0.5e3 days at the initial concentrations of 50e5500 mg/L, but 3e6 days at environmentally relevant concentrations (0.05e0.5 mg/L) with lag phases of 2e4 days using sediment and water collected from rivers (Klecka et al., 2001). To increase the biodegradation efficiency, methods such as membrane bioreactor and microorganism immobilization have been developed. Although membrane bioreactor could bear higher volume loadings, efficiency of membrane bioreactor to degrade BPA was only slightly higher than that of ordinary activated sludge reactor (Chen et al., 2008). This is because membrane bioreactor could only increase biomass level, not the substrate (target pollutant) concentration. Microorganism immobilization is more useful in degrading high concentration of pollutant by maintaining high level of microorganisms in carriers, which is helpful to solve normal problems of biodegradation system such as high substrate toxicity for cells and loss of large number of microorganisms (Zhao et al., 2009). BPA levels in the influent of wastewater treatment plants are usually in the range of ng/L or mg/L, which cannot be degraded effectively in modern wastewater treatment plants. Effluent of wastewater treatment plants is a major source for BPA entering surface waters. Therefore new technique that can degrade low concentration of BPA effectively is in great need. Molecularly imprinted polymer microspheres (MIPMs) are a class of smart sorbents that exhibit high affinity and selectivity. MIPMs have advantages such as physical robustness, resistance to elevated temperature and pressure, and inertness towards organic solvents, acids or bases (Tamayo et al., 2007). In our previous study, BPA-imprinted MIPMs were able to selectively remove trace phenolic estrogens (BPA and its analogues) from different sources of water. The selective binding characters of MIPMs enable them to have better removal efficiencies compared with non-selective sorbents such as active carbon (Lin et al., 2008). Therefore, we suggested that using MIPMs and activated sludge together, the selective adsorption of MIPMs would increase concentrations of substrates (also the target pollutants) effectively, which will enhance the degradation of trace target pollutants. To the best of our knowledge, no paper using MIPMs with activated sludge was reported. Therefore, the impacts of BPA-imprinted MIPMs on biodegrading BPA by acclimated activated sludge were studied in this paper. Factors such as heavy metals, humic acid (HA), different kinds of water and initial BPA concentration on the degradation efficiency were evaluated. BPA biodegradation pathway under our experimental conditions is not clear. But according to the pathway proposed by Spivack et al. (1994), HAP is one of the main metabolites. And HAP is the only major intermediate metabolite that has slight estrogenic activity among all the BPA biodegradation
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 8 9 e1 1 9 8
metabolites (Ike et al., 2002). Therefore, BPA and HAP were used as indexes to evaluate biodegradation efficiency.
2.
Materials and methods
2.1.
Materials
BPA, Bisphenol C (BPC), Bisphenol Z (BPZ), 4-vinylpyridine (4VP), HAP, trimethylolpropane trimethacrylate (TRIM) and HPLC-grade acetonitrile and methanol were provided by Sigma (St. Louis, MO). Analytical grade azobisisobutyronitrile (AIBN), dichloromethane, active carbon and other chemicals were obtained from Kemiou Company (Tianjin, China). TRIM and 4-VP were purified prior to use via general distillation methods in vacuo under nitrogen protection to remove the polymerization inhibitor. AIBN was recrystallized from methanol and then dried at room temperature in vacuum prior to use. Unless indicated, triple distilled water was used throughout. Figure S1 (supporting information) showed the molecular structures of chemicals used in this study.
2.2.
Apparatus and analytical conditions
HPLC analyses were performed on a Waters symmetry C18 column (5 mm, 250 mm 4.6 mm i.d.) using a Waters 1525 HPLC system with 2487 dual l absorbance detector operating at 281 nm (Waters, USA). The mobile phase was a mixture of water: acetonitrile (6:4). The injected sample volume was 20 mL and the flow-rate of the mobile phase was 1 mL/min. The oven temperature was set at 25 C. The calibration curves for all compounds had good linearity, with correlation coefficients higher than 0.999 over the studied concentration ranges (10e500 mg/L and 22.8e1140 mg/L, respectively). The limit of detection (LOD, a signal-to-noise ratio of 3) for BPA, BPC, BPZ, HAP was 5, 18, 38 and 9 mg/L, respectively. The limit of quantification (LOQ, a signal-to-noise ratio of 10) was 17, 60, 125 and 30 mg/L, respectively. BPZ (250 or 20 mg/L) was used as internal standard. The BPZ recovery rates in all tests were in the range of 95e105%.
2.3.
Preparation and characters of MIPMs
MIPMs were synthesized using precipitation polymerization conditions optimized in our lab (Lin et al., 2008). Briefly, the template BPA (6 mmol), monomer 4-VP (6 mmol), cross-linker TRIM (12 mmol) and free-radical initiator AIBN (40 mg) weredissolved in 250 mL acetonitrile. The solution was degassed in an ultrasonic bath for 5 min, purged with oxygen-free nitrogen for 10 min. The flask was then attached to a rotor-arm and rotated about 50 rpm at 65 C for 24 h. After centrifugation, the microspheres were extracted using methanol: acetic acid (9:1) for nine times and acetonitrile for five times to remove the template. Then microspheres were dried in vacuo overnight at 25 C. Non-imprinted microspheres (nMIPMs) were prepared under identical conditions except that the template was omitted. To detect binding capacity, BPA solutions (22.8e1140 mg/L in acetonitrile) were added to 10 mg MIPMs respectively. The samples were shaken at 25 C for 24 h. BPA concentration in
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the supernatant (free BPA) was analyzed by HPLC. The bound BPA was calculated by subtracting the free amount of BPA from its initial level. Scatchard plot was constructed by plotting the ratios of bound to free BPA concentration against the bound concentration. The absorption capacity (the maximum number of binding sites, Bmax) was determined from the equation, (B/[free]) ¼ (B/Kd) þ (Bmax/Kd), where Kd is the equilibrium dissociation constant, B the concentration of bound BPA, [free] the concentration of free BPA. The dissociation constant (Kd) was calculated from the Scatchard plot. To analyze binding selectivity, a range of structural analogues of BPA (2 mL BPA, BPC, BPZ and HAP solutions, 57, 64, 67 and 31.5 mg/L each in acetonitrile) were added to 10 mg MIPMs separately. The samples were shaken at 25 C for 24 h. IPB (imprinting-induced promotion of binding, IPB ¼ (Cmip Cnip)/Cnip 100% was used to demonstrate the specificity of MIPMs due to the molecular imprinted effect. Cmip was the amount of BPA or its analogues bound to MIPMs, and Cnip was the corresponding value for nMIPMs.
2.4.
Degradation test
Activated sludge was collected from the secondary sedimentation tank of wastewater treatment station of Wuhan Beer Company, China. Eight liters of sludge were mixed with 24 L of the inorganic salt solution (1 g/L NaCl, 1 g/L K2HPO4$3H2O, 0.5 g/L NH4Cl and 0.4 g/L MgSO4$7H2O) in an incubation reactor. Proper ratios of carbon, nitrogen and phosphate are necessary for acclimation efficiency. BPA was the sole carbon and energy source, while the inorganic salt solution provided nitrogen and phosphate. Considering the potential toxicity of BPA to microorganisms in the sludge, the acclimation process was conducted at room temperature by increasing BPA levels gradually from 10 to 450 mg/L for at least 8-week. When the activated sludge was acclimated completely after 8-week incubation, BPA removal efficiency was steadily above 90% and the mixed liquor suspended solid (MLSS) was in the range of 3000e3500 mg/L. L16 (43) table including three factors (shaking speed of water bath incubator, pH, temperature) and four levels were used to explore the optimal BPA-degrading conditions (Table 1). Completely acclimated activated sludge was taken from the incubation reactor and washed with triple distilled water three times to remove the residual BPA. After centrifugation (300 rpm, 10 min), 5 g condensed activated sludge was distributed into 100 mL BPA synthetic water sample (containing BPA and inorganic salt solution in distilled water with MLSS 3000e3100 mg/L) to degrade BPA under different shaking speed of water bath incubator, pH and temperature for 4 h. The initial concentration of BPA was 500 mg/L. Each experimental group was conducted in duplicate. Variable with high R value has strong effect on the degradation. Active carbons were washed first with 0.1 mol/L hydrochloric acid and then with distilled water until pH level of the washing elute was 7. Finally they were dried at 100 C for 24 h before use. BPA-imprinted MIPMs (10 mg), nMIPMs (10 mg) and active carbons (10 mg) were applied to BPA synthetic water sample containing 5 g activated sludge, respectively. Experiments were conducted at the optimal degrading conditions and carried out in duplicate. Mixture of 5 g activated sludge
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Table 1 e Orthogonal test of acclimated activated sludge on BPA biodegradation. No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 K1b K2 K3 K4 Rc
Factor Shaking speed of water bath incubator (r/min)
pH
Temperature
0 0 0 0 50 50 50 50 100 100 100 100 150 150 150 150 23.28 28.01 32.03 36.84 13.55
5 6 7 8 5 6 7 8 5 6 7 8 5 6 7 8 27.33 27.58 33.73 31.52 6.15
25 30 35 40 30 25 40 35 35 40 25 30 40 35 30 25 17.32 26.32 42.02 34.5 24.7
Degradated BPA (mg)a
7.32 16.13 40.28 29.41 22.44 13.48 34.8 41.31 42.18 36.44 21.33 28.19 37.36 44.3 38.53 27.16
a Degradated BPA: Qd ¼ Q0 Qe Qx,. Q0 the initial BPA level (mg), Qe and Qx were the amount of BPA (mg) in the water phase and the solid phase respectively. b Ki (i ¼ 1, 2, 3. . .)was defined as mean value of sum of degradated amount of every level and the optimal level of factor can be confirmed by comparing the value of Ki. c R value refers to the result of extreme analysis; R ¼ max {K1, K2, K3, K4}min{K1, K2, K3, K4}. Factor with high R value has strong effect on the degradation.
with BPA synthetic water samples was used as activated sludge control groups to evaluate the degradation capacity of activated sludge. BPA synthetic water samples contained 10 mg MIPMs were used as MIPMs control groups to detect BPA level that was only adsorbed by MIPMs. Because BPA leaking
from MIPMs (template bleeding) will affect the accurate determination of BPA treatment efficiency, BPA bleeding tests were preformed by mixing 10 mg MIPMs with 5 g activated sludge in distilled water for 5 days to evaluate BPA leakage during water treatment. During activated sludge treatment, the removal of BPA should be attributed to the adsorption of BPA by different adsorbents (including the sludge, active carbon or MIPMs), and the biodegradation by activated sludge. In order to calculate the degraded BPA level, the amounts of BPA in the solid phase and in the water phase must be detected separately. The amount of BPA in the solid phase was the adsorbed BPA (BPA adsorbed by sludge or different sorbents). And the amount of BPA in water phase was the BPA that was not adsorbed nor degraded. At each designed sampling time (0, 1, 2, 3, 4 h), two samples were taken out of the shaking water bath incubator and immediately centrifuged at 6000 rpm for 3 min to separate the solid phase (the sludge and the sorbent together) and the water phase. The solid phase was extracted by 20 mL methanol 5 times. The water phase was extracted by 70 mL dichloromethane 3 times. Then the methanol or dichloromethane was volatilized using a rotary evaporator (Heidolph, Germany) at 55 C. The dried residue was re-dissolved by 2 mL acetonitrile. The amount of degraded BPA was calculated as: Qd ¼ Q0 Qe Qx. The degradation rate % ¼ (Qd/Q0) 100%. Where Qd was the amount of degraded BPA (mg), Q0 the initial BPA level (mg), Qe and Qx were the amount of BPA (mg) in the water and solid phase respectively. The first-order reaction equation: lnC ¼ kt þ A, was used to describe the degradation processes. C is the substrate (BPA) concentration (mg/L), calculated as: C ¼ C0 Cd, Where C0 is the initial BPA concentration (mg/L), Cd (Cd ¼ Qd/V) is the degraded BPA concentration (mg/L) (not include the BPA absorbed by sludge or sorbent), k is the biodegradation rate constant (h1), t is the time period (h), A is a constant. The degradation half-life (t1/2) of BPA is ln2/k. AgNO3, HgCl2 or HA was added to the BPA synthetic water sample respectively to get a final concentration of 10 mg/L. Except distilled water, environment water collected from Donghu Lake and Hanjiang River (Wuhan, China) was also used to prepare the BPA synthetic water samples. Total organic carbon (TOC), chemical oxygen demand (COD), metal ions, total
Table 2 e BPA biodegradation rate constants (k) and half-lives (t1/2) of different treated groups. Interference
Sorbent Sludge þ active carbon
Sludge a
k Distilled waterd Distilled water with AgNO3d Distilled water with HgCl2d Distilled water with HAd Lake waterd River waterd River water (20 mg/L) a b c d
0.6145 0.2757 0.2536 0.6808 0.225 0.2007 0.1145
t1/2
b
1.13 2.51 2.73 1.02 3.08 3.45 6.05
k is the biodegradation rate constant(h1). t1/2 is the half-life(h). r2 is correlation coefficient. the initial BPA level is 500 mg/L.
2c
r
0.9902 0.9917 0.9961 0.9919 0.9995 0.9981 0.9911
a
b
k
t1/2
0.7549 0.2879 0.2618 0.8003 0.2657 0.2344 0.1365
0.92 2.41 2.65 0.87 2.61 2.96 5.07
2c
r
0.9987 0.9986 0.9981 0.9992 0.9982 0.9983 0.9907
Sludge þ nMIPMs a
b
k
t1/2
0.7629 0.2987 0.2732 0.8242 0.2815 0.2622 0.1503
0.91 2.32 2.54 0.84 2.46 2.64 4.61
2c
r
0.9968 0.9964 0.9983 0.9989 0.9901 0.99 0.9910
Sludge þ MIPMs ka
t1/2b
r2c
0.8391 0.3327 0.3297 0.9458 0.3361 0.2985 0.1893
0.83 2.08 2.10 0.73 2.06 2.32 3.66
0.9909 0.9974 0.9964 0.9953 0.9974 0.9992 0.9973
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bacteria count and other parameters of lake and river water were detected according to Sanitary Standard for Drinking Water, Ministry of Health, China (2001). Then these BPA synthetic water samples (500 mg/L or 20 mg/L) were applied to degradation test under the optimal degrading conditions and were carried out in duplicate.
3.
Results and discussion
3.1.
Binding characters of MIPMs and nMIPMs
phase, no BPA was detected in MIPMs-sludge group after 2 h treatment, compared with higher than 2.83 mg BPA in other groups. And high levels of HAP remained in other groups (higher than 8.48 mg) except only 0.60 mg HAP was detected in MIPMs-sludge group after 4 h treatment. In solid phase, levels of BPA (1.93 mg) and HAP (9.31 mg) in MIPMs-sludge group were a little bit lower than those in active carbon-sludge group (2.45 and 10.03 mg respectively) after 4 h treatment (Figs. 2 and 3). Among all groups, MIPMs-sludge group had the highest degraded BPA level at each tested time (Fig. 1), the highest degradation rate constant (Table 2) and degradation rate (Table 3), but the shortest degradation half-lives (Table 2). On the other hand, non-selective sorbents active carbon and nMIPMs could adsorb fewer amounts of BPA and HAP (Figs. 2 and 3). Therefore the speed and efficiency to degrade BPA and HAP in active carbon-sludge and nMIPMs-sludge groups were also higher than activated sludge group, but lower than MIPMs-sludge group (Figs. 1e3, Tables 2 and 3). It was obvious that selective increasing levels of substrates (BPA and HAP) by MIPMs were critical to enhance BPA biodegradation.
Scanning electron micrographs of MIPMs and nMIPMs were shown in Figure S2 (supporting information). Binding isotherm and Scatchard plot of MIPMs and nMIPMs were shown in Figure S3 (supporting information). The Bmax for the MIPMs and nMIPMs used in this study were 5.33 mg/g and 0.96 mg/g respectively. IPB values were 318, 221, 214 and 183% respectively for BPA, BPC, BPZ and HAP.
3.2.
Optimal conditions for BPA degradation 3.4. Effects of heavy metals and HA on degradation efficiency
From orthogonal test (Table 1), factors that can influence the degradation were listed as: temperature > shaking speed of water bath incubator > pH value. Shaking speed 150 rpm, 35 C and pH ¼ 7 were the optimized degradation conditions. Further studies were carried out under these optimal conditions.
Heavy metals are major components that may affect water treatment efficiency. Compared with other heavy metals, Hg and Ag are the most potent antiseptics and can reduce the biomass of activated sludge significantly (Battistoni et al., 1993; Mowat, 1976). Therefore AgNO3 and HgCl2 were used as models of heavy metals in this study. Compared with distilled water, addition of AgNO3 or HgCl2 decreased degradation rate (Table 3) but increased half-lives of degradation (Table 2). More BPA but less HAP remained in water and solid phase combined with less degraded BPA (Figs. 1e3). It was obvious that heavy metals reduced the degradation capability of activated sludge, which led to less degraded BPA and less HAP although there was high level of BPA in water and solid phase (Figs. 1e3). Heavy metals could combine with biomolecules and act as potent enzyme inhibitors to hamper the activities of biodegradation enzymes and biodegradation processes (Fergusson, 1990; Passow et al., 1961; Poli et al., 2009). Heavy metals could also reduce the bacteria biomass that is respond for biodegradation. Toxicity of heavy metals on degradation bacterium and enzymes reduced
3.3. Effects of different sorbents on degradation efficiency After 5 days treatment, no BPA was detected either in the solid or water phase of BPA bleeding test groups. BPA leaking from MIPMs is not a problem for the usage of MIPMs in water treatment. In distilled water, addition of sorbents promoted biodegradation by increasing the degraded BPA level (Fig. 1) and degradation rate (Table 3) but decreasing half-lives of degradation (Table 2). MIPMs can adsorb BPA and its structural analogous HAP selectively (Figs. 2 and 3). Thus MIPMs-sludge groups had the highest level of BPA and HAP in solid phase at the 1st and 2nd h (Figs. 2b and 3b). High levels of substrates (BPA and HAP) accelerated the degradation efficiency. In water
Table 3 e BPA biodegradation rate after 4 h treatment. Degradation rate(%)a Sludge Distilled waterb Distilled water with AgNO3b Distilled water with HgCl2b Distilled water with HAb Lake waterb River waterb River water(20 mg/L)
90.38 67.74 62.67 93.97 59.63 54.65 37.00
1.05 1.44 1.06 1.18 3.13 1.37 3.96
Active carbon þ sludge 94.89 68.89 64.80 95.89 64.75 60.16 42.00
1.77 1.64 2.81 0.66 1.60 2.72 1.70
nMIPMs þ sludge 94.92 70.41 67.12 96.30 66.23 63.24 44.90
0.72 1.58 2.7 0.48 2.83 3.66 1.13
MIPMs þ sludge 96.14 74.30 74.26 97.89 73.89 69.24 53.60
0.92 2.82 1.73 0.31 1.02 2.63 3.82
a Degradation rate % ¼ (Qd/Q0) 100%. Qd was the amount of degraded BPA (mg): Qd ¼ Q0 Qe Qx. Q0 was the initial BPA amount (mg), Qe and Qx were the amount of BPA (mg) in the water phase and the solid phase respectively. b the initial BPA level is 500 mg/L.
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Fig. 1 e Degraded BPA level in different treated groups. The initial BPA level is 500 mg/L.
the degradation capability of activated sludge, which resulted in longer degradation half-lives (Table 2) and lower degradation rate (Table 3). Although AgNO3 or HgCl2 reduced degradation capability of activated sludge, applying MIPMs to activated sludge still increased the degradation rate (Table 3), the degraded BPA level and HAP level (Figs. 1e3), but decreased half-lives of degradation (Table 2). Heavy metals increased the selective adsorption of MIPMs, but reduced the adsorption capacity of active carbon (Figs. 2b and 3b). It was reported that the exchange of protons between the functional groups of the sorbents and cations (Ag2þ or Hg2þ in this case) removed the hydrogen bond donor groups necessary for selective retention. This effect was more significant to non-selective absorption than in selective retention (Hu et al., 2007). Thus, heavy metals reduced the adsorption capacity of active carbon and nMIPMs but did not affect the adsorption capacity of MIPMs (Figs. 2b and 3b). Selective adsorption of MIPMs led to the highest BPA and HAP levels from the 1st to the 3rd h in solid phase of MIPMs-sludge group. High levels of substrates (BPA and HAP) accelerated the degradation efficiency of remaining alive microorganisms to increase biodegradation efficiency and degradation speed at some extent. Therefore, BPA was almost completely removed in water phase of MIPMs-sludge groups (0.17 or 0.5 mg in the existence of AgNO3 or HgCl2 respectively), compared with higher than 3.45 or 4.08 mg respectively in other groups after 4 h treatment. While HAP levels in water phase of MIPMs-sludge groups were 6.99 and 9.68 mg respectively, lower than those in other groups (higher than 10.98 and 11.97 mg respectively). And levels of BPA (lower than 12.68 mg) and HAP (lower than 9.48 mg) in solid phase were similar to those in active carbon groups (lower than 11.74 mg or 8.72 mg, respectively) at the 4th h (Figs. 2 and 3). The differences on degradation rate (Table 3) and degradation half-lives (Table 2) among MIPMs-sludge and other groups were more significant in water containing heavy metals than in distilled water. These data indicated that the
capability of MIPMs to promote degradation efficiencies was more significantly in water containing HgCl2 or AgNO3 than in distilled water. Furthermore, MIPMs were better than active carbon to enhance biodegradation efficiencies in water containing heavy metals. HA is another factor that may affect the biodegradation efficiency. The level of HA in environmental water is about 1e10 mg/L (Li and Lee, 2001). HA was reported to increase, inhibit, or have no effect on the biodegradation of polycyclic aromatic hydrocarbons and aromatic pollutants (Burgos et al., 2000; Amador and Alexander, 1988; Holman et al., 2002; Larsson et al., 1988). In this study, water containing HA had higher degradation rate constants (Table 2) and degradation rate (Table 3), but shorter degradation half-lives (Table 2). HA increased the efficiency and speed of activated sludge to eliminate BPA and HAP. These may because the strong positive correlation between bacterial biomass and the concentration of HA (Hessen, 1985; Tranvik and Ho¨fle, 1987). Higher HA level will increase bacterial biomass, which is helpful to increase degradation capability. In water containing HA, addition of MIPMs still enhanced degradation efficiency by increasing degradation rate constant (Table 2) and degradation rate (Table 3) but reduced degradation half-lives (Table 2). Detail analysis showed that HA increased the selective adsorption of MIPMs, but reduced the adsorption capacity of active carbon (Figs. 2b and 3b). Similar results were also observed in our previous study (Lin et al., 2008). HA increased the selective adsorption of MIPMs because functional monomer 4-VP did not have an effective molecular interaction with HA. But HA can be easily adsorbed by relatively hydrophobic stationary phase such as active carbon to interfere the adsorption of target pollutants (Kubo et al., 2003; Lin et al., 2008). MIPMs enriched much higher BPA (14.88 mg) and HAP (19.64 mg) level in solid phase at the 1st h than those in other groups (less than 7.91 and 10.96 mg, respectively) (Figs. 2b and 3b). High levels of substrates (BPA and HAP) led to rapid and effective reduction of BPA and HAP in
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Fig. 2 e BPA level in different treated groups. (a) water phase (b) solid phase. The initial BPA level is 500 mg/L.
MIPMs-sludge group. No BPA or HAP was detected in water phase at the 2nd or the 4th h respectively in MIPMs-sludge group, compared with higher than 4.56 mg BPA or 2.97 mg HAP in other groups (Figs. 2a and 3a). After 4 h treatment, BPA and HAP in solid phase were 1.06 and 9.22 mg in MIPMs-sludge group, lower than those of active carbon group (1.56 and 12.37 mg respectively) (Figs. 2b and 3b). The enhancement of MIPMs in degradation efficiencies was more significant than that of active carbon in water containing high level of HA (Figs. 1e3, Tables 2 and 3).
3.5. Effects of different kinds of water on degradation efficiency Environmental lake and river water were applied to degradation tests to evaluate the practical usage of MIPMs. The physiochemical characters of Hanjiang River and Donghu Lake water used in this study were listed in Table S1 (supporting
information). When lake or river water instead of distilled water was used, the degradation rate constants (Table 2) and degradation rates (Table 3) decreased but degradation halflives increased (Table 2). The degradation capability of activated sludge was inhibited by river or lake water as the degraded BPA level decreased (Fig. 1) although adsorbed BPA level did not reduce compared with distilled water (Figs. 2b and 3b). This inhibition could be attributed to the contaminants in lake and river water. Factors such as COD level, heavy metals and bacterium in the river and lake water (Table S1, supporting information) can affect the degradation capability of activated sludge. In distilled water, microorganisms in the activated sludge utilized BPA as the only carbon source. Water with certain COD level contains other organic substrates, which can be more easily degraded by activated sludge than BPA. Therefore BPA degradation was slowed down in water with higher initial COD level (Urase and Kikuta, 2005; Zhao et al., 2008). Heavy metals (Table S1, supporting information) in lake and
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Fig. 3 e HAP level in different treated groups. (a) water phase (b) solid phase. The initial BPA level is 500 mg/L.
river water also reduced biodegradation capability of activated sludge (Figs. 1e3, Tables 2 and 3). Some bacterium (such as pseudomonas fluorescens) could produce bacterio-toxins to inhibit or kill other bacterium (Lewis, 1929; Terpstra, 1947), which would reduce the biodegradability of activated sludge. Contaminants in lake and river water decreased the biodegradation capability of activated sludge. Application of MIPMs to river and lake water increased degradation rate constants (Table 2) and degradation rates (Table 3), thereby decreased half-lives of degradation (Table 2). Compared with distilled water, MIPMs had much higher adsorption capabilities than those of active carbon in river and lake water (Figs. 2b and 3b). These were because MIPMs could resist the interference of pollutants that normally reduce the adsorption efficiency of active carbon in environmental water (Lin et al., 2008). Higher substrates level is optimal to increase degradation efficiencies of alive microorganisms in the activated sludge. After 4 h treatment, BPA was completely
removed from water phase (either lake or river water) of MIPMssludge groups, compared with higher than 3.72 mg BPA in other groups (Fig. 2a). And HAP levels (less than 6.63 mg) in water phase of MIPMs-sludge groups were the lowest among all the groups (higher than 7.64 mg) (Fig. 3a). In solid phase, BPA (lower than 15.38 mg) and HAP (lower than 9.32 mg) in MIPMs-sludge groups were almost similar to those of other groups (lower than 14.67 mg and 8.07 mg respectively) at the 4th h (Figs. 2b and 3b). The differences on degraded BPA level (Fig. 1), degradation rate (Table 3) and degradation half-lives (Table 2) among MIPMssludge and other groups were more significant in lake or river water than in distilled water. MIPMs were more suitable than active carbon to be mixed with activated sludge to increase BPA biodegradation efficiencies in complicated environmental water. Because only trace BPA was existed in environmental water, river water containing low concentration of BPA (20 mg/ L, 500 mL) was also applied to biodegradation test. Compared
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Fig. 4 e Biodegradation of river water containing 20 mg/L initial BPA level (a) Degraded BPA level. (b) BPA level (c) HAP level.
with river water containing 500 mg/L BPA, lower degradation rate (Table 3) and degraded BPA level (Fig. 4), but higher degradation half-lives (Table 2) were observed in water containing 20 mg/L BPA. Low initiated BPA concentration means low substrate level, which decreased degradation efficiencies significantly. Although low initiated BPA level reduced degradation capability, addition of adsorbs to 20 mg/L BPA groups increased degradation efficiencies. MIPMs enriched trace BPA and HAP effectively because both BPA and HAP levels in solid phase during the 1st and 2nd h were the highest in MIPMs-sludge groups. Higher substrate (BPA and HAP) levels promoted the degradation. After 4 h treatment, MIPMs-sludge group had the lowest BPA and HAP level in water phase (Fig. 4b and c). And lower BAP or HAP levels in solid phase than those in active carbon-sludge group (Fig. 4b and c) were observed. MIPMssludge group had the highest degraded BPA level (Fig. 4a) and degradation rate (Table 3) but the shortest degradation halflives (Table 2). The differences on degraded BPA level, degradation rate and degradation half-lives among MIPMs-sludge and other groups were more significant in 20 mg/L groups than in 500 mg/L groups (Fig. 4, Tables 2 and 3). The enhancement of MIPMs in degradation efficiencies was more significant in water containing lower BPA level. Selective increasing substrates levels were very important to enhance degradation efficiencies in environmental water containing trace pollutants. Furthermore, the degradation rates in MIPMs-sludge and active carbon-sludge groups were 53.6% and 42% respectively in 20 mg/L BPA groups, compared with 69.24% and 60.16% respectively in 500 mg/L BPA groups (Table 3). And the degradation half-lives of MIPMs-sludge and active carbon-sludge groups were 3.66 h and 5.07 h respectively in 20 mg/L BPA groups, but 2.32 h and 2.96 h respectively in 500 mg/L BPA samples (Table 2). Compared with active carbon, MIPMs is much more suitable to be used with activated sludge to degrade trace pollutants in complex environmental water.
4.
Conclusions
In this study, BPA-imprinted MIPMs were mixed with activated sludge to evaluate the potential impacts of MIPMs on biodegradation. Our results proved that the application of MIPMs enhanced biodegradation efficiency of activated sludge effectively via increasing levels of substrates (BPA and its metabolites) selectively. This enhancement was more significant in environmental water containing trace pollutants, and in water containing different interferences such as heavy metals and HA. MIPMs were more suitable than active carbon to be used as enhancer for BPA biodegradation. Combined usage of MIPMs and activated sludge provides a reliable and practical solution to degrade trace environmental pollutants in environmental water effectively.
Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 30972435, 30771775, 20728708 and 30771776) and NCET Program by the Chinese Ministry of Education (No. NCET-06-0640).
Appendix. Supplementary data Supplementary data associated with the article can be found in online version, at doi:10.1016/j.watres.2010.11.014.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 9 9 e1 2 1 2
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Indicator compounds for assessment of wastewater effluent contributions to flow and water quality Eric R.V. Dickenson a, Shane A. Snyder b, David L. Sedlak c, Jo¨rg E. Drewes a,* a
Advanced Water Technology Center (AQWATEC), Environmental Science and Engineering Division, Colorado School of Mines, Golden, CO 80401, USA b Applied Research and Development Center (ARDC), Water Quality Research and Development Division, Southern Nevada Water Authority, Henderson, NV 89015, USA c Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA 94720, USA
article info
abstract
Article history:
Numerous studies have reported the presence of trace (i.e., ng/L) organic chemicals in
Received 6 March 2010
municipal wastewater effluents, but it is unclear which compounds will be useful to
Received in revised form
evaluate the contribution of effluent to overall river flow or the attenuation processes that
30 October 2010
occur in receiving streams. This paper presents a new approach that uses a suite of
Accepted 9 November 2010
common trace organic chemicals as indicators to assess the degree of impact and atten-
Available online 16 November 2010
uation of trace organic chemicals in receiving streams. The utility of the approach was validated by effluent monitoring at ten wastewater treatment plants and two effluent-
Keywords:
impacted rivers with short retention times (<17 h). A total of 56 compounds were partic-
Indicator
ularly well suited as potential indicators, occurring frequently in effluent samples at
Trace organic chemical
concentrations that were at least five times higher than their limit of quantification.
Wastewater treatment
Monitoring data from two effluent-impacted rivers indicated that biotransformation was
Performance monitoring
not important for these two river stretches, whereas photolysis attenuation was possibly
Pharmaceuticals
important for the shallow river. The application of this approach to receiving waters and
Water reclamation and reuse
water reclamation and reuse systems will allow for more effective allocation of resources in future monitoring programs. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Drinking water sources that receive treated wastewater discharge or that are augmented in part through potable water reuse can contain trace organic chemicals at concentrations that may have the potential to elicit adverse effects to aquatic life or human health. A 30-year old scoping study published by the United States (U.S.) Environmental Protection Agency in 1980 estimated that about 15 million people in the U.S. were served by surface supplies containing at least 10 percent treated wastewater at low flow conditions and 4 million people use municipal supplies that contain close to 100 percent treated wastewater
during low flow conditions (Swayne et al., 1980). With increasing water demand and dwindling water resources in many communities the proportion of treated wastewater impacted drinking water supplies in the U.S. has likely risen significantly. In this decade, several monitoring campaigns (e.g., Kolpin et al., 2002; Glassmeyer et al., 2005) indicated the presence of trace organic chemicals in U.S. surface waters and groundwaters susceptible to treated wastewater discharge. More recently, Benotti et al. (2009) reported the presence of wastewater-derived chemicals in finished water of 19 U.S. drinking water utilities. These chemicals span a wide range of categories, such as pharmaceutical residues, personal care products, household
* Corresponding author. Tel.: þ1 (303) 273 3401; fax: þ1 (303) 273 3413. E-mail address:
[email protected] (J.E. Drewes). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.012
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chemicals, suspected endocrine disrupting compounds, transformation products, and others. Despite the strong interest in these findings, the occurrence of wastewater-derived trace organic compounds at concentrations in the nanogram-perliter (ng/L) range has been reported sporadically over the last 40 years. The recently reported detection of these compounds was driven mainly by the availability of more sensitive analytical instruments. The most important sources for release of trace organic chemicals into surface and groundwater are discharges from municipal wastewater treatment plants (WWTPs), industrial manufacturing processes, leaky sewers, combined sewer overflows, onsite wastewater treatment systems, agricultural practices including confined animal feeding operations (Drewes and Shore, 2001). Generally, the occurrence levels of these trace organic chemicals in drinking water sources primarily depend upon physicochemical properties, their fate in the environment after discharge, and the relative contribution of treated wastewater to the overall flow. However, the degree of treated wastewater impact for the majority of occurrence studies is frequently either not reported or not known. Additional factors affecting occurrence are different usage patterns (e.g., pharmaceutical prescription practices), which can vary with region, per-capita water consumption (resulting in different levels of dilution) and substitution and phase-out programs for specific chemicals. This denotes that the occurrence pattern of currently detected trace organic chemicals is not static. Providing certain chemicals occur at quantifiable concentrations, conservative and nonconservative anthropogenic indicator markers can be used to assess wastewater contamination in receiving waters. Conservative inorganic markers, such as boron (B) isotopes (Vengosh et al., 1994; Bassett et al., 1995) and gadolinium (Gd) (Verplanck et al., 2005), have been successfully used to indicate wastewater impact, but they have their limitations. In order for B isotopes to be applicable to a particular site they need to be well characterized in all relevant water sources and the resulting B isotopic signatures need to be distinguishable from background water sources. Also, the occurrence of Gd is usually associated with and limited to communities with magnetic resonance imaging facilities. Conservative organic markers may provide a more robust alternative, where several studies have proposed select markers to indicate the influence of wastewater into receiving water bodies (Buerge et al., 2003, 2008, 2009; Clara et al., 2004; Fono and Sedlak, 2005; Glassmeyer et al., 2005; Nakada et al., 2008). However, little information is available on the use of frequently occurring nonconservative indicators to indicate the reduction of co-occurring compounds with similar physiochemical and biodegradable characteristics in receiving rivers. These types of indicator compounds are important as they provide a more complete evaluation regarding the attenuation of wastewater-derived chemicals that is not solely due to dilution. However, implementing suitable conservative and nonconservative indicator compounds is currently hindered since a comprehensive identification of unregulated trace organic chemicals that commonly occur in treated wastewater effluents regardless of location is lacking. Therefore, the objective of this study was to identify and verify viable indicator compounds by evaluating the occurrence of the most frequently detected trace organic chemicals occurring in North American conventional secondary- or tertiary-treated
wastewater effluents and to assess the fate and transport of trace organic chemicals by monitoring select indicator compounds in two rivers highly impacted by treated wastewater discharge.
2.
Materials and methods
2.1.
Full-scale monitoring
Ten full-scale wastewater treatment facilities were selected to evaluate the occurrence of trace organic chemicals in secondary, tertiary, or membrane bioreactor treated effluents (Table 1). Biologically treated effluent samples were collected prior to disinfection with exceptions to facilities 2 and 8, where biologically treated effluent samples were collected after chloramine and chlorine application, respectively. Multiple sampling events were performed at some facilities (Table 1). Efforts were made to reduce, if not eliminate, the use of plastics during the sampling process because of the propensity to either cause adsorptive losses of target compounds or leaching of compounds targeted in the study into the sample (i.e., bisphenol A). Only glass or metal collection containers were used during sample collections, and if plastic tubing was employed, well-conditioned tubing was used. Initially, samples were collected directly in a single, cooled 20 L glass container, then split into various sample containers and shipped overnight to participating laboratories. Samples were collected as composites over a short period (2e4 h). Prior to sampling, the normal operation of the plant was confirmed by assuring that operational parameters were within their technical design specifications. Care was also taken not to collect samples within 48 h following a rain event.
2.2.
Receiving river monitoring
The attenuation of select trace organic compounds was assessed at two river sites impacted by treated wastewater discharge. A sampling campaign was conducted at river site 1 during the day of June 21, 2006. This river, which usually is dry and only carries water upstream of the site during flooding conditions, received chlorinated (followed by dechlorination) secondary-treated wastewater (non-nitrified; trickling filter process) from a 150 ML/day plant, which made up 100% of the flow in the river (no flow upstream of the discharge). Downstream water flows were monitored on the day of sampling using USGS stream gauging stations. The average flow on the day of sampling was 1.0 m3/s. The studied river stretch was not influenced by major river tributaries or other treated wastewater outfalls. Synoptic grab sampling occurred downstream of the discharge (8.3 km), which represented an estimated travel time of 6 h based on time studies performed by the USGS using peak flow measurements. During the time of sampling the weather was sunny, clear skies, zero precipitation, and the air temperature ranged from 31.7 to 36.1 C as recorded by the National Weather Service. The river stretch between the discharge and downstream sampling point was shallow, ranging from 10 cm to 35 cm in depth. Site and water quality data at river site 1 are presented in Table 2. River site 1 was characterized by high TOC (14 mg/L), ammonia (20 mg N/L), and phosphate (10 mg/L) concentrations.
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Table 1 e Conventional full-scale wastewater treatment facilities in the U.S. Facility
Capacity
Treatment train
# Sampling events
Facility 1
40 mgd 151 ML/day 6 mgd 23 ML/day 2.5 mgd 9.5 ML/day 20 mgd 76 ML/day 88.5 mgd 335 ML/day 14 mgd 53 ML/day 21 mgd 79 ML/day 15 mgd 57 ML/day 25 gpm 95 L/min 5 mgd 19 ML/day
Primary, secondary (trickling filter; partly nitrifying), disinfection (chloramine) Primary, secondary (activated sludge; non nitrifying), disinfection (chloramine), microfiltration Primary, secondary (activated sludge; partly nitrifying/denitrifying), disinfection (chloramine) Primary, secondary (activated sludge; nitrifying), chemical phosphorus-removal, ultrafiltration, disinfection (ozone) Primary, secondary (activated sludge; nitrifying), tertiary filtration, disinfection (UV irradiation) Primary, secondary (activated sludge; nitrifying), disinfection (chloramine) Primary, secondary (activated sludge; nitrifying), tertiary filtration, disinfection (chloramine) Primary, secondary (activated sludge; nitrifying/denitrifying), tertiary filtration, disinfection (chlorine) Primary, MBRb (activated sludge; nitrifying/denitrifying)
2
MBR (activated sludge; nitrifying/denitrifying), disinfection (UV irradiation)
2
Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Facility 9a Facility 10
2 1 2 4 1 1 3 2
Primary treatment: mechanical treatment. ML/day e Million liters per day; mgd e million gallons per day. a Pilot-scale system. b Membrane bioreactor.
At river site 2 a sampling campaign was conducted on April 30, 2006. The river sampled at site 2 received a blend of nonnitrified (130 ML/day) and nitrified (375 ML/day) chlorinated (followed by dechlorination) secondary-treated wastewater (activated sludge treatments), which made up 35% of the flow in the river during the time of sampling, since there was an existing upstream flow. The studied river stretch was not influenced by major river tributaries or other treated wastewater outfalls. Water flows were monitored on the day of sampling using USGS downstream gauging stations. The average flow on the day of sampling was 5.7 m3/s. Synoptic and 1 h time-composite sampling occurred at four locations downstream of the WWTP. These locations are representative of approximate travel times of 30 min, 5 h, 12 h, and 17 h downstream from the point of wastewater discharge, as determined by previous tracer studies performed at similar river flow conditions. The approximate travel distances of the river from the point of discharge were 0.8, 8, 19, and 27 km, respectively. During the time of sampling the weather was mostly cloudy or scattered clouds with 0.25 mm of precipitation. The air temperature ranged from 7.2 to 19.4 C as recorded by the National Weather Service. The river stretch between the discharge and downstream sampling points was approximately 1.2 m. Site and water quality data at river site 2 are presented in Table 2. The river at site 2 was characterized by relatively high TOC levels (8 mg/L), but it had lower ammonia (<0.1 mg N/L) and moderate phosphate concentrations (2.5 mg/ L) though it was higher in nitrate levels (3 mg N/L) than the river at site 1.
2.3.
Analytical methods
Multiple methods were used to quantify trace organic chemicals. Liquid chromatography (LC) followed by UV detection was
used for the analysis of total EDTA (Bedsworth and Sedlak, 2001). LC with tandem mass spectrometry (LC/MS-MS) was used for the analysis for a suite of pharmaceutical residues, personal care products, suspected endocrine disrupting compounds and pesticides (Trenholm et al., 2006; Vanderford and Snyder, 2006). Gas chromatography/mass (GC/MS) spectrometry was used for a suite of pharmaceutical residues, pesticides and chlorinated flame retardant compounds (Reddersen and Heberer, 2003; Hoppe-Jones et al., 2010). Gas chromatography/tandem mass (GC/MS-MS) spectrometry was used for pharmaceutical b-blockers (Fono and Sedlak, 2005, Kolodziej et al., 2004) and N-nitrosodimethlyamine (NDMA) (Mitch et al., 2003). Samples were extracted within two weeks and were stored at 4 C until analyses of extracts were completed. Field and laboratory blanks were processed like field samples. Findings from two analytical Round Robin experiments and split samples analyzed during field monitoring efforts indicated that the methods employed during this study were comparable for common analytes (i.e., diclofenac, gemfibrozil, ibuprofen, naproxen, tris[2-chloroethyl]-phosphate) with relative standard deviations (RSDs) of less than 30% (Drewes et al., 2008).
3.
Results and discussion
3.1. Selection of potential indicator compounds from a literature survey Numerous past studies have reported the presence of trace organic chemicals in effluents of North American (U.S. and Canada) conventional wastewater treatment facilities. However, if trace organic chemicals do not occur at concentrations significantly above their detection limits and at high
1.53 1.58 1.57 1.52 N/R N/M 8.5 8.3 8.0 7.9 N/R 2.6 91 91 89 100 65 83 N/M e not measured; N/R e not reported; DO e dissolved oxygen; TOC e total organic carbon; SUVA e ultraviolet absorbance at 254 nm/TOC. a Sample collected at site 4 in Fono et al. (2006) on September 1, 2005 by the USGS. b Sample collected at site 3 in Gross et al. (2004).
2.7 2.7 2.3 2.5 1.47 N/R 3.7 3.2 2.6 3.0 3.8 6.9 990 964 970 1050 618 N/R 0.8 8 19 27 N/R 12 River Site 2 (5.7 m3/s) Downstream location #1 Downstream location #2 Downstream location #3 Downstream location #4 Trinity Rivera (23 m3/s) Santa Ana Riverb (1.4 m3/s)
0.5 5 12 17 264 7.5
7.7 7.8 7.6 7.7 8.4 N/R
14.3 16.2 18.7 18.0 30.2 N/R
7.8 8.9 9.0 7.2 7.5 8.5
0.10 0.10 0.09 0.15 <0.04 N/R
1.21 1.24 14.2 14.0 112 128 10 11 0.7 0.5 17 23 1182 1159 <2 <2 N/M N/M 0 8.3 River Site 1 (1.0 m3/s) River discharge Downstream location
0 6
7.6 7.6
Cond. (uS/cm) DO (mg/L) Distance from WWTP discharge (km)
Travel time from WWTP discharge (h)
pH
Temp. (C)
Ammonia (mg N/L)
Nitrate (mg N/L)
Phosphate (mg/L)
Chloride (mg/L)
TOC (mg/L)
SUVA (L/mg m)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 9 9 e1 2 1 2
Sample location
Table 2 e River site information and water quality data at river sites 1 and 2 and the Santa Ana and Trinity Rivers studied by Lin et al. (2006) and Fono et al. (2006), respectively.
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frequencies, many of these compounds may not represent good indicator candidates for monitoring efforts. Using these criteria, compound occurrence data was screened and compounds that did not occur at a frequency above 80% or were not present in secondary- or tertiary-treated wastewater at concentrations at least five times higher than their respective limits of quantification were eliminated. Compounds considered during screening are presented in Table SI. Based on this analysis a list of 52 potential indicator compounds was identified in North American wastewater effluents (Table 3). For most compounds, their occurrence in effluent was reported in more than one study and in multiple WWTP effluents, where the median total number of WWTPs was 13. Pharmaceutical residues and fragrance compounds were the dominant types of compounds identified among the compounds targeted in this study. It is noteworthy, that this screening of compounds is biased through the application of analytical methods that targeted compounds that were of interest to the researchers who initiated the study. It is possible that other viable indicator compounds are present, but analytical methods may not exist for these compounds or existing methods have not been applied to measure these compounds in treated wastewater. A successful indicator compound is one that can be used to quantify changes in concentration that occur in receiving waters. However, it is difficult to detect the loss of a compound, if it is initially present at a concentration near the detection limit. Therefore, it is necessary to consider a compound’s concentration relative to the limit of quantification (LOQ). To address this issue, Sedlak et al. (2004) proposed a detection ratio (DR):
DR ¼
½Concentrationmedian ½LOQ
(1)
The detection ratio was calculated for a compound for a given study based on the reported median concentration and the LOQ. However, when the median concentration was not reported or could not be calculated for a given study, the reported average concentration was used in Eq. (1). In this study a detection ratio of 5 was selected to identify potential indicator compounds as this ratio allows attenuation evaluation of a particular compound in excess of 80%. Potential indicator compounds with detection ratio 5 are reported in Table 3. The detection ratios reported in Table 3 are representative average detection ratios across studies. The LOQ was sometimes variable among studies that employed different analytical methods. Therefore, in order to compare detection ratios evenly among studies the median concentrations were normalized by the same LOQ. LOQs employed in Eq. (1) were based upon reported analytical methods that are less than 30 ng/L, which allows for a greater probability of detection of a compound. These LOQs are reported in Table 3 and ranged from 0.25 to 30 ng/L, with the majority of LOQs between 1 and 10 ng/L. The number of WWTPs evaluated for a given study varied, therefore in order to take into account this bias, the weighted-average detection ratio (and standard deviation) that considers the number of WWTPs that was examined per study was calculated and represents the detection ratio reported in Table 3. With the exception of eight compounds, the detection ratio exceeded 10 for most of the compounds (Table 3). Therefore,
Table 3 e Average detection frequencies (DF) and detection ratios (DR) calculated from literature for potential organic indicator compounds in North American treated wastewater effluents. Method detection limit (MDL) and limit of quantification (LOQ) and corresponding analytical method and reference are listed. Compound
Compound category Fragrance PhAC DBP
Isobornyl acetate Hexylcinnamaldehyde Benzophenone
Fragrance Fragrance UV absorber
Terpineol Codeine
Fragrance PhAC
Fluoxetine
PhAC
Musk xylene TDCPP Methyl salicylate Bisphenol A Propylparaben g-Methyl ionine Propranolol Metoprolol Caffeine
Oxybenzone Dibutyl phthalate Acetyl cedrene OTNE, ethanone TCEP
Benzyl acetate Diclofenac
Simonich et al. (2002) Glassmeyer et al. (2005) Sedlak et al. (2005a), Najm and Trussell (2001) Simonich et al. (2002) Simonich et al. (2002) Glassmeyer et al. (2005), Loraine and Pettigrove (2006), Drewes et al. (2009) Simonich et al. (2002) Glassmeyer et al. (2005), Benotti and Brownawell (2007)
Analytical Method
Ref.
12 10 8
100 91 88
n/a n/a 7
5 5 5
n/a n/a 3
n/a n/a n/a
2 15 10
GC/MS LC/MS GC/MS
Simonich et al. (2002) Glassmeyer et al. (2005) Najm and Trussell (2001)
12 12 16
100 100 100
n/a n/a 0
6 7 7
n/a n/a 1
n/a n/a n/a
4 2 25
GC/MS GC/MS LC/MS-MS
Yang and Carlson (2004) Simonich et al. (2002) Trenholm et al. (2008)
12 11
100 84
n/a 6
8 9
n/a 1
n/a n/a
5 15
GC/MS LC/MS
Simonich et al. (2002) Glassmeyer et al. (2005)
5
94
14
10
3
0.19
1
LC/MS-MS
Trenholm et al. (2006)
12 10 12 7 6 12 25
92 100 100 100 83 100 79
n/a n/a n/a n/a n/a n/a 14
10 10 11 14 16 17 19
n/a n/a n/a n/a n/a n/a 6
n/a n/a n/a n/a n/a n/a 0
1 30 4 1 0.25 2 1
GC/MS GC/MS GC/MS LC/MS-MS LC/MS-MS GC/MS GC/MS/MS
Simonich et al. (2002)
18
98
3
20
21
0
10
GCeMS/MS Fono and Sedlak (2005)
15
81
13
21
27
n/a
10
LC/MS-MS
Trenholm et al. (2006)
11
91
8
23
8
0.48
1
LC/MS-MS
Trenholm et al. (2006)
6 12 12 16
100 100 100 94
n/a n/a n/a 9
24 25 28 28
n/a n/a n/a 8
n/a n/a n/a 1.6
25 7 4 10
GC/MS-MS GC/MS GC/MS LC/MS-MS
Simonich et al. (2002) Simonich et al. (2002) Simonich et al. (2002) Trenholm et al. (2006)
12 28
100 75
n/a 29
29 36
n/a 23
n/a 0.14
3 1
GC/MS LC/MS-MS
Simonich et al. (2002) Trenholm et al. (2006)
Simonich et al. (2002) Vanderford and Snyder (2006) Trenholm et al. (2008) Simonich et al. (2002) Fono and Sedlak (2005)
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Snyder et al. (2007), Vanderford et al. (2003) Fragrance Simonich et al. (2002) Flame retardant Glassmeyer et al. (2005) Fragrance Simonich et al. (2002) Plasticizer Drewes et al. (2005) Biocide Trenholm et al. (2008) Fragrance Simonich et al. (2002) Beta Blocker Fono et al. (2006), Sedlak et al. (2005b), Fono and Sedlak (2005) Beta Blocker Fono et al. (2006), Sedlak et al. (2005b) Stimulant Glassmeyer et al. (2005), Snyder et al. (2007), Vanderford et al. (2003) UV absorber Snyder et al. (2007), Drewes et al. (2009) Plasticizer Drewes et al. (2009) Fragrance Simonich et al. (2002) Fragrance Simonich et al. (2002) Flame retardant Glassmeyer et al. (2005), Snyder et al. (2007), Vanderford et al. (2003) Fragrance Simonich et al. (2002) PhAC Snyder et al. (2007), Vanderford et al. (2003), Drewes et al. (2002), Miao et al. (2004), Fono et al. (2006), Sedlak et al. (2005b)
Total # Averageb Stand. Averageb Stand. MDL LOQc DR dev. (ng/L) (ng/L) WWTPsa DF (%) dev. (%)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 9 9 e1 2 1 2
Hexyl salicylate Diphenhydramine NDMA
Occurrence reference
(continued on next page)
Compound
p-t-Bucinal Musk ketone
Compound category Fragrance Fragrance
Methyl dihydrojasmonate Fragrance Benzyl salicylate Fragrance Ibuprofen Pharmaceutical
Pharmaceutical
Ofloxacin
Pharmaceutical
Sulfapyridine Nonylphenol
Pharmaceutical Surfactant
Dilantin
Pharmaceutical
Clarithromycin Carbamazepine
Pharmaceutical Pharmaceutical
Galaxolide
Fragrance
Primidone Erythromycin
Pharmaceutical Pharmaceutical
Iopromide Naproxen
X-ray contrast agent Pharmaceutical
Indol-3-butyric acid
Plant Hormone
Simonich et al. (2002) Snyder et al. (2007), Simonich et al. (2002) Simonich et al. (2002) Simonich et al. (2002) Snyder et al. (2007), Vanderford et al. (2003), Miao et al. (2004), Fono et al. (2006), Sedlak et al. (2005b) Benotti and Brownawell (2007), Snyder et al. (2007), Vanderford et al. (2003) Miao et al. (2004), Sedlak et al. (2005b) Miao et al. (2004) Drewes et al. (2005), Snyder et al. (1999) Snyder et al. (2007), Vanderford et al. (2003) Miao et al. (2004) Glassmeyer et al. (2005), Benotti and Brownawell (2007), Snyder et al. (2007), Drewes et al. (2002) Glassmeyer et al. (2005), Snyder et al. (2007), Simonich et al. (2002) Drewes et al. (2002) Glassmeyer et al. (2005), Snyder et al. (2007), Miao et al. (2004), Yang and Carlson (2004) Snyder et al. (2007), Vanderford et al. (2003) Snyder et al. (2007), Vanderford et al. (2003), Drewes et al. (2002), Miao et al. (2004), Fono et al. (2006), Sedlak et al. (2005b) Drewes et al. (2009)
Total # Averageb Stand. Averageb Stand. MDL LOQc DR dev. (ng/L) (ng/L) WWTPsa DF (%) dev. (%)
Analytical Method
Ref.
12 13
100 87
n/a 7
41 42
n/a 24
n/a 5.5
1 10
GC/MS GC/MS
Simonich et al. (2002) Simonich et al. (2002)
12 12 28
100 100 78
n/a n/a 21
43 44 49
n/a n/a 30
n/a n/a 0.18
2 2 1
GC/MS GC/MS LC/MS-MS
Simonich et al. (2002) Simonich et al. (2002) Trenholm et al. (2006)
7
100
0
54
21
0.29
1
LC/MS-MS
Trenholm et al. (2006)
15
93
7
67
22
2
n/a
LC/MS/MS
Miao et al. (2004)
8 13
100 100
n/a 0
81 85
n/a 17
1 15
n/a n/a
LC/MS/MS GC/MS
Miao et al. (2004) Drewes et al. (2005)
6
100
0
87
4
0.33
1
LC/MS-MS
Trenholm et al. (2008)
8 21
80 88
n/a 13
87 94
n/a 28
1 n/a
n/a 1
LC/MS/MS LC/MS-MS
Miao et al. (2004) Trenholm et al. (2006)
23
100
0
95
60
5.8
10
GC/MS-MS
Trenholm et al. (2006)
5 24
100 83
n/a 14
115 124
n/a 41
<1 0.22
1 1
GC/MS LC/MS-MS
Drewes et al. (2002) Trenholm et al. (2006)
6
100
0
125
5
0.58
1
LC/MS-MS
Trenholm et al. (2006)
28
92
8
126
120
0.23
1
LC/MS-MS
Trenholm et al. (2006)
6
100
n/a
127
n/a
n/a
1
LC/MS-MS
Trenholm et al. (2008)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 9 9 e1 2 1 2
Hydrocodone
Occurrence reference
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Table 3 (continued)
Pharmaceutical
Meprobamate
Pharmaceutical
Triclosan
Biocide
Gemfibrozil
Pharmaceutical
Tonalide (AHTN)
Fragrance
DEET
Insecticide
Triclocarban
Biocide
Sulfamethoxazole
Pharmaceutical
Total EDTA
Chelating agent
Glassmeyer et al. (2005), Benotti and Brownawell (2007), Snyder et al. (2007), Vanderford et al. (2003), Sedlak et al. (2005b), Batt et al. (2006) Snyder et al. (2007), Vanderford et al. (2003) Glassmeyer et al. (2005), Snyder et al. (2007), Loraine and Pettigrove (2006), Drewes et al. (2009), Halden and Paull (2005), McAvoy et al. (2002) Snyder et al. (2007), Vanderford et al. (2003), Miao et al. (2004), Fono et al. (2006), Sedlak et al. (2005b) Glassmeyer et al. (2005), Simonich et al. (2002) Glassmeyer et al. (2005), Snyder et al. (2007), Vanderford et al. (2003), Loraine and Pettigrove (2006), Drewes et al. (2009) Drewes et al. (2009), Halden and Paull (2005) Glassmeyer et al. (2005), Benotti and Brownawell (2007), Snyder et al. (2007), Miao et al. (2004), Sedlak et al. (2005b), Yang and Carlson (2004) Drewes et al. (2003)
26
86
14
241
222
0.19
1
LC/MS-MS
Trenholm et al. (2006)
6
83
10
242
37
0.16
1
LC/MS-MS
Trenholm et al. (2006)
27
98
11
303
328
0.39
1
LC/MS-MS
Trenholm et al. (2006)
28
92
11
308
401
0.17
1
LC/MS-MS
Trenholm et al. (2006)
22
100
0
357
22
n/a
3
GC/MS
Simonich et al. (2002)
23
89
16
380
414
0.44
1
LC/MS-MS
Trenholm et al. (2006)
7
100
0
382
58
n/a
0.25
LC/MS-MS
Trenholm et al. (2008)
32
94
7
426
470
0.22
1
LC/MS-MS
Trenholm et al. (2006)
1
100
n/a
656
n/a
n/a
18
GC/MS
Drewes et al. (2003)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 9 9 e1 2 1 2
Trimethoprim
WWTP: Wastewater Treatment Plant; n/a: Not Available. a Total number of plants where a compound was measured. Summed across studies. b Weighted-average DF or DR across studies. c When the LOQ was not reported and the MDL was only reported, the MDL was used as the LOQ in Eq. (1) to calculate DR.
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1206
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at least 1-log removal can be assessed for these potential indicator compounds. The detection ratio was exceeding 100 for 14 potential indicator compounds, thus 2-log removal can be potentially quantified for these compounds. However, the detection ratio was less than 100 for some of these 14 compounds (i.e., erythromycin, naproxen, trimethoprim, triclosan, gemfibrozil, DEET) for a given study. Therefore, 2-log removal may not always be able to be determined. Conversely, iopromide, meprobamate, tonalide, triclocarban, and sulfamethoxazole always had detection ratios exceeding 100 across all studies and are possibly more suitable indicator compounds to consistently assess 2-log removal across a downstream treatment process or receiving environment. The variability of detection ratios for some compounds (e.g., naproxen, DEET, gemfibrozil, sulfamethoxazole) was considerably high, which could be due to different local usage patterns and/or differing degrees of removals of these compounds during conventional wastewater treatment, but the average DF for these compounds was consistently above 80%. Despite this variability, nearly all of the compounds had detection ratios >5 across studies indicating at least >80% attenuation of these compounds can be quantified. Along with a compound needing to be easily detected it is also necessary for a compound to be frequently detected in order for it to be a viable indicator compound. If it does not frequently occur, then it is not suitable to assess attenuation removal factors. In this study a detection frequency of at least 80% was used to identify potential indicator compounds. Table 3 lists the average detection frequency for identified indicator compounds, which is an average detection frequency across studies. Similar to the average detection ratio the detection frequency is a weighted-average that considers the number of WWTPs that was examined per study. Table 3 lists the detection frequency for potential indicator compounds, where almost all the compounds had a detection frequency exceeding 80%, where the median was 100%. Propranolol, diclofenac, and ibuprofen were exceptions exhibiting detection frequencies between 75 and 80%. However, these compounds were included as they are near the >80% cutoff and higher frequencies were observed in the validation study as discussed in the following section. These results suggest all of the compounds in Table 3 occur frequently in treated wastewater effluent and are potentially suitable indicator compounds.
3.2. Indicator compounds and detection ratios in secondary- or tertiary-treated effluents Ten full-scale conventional wastewater treatment facilities located in the U.S. (Table 1) were selected to validate the occurrence of the proposed indicator compounds (Table 3) in conventionally treated secondary- or tertiary-treated effluents. For this screening effort, 39 trace organic chemicals were selected (Table S2, Supplemental Information). The detection ratios for these trace organic chemicals were averaged across wastewater treatment plants and results are presented in a box and whisker plot in Fig. 1. Note, due to budget constraints not all the compounds were measured at every facility as only selected analytical methods were employed for samples from a given facility. The total number of WWTP effluents evaluated
for the occurrence of trace organic chemicals and the detection frequencies are noted within Fig. 1. Considering the monitoring results from these facilities, detection ratios and frequencies are in agreement with the proposed indicator compounds listed in Table 3. Twenty-nine out of the 39 compounds targeted in Fig. 1 are detected greater than 80% of the time. Detection frequencies are less than 80% for ketoprofen, estriol, estrone, mecoprop, atrazine, and acetaminophen, which confirmed detection frequencies of less than 80% calculated for these compounds reported in previous studies (data not shown). The detection ratio values are greater than five (black horizontal line within Fig. 1) for some of the potential indicator compounds identified in Table 3. Three additional compounds, atenolol, simvastatin hydroxy acid, and TCPP, were identified as potential indicator compounds that were not evaluated during the literature survey but met the requirements to serve as an indicator. Compounds with detection ratio values below 5, such as atrazine, diazepam, estriol, and estrone confirm low detection ratios of <1, 2, and 3, respectively, based on the literature survey (data not shown). Therefore, it is proposed that these compounds would not be suitable indicator compounds for assessing >80% removal. Effluent monitoring from past studies and at ten different wastewater treatment facilities examined in this study confirmed similar occurrence patterns of proposed indicator compounds in North American conventional secondary- and tertiary-treated wastewater effluents regardless of location. However, it is likely that the occurrence of these indicator compounds can vary between different countries due to different usage patterns and water consumption practices. For example, clofibric acid, a breakdown product of a blood lipid regulator, is widely administered in middle European countries but rarely used in the U.S. As a consequence, clofibric acid concentrations in treated wastewater effluents in Switzerland (Tauxe-Wuersch et al., 2005) and Germany (Ternes, 1998) exhibited >50 times higher median concentrations than observed in North America (Drewes et al., 2002; Lishman et al., 2006; Miao et al., 2002) (Table 4). Note, clofibric acid levels are similar between North American and the United Kingdom (Kasprzyk-Hordern et al., 2009). Another important factor to consider in the occurrence pattern of trace organic chemicals in treated wastewater is the strength of treated wastewater. The per-capita indoor water consumption is usually a factor of 2e3 times lower in Europe as compared to the U.S. resulting in higher concentrated wastewater in Europe (American Water Works Association, 2009; Bundesverband der Energie- und Wasserwirtschaft, 2009). For pharmaceutical residues, such as carbamazepine and diclofenac, representing drugs administered both in European countries and North America in similar amounts, this would result in a lower occurrence level in North America. Note, these chemicals are not well attenuated during activated sludge wastewater treatment (Castiglioni et al., 2005; Clara et al., 2005a; Drewes et al., 2008; Joss et al., 2005; Kasprzyk-Hordern et al., 2009; Radjenovic et al., 2009; TauxeWuersch et al., 2005) and therefore, treatment effects on occurrence levels in wastewater treatment effluents can be assumed negligible. Indeed, concentrations of carbamazepine and diclofenac are by a factor of >10 times higher in treated wastewater effluents in Germany (Deng et al., 2003; Ternes, 1998; Ternes et al., 2003), Austria (Clara et al., 2004; Clara et al., 2005b;
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0 100 75 75 75 71 75 100 100 57 10 89 100 67 50 80 100 100 100 80 100 13 40 100 100 100 100 100 80 100 100 100 100 100 100 100 100 88 86
Acetaminophen Atenolol Atorvastatin Atorvastatin (o-Hydroxy) Atorvastatin (p-Hydroxy) Atrazine Bisphenol A Carbamazepine DEET Diazepam Dichlorprop Diclofenac Dilantin Erythromycin-H2O Estriol Estrone Fluoxetine Gemfibrozil Hydrocodone Ibuprofen Iopromide Ketoprofen Mecoprop Meprobamate Metoprolol Naproxen NDMA Norfluoxetine Primidone Propranolol Salicylic acid Simvastatin hydroxy acid Sulfamethoxazole TCEP TCPP TDCPP Total EDTA Triclosan Trimethoprim
0.1
Fig. 1 e Box and whisker plots of the detection ratio of trace organic chemicals in U.S. treated wastewater effluents. The total number of WWTP effluents evaluated and detection frequency (%) for a given compound are listed horizontally and numerically within the graph.
Kreuzinger et al., 2004; Strenn et al., 2004), Switzerland (Kahle et al., 2009; Tauxe-Wuersch et al., 2005), and The United Kingdom (Ashton et al., 2004; Hilton and Thomas, 2003; Kasprzyk-Hordern et al., 2009) as compared to concentrations observed in North American (Benotti and Brownawell, 2007; Drewes et al., 2002; Glassmeyer et al., 2005; Miao et al., 2002; Sedlak et al., 2005a,b; Snyder et al., 2007; Vanderford et al., 2003) effluents (Table 4). Therefore, the occurrence pattern of trace organic chemicals in treated wastewater effluent is country
specific and occurrence studies conducted in one country are not necessarily applicable to other regions of the world. When considering the occurrence of trace organic compounds in treated wastewater effluent disinfection treatment must be considered. In North America, disinfection is commonly applied to secondary- or tertiary-treated wastewater employing disinfection processes including chlorine, chloramine, UV irradiation, and ozone processes. Little removal of trace organic chemicals has been observed, with a few
Table 4 e Carbamazepine, clofibric acid and diclofenac occurrence concentrations and detection frequencies in wastewater treatment effluents in different countries.a Country
Carbamazepine b
Germany Austria Switzerland United Kingdom North America
Clofibric Acid b
Diclofenac
Median conc. (ng/L)
# of Plants
DF (%)
Median conc. (ng/L)
# of Plants
DF (%)
Median conc.b (ng/L)
# of Plants
DF (%)
2100 1123 840 1662 94
30 17 9 2 21
100 100 100 100 88
360 n/a 200 11 4
49 n/a 3 2 8
96 n/a 83 44 58
868 1587 1000 422 37
69 6 3 6 22
100 100 100 90 89
n/a e not available; DF e detection frequency. a Occurrence median concentrations are based on the studies reported in the text. b Average concentration across studies, which were weighted considering the number of WWTPs that was examined for a given study.
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exceptions, during full-scale wastewater chloramination (e.g., 2.6 mg/L residual concentration; 2 h contact time) and UV disinfection processes (e.g., 40 mJ/cm2) (Drewes et al., 2008, 2009). However, the application of chlorine, where free chlorine is present in the aqueous phase, can lead to the selective reduction of some trace organic chemicals (Westerhoff et al., 2005). Chlorine acts as an oxidant or an electrophile, reacting selectively with certain functional groups on organic compounds (Deborde and von Gunten, 2008). Chlorine can selectively react with phenolic compounds, such as acetaminophen, aliphatic amine groups, such as those on propanolol, and amines within heterocyclic structures, such as trimethoprim (Pinkston and Sedlak, 2004; Dodd and Huang 2007). Ozone oxidation can also lead to the oxidation of some trace organic chemicals (Snyder et al., 2006) and Dickenson et al. (2009) identified which indicator compounds, presented in this paper, are reactive or nonreactive during ozone treatment of treated wastewater. Thus, when selecting trace organic chemicals that can serve as indicator compounds, the type and degree of wastewater disinfection needs to be considered. The list of viable indicator compounds identified in North American treated wastewater effluents can be used to tailor monitoring strategies or to assess attenuation in receiving streams. However, the detection criteria of proposed indicator compounds needs to be confirmed for a given facility as local consumption, environmental and treatment practices could potentially affect their occurrence. For example, seasonality (i.e., wet vs. dry) could affect the occurrence of trace organic chemicals, which was not considered in this study. During the wet season, treated wastewater can potentially become more diluted and thus indicator compound levels in effluents are decreased.
3.3. Attenuation of indicator compounds in highly impacted receiving waters The attenuation of select indicator compounds in two wastewater effluent-impacted rivers was assessed. During the time of sampling, river sites 1 and 2 were 35 and 100% impacted, respectively. While many rivers across the U.S. are likely receiving wastewater discharge to much lesser degree, these two study sites provided a better opportunity to monitor attenuation for compounds after wastewater discharge. Concentrations of detectable trace organic chemicals at downstream locations of river sites 1 and 2 are presented in Fig. 2. Most of the studied compounds did not decrease substantially in concentration downstream of the discharges. Interestingly, ibuprofen, gemfibrozil, and naproxen concentrations did not decrease, although the same compounds have been observed to transform during similar travel times in the shallow (w0.3 m) Santa Ana River, California (Lin et al., 2006). The Santa Ana River is also an effluent-dominated river, and Lin et al. (2006) observed attenuation of gemfibrozil, ibuprofen, and naproxen along a 12 km (travel time 7.5 h) stretch with half-lives of 5.4, 3.7, and 2 h, respectively. Lin et al. (2006) performed laboratory microcosm batch experiments that contained sediment from the river, which suggested biotransformation was the principal removal mechanism for ibuprofen and gemfibrozil, whereas biotransformation and photolysis were both important for naproxen removal. A major difference between
river site 1 and Santa Ana River is the wastewater quality applied to the receiving rivers. Site 1 received non-nitrified secondarytreated wastewater, which was typified with high ammonia (w20 mg N/L) and TOC (w14 mg/L) concentrations, where this high oxidant demand attributed to the low observed DO levels (<2 mg/L) in this river. Therefore, aerobic conditions were not maintained and biotransformation of these compounds under aerobic conditions could not occur. On the other hand, the Santa Ana River received nitrified tertiary-treated wastewater, which is characterized by low ammonia concentrations (<0.04 mg N/L) and low TOC concentrations (2.6 mg/L), which allowed for aerobic conditions (DO ¼ 8.5 mg/L) to persist and probably led to the attenuation of these compounds in this shallow river. However, some compounds did decrease in concentration at site 1, such as total EDTA, sulfamethoxazole, diclofenac, trimethoprim, and carbamazepine, which decreased by 47, 33, 27, 20 and 12%, respectively. These removals are probably not due to biological attenuations, since aerobic conditions were not present at site 1, and other more bioamenable compounds were not removed (i.e., ibuprofen, gemfibrozil). Some of these compounds are known to be recalcitrant (i.e., diclofenac, carbamazepine). On the day of sampling at river site 1 the weather was sunny with clear skies, sampling occurred in the summer and the river was shallow with a depth of 10e30 cm, so nearsurface conditions were present. Therefore, these observed attenuations could be due to photolysis where past river field and controlled laboratory-scale studies have reported the degradation of these compounds due to photolysis (Abella´n et al., 2009; Andreozzi et al., 2002, 2003; Boreen et al., 2004; Fono et al., 2006; Kari and Giger, 2002; Tixier et al., 2003; Xue et al., 1995). Ibuprofen, gemfibrozil, and naproxen concentrations also did not decrease in river site 2. This is most likely due to a lower biological activity for this river than in the Santa Ana River. The Santa Ana River is a shallow river allowing close interactions of compounds with the riverbed and subsequent microbial activity. However, river site 2 is a deeper river with a typical depth of 1.2 m, which allows less opportunity for the compounds to interact with the riverbed. Fono et al. (2006) studied the attenuation of gemfibrozil, ibuprofen, and naproxen in a larger (22 m3/s) and deeper river, Trinity River, Texas, which had an average depth of 2 m and an average width of 30 m. They observed attenuation of these compounds along a 500 km stretch (2 week travel time). However, unlike the study in the Santa Ana River by Lin et al. (2006), Fono et al. (2006) observed slower attenuation rates where half-lives of 2.89, 4.6, and 4.2 days were observed. Fono et al. (2006) also performed laboratory-scale microcosm experiments containing only river water (no sediment), and like Lin et al. (2006), these tests supported biotransformation as the important attenuating process for these compounds. Therefore, deeper depths at river site 2 provided less opportunity for the compounds to interact with the riverbed and longer travel times may be required to observe attenuation of these compounds in this river. Past studies have identified individual compounds, including propanolol and caffeine, as suitable anthropogenic markers for treated wastewater contamination in receiving waters (Buerge et al., 2003; Clara et al., 2004; Fono and Sedlak, 2005). However, the results presented here demonstrate that there is a wider range of refractory trace organic chemicals
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 1 9 9 e1 2 1 2
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Fig. 2 e Concentrations of detectable indicator compounds downstream of wastewater treatment plants at river sites 1 and 2.
(i.e., meprobamate, TCEP, TCPP, carbamazepine), in some cases arguably better (i.e., caffeine is not refractory and can easily contribute to field and laboratory blanks), that can be used to indicate the degree of treated wastewater impact to a receiving body. This is significant as a suite of select indicators can be used as anthropogenic markers, which provides confirmation regarding the degree of treated wastewater contamination. Kahle et al. (2009) successfully employed concentration ratios of multiple marker compounds (e.g., primidone/carbamazepine) to quantify wastewater contamination in Switzerland ground and surface waters. Similarly, indicator concentration ratios between photolabile (i.e., sulfamethoxazole) or biotransformed (i.e., gemfibrozil) indicator compounds and refractory compounds can be used to identify those systems where in-river attenuation is important, such as surface waters with longer hydraulic residence times.
4.
Conclusion
The screening approach developed in this study that utilized detection frequency and ratio as selection criteria and subsequent validation via full-scale monitoring supported the identification of 50þ viable indicator compounds in North American treated wastewater effluents to assess fate and transport of trace organic chemicals of emerging concern for receiving environments. There is a high probability that the identified indicator compounds will occur in effluents of other North American wastewater treatment facilities regardless of location. Recalcitrant, photolabile, and bioamenable indicator compounds were used to assess attenuation at two river sites with short retention times (<17 h). Recalcitrant indicator compounds, such as TCEP, TCPP, and meprobamate, indicate dilution impacts were minimal for the studied river stretches.
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Biotransformed indicator compounds, such as ibuprofen and gemfibrozil, indicate that biotransformation attenuation was likely not important for the two river sites. Photolabile indicator compounds, such as diclofenac and sulfamethoxazole, indicate that photolysis attenuation was possibly a factor at river site 1. The application of indicators can be used to tailor monitoring programs to assess attenuation of similar reactive trace organic chemicals in receiving streams.
Acknowledgements The authors thank the WateReuse Research Foundation (WRF-014-01) for its financial, technical, and administrative assistance in funding and managing the project through which this information was derived. The authors also gratefully acknowledge the financial contributions from the Water Environment Research Foundation (WERF-04-HHE-1CO). The comments and views detailed herein may not necessarily reflect the views of WRF or WERF, its officers, directors, affiliates or agents. The authors are also grateful to the participating utilities for their technical and administrative support. The authors would like to thank Brett Vanderford, Rebecca Trenholm, and Janie Zeigler at SNWA, Mong Hoo Lim and Edward Kolodziej at UC-Berkeley, and John Luna, Christiane Hoppe, Gary Wang and Dean Heil at CSM for their assistance in reviewing literature articles, sample logistics and analysis.
Appendix. Supplementary data Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.11.012.
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Kreuzinger, N., Clara, M., Strenn, B., Vogel, B., 2004. Investigation on the behaviour of selected pharmaceuticals in the groundwater after infiltration of treated wastewater. Wat. Sci. Tech. 50, 221e228. Lin, A.Y.-C., Plumlee, M., Reinhard, M., 2006. Natural attenuation of pharmaceuticals and alkylphenol polyethoxylate metabolites during river transport: photochemical and biological transformation. Environ. Toxicol. Chem. 25, 1458e1464. Lishman, L., Smyth, S.A., Sarafin, K., Kleywegt, S., Toito, J., Peart, T., Lee, B., Servos, M., Beland, M., Seto, P., 2006. Occurrence and reductions of pharmaceuticals and personal care products and estrogens by municipal wastewater treatment plants in Ontario, Canada. Sci. Total Environ. 367, 544e558. Loraine, G.A., Pettigrove, M.E., 2006. Seasonal variations in concentrations of pharmaceuticals and personal care products in drinking water and reclaimed wastewater in southern California. Environ. Sci. Technol. 40, 687e695. McAvoy, D., Schatowitz, B., Jacob, M., Hauk, A., Eckhoff, S., 2002. Measurement of triclosan in wastewater treatment systems. Environ. Toxicol. Chem. 21, 1323e1329. Miao, X.-S., Koenig, B.G., Metcalfe, C.D., 2002. Analysis of acidic drugs in the effluents of sewage treatment plants using liquid chromatography-electrospray ionization tandem mass spectrometry. J. Chromatogr. A 952, 139e147. Miao, X.-S., Bishay, F., Chen, M., Metcalfe, C.D., 2004. Occurrence of antimicrobials in the final effluents of wastewater treatment plants in Canada. Environ. Sci. Technol. 38, 3533e3541. Mitch, W.A., Gerecke, A.C., Sedlak, D.L., 2003. A n-nitrosodimethylamine (NDMA) precursor analysis for chlorination of water and wastewater. Water Res. 37, 3733e3741. Najm, I., Trussell, R.R., 2001. NDMA formation in water and wastewater. J. Am. Water Work Assoc. 93, 92e99. Nakada, N., Kiri, K., Shinohara, H., Harada, A., Kuroda, K., Takizawa, S., Takada, H., 2008. Evaluation of pharmaceuticals and personal care products as watersoluble molecular markers of sewage. Environ. Sci. Technol. 42, 6347e6353. Pinkston, K.E., Sedlak, D.L., 2004. Transformation of aromatic ether- and amine-containing pharmaceuticals during chlorine disinfection. Environ. Sci. Technol. 38, 4019e4025. Radjenovic, J., Petrovic, M., Barcelo´, D., 2009. Fate and distribution of pharmaceuticals in wastewater and sewage sludge of the conventional activated sludge (CAS) and advanced membrane bioreactor (MBR) treatment. Water Res. 43, 831e841. Reddersen, K., Heberer, T., 2003. Multi-compound methods for the detection of pharmaceutical residues in various waters applying solid phase extraction (SPE) and gas chromatography with mass spectrometric (GC-MS) detection. J. Separ. Sci. 26, 1443e1450. Sedlak, D.L., Huang, C.H., Pinkston, K.E., 2004. Strategies for selecting pharmaceuticals to assess attenuation during indirect potable water reuse. In: Ku¨mmerer, K. (Ed.), Pharmaceuticals in the environment. Springer Publishers, Berlin. Sedlak, D., Deeb, R., Hawley, E., Mitch, W.A., Durbin, T., Mowbray, S., Carr, S., 2005a. Sources and fate of nitrosodimethylamine and its precursors in municipal wastewater treatment plants. Water Environ. Res. 77, 32e39. Sedlak, D., Pinkston, K., Huang, C.-H., 2005b. Occurrence Survey of Pharmaceutically Active Compounds. American Water Works Research Foundation, Denver, CO. Simonich, S.L., Federle, T.W., Eckhoff, W.S., Rottiers, A., Webb, S., Sabaliunas, D., de Wolf, W., 2002. Removal of fragrance
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Composition of bacterial and archaeal communities in freshwater sediments with different contamination levels (Lake Geneva, Switzerland) Laurence Haller a, Mauro Tonolla b,d, Jakob Zopfi c, Raffaele Peduzzi b, Walter Wildi a, John Pote´ a,* a
University of Geneva, Institute F.A. Forel, 10 route de Suisse, CP 416, CH-1290 Versoix, Switzerland Microbial Ecology, Microbiology Unit, Plant Biology Department, University of Geneva, 30, Quai Ernest-Ansermet, CH-1211 Geneva, Switzerland c University of Lausanne, Institute of Geology and Paleontology, Laboratory of Biogeosciences, CH-1015 Lausanne, Switzerland d Cantonal Institute of Microbiology, Via Mirasole 22A, CH-6500 Bellinzona, Switzerland b
article info
abstract
Article history:
The aim of this study was to compare the composition of bacterial and archaeal
Received 31 August 2010
communities in contaminated sediments (Vidy Bay) with uncontaminated sediments
Received in revised form
(Ouchy area) of Lake Geneva using 16S rRNA clone libraries. Sediments of both sites were
8 November 2010
analysed for physicochemical characteristics including porewater composition, organic
Accepted 14 November 2010
carbon, and heavy metals. Results show high concentrations of contaminants in sediments
Available online 20 November 2010
from Vidy. Particularly, high contents of fresh organic matter and nutrients led to intense mineralisation, which was dominated by sulphate-reduction and methanogenesis. The
Keywords:
bacterial diversity in Vidy sediments was significantly different from the communities in
Lake Geneva
the uncontaminated sediments. Phylogenetic analysis revealed a large proportion of
Sediment pollution
Betaproteobacteria clones in Vidy sediments related to Dechloromonas sp., a group of dech-
Heavy metal
lorinating and contaminant degrading bacteria. Deltaproteobacteria, including clones related
Microbial diversity
to sulphate-reducing bacteria and Fe(III)-reducing bacteria (Geobacter sp.) were also more
16S rRNA
abundant in the contaminated sediments. The archaeal communities consisted essentially
Clone library
of methanogenic Euryarchaeota, mainly found in the contaminated sediments rich in organic matter. Multiple factor analysis revealed that the microbial community composition and the environmental variables were correlated at the two sites, which suggests that in addition to environmental parameters, pollution may be one of the factors affecting microbial community structure. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Untreated or only partly treated wastewaters including industrial, agricultural and domestic effluents constitute the main contamination sources in aquatic environments. Sediment contamination is usually due to inorganic and organic
compounds including heavy metals (HMs) and hydrophobic organic compounds, such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) (e.g. Koelmans et al., 2001; Eggleton and Thomas, 2004). The increasing contamination of sediments by inorganic and organic micro-pollutants is a big
* Corresponding author. Tel.: þ41 22 379 03 21; fax: þ 41 22 379 03 29. E-mail address: [email protected] (J. Pote´). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.11.018
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concern in aquatic ecosystems (Fo¨rstner and Wittmann, 1979; Pardos et al., 2004; Schwarzenbach et al., 2006). Sediment contamination by HMs and other micro-pollutants might cause potential adverse effects to ecosystems and also pose human health risks (Salomons and Fo¨rstner, 1984; Verweij et al., 2004; Wang et al., 2004). The main environmental risk is remobilization of the contaminants and their return to the hydrosphere either by sediment re-suspension or by infiltration into the groundwater (Wildi et al., 2004). Therefore, the removal of HMs from wastewater or their accumulation in sediments should be examined extensively (e.g. Wang et al., 2004; Wildi et al., 2004). Microbial communities may be sensitive indicators for pollution in aquatic ecosystems and may be applied for biomonitoring and assessment purposes (Pronk et al., 2009). Heavy metal contamination, for example, may lead to a reduction of bacterial diversity (Sandaa et al., 1999). However, other studies observed either an increase in microbial diversity along with heavy metal contamination (Sorci et al., 1999) or no significant variation (Gillan et al., 2005). Other environmental factors as well as the time of exposure might explain these differences. Lake Geneva is the largest freshwater reservoir of Western Europe with a volume of 89 km3 and a maximum depth of 309 m. It is a monomictic temperate lake, with early spring overturn not occurring every year. The lake was considered eutrophic in 1970s and 1980s, but has become mesotrophic after drastic reduction of phosphorus inputs (Dorioz et al., 1998). Approximately 700,000 people are supplied with water from Lake Geneva. The city of Lausanne, located on the northern shore, discharges the largest volume of treated wastewater into the nearby Bay of Vidy. The wastewater treatment plant (WWTP) of the city treats nowadays approximately 220,000 equivalent-inhabitants of wastewater. The WWTP effluent is released into the Bay of Vidy 700 m from the shore at 30 m depth. As a consequence, Vidy Bay is the most contaminated area of Lake Geneva. Published data document the accumulation of contaminants close to a recreational area as well as the related ecological impacts and health risks (Loizeau et al., 2004; Pardos et al., 2004; Wildi et al., 2004). Several studies focused on the quality of bottom sediments in the Bay of Vidy and on the spatial distribution of organic matter, faecal indicator bacteria, heavy metals, and hydrophobic organic compounds (Pote´ et al., 2008; Haller et al., 2009). However, there is still a paucity of information regarding sedimentary microbial communities in Lake Geneva and particularly around the WWTP discharge outlet. Sediments are complex habitats densely colonised by diverse groups of microorganisms, which play key roles in biogeochemical cycling, aquatic food webs and the remobilization of heavy metals (Nealson, 1997; Lors et al., 2004; Ye et al., 2009). Many studies have been performed to examine the composition and variability of microbial communities in extreme or complex aquatic ecosystems. However, only a few studies compared microbial community structures in contaminated and uncontaminated sediments (Powell et al., 2003; Zhang et al., 2008). The aim of the present study was to compare the composition of the sediment-associated microbial communities in the contaminated Bay of Vidy with a closeby less polluted site in the Ouchy area. The Bay of Vidy is currently used as a model system for several limnological,
biogeochemical, and ecotoxicological studies. This research represents the first assessment of Bacteria and Archaea in contaminated and uncontaminated sediments of Lake Geneva and serves as important background information for these studies. For a better understanding of the microbial community structures, molecular analyses were complemented by a detailed physicochemical characterisation of the sediments.
2.
Materials and methods
2.1.
Study site description and sampling procedure
In August 2005, sediment was collected at two locations in Lake Geneva (Fig. 1): (i) within the Bay of Vidy near the outlet pipe of the WWTP of Lausanne (Swiss coordinates X: 534682, Y: 151410) and (ii) near the Ouchy area (Swiss coordinates X: 537985, Y: 150390). Sampling was done from R/V “La Licorne” using a core sampler (Benthos Inc, USA). Three cores (6.7 cm i.d., 1.5 m length) were retrieved from each site at a depth of 40 m. For microbiological analyses, the cores were opened longitudinally and sliced into 2 cm thick sections until 10 cm depth. The sediment samples were placed into sterile plastic containers, stored in an icebox and treated in the laboratory within 24 h. For chemical analysis, the intact sediment cores were transported to the laboratory and stored vertically in a cold-room at 4 C until analysis.
2.2.
Chemical analysis
Two cores per site were used for the chemical analyses: one was used to measure organic matter, nutrients and HMs contents and the other one was used to determine sulphur and iron concentrations and the porewater constituents. Organic matter and nutrients: the cores were opened longitudinally and sliced every 2 cm down to a depth of 10 cm. Before analysis, sediment samples were air-dried at ambient room temperature. The particle grain size was measured with a laser Coulter LS-100 diffractometer (Beckman Coulter, Fullerton, CA, USA), after a 5-min ultrasonic dispersal in deionised water according to the method described by Loizeau et al. (1994). The proportions of three major size classes (clay < 2 mm; silt 2e63 mm; and sand > 63 mm) were determined from size distributions. Total organic matter content in sediments was estimated by loss on ignition (LOI) at 550 C for 1 h in a Salvis oven (Salvis AG Emmenbru¨cke, Luzern, Switzerland) on 5 g of dried sediments. Total organic carbon (TOC) was determined by titrimetry following acid oxidation on 5 g of dried sediments. Total nitrogen (TN) was determined according to Kjeldahl (APHA, 1985) on 2 g of dried sediments. Total phosphorus (TP) and its different forms were measured on 150 mg of dried sediments with a spectrophotometer (Helios Gamma UVeVis, Thermo Scientific, USA) at 850 nm, following the fractionation scheme of Williams et al. (1976) as modified by Burrus et al. (1990). The results are expressed in mg kg1 dry weight sediment (ppm). Solid phases: sulphur and iron contents were determined every centimeter down to 10 cm depth. The sediment was sliced at room temperature in a N2-filled glove bag and fixed in 50 mL tubes containing 10 mL zinc acetate solution (10%). Sulphide was extracted as acid volatile sulphur [AVS;
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 1 3 e1 2 2 8
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Fig. 1 e Study area with the two sampling locations in the Bay of Vidy and the Ouchy area. The upper left corner of the map represents 46 320 0000 N and 6 330 0000 E and the lower right corner represents 46 290 0000 N and 6 380 0000 E.
dissolved sulphide (H2S) and iron sulphides (FeS)] and chromium reducible sulphur [CRS; primarily pyrite (FeS2), elemental sulphur (S0), and some organic sulphur]. AVS and CRS were measured by a two step distillation process with cold 6 N HCl followed by boiling 1 M acidic CrCl2 solution (Fossing and Jørgensen, 1989; Zopfi et al., 2008). Poorly cristalline Fe(III)oxides were extracted with 0.5M HCl according to Thamdrup et al. (1994). Total contents of Cu, Cd, Cr, Zn, and Pb were determined by quadrupole-based Inductively Coupled PlasmaeMass Spectrometry (ICPeMS) (HP 4500, Agilent) following the digestion of 1 g of dried sediment in analytical grade 2 M HNO3 (Pardos et al., 2004). Total Hg was quantified by atomic absorption spectrophotometry (Advanced Mercury Analyser; AMA 254, Altec, Czech Rep.) according to Hall and Pelchat (1997) and Ross-Barraclough et al. (2002). The method is based on sample combustion, gold amalgamation and atomic absorption spectrometry (AAS). Average values of triplicate measurements are expressed in mg kg1 dry weight sediment (ppm). Porewater constituents: porewater was harvested by centrifugation (4000 rpm) under an N2 atmosphere to avoid Fe2þ and H2S oxidation. Samples for dissolved Fe2þ were acidified with 0.5 M HCl and analysed by the photometric Ferrozine method (Thamdrup et al., 1994). Dissolved sulphide was determined on Zn-acetate fixed samples using the colorimetric methyleneblue method (Cline, 1969; Zopfi et al., 2008). The major anions 3 Cl, SO2 4 , NO3 , and PO4 were determined by ion-chromatography on a DIONEX DX-120 system using an IonPac AS14A
anion exchange column, Na2CO3/NaHCO3 (8 mM/1 mM) as eluent, an Anion Self-Regenerating Suppressor (ASRS 300, 4 mm) module and a conductivity detector.
2.3.
DNA extraction
The samples from 0 to 2 cm and 4e6 cm depth were used for microbiological analyses. Total DNA was extracted from 250 mg of sediment using the PowerSoil DNA Isolation Kit (Mo-Bio Laboratories, Carlsbad, CA, USA), according to the manufacturer’s instructions. The concentration of extracted DNA was measured spectrophotometrically (OD260) and aliquots were used to estimate DNA quality by electrophoresis on 0.8% agarose gel stained with ethidium bromide (10 mg ml1, SigmaeAldrich Chemie GmbH, Buchs, Switzerland). DNA extracts were stored at 20 C until used for PCR amplification.
2.4.
PCR amplification
Nearly complete bacterial 16S rRNA genes were amplified using the primers 26f (50 -AGAGTTTGATCATGGCTCA-30 ) and 1392r (50 -GTGTGACGGGCGGTGTGTA-30 ) (Brosius et al., 1981; Lane, 1991). Archaeal 16S rRNA genes were amplified using the primers 109f (50 -ACKGCTCAGTAACACGT-30 ) and 915r (50 GTGCTCCCCCGCCAATTCCT-30 ) (Grobkopf et al., 1998; Stahl and Amann, 1991). Both archaeal and bacterial PCR amplifications were performed separately using the Taq PCR Master Mix Kit (Qiagen,
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 1 3 e1 2 2 8
Basel, Switzerland). Each PCR reaction was carried out in a volume of 50 mL, containing 1 PCR buffer, 1.5 mM of MgCl2, 200 mM of each dNTP, 0.3 mM of each primer, 2.5 units of Taq DNA polymerase, 2.5 mg ml1 of bovine serum albumin (Invitrogen, Basel, Switzerland) and 2 mL of DNA (about 100 ng) of each sample. The following conditions were used for PCR amplification: initial denaturation at 94 C for 5 min; 35 cycles of denaturation (94 C for 30 s), annealing (52 C for 30 s), extension (72 C for 1 min) and a final extension step of 10 min at 72 C. PCR products (10 mL) were separated by electrophoresis on 0.8% agarose gels and visualised by ethidium bromide staining and UV illumination.
2.5.
Clone library construction and DNA sequencing
PCR products were purified using the NucleoSpin Extract II Kit (MachereyeNagel, Oensingen, Switzerland) according to the manufacturer’s instructions. The amplified DNA was then quantified using the PicoGreen dsDNA Quantitation Reagent (Molecular Probes Inc., Eugene, OR, USA) and a TD-700 Fluorometer (Turner Designs Sunnyvale, CA, USA). Approximately 20e30 ng of amplified 16S rDNA were cloned into competent Escherichia coli cells using the TOPO TA cloning kit (Invitrogen, Basel, Switzerland) following the manufacturer’s recommendations. The transformed cells were plated on LB medium containing 50 mg L1 ampicillin, 60 mg L1 of IPTG (isopropylb-D-thiogalactopyranoside), and 100 mg L1 of X-gal (5-bromo4-chloro-3-indolyl-b-D-galactopyranoside), and incubated overnight at 37 C. White recombinants were transferred to LB medium plates for 24 h. 80 and 40 recombinants were picked from samples taken at 0e2 cm and 4e6 cm, respectively, in each location, to constitute the Bacteria clone libraries. For the Archaea, only 2 recombinants (1 per site) were found at 0e2 cm and 34 colonies were picked from 4 to 6 cm. The insert size of all picked colonies, was determined by direct PCR using M13 forward and reverse primers included in the cloning kit. The products were subsequently purified with the NucleoSpin Extract II Kit and sequenced using the BigDye Terminator Cycle Sequencing Ready reaction kit (Applied Biosystems International, Inc., Rotkreuz, Switzerland). Sequences were obtained on an ABI PRISM 310 Genetic Analyser, (Perkin Elmer, Schwerzenbach, Switzerland) using either the sequencing primer M13 for archaeal clones or primer 27f for bacterial clones (Lane, 1991).
2.6.
detection program was used to exclude chimeras (Maidak et al., 1999). NCBI BLAST (http://www.ncbi.nih.gov) was used to identify the most closely related 16S rRNA gene sequences. The partial 16S rRNA gene sequences were then aligned in the Clustal W implementation of MEGA 3.0 (Kumar et al., 2004). The same program was used to produce neighbour-joining phylogenetic trees (Kimura-2 correction; bootstrap values for 500 replicates). The sequences were identified using the ribosomal database project classifier (Wang et al., 2007). Sequences from this study have been deposited with the EMBL database under accession numbers FN679050eFN679294. The sequences were assigned to individual OTUs based on the 97% sequence similarity criterion. The number of OTUs and the rarefaction curves were estimated from the sequence data using Mothur v.1.12.3 (Schloss et al., 2009). Comparison between the clone libraries from the two sites was done on the basis of genetic diversity by means of the parsimony test using Mothur v.1.12.3 (Schloss et al., 2009) and the the FST-test using the program Arlequin, v.2.0 (Schneider et al., 2000). A Mantel test using Spearman correlation (Mantel, 1967) was applied to relate the microbial community compositions from the two sites with environmental variables. Multiple factor analysis (MFA) was done to obtain an integrative picture of the relationship between the bacterial community structures and the environmental factors across the 2 sites. The environmental parameters were separated into 2 matrices, one including organic matter contents and nutrient variables and one with the heavy metal concentrations. The MFA allows the simultaneous ordination of a composite table obtained by the juxtaposition of the species and environmental datasets after weighting the different matrices (Escofier and Pages, 1994). The final ordination plot shows global points indicating the relative positions of the objects described by the combination of datasets. Each global point is surrounded by partial points indicating the relative positions of the datasets taken separately (Escofier and Pages, 1994; Becue-Bertaut and Pages, 2008). These statistical analyses were carried out in “R”, a free software environment for statistical computing and graphics, using the Vegan library (R Development Core Team, 2005).
3.
Results
3.1.
Chemical analysis
Phylogenetic analysis
The obtained sequences were checked and edited using the program EditSeq (DNAStar Inc., Madison, WI, USA). A chimera
Some general sediment characteristics including particle grain size, organic matter and nutrient contents are presented in Table 1. These sediments were mostly composed of silts,
Table 1 e General characterisation of the contaminated (Vidy) and non-contaminated (Ouchy) sediment sections used for clone library construction. Sediment section Vidy 0e2 cm Vidy 4e6 cm Ouchy 0e2 cm Ouchy 4e6 cm
OM (%)
TOC (%)
TN (mg kg1)
NH4-N (mg kg1)
TP (mg kg1)
Clay/silt/sand proportion (%)
18.7 23.7 5 4.3
10.8 13.7 2.9 2.5
11.6 13.5 2.2 1.7
1.6 1.6 0.5 0.4
6783.8 9650.9 862.1 759.5
0/64/35 0/65/34 0.9/78/21 0.6/80/19
OM: Organic matter; TOC: Total organic carbon; TN: Total nitrogen; NH4-N: Ammonium; TP: Total phosphorus.
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approximately 65% and 80% for Vidy and Ouchy, respectively. Average organic matter contents in Vidy Bay sediments were much higher (21%) than in Ouchy sediments (4.5%). Average nutrient concentrations such as total nitrogen, ammonium and total phosphorus were also higher in Vidy sediments (12.6 ppm, 1.6 ppm and 8217 ppm, respectively) while they were considered low in the sediments from Ouchy (2 ppm, 0.5 ppm and 810 ppm, respectively). However, at both stations, no substantial differences in the nutrient contents between samples collected at 0e2 cm and 4e6 cm were observed. Porewater concentrations of major ions, including Fe2þ, H2S (¼H2S þ HS þ S2) and SO2 4 are shown in Fig. 2. High concentrations of porewater PO3 4 , reaching about 270 mM were only detected in the uppermost cm of Vidy Bay sediments (not shown). Nitrate was not detected in either one of the two cores. Porewater sulphate in the Ouchy sediment decreased from about 550 mM at the surface to 0 mM at 7 cm depth, whereas it was essentially depleted in the Vidy Bay sediment. Dissolved Fe2þ concentrations were only high in Vidy sediment where they reached 260 mM. It is unlikely that under such Fe2þ-rich conditions free sulphide exists in porewater as it rapidly reacts with iron to form FeS. The measured porewater sulphide in the Vidy core represents thus mainly soluble FeS complexes or colloidal FeS phases. Minor amounts of poorly crystalline Fe(III)oxides were only detected in the uppermost section of the Ouchy sediment core. Below 1 cm depth, and in the whole core from the Vidy Bay, HCl extractable Fe(III) was absent (Fig. 2). Differences in the iron-sulphur geochemistry exist between the two sites as indicated also by AVS and CRS data (Fig. 3). Unlike the Vidy sediments, solid phase sulphur species in the Ouchy sediments accumulated gradually with depth and reached a plateau at around 3 cm. Less reduced sulphur species such as S0, FeS2, Fe3S4 may play a role as indicated by higher CRS contents. Iron contents in the Vidy sediments were about 50% higher than in the Ouchy site and a constant Fe/S ratio of 1 along the whole core suggests FeS as dominant phase. The contents of heavy metals (in mg kg1) are reported in Table 2. All measured metal concentrations, except for Cr, were higher in the Vidy sediments, where peak concentrations reached 2.8 mg kg1 for Cd, 181.4 mg kg1 for Cu, 164.7 mg kg1 for Pb, and 2.3 mg kg1 for Hg.
3.2.
Bacterial 16S rRNA gene clone libraries
At each site, sediment samples from 0 to 2 cm and 4e6 cm depth were used for clone library construction of bacterial 16S rRNA genes. Phylogenetic analysis showed that approximately 85% of the retrieved clones fell into known divisions with the rest remaining unclassified. Seven and twelve divisions were identified in the sediments from Vidy and Ouchy, respectively (Fig. 4). The dominant groups (Beta-, Gamma-, Delta-proteobacteria and Bacteroidetes) were found at both sites and all depths. Most sequences in the Bay of Vidy were affiliated with Betaproteobacteria (37%), Deltaproteobacteria (15%), Gammaproteobacteria (15%), and Bacteroidetes (23%). Also in the Ouchy sediments, most sequences were related to Betaproteobacteria (22%), Deltaproteobacteria (6%), Gammaproteobacteria (24%), and Bacteroidetes (12%). The sequences were assigned to individual OTUs based on their phylogenetic positions and the 97% sequence similarity
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criterion. The sequences (n ¼ 208) were grouped into 132 OTUs: 40, 26, 53, and 32 for Vidy 0e2 cm, Vidy 4e6 cm, Ouchy 0e2 cm, and Ouchy 4e6 cm, respectively (Table 3). The number of unique OTUs was higher in Ouchy than in Vidy sediments, 38% and 54% of the OTUs were exclusively found in Vidy and Ouchy, respectively. Only 8% of all OTUs were shared between the libraries from both sites. The OTU richness estimates based on rarefaction curves suggests a higher bacterial diversity for the sediments from Ouchy than from Vidy (Fig. 5). By means of the parsimony test (P-Test) and the FST-test, a significant genetic differentiation was observed between the sediment bacterial communities of the two sites at all depths (0e2 and 4e6 cm), as well as between the two samples from Ouchy taken at different depths (Table 4). Significance for both tests signals less genetic diversity within each community than for two communities combined and that the different communities harbour distinct phylogenetic lineages (Martin, 2002). Neighbour-joining phylogenetic trees of Betaproteobacteria (Fig. 6), Deltaproteobacteria (Fig. 7), and Gammaproteobacteria (Fig. 8) are presented. The microbial communities of both sites were correlated with the environmental variables, as indicated by the Mantel test result (r ¼ 0.9429, p ¼ 0.044). A more detailed picture of the relationship between bacterial community structures and environmental factors at the two sites was obtained by MFA (Fig. 10). Results show that (i) the sampling sites Ouchy and Vidy were clearly different in terms of their microbial communities and environmental factors; (ii) the two sampling depths in Vidy were not significantly different in terms of microbial communities, OM, nutrients and heavy metals; (iii) the similarity among the datasets from Ouchy is much lower than from Vidy.
3.3.
Archaeal 16S rRNA gene clone libraries
All Archaea found in both sites fell into the Euryarchaeota division and most of them were from 4 to 6 cm of depth, except for 2 clones, which were retrieved from surface sediments: one from Vidy and one from Ouchy. The 36 obtained sequences were grouped into 18 OTUs: 1 OTU for Vidy 0e2 cm, 5 OTUs for Vidy 4e6 cm, 1 OTU for Ouchy 0e2 cm, and 12 for Ouchy 4e6 cm. A large proportion of these Euryarchaeota phylotypes, mostly originating from Vidy, were assigned to the methanogenic families Methanosaetaceae and Methanomicrobiaceae (Fig. 9).
4.
Discussion
Sediments were sampled in the area of Ouchy, a site close to the city of Lausanne but without known impact of contaminated water, and in the Bay of Vidy near the WWTP outlet pipe. The Bay of Vidy, contaminated with heavy metals, hydrophobic organic compounds and faecal bacteria, is currently the most polluted area of Lake Geneva (Pardos et al., 2004; Pote´ et al., 2008; Haller et al., 2009). Organic matter contents ranged from 18.7% (0e2 cm) to 23.7% (4e6 cm) and were much higher than in the Ouchy area (max. 5%) or
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Fig. 2 e Concentration profiles of porewater sulphate and sulphide in the sediments from Vidy and Ouchy (above). The concentrations of iron(II) in the porewater and contents of solid phase iron(III)oxides are shown below.
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Fig. 3 e Depth profiles of acid volatile sulphur (AVS [ H2S D FeS), chromium reducible sulphur (CRS [ S0 D FeS2 D Fe3S4), and total reducible sulphur (TRS [ AVS D CRS) in the sediments from Vidy and Ouchy.
elsewhere in Lake Geneva (max. 5e8%) (Pote´ et al., 2008). The intense mineralisation of organic matter, indicated by high porewater phosphate, led to strongly reducing conditions in the Vidy sediments. Porewater sulphate was used up by sulphate-reducing bacteria and iron was essentially present as FeS. Due to the absence of any other significant oxidant, methanogenesis is suspected to be the major process for organic matter degradation at this site. The conditions were less reducing in the Ouchy sediments, where iron-reduction may have prevailed in the uppermost 1e2 cm of the sediment profile. Underneath, at 2.5 cm depth, the sediment was sulphidic due to the activity of sulphate-reducing bacteria. Below 7 cm depth, where sulphate was depleted, methanogenic conditions prevailed (Canfield and Thamdrup, 2009). Heavy metal concentrations in the Bay of Vidy were up to six times higher than in Ouchy. According to the Canadian Sediment Quality Guidelines for the Protection of Aquatic Life (Conseil Canadien des Ministres de l’Environnement, 1999) the heavy metal concentrations in Vidy Bay were 2e8 times higher than reported PELs (probable effect levels). These results are in agreement with previous data from the same sampling site
(Pote´ et al., 2008). In that survey, hydrophobic organic compounds, such as PAHs, PCBs and OCPs were investigated and concentrations of up to 2, 156 and 45 mg kg1 were determined, respectively. These values are considered high and above average levels for Lake Geneva (Corvi et al., 1986). It appears clearly that the sediments around the WWTP outlet pipe in the Bay of Vidy are heavily contaminated with many kinds of pollutants, possibly representing a significant source of toxicity for microbial communities and benthic organisms. Results from this study indicate that the dominant phylogenetic groups at both sites were the Beta-, Gamma- and Deltaproteobacteria and Bacteroidetes, which is in agreement with 16S rRNA analyses of lake bacterioplankton (Zwart et al., 2002; Glo¨ckner et al., 2000; Hiorns et al., 1997). Nevertheless, some differences between the two sites were observed in the relative proportion of the different Proteobacteria subdivisions. Proteobacteria accounted for 64% of the clones in Vidy and 55% of the clones in Ouchy. Proportions of Beta- and Deltaproteobacteria were much higher in Vidy Bay that in Ouchy sediments. In contrast, the Gammaproteobacteria were more abundant in Ouchy sediments (Fig. 4).
Table 2 e Depth variation of heavy metal contents (mg kgL1 dry weight sediment)a in Vidy Bay and Ouchy sediments. Depth
0e2 cm 2e4 cm 4e6 cm 6e8 cm 8e10 cm
Cu
Zn
Cd
Pb
Cr
Hg
Ouchy
Vidy
Ouchy
Vidy
Ouchy
Vidy
Ouchy
Vidy
Ouchy
Vidy
Ouchy
Vidy
54.7 68.3 86.8 135.5 136.5
142.5 135.9 133.6 181.4 161.4
126.8 155.1 218.1 305.5 242.8
341.3 327.8 344.9 446.8 518.2
0.5 0.7 1.2 1.7 1.2
1.6 1.5 1.7 2.3 2.8
38.8 49.7 75 106.5 91
89.4 89.3 100.7 135.9 164.7
60.3 63 69.7 88.4 81.4
74.2 68.3 67.9 77.2 88.9
0.2 0.6 1 1.3 1.7
1.2 1 2.3 2.3 2.2
a Total variation coefficients for triplicate measurements were <15% for all elements.
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Fig. 4 e Relative abundance of bacterial taxonomic groups in the clone libraries established with sediments from the Bay of Vidy and the Ouchy area.
Betaproteobacteria appear to be one of the major divisions present in freshwater systems (Zwart et al., 2002; Hahn, 2006). In this study they were also well represented in both locations (37% of clones in Vidy, 22% in Ouchy), particularly in surface sediments where they were dominant (Fig. 6). A prevalent group of bacteria in Vidy sediments, belonged to the Rhodocyclaceae, which are phenotypically and ecologically very diverse. Twenty clones of this family were affiliated to Dechloromonas, a genus containing many heterotrophic and facultative anaerobic chlorate-, perchlorate- or nitrate respiring bacteria (Wolterink et al., 2005). Members of this genus such as Dechloromonas aromatica are found in aquatic habitats and are capable of oxidising aromatic compounds such as toluene, benzoate, and chlorobenzoate. D. aromatica is currently the only pure culture being able to degrade anaerobically benzene with nitrate as electron acceptor (Coates et al., 2001) A few clones, also found only in Vidy sediments, were related to Methylophilus (Methylophilaceae) a group of methylotrophic organisms. However, while methanol is oxidised as the sole carbon and energy source, some species may grow also on a limited range of other carbon compounds such as methylamines, formate, glucose, and fructose (Jenkins et al., 1987). Similar Methylophilaceae sequences were found in wastewater treatment pools in China (AY863077) and in freshwater calcareous mats (EF580978). The remaining Betaproteobacteria clones were found in both sites and included various genera. Several clones were affiliated to Propionivibrio (Rhodocyclaceae), which are aerotolerant or obligate anaerobic chemoorganotrophic bacteria. These bacteria are typical inhabitants
Table 3 e Distribution of Bacteria phylotypes in clone libraries from Vidy and Ouchy sediments (Lake Geneva). “A” stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section. Vidy A Vidy B Ouchy A Ouchy B Total no of sequences Total no of OTUs Percentage of unique OTUs
68 40 17
41 26 10
60 53 30
40 32 18
Fig. 5 e Rarefaction curves for the Bacteria 16S rRNA gene sequences retrieved from Vidy and Ouchy sediment samples (depth intervals: 0e2 cm and 4e6 cm). Operational taxonomic units were defined with a 97% sequence similarity cut-off.
of anaerobic, muddy freshwater sediments (Tanaka et al., 2003). Two further clones were related to Thiobacillus (Hydrogenophilaceae), which are ubiquitous in soils and sediments, and may grow on reduced sulphur compounds. Compared to Betaproteobacteria, the Deltaproteobacteria subdivision (Fig. 7) was less abundant (15% and 6% of the clones in Vidy and Ouchy sediments, respectively). Most Deltaproteobacteria are sulphate-, iron- or proton-reducing (syntrophic) bacteria that play major roles in anoxic settings like meromictic lakes and sediments (Lehours et al., 2007; Karr et al., 2005). The large number of clones related to sulphate-reducing bacteria (Desulfobacteraceae) in Vidy Bay was not unexpected since sulphate consumption in the sediment was obvious from the porewater data. Moreover, the pool of total reduced sulphur (TRS) was quite high, especially in Vidy sediments. Several additional clones, only found in Vidy sediments, were related to Geobacter sp. (Geobacteraceae). These anaerobic bacteria have been isolated from freshwater sediments, soils and subsurface environments. They are traditionally considered chemoorganotrophic Fe(III)-reducing bacteria. However, most
Table 4 e Summary of FST and P-test results for the comparison of microbial communities between Vidy and Ouchy sediments. “A” stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section. P valuea Group Vidy A vs Vidy B Ouchy A vs Ouchy B Vidy A vs Ouchy A Vidy B vs Ouchy B a NS, not significant.
FST-test
P test
NS < 0.00 0.001 0.005
NS 0.001 0.001 0.001
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Fig. 6 e Neighbour-joining phylogenetic tree of Betaproteobacteria 16S rRNA gene sequences retrieved from Vidy Bay and Ouchy sediments. “A” in clone names stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section. Bootstrap values > 50% are shown (500 replicates). The scale bar represents 2% estimated sequence divergence.
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Fig. 7 e Neighbour-joining phylogenetic tree of Deltaproteobacteria 16S rRNA gene sequences from Vidy Bay and Ouchy sediments. “A” in clone names stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section. Bootstrap values >50% are shown (500 replicates). The scale bar represents 2% estimated sequence divergence.
species utilise a wide range of alternative electron acceptors, 0 including NO 3 , S , and other sulphur compounds (Coates et al., 1998). Similar sequences related to Geobacter sp. were retrieved from anthropogenically impacted urban creek sediment (EU284415) and from an aquifer where Fe(III) reduction was associated with aromatic hydrocarbon degradation (Nevin et al., 2005; AY653549). Another group of clones exclusively found in Vidy sediments belonged to the
Synthrophaceae, with one clone related to Smithella sp. Similar sequences were identified as core microorganisms involved in the anaerobic digestion of sludge (Riviere et al., 2009; CU922073). The Gammaproteobacteria were more abundant in Ouchy sediments than in Vidy and were phylogenetically diverse (Fig. 8). Clones from both sites were affiliated to Methylobacter sp. (Methylococcaceae), which are characterised by their
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Fig. 8 e Neighbour-joining phylogenetic tree of Gammaproteobacteria 16S rRNA gene sequences from Vidy Bay and Ouchy sediments. “A” in clone names stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section. Bootstrap values >50% are shown (500 replicates). The scale bar represents 2% estimated sequence divergence.
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Fig. 9 e Neighbour-joining phylogenetic tree showing 16S rRNA gene sequences of Archaea retrieved from Vidy Bay and Ouchy sediments. “A” in clone names stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section. Bootstrap values >50% are shown (500 replicates). The scale bar represents 5% estimated sequence divergence.
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specialised metabolism restricted to the oxidation of methane or methanol. Similar sequences were retrieved from a permafrost soil in Siberia (Liebner et al., 2009; EU124843) and an Arctic wetland soil (Wartiainen et al., 2006; AJ414655). Surprisingly, a few clones from Vidy sediments were related to phototrophic purple sulphur bacteria (Chromatiaceae) despite the fact that there is not much light to be expected at 30 m depth. However, some species can also grow under chemotrophic conditions in the dark, either autotrophically or heterotrophically using oxygen as terminal electron acceptor. Sequences similar to ours were retrieved from an anaerobic digestor (Riviere et al., 2009) and the sediment surface of Fayetteville Green Lake, USA (FJ437977). Several groups of Gammaproteobacteria clones remained unclassified but with similar sequences found in bacterioplankton communities of Lake Michigan (Mueller-Spitz et al., 2009. EU640647), river sediments (Li et al., 2008. EF590053), mangroves (EF125457) and agricultural soils (FJ444695). A large number of sequences affiliated with the division Bacteroidetes (CytophagaeFlexibactereBacteroidetes) were found in both sites, particularly in Vidy sediments. Bacteroidetes constitute the second largest group in Vidy sediments after the Betaproteobacteria, and the third largest group in Ouchy sediments (Fig. 4). Bacteroidetes phylotypes were diverse and their closest relatives were sequences found in freshwater lakes (Mueller-Spitz et al., 2009), anthropogenically impacted sediments, tundra soils (Liebner et al., 2008) and aquifers. Most of the sequences found in the 2 investigated sites remained unclassified. One clone found in Vidy sediments was affiliated to the genus Cytophaga sp. and one clone from Ouchy was related to the genus Flavobacterium sp. A large proportion of the Euryarchaeota phylotypes, mostly retrieved from Vidy sediments, were related to methanogens like Methanosaeta sp. (Methanosaetaceae) and Methanomicrobiales (Fig. 9). A few species of Methanosaeta sp. have been isolated from anaerobic sewage digestors or sewage sludge (Zinder et al., 1984; Huser et al., 1982). Similar sequences to the clones found in this study, were retrieved from anaerobic sludge and a meromictic lake (Lehours et al., 2007). The rest of the archaeal sequences were only distantly related to any cultured species but similar to sequences retrieved from lake sediments (Pouliot et al., 2009; AY531743, EU782007), Arctic peat (AM712495) and the anoxic zone of a hydropower plant reservoir in the Brazilian Amazon (GU127420 and GU127500). The two investigated sites differ clearly in terms of sediment chemical parameters and degree of pollution. It was therefore expected that the bacterial community composition would be different and reflect the differences in environmental conditions. Fig. 4 and Figs. 6e9 clearly show the diverse bacterial and archaeal lineages detected in the two sites. The results of the genetic diversity tests confirm the significant genetic differentiation of the sediment bacterial communities between the two sites at all depths. Seven and twelve divisions were identified in the sediments from Vidy and Ouchy, respectively. Among them Nitropsira, Planctomycetes, Verrucomicrobia and Gemmatimonadetes were only detected in Ouchy sediments. The apparent lower bacterial diversity in Vidy sediments may be explained by the high levels of a broad range of pollutants, which may induce adverse biological effects on microbial communities. The bacterial community
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composition changed with depth in the uncontaminated sediment. Conversely, no statistically significant variations were observed for the two sediment layers (0e2 and 4e6 cm) in the Bay of Vidy, which is explained by the high sedimentation rates and the non-consolidated nature of the sediment, permitting mobilisation and vertical mixing. The microbial composition of both sites was correlated with the environmental variables, as shown by the Mantel correlation test (r ¼ 0.9429, p ¼ 0.044). This result suggests that the diversity of microbial communities may be affected by nutrients, organic matter as well as the degree of pollution. Many environmental variables are implicated, which is a situation inherent to all field studies. The Bay of Vidy is contaminated by all kinds of contaminants but also with high quantities of organic matter and nutrients. Under these conditions it is difficult to separate out the influence of the different environmental variables on microbial diversity and community composition. To learn more about the relative importance of individual environmental factors, microcosm studies will be required. The integrative picture of the relationship between bacterial community structures and environmental factors at the two sites (Fig. 10) indicated that the sampling sites Ouchy and Vidy were clearly different with respect to both. Previous results already showed that the bacterial diversity in comparable contaminated and uncontaminated environments may differ significantly. The difference may be explained by the nature of pollution and a wide diversity of organic carbon
Fig. 10 e The Multiple factor analysis (MFA) is a PCA-based technique allowing the simultaneous ordination of a composite table obtained by the juxtaposition of the species and the two environmental datasets, after weighting the different matrices The superimposed representation shows one global point for each site, Vidy and Ouchy, at each depth (“A” stands for the 0e2 cm sediment section and “B” for the 4e6 cm sediment section). The three associated partial points correspond to the three datasets (microbial composition, organic matter and nutrients and heavy metals). The values on the axes indicate the percentage of total variation.
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sources (Sandaa et al., 1999; Sorci et al., 1999; Zhang et al., 2008). The polluted environment of Vidy Bay may have selected, among the dispersed microbes in sediments, certain functional bacterial groups which adapted to these conditions and became more dominant in that particular environment.
5.
Conclusion
This is the first study reporting on the microbial community structures of Bacteria and Archaea in contaminated and uncontaminated sediments of Lake Geneva. Results show that the sediments of the two sites differed clearly in their organic matter and nutrient contents. Intense mineralisation of organic matter under sulphate-reducing and methanogenic conditions was indicated for the sediments from Vidy Bay. Furthermore, results confirm data of previous studies showing that the area around the WWTP outlet pipe in the Vidy Bay is heavily contaminated with various organic and inorganic pollutants. Phylogenetic analysis of sedimentary prokaryotes revealed that (i) archaeal and bacterial communities differed significantly between the contaminated and the noncontaminated sediments. (ii) For both sites, a correlation was observed between the microbial community structure and environmental variables suggesting that microbial diversity may be affected by nutrients, organic matter content and by the degree of pollution. (iii) Betaproteobacteria was the dominant bacterial group, representing more than 30% of the clones from surface sediments at both sites. (iv) A large proportion of Betaproteobacteria clones, mostly from Vidy sediments, were related to the reductively dechlorinating Dechloromonas sp. (iv) Consistent with geochemical data, Deltaproteobacteria including clones related to iron- (Geobacter sp.) and sulphatereducing bacteria, were relatively more abundant in the contaminated sediments. (v) The archaeal communities were dominated by methanogenic Euryarchaeota, particularly in the organic matter-rich Vidy Bay sediments. This study suggests that each site harbours a specific sediment microbial community. The apparent lower bacterial diversity in Vidy sediments may be explained by the significant concentrations of contaminants, which may induce adverse biological effects on benthic metazoa and microbes. However, given the long history of pollution in the bay, specific bacterial and archaeal communities may well have adapted to these particular conditions. Hence, more research on microbial community composition and specific activities of microorganisms inhabiting similar environments should be performed, in order to improve the understanding how pollution and eutrophication may affect microbial communities.
Acknowledgements We thank Nadia Ruggeri-Bernardi (Cantonal Institute of Microbiology, Bellinzona, Switzerland) for assisting with the molecular analysis, Dr. Pierre Rossi and Noam Shani (EPFL-IIELBE, Switzerland) and Dr. Mathias Currat (University of Geneva, Switzerland) for helping with the statistical analyses. Philippe Arpagaus (Institute FA Forel) is acknowledged for
navigating R/V “La Licorne” and the Municipality of Lausanne for financial support.
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Evaluation and improvement of total organic bromine analysis with respect to reductive property of activated carbon Yao Li a, Xiangru Zhang a,*, Chii Shang a, Stuart W. Krasner b a
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China b Metropolitan Water District of Southern California, 700 Moreno Ave., La Verne, CA 91750, USA
article info
abstract
Article history:
A collective parameter and a toxicity indicator for all the halogenated organic disinfection
Received 4 July 2010
byproducts in a water sample is total organic halogen (TOX), which can be differentiated as
Received in revised form
total organic chlorine (TOCl), total organic bromine (TOBr) and total organic iodine. The
22 September 2010
TOX method involves concentration of organic halogens from water by adsorption onto
Accepted 28 September 2010
activated carbon (AC). A previous study showed that a portion of TOCl can be reduced to
Available online 7 October 2010
chloride during the adsorption procedure, which can be minimized by ozonation of the AC. In this study, a portion of TOBr was sometimes found to be reduced by AC to bromide, and
Keywords:
the reduction was generally less than that of corresponding TOCl. The results suggested
Disinfection byproducts
that around 10% of brominated Suwannee River fulvic acid was reduced to bromide.
Total organic halogen
However, some brominated amino compounds (especially glycylglycine, phenylalanine,
Total organic bromine
and cytosine) were found to be more reactive with the AC. For the iodinated compounds
Drinking water
studied, the reduction to iodide was not significant. The method for the TOBr measurement
Activated carbon
was improved by using ozonated AC when reduction occurred on the original AC. The improved method was also evaluated on treated wastewater and swimming pool water samples. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
When bromide is present in water (e.g., due to saltwater intrusion, connate water, oil field brines), hypobromous acid will be rapidly formed with the addition of chlorine or other disinfectants. Hypobromous acid undergoes reactions with organic matter in the water to form organic disinfection byproducts (DBPs) that contain bromine (Cowman and Singer, 1996; Richardson, 1998; Richardson et al., 2003; Xie, 2004). Kinetic studies have shown that the reaction of organic matter with hypobromous acid is much faster than that with hypochlorous acid (Westerhoff et al., 2003; Acero et al., 2005; Echigo and Minear, 2006; Hua et al., 2006). Research has shown that
brominated DBPs generally are dozens to hundreds times more toxic than their chlorinated analogues (Plewa and Wagner, 2009). For instances, bacterial studies have shown that bromoacetic acid is 201.3 times more mutagenic in Salmonella typhimurium strain TA100 than chloroacetic acid; mammalian cell studies have shown that bromoacetic acid is 89.8 times more cytotoxic in Chinese hamster ovary cells than chloroacetic acid; bromoacetic acid is 23.6 times more genotoxic in Chinese hamster ovary cells than chloroacetic acid (Plewa et al., 2004). With the presence of iodide in water, iodinated DBPs can also be formed during disinfection (Bichsel and von Gunten, 1999). Iodinated DBPs might be several times more toxic than their brominated analogues (Plewa et al.,
* Corresponding author. Tel.: þ86 852 2358 8479; fax: þ86 852 2358 1534. E-mail address: [email protected] (X. Zhang). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.038
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2004), but they are typically formed at lower concentrations (Krasner et al., 2006). Even though brominated (and iodinated) DBP species are being increasingly discovered, numerous of them remain unknown (Krasner et al., 2006; Ding and Zhang, 2009). A collective parameter to give an estimation of all forms of organic-bound halogenated DBPs (Jekel and Roberts, 1980) is total organic halogen (TOX). As “a master parameter” and “a toxicity indicator” for halogenated organic DBPs, TOX has been studied and applied in more than 800 journal papers (Singer and Chang, 1989; Li et al., 2002, 2010 and references therein). An improvement in TOX measurement will surely benefit researchers and practitioners in the more accurate study/control of halogenated organic DBPs in drinking waters, wastewaters, swimming pool waters, etc. The components of TOX include total organic chlorine (TOCl), total organic bromine (TOBr) and total organic iodine (TOI). TOX, TOCl, TOBr, and TOI can be measured with the adsorptionepyrolysis method based on Standard Method 5320B (APHA et al., 1995; Hua and Reckhow, 2006). The first two steps of this method involve enrichment of organic halogens from water by adsorption onto activated carbon (AC), and elimination of inorganic halides present on the AC by competitive displacement by nitrate ions. Because AC can also act as a reductant, if some halogenated DBPs are reduced to inorganic halides when in contact with AC, they will be removed from the AC during the rinse step with nitrate, leading to an underestimation of the amount of TOX present. In a previous study, a portion of TOCl has been found to be reduced during the adsorption procedure, where w20% of chlorinated Suwannee River fulvic acid (SRFA) was reduced to chloride by AC (Li et al., 2010). For the same concentrations, brominated (and iodinated) DBPs are thought to have significantly higher adverse health effects than their chlorinated analogues (Plewa and Wagner, 2009), so there is a critical need to evaluate and improve the accuracy of the TOBr (and TOI) measurement. In the current research, the reduction of TOBr by AC during the TOX measurement was evaluated, and the extent to which this reduction affects the measurement of TOBr was explored with various types of organics. Also, according to the previous study, AC that was slightly oxidized by ozone can fully or partially inhibit the reductive property of the AC. Thus, whether ozonated AC can also inhibit the reduction of TOBr but still maintain its adsorption capacity was investigated. In addition, the reductions of some iodine- and chlorine-containing DBPs (i.e., TOI and TOCl) by AC were evaluated and compared following a similar procedure.
2.
Materials and methods
2.1.
Preparation of halogenated samples
All solutions used in this study were prepared with ultrapure water (18.2 MU/cm) supplied by a NANOpure system (Barnstead). A chlorine stock solution (5000e5500 mg/L as Cl2) was prepared by absorption of ultra high-purity chlorine gas with a 1.0 M NaOH solution. By following the method outlined by Pinkernell et al. (2000), a bromine stock solution (13 mg/L as
Br2) was prepared from a 0.20 mM solution of potassium bromide by addition of 0.25 mM of an ozone solution at pH 4 (10 mM phosphate buffer). The bromine solution was standardized by Standard Method 4500F (APHA et al., 1995). After the pH was adjusted to 11 by sodium hydroxide, the bromine solution was stable for several days when stored at 4 C. The preparation of an iodine stock solution followed a similar procedure. The bromine and iodine stock solutions were adjusted to pH 6.5 before use. Compared to the commercial ones, the bromine and iodine stock solutions generated in such a method minimized the levels of inorganic halides in them by over 50%. SRFA from the International Humic Substances Society was dissolved into ultrapure water to prepare a SRFA stock solution. One brominated SRFA sample was prepared. The initial concentrations of SRFA and bromine were 3 mg/L as C and 2 mg/L as Br2, respectively. Bromide is naturally present in many source waters across the world, with the highest natural level of w2 mg/L present in Israel’s source water (Richardson et al., 2003). The high concentration of bromine (from oxidation of bromide during chlorination) was used to magnify the possible reactions and products. The pH of the sample was w6.8. After a reaction time of 5 d at ambient temperature (20 C), the sample was measured for bromine residual with the DPD ferrous titrimetric method (APHA et al., 1995). After 5 d, no residual bromine was left in the brominated SRFA sample. In addition, one chlorinated SRFA sample with/without ultrafiltration was prepared based on a previous study (Li et al., 2010). The initial concentrations of SRFA and chlorine were 3 mg/L as C and 5 mg/L as Cl2, respectively, which were used to simulate the typical concentrations in drinking water treatment. The reaction lasted for 5 d at ambient temperature. After 5 d, no residual chlorine was left in the chlorinated SRFA sample. Ultrafiltration was used to flush out most of the inorganic ions in the chlorinated SRFA sample. The objective of this step was to remove chloride ions remaining after chlorination, so that when chlorinated DBPs was degraded on the AC to release chloride, the amount released could be seen over the background level. Detailed information on the ultrafiltration can be found in a previous study (Li et al., 2010). For the iodinated SRFA sample preparation, the initial concentrations of SRFA and iodine were 3 mg/L as C and 1.27 mg/L as I2, respectively. In natural waters, iodide is found at concentrations of 0.5e212 mg/L (Moran et al., 2002), whereas the higher concentration of iodine was used to magnify the possible reactions and products. The reaction also lasted for 5 d at ambient temperature and no residual iodine was left after 5 d. In other research, inorganic chloramines have been reported to be reduced by AC to chloride (Bauer and Snoeyink, 1973). It has been demonstrated that chlorination of some amino compounds forms organic chloramines, which are one type of DBPs in TOCl that could be reduced by AC (Li et al., 2010). Therefore, eight amino compounds were used as model compounds, including glycine, glycylglycine, cytosine, leucine, methylamine, adenine, phenylalanine, and tryptophan. For the preparation of brominated model compounds, 1 mM of each amino compound was dissolved in a 12.5 mM bromine solution (2 mg/L as Br2). The pH of the mixture was around 7.2.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 2 9 e1 2 3 7
After a reaction time of 2 h at ambient temperature, the samples were measured for bromine residuals, and no bromine residuals were left. Chlorinated and iodinated amino compounds were prepared with a similar procedure. Briefly, 1 mM of each amino compound was dissolved in a 0.1 mM chlorine solution (7.1 mg/L as Cl2) or a 5 mM iodine solution (1.27 mg/L as I2). After a reaction time of 2 h at ambient temperature, no chlorine or iodine residuals were left. Three real water samples were also evaluated, including two wastewater samples from Hong Kong (one from a primary effluent and the other from a secondary effluent), and one swimming pool water sample (from a Hong Kong indoor swimming pool with a water temperature of w24.5 C). The characteristics and chlorination of the three water samples are shown in the Supplementary Information. These chlorinated water samples were expected to contain different levels of organic chloramines/bromamines and thus exhibit different TOCl/TOBr concentrations when measured with original and ozonated AC columns.
2.2.
Measurement of TOCl, TOBr, TOI, Cl, Br, and I
TOCl, TOBr, and TOI were determined using an AC adsorption and pyrolysis method with off-line ion chromatography as a halide detector (Hua and Reckhow, 2006). Sample preparation and AC adsorption followed Standard Method 5320B (APHA et al., 1995). Pre-packed AC columns were obtained from Mitsubishi Corporation. Halogenated samples were adjusted to pH 2 with nitric acid and then passed through two consecutive AC columns in a 3-channel adsorption module (TXA03C, Mitsubishi). After that, the AC columns were washed with 5 mL of 5000 mg/L as NO 3 of potassium nitrate (with a flow rate of 3 mL/min) to remove inorganic halides and were subsequently subjected to pyrolysis at 1000 C with an AQF-100 automatic quick furnace (Mitsubishi). The hydrogen halide and halogen gases from the pyrolysis unit were trapped by 5 mL of 0.003% hydrogen peroxide absorbent (freshly made daily), which contained 2 mg/L of phosphate serving as an internal standard to estimate the volume variations induced by the GA-100 gas absorption unit (Mitsubishi). An ICS-3000 ion chromatography system (Dionex, Sunnyvale, CA) equipped with an IonPac analytical column (AS19, 4 250 mm) and a guard column (AG19, 4 50 mm) was used. The eluent was generated by an EGC potassium hydroxide cartridge at a flow rate of 1 mL/min. Chloride and Br ions were determined with an isocratic eluent of 10 mM KOH from 0 to 10 min followed by a linear gradient eluent of 10e45 mM KOH from 10 to 25 min. Iodide was determined with an isocratic eluent of 10 mM KOH from 0 to 10 min followed by a linear gradient eluent of 10e58 mM KOH from 10 to 40 min. The concentrations of the halides were quantified with a conductivity detector. The practical quantitation limits for TOCl, TOBr, and TOI in a 40mL sample were 0.002 mg/L as Cl, 0.002 mg/L as Br, and 0.009 mg/L as I, respectively. The concentrations of Cl, Br, and I in a sample were measured with the same ion chromatograph under the same instrument settings. The practical quantitation limits for Cl, Br, and I were 0.010, 0.010, and 0.050 mg/L, respectively. The relative standard deviations (RSDs) for the Cl measurement in 7 aliquots of a standard NaCl solution (0.010 mg/L as Cl in
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ultrapure water) and a chlorinated SRFA sample were 0.05% and 0.60%, respectively. The RSDs for the Br measurement in 7 aliquots of a standard NaBr solution (0.010 mg/L as Br in ultrapure water) and a brominated SRFA sample were 0.10% and 0.75%, respectively. The RSDs for the I measurement in 7 aliquots of a standard KI solution (0.050 mg/L as I in ultrapure water) and an iodinated SRFA sample were 0.05% and 0.55%, respectively. Unless otherwise specified, triplicates of a sample were analyzed for TOCl, TOBr, TOI, Cl, Br, and I.
2.3.
Reactions of halogenated samples with AC
Two 20-mL aliquots of a brominated DBP sample were collected in two vials. One aliquot was used as a control, and the other aliquot was allowed to react with AC. The AC was purchased from Mitsubishi (coconut-based with particle sizes of 100e200 mesh and a very low halide background of 0.4 mg Cl/40 mg AC), and was the same as the one packed in the AC columns for TOX analyses. The aliquot was spiked with 40 mg of the AC and was adjusted to pH 2 immediately (to simulate the TOX measurement procedure). After a contact time of 5 min, the aliquot was filtered with a syringe coupled with a 0.45 mm Durapore PVDF membrane filter (Millipore Corporation). The filtrate was collected and adjusted back to pH 7 for determination of the Br concentration. As the nitrate peak overlapped with the Br peak in the ion chromatograph, a chloride solution was used to substitute for the nitrate wash. The syringe filter was rinsed three times (to rinse out all the Br in the AC and the syringe filter), each time with 10 mL of 6008 mg/L of a chloride solution, which was used to simulate 5000 mg/L of a nitrate solution, and the Br concentration in each filtrate (10 mL) was measured. Finally, the total Br concentration in the aliquot after contact with the AC was calculated by combining the Br concentrations in all the filtrates. It was designated as the one with a contact time of “5 min” with the AC. For the aliquot used for a control, it was treated in the same way, except that no AC was used, and thus was designated as the one with a contact time of “0 min” with the AC. Since the AC might contain some rinsable Br ions, another control was conducted as follows: 20 mL of ultrapure water was spiked with 40 mg of the AC. After a contact time of 5 min, the sample was filtered with a syringe coupled with a 0.45 mm Durapore PVDF membrane filter, followed by rinsing the syringe filter with 10 3 mL of a 6008 mg/L chloride solution. The chloride solution was found not to contain any measurable Br ions. The Br concentrations in all the filtrates were measured and combined. The total Br concentration would be deducted from the Br concentration in the aliquot with a contact time of 5 min with the AC. The iodinated and (ultrafiltered) chlorinated DBP samples were treated with the similar procedures, except that the 5000 mg/L nitrate solution was used to rinse the syringe filter.
2.4.
Treatment of AC
The results in a previous study showed that AC treated with ozone minimized the reduction of a portion of the TOCl to Cl (Li et al., 2010). In this study, ozone gas from an ozone generator (10K-2U, Enaly) was absorbed in ultrapure water to
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3.
Results and discussion
3.1.
Reactions of brominated SRFA with AC
Fig. 2 shows the Br concentrations in the brominated SRFA sample before and after reaction with the AC. The Br concentration in the brominated SRFA sample was 1.70 mg/L and the measured TOBr concentration was 0.28 mg/L as Br. After reaction with the AC, the Br concentration in the brominated SRFA sample increased to 1.73 mg/L. Such an increase was not significant ( p > 0.05) as shown in Supplementary Information Table S1. The net Br increment in the 5-min contact was 0.03 mg/L, which means that 0.03 mg/L of TOBr may have been reduced to Br in 5 min. Considering that the measured TOBr concentration in brominated SRFA sample was 0.28 mg/L as Br, the measurement error for the brominated SRFA sample with the standard method may be estimated as 0.03/(0.28 þ 0.03) ¼ 9.8%. As a comparison, the ultrafiltered chlorinated SRFA sample was also used to react with the AC (Fig. 2). After a contact time of 5 min, the Cl concentration in the ultrafiltered sample increased from 0.18 to 0.31 mg/L, indicating a significant
2.0 0 min
Cl− or Br− conc. (mg/L)
prepare a w15 mg/L ozone stock solution, which was diluted immediately to prepare ozone solutions ranging from 0.25 to 10 mg/L. Ten mL of each diluted ozone solution was passed through an AC column immediately at a flow rate of 2 mL/min. It was found that 10 mL of a 2.4 mg/L ozone solution was the optimal ozone dose for treating the AC (Fig. 1). Accordingly, to prepare an ozonated AC column, 10 mL of a 2.4 mg/L ozone solution was passed through an AC column immediately at a flow rate of 2 mL/min. The ozonated AC column was kept in a fume hood for over 24 h until used for TOX analysis. The TOCl, TOBr and TOI recoveries with the ozonated and original AC columns were tested with monochloroacetic acid, monobromoacetic acid and monoiodoacetic acid, which have been used to test the recoveries by Hua and Reckhow (2006).
5 min
1.6 1.2 0.8
0.4
5 min 0 min
0.0
Cl− conc. in chlorinated SRFA + AC
Br− conc. in brominated SRFA + AC
Fig. 2 e ClL and BrL concentrations in the brominated SRFA and the ultrafiltered chlorinated SRFA samples after a contact time of 0 or 5 min with AC.
increase ( p < 0.05). The net Cl increment in 5 min was 0.13 mg/L. The measured TOCl concentration in the ultrafiltered sample was 0.42 mg/L as Cl, thus the measurement error for the ultrafiltered sample with the standard method can be estimated as 0.13/(0.42 þ 0.13) ¼ 23.6%. The results show that the chlorinated SRFA can be reduced by AC, whereas it seems as if the TOBr reduction, at least for the brominated SRFA, occurred in a less extent. It needs pointing out that, to observe the Cl increment from the reduction of chlorinated SRFA by the AC, the use of the “ultrafiltered” chlorinated SRFA sample to react with the AC was a choice with no alternative because of the high Cl concentration in the original sample. As shown later in Section 3.3, the TOCl concentrations in the “original” chlorinated SRFA sample measured with AC and ozonated AC were 0.484 and 0.600 mg/ L as Cl, respectively. The net TOCl increment corresponded an improvement of 19.3%, which further confirms that the reduction of the TOBr in the brominated SRFA occurred in a less extent than that of the TOCl in the chlorinated SRFA. Finally, the reduction of the TOI in the iodinated SRFA sample by the AC was barely detectable.
TOCl or TOBr conc. (mg/L as Cl or Br)
0.40 TOCl TOBr
3.2.
0.30
0.20
0.10 0
2
4 6 O3 concentration (mg/L)
8
10
Fig. 1 e TOCl and TOBr levels in a chlorinated SRFA sample measured with AC columns that were treated with different ozone doses (10 mL). The chlorinated SRFA sample was prepared as follows: SRFA 3 mg/L as C, BrL 0.4 mg/L, alkalinity 90 mg/L as CaCO3, chlorine dose 5 mg/L as Cl2, and chlorine contact time 5 d (with no free chlorine residual at end of 5 d).
Reactions of brominated amino compounds with AC
The concentrations of the brominated amino compounds measured as TOBr are shown in Table 1. As a comparison, the concentrations of chlorinated and iodinated amino compounds measured as TOCl and TOI, respectively, are also shown in this table. For brominated cytosine, TOBr was detected at a significant level (0.772 mg/L as Br), whereas the TOBr concentrations of the other brominated amino compounds were below 0.10 mg/L as Br. Considering that the brominated amino compounds were prepared using a bromine solution of 2 mg/L as Br2, cytosine had a 38.6% bromine utilization, whereas the other amino compounds had <4% bromine utilization. The results in Table 1 also showed that the TOX concentrations in both brominated and chlorinated cytosine were significant. Among the eight brominated amino compounds, after a 5min contact with AC, the Br concentrations increased obviously in five samples (and presented no discernible changes in
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Table 1 e TOCl, TOBr and TOI in halogenated amino compounds. Amino compound
Molecular weight
TOCl (mg/L as Cl) in chlorinated amino compound
TOBr (mg/L as Br) in brominated amino compound
TOI (mg/L as I) in iodinated amino compound
75.07 31.06 131.17 132.12 111.10 135.13 165.19 204.20
0.031 0.002 0.007 0.339 1.218 0.031 0.009 0.049
0.024 0.015 0.044 0.056 0.772 0.023 0.064 0.037
0.051 0.013 0.035 0.090 0.155 0.028 0.088 <0.009
Glycine Methylamine Leucine Glycylglycine Cytosine Adenine Phenylalanine Tryptophan
other three samples). Fig. 3a shows the Br concentrations in the brominated glycine, glycylglycine, leucine, phenylalanine, and cytosine samples. After a 5-min contact with AC, the Br concentrations in the five samples increased significantly ( p < 0.05). The net Br increments in 5 min were 0.056, 0.119, 0.066, 0.082, and 0.121 mg/L, respectively. For the brominated glycylglycine, phenylalanine, and cytosine, the Br increments were more significant. Because the measured concentrations of TOBr for the brominated glycylglycine, phenylalanine, and cytosine samples were 0.056, 0.064, and 0.772 mg/L as Br, respectively (see Table 1), the measurement errors for the brominated amino compounds with the standard method can be calculated as 68.0%, 56.2% and 13.5%, respectively. For the
Br− conc. (mg/L)
5 min 0 min
5 min 0 min 5 min 0 min
R2NH þ HOBr / R2NBr þ H2O
RNH2 þ 2HOBr / RNBr2 þ 2H2O Bauer and Snoeyink (1973) studied the reactions of inorganic chloramines with AC. Zhang and Minear (2006) and Li et al. (2010) examined the reactions of organic chloramines with AC. Likewise, the reactions of some organic bromamines with AC are proposed:
a 2.8 2.4
brominated glycylglycine and phenylalanine, the measurement errors were relatively high because the amounts of TOBr formed were so low. Nonetheless, this experiment demonstrated that some of the brominated amino compounds could be reduced in part by the AC. During disinfection, amino compounds may react with bromine to form organic bromamines:
5 min 0 min
2.0
R2NBr þ H2O þ C* / R2NH þ C*O þ Hþ þ Br 5 min
1.6
0 min
RNBr2 þ H2O þ C* / 0.5RN]NR þ C*O þ 2Hþ þ 2Br 1.2 brominated glycine + AC
b 5.6 Cl− or I− conc. (mg/L)
5 min
4.2
brominated brominated brominated brominated glycylglycine leucine phenylalanine cytosine + AC + AC + AC + AC 5 min 0 min
0 min 5 min 0 min
2.8
1.4
0.0
0 min 5 min
0 min 5 min
Cl conc. in Cl conc. in Cl conc. in l conc. in l conc. in chlorinated chlorinated chlorinated iodinated iodinated glycine + AC glycylglycine cytosine + AC glycine + AC glycylglycine + AC + AC
Fig. 3 e (a) BrL concentrations in the brominated amino compound samples after a contact time of 0 or 5 min with the AC. (b) ClL and IL concentrations in the chlorinated and iodinated amino compound samples after a contact time of 0 or 5 min with the AC.
where C* and C*O represent the carbon surface and a surface oxide, respectively. In order to find the trend of halogenated DBP reactions with AC, some chlorinated and iodinated amino compounds were also evaluated. Fig. 3b shows the Cl concentrations in the chlorinated glycine, glycylglycine, and cytosine samples before and after a 5-min contact with the AC. As shown in Fig. 3b, the net Cl increases in 5 min were 0.608, 0.238, 0.413 mg/L, respectively. Because the measured TOCl concentrations of chlorinated glycine, glycylglycine, and cytosine samples were 0.03, 0.34 and 1.22 mg/L as Cl, respectively, the measurement errors for the chlorinated amino compounds with the standard method were 95.3%, 41.3%, and 25.3% respectively. The I concentrations in the iodinated glycine, glycylglycine, and cytosine samples before and after a 5-min contact with the AC were also examined. The net I increases in 5 min were 0.001, 0.012, and 0.011 mg/L, respectively, which were not significant ( p > 0.05). In terms of the relative reduction of TOCl, TOBr, and TOI, some instances of TOBr and all of the TOI examples for the amino compounds showed insignificant reductions. The best
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comparisons (at least for TOCl and TOBr) were for glycine, glycylglycine, and cytosine, where the reductions were higher for TOCl. Nonetheless, the results suggested that some of the brominated amino compounds could be reduced by the AC, but the extent of the reduction was lower than that of the chlorinated amino compounds.
3.3. Improvement of the TOBr measurement with ozonated AC The reductive property of the AC was demonstrated to cause the systematic error in the TOCl measurement, so the AC columns were slightly oxidized by ozone with the same procedure described in the previous study (Li et al., 2010). When either the ozonated or original AC columns were used in the TOX measurement procedure, the TOCl, TOBr and TOI recoveries with monochloroacetic acid, monobromoacetic acid and monoiodoacetic acid were all within 93.6%e99.2%, which were comparable to the recoveries reported by Hua and Reckhow (2006). The results indicated that slight oxidation of the AC with ozone still well maintained its adsorption capacity for TOCl, TOBr and TOI. Then the ozonated AC columns were used to compare with the original AC columns in the TOBr analyses. As shown in Fig. 4, the brominated SRFA sample and five brominated amino compounds (glycine, glycylglycine, leucine, phenylalanine, cytosine) were used to examine the adsorption efficiency and reductive property of the ozonated AC. Chlorinated
0.12 AC ozonated AC
0.8
0.08
0.6 0.4
0.04
0.2 0.00
b
2.4
TOCl, TOBr or TOI conc. (mg/L as Cl, Br or I)
brominated brominated brominated brominated brominated glycine glycylglycine leucine phenylalanine cytosine
2.0
TOBr conc. (mg/L as Br)
1.0
0.0
0.8 AC ozonated AC 0.6
1.6 0.4
1.2 0.8
0.2
TOCl or TOBr conc. (mg/L as Cl or Br)
TOBr conc. (mg/L as Br)
a
cytosine, iodinated cytosine, and chlorinated SRFA samples were also measured for comparison. As shown in Fig. 4a, the TOBr concentrations in the brominated glycine sample measured with the original and ozonated AC columns were 0.024 0.002 and 0.030 0.003 mg/L as Br, respectively; the difference was 0.006 mg/L as Br. The TOBr concentrations in the brominated glycylglycine sample measured with the original and ozonated AC columns were 0.056 0.003 and 0.100 0.010 mg/L as Br, respectively, where the difference (0.044 mg/L as Br) was substantial. Likewise, the TOBr concentrations in the brominated leucine sample measured with the original and ozonated AC columns were 0.044 0.007 and 0.065 0.008 mg/L as Br, respectively, with a difference of 0.022 mg/L as Br; in the brominated phenylalanine sample the TOBr concentrations were 0.064 0.007 and 0.098 0.007 mg/L as Br, respectively, with a difference of 0.034 mg/L as Br; and in the brominated cytosine sample the TOBr concentrations were 0.772 0.032 and 0.925 0.098 mg/L as Br, respectively, with a difference of 0.153 mg/L as Br. Statistical analyses show that the TOBr concentrations in the five brominated amino compounds measured with the ozonated AC columns were significantly higher than the corresponding ones measured with the original AC columns ( p < 0.05). These results suggested that the slight oxidation of the AC with ozone might effectively inhibit its reductive property on the brominated amino compounds. It is of note that the effect of the ozonated AC was different among different amino acids, which may be ascribed to the different
0.4 0.0
chlorinated cytosine
brominated cytosine
Iodinated cytosine
chlorinated SRFA
brominated SRFA
0.0
Fig. 4 e (a) TOBr concentrations in different brominated amino compound samples measured with the original and ozonated ACs. (b) TOCl, TOBr, and TOI concentrations in different halogenated cytosine and SRFA samples measured with the original and ozonated ACs.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 2 9 e1 2 3 7
NeBr bond strengths and steric effects of the formed organic bromamines. Fig. 4b compares the impact of ozonation of the AC on the TOCl, TOBr and TOI concentrations of halogenated cytosine. The TOCl concentrations in the chlorinated cytosine samples measured with the original and ozonated AC columns were 1.56 0.097 and 1.97 0.082 mg/L as Cl, respectively (a significant increase, p < 0.05). The difference was 0.417 mg/L as Cl. On the basis of the TOCl concentration measured with the ozonated AC, the incremental improvement can be calculated as 0.417/(1.56 þ 0.417) ¼ 21.1%. Likewise, the TOBr concentrations in the brominated cytosine sample measured with the original and ozonated AC columns were 0.772 0.032 and 0.925 0.098 mg/L as Br, respectively (a significant increase, p < 0.05), the net increment was 0.153 mg/L as Br, and the improvement was 16.5%. However, the TOI concentrations in the iodinated cytosine sample measured with the original and ozonated AC columns were 0.155 0.010 and 0.170 0.013 mg/L as I, respectively (not a significant increase, p > 0.05), the net increment was 0.015 mg/L as I, and the improvement was only 8.8%. The TOCl and TOBr concentrations in chlorinated and brominated SRFA samples were also compared in Fig. 4b. The concentrations of TOCl measured with the original and ozonated AC columns were 0.484 0.044 mg/L and 0.600 0.024 mg/L as Cl, respectively (a significant increase, p < 0.05). The net improvement was 19.3%. The concentrations of TOBr measured with the original and ozonated AC columns were 0.278 0.007 and 0.300 0.015 mg/L as Br, respectively (a significant increase, p < 0.05). The net improvement was 7.3%. The results showed a similar impact of the AC column on the TOX reduction: TOI < TOBr < TOCl. The reduction of TOCl, TOBr, and TOI by the AC was likely associated with organic haloamines, which may inherit certain oxidation power from the precursor halogens (whose oxidation potentials are in the order of HOI < HOBr < HOCl). To confirm the reduction inhibition with the ozonated AC, the bromide concentrations of the brominated amino compound and SRFA samples were tested before and after these samples reacted with the original and ozonated ACs. Fig. 5a shows the Br concentrations in different brominated amino compound samples before and after reactions with the ACs. After a contact time of 5 min, the Br concentration in the brominated glycine sample with the original AC increased from 2.35 to 2.41 mg/L (not a significant increase, p > 0.05), whereas in the sample with the ozonated AC it was 2.38 mg/L. Alternatively, the Br concentration in the brominated glycylglycine sample with the original AC increased from 2.32 to 2.44 mg/L (a significant increase, p < 0.05), whereas in the sample with the ozonated AC it was less (2.38 mg/L). The Br decrement between the original and ozonated ACs was 0.06 mg/L, which was close to the corresponding TOBr increment (0.05 mg/L as Br, Fig. 4a). Similar results were obtained with brominated leucine and phenylalanine samples. As shown in Fig. 5b, the Br concentration in the brominated SRFA sample with the original AC increased from 1.68 mg/L to 1.74 mg/L (not a significant increase, p > 0.05), whereas in the sample with the ozonated AC (1.69 mg/L) it was close to the initial Br concentration. Alternatively, the Cl concentration in the ultrafiltered chlorinated SRFA sample
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with the original AC increased from 0.320 to 0.427 mg/L (a significant increase, p < 0.05), whereas in the sample with the ozonated AC (0.350 mg/L) it was close to the initial Cl concentration. Similar results were obtained with the chlorinated and brominated cytosine samples. The results demonstrated that the ozonated AC inhibited the reduction of TOCl and TOBr. However, the impact on TOBr was much less than that on TOCl. This may have been due (in part) to there being (in general) much less reduction of TOBr than TOCl on the original AC.
3.4. Measurement of TOCl and TOBr concentrations with ozonated AC for chlorinated wastewater effluents and swimming pool water samples Two chlorinated wastewater effluent samples and one chlorinated swimming pool water sample were used as alternative organic matter sources to evaluate the TOX adsorption and reduction inhibition by the ozonated AC. After treatment, TOCl and TOBr concentrations in all water samples were measured with the original and ozonated AC columns. As shown in Fig. 6, the TOCl concentrations were 0.268 0.030 and 0.346 0.033 mg/L as Cl respectively in the primary effluent (a significant increase, p < 0.05), and 0.176 0.007 and 0.204 0.018 mg/L as Cl respectively in the secondary effluent
Fig. 5 e (a) BrL concentrations in the brominated amino compound samples after a contact time of 0 or 5 min with the original and ozonated ACs. (b) ClL and BrL concentrations in the chlorinated SRFA, brominated SRFA, chlorinated cytosine, and brominated cytosine samples after a contact time of 0 or 5 min with the original and ozonated ACs.
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TOCl or TOBr conc. (mg/L as Cl or Br)
1.2 1.0 0.8
4. AC ozonated AC
0.6 0.4 0.2 0.0 TOCl TOBr TOCl TOBr TOCl TOBr chlorinated primary chlorinated secondary chlorinated swimming pool water wastewater effluent wastewater effluent
Fig. 6 e TOCl and TOBr concentrations in the chlorinated wastewater primary effluent, chlorinated wastewater secondary effluent, and chlorinated swimming pool water samples measured with the original and ozonated ACs.
(a significant increase, p < 0.05). The net increments in TOCl concentrations were 0.078 and 0.028 mg/L as Cl, respectively. The error bars slightly overlapped for the TOCl measurements for the secondary effluent. The TOBr concentrations were 0.142 0.011 and 0.195 0.015 mg/L as Br respectively in the primary effluent (a significant increase, p < 0.05), and 0.447 0.035 and 0.487 0.020 mg/L as Br respectively in the secondary effluent (not a significant increase, p > 0.05). The net increments in TOBr concentrations were 0.053 and 0.040 mg/L as Br, respectively. The error bars partially overlapped for the TOBr measurements for the secondary effluent. The results showed that the TOCl and TOBr concentrations measured with the ozonated AC increased significantly, especially for the chlorinated primary effluent sample. Because the ammonia concentration in the primary effluent was much higher than that in the secondary effluent, the N/Br ratio was much higher in the primary effluent. Galal-Gorchev and Morris (1965) demonstrated that the formation of inorganic bromamine species was impacted by the N/Br ratio. This and differences in the organic matter makeup (e.g., organic nitrogen content) of the two wastewater effluents may have also impacted organic haloamine formation. Certain organic haloamines are considered to be important compounds reduced by AC. As shown in Fig. 6, the TOCl concentrations in the bromidespiked swimming pool water sample were 0.966 0.019 and 1.002 0.022 mg/L as Cl, respectively (a significant increase, p < 0.05), and the TOBr concentrations were 0.089 0.008 and 0.091 0.004 mg/L as Br, respectively (not a significant increase, p > 0.05). The net increments were 0.036 mg/L as Cl and 0.002 mg/L as Br. However, the error bars partially overlapped for both TOCl and TOBr. Compared with the primary wastewater effluent sample, the increments for TOCl and TOBr were relatively small. The difference in results may have been due (in part) to the low concentrations of the ammonia and organic nitrogen content in the swimming pool water sample, where breakpoint chlorination should have been achieved. The results again suggest the importance of organic haloamines to the TOX reduction by AC.
Conclusions
The results showed that brominated DBPs may be reduced by the AC used in the TOX standard method, but the reduction was lower than that of the chlorinated DBPs. Around 10% of the TOBr in the brominated SRFA sample was reduced by the AC, which was less than what was observed with the chlorinated SRFA (around 20%). The reduction of the TOI in the iodinated SRFA by the AC was negligible. A similar trend was observed for some halogenated amino compounds, i.e., the impact of the AC on the TOX reduction was in the order of TOI < TOBr < TOCl. The reductions in TOBr by the AC were significant in the brominated glycylglycine, phenylalanine, and cytosine samples, leading to the measurement errors with the standard method up to 68.0%, 56.2% and 13.5%, respectively. TOBr measurements may be improved by using the ozonated AC, which can minimize the reduction of the brominated DBPs during the adsorption procedure in cases where it occurred. The TOCl and TOBr concentrations in the chlorinated primary wastewater effluent were improved dramatically when measured with the ozonated AC columns. The results suggest the importance of organic haloamines to the TOX reduction by the AC.
Acknowledgments The work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKUST622808).
Appendix. Supplementary information Supplementary information associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.09. 038.
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Evaluation and modeling of benzalkonium chloride inhibition and biodegradation in activated sludge Chong Zhang a, Ulas Tezel b, Kexun Li a,*, Dongfang Liu a, Rong Ren a, Jingxuan Du a, Spyros G. Pavlostathis b a b
The College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0512, USA
article info
abstract
Article history:
The inhibitory effect and biodegradation of benzalkonium chloride (BAC), a mixture of
Received 17 March 2010
alkyl benzyl dimethyl ammonium chlorides with different alkyl chain lengths, was
Received in revised form
investigated at a concentration range from 5 to 20 mg/L and different biomass concen-
30 August 2010
trations in an activated sludge system. A solution containing glucose and mineral salts was
Accepted 29 September 2010
used as the wastewater in all the assays performed. The inhibition of respiratory enzymes
Available online 7 October 2010
was identified as the mode of action of BAC as a result of oxygen uptake rate analysis performed at BAC concentrations ranging between 5 and 70 mg/L. The glucose degradation
Keywords:
in the activated sludge at different BAC and biomass concentrations was well-described
Benzalkonium chloride
with Monod kinetics with competitive inhibition. The half-saturation inhibition constant
Inhibition
(KI) which is equivalent to EC50 of BAC for the activated sludge tested ranged between 0.12
Biodegradation
and 3.60 mg/L. The high KI values were recorded at low BAC-to-biomass ratios, i.e. less than
Modeling
10 mg BAC/g VSS, at which BAC was almost totally adsorbed to biomass and not
Activated sludge
bioavailable. BAC degradation started as soon as glucose was totally consumed. Although BAC was almost totally adsorbed on the biomass, it was degraded completely. Therefore, BAC degradation was modeled using two-phase biodegradation kinetics developed in this study. This model involves rapid partitioning of BAC to biomass and consecutive degradation in both aqueous and solid phases. The aqueous phase BAC degradation rate was twenty times, on average, higher than the solid phase degradation rate. The specific aqueous (kI1) and solid (kI2) phase BAC utilization rate constants were 1.25 and 0.31 mg BAC/g VSS h, respectively. The findings of this study would help to understand the reason of extensive distribution of quaternary ammonium compounds in wastewater treatment plant effluents and in natural water systems although QACs are biodegradable, and develop strategies to avoid their release and accumulation in the environment. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Benzalkonium chloride (BAC) is a mixture of alkyl benzyl dimethyl ammonium chlorides with C8 to C18 alkyl groups. BAC, which is a group of quaternary ammonium compounds
(QACs), is the active ingredient of many pharmaceutical formulations, cosmetics, commercial disinfectants, industrial sanitizers and food preservatives (Tezel and Pavlostathis, 2009). About 75% of the QACs consumed in domestic and industrial applications annually are released into wastewater
* Corresponding author. Tel./fax: þ86 22 23501117. E-mail address: [email protected] (K. Li). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.037
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 3 8 e1 2 4 6
treatment systems. BAC is the most frequently found QAC group worldwide in municipal wastewater at concentrations ranging between 20 and 300 mg/L (Martinez-Carballo et al., 2007; Clara et al., 2007). The QACs present in the wastewater upset activated sludge process (Boethling, 1984). The EC50 values for hexadecyl trimethyl ammonium bromide and dodecyl benzyl dimethyl ammonium chloride obtained from a respirometric assay conducted with activated sludge ranged between 10 and 40 mg/L (Reynolds et al., 1987). The EC50 of a mixture of alkyl trimethyl ammonium chlorides (C14e18) for unacclimated activated sludge determined based on the inhibition of [14C] glucose uptake was 28 mg/L (Larson and Schaeffer, 1982). Another study showed that didecyl dimethyl ammonium chloride inhibited the COD removal in a rotating biological contactor at concentrations above 20 mg/L and the biofilm was totally eliminated at 160 mg/L. A variety of physiologically different microorganisms participate in the wastewater treatment process, therefore the response of each species to QAC inhibition is expected to be different. For instance, QACs are particularly toxic to nitrifiers. Benzalkonium chloride was inhibitory to a mixed nitrifying culture at 10e15 mg/L with a non-competitive inhibition coefficient equal to 1.5 mg/L (Yang, 2007). Overall, these studies suggest that QACs are inhibitory to activated sludge microbial community at concentrations higher than what is found in the wastewater. However, sudden discharges of QACs resulting in temporarily high levels in treatment plants could upset plant function. BACs rapidly and strongly adsorb onto biomass or are biodegraded during the biological wastewater treatment. Therefore, adsorption and biotransformation are the main routes of BAC removal from the wastewater. Average removal up to 99% by means of adsorption and biodegradation is reported in wastewater treatment systems (Clara et al., 2007; Boethling, 1984). Microorganisms that utilize QACs as the carbon and energy source at high concentrations have been identified in the activated sludge. The majority of the QAC degraders in the activated sludge are classified in the genus Pseudomonas (Dean-Raymond and Alexander, 1977; Geftic et al., 1979; van Ginkel et al., 1992; Nishihara et al., 2000; Kaech and Egli, 2001; Nishiyama and Nishihara, 2002; Takenaka et al., 2007; Liffourrena et al., 2008). Other species that can catabolize various QACs are Xanthomonas sp. (DeanRaymond and Alexander, 1977) and Aeromonas sp. (Patrauchan and Oriel, 2003). Until recently, few studies had focused on the biotransformation/biodegradation of BAC (Patrauchan and Oriel, 2003; van Ginkel, 2004; Qin et al., 2005). According to the results of these studies, BAC biotransformation commences with the fission of the alkyl group from the quaternary nitrogen resulting in the formation of benzyl dimethyl amine as the first intermediate. Benzyl dimethyl amine is then converted to ammonia through either two demethylation followed by a debenzylation or a debenzylation followed by two demethylation processes. Although biodegradation potential and mechanism of BAC and other QACs have been elucidated, none of the studies presented above reported the biodegradation kinetics of QACs. In spite of the fact that, the information on the inhibitory effects and biodegradation of BAC as well as its adsorption to activated sludge is present in the literature, the interaction of these processes, i.e. adsorption, inhibition and biodegradation,
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and how it affects the overall fate of BAC in the activated sludge is not well understood. Given the toxicity of BAC to aquatic organisms and the role in the induction of antibiotic resistance in the environment (Gaze et al., 2005), BAC has to be removed completely in the wastewater treatment systems (i.e., activated sludge) before wastewater and the residual (i.e., sludge) are discharged to the environment. As the ultimate biodegradation of BAC is the main goal, BAC inhibition and biodegradation kinetics in activated sludge systems need to be well-understood. The objectives of this study were to: (a) investigate the potential inhibitory effect and biodegradation of BAC in activated sludge; and (b) develop a comprehensive dynamic model to elucidate the fate and effect of BAC in activated sludge. All the experiments were carried out with a mixed aerobic culture at a range of BAC and biomass concentration.
2.
Materials and methods
Details on the a) properties and characterization of benzalkonium chloride; b) mixed aerobic heterotrophic culture used in all assays; c) analytical methods; d) model simulations and parameter estimation; and e) adsorption kinetics and isotherm assays are given in the Supplementary Material (Text S1eS5).
2.1.
Respirometric assay
Inhibition of BAC in activated sludge was investigated based on the oxygen uptake rate. A 100 mL sample of mixed aerobic heterotrophic culture in the endogenous growth phase was transferred into a series of Erlenmeyer flasks. A glucose solution, which served as carbon/energy source, and BAC at desired concentrations were added and the total liquid volume was adjusted to 100 mL with culture media. The glucose COD in the bottles was about 300 mg/L. The culture series included six bottles that were amended with BAC resulting in total BAC concentrations of 5, 10, 20, 30, 50 and 70 mg/L. Two additional flasks were prepared: seed blank and reference which consisted of only seed and culture media and seed, culture media, and glucose (300 mg COD/L), respectively. Dissolved oxygen in each flask was measured during the time course using a DO meter while the content was continuously mixed. The oxygen uptake rate (OUR) of each culture at different BAC concentrations was determined by calculating the slope of DO versus time curve using a linear regression performed by using Sigma Plot Version 10 software (Systat Software Inc., San Jose, CA, USA). The specific oxygen uptake rate (SOUR) of each culture was determined by normalizing the OUR to the volatile suspended solids (VSS) concentration in each individual flask. At the end of the assay, BAC concentration in each flask was measured to verify that BAC was not degraded during the course of the assay.
2.2. Batch inhibition assay using the mixed aerobic culture The inhibitory effect of BAC on glucose utilization and BAC biodegradation in the mixed heterotrophic culture was tested
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in a batch assay. The assay was performed in 1.5-L glass reactors stirred with a Teflon-coated stirring bar and aerated with compressed air. A sample of mixed liquor from the mixed aerobic heterotrophic culture was transferred to each reactor. Glucose and NH4Cl were added as the carbon and nitrogen source. The initial total glucose concentration was about 300 mg COD/L in the reactor. The cultures were then amended with BAC resulting in a total initial BAC concentration of 5, 10, and 20 mg/L, respectively. The total liquid volume was adjusted to 1 L with the mineral media. The initial pH in the culture was 7.0 and the reactor was maintained at 25 C. During the incubation period, the DOC and BAC concentration was measured at pre-specified time intervals. pH, TSS, VSS were measured at the beginning and at the end of the incubation period. Another batch assay testing the effect of biomass concentration on the inhibition and biodegradation of BAC in the mixed heterotrophic culture was performed using the same methodology described above. The biomass concentration in each reactor was adjusted by diluting the stock mixed culture with mineral media. The biomass concentration tested in this study ranged from 180 to 1300 mg VSS/L and the BAC concentration applied was either 5 or 10 mg/L. DOC and BAC concentration, VSS and pH were measured during the test period. All the assays described above were performed in duplicate. A dynamic model delineating the effect of BAC on substrate (glucose) utilization and BAC degradation was developed using the results of the assays described above. The model simulations and parameter estimation procedures used are given in Text S4.
2.3.
A DISSOLVED OXYGEN (mg/L)
8
3.
Results and discussion
3.1.
BAC inhibition assessment via oxygen consumption
The impact of BAC on the oxygen uptake rate of the mixed heterotrophic culture was assessed at different BAC concentrations ranging from 5 to 70 mg/L (Fig. 1(A)). The dissolved oxygen present in the reference culture in which there was no BAC was depleted in less than 25 min. The SOUR of the reference culture was measured as 49 mg O2/g VSS h which indicates that the culture was at the exponential growth phase. Although, nitrifiers were present in the activated sludge, their population was low enough to assume that the major fraction of the oxygen is consumed by the heterotrophic population (Fig. S1). The SOUR of the cultures decreased exponentially as the BAC concentration increased. The SOUR approached to that of seed culture, which was amended with neither glucose nor BAC, at the highest BAC concentration tested which indicates that the culture was at the endogenous respiration phase and did not utilize the added glucose (Fig. 1(B)).
6
4 BAC Conc. (mg/L) 0 30 5 50 10 70 20
2
0 6
8 10 12 14 16 18 20 22
TIME (Min) SOUR (mg O2/g VSS.hr)
B
50 40 30 20 10 0 0
20
40
60
80
BAC CONC. (mg/L)
Adsorption kinetic and isotherm assays
An adsorption kinetic assay was carried out to determine the time required for the adsorption of BAC to reach equilibrium. Subsequently, an adsorption isotherm assay was conducted to determine the BAC adsorption capacity of the activated sludge. Both assays are described in detail in Text S5.
blank
Fig. 1 e The profile of dissolved oxygen consumption (A) and specific oxygen uptake rate of activated sludge used in this study at different BAC concentrations (0e70 mg/L).
The effective BAC concentration that reduces the SOUR to half of the reference culture SOUR (EC50) is calculated as 22 mg/ L Boethling (1984) reported a range between 20 and 50 mg/L as the EC50 value of BAC for acclimated and unacclimated activated sludge in his review on cationic surfactants. Moreover, the EC50 of BAC for Pseudomonas putida was reported as 6 mg/L (Sutterlin et al., 2008). The results of the respirometric assay performed in this study revealed that BAC affects oxygen uptake rate therefore the primary mode of action of BAC in activated sludge is the inhibition of the respiratory enzymes. Inhibition of other terminal electron accepting processes (TEAPs) such as denitrification at the same concentration range was recently reported (Tezel and Pavlostathis, 2009).
3.2.
Modeling BAC inhibition and biodegradation
Glucose utilization as well as BAC biodegradation by activated sludge at 5, 10 and 20 mg/L BAC was investigated in another batch assay. The time at which half of the glucose was utilized (t1/2) in the reference culture which did not receive any BAC was calculated as 0.6 h (Fig. S2). The glucose utilization slowed
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 3 8 e1 2 4 6
down at the higher BAC concentrations (Fig. 2). The glucose consumption t1/2 values were 1.6, 2.3 and 10.2 h at 5, 10 and 20 mg BAC/L. On the other hand, the BAC concentration was almost constant and equal to the initial concentration during the utilization of glucose (Fig. 2). Following the utilization of the major fraction of the glucose present in the cultures, BAC degradation was initiated. Given that the major fraction of the microbial community used in this study was composed of heterotrophs, and nitrifiers cannot degrade BAC (Yang, 2007), BAC was consumed by the heterotrophic microbial community in the activated sludge which also consumed glucose. The
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TIME (hr) Fig. 2 e Observed and simulated glucose and BAC utilization profiles in the cultures amended with (A) 5, (B) 10, and (C) 20 mg BAC/L at 500 mg VSS/L (Error bars represent one standard deviation of the means). In glucose utilization simulations; k [ 0.41 g COD/g VSS h, Ks [ 22 mg COD/L, Y [ 0.6 g COD/g COD and b [ 0.0025 hL1 was used and KI was estimated. In BAC utilization simulations, k, Ks, Y and b were kept constant and (A) KI [ 0.41 mg/L and kI1, kI2, KSI were estimated, (B) KI [ 0.30 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/L and, kI2 was estimated, (C) KI [ 0.12 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/L, and kI2 was estimated.
1241
calculated half-life of BAC in the cultures at 5, 10 and 20 mg/L BAC was 20.7, 21.5 and 36.9 h. The t1/2 value for BAC degradation are strongly correlated (r2 ¼ 0.999) to the t1/2 value for glucose utilization (Fig. S3). This result supports two conclusions: (1) the initial delay in the BAC degradation observed in all cultures was not related to acclimation; given that the inoculum used in this study was obtained from a wastewater treatment facility serving a very complex industrial area, the probability of the microbial community to have been exposed to QACs is high; and (2) the delay in the glucose degradation caused by BAC inhibition was the major reason for the retardation in the BAC degradation. An adsorption kinetic assay was performed in order to evaluate the dynamics of BAC partitioning in the activated sludge. The liquid phase BAC concentration reached equilibrium in half an hour which indicates that BAC sorption to the activated sludge at the concentration used was instantaneous (Fig. S4). The rapid attainment of equilibrium is consistent with previously published reports on the adsorption of quaternary ammonium compounds on a variety of municipal sludge (Ismail et al., 2010). Adsorption of BAC on activated sludge was investigated at BAC concentrations up to 70 mg/L. The Freundlich isotherm was used to model the equilibrium (Fig. S5). The estimated values for KF, capacity factor/sorption affinity and n, Freundlich exponent were 42.1 1.4 (mg/g VSS)(L/mg)n and 0.25 0.01, respectively (r2 ¼ 0.995). Similar constants for adsorption of BAC on activated sludge were reported in other studies (Ismail et al., 2010; Garcia et al., 2006). The Freundlich isotherm model represented well the BAC adsorption on activated sludge. The results of BAC adsorption kinetic and isotherm assays suggest that BAC is rapidly and extensively adsorb on activated sludge. Based on the adsorption isotherm, the calculated equilibrium liquid phase BAC concentration in the cultures at 5, 10, and 20 mg BAC/L was 0.003, 0.02 and 0.32 mg/L, respectively. Given that above 99% of BAC was adsorbed on the activated sludge and the desorption of adsorbed BAC was almost negligible under the test conditions (Ismail et al., 2010), the biodegradation of BAC proceeds both in the liquid and the solid phases. By using the facts demonstrated above which are: (1) BAC inhibits respiratory enzymes; (2) BAC degradation starts after the utilization of the major fraction of readily degradable COD; (3) BAC instantaneously and extensively partitions to the activated sludge; and (4) BAC gets degraded in both the liquid and solid phases, and assuming that all microbial cells in the activated sludge community are capable of BAC degradation, a model composed of four ordinary differential Eq. (1)e(4) and one algebraic Eq. (5) was developed. The Monod equation (Rittmann and McCarty, 2001) is the foundation of the model and used for modeling COD substrate utilization Eq. (1), and BAC utilization Eqs. (3) and (4) as well as the microbial growth Eq. (3). A novel approach was developed to model the biodegradation of BAC in the activated sludge and a schematic of the concept is given in Fig. 3. Based on this approach, BAC is instantaneously adsorbed on the biomass and gets degraded at different rates in the liquid Eq. (3) and the solid phases Eq. (4). We assumed that the same enzyme or group of enzymes
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 3 8 e1 2 4 6
BAC
product kI1
adsorption
aqueous phase
kI2
BAC
biomass
enzyme
Fig. 3 e Conceptual model of BAC degradation in activated sludge.
catalyzes the BAC degradation in the liquid and the solid phase, however other processes such as diffusion in the membrane, also may affect the degradation of biomass-sorbed BAC. Therefore, kI2 Eq. (4) is expressed as the observed biodegradation rate and may be composed of true maximum specific utilization rate plus membrane migration (diffusion within the membrane) rate. Biodegradation of dodecyl trimethyl ammonium chloride at different rates in the liquid and solid phases of a sediment slurry was previously demonstrated in another study (Shimp and Young, 1988), which also indicates that such a phenomenon occurs not only in biological reactors but also other natural environments in which QACs sorb. Therefore, combining phase distribution and biodegradation kinetics in such a way demonstrated above would contribute to a better understanding of the dynamics of pollutants in both engineered and natural systems. The Freundlich isotherm equation is incorporated in the model to calculate total BAC concentration as it was degraded in both the liquid and solid phases. An inhibition factor, (1 þ I/ KI), was included in both Eqs. (1) and (2) to reflect competitive inhibition as the inhibition mechanism for BAC which was justified by fact (1) given above. A switching factor, KS/(KSþS), was included in Eqs. (3) and (4) to reflect the substrate competition which was justified by fact (2) listed above. The biomass growth on BAC was assumed to be very small compared to the growth on glucose therefore this term (YdI/ dt) was neglected in Eq. (2). dS kSX ¼ dt Ks 1 þ I þ S
(1)
dX kSX bX ¼Y dt Ks 1 þ I þ S
(2)
dCe kI1 Ce X Ks ¼ dt KsI þ Ce Ks þ S
(3)
dqe kI2 qe X Ks ¼ dt KsI þ qe Xinit Ks þ S
(4)
I ¼ qe Xinit þ Ce
(5)
KI
KI
In the above equations, S is the glucose concentration (mg COD/L), X is the active biomass concentration (mg VSS/L), Ce is the liquid phase BAC concentration (mg/L), qe is the solid phase BAC concentration (mg BAC/g VSS), I is the total BAC concentration (mg BAC/L). The parameters used in the model equations include: k, maximum specific glucose utilization
rate constant (mg COD/mg VSS h); KS, glucose half-saturation coefficient (mg COD/L); Y, true yield coefficient (g VSS/g COD); b, biomass decay rate constant (h1); KI, “observed” inhibition coefficient (mg BAC/L); kI1, maximum specific liquid phase BAC utilization rate constant (mg BAC/mg VSS h); kI2, “observed” solid phase BAC utilization rate constant (mg BAC/ mg VSS h); KSI, BAC half-saturation coefficient (mg BAC/L); and Xinit, initial biomass concentration (g VSS/L). Before simulating the effect of BAC and its biodegradation in the activated sludge, the key parameters, i.e. k, KS, Y and b, of Monod growth equations were estimated. The estimation of each parameter was done by using the glucose consumption profile in the reference culture (Fig. S2). The range for each parameter value was limited by typical parameter values reported for activated sludge (Tchobanoglous et al., 2003). The RMSD of the fit was 24.7 (9.7% of the initial conc.) and the estimated values for each parameter are given in Table 1. The estimated parameter values were kept as constants for the rest of the simulations. The identifiability of each parameter estimated was determined using local sensitivity functions obtained by Sensitivity Toolbox of Berkeley-Madonna (Gujer, 2008) (Fig. S6). According to the sensitivity analysis, sensitivity is largest for k which indicated that a minor change in the k would have the largest effect on the model output S. From the visual inspection of the sensitivity figure (Fig. S6), it was obvious that the sensitivity of Y and b had exactly the same form, while k and Ks were different. Given that Y and b were the parameters describing mainly the biomass growth, the change of one of Y or b can be compensated by an appropriate adjustment of the other. Thus, these two parameters could not be identified uniquely from the data used. On the other hand, the values estimated for k and Ks were absolute. The identifiability of Y and b was less of concern in this study because the sensitivity of S to these parameters dominated after the major fraction of the substrate was utilized. Moreover, curve fitting performed with 15 randomly selected initial estimate values for each of four parameters within the constraints specified in Table 1 resulted in the estimation of the same value for Y and b. Overall, Y and b could be identifiable within the constraints used in the curve fitting. The glucose and BAC utilization at different BAC concentrations were simulated and KI, kI1 and kI2 were estimated using the glucose and BAC utilization profiles in the cultures amended with 5, 10 and 20 mg BAC/L (Fig. 2). The estimation was performed in two steps.
Table 1 e Estimated model parameters and previously reported range of typical parameter values used for parameter estimation in this study. Parameter k, g COD/g VSS h Ks, mg COD/L Y, g VSS/g COD b, h1 c2
Estimated value
Typical value rangea
0.41 0.06 22 19.8 0.6 0.9 0.0025 0.321 3086
0.08e0.41 10e60 0.3e0.6 0.0025e0.0060
a (Tchobanoglous et al., 2003).
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2009; van Ginkel and Kolvenbach, 1991; Ying, 2006). On the other hand, Tezel (2009) reported more than an order of magnitude higher degradation rate for a BAC, tetradecyl benzyl dimethyl ammonium chloride (C14BDMA-Cl) in a BACenriched culture. The Monod-type specific C14BDMA-Cl utilization rate constant for the BAC enrichment culture, which utilizes BAC as the sole carbon and energy source for almost three years, was 0.03 mg C14BDMA-Cl/mg VSS h. The BACenriched culture is mainly composed of Pseudomonas spp. which is the primary species known to degrade various types QACs. Pseudomonas spp. accounts for 2e12% of the activated sludge microbial community (Dias and Bhat, 1964). The low BAC utilization rate constant estimated by assuming all of the
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In all simulations the previously estimated parameter values at each BAC concentration were kept constant. The initial estimation of the aforementioned parameters, i.e. KSI, kI1 and kI2, was done using the BAC utilization profiles of the culture amended with 5 mg BAC/L (Fig. 2(A)). The estimated KSI, kI1 and kI2 values were 0.6 mg/L, 0.0013 mg BAC/mg VSS h and 0.00028 mg BAC/mg VSS h, respectively (RMSD: 0.15 (2.9%)). The sensitivity analysis revealed that each parameter was identified uniquely from the data sets used (Fig. S7). The liquid phase BAC degradation was twenty times faster than the solid phase BAC degradation (Fig. S8). However, given the high adsorption affinity of BAC, the solid phase transformation is the limiting step in the BAC removal in activated sludge systems. The specific BAC utilization rate constants are consistent but lower than the first order liquid and solid phase dodecyl trimethyl ammonium chloride, a monoalkonium chloride (MAC), degradation rate constants obtained in experiments performed using sediments which were 0.0032 0.0008 h1 and 0.0009 0.0002 h1, respectively (Shimp and Young, 1988). The lower rate constants obtained in the present study are attributed to the type of QAC used which was confirmed by other studies indicating that BACs are less biodegradable than the MACs (Tezel and Pavlostathis,
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10
BAC CONC. (mg/L)
Step 1 e estimation of inhibition constant, KI
Since the BAC concentration was almost constant throughout the glucose utilization period, KI was estimated using only Eqs. (2) and (3) in the first step (Fig. 2). During KI estimations, only glucose consumption profiles at different BAC concentrations were used (Fig. 2). Since only KI was estimated, its value was identified uniquely from the data sets used for curve fitting. Altogether three KI values were obtained for cultures tested at the three BAC concentrations. These KI values were 0.383 0.015, 0.292 0.014 and 0.120 0.006 mg/L at 5, 10 and 20 mg BAC/L, respectively. The corresponding RMSD (the values in parentheses represent the coefficient of variation with respect to the initial concentration) of the fits was 7.9 (2.6%), 10.7 (3.4%) and 17.1 (5.2%), respectively. The mean KI was calculated as 0.28 0.15 mg/L. The mean KI value for BAC is at least two orders of magnitude lower than what is observed for conventional activated sludge which is attributed to the lower biomass concentration (450e650 mg VSS/L) used in our experiments compared to conventional activated sludge process (ca. 2000 mg VSS/L). On the other hand, inhibitory concentrations ranging from 0.2 to 6 mg QAC/L were reported for dilute activated sludge systems (Boethling, 1984). However, the biomass concentrations used in these studies were unknown. In addition, Microtox was used to determine the acute inhibitory concentration of the BAC mixture used in our experiments. The 5-min and 15-min EC50 values were 0.22 mg/ L (r2 ¼ 0.95, and 95% confidence range ¼ 0.17e0.27 mg/L) and 0.14 mg/L (r2 ¼ 0.88, and 95% confidence range ¼ 0.10e0.20 mg/ L). These results suggest that the mean KI obtained in the batch inhibition assay is the “minimum inhibitory concentration” for the activated sludge tested. KI may increase as the biomass concentration increases. This phenomenon is discussed in a subsequent section.
GLUCOSE (mg COD/L)
3.2.1.
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TIME (hr) Fig. 4 e Observed and simulated glucose and BAC utilization profiles in the cultures amended with 5 mg BAC/L at (A) 615, (B) 394 and (C) 179 mg VSS/L (Error bars represent one standard deviation of the means). In glucose utilization simulations; k [ 0.41 g COD/g VSS h, Ks [ 22 mg COD/L, Y [ 0.6 g COD/g COD and b [ 0.0025 hL1 was used and KI was estimated. In BAC utilization simulations, k, Ks, Y and b were kept constant and (A) KI [ 2.04 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/L and kI2 was estimated, (B) KI [ 0.38 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/ L, and kI2 was estimated, (C) KI [ 0.14 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/L, and kI2 was estimated.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 3 8 e1 2 4 6
3.3. Effect of BAC-to-biomass ratio on inhibition and biodegradation The toxicity of BAC varies depending on the biomass concentration in the activated sludge most probably due to the extent of adsorption. Therefore, BAC-to-biomass ratio plays a crucial role in identifying inhibition and assessing BAC degradation since inhibition directly affects the BAC degradation by prolonging the half-life of the substrate COD utilization. The glucose and BAC utilization was tested at various biomass concentrations ranging from 179 to 1280 mg VSS/L and at 5, 10 and 20 mg BAC/L resulting in a BAC:VSS ratio ranging between 8 and 38 mg/g in a series of batch assays (Figs. 4 and 5, Fig. S9). The KI and kI2 values were estimated for each set of data following the two steps described above. The glucose and BAC utilization profiles presented in Figs. 4 and 5 were used for the estimation of these parameters. The estimated KI (mg/L) and kI2 (mg BAC/mg VSS h) values in the cultures having 615, 394 and 179 mg VSS/L and amended with 5 mg BAC/L were 2.04 and 0.0002, 0.38 and 0.0003, and 0.14 and 0.0003, respectively (Fig. 4). These parameter values in the cultures having 1280, 701 and 437 mg VSS/L and amended with 10 mg BAC/L were 3.61 and 0.0002, 0.56 and 0.0003, and 0.23 and 0.0004, respectively (Fig. 5). At both BAC concentrations, kI2 was constant having a mean value of 2.89 0.73 104 mg BAC/mg VSS-h. On the contrary, KI was high at the highest VSS concentration tested at both BAC concentrations and in a range of 0.14e0.56 mg/L at the rest of VSS concentrations tested. A comprehensive plot was created by using the estimated KI and kI2 as well as the BAC adsorption isotherm at different BAC-to-biomass (BAC:VSS) ratios tested in this study (Fig. 6). Multi-dimensional analysis of the data obtained in this study
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BAC CONC. (mg/L)
species present in the activated sludge are capable of BAC degradation may suggest that only about 4% (the ratio of BAC utilization rates of activated sludge and BAC enrichment community) can degrade BAC. The profiles of glucose and BAC utilization in the cultures at 10 and 20 mg BAC/L were simulated and only kI2 was estimated at each BAC concentration using the previously estimated parameter values as constants. The estimated kI2 values were 0.0004 (RMSD ¼ 0.24 (2.4%)) and 0.0013 (RMSD ¼ 0.63 (3.2%)) mg BAC/mg VSS h, respectively. Thus, the estimated kI2 values increased and approached the kI1 value as the BAC concentration increased from 5 to 20 mg/L. As it was discussed above, kI2 was a lumped parameter which may represent both biodegradation and membrane migration. The BAC concentration may affect the dominance of one or the other. For instance, the membrane migration process is the rate limiting step at low BAC concentrations at which BAC sorption is heterogeneous through the biomass surface, thus there is a concentration gradient on the biomass. On the contrary, the membrane migration process diminishes at high BAC concentrations at which the biomass surface is saturated, and the biodegradation rate in the solid phase, therefore, becomes equal to that in the liquid phase. The adsorption isotherm presented in this study supports that saturation of biomass was reached at 20 mg BAC/L (Fig. S5). The phenomenon presented here is discussed in detail below.
GLUCOSE (mg COD/L)
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0
C
400
20
300
15
200
10
100
5
0
0 0 10 20 30 40 50 60 70 80
TIME (hr) Fig. 5 e Observed and simulated glucose and BAC utilization profiles in the cultures amended with 10 mg BAC/L at (A) 1280, (B) 701 and (C) 437 mg VSS/L (Error bars represent one standard deviation of the means). In glucose utilization simulations; k [ 0.41 g COD/g VSS h, Ks [ 22 mg COD/L, Y [ 0.6 g COD/g COD and b [ 0.0025 hL1 was used and KI was estimated. In BAC utilization simulations, k, Ks, Y and b were kept constant and (A) KI [ 3.61 mg/L and kI1, kI2, KSI were estimated, (B) KI [ 0.56 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/L and, kI2 was estimated, (C) KI [ 0.23 mg/L, kI1 [ 0.0013 mg BAC/mg VSS-h, KSI [ 0.6 mg/L, and kI2 was estimated.
suggests that BAC does not exert an inhibitory effect to activated sludge at and below 10 mg BAC/g VSS at which almost all of the BAC is adsorbed on the biomass. The inhibition increases as BAC:VSS increases, and KI approaches to a constant value which is defined as “minimum inhibitory concentration (MIC)”. The MIC was reached at the BAC:VSS around 28 mg BAC/g VSS at which the liquid phase BAC concentration (Ce) is equal to the MIC. This implies that BAC is inhibitory only if it is in the liquid phase. The kI2 was constant around 3.08 0.83 104 mg BAC/mg VSS-h at between 7 and 28 mg BAC/g VSS. A sudden increase in the kI2 was obtained at
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 3 8 e1 2 4 6
0.35
4
Ceq
0.0012 3
0.0006 0.0004
KI (mg BAC/L)
-1
kI2 (hr )
0.0010 0.0008
KI
0.30 0.25 0.20
2
kI2
0.15
0.05 0 5
10
15
20
25
30
35
0.00 40
BAC:VSS (mg BAC/g VSS)
Fig. 6 e Profile of liquid phase BAC concentration (Ceq: solid line e), estimated inhibition coefficient (KI: hollow circle B) and solid phase BAC utilization rate constant (kI2: upward triangle 6) at different BAC-to-biomass ratios (BAC:VSS).
32 mg BAC/g VSS at which the available biomass surface reached saturation by BAC according to the adsorption isotherm obtained in this study. The kI2 approached the kI1 value at that particular BAC:VSS at which the rate limiting step, i.e. membrane migration, diminished and biodegradation dominated. The kI2 stayed constant and equal to kI1 above 32 mg BAC/g VSS (Fig. 6).
4.
in higher than typical BAC removal efficiencies. The model, which integrates inhibition, adsorption and biodegradation processes to simulate the dynamics of BAC in the activated sludge, developed in this study may effectively be used to simulate the dynamics of other compounds (e.g., triclosan, triclocarban, linear alkyl and alkyl benzene sulfonates, perfluoroalkyl carboxylates and sulfonates etc) with properties and behavior similar to QACs.
0.10
1
0.0002 0.0000
Ceq (mg/L)
0.0014
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Conclusions
In this study, the dynamics of BAC in an activated sludge system were investigated. Respiratory inhibition, adsorption and biodegradation were identified as the three major processes which affect the fate of BAC in activated sludge. A comprehensive model was developed by integrating these processes into the Monod equation. The model agreed well with the data obtained from a series of batch assays performed. In conclusion, BAC inhibits oxygen uptake and use, thereby causing prolonged COD substrate utilization. BAC degradation initiates after the major portion of the readily degradable COD is utilized. Therefore, a delay in the readily degradable COD utilization causes retardation in the BAC degradation, as well. A major fraction of BAC instantly adsorbs on the biomass. Biodegradation of BAC proceeds both in the liquid and solid phases, however the solid phase BAC degradation is about twenty times slower than in the liquid phase. Given the low BAC concentrations found in municipal wastewaters, BAC is unlikely to be toxic in wastewater treatment. However, since the biodegradation rate of BAC is very slow, a major fraction of BAC is likely to be transferred and accumulated in the environment, especially in anaerobic compartments. In order to mitigate this problem, we suggest the implementation of activated sludge systems with long solid retention times such as extended aeration or employment of attached growth systems. These systems would favor the prolonged retardation of BAC by facilitating the adsorption on the biomass and increase in the degradation rate, resulting
Acknowledgements This work was financially supported by the National Water Pollution Control and Treatment Technological project (No. 2008ZX07314-002) and the National Natural Science Foundation of China (No. 50908117).
Appendix. Supplementary data Supplementary data associated with this article can be found in the on-line version, at doi:10.1016/j.watres.2010.09.037.
references
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Determination of selected quaternary ammonium compounds by liquid chromatography with mass spectrometry. Part I. Application to surface, waste and indirect discharge water samples in Austria. Environ. Pollut. 145 (2), 489e496. Nishihara, T., Okamoto, T., Nishiyama, N., 2000. Biodegradation of didecyl dimethyl ammonium chloride by Pseudomonas fluorescens TN4 isolated from activated sludge. J. Appl. Microbiol. 88, 641e647. Nishiyama, N., Nishihara, T., 2002. Biodegradation of dodecyl trimethyl ammonium bromide by Pseudomonas fluorescens F7 and F2 isolated from activated sludge. Microbes Environ. 17, 164e169. Patrauchan, M.A., Oriel, P.J., 2003. Degradation of benzyl dimethyl alkyl ammonium chloride by Aeromonas hydrophila sp K. J. Appl. Microbiol. 94, 266e272. Qin, Y., Zhang, G.Y., Kang, B.A., Zhao, Y.M., 2005. Primary aerobic biodegradation of cationic and amphoteric surfactants. J. Surfactants Detergents 8, 55e58. Reynolds, L., Blok, J., Demorsier, A., Gerike, P., Wellens, H., Bontinck, W.J., 1987. Evaluation of the toxicity of substances to be assessed for biodegradability. Chemosphere 16, 2259e2277. Rittmann, B.E., McCarty, P.L., 2001. Environmental Biotechnology: Principles and Applications. McGraw-Hill, Boston. Shimp, R.J., Young, R.L., 1988. Availability of organic-chemicals for biodegradation in settled bottom sediments. Ecotoxicol. Environ. Saf. 15 (1), 31e45. Sutterlin, H., Alexy, R., Kummerer, K., 2008. The toxicity of the quaternary ammonium compound benzalkonium chloride alone and in mixtures with other anionic compounds to bacteria in test systems with Vibrio fischeri
and Pseudomonas putida. Ecotoxicol. Environ. Saf. 71 (2), 498e505. Takenaka, S., Tonoki, T., Taira, K., Murakami, S., Aoki, K., 2007. Adaptation of Pseudomonas sp strain 7-6 to quaternary ammonium compounds and their degradation via dual pathways. Appl. Environ. Microbiol. 73, 1797e1802. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering: Treatment, Disposal and Reuse, Fourth ed. McGraw-Hill Professional, New York, NY. Tezel, U., 2009. Fate and effect of quaternary ammonium compounds in biological systems. Ph. D. Thesis, Georgia Institute of Technology, Atlanta, GA. Tezel, U., Pavlostathis, S.G., 2009. Transformation of benzalkonium chloride under nitrate reducing conditions. Environ. Sci. Technol. 43 (5), 1342e1348. van Ginkel, C.G., Kolvenbach, M., 1991. Relations between the structure of quaternary alkyl ammonium salts and their biodegradability. Chemosphere 23 (3), 281e289. van Ginkel, C.G., Vandijk, J.B., Kroon, A.G.M., 1992. Metabolism of hexadecyl trimethyl ammonium chloride in Pseudomonas strain-B1. Appl. Environ. Microbiol. 58, 3083e3087. van Ginkel, C.G., 2004. Biodegradation of cationic surfactants. In: Zoller, U. (Ed.), Handbook of Detergents Part B: Environmental Impact, vol. 121. Marcel Dekker, Inc., New York, USA. Yang, J., 2007. Fate and effect of alkyl benzyl dimethyl ammonium chloride in mixed aerobic and nitrifying cultures. MS Thesis. Georgia Institute of Technology, Atlanta, GA. Ying, G.G., 2006. Fate, behavior and effects of surfactants and their degradation products in the environment. Environ. Int. 32 (3), 417e431.
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Molecular subtypes of Campylobacter spp., Salmonella enterica, and Escherichia coli O157:H7 isolated from faecal and surface water samples in the Oldman River watershed, Alberta, Canada C. Jokinen a, T.A. Edge b, S. Ho a, W. Koning c, C. Laing a, W. Mauro a, D. Medeiros d, J. Miller e, W. Robertson d,1, E. Taboada a, J.E. Thomas f, E. Topp g, K. Ziebell h, V.P.J. Gannon a,* a
Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Box 640, Township Road 9-1, Lethbridge, Alberta, Canada T1J 3Z4 Aquatic Ecosystem Protection Research Division, Water Science and Technology Directorate, National Water Research Institute (NWRI), Environment Canada, Box 5050, Burlington, Ontario, Canada L7R 4A6 c Alberta Environment, 2938 11 St. N.E., Calgary, Alberta, Canada T2E 7L7 d Water Air and Climate Change Bureau, Health Canada, 269 Laurier Ave W, Ottawa, Ontario, Canada K1A 0K9 e Agriculture and Agri-Food Canada, Box 3000, Lethbridge, Alberta, Canada T1J 4B1 f University of Lethbridge, Biological Sciences Department, 4401 University Drive West, Lethbridge, Alberta, Canada T1K 3M4 g Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario, Canada N5V 4T3 h Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, 110 Stone Rd. W, Guelph, Ontario, Canada N1G 3W4 b
article info
abstract
Article history:
Campylobacter spp., Salmonella enterica, and Escherichia coli O157:H7 isolated from 898 faecal,
Received 10 June 2010
43 sewage, and 342 surface water samples from the Oldman River were characterized using
Received in revised form
bacterial subtyping methods in order to investigate potential sources of contamination of
29 September 2010
the watershed. Among these pathogens, Campylobacter spp. were the most frequently
Accepted 1 October 2010
isolated from faecal, sewage, and surface water samples (266/895, 11/43, and 91/342, respectively), followed by Salmonella (67/898, 8/43, and 29/342, respectively), and E. coli O157:H7 (16/898, 2/43, and 8/342, respectively). Salmonella Rubislaw was the most common
Keywords:
serovar isolated from water. This serovar was also isolated from two wild bird species.
Comparative genomic fingerprinting
Most other serovars isolated from water were either not isolated from animals or were
flaA
isolated from multiple species. E. coli O157:H7 was predominantly isolated from cattle. The
PFGE
most common phage-types of this pathogen from cattle were also the most common
Phage-type
among water isolates, and there were exact pulsed field gel electrophoresis and compar-
Serovar
ative genomic fingerprint matches between cattle, sewage, and water isolates. Campylobacters were commonly isolated from surface waters and faeces from most animal species.
Abbreviations: BB, Bolton’s broth; BPW, buffered peptone water; CGF, comparative genomic fingerprinting; PFGE, pulsed field gel electrophoresis; PT, phage-type; RFLP, restriction fragment length polymorphism; UPGMA, unweighted pair group method with arithmetic mean; VTEC, verotoxin-producing E. coli. * Corresponding author. Tel.: þ1 403 382 5514; fax: þ1 403 381 1202. E-mail addresses: [email protected] (C. Jokinen), [email protected] (T.A. Edge), [email protected] (W. Koning), [email protected] (D. Medeiros), [email protected] (J. Miller), [email protected] (J.E. Thomas), [email protected]. ca (E. Topp), [email protected] (K. Ziebell), [email protected] (V.P.J. Gannon). 1 Retired from: Water Air and Climate Change Health Bureau, Health Canada, 269 Laurier Ave W, Ottawa, Ontario, Canada, K1A 0K9. 0043-1354/$ e see front matter ª 2010 Published by Elsevier Ltd. doi:10.1016/j.watres.2010.10.001
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Restriction fragment length polymorphism of the Campylobacter flaA gene identified several location and host species-specific (cattle, goose, pig) fingerprints. Molecular subtyping of these bacterial pathogens shows considerable promise as a tool for determining the sources of faecal pollution of water. ª 2010 Published by Elsevier Ltd.
1.
Introduction
Waterborne disease is a significant cause of morbidity and mortality worldwide (Hrudey and Hrudey, 2007; Yoder et al., 2008b). While much of this waterborne disease can be traced to contamination of water with human waste, in many regions animal waste in water can also represent a significant human health risk. Bacterial pathogens derived from animal waste such as Campylobacter jejuni, non-typhoid Salmonella enterica, and Escherichia coli O157:H7 are the most frequently associated with waterborne disease outbreaks in the United States (Yoder et al., 2008a and 2008b). Illness associated with these agents has resulted from inadequate treatment of contaminated drinking water supplies such as wells, contaminated surface waters used for recreation, and water used in the irrigation of fruits and vegetables (Yoder et al., 2008a). Many small communities within southern Alberta rely on the Oldman River for drinking water, recreation, and irrigation of field crops. This region has high levels of mixed animal agriculture and one of the highest incidences of gastroenteritis in Canada (Khakhria et al., 1996). Knowledge of the frequency of occurrence of the three most important zoonotic, bacterial pathogens (C. jejuni, S. enterica, and E. coli O157:H7) in surface water would be helpful in determining the human health risks associated with accidental consumption of this water. Differences in the isolation rates of these specific pathogens from animal waste would help to identify whether certain animal species may present a greater risk to human health than others. Finally, the discovery of common pathogenic subtypes in animal waste and water would provide evidence for specific animal sources of these pathogens. In this study the distribution of these three important zoonotic pathogens was determined, and the subtypes of these pathogens isolated in animal faecal sources, untreated human sewage, and flowing surface waters of the Oldman River watershed in southern Alberta, Canada were compared.
2.
Methods
2.1.
Study area
The study area is located in a semi-arid region within the Oldman River drainage basin of southern Alberta, and occupies approximately 26,000 km2. It is the region’s principle source of water used for agriculture, maintenance of livestock operations, recreation, and residential purposes in both rural and urban centres. Flowing surface waters in the basin are influenced by mountain snowmelt, rainfall runoff, and tile drainage (Koning et al., 2006).
2.2.
Water and faecal sample collection
From July 2005 to November 2007, a total of 342 surface water samples were collected from nine different sites within the Oldman River basin and analysed for the presence of three pathogens, Campylobacter spp., E. coli O157:H7, and Salmonella spp. No water samples were collected from December to March in any year. Using gloves and hip waders, water samples were collected approximately 1e2 m from shore by submersing a sterile, polyethylene glycol bottle through the water column at a depth of 20e30 cm. From May 2004 to November 2007, a total of 43 untreated human sewage samples were collected from the Fort Macleod sewage treatment plant and 898 faecal samples (buffalo, n ¼ 7; cattle, n ¼ 215; goat, n ¼ 33; sheep, n ¼ 82; deer, n ¼ 113; chicken, n ¼ 77; duck, n ¼ 38; goose, n ¼ 81; pelican, n ¼ 19; other birds, n ¼ 25; horse, n ¼ 80; pig, n ¼ 57; cat, n ¼ 14; dog, n ¼ 36; human, n ¼ 16; small mammals, n ¼ 5) were collected from domestic and wild animal species within the watershed and analysed for the same pathogens as were the water samples. Faecal samples were collected from the ground. An attempt was made to collect fresh samples; however, it was difficult to ensure freshness of wildlife faecal samples. Following collection, all samples were placed on ice. Water samples were processed within 24 h of collection and faecal samples were processed within 6 h. Animal faecal samples that were collected from the same municipal district or county were considered to be from the same geographic location in the analysis.
2.3. Processing of faecal and water samples for pathogen detection and confirmation Approximately 5 g of faecal matter and 5 mL of phosphate buffered saline (PBS; 137 mM NaCl, 8.1 mM Na2HPO4, 1.5 mM KH2PO4, 2.7 mM KCl, pH 7.4) were mixed together to form a uniform slurry. One-millilitre aliquots of the PBS-faecal samples were added to 20 mL of buffered peptone water (BPW; Oxoid Ltd., Basingstoke, Hampshire, England) for the preenrichment of E. coli O157:H7 (Chapman et al., 1994) and Salmonella spp. (D’Aoust and Purvis, 1998), and to 20 mL of Bolton’s broth supplemented with 20 mg L1 cefoperazone, 20 mg L1 vancomycin, 20 mg L1 trimethoprim, 50 mg L1 cycloheximide, and 5% lysed horse blood (BB; Oxoid Ltd., Basingstoke, Hampshire, England) for the enrichment of Campylobacter spp. (Diergaardt et al., 2004). Water sample filtration, as well as the enrichment, isolation, and confirmation of Campylobacter spp., Salmonella spp., and E. coli O157:H7 were carried out for water and faecal samples according to Jokinen et al. (2010).
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2.4.
Subtyping of faecal and waterborne pathogens
Serotyping of Salmonella isolates was performed by the Public Health Agency of Canada, Laboratory for Foodborne Zoonoses, at the Office International des E´pizooties Salmonella Reference Laboratory in Guelph, Ontario. Serotyping and verotoxin detection of E. coli O157:H7 isolates was carried out at the Verotoxin-Producing E. coli (VTEC) Reference Laboratory in Guelph, Ontario, at the Public Health Agency of Canada, Laboratory for Foodborne Zoonoses. Phage-typing of E. coli O157:H7 isolates was carried out as described by Ahmed et al. (1987) and extended by Khakhria et al. (1990). Two different advanced molecular methods, PFGE (pulsed field gel electrophoresis) and CGF (comparative genomic fingerprinting), were also used to type the E. coli O157:H7 isolates. PFGE of these isolates was carried out according to the Centers for Disease Control and Prevention manual standard PFGE protocol (www. cdc.gov/pulsenet). Genomic DNA was digested with XbaI (Invitrogen, Burlington, ON) and analysed in 1% Seakem Gold agarose gels (Lonza, Rockland, ME USA) in 0.5 TBE buffer at 14 C using the CHEF DRIII system (Bio-Rad, Mississauga, ON) with the following parameters; switch time of 2.2 and 54.2 s, angle of 120 , voltage 200 V and a temperature of 14 C. The XbaI-digested DNA from S. enterica Braenderup H9812 was used as a molecular size marker. Each unique banding pattern (fingerprint) is based on at least a single band difference between strains. CGF of the E. coli O157:H7 isolates was performed according to the methods of Laing et al. (2008). Briefly, the E. coli O157:H7 CGF tests for the presence or absence of 23 loci, which were previously found to be highly variable in the E. coli O157 genome. The binary (0 and 1) results for the 23 loci serve as the fingerprint for each strain and the relatedness among strains can be determined by assessing their similarity of loci presence/absence. CGF has been used to identify epidemiologically related strains from endemic strains and to identify genomic differences within epidemiologically related strains. A randomly chosen subset of the Campylobacter isolates obtained from water (62 isolates) and faecal samples (253 isolates) were analysed using restriction fragment length polymorphism (RFLP) typing of the flaA gene according to the methods of Nachamkin et al. (1993) and Harrington et al. (2003). For all pathogens, isolates obtained from the same sample with identical serovars and/or fingerprints were removed from the analyses.
of the high resolution of CGF (Laing et al., 2008), clusters were assigned at the 95% similarity level, allowing for a one locus difference (i.e. 1/23, 4.3%) between isolates in the same cluster. Any isolates with two or more differences were assigned to different clusters. A host species-specific cluster was defined as a group containing one or more isolates from the same host source with 95e100% similarity in their fingerprints. A host group-specific cluster was defined as a group containing one or more isolates from biologically similar groups of animals such as ruminants or birds.
3.
Results
Of the three pathogens analysed in this study, Campylobacter was most frequently detected in animal faecal samples (266/895, 29.7%), untreated human sewage (11/43, 25.6%), and surface water (91/342, 26.6%), followed by Salmonella spp. (67/898, 7.5%; 8/43, 18.6%; and 29/342, 8.5%, respectively) and E. coli O157:H7 (16/898, 1.8%; 2/43, 4.7%; and 8/342, 2.3%, respectively; Fig. 1).
3.1.
Campylobacter spp. distribution
A total of 266 faecal samples from 16 different animal species, 11 untreated human sewage samples, and 91 water samples were positive for Campylobacter spp. (Figs. 1 and 2). The majority of these isolates were identified as C. jejuni (279/368, 75.8%), while C. coli and other Campylobacter spp. accounted for 17.9% (66/368) and 9.2% (34/368), respectively (Fig. 3). More than one species of Campylobacter was isolated from only a small proportion of the water (4/91, 4.4%) and faecal (7/266, 2.6%) samples (data not shown). C. jejuni was isolated at least once from each of the 18 different sources. In contrast, C. coli was isolated at least once from seven different animal species, human sewage, and water, and Campylobacter spp. other than C. coli or C. jejuni were isolated at least once from six different animal species, human sewage, and water. C. jejuni was isolated at the greatest frequency from all sources with the exception of deer and pig faeces. C. coli and ‘other’ Campylobacter spp. were isolated at the greatest frequency from pig and deer faeces, respectively.
32
Analysis of fingerprint data
Cluster analyses of the Campylobacter spp. flaA-RFLP profiles and the E. coli O157:H7 PFGE banding patterns were performed with Bionumerics (Version 5.1, Applied Maths, BVBA, Austin, TX) using the UPGMA clustering algorithm and the dice similarity coefficient with an optimization of 1.5% and a tolerance of 1.5%. A cluster was defined as a group of C. jejuni isolates with identical flaA-RFLP fingerprints (banding patterns), or a group of E. coli O157:H7 isolates with identical PFGE fingerprints (banding patterns). Any isolates with at least a single band difference between strains were assigned to different clusters. Hierarchical clustering of the CGF data was performed with the statistical package “R” using the hclust method with Euclidean distance and average linkage. Because
28 Isolation R ate (%)
2.5.
24 20 16 12 8 4 0 Campylobacter spp.
Salmonella spp.
E. coli O157:H7
Fig. 1 e Percentage (%) of animal faecal (n [ 898, white), sewage (n [ 43, grey), and surface water (n [ 342, black) samples positive for Campylobacter spp., Salmonella spp., and E. coli O157:H7. Samples were collected from the Oldman River watershed. 895 animal faecal samples were analysed for Campylobacter spp.
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Other birds Dog Pelican Cat Human
Type of Animal Faeces
Horse Deer Goat Goose Gull Sheep Duck Pig Cattle Chicken Turkey Buffalo 0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 Isolation Rate (%)
Fig. 2 e Percentage (%) of animal faecal samples positive for Campylobacter spp. (white), Salmonella spp. (grey), and E. coli O157:H7 (black). Only those faecal sources from which pathogens were isolated are included. (Other birds, n [ 19; Dog, n [ 36; Pelican, n [ 19; Cat, n [ 14; Human, n [ 16; Horse, n [ 80; Deer, n [ 112; Goat n [ 33; Goose, n [ 80; Gull, n [ 3; Sheep, n [ 74; Duck, n [ 38; Pig, n [ 57; Cattle, n [ 199; Chicken, n [ 75; Turkey, n [ 3; Buffalo, n [ 7).
3.2.
C. jejuni flaA-RFLP typing
Two hundred and fifty three C. jejuni isolates, each obtained from different animal faecal samples, were further characterized via RFLP of the flaA gene. Approximately 40% of these faecal isolates (102/253) grouped into various host speciesspecific or host group-specific (e.g. birds) clusters (Table 1). The remaining isolates were “singletons” that did not form clusters (75/253), or they were grouped into mixed-species clusters (76/253). With respect to the host species-specific clusters, some RFLP fingerprints appeared to be unique to specific geographic areas, whereas other fingerprints were observed in more than one geographic area. Where geographic location is known (11/ 22, 50.0%), five clusters (45.5%) included animals of the same species sampled from different locations, while 6 clusters (54.5%) included different animals of the same species sampled from the same geographic location. The same fingerprints were also detected over long periods of time. Eleven of the 22 host species-specific clusters (50.0%) contained isolates obtained from animals sampled on different days, separated by months and up to three years. The remaining 11 (50.0%) clusters contained isolates obtained from animals of the same species sampled on the same day. Of the C. jejuni isolates obtained from water samples, 62 isolates were further characterized via RFLP of the flaA gene. Evidence of overlap between water and faecal clusters was
observed (Fig. 4). Thirty-nine percent of the water isolates (24/ 62) grouped with at least one faecal isolate, resulting in eight faecal-water clusters. Three of these clusters contained water and mixed-species faecal isolates (data not shown). The remaining water isolates did not cluster with any faecal isolates but instead formed water-only clusters (22/62, 6 clusters) or were singletons (16/62). Five clusters contained both water and either cattle-specific, goose-, duck and goose-, or pig-specific faecal isolates, accounting for 16/62 (25.8%) water isolates and 12/253 (4.7%) faecal isolates. Some of the water-only clusters contained isolates with identical fingerprints from the same site detected over 3 years (data not shown). There were also water-only clusters that contained isolates with identical fingerprints from different sites over three months, and also between different sites on the same sampling date.
3.3. Distribution and molecular typing of S. enterica subsp. enterica High serovar diversity was observed among the Salmonella spp. isolates from faecal, sewage, and water sources (Table 2). Of the faecal samples from which at least one of the three pathogens was isolated, Salmonella spp. were not isolated from dog, cat, human, deer, goat, or buffalo faeces (Fig. 2). Among the samples positive for Salmonella spp., a total of 36 different serovars were observed (Table 2). Twenty-six different Salmonella serovars were isolated from animal faeces, five
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Water Sewage Pig Deer Turkey Goat Duck
Source
Goose Cattle Dog Pelican Cat Human Horse Gull Sheep Chicken Buffalo 0
10
20
30
40
50
60
70
80
90
100
Isolation Rate (%) Fig. 3 e Distribution (%) of Campylobacter jejuni (white), C. coli (grey), and other Campylobacter spp. (black) isolated from animal faecal, sewage, and surface water samples collected from the Oldman River watershed. Samples containing more than one species of Campylobacter are indicated by a white bar. Only those sources from which Campylobacter spp. were isolated are included. (Water, n [ 91; Sewage, n [ 11; Pig, n [ 25; Deer, n [ 22; Turkey, n [ 2; Goat, n [ 7; Duck, n [ 16; Goose, n [ 21; Cattle, n [ 88; Dog, n [ 1; Pelican, n [ 1; Cat, n [ 1; Human, n [ 2; Horse, n [ 10; Gull, n [ 1; Sheep, n [ 29; Chicken, n [ 34; Buffalo, n [ 6).
different serovars were isolated from untreated human sewage, and 11 different serovars were isolated from surface water samples. More than one serovar was isolated from each Salmonella-positive source with the exception of horse faeces. Very little overlap between sources of specific Salmonella spp. serovars was observed, since more than 80% (29/36) of the serovars detected were isolated from single sources (Table 2). However, five serovars were isolated from multiple faecal sources (Serovars Give variant 15þ, Hadar, Heidelberg, Indiana, and Typhimurium variant Copenhagen) and four serovars were isolated from at least one animal species or untreated human sewage sample, as well as from water (Serovars Give variant 15þ, Heidelberg, Rubislaw, and I:11:r:-). Serovar Rubislaw was isolated at the greatest frequency from water (21/29, 72.4%) and was also isolated from sparrow and swallow faeces. In addition to water, serovar Give var. 15þ was isolated from bird (goose, duck, gull, and sparrow) and ruminant (sheep and cattle) faeces, serovar Heidelberg was isolated from untreated human sewage and chicken, pig, and horse faeces, and I:11:r:- was isolated from cattle faeces.
3.4. Distribution of phage-types (PTs) among E. coli O157:H7 isolates Cattle were the primary source of E. coli O157:H7 isolates examined in this study (Fig. 2). Five different PTs were identified among the E. coli O157:H7 isolates (Table 3). The two PTs identified at the greatest frequency (PT 14a and PT 8) were isolated from both water and faecal sources. PT 8 was isolated from water and cattle faeces only, while PT 14a was isolated from each of the E. coli O157:H7 sources, with the exception of goose faeces. Although PT 14a was isolated from duck faeces and sewage, this PT was identified in more than 78% of the cattle faecal isolates. The remaining three PTs (PT 21, PT 33, and PT 34) were each isolated from only one source (goose, water, and cattle faeces, respectively).
3.5.
E. coli O157:H7 CGF and PFGE typing
E. coli O157:H7 isolates from water (n ¼ 8), faecal (n ¼ 16), and sewage (n ¼ 2) were further characterized by CGF and PFGE
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Table 1 e Analysis of Campylobacter jejuni sources among clusters obtained by flaA-RFLP. Isolates were obtained from animal faeces collected from the Oldman River watershed. The number of samples refers to discrete faecal samples obtained from separate sampling events. Only results for clusters containing multiple faecal isolates are shown. Cluster #
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Sample type
No. samples
Isolated from same geographic area?
Isolated on same date?
Buffalo Cattle Cattle Cattle Cattle Cattle/Deer Sheep Sheep Cattle/Sheep Cattle/Sheep Chicken Chicken Chicken Chicken Chicken Goose Goose Duck/Goose Duck/Goose Pig Pig Pig
2 4 3 3 7 1/1 7 2 10/5 3/2 5 9 4 2 2 3 4 4/4 2/1 8 2 2
na N na Y N na Y Y Y na na Y na na na N na N na N na Y
Y Nb Y Y Na Na Na Na Nb Nc Y Y Y Y Y Na Y Na Nb Nb Y Y
Y, yes; N, no; na, municipal district/county information not available; In order to be assigned a “Y”, all isolates within the same cluster must have been isolated from the same geographic area or isolated on the same date. Samples were collected over: a Different months of the same year. b Two different years. c Three different years.
fingerprinting. Both CGF 95% and PFGE yielded five clusters containing more than one E. coli O157:H7 isolate. One of the five clusters contained only water isolates (data not shown), and four of the clusters contained faecal isolates (Table 4). The remaining isolates did not group into any of these five clusters and had unique fingerprints (singletons). The multi-isolate clusters were generally comprised of the same isolates regardless of the typing method used; i.e. there was 85% concordance between the PFGE and CGF data (Table 4). Clusters 2 and 3 contained the same cattle isolates regardless of the method used. Isolates from two different PFGE clusters (clusters 1 and 4) grouped into only one CGF 95% cluster (cluster 1). The main difference between the two methods was that the members of a CGF 95% cluster (cluster 4), containing 3 cattle isolates and one duck isolate, were all singletons according to PFGE. When overlap between faecal and water isolates was examined (Fig. 5), both methods identified a cluster that contained water, cattle, and sewage isolates (cluster 1), and also a cluster that contained both cattle and water isolates (cluster 2). All isolates with the same fingerprint
were also of the same PT with the exception that one CGF cluster contained isolates from PT 8 and 14a (Table 4). Both methods identified the same fingerprint in two different cattle from the same geographic area (cluster 3), which was not found in animals from any other locations (Table 4). Another unique CGF fingerprint was observed in two animals of different species that were obtained from the same geographic area (cluster 4). With respect to sampling dates, identical fingerprints were obtained from more than one sample collected on the same day and also during different months of the year (Table 4).
4.
Discussion
The primary objectives of this work were to examine the distribution of Campylobacter spp., S. enterica, and E. coli O157:H7 in human, wildlife, and livestock faecal sources, to compare subtypes of these pathogens to those isolated from surface water samples, and finally, to determine if animal host species-specific subtypes could be detected in these surface waters. Pathogen subtyping is used by public health laboratories around the world to determine the relatedness of pathogens in disease outbreak investigations (Swaminathan et al., 2001); therefore, we also wanted to assess the usefulness of molecular typing in determining the relatedness of pathogens identified in faecal sources. Salmonella spp. serotyping has been shown to be an invaluable tool in epidemiological surveillance. It can provide information about serovar distribution in animals and humans, and how this distribution changes over time or during different seasons (van Duijkeren et al., 2002). This information in turn may be useful in the development of risk assessment models. Salmonella spp. serotyping has also demonstrated potential in pinpointing specific locations of contaminating sources in the pre-harvest turkey production environment (Nayak and Stewart-King, 2008). In the current study, the overlap between Salmonella serovars isolated from water and faecal samples identified several possible sources of contamination, including chickens, geese, ducks, sparrows, swallows, pigs, sheep, cattle, horses, and human sewage. Salmonella serovar Rubislaw was the most prevalent serovar isolated from water samples. It was also isolated once from a swallow and once from a sparrow; however, since only four birds were sampled, it is difficult to determine the significance of these isolations with respect to the high frequency of isolation of this serovar from water. According to five years of province-wide laboratory surveillance data from Canada (for an example, see Demczuk and Pankhurst, 2007), serovar Rubislaw has rarely been isolated from non-human sources in any province other than Alberta. Throughout 2000e2001, serovar Rubislaw was the most commonly isolated (52.4%) Salmonella serovar from irrigation canals and reservoirs located within the Oldman River watershed in Alberta (Gannon et al., 2004), suggesting that a significant source of serovar Rubislaw is contributing to the contamination of water specifically in the Oldman River watershed. However, the source of this serovar is not yet clear, nor do we know if there is a geographically isolated source of contamination, or if serovar Rubislaw
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n = 3, 33%
n = 6, 67%
n = 4, 67%
Cluster 4 Sources:
water
n =1, 25%
n = 2, 33%
n = 3, 75%
Cluster 17 Sources:
cattle
water
Cluster 19 Sources:
goose
water
n = 1, 50%
n = 1, 14%
n = 1, 50%
n = 6, 86% Cluster 23 Sources:
water
duck / goose
Cluster 24 Sources:
cattle
water
pig
Fig. 4 e Overlap of water and faecal sources of Campylobacter jejuni among clusters obtained by flaA-RFLP. The number of samples refers to discrete water and/or faecal samples obtained from separate sampling events. Clusters 23 and 24 contained singleton faecal samples.
contamination of water is widespread and also occurs in other watersheds. Alternatively, there were many serovars isolated from various animal faecal samples that were not isolated from any of the water samples. This may suggest that certain animals were not important contributing sources of faecal contamination of the surface waters during the sampling periods in this study. While the current study used identical culture-based methods for the isolation of Salmonella from both water and faeces, factors such as cultivation bias have been shown to significantly skew the serovar-specific Salmonella selective isolation frequencies (Singer et al., 2009). Although it would be best to use both culture-based and culture-independent methods of Salmonella detection, there are advantages and disadvantages inherent to both approaches (Girones et al., 2010). Campylobacter isolates obtained from water and faecal samples in the current study were characterized using RFLP of the flaA gene. This method was chosen because it has been used successfully to discriminate between outbreak strains (Nachamkin et al., 1993), and recently has also shown to be useful in epidemiological surveillance (Huang et al., 2009). While MLST (multilocus sequence typing) is considered the gold standard for Campylobacter spp. typing, it is a laborious and costly procedure. RFLP of the flaA gene appears to be a suitable preliminary typing method (O’Reilly et al., 2006), and may also serve as an alternative to MLST when used in routine surveillance (Djordjevic et al., 2007). C. jejuni is readily isolated from surface waters and a variety of animal hosts (Jokinen et al., 2010), and several host species-specific flaA-RFLP clusters were generated in the
current study, demonstrating that this method of subtyping may be useful in the identification of various animal sources of faecal contamination including buffalo, cattle, sheep, chickens, geese, pigs, and birds. Specifically, C. jejuni isolates from water that clustered with host species-specific faecal isolates identified cattle, pigs, ducks, and geese as possible sources of water contamination. Sixty-one percent of the water isolates typed by RFLP of the flaA gene did not cluster with any of the faecal isolates in this study. This suggests that animal species not tested in this study (possibly certain other wild species) are contributing to surface water contamination by Campylobacter, although it is also possible that a larger sample size of the domestic and wild animal species sampled in this study would have increased the number of matching animal and water isolate flaA-RFLP profiles. Of the host species-specific flaA-RFLP clusters identified, some were obtained from different animals from the same geographic area, suggesting that this method of typing may be useful not only in identifying the species of animal that shed the organism but also specific sites and groups of animals responsible for contamination of water by these pathogens. There were also examples of clusters containing isolates from the same species of animal obtained at different times throughout the three-year sampling period, suggesting that flaA profiles persist over time. Other molecular subtyping techniques, such as MLST have identified location and species-specific clusters of Campylobacters (Ragimbeau et al., 2008; French et al., 2009; Hannon et al., 2009; Huang et al., 2009). Although flaA-RFLP may not be
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Table 2 e Distribution (%) of Salmonella serovars isolated from animal faeces, sewage, and surface water collected from the Oldman River watershed. Serovars isolated from both water and faecal sources are highlighted grey. Only those sources from which Salmonella species were isolated are included. (Chicken, n [ 14; Goose, n [ 8; Duck, n [ 3; Pelican, n [ 5; Other Bird, n [ 6; Cattle, n [ 2; Sheep n [ 18; Horse n [ 1; Pig, n [ 10; Sewage, n [ 8; Water, n [ 29). Chicken Goose Duck Pelican Other birdb Cow Sheepc Horse Pig Sewage Water
Source Salmonella enterica serovar distribution (%)
Anatum Bovismorbificans Bradford Brandenburg Bredeney Derby Give var. 15þ Hadar Heidelberg I:11:r:IIIb:61:-:1,5 Indiana Infantis Mbandaka Muenchen Rubislaw Saintpaul Senftenberg Stanley Tennessee Typhimurium Typhimurium Copenhagen Othera >1 Serovar
14 30 10 20 13 25
33
29 50
50
100
7 3
6
80 100
20
38 50
50
3 21
89 50
33
17 10 3 13 33
72
25 3 13 20 3 33 21 14
13 25
10 40 20
17
50
11 6
20 10
13 25
10 24
a Includes 14 ‘other’ serovars. b Includes gull, sparrow, swallow, and vulture. c Serovar data not available for one sample.
as discriminatory as other multilocus typing methods, we were still able to identify source-specific clusters. Phage-typing of E. coli O157:H7 is often used in conjunction with higher resolution molecular typing techniques such as PFGE (Karmali et al., 2010) in order to help with the interpretation of PFGE data or to eliminate further analyses of isolates unrelated to those implicated in an outbreak. In the current study we applied phage-typing, PFGE fingerprinting, and CGF of isolates in order to assess whether or not the E. coli O157:H7 PTs and/or molecular fingerprints detected in human and
Table 3 e Distribution of E. coli O157:H7 PTs isolated from animal faeces, sewage, and surface water collected from the Oldman River watershed. PTs isolated from both water and faecal sources are highlighted grey. Only those sources from which E. coli O157:H7 species were isolated are included. E. coli O157:H7 Phageetype Distribution (No.) Source 8 Duck Goose Cow 2 Sewage Water 4
14a 1
21
33
34
No PT Data
>1 PT
1 11 1 4
1 1
1 1
2
animal faeces could also be detected in surface waters within the same watershed. Results from the present study as well as other research suggest that various E. coli O157:H7 PTs and molecular fingerprints are widespread in the environment (Zhang et al., 2007; Laing et al., 2008); and while it is accepted that cattle are the primary reservoir of E. coli O157:H7, this pathogen may also be isolated occasionally from other animal species and the environment (Oporto et al., 2008; La Ragione et al., 2009). The most common PTs of this pathogen were observed in both cattle and water isolates, and there were exact matches of molecular fingerprints between sewage, cattle, and water isolates. Molecular fingerprinting of E. coli O157:H7 may also provide valuable information on the distribution and persistence of fingerprints at specific locations over time. Results of this study suggest that molecular fingerprinting can provide more precise information that may also be useful in pinpointing the source(s) responsible for contamination of water. This study showed that identical fingerprints could be isolated from different cattle from the same geographic area which were distinct from those fingerprints obtained from cattle from other geographic areas. Identical fingerprints were also obtained from animals and specific surface water sampling locations which were distinct from isolates obtained from any other animals and surface water sampling locations. This study also demonstrated that both CGF and PFGE could
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Table 4 e Analysis of E. coli O157:H7 sources among clusters obtained by PFGE fingerprints and CGF. Isolates were obtained from animal faeces and sewage collected from the Oldman River watershed. The number of samples refers to discrete water and/or faecal samples obtained from separate sampling events. Cluster #
Sample type
No. samples
PFGE, 100%
1 2 3 4
cattle/sewage cattle cattle cattle
2/1 1 2 2
CGF, 95%
1 2 3 4
cattle/sewage cattle cattle cattle/duck
5/1 1 2 3/1
Phage-type
Isolated from same geographic area?
Isolated on same date?
14a 14a 14a 14a
na e Y na
N e Y Y
8, 14a/14a 14a 14a 14a
na e Y Y
N e Y N
Y, yes; N, no; na, municipal district/county information not available; –, not applicable. Only results for clusters containing multiple faecal isolates are shown. In order to be assigned a “Y”, all isolates within the same cluster must have been isolated from the same geographic area or isolated on the same date.
generate identical clusters, supporting the use of CGF as an alternative to PFGE (Laing et al., 2008). An interesting finding from our CGF analysis is that although clustering at 95% stringency yielded host speciesspecific clusters that could be useful for determining sources of contamination, analysis at 100% stringency provided enhanced discrimination that could, in addition, allow the separation of isolates by date and geographic location. High resolution genotyping methods such as CGF allow the stringency of clusters to be adjusted depending on the objective,
and they could prove to be useful in pinpointing specific groups of animals or sources of faecal contamination. Although CGF of E. coli O157:H7 isolates shows promise, the inherent deficiency in using this particular organism for determining sources of faecal contamination is its low prevalence in surface waters and its limited host range. It may also be possible, however, to apply similar high-resolution molecular typing techniques such as CGF to organisms which are more prevalent and have a broader host range such as Campylobacter spp., to identify other species sources of surface
A n = 1,
n = 1,
25%
50% n = 2,
n = 1,
50%
50%
n = 1, 25% PFGE Cluster 2 Sources:
PFGE Cluster 1 Sources: cattle
sewage
water
cattle
water
B n = 2, 25%
n = 1, 50% n = 1, 50%
n = 1,
n = 5,
13%
63% CGF Cluster 1 Sources:
cattle
sewage
CGF Cluster 2 Sources: water
cattle
water
Fig. 5 e Overlap of water and faecal sources of E. coli O157:H7 among clusters obtained by PFGE fingerprinting (A) and corresponding CGF profiles (B). The number of samples refers to discrete water and/or faecal samples obtained from separate sampling events.
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water contamination by identifying location- and sourcespecific clusters of the organism.
5.
Conclusions
From the current study, the following conclusions can be drawn: The high prevalence of zoonotic pathogens in domestic animal faeces, municipal sewage, and in flowing waters found in this watershed underlines the risks to human health associated with exposure to these potential sources of infection. Bacterial pathogen subtyping has the potential to identify not only host species sources, but also the location and/or specific groups of animals that are the sources of water contamination. This specific information may in many cases be more helpful and effective in watershed management than simply determining the animal species responsible for contamination.
Acknowledgements This study was funded in part by the Agriculture Policy Framework’s National Water Quality Surveillance Research Initiative through an agreement between Agriculture and Agri-Food Canada and Health Canada; Environment Canada under the National Agricultural Environmental Standards Initiative (NAESI); and the Public Health Agency of Canada. The authors thank Ray Walker of Alberta Environment and Bruce Beasley of Agriculture and Agri-Food Canada for their sampling contributions. We would also like to thank Steven Mutschall, Allison Kindt, Anita James, Susan Ross, Rommy Rodriguez, and Garrett Kennedy of the Public Health Agency of Canada for their excellent assistance with laboratory analyses. We would like to thank Linda Cole, Betty Wilkie, Ketna Mistry and Ann Perets of the OIE Salmonella Reference Laboratory, as well as Irene Yong and Nina Enriquez of the E. coli Reference Laboratory of the Public Health Agency of Canada in Guelph, Ontario, for serotyping and PFGE analysis of isolates.
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Available at www.sciencedirect.com
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Applying an electric field in a built-in zero valent iron e Anaerobic reactor for enhancement of sludge granulation Yiwen Liu, Yaobin Zhang*, Xie Quan, Shuo Chen, Huimin Zhao Key Laboratory of Industrial Ecology and Environmental Engineering, Ministry of Education, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
article info
abstract
Article history:
A zero valent iron (ZVI) bed with a pair of electrodes was installed in an upflow anaerobic
Received 16 July 2010
sludge blanket (UASB) reactor to create an enhanced condition to increase the rate of
Received in revised form
anaerobic granulation. The effects of an electric field and ZVI on granulation were inves-
27 September 2010
tigated in three UASB reactors operated in parallel: an electric field enhanced ZVI-UASB
Accepted 2 October 2010
reactor (reactor R1), a ZVI-UASB reactor (reactor R2) and a common UASB reactor (reactor
Available online 19 October 2010
R3). When a voltage of 1.4 V was supplied to reactor R1, COD removal dramatically increased from 60.3% to 90.7% over the following four days, while the mean granule size
Keywords:
rapidly grew from 151.4 mm to 695.1 mm over the following 38 days. Comparatively, COD
Anaerobic reactor
removal was lower and the increase in granule size was slower in the other two reactors (in
Zero valent iron
the order: R1 > R2 > R3). The electric field caused the ZVI to more effectively buffer acidity
Granulation
and maintain a relatively low oxidationereduction potential in the reactor. In addition, the
Electric field
electric field resulted in a significant increase in ferrous ion leaching and extracellular polymeric substances (EPS) production. These changes benefited methanogenesis and granulation. Scanning electron microscopy (SEM) images showed that different microorganisms were dominant in the external and internal layers of the reactor R1 granules. Additionally, fluorescence in situ hybridization (FISH) analysis indicated that the relative abundance of methanogens in reactor R1 was significantly greater than in the other two reactors. Taken together, these results suggested that the use of ZVI combined with an electric field in an UASB reactor could effectively enhance the sludge granulation. Crown Copyright ª 2010 Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Upflow anaerobic sludge blanket (UASB) reactors are considered one of the most effective anaerobic reactors for digestion of organic substances in various effluents (Seghezzo et al., 1998; Lettinga et al., 1993). The presence of granular sludge is a major characteristic of the UASB reactor (Ghangrekar et al., 2005). The excellent settling property of granular sludge leads to a high level of biomass and rich microbial diversity being maintained in the reactor, which leads to high biodegradation efficiency. However, sludge granulation is a long-term process (Hulshoff Pol et al., 1983; Hickey et al., 1991) that generally takes
three to eight months. There has been a great deal of effort to increase the rate at which the granulation process occurs through microbiological, physico-chemical and hydraulic methods (El-Mamouni et al., 1997; Lettinga et al., 1993; Mahoney et al., 1987; Arcand et al., 1994). It was recently reported that multivalent metal ions such as Ca2þ, Mg2þ, Al3þ and Fe2þ could enhance granulation (Mahoney et al., 1987; Schmidt and Ahring, 1993; Yu et al., 2000, 2001a, 2001b) through charge neutralization and double-layer compression that may condense the diffusive double-layers to form a relatively strong effect of van der Waals forces. Moreover, it is believed that these metal ions can bind to extracellular
* Corresponding author. Tel.: þ86 411 84706460; fax: þ86 411 84706263. E-mail address: [email protected] (Y. Zhang). 0043-1354/$ e see front matter Crown Copyright ª 2010 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.002
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polymeric substances (EPS) of anaerobic sludge to form more stable complexes, thereby maintaining the integrity of the granules (Rudd et al., 1984; Mahoney et al., 1987). Granular sludge is composed of methanogens, acidogens and other types of microorganisms, among which methanogens present the slowest growth rate and are most influenced by operational conditions. Generally, the successful start-up of an UASB reactor indicates that the reactor has achieved relatively high COD removal efficiency and methane production, which is dependent on methanogenesis. Therefore, granulation can be considered a process that leads to the development of methanogens in the granule (Tiwari et al., 2006). Accordingly, creating a favorable environment for the growth of methanogens in the reactor is essential to accelerating sludge granulation. Zero valent iron (ZVI) is a reducing agent that is expected to help create an enhanced anaerobic environment that may improve the performance of UASB due to its reductive property. When utilized in an anaerobic environment, ZVI can serve as an electron donor to lower oxidationereduction potential (ORP) and buffer acid produced by acidogens, which are crucial to maintain a stable and favorable condition for methanogens. Therefore, we developed a ZVI packed UASB reactor (ZVI-UASB) in our laboratory. Our previous work demonstrated that the ZVI-UASB reactor had higher performance with respect to the removal of COD and color than a normal UASB without the ZVI. Additionally, the performance of the reactor was found to be closely related to the reduction reaction of ZVI (Fe0 2e ¼ Fe2þ) (Zhang et al., in press). It is believed that proper electric stimulation can promote microbial metabolism (Thrash and Coates, 2008), thereby leading to higher biochemical performance. Indeed, there are several examples of bio-electrochemical methods that have been established and applied in biological electrocatalysis (Schlegel and Lafferty, 1965; Islam and Suidan, 1998) and biofuel cells (Chaudhuri and Lovley, 2003; Logan et al., 2006). In the field of wastewater treatment, it has been reported that hydrogen produced from the cathode can serve as a substrate for denitrification and dechlorination, thereby improving the efficiency of pollutant degradation (Hayes et al., 1998; Son et al., 2006; Guiot et al., 2008; Aulenta et al., 2008). Moreover, it is believed that an electric field can enhance the surface reaction by promoting the ion migration rate. Therefore, when an electric field is supplied to the ZVI bed, it is likely to intensify the reaction of ZVI and further improve the anaerobic process. However, no studies have identified a method of enhancing anaerobic granulation using an electric field to date. In this study, ZVI combined with an electric field was installed in an UASB reactor with the goal of accelerating anaerobic granulation. The effectiveness of this method was then investigated and the potential mechanisms were explored.
2.
Materials and methods
2.1.
Experimental set-up
A ZVI bed (4120 mm 200 mm) was installed at 3/5 depth in a transparent acrylic plastic UASB reactor (4140 mm 1200 mm)
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to form an electric field enhanced ZVI-UASB reactor (hereafter referred to as reactor R1). The ZVI bed was constructed of a cylindrical acrylic plastic tube (4120 mm 200 mm) packed with a mixture of waste scrap iron (300 g, 20# steel) and waste granular activated carbon (150 g). The goal of mixing the granular carbon (45 mm 10 mm) and the ZVI was to reduce conglutination among the scrap iron and to improve the hydraulic distribution in the ZVI bed. A pair of graphite plate electrodes (180 mm 70 mm 15 mm) was then inserted into the ZVI bed with a distance between the electrodes of 70 mm and supplied by a regulated DC power source. Scrap iron (about 8 mm 4 mm 2 mm) collected from a machine shop was soaked in 1% NaOH solution for 24 h, after which it was washed with dilute HCl and water to clean its surfaces. The granular carbon was then pre-adsorbed in the wastewater used in the study until equilibrium to eliminate any adsorption effect. The control experiments were conducted in the following two reactors, a ZVI-UASB reactor that was the same as reactor R1, but without the electrodes (hereafter referred to as reactor R2), and a common UASB reactor that was the same as reactor R1, except without the ZVI bed and the electrodes (hereafter referred to as reactor R3). These three reactors were operated with a hydraulic retention time (HRT) of 24 h at a temperature controlled at 35 1 C using a heating jacket system. The influent COD increased gradually from 1400 mg/L (1.4 kg/m3/d) to 8000 mg/L (8 kg/m3/d) for all three reactors during the 80 day operation period. To further clarify the function of electricity, reactor R1 was operated with and without the voltage supply in sequence. During the initial 22 days of start-up, reactor R1 was operated without voltage, which resulted in the conditions being the same as for reactor R2. From day 23, a voltage of 1.4 V (I ¼ 150 mA) was supplied to the ZVI bed of reactor R1. The real potential determined between the electrodes was about 1.2e1.3 V. Although the potential was close to the theoretical value of electrolysis of water (1.23 V), the resistance and overpotential in this system was far greater than the classical electrolytic solution (H2SO4 or NaOH solution). Therefore, the wastewater in the reactor could not be electrolyzed.
2.2.
Sludge and wastewater
Sludge obtained from a sedimentation tank in Chunliu municipal sewage plant in Dalian (China) was used as the seed sludge. Following removal of large debris with a sieve, the ratio of volatile suspended sludge to total suspended sludge (VSS/TSS) was 0.74 and the sludge volume index (SVI) was 53 mL/g. Five liters of these seed sludges were inoculated into each of the reactors with an initial TSS of 14.88 g/L. An artificial wastewater was used in this study. Specifically, sucrose, NH4Cl and KH2PO4 were added as the carbon, nitrogen, and phosphorus sources, respectively, to give a COD: N: P ratio of 200: 5: 1. The trace elements were added according to the following composition: 1 mL/L of a trace element solution containing Zn at 0.37 mmol/L, Mn at 2.5 mmol/L, Cu at 0.14 mmol/L, Co at 8.4 mmol/L, Ni at 0.25 mmol/L, H3BO3 at 0.8 mmol/L and EDTA at 3.4 mmol/L. The pH of the influent wastewater was adjusted to 8 using NaHCO3 solution.
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2.3.
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Analysis
measured with a wet type gas meter (LMF-2, Changchun, China), after which its volume was calculated as standard temperature and pressure (STP). The composition of the biogas was analyzed by gas chromatography (Shimadzu, GC2010/TCD, Japan) according to the method described by Jiang et al. (2007). The concentration of Fe (II) in the aqueous phase was determined using ortho phenanthroline spectrophotometry at 510 nm (Techcomp, UV-2301, Shanghai, China).
TSS, VSS and COD were determined according to Standard Methods for the Examination of Water and Wastewater (APHA, 1998). The ORP was measured using an ORP combination class-body redox electrode (Sartorius PY-R01, Germany). The pH was recorded using a pH analyzer (Sartorius PB-20, Germany). Biogas collected from the reactors was
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was conducted according to the method described by Wu et al. (2001). Fluorescence labels of the oligonucleotide probes used in this study included EUB338 (Bacteria), ARC915 (Archaea) and MB1174 (Methanobacteriaceae) (Raskin et al., 1994; Sekiguchi et al., 1999). The samples were observed under a confocal laser scanning microscope (Leica SP2, Heidelberger, Germany). The FISH images obtained were imported to Image-Pro Plus 6.0 for analysis of the relative abundance of microorganisms.
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Results and discussion
3.1. COD removal and pH changes in the reactors during start-up
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Fig. 2 e Characterization of the granular sludge during start-up. (a) Time series of mean granule size in each reactor, (b) size distribution of the granules from the three reactors on day 80 (inoculum sludge is presented for comparison).
The iron content in the granules was determined by atomic absorption spectroscopy (Vlyssides et al., 2009). The settling velocity of the granules was determined by measuring the mean time required for an individual granule to fall a certain distance in a measuring cylinder. The morphology of the granular sludge was examined using a scanning electron microscope (SEM; Quanta 200 FEG, The Netherlands). For the SEM observation, the sludge was immobilized in a 2.5% glutaraldehyde solution, dehydrated in graded water-ethanol solutions, then lyophilized and sputter-coated with gold (Zhou et al., 2006). The average granular sludge size was measured using a Malvern Mastersizer 2000 (UK). Each size distribution obtained was calculated by instrument software. EPS was extracted using a cation exchange resin (CER) according to the method described by Frolund et al. (1996). Polysaccharide in the EPS was determined by a sulfuric acideanthrone method and protein in the EPS was analyzed according to the method described by Lowry et al. (1951). Fluorescence in situ hybridization (FISH) was used to determine the abundance of methanogens in the granules. FISH
To investigate the effects of the electric field on the start-up, reactor R1 was operated with and without voltage successively. During the initial 22 days from inoculation, reactor R1 was operated without voltage, during which time it was identical to reactor R2. As shown in Fig. 1, the COD removal in reactor R1 and R2 ranged from 60% to 70% and the effluent pH of both reactors was maintained between 6.0 and 6.5. When compared with these two reactors, reactor R3 showed less COD removal (50%e60%) and lower pH stability (5.0e6.5). As a voltage of 1.4 V was supplied to reactor R1 from day 23, its effluent COD decreased dramatically from 1082 mg/L to 229 mg/L over the next four days. In addition, the effluent pH increased to 6.5e7.0 from 6.0 to 6.5. Although the influent COD concentration rose rapidly from 2500 mg/L to 6500 mg/L after seven days, the COD removal in reactor R1 still increased to 97.1%. The removal efficiency fell to 90.8% and the pH decreased from 6.6 to 6.1 when the influent COD increased to 7500 mg/L on day 36; however, the COD removal recovered to more than 94.5% and the pH recovered to 6.5 after two days. The COD removal was then maintained between 92.2% and 95.4% and the pH was maintained between 6.4 and 6.8 in reactor R1 for the remainder of the experiment. Reactor R2 required 17 days to elevate the COD removal from 68.1% to 91.4% from day 23. In addition, when the influent COD increased from 2500 mg/L to 7000 mg/L, reactor R2 showed a gradual decrease in COD removal from 91.5% to 74.1% and a decline in pH from 6.5 to 5.6. After these decreases, nearly ten days were required for the COD removal to recover to 90%. The increase in COD removal was the slowest for reactor R3, with the highest level of 85% not being attained until day 63. After day 63, the COD removal in R3 showed a declining trend, reaching 76.5% on day 80. The pH of reactor R3 varied from 5.5 to 6.3 throughout the experimental period. It is well known that acidogens breaks down organic matters into H2, acetic acid and CO2, and methanogens convert these intermediate products to CH4. In the processes, methanogenesis is a major way to mineralize organic substrates. However, methanogenic metabolism is slow and often influenced by operational conditions. Improper conditions such as fluctuating organic loading rate may lead to imbalance between acidogenesis and methanogenesis so as to accumulate organic acids, which may further deteriorate the methanogenesis. Therefore, to maintain a near neutral pH extent is crucial for the performance and start-up of an UASB
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Fig. 3 e Images of the granular sludge on day 80. (a) Digital image of sludge from reactor R1, (bed) SEM observation of granules from reactor R1, R2 and R3, respectively, (e, g) a higher magnification of the surface (ex) of granules from reactor R1 and R3, (f, h) a higher magnification of the inner layer (in) of granules from reactor R1 and R3.
reactor. The ZVI in reactor R1 and R2 could buffer acid produced by acidogens according to the reaction, Fe0 þ 2Hþ ¼ Fe2þ þ H2, which likely helped the reactor maintain a favorable pH for methanogenesis (6.8e7.2). This reaction may have intensified in response to the application of electricity to the ZVI. Also, iron was documented as
a component of the essential enzymes that drive numerous anaerobic reactions. Oleszkiewicz and Sharma (1990) reported limited conversion of COD at deficient concentrations of iron. Thus, ZVI could be responsible for the significant COD removal. In this process, the ZVI reaction was the major reason for the improved performance, but the reaction might
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Table 1 e Composition of extracellular polymeric substances (EPS) extracted from reactor sludges. Inoculum sludge is presented for comparison. Sludge
EPS Polypeptides (mg protein/g VSS) 15.33 26.78 23.13 16.32
Inoculum Reactor R1 Reactor R2 Reactor R3
0.54 1.06 0.93 0.60
Polysaccharides (mg glucose/g VSS) 9.90 18.13 13.47 10.54
0.39 0.53 0.62 0.41
be weak. The electric field could intensify the ZVI reaction so as to enhance the roles of ZVI in anaerobic process.
3.2.
Granulation
The granule size variation during 80 days of operation is shown in Fig. 2. The mean granule size in reactor R1 increased from 86.2 mm to 151.4 mm during the initial 22 days without voltage (Fig. 2a), which was similar to that observed in reactor R2. Comparatively, the granule size in reactor R3 only rose to 88.9 mm during this period. As the voltage was supplied to reactor R1 from day 23, a marked increase in the granule size was observed. The mean granule size reached 695.1 mm on day 60, which was almost five times greater than on day 22. Afterwards, it gradually reached a relatively steady size of 723.5 mm on day 80. Comparatively, the granule size in reactor R2 only reached 189.8 mm on day 30, after which it grew slowly to 216.79 mm on day 80. The granule size in reactor R3 grew the most slowly, being only 113.0 mm on day 80. As shown in Fig. 2b, the size of the granules in reactor R1 presented a near normal distribution. The settling velocity of the granular sludge in each reactor was determined on day 80. The velocity was 56.1 m/h in reactor R1, which was much higher than the 38.6 m/h and 17.1 m/h observed in reactor R2 and R3, respectively. It is believed that the general range of settling velocities in UASB sludge is 2e90 m/h (Lettinga et al., 1983), and a settling velocity of 50e60 m/h is believed to be ideal (Hulshoff Pol et al., 2004) because it reduces washout of the sludge while maintaining a uniform distribution of sludge in the reactor.
3.3.
Morphology of granules
Digital and SEM images of the granular sludge are displayed in Fig. 3. The digital image of sludge taken from reactor R1 revealed a compact structure (Fig. 3a). The SEM images revealed that the texture of granules in reactor R1 was clear-
Table 2 e Contents of CH4 and H2 in the biogas of each reactor from day 70 to day 80. Reactor Reactor R1 Reactor R2 Reactor R3
Average CH4 (%) 50.71 1.01 45.92 0.93 42.49 0.96
Average H2 (%) Not detectable 0.11 0.02 0.73 0.07
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cut, rigid and stable (Fig. 3b), while the granules in reactors R2 (Fig. 3c) and R3 (Fig. 3d) showed a loose and rough surface. A higher magnification of SEM showed that cocci prevailed the surface of the granules from reactor R1 (Fig. 3e), while rodshaped (or filamentous) species dominated the inner layer (Fig. 3f). It is believed that filamentous Methanothrix are important to granule formation (De Zeeuw, 1987). Dubourgier et al. (1987) suggested that the granulation process started as a result of the bridging of microflocs by Methanothrix filaments, after which other bacteria and acidogens colonized the flocs to form sludge with increasing granule sizes. According to the description of the granular structure by Macleod et al. (1990), methanogens are primarily located in the interior layer, while acetate producers and fermentative bacteria form the exterior layer. This structure can prevent the exposure of susceptible methanogens to stressful changes in the environment such as low pH and toxic substances. From this point of view, the cocci observed on the surface in the present study were likely acidogens. Evaluation of the granules of reactor R3 revealed that the microorganisms on the exterior and interior did not differ greatly (Fig. 3f and h), and acidogen-like and Methanothrix-like microorganisms were intertwined, indicating that a double-layer structure had not formed.
3.4.
Fe2þ leaching
Fe2þ is a product of the ZVI reaction, so its amount can reflect the intensity of the reaction. Iron leaching under anaerobic conditions resulted in ferrous iron. As shown in Fig. 4, the Fe2þ concentration in both reactor R1 and R2 ranged from 38 mg/L to 40 mg/L during the first 22 days. As voltage was supplied, the Fe2þ concentration in reactor R1 rapidly increased to 61e63 mg/L in the following days. According to the reaction of ZVI, the production of more Fe2þ indicates that more Hþ was consumed. Therefore, the pH in reactor R1 was well maintained in a near neutral range. According to the DLVO theory, the divalent metal ions could decrease electric repulsion and facilitate cell-to-cell interaction between bacteria (Mahoney et al., 1987; Schmidt and Ahring, 1993). Therefore, ferrous iron ions could enhance cell aggregation. Yu et al. (2000) reported that Fe2þ at a concentration of 300 mg/L (dosage in the form of FeCl2) enhanced the granulation process, while a dose of FeCl2 less than 150 mg/L (Fe2þ) had little effect on sludge granulation. However, in the present study, a lower Fe2þ “dose” (61e63 mg/ L) resulted in significant enhancement of granulation. The role of iron in cell aggregation and its other functions, such as interaction with EPS, indicate that it would be involved in the formation of sludge. The amount of iron in the sludge could partially reflect the level of these effects. After 80 days of operation, the iron content of the sludge from reactor R1 was the greatest (22.45 mg Fe/g VSS, data not shown), while it was only 11.59 mg Fe/g VSS in sludge from reactor R2 and 3.94 mg Fe/g VSS in that of reactor R3. Together with the results of Fig. 4, the iron content of the sludge was related to the Fe2þ concentration in the bulk liquid. Fe2þ leaching increased the iron content of the sludge, which benefited the formation of the granule matrix with EPS. The ZVI reaction was assumed to be essential for enhanced granulation. ZVI serving as a reductant could decrease the
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Fig. 6 e FISH images of granular sludge in different reactors. (aec) Granules from reactor R1, R2 and R3 respectively, hybridized with specific probes for Archaea and Bacteria (ARC915-FITC, green and EUB338-CY3, red), (def) granules from reactor R1, R2 and R3 respectively, hybridized with specific probes for Archaea and H2-utilizing methanogens (ARC915-FITC, green and MB1174-CY3, red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
ORP, and the ORP level in the reactor could reflect the rate of the ZVI reaction. The ORP values of the reactors were recorded from day 30 to day 80 and the results are shown in Fig. 5. From the figure, the ORP values of reactor R1 and R2 were more negative than that of reactor R3, indicating that ZVI could effectively decrease the ORP. Moreover, the ORP of reactor R1 ranged from 340 mV to 370 mV, while it ranged from 270 mV to 320 mV in reactor R2. These results indicated that the ORP was further decreased under the voltage supply, implying that the reaction of ZVI was enhanced under an electric field, which was in agreement with the results of Fe2þ leaching (seen in Fig. 4). It is well known that a lower ORP is instrumental in the survival and growth of obligate anaerobes, especially methanogens. These results indicated that an electric field could enhance the ZVI reaction to further create a favorable environment for the development of methanogens, thereby enhancing the granulation.
3.5.
EPS content of the sludge
EPS secreted by bacteria can mediate cohesion as well as adhesion of cells, which is crucial to maintenance of the structural integrity of anaerobic granules (Schmidt and Ahring, 1996; Shen et al., 1993; Liu et al., 2003). Therefore, the EPS content of the sludge is an important factor in anaerobic granulation (Zhou et al., 2006). Proteins and polysaccharides are the two dominant compositions in extracted EPS, which are believed to represent the entire EPS of the sludge (Yu et al., 2006). After 80 days of operation, these two compositions extracted from the sludge of each reactor and from the inoculum were analyzed. As shown in Table 1, both
compounds increased in all three reactors when compared with the inoculum. Indeed, the level of proteins and polysaccharides extracted from reactor R1 was nearly double that of the inoculum, and was higher than the levels in reactor R2 and R3. The levels of these compounds in reactor R3 were only slightly higher than those of the inoculum. The different EPS contents in the three reactors were related to Fe2þ leaching. EPS preferred to bind with divalent metals to form a stable three dimensional structure to maintain the structural integrity of the granule (Rudd et al., 1984; Cammarota and Sant’Anna, 1998), which was important for the response of the granule to stressful changes. Thus, as the Fe2þ content of the sludge increased, more EPS was bound and immobilized in the sludge (Shen et al., 1993).
3.6.
Gas composition
It has been suggested that H2 production accompanies the ZVI reaction (acid buffering); therefore, its production should increase in response to the application of electricity because the reaction was enhanced. However, no hydrogen was detected in the biogas of reactor R1, while it was detected in the biogas produced in reactors R2 and R3 (shown in Table 2). These results were contrary to the expected results based on the explanation presented above. This may have been due to the presence of hydrogenotrophic methanogens that could utilize H2 and CO2 for the production of CH4. Based on these biogas results, we assumed that hydrogenotrophic methanogens were present in R1, and that they were so active that they consumed all of the H2 produced by acidogenesis and the ZVI reaction (Daniels et al., 1987). Accordingly, the percentage
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of CH4 in the biogas of reactor R1 was the greatest, 50.71% (shown in Table 2), while it was 45.92% in reactor R2 and 42.49% in reactor R3, respectively. Certainly, such little difference in the H2 percentage was insufficient to create obvious differences in CH4 production. It is assumed that the ZVI in conjunction with the voltage also enhanced the growth of other methanogenic species. CH4 production in reactor R1 was in the range of 40.3e45.1 L/d from day 70e80, amounting to 296e332 mL CH4/g CODremoved (STP), which was close to the theoretical CH4 production from per gram removed COD, i.e., 350 mL CH4/g CODremoved (Toprak, 1995).
and reduction in start-up time. These findings indicate that coupling of ZVI and an electric field was an effective method of enhancing sludge granulation, which is meaningful for wider application of anaerobic reactors in the wastewater treatment associated with engineering. This method also improved wastewater treatment efficiency during the anaerobic startup period, enabling the anaerobic reactor to more easily respond to the requirements of various environmental criterions.
3.7.
Acknowledgments
FISH analysis
FISH was used to analyze the specific microbial composition of the granules after 80 days of operation, and the results are shown in Fig. 6. The primary microorganisms in the anaerobic reactors consisted of Bacteria and Archaea; therefore, the sum of these two domains was considered to be 100% (Griffin et al., 1998). In addition, it is well known that methanogens are members of Archaea and acidogens are members of Bacteria. In most anaerobic reactors, methanogens can represent Archaea (Sekiguchi et al., 1999). According to analysis conducted using Image-Pro Plus 6.0, the relative abundance of methanogens in reactor R1 was 86.25%, while it was 74.81% in reactor R2 and 63.31% in reactor R3. These findings are coincident with their performance in terms of COD removal and granulation. As shown in Fig. 6d, e and f, the relative abundance of H2-utilizing methanogens in the total population of methanogens was 29.97%, 22.19% and 13.24% in reactor R1, R2 and R3, respectively, which could explain the different H2 levels in the biogas. The high percentage of methanogens in the granules of reactor R1 was ascribed to the following reasons. The reaction of ZVI could help to maintain a near neutral pH and lower ORP level, which were beneficial to the growth of methanogens. Additionally, electricity could enhance the reaction of ZVI to further intensify the function of ZVI. Due to the formation of a favorable environment for the growth of methanogens, the start-up of the reactor and process of sludge granulation were effectively accelerated. Moreover, Fe2þ as a product of the reaction of ZVI promoted the aggregation of granules and colligation with EPS, both of which helped to form a stable and intact structure for methanogenesis and granulation.
4.
Conclusion
The application of ZVI in combination with an electric field installed in an UASB reactor effectively sped up sludge granulation and improved the performance of the reactor. When an electric field of 1.4 V was supplied to reactor R1, COD removal increased dramatically from 60.3% to 90.7% in four days and the granule size rapidly increased from 151.4 mm to 695.1 mm in 38 days. The electric field enhanced the ZVI reaction to decrease ORP values and buffer acidity, which created a favorable environment for the growth of methanogens. At the same time, increases in the Fe2þ dissolution and EPS content were helpful for the growth of granule size. These factors all led to the observed increase in granulation
This study was conducted with financial support from the National Key Scientific and Technology Project for Water Pollution Treatment of China (2008ZX07208-004, 2008ZX07208-002) and the Program for Changjiang Scholars and Innovative Research Team at the University of China (IRT0813).
reference
APHA, 1998. Standard Methods for the Examination of Water and Wastewater, twentieth ed. American Public Health Association/American water work Association/Water environment Federation, Washington D.C., USA. Arcand, Y., Guiot, S.R., Desrochers, M., Chavarie, C., 1994. Impact of the reactor Hydrodynamics and organic loading on the size and Activity of anaerobic granules. Chemical Engineering Journal and the Biochemical Engineering Journal 56 (1), B23eB35. Aulenta, F., Canosa, A., Majone, M., Panero, S., Reale, P., Rossetti, S., 2008. Trichloroethene dechlorination and H2 evolution are alternative biological pathways of electric charge utilization by a dechlorinating culture in a bioelectrochemical system. Environmental Science and Technology 42 (16), 6185e6190. Cammarota, M.C., Sant’Anna, G.L., 1998. Metabolic blocking of exopolysaccharides synthesis: effects on microbial adhesion and biofilm accumulation. Biotechnology Letters 20 (1), 1e4. Chaudhuri, S.K., Lovley, D.R., 2003. Electricity generation by direct oxidation of glucose in mediatorless microbial fuel cells. Nature Biotechnology 21 (10), 1229e1232. Daniels, L., Belay, N., Rajagopal, B.S., Weimer, P.J., 1987. Bacterial methanogenesis and growth from CO2 with elemental iron as the sole source of electrons. Science 237 (4814), 509e511. De Zeeuw, W.J., 1987. Granular sludge in UASB reactors. In: Lettinga, G., Zehnder, A.J.B., Grotenhuis, J.T.C., Hulshoff Pol, L. W. (Eds.), Granular Anaerobic Sludge: Microbiology and Technology. Microbiology and Technology. Pudoc, Wageningen, The Netherlands, pp. 132e145. Dubourgier, H.C., Prensier, G., Albagnac, G., 1987. Structure and microbial activities of granular anaerobic sludge. In: Lettinga, G., Zehnder, A.J.B., Grotenhuis, J.T.C., Hulshoff Pol, L. W. (Eds.), Granular Anaerobic Sludge: Microbiology and Technology. Pudoc, Wageningen, The Netherlands, pp. 18e33. El-Mamouni, R., Leduc, R., Guiot, S.R., 1997. Influence of the starting microbial nucleus type on the anaerobic granulation dynamics. Applied Microbiology and Biotechnology 47 (2), 189e194. Frolund, B., Palmgren, R., Keiding, K., Nielsen, P.H., 1996. Extraction of extracellular polymers from activated sludge
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using a cation exchange resin. Water Research 30 (8), 1749e1758. Ghangrekar, M.M., Asolekar, S.R., Joshi, S.G., 2005. Characteristics of sludge developed under different loading conditions during UASB reactor start-up and granulation. Water Research 39 (6), 1123e1133. Griffin, M.E., McMahon, K.D., Mackie, R.I., Raskin, L., 1998. Methanogenic population dynamics during start-up of anaerobic digesters treating municipal solid waste and biosolids. Biotechnology and Bioengineering 57 (3), 342e355. Guiot, S.R., Cimpoia, R., Kuhn, R., Alaplantive, A., 2008. Electrolytic methanogenic e methanotrophic coupling for tetrachloroethylene bioremediation: proof of concept. Environmental Science and Technology 42 (8), 3011e3017. Hayes, A.M., Flora, J.R.V., Khan, J., 1998. Electrolytic stimulation of denitrification in sand columns. Water Research 32 (9), 2830e2834. Hickey, R.F., Wu, W.M., Veiga, M.C., Jones, R., 1991. Start-up, operation, monitoring and control of high-rate anaerobic treatment systems. Water Science and Technology 24 (8), 207e255. Hulshoff Pol, L.W., de Zeeuw, W.J., Velzeboer, C.T.M., Lettinga, G., 1983. Granulation in UASB-reactors. Water Science and Technology 15 (8e9), 291e304. Hulshoff Pol, L.W., Lopes, S.I.D., Lettinga, G., Lens, P.N.L., 2004. Anaerobic sludge granulation. Water Research 38 (6), 1376e1389. Islam, S., Suidan, M.T., 1998. Electrolytic denitrification: long term performance and effect of current intensity. Water Research 32 (2), 528e536. Jiang, S., Chen, Y.G., Zhou, Q., 2007. Effect of sodium dodecyl sulfate on waste activated sludge hydrolysis and acidification. Chemical Engineering Journal 132 (1e3), 311e317. Lettinga, G., Hulshoff Pol, L.W., Homba, S.W., Grin, P., Jong de, P., Roersma, R., Jspeert, P.I., 1983. The use of a floating settling granular sludge bed reactor in anaerobic treatment. In: van den Brink, W.J. (Ed.), Proceedings of the European Symposium on Anaerobic Waste Water Treatment. Noordwijkerhout, The Netherlands, pp. 411e429. Lettinga, G., Deman, A., Vanderlast, A.R.M., Wiegant, W., Vanknippenberg, K., Frijns, J., Vanbuuren, J.C.L., 1993. Anaerobic treatment of domestic sewage and waste-water. Water Science and Technology 27 (9), 67e73. Liu, Y., Xu, H.L., Yang, S.F., Tay, J.H., 2003. Mechanisms and models for anaerobic granulation in upflow anaerobic sludge blanket reactor. Water Research 37 (3), 661e673. Logan, B.E., Hamelers, B., Rozendal, R.A., Schrorder, U., Keller, J., Freguia, S., Aelterman, P., Verstraete, W., Rabaey, K., 2006. Microbial fuel cells: methodology and technology. Environmental Science and Technology 40 (17), 5181e5192. Lowry, O.H., Rosebrough, N.J., Farr, A.L., Randall, R.J., 1951. Protein measurement with the folin phenol reagent. Journal of Biological Chemistry 193 (1), 265e275. Macleod, F.A., Guiot, S.R., Costerton, J.W., 1990. Layered structure of bacterial aggregates produced in an upflow anaerobic sludge bed and filter reactor. Applied and Environmental Microbiology 56 (6), 1598e1607. Mahoney, E.M., Varangu, L.K., Cairns, W.L., Kosaric, N., Murray, R. G.E., 1987. The effect of calcium on microbial aggregation during UASB reactor start-up. Water Science and Technology 19 (1e2), 249e260. Oleszkiewicz, J.A., Sharma, V.K., 1990. Stimulation and inhibition of anaerobic processes by heavy-metals e a review. Biological Wastes 31 (1), 45e67. Raskin, L., Stromley, J.M., Rittmann, B.E., Stahl, D.A., 1994. Groupspecific 16s ribosomal-RNA hybridization probes to describe
natural communities of methanogens. Applied and Environmental Microbiology 60 (4), 1232e1240. Rudd, T., Sterritt, R.M., Lester, J.N., 1984. Complexation of heavy metals by extracellular polymers in the activated sludge process. Water Pollution Control Federation 56 (12), 1260e1268. Schlegel, H.G., Lafferty, R., 1965. Growth of Knallgas bacteria (Hydrogenomonas) using direct electrolysis of culture medium. Nature 205 (4968), 308. Schmidt, J.E., Ahring, B.K., 1993. Effects of magnesium on thermophilic acetate-degrading granules in upflow anaerobic sludge blanket (UASB) reactors. Enzyme and Microbial Technology 15 (4), 304e310. Schmidt, J.E., Ahring, B.K., 1996. Granular sludge formation in upflow anaerobic sledge blanket (UASB) reactors. Biotechnology and Bioengineering 49 (3), 229e246. Seghezzo, L., Zeeman, G., van Lier, J.B., Hamelers, H.V.M., Lettinga, G., 1998. A review: the anaerobic treatment of sewage in UASB and EGSB reactors. Bioresource Technology 65 (3), 175e190. Sekiguchi, Y., Kamagata, Y., Nakamura, K., Ohashi, A., Harada, H., 1999. Fluorescence in situ hybridization using 16S rRNAtargeted oligonucleotides reveals localization of methanogens and selected uncultured bacteria in mesophilic and thermophilic sludge granules. Applied and Environmental Microbiology 65 (3), 1280e1288. Shen, C.F., Kosaric, N., Blaszczyk, R., 1993. The effect of selected heavy-metals (Ni, Co and Fe) on anaerobic granules and their extracellular polymeric substance (EPS). Water Research 27 (1), 25e33. Son, A., Lee, J., Chiu, P.C., Kim, B.J., Cha, D.K., 2006. Microbial reduction of perchlorate with zero-valent iron. Water Research 40 (10), 2027e2032. Thrash, J.C., Coates, J.D., 2008. Review: direct and indirect electrical stimulation of microbial metabolism. Environmental Science and Technology 42 (11), 3921e3931. Tiwari, M.K., Guha, S., Harendranath, C.S., Tripathi, S., 2006. Influence of extrinsic factors on granulation in UASB reactor. Applied Microbiology and Biotechnology 71 (2), 145e154. Toprak, H., 1995. Temperature and organic loading dependency of methane and carbon-dioxide emission rates of a full-scale anaerobic waste stabilization pond. Water Research 29 (4), 1111e1119. Vlyssides, A., Barampouti, E.M., Mai, S., 2009. Influence of ferrous iron on the granularity of a UASB reactor. Chemical Engineering Journal 146 (1), 49e56. Wu, J.H., Liu, W.T., Tseng, I.C., Cheng, S.S., 2001. Characterization of microbial consortia in a terephthalate-degrading anaerobic granular sludge system. Microbiology-UK 147, 373e382. Yu, H.Q., Fang, H.H.P., Tay, J.H., 2000. Effect of Fe2þ on sludge granulation in upflow anaerobic sludge blanket reactor. Water Science and Technology 41 (12), 199e205. Yu, H.Q., Fang, H.H.P., Tay, J.H., 2001a. Enhanced sludge granulation in upflow anaerobic sludge blanket (UASB) reactors by aluminum chloride. Chemosphere 44 (1), 31e36. Yu, H.Q., Tay, J.H., Fang, H.H.P., 2001b. The roles of calcium in sludge granulation during UASB reactor start-up. Water Research 35 (4), 1052e1060. Yu, T., Lei, Z., Sun, D.Z., 2006. Functions and behaviors of activated sludge extracellular polymeric substances (EPS): a promising environmental interest. Journal of Environmental Sciences-China 18 (3), 420e427. Zhang, Y.B., Jing, Y.W., Zhang J.X., Sun, L.F. and Quan, X. Performance of a ZVI-UASB reactor for azo dye wastewater treatment. Journal of Chemical Technology & Biotechnology, in press. Zhou, W.L., Imai, T., Ukita, M., Sekine, M., Higuchi, T., 2006. Triggering forces for anaerobic granulation in UASB reactors. Process Biochemistry 41 (1), 36e43.
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Available at www.sciencedirect.com
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Effects of free cyanide on microbial communities and biological carbon and nitrogen removal performance in the industrial activated sludge process Young Mo Kim a,c, Dae Sung Lee b, Chul Park c, Donghee Park b,**, Jong Moon Park a,* a
Advanced Environmental Biotechnology Research Center, Department of Chemical Engineering, School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, Republic of Korea b Department of Environmental Engineering, Kyungpook National University, Daegu 702-701, Republic of Korea c Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USA
article info
abstract
Article history:
The changes in process performance and microbial communities under free cyanide (CN)
Received 31 May 2010
were investigated in a lab-scale activated sludge process treating industrial wastewater.
Received in revised form
The performance of phenol degradation did not appear to be adversely affected by
28 September 2010
increases in CN concentrations. In contrast, CN was found to have an inhibitory effect on
Accepted 7 October 2010
SCN biodegradation, resulting in the increase of TOC and COD concentrations. Nitratation
Available online 19 October 2010
also appeared to be inhibited at CN concentrations in excess of 1.0 mg/L, confirming that nitrite-oxidizing bacteria (NOB) is more sensitive to the CN toxicity than ammonia
Keywords:
oxidizing bacteria (AOB). After CN loads were stopped, SCN removal, denitrification, and
Free cyanide
nitrification inhibited by CN were recovered to performance efficiency of more than 98%.
Activated sludge
The AOB and NOB communities in the aerobic reactor were analyzed by terminal restric-
T-RFLP
tion fragment length (T-RFLP) and quantitative real-time PCR (qPCR). Nitrosomonas europaea
qPCR
lineage was the predominant AOB at all samples during the operation, but an obvious
Nitrifying bacteria
change was observed in the diversity of AOB at the shock loading of 30 and 50 mg/L CN,
Denitrifying bacteria
resulting in Nitrosospira sp. becoming dominant. We also observed coexisting Nitrospira and Nitrobacter genera for NOB. The increase of CN loading seemed to change the balance between Nitrospira and Nitrobacter, resulting in the high dominance of Nitrobacter over Nitrospira. Meanwhile, through using the qPCR, it was observed that the nitrite-reducing functional genes (i.e., nirS ) were dominant in the activated sludge of the anoxic reactor, regardless of CN loads. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Biological nitrification coupled to denitrification is commonly used in wastewater treatment plants to simultaneously remove carbon and nitrogen. Nitrification is achieved through the aerobic oxidation of ammonium (NHþ 4 ) or ammonia (NH3)
into nitrite (NO 2 ) by ammonia oxidizing bacteria (AOB), often Nitrosomonas spp., and followed by the oxidation of the nitrite (NO 2 ) into nitrate (NO3 ) by nitrite-oxidizing bacteria (NOB), often Nitrobacter spp. The former is called nitritation and the latter nitratation. Denitrification consists of consecutive reaction steps in which nitrate is reduced to dinitrogen gas by
* Corresponding author. Tel.: þ82 54 279 2275; fax: þ82 54 279 2699. ** Corresponding author. Tel.: þ82 53 950 7566; fax: þ82 53 950 7879. E-mail addresses: [email protected] (D. Park), [email protected] (J.M. Park). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.003
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denitrifying bacteria using the organic matter of wastewater under anoxic conditions: the reduction of nitrate via nitrite and nitric oxide to nitrous oxide or dinitrogen gas (Zumft, 1997). Although the biological processes treating industrial wastewater are efficient and reliable, they may be susceptible to disturbances and toxic loadings (Juliastuti et al., 2003; Mertoglu et al., 2008; Kim et al., 2009). In particular, the presence of cyanide in industrial wastewaters from mining, coke or steel industries leads to severe problems for biological wastewater treatment (Wild et al., 1994). It is known that cyanide inhibits nitrification and denitrification in activated sludge systems (Kelly et al., 2004; Kim et al., 2008a), with only 1e2 mg/L of free cyanide in the form of HCN or CN being more toxic to biological processes than thiocyanate and metalecyanide complexes (Park and Ely, 2008). Although the inhibitory effects of free cyanide on microbial reactions have been investigated by many researchers, most of them have focused on the threshold levels of free cyanide, at which pure or mixed cultures can tolerate without any inhibition (Kim et al., 2008b; Neufeld et al., 1986; Park and Ely, 2008). A few researchers have investigated the process performances on free cyanide (Lewandowski, 1984; Richards and Shieh, 1989). However, little is known about the microbial populations of nitrifiers and denitrifiers in an activated sludge system treating industrial wastewater containing free cyanide. Therefore, this study aimed to investigate bacterial populations relevant to nitrification and denitrification processes, particularly those in an activated sludge system treating wastewater from a coke plant under free cyanide shock loading. Diversity surveys, and the relationship of the bacterial abundance and activity to the overall processing conditions, may lead to an understanding of the basis of the process instability under free cyanide shock loading.
2.
Materials and methods
2.1.
Actual wastewater and microbial inoculums
Actual wastewater was collected from the full-scale wastewater treatment plant (WWTP) of a coke manufacturing plant in Pohang, Korea. During the operation of our reactor, the concentrations of pollutants in the raw wastewater were as follows: 1950e2325 mg/L of chemical oxygen demand (COD), 606e644 mg/L of total organic carbon (TOC), 190e229 mg/L of phenol, 186e218 mg-N/L of total nitrogen (TN), 103e119 mg-N/ L of ammonia, 405e486 mg/L of thiocyanate (SCN) and 15.0e50.0 mg-CN/L of free cyanide (CN). The pH was in the range of 8.9e9.4. To mimic the full-scale process as closely as possible, no supplementary nutrients were added into the raw wastewater. Activated sludge from the anoxic and aerobic tanks of the full-scale process was sampled and used as microbial inoculums for the anoxic and aerobic reactors of the lab-scale process, respectively.
2.2.
Set-up and operation of the lab-scale reactor
The lab-scale pre-denitrification process reactor, consisting of an anoxic reactor, an aerobic reactor, and a settler, was designed to mimic a full-scale one (Kim et al., 2008b). The working volumes of the anoxic and aerobic tanks were 8 L and
16 L, respectively. The reactor was operated in down flow mode. In the anoxic reactor, a variable-speed stirrer was used to maintain suspended-growth system. In the aerobic reactor, compressed air was supplied for mixing and aeration. Effluent from the aerobic reactor was allowed to flow into a settler for the settling of sludge, which was then recycled to the anoxic reactor. The concentration of suspended solids in each reactor was maintained at 3500e4000 mg/L, similar to that of the fullscale process. A temperature controller was used to keep each reactor at 33e34 C and the pH of the aerobic reactor was controlled at 7.3e7.5 by adding a 1 N NaOH solution. The dissolved oxygen (DO) concentration in the aerobic reactor was more than 4.0 mg/L, while the DO level of the anoxic reactor was maintained below 0.3 mg/L. The total hydraulic retention time (HRT) of the reactor was 16.7 h and the internal recycling ratio of nitrified effluent was 5. The sludge retention time (SRT) was 24 days. The performance of the reactor was monitored under four CN shock loadings of 3.6, 4.8, 7.2, and 12 mg/L d. The shock loadings were continuously given for 5 days by increasing the influent CN concentration to 15, 20, 30 and 50 mg/L respectively. Extra CN was added into the feed as potassium cyanide (Junsei Chemical Co. Ltd., Japan), while the other influent components were maintained equal to those of normal wastewater. After each shock loading, the reactor was allowed to recover fully from the effect of the shock without CN loading.
2.3.
Microbial activity test
To investigate microbial activities of nitrifiers and denitrifiers during the shock loading tests, nitrification and denitrification rates were evaluated through batch experiments with synthetic medium containing ammonia or nitrate, but without CN. Batch experiments for nitrification and denitrification activity were carried out in 500 mL Erlenmeyer flasks filled with 100 mL of test solution containing 50 mg-N/L of ammonia and nitrate ion, respectively. Each flask was inoculated with activated sludge sampled directly from the reactor, and then agitated on a thermostatic shaker at 200 rpm and 35 C, maintaining the pH at 7.5 (Kim et al., 2007, 2008a). The specific nitrification and denitrification rate were calculated following the equation provided by Kim et al. (2008b).
2.4.
DNA extraction
One milliliter of the sample was centrifuged at 16,000 g for 5 min before the supernatant was decanted. The pellet was washed with 1 mL of deionized and distilled water (DDW) and centrifuged again in the same manner to ensure a maximal removal of residual medium. The supernatant was carefully removed, and the pellet was resuspended in 100 mL of DDW. All DNA in the suspension was immediately extracted using an automated nucleic acid extractor (Magtration System 6GC, PSS, Chiba, Japan). Purified DNA was eluted with 100 mL of TriseHCl buffer (pH 8.0) and stored at 20 C for further analyses.
2.5.
T-RFLP analysis
T-RFLP was used to analyze the nitrifying bacteria community in the pre-denitrification process reactor based on the known
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16S rDNA genes of AOB and NOB, as was described in the protocol of a previous study (Siripong and Rittmann, 2007). Considering the low concentration of DNA from the nitrifiers, we amplified DNA through nested PCR, using the universal primers 11f and 1492r (Table 1), followed by the specific amplification of nitrifier genes (Nitrifier-specific reverse primer: Nso1225r, NIT3r, Ntspa685r, Forward primer: Eub338f included phosphoramidite dye 6-FAM, Table 1) (Siripong and Rittmann, 2007). We used 2 mL of template DNA for the universal amplification step and 1 mL of the universal amplification product as the template for the nitrifier-specific amplification. Finally, we purified the PCR products and digested 16S rDNA gene amplicons with MspI restriction endonuclease (Siripong and Rittmann, 2007). Digested PCR products were run through an ABI 3130XL Genetic Analyzer (Applied Biosystems, Foster City, USA) at the SolGent Company (Korea). The peak results were analyzed using the Peak Scanner software v 1.0 (https:// products.appliedbiosystems.com; Applied Biosystems, Foster City, USA). Details of the PCR conditions, product purification and restriction digestion are provided elsewhere (Siripong and Rittmann, 2007).
2.6.
qPCR analysis
To investigate the changes in the nitrifying and denitrifying bacteria populations according to the variation of the process
performance, all qPCR assays were performed using a 7300 Real-Time PCR system (Applied Biosystems, Foster City, USA). To determine the amount of the nitrifying bacteria, four independent qPCR assays were conducted by quantifying total bacterial 16S rDNA, ammonia oxidizing bacterial 16S rDNA, Nitrospira spp. 16S rDNA, and Nitrobacter spp. 16S rDNA (Table 1). Each capillary tube was separately loaded with 2 mL of template DNA (at 14e26 ng/mL), followed by 4.0 pmol of the forward and reverse primers (1 mL), together with 2.0 pmol of the TaqMan probe (0.5 mL) corresponding to each primer and probe set, 12.5 mL of TaqMan Universal PCR Master Mix (No 4304437 Applied Biosystems, New Jersey, USA), and PCR-grade sterile water for a final volume of 25 mL. The amount of total bacterial 16S rDNA was amplified using primer (1055f and 1392r) (Ferris et al., 1996). The TaqMan probe (16S Taq1115) was modified by the 1114f primer (Harms et al., 2003). The PCR program was 2 min at 50 C, 10 min at 95 C; 45 cycles of 30 s at 95 C, 60 s at 50 C, and 40 s at 72 C. To determine the amount of AOB 16S rDNA genes, two forward primers (CTO 189A/B and CTO 189C), one reverse (RT1r), and the TaqMan probe (TMP1) were used as described previously by Hermansson and Lindgren (2001). The PCR program for AOB 16S rDNA quantification included 2 min at 50 C, 10 min at 95 C; 40 cycles of 30 s at 95 C, 60 s at 60 C. The Nitrospira spp. 16S rDNA primers (NSR 1113f/NSR 1264r) (Dionisi et al., 2002) and the TaqMan probe (NSR 1143Taq) (Harms et al.,
Table 1 e Primers and probes used in T-RFLP and qPCR. Target For T-RFLP Bacterial 16S rDNA Bacterial 16S rDNA AOB 16S rDNA Nitrobacter 16S rDNA Nitrospira 16S rDNA For qPCR Bacterial 16S rDNA
AOB 16S rDNA
Nitrospira spp. 16S rDNA
Nitrobacter spp. 16S rDNA
narG gene nirS gene nirK gene nosZ gene
Primer/probe
Sequence (50 e30 )
References
11f 1492r Eub 338f Nso 1225r NTT 3r Ntspa 685r
50 -GTTTGATCCTGGCTCAG-30 50 -TACCTTGTTACGACTT-30 50 -(6-FAM)-ACTCCTACGGGAGGCAGC-30 50 -CGCCATTGTATTACGTGTGA-30 50 -CCTGTGCTCCATGCTCCG-30 50 -CGGGAATTCCGCGCTC-30
Kane et al., 1993 Lin and Stahl, 1995 Amann et al., 1990 Mobarry et al., 1996 Wagner et al., 1995 Regan et al., 2002
1055f 1392r 16S Taq1115 CTO 189fA/Ba CTO 189fCa RT1r TMP1 NSR 1113f NSR 1264r NSR 1143Taq Nitro 1198f Nitro 1423r Nitro 1374Taq narG 1960m2f narG 2050m2r nirS 1f nirS 3r nirK 876 nirK 1040 nosZ 2f nosZ 2r
50 -ATGGCTGTCGTCAGCT-30 50 -ACGGGCGGTGTGTAC-30 50 -(6-FAM)-CAACGAGCGCAACCC-(TAMRA)-30 50 -GGAGRAAAGCAGGGGATCG-30 50 -GGAGGAAAGTAGGGGATCG-30 50 -CGTCCTCTCAGACCARCTACTG-30 50 -(6-FAM)-CAACTAGCTAATCAGRCATCRGCCGCT-(TAMRA)-30 50 -CCTGCTTTCAGTTGCTACCG-30 50 -GTTTGCAGCGCTTTGTACCG-30 50 -(6-FAM)-AGCACTCTGAAAGGACTGCCCAGG-(TAMRA)-30 50 -ACCCCTAGCAAATCTCAAAAAACCG-30 50 -CTTCACCCCAGTCGCTGACC-30 50 -(6-FAM)-AACCCGCAAGGAGGCAGCCGACC-(TAMRA)-30 50 -TAYGTSGGGCAGGARAAACTG-30 50 -CGTAGAAGAAGCTGGTGCTGTT-30 50 -TACCACCCSGARCCGCGCGT-30 50 -GCCGCCGTCRTGVAGGAA-30 50 -ATYGGCGGVCAYGGCGA-30 50 -GCCTCGATCAGRTTRTGGTT-30 50 -CGCRACGGCAASAAGGTSMSSGT-30 50 -CAKRTGCAKSGCRTGGCAGAA-30
Ferris et al., 1996 Ferris et al., 1996 Harms et al., 2003 Hermansson and Lindgren, Hermansson and Lindgren, Hermansson and Lindgren, Hermansson and Lindgren, Dionisi et al., 2002 Dionisi et al., 2002 Harms et al., 2003 Graham et al., 2007 Graham et al., 2007 Graham et al., 2007 Lo´pez-Gutie´rrez et al., 2004 Lo´pez-Gutie´rrez et al., 2004 Braker et al., 1998 Braker et al., 1998 Henry et al., 2004 Henry et al., 2004 Henry et al., 2006 Henry et al., 2006
a A mixture of CTO 189fA/B and CTO 189fC at the weight ratio of 2:1 was used as the forward primer.
2001 2001 2001 2001
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2003) were tested. PCR amplification consisted of 2 min at 50 C, 10 min at 95 C; 50 cycles of 30 s at 95 C, 60 s at 60 C. Lastly, the amount of Nitrobacter spp. from Graham et al. (2007) was amplified using primer (Nitro 1198f/Nitro 1423r) and TaqMan probe (Nitro 1374Taq). The program used for amplification was 2 min at 50 C, 10 min at 95 C; 50 cycles of 20 s at 94 C, 60 s at 58 C, and 40 s at 72 C. Meanwhile, the denitrifying functional genes were quantified with SYBR Premix Ex Tag (Takara, Japan). Amplification reactions were performed in a volume of 25 mL loaded with 2 mL of template DNA (at 17.5e22.5 ng/mL), 4.0 pmol of the forward and reverse primers (1 mL), together with 0.5 mL (1X) of the ROX reference dye (50X), 12.5 mL of SYBR Premix, and PCR-grade sterile water. The qPCR program for 16S rDNA amplification using (primer 1055f and 1392r) was 30 s at 95 C; 30 cycles of 15 s at 95 C, 20 s at 55 C, and 31 s at 72 C. Primers designed by Lo´pezGutie´rrez et al. (2004) were used to determine the amount of narG gene. The PCR program for narG gene quantification included 30 s at 95 C; 35 cycles of 15 s at 95 C, 30 s at 58 C, and 31 s at 72 C. The nirS gene PCR amplification using primers (nirS 1f and nirS 3r) (Braker et al., 1998) consisted of 30 s at 95 C; 30 cycles of 15 s at 95 C, 20 s at 60 C, and 31 s at 72 C. The PCR condition for nirK gene included 30 s at 95 C; 30 cycles of 15 s at 95 C, 30 s at 58 C, and 31 s at 72 C. Lastly, Primers (nosZ 2f and nosZ 2r) designed by Henry et al. (2006) were used to determine the amount of nosZ gene. The program used for amplification was 30 s at 95 C; 30 cycles of 15 s at 95 C, 30 s at 60 C, and 31 s at 72 C.
All experiments were performed in duplicate per sample and all PCR runs included control reactions without the template. The specificity of each PCR assay was confirmed using both melting curve analysis and agarose gel electrophoresis. The gene copy numbers were calculated through a comparison of threshold cycles obtained in each PCR run with those of known standard DNA concentrations. Standards were prepared using serially diluted plasmid DNA with 103e108 gene copies/mL. Standard curves for the 16S rDNA, AOB, Nitrospira spp., Nitrobacter spp., narG, nirS, nirK, and nosZ assays were generated by plotting the threshold cycle values versus log10 of the gene copy numbers. The amplification efficiency (E ) was estimated using the slope of the standard curve through the following formula: E ¼ ð101=slope Þ 1. The efficiency of PCR amplification for each gene was between 90% and 100%.
2.7.
Analytical methods
The collected samples were centrifuged at 3500 rpm for 3 min (MF550, Hanil Sci. Ind., Korea), and then the supernatants were used for the following analyses. According to standard methods (APHA, 1998), COD, ammonia, phenol, and SCN were analyzed by the colorimetric method with a spectrophotometer (Genesys TM-5, Spectronic Inc., USA). The CN concentration was determined by the pyridine-pyrazolone method after distillation. Nitrite and nitrate ions were measured with an ion
Fig. 1 e Variation of influent- and effluent concentrations of CNL in the lab-scale pre-denitrification process during the shock loading of CNL.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 6 7 e1 2 7 9
chromatograph (ICS-1000, Dionex Co., USA). TOC, inorganic carbon (IC), and TN were measured with a TOC/TN analyzer (TOC-V csu, TNM-1, Shimadzu Co., Japan).
3.
Results and discussion
3.1.
The pollutants removal performance
After stable operation of the lab-scale reactor was achieved, extra CN was added into the feed for 5 days to examine its effects on the nitrification and denitrification as well as the removal of other pollutants. Fig. 1 shows the variation of effluent CN from the reactors under different CN shock loading conditions. During the first shock loading (15 mg/L for 5 days), CN concentration in the effluent of the anoxic reactor was maintained below 0.4 mg/L. As the loading concentration of CN into the anoxic reactor increased to 50 mg/L, however, CN concentration in the effluent from the anoxic reactor gradually increased to 2.0 mg/L, thus the CN removal efficiency of the anoxic reactor slightly decreased from 84% to
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76%. Meantime, the remaining CN in the aerobic reactor was completely removed in the aerobic reactor during the first and second shock loading periods. From third shock loading (30 mg/L for 5 days), however, over 1.0 mg/L CN flowed from the anoxic reactor into the aerobic reactor, resulting in the detection of CN in the range of below 0.2 mg/L. It must be noted that removal efficiency of CN in the aerobic reactor was not so efficient in this study. The removal efficiency of phenol was not affected by the shock loading of CN (Fig. 2(a)), and was always maintained at higher than 98%. The aerobic degradation of phenol is known to be fast in an activated sludge system, so its degradation may not be inhibited by CN. Staib and Lant (2007) also did not observe a toxic effect on phenol biodegradation by CN in their study with real cokes wastewater. On the other hand, the CN shock loading test showed inhibition of SCN removal performance in the pre-denitrification process (Fig. 2(b)). This means that CN is an inhibitor of SCN biodegradation. Figs. 1 and 2(b) show the SCN removal pattern corresponding to the variation of CN concentration. When the CN concentration flowing into the aerobic reactor reached to 2.0 mg/L, SCN removal was totally inhibited. This result was similar to the
Fig. 2 e Variation of influent- and effluent concentrations and final removal efficiencies of (a) phenol, (b) SCNL in the lab-scale pre-denitrification process during the shock loading of CNL.
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threshold level of CN on SCN removal reported in another previous study (Staib and Lant, 2007). Fig. 3 shows the COD and TOC values of the effluent of the anoxic and aerobic reactors, as well as final efficiency at different CN shock loading. Before increasing CN concentration in the influent feed to 30 mg/L, effluent COD and TOC concentrations in the anoxic and aerobic reactors were consistent with no inhibition of carbon removal. Final removal efficiencies of COD and TOC were maintained at 88%. When the CN concentration in the influent increased to 30 mg/L, however, COD and TOC removal efficiencies decreased to about 81%, and then recovered to normal performance level when the shock loading was stopped. Under the fourth shock load corresponding to 50 mg/L CN in the influent, final removal efficiencies of COD and TOC were observed at 67% and 72%, and the effluent COD and TOC concentrations in the aerobic reactor reached to 700 mg/L and 150 mg/L, respectively. This might be due to the inhibition of SCN biodegradation by CN. Effluent concentrations of COD and TOC increased in ratios of about 1.5 mg-COD/ mg-SCN and 0.4 mg-TOC/mg-SCN with increasing SCN concentration in the aerobic reactor (Fig. 2(b) and 3). To sum up,
the removal behavior of organic matters was affected by SCN biodegradation efficiency.
3.2.
Nitrification and denitrification performance
The main role of the aerobic reactor in the activated sludge system was to nitrify ammonia into nitrite and/or nitrate. Fig. 4(a) shows not only the variation of ammonia concentration, but also the total removal efficiency via nitrification. The ammonia concentration in the feed of industrial wastewater ranged between 103 and 119 mg-N/L. Until the second CN shock loading (20 mg/L for 5 days) was done, the ammonia concentration in the aerobic effluent was maintained at below 2.0 mg-N/L, corresponding to a total removal efficiency of more than 97%. Therefore, it took only 5 days to stabilize the reactor performance. Under the third shock load (30 mg/L for 5 days), ammonia concentration in the aerobic reactor effluent reached to 36 mg-N/L, and the total ammonia removal efficiency decreased to 67%. After the 30 mg/L CN loading was stopped, the system took 14 days to come back to normal performance level. Finally, when the CN
Fig. 3 e Variation of influent- and effluent concentrations and final removal efficiencies of (a) COD (b) TOC in the lab-scale pre-denitrification process during the shock loading of CNL.
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Fig. 4 e Variation of influent- and effluent concentrations of (a) ammonia, (b) nitrite/nitrate in the lab-scale pre-denitrification process during the shock loading of CNL.
concentration in the influent feed increased to 50 mg/L, ammonia concentration in the aerobic reactor effluent increased to 100 mg-N/L and nitrification efficiency sharply decreased to 8.0%, resulting in severe nitrification performance damage. Contrary to the 0.1 mg/L CN threshold level on nitrification observed in a batch system (Neufeld et al., 1986), above 1.0 mg/L CN inflow inhibited nitrification performance in this continuous system. Interestingly, the threshold level of the CN on nitrification was the same as that on SCN removal in this study. The nitrification performance, which was severely damaged by CN shock loading, could be gradually recovered to normal performance level, but took much longer than SCN removal recovery. Fig. 4(b) shows variation of the NOxeN (nitrite þ nitrate) concentration generated from nitrification and denitrification reactions at different CN shock loadings. During the first shock loading (15 mg/L for 5 days), about only 2.5 mg/L CN flowed into the anoxic reactor due to the diluting effect of the internal recycling ratio. However, denitrification efficiency decreased to 75%, and a concentration of nitrite in the anoxic
effluent gradually accumulated. In addition, some of the nitrate recycled from the aerobic reactor was not fully denitrified. As more CN was removed in the anoxic reactor (Fig. 1), nitrate concentration decreased to below 1.0 mg-N/L, but nitrite concentration increased to 15 mg-N/L. These results indicated that the denitrification performance catalyzed by the nitrite reductase might be more sensitive to CN toxicity than by the nitrate reductase. Meanwhile, nitrite concentration gradually accumulated 17 mg-N/L in the aerobic effluent. This result might be due to the inhibition of the nitrite oxydase by nitrite accumulation (Kelly et al., 2004) as well as increased nitrite concentration flowing from the anoxic effluent. At the second shock loading, we could not find denitrification inhibition. This indicates that the denitrifying bacteria might have adapted to toxic CN to some degree during the first shock loading of CN. As the CN concentration into the aerobic reactor increased to over 1.0 mg/L at the third shock loading, nitrate concentration began to drop from 40 to 5.0 mg-N/L sharply, and nitrite concentration accumulated to 47 mg-N/L, while no significant increase in ammonia
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was seen. This result suggests NOB may be more sensitive to the toxic CN shock than AOB. A similar result was observed in a previous study (Kelly et al., 2004), but the exact reason for the increased sensitivity of the NOB vs. the AOB remains unidentified (Kelly et al., 2004). Meanwhile, denitrification was achieved irrespectively of nitrification inhibition, and the pH level was also maintained at between 8.5 and 9.0, but ORP level slightly increased to 170 mV. After the reactor performance was stabilized, the fourth shock loading was begun. As soon as increased CN concentration flowed into the aerobic reactor, the nitrification performance was totally inhibited. No nitrate concentration was detected and nitrite concentration decreased to below 2.0 mg-N/L. In the aerobic reactor, the pH level increased to 8.3 and the ORP level decreased to 200 (data not shown). Therefore, 2.0 mg/L CN could be considered the threshold concentration on the nitrification performance in this activated sludge process (Figs. 1 and 4). In the case of the denitrification performance at the fourth shock loading, it could be determined that the denitrification did not occur through the toxicity of high concentration CN as well as providing no substrate such as nitrite or nitrate ion by the nitrification inhibition. Fig. 5 shows the variation of TN concentrations in the reactor and the total removal efficiency. The concentration of TN in raw wastewater ranged between 186 and 218 mg-N/L, and the TN was consistently kept below 60 mg-N/L through the pre-denitrification process until before the 30 mg/L CN shock loading. At the third and fourth CN shock loading, however, nitrification and SCN biodegradation were inhibited, resulting
in the increase of effluent TN concentration to 125 mg-N/L, corresponding to the decrease of TN removal efficiency to 36%. In the range of the CN shock loading from 15 to 20 mg/L, more than 90% of TN in the final effluent was in the form of nitrites and nitrates generated by nitrification. At the fourth shock loading, however, the fraction of nitrites and nitrates in the effluent reduced to almost zero and the fraction of other nitrogen compounds, such as ammonia and SCN, accounted for all TN concentrations in the final effluent.
3.3.
Microbial activity
Batch experiments in observation of the variations of nitrification and denitrification activities in the activated sludge process were carried out. The specific nitrification and denitrification rate in the batch test was analyzed through the variations of ammonia, nitrite and nitrate concentrations. As shown in Fig. 6, nitrifying and denitrifying bacteria activities were changed according to CN shock loading. Until the third shock loading (from 10 to 30 mg/L CN concentration inflow), the specific nitrification rate was not drastically changed according to the variation of CN inflow. The specific nitrification rate on normal performance was maintained at about 4.0 mg-N/g-VSS h. As CN concentration in the influent reached 50 mg/L, however, the specific nitrification rate decreased four times more than the normal performance. Therefore, we could additionally confirm that the nitrification activity significantly decreased through the batch test as the continuous process appeared to be inhibited. Like the recovery
Fig. 5 e Variation of influent- and effluent concentrations and final removal efficiencies of TN in the lab-scale pre-denitrification process during the shock loading of CNL.
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Fig. 6 e Variation of nitrification and denitrification activities by batch test during the shock loading of CNL.
pattern of the nitrification performance shown in Fig. 4, the specific nitrification rate increased to its normal performance level after the shock loads were stopped. As shown in Fig. 6, the specific denitrification rate changed rapidly, unlike the variation of the nitrification activity. Under normal conditions, the specific denitrification rate maintained in the range of 7.3e8.6 mg-N/g-VSS h, but it considerably decreased by more than half, when CN was added into the process. It is very interesting that the complete denitrification of the process was achieved even during the second and third shock loading periods, in spite of the considerable decrease of the denitrification rate (Figs. 4 and 6). Similarly, upon the administration of the fourth shock load (50 mg/L CN), the denitrification activity was significantly inhibited, corresponding to the decrease of the specific denitrification rate from 8.4 to 0.4 mg-N/g-VSS h. After being returned into normal condition, however, the denitrification activity of the process rapidly recovered to the normal performance.
3.4.
Microbial community
We determined the nitrifying bacterial communities present in the activated sludge process using T-RFLP designed for the identification of AOB and NOB with terminal fragment (TF) lengths (Regan et al., 2002). Figs. 7 and 8 show electropherograms of AOB, Nitrobacter-specific NOB, and Nitrospira-specific NOB, respectively, according to the variation of CN shock loading. As shown in Fig. 7, AOB-targeted T-RFLP allowed us to differentiate between AOB groups. All samples from each different loading condition showed a peak at 164 bp, a signature peak of Nitrosomonas europaea/eutropha and Nitrosomonas marina lineage (Table 2). Because the influents are from industrial wastewater, marine AOB species are not relevant. Besides the major peak at 164 bp, we detected a peak at 273 bp, which represents the potential presence of N. europaea/eutropha, Nitrosomonas oligotropha, Nitrosomonas cryotolerans, or
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Nitrosomonas communis lineage (Table 2). We also observed a peak at 102 bp, which indicates the presence of the Nitrosospira lineage. This 102 bp peak was detected from the third shock load (30 mg/L CN). When the nitrification was inhibited, the 102 bp peak appeared, and became the major peak of the 50 mg/L CN loading period when the nitrification was totally inhibited. As the nitrification recovered to normal performance, the 102 bp peak gradually disappeared. To obtain a finer understanding of the AOB community present in the process, AOB 16S rDNA gene based cloning and sequencing was performed using the AOB-target primer (Nso1225r and Eub338f) without the fluorescent dye (Table 1). Eighty-two of total 87 AOB clones from the reactor were closely associated with N. europaea in the N. europaea/ eutropha lineage and N. nitrosa in the N. communis lineage, and the rest of the AOB clones related to the Nitrosospira lineage were also detected. As a result, through the 16S rDNA gene sequences, the microorganisms corresponding to the peaks at 102 bp, 164 bp, and 273 bp could be identified as Nitrosospira, N. europaea, and N. nitrosa lineage, respectively (Table 2). Thus, the high peak at 164 bp implies the dominance of N. europaea of AOB in this activated sludge process, irrespective of the variation of CN shock loading. N. nitrosa corresponding to the 273 bp peak was dominant with N. europaea at each recovery stage, but became a minor population during CN loading (Fig. 7). Meanwhile, Nitrosospira sp. was found to be predominant in habitats exposed to the highly toxic CN concentration (Fig. 7). Although microbial shifts were clearly not observed from Nitrosomonas sp. to Nitrosospira sp. in the inhibited nitrifying system, due to the dominance of N. europaea, these results showed Nitrosospira sp. could tolerate higher CN toxicity. Many studies have found a competitive dominance between the two AOB species in habitats exposed to low-substrate or DO and metal toxicity (Schramm et al., 1999; Mertoglu et al., 2008), but there have been few reports on the AOB community changing in relation to CN toxicity. Based on Nitrobacter-specific T-RFLP, Fig. 8(a) shows a prominent peak at 137 bp, which belongs to Nitrobacter species. We also found TF sizes at 93, 104, 125, 163, and 273 in the samples. These unexpected peaks could be the result of an incomplete digestion, uncharacterized Nitrobacter species, or imperfectly matched primer (Siripong and Rittmann, 2007). The results of Nitrospira-specific T-RFLP showed two dominant peaks at 272 and 334 bp. (Fig. 8(b)). The peak at 272 corresponds to several Nitrospira clones in the database. The 334 TF belongs to one of the Nitrospira moscoviensis strains (Siripong and Rittmann, 2007). The Nitrospira species corresponding to the peak at 272 bp and 334 bp was a consistently dominant population under the variation of CN loading, but the dominance between the Nitrospira species in the reactor shifted to the Nitrospira species corresponding to the peak at 136 bp and 164 bp, when the totally inhibited nitrification of the 50 mg/L CN loading gradually recovered. Fig. 9(a) shows the changes in the 16S rDNA gene copies for the total bacteria, AOB, Nitrobacter, and Nitrospira, quantified using qPCR assays in the aerobic reactor of the predenitrification process under CN shock loadings. In all samples, the total bacterial population in the aerobic reactor ranged from 1.2 1013 to 3.6 1013 copies/L and remained
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Fig. 7 e T-RFLP profiles of AOB in the aerobic reactor during the shock loading of CNL.
constant during the shock loading of CN. These values are in the same order of magnitude as those obtained from activated sludge samples of the sewage from WWTPs (Limpiyakorn et al., 2005). The concentration of AOB determined using the AOB 16S rDNA assay was almost consistent. However, while the AOB number slightly decreased at every CN shock load stage, it gradually increased at the recovery stage. As the 2.0 mg/L CN concentration flowed into the aerobic reactor, however, an approximately 3-fold decrease was observed in the number of AOB 16S copies/L. After CN loading was stopped, the number of AOB 16S copies/L recovered to the level of the normal performance. Thus, the increase or decrease of the AOB number might affect nitrification activity (Fig. 6). Meanwhile, the percentages of the AOB within the total bacteria varied from 0.36 to 1.71% in the aerobic reactor. Contrary to expectation, the activated sludge with normal nitrification activity did not have a higher percentage of AOB than that which had inhibited nitrification activity. Thus, the AOB/bacterial ratio did not correlate with the nitrification activity.
We also observed coexisting Nitrospira and Nitrobacter genera for NOB. The Nitrospira and Nitrobacter populations in the initial operating condition were similar, at 1.0 1011 copies/L and 2.0 1010 copies/L, respectively. However, a shift in the NOB community was observed as the CN loading progressed. The 16S rDNA gene concentration of the Nitrobacter increased to a range of 2.0 1010e6.2 1010 copies/L, and the percentages of the Nitrobacter population within the total bacteria also sharply increased from 0.07 to 0.54% in the aerobic reactor, irrespective of CN shock loading. Finally, after the 30 mg/L CN shock loading, the Nitrobacter populations in the nitrifying system were higher than the Nitrospira populations. On the other hand, the number of Nitrospira gradually decreased from 1.0 1011 to 2.2 1010 copies/L, until the third shock loads (30 mg/L CN). When the 50 mg/L CN concentration in the influent was allowed to flow into the activated sludge process, a 10-fold decrease was observed in the number of Nitrospira 16S copies/L. The percentage of the Nitrospira population within the total bacteria shrank to 0.01%. The previous research reported that Nitrospira were far more
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Fig. 8 e T-RFLP profiles of (a) Nitrobacter (b) Nitrospira in the aerobic reactor during the shock loading of CNL.
sensitive to the toxicity of free ammonia than Nitrobacter (Blackburne et al., 2007). Meanwhile, the abundance of narG, nirS, nirK, and nosZ genes of denitrifying bacteria was investigated during the CN shock loading in the activated sludge process. The narG, nirS, nirK, and nosZ target molecules were less abundant than the 16S rDNA gene copies for the total bacteria: total bacteria ranged from 1.8 1013 to 3.7 1013 copies/L, narG ranged from 3.8 109 to 1.7 1010 copies/L, nirS ranged from 1.9 y 1012 to 1.3 y 1013 copies/L, nirK ranged from 4.4 1010 to 1.7 1011 copies/ L, and nosZ ranged from 1.7 1011 to 8.3 1011 copies/L (Fig. 9 (b)). In the activated sludge process treating industrial wastewater, the gene copy numbers per liter of the nirS gene were higher than those of the narG and nosZ genes at all
Table 2 e Expected TF sizes and their corresponding AOB and NOB groups based on T-RFLP of 16S rDNA gene (Siripong and Rittmann, 2007). TF size (bp) 164e166, 276 276 276 166 276 105e107 141, 196 133, 194, 265e267, 277,333
Nitrifying bacteria group Nitrosomonas europaea/eutropha lineage Nitrosomonas oligotropha lineage Nitrosomonas cryotolerans lineage Nitrosomonas marina lineage Nitrosomonas communis lineage Nitrosospira lineage Nitrobacter species Nitrospira species
sampling points. This trend implies that there is a greater abundance of genes for the nitrite-reducing genes than for the nitrate and nitrous oxide reducing genes. There was no major difference in the copy numbers of all of the genes during the CN shock loading progression, but the copy numbers of narG and nosZ genes decreased 3-fold after the 50 mg/L CN in the influent was allowed to flow into the activated sludge process. Meanwhile, for nirS, the copy numbers of genes detected were much higher than those for nirK at all sampling points. It is known that the more taxonomically diverse nirK denitrifiers are more sensitive to environmental changes than the nirS denitrifiers; however, the latter are more abundant (Yoshie et al., 2004). According to CN shock loading, however, the sensitivity of nirK denitrifiers to CN toxicity was not found in this study. To evaluate the abundance of denitrifies relative to total bacteria, percentages of denitrification genes in proportion to 16S rDNA were calculated, which resulted in proportions of around 0.03%, 17.9%, 0.34%, and 1.41% for narG, nirS, nirK, and nosZ genes respectively. The maximum abundance of nirS relative to 16S rDNA was 31%, confirming the high proportion of denitrifiers to total bacteria in this activated sludge process. However, a correlation between the abundance of functional genes and the denitrifying activity was not observed in the experimental period. Lastly, it should be noted that the presence of functional genes for target microorganisms in the activated sludge samples does not necessarily indicate that the corresponding bacteria present in the activated sludge will display the expected activities (Philippot and Hallin, 2005).
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but Nitrosospira sp. became dominant at the 30 and 50 mg/L CN shock loadings. Among the NOB, Nitrobacter and Nitrospira co-existed, Nitrospira seem to be more sensitive to CN. Meanwhile, in denitrifying genes from industrial activated sludge, nitrite-reducing functional genes (i.e., nirS ) were dominant in the anoxic reactor.
Acknowledgement This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0001437). This work was also partially supported by the second phase of the Brain Korea 21 Program in 2010 as well as by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the MEST (2009-0093819). The authors thank Dr. Seungyong Lee, Dr. Seung Gu Shin, and Min Ji Kim for assistance during this work.
references
Fig. 9 e Changes in copies per liter of (a) the total bacteria, AOB, Nitrobacter, and Nitrospira (b) the total bacteria, narG, nirS, nirK, nosZ in the lab-scale pre-denitrification process during the shock loading of CNL.
4.
Conclusions
The microbial communities and reactor performance under gradually increased CN loading were monitored in a lab-scale industrial activated sludge process using T-RFLP and qPCR. The performance of phenol degradation did not appear to be adversely affected by increases in CN concentrations. In contrast, CN- significantly inhibited SCN biodegradation, resulting in the increase of TOC and COD concentrations. Nitratation also appeared to be inhibited at CN concentrations in excess of 1.0 mg/L, confirming that NOB is more sensitive to the toxic CN than AOB. After CN loads were stopped, SCN removal, denitrification, and nitrification affected by CN toxicity recovered to normal performance. During the operation, N. europaea as the AOB was dominant,
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Direct and indirect photolysis of sulfamethoxazole and trimethoprim in wastewater treatment plant effluent Christopher C. Ryan, David T. Tan, William A. Arnold* Department of Civil Engineering, University of Minnesota, 500 Pillsbury Dr. SE, Minneapolis, MN 55455, USA
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abstract
Article history:
The photolysis of two antibacterial compounds, sulfamethoxazole and trimethoprim, was
Received 27 June 2010
studied in wastewater effluent. The rate of loss of sulfamethoxazole was enhanced in
Received in revised form
wastewater effluent due to indirect photolysis reactions, specifically reactions with
7 October 2010
hydroxyl radicals and triplet excited state effluent organic matter. Photolysis in the pres-
Accepted 9 October 2010
ence of natural organic matter, however, did not lead to enhanced degradation of sulfa-
Available online 16 October 2010
methoxazole. Trimethoprim was also found to be susceptible to indirect photolysis in wastewater effluents, with hydroxyl radical and triplet excited effluent organic matter
Keywords:
being the responsible species. Deoxygenation of solutions led to more rapid direct
Pharmaceuticals
photolysis of sulfamethoxazole and trimethoprim, indicating that direct photolysis
Photolysis
proceeds through a triplet excited state, which was verified by demonstrating that
Wastewater
trimethoprim is a singlet oxygen sensitizer. In the wastewater effluents tested, photolysis
Dissolved oxygen
could be apportioned into direct photolysis (48% for sulfamethoxazole, 18% for trimetho-
Triplet organic matter
prim), reaction with hydroxyl radicals (36% and 62%, respectively) and reaction with triplet
Nitrate
excited effluent organic matter (16% and 20%, respectively). These results indicate that allowing photolysis in wastewater stabilization ponds or wastewater treatment wetlands may lead to enhanced pharmaceutical removal prior to discharge and that effluent organic matter has different photoreactivity than natural organic matter. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Sulfamethoxazole and trimethoprim are two human-use antibacterial compounds that are often prescribed together to treat various bacterial infections. Sulfamethoxazole belongs to the sulfonamide class of antibacterial compounds, while trimethoprim does not belong to any specific class. Antibiotics/antibacterials that are used by humans are not entirely metabolized by the digestive system and pass into the sanitary sewer system. At wastewater treatment plants, some fraction of the drugs entering the plants are degraded, but a portion may pass through, either sorbed to the waste solids or dissolved in the liquid effluent (Renew and Huang, 2004; Brown et al., 2006; Go¨bel et al., 2007; Batt et al., 2007).
The discharge of effluent or the application of solids to the land surface leads to the contamination of environmental systems with the residual pharmaceuticals (Kolpin et al., 2002; Kinney et al., 2008; Barber et al., 2009). Concerns about sulfamethoxazole and trimethoprim are related to the potential for resistance to be developed to this drug combination because of its widespread use. Since these compounds first began to be used in combination in 1968, the frequency of bacterial isolates showing resistance to the combination has gradually increased (Huovinen et al., 1995). Besides resistance developed through normal use, concerns exist about resistance developing due to bacteria being exposed to the drugs at low concentrations in the environment (Daughton and Ternes, 1999).
* Corresponding author. Tel.: þ1 612 625 8582; fax: þ1 612 626 7750. E-mail address: [email protected] (W.A. Arnold). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.005
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 8 0 e1 2 8 6
When sulfamethoxazole and trimethoprim reach environmental systems, there are multiple routes for their possible removal, including photodegradation, biodegradation, and partitioning to sediments. Focusing on photodegradation, previous work has found sulfamethoxazole to degrade predominantly by direct photolysis (Boreen et al., 2004; Lam and Mabury, 2005). Reported solar quantum yields (F) for photolysis of sulfamethoxazole are 0, 0.5, and 0.09 for the three protonation states, with the 0.09 value being relevant for most environmental conditions (pH > 7) (Boreen et al., 2004). Indirect photolysis was found to be important for sulfamethoxazole in one study, for adding nitrate or humic acids to solutions increased the degradation rates above what was observed for distilled water solutions (Andreozzi et al., 2003). Sulfamethoxazole was also found to act as a photosensitizer (Zhou and Moore, 1997), producing singlet oxygen and radical species. For trimethoprim, direct photolysis proceeds at slow rates under environmentally relevant conditions when compared to other pharmaceutical compounds (Lunestad et al., 1995; Zhou and Moore, 1997). A quantum yield of 3 104 (in methanol) was found, and trimethoprim degradation was sensitized by aromatic ketones (Dedola et al., 1999). Zhou and Moore (1997) found that trimethoprim did not generate singlet oxygen or radicals in solution upon photolysis. Photolysis is a potential means to limit the release of pharmaceuticals carried by wastewater effluents into the environment. An engineered ultraviolet light photolysis system could be used to photodegrade the compounds. Alternatively, photolysis in sunlight may occur in stabilization ponds or treatment wetlands. The photochemistry of pharmaceutical compounds in a wastewater matrix, however, has not yet been thoroughly evaluated. Natural surface waters are dominated by natural organic matter (NOM) as a photosensitizer, whereas wastewater effluents contain effluent organic matter (EfOM). EfOM has different characteristics than NOM (Shon et al., 2006), which may affect its photoreactivity. Additionally, wastewater effluents that have gone through a nitrification process will have potentially high levels of nitrate, which is a photosensitizer for the production of hydroxyl radicals (Blough and Zepp, 1995). In effluent dominated streams, the dissolved constituents in the effluent (organic matter, nitrate) may impact photolysis more so than the dissolved constituents in the upstream waters (e.g., natural organic matter), pointing to the need to understand photolysis in the wastewater matrix. The goal of this study was to examine various aspects of the direct and indirect photolysis of sulfamethoxazole and trimethoprim in wastewater effluents. The photolysis rates in ultrapure water, natural water, and wastewater effluent were compared to determine the important processes in each matrix.
2.
Materials and methods
2.1.
Chemicals
Sulfamethoxazole (98%), trimethoprim (98%), 4-chlorobenzoic acid (pCBA; 99%), perinaphthenone (97%), cimetidine (99%), 2-propanol (IPA; 99.5%), 4-nitroacetophenone (PNAP; 98%) and pyridine (99%) were purchased from SigmaeAldrich. Isoprene
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(99%), 4-nitroanisole (PNA; 99%) and 30 -methoxyacetophenone (98%) were purchased from Acros Organics. Nitrogen (zero grade) and oxygen (ultrapure grade) were purchased from Minneapolis Oxygen Company. Argon (zero grade) was obtained from Airgas. All solvents were high-performance liquid chromatography (HPLC) grade. Chemicals were used as received except for PNA, which was recrystallized before use. Ultrapure (Milli-Q) water was obtained from a Millipore Simplicity UV purification system. Samples of final effluent from wastewater treatment plants were collected in 1-L glass bottles, filtered through 0.2 mm filters, acidified with sulfuric acid to pH 2, and stored at 4 C until use in experiments. The pH was readjusted to 8.0 with sodium hydroxide before initiating photolysis experiments.
2.2.
Analytical methods
Concentrations were quantified using an Agilent Technologies 1200 Series HPLC equipped with UV/visible and photodiode array detectors. All compounds were analyzed on a Supelco Ascentis RP Amide 150 mm 4.6 mm, 5 mm column. For sulfamethoxazole and trimethoprim, a methanol:pH 3 phosphate buffer gradient method was used, starting at 20:80 and changing to 50:50 over 1 min and then holding for 4 min, with a 1 ml/min flow rate and a detection wavelength of 274 nm. For PNAP and PNA, a 50:50 acetonitrile:pH 3 phosphate buffer mobile phase at a flow rate of 1 ml/min was used, with detection wavelengths of 254 nm and 280 nm respectively. Cimetidine analysis was carried out with a 5:10:85 methanol:acetonitrile:pH 3 phosphate buffer mobile phase at a flow rate of 1 ml/min with a detection wavelength of 219 nm. Analysis for pCBA used an isocratic 75:25 methanol:pH 3 phosphate buffer mobile phase at a flow rate of 1 ml/min with a detection wavelength of 240 nm. Dissolved organic carbon concentrations of the wastewater effluents were determined using a Sievers 900 portable TOC analyzer and nitrate concentrations were measured with a Metrohm 761 compact ion chromatograph. UVevisible light absorption spectra of trimethoprim were obtained with a Shimadzu 1601-PC spectrophotometer. UVevisible light absorption spectra for sulfamethoxazole have been reported previously (Boreen et al., 2004).
2.3.
Photolysis
Laboratory photolysis experiments were conducted using a Suntest CPS þ solar simulator with a UV-Suprax optical filter (Atlas Materials Testing Solutions) with the light intensity set at 765 W/m2. Samples were held in quartz test tubes (o.d. ¼ 1.3 cm, i.d. ¼ 1.1 cm, V ¼ 10 ml) set at an angle of 30 from horizontal. Tubes were filled with approximately 7 ml of solution of the desired composition. Deoxygenated samples had nitrogen or argon gas bubbled through them for 5 min and were subsequently capped and sealed. As subsamples were taken from the tubes, the appropriate gas, either nitrogen or argon, was injected into the headspace of the vials to replace the lost volume. In determination of the quantum yield of trimethoprim, PNA/pyridine and PNAP/pyridine actinometers were used (Dulin and Mill, 1982), and the quantum yield was determined by comparison of the first order rate constant for
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 8 0 e1 2 8 6
0.0
-0.5
ln(C/C0)
trimethoprim to that of PNA or PNAP, which have known quantum yields. The ratio of rate constants and the spectral overlap integrals (determined from the molar absorptivities determined from UVevisible spectra of trimethoprim and the actinometers and the manufacturer-reported lamp output spectrum) were used to calculate the quantum yield as described by Leifer (1988). Quantum yields were determined in pH 5 Milli-Q water adjusted with phosphoric acid and in 10 mM phosphate pH 8 buffered Milli-Q water under both air saturated and deoxygenated conditions. These pH values were selected to evaluate the photolysis of the protonated (positively charged) and deprotonated (neutral) forms of trimethoprim (pKa of 6.7; Zhou and Moore, 1997). Experiments investigating indirect photolysis compared the behavior of 1 mM solutions of either sulfamethoxazole or trimethoprim in a 10 mM phosphate pH 8 buffered Milli-Q water, wastewater effluent from the Blue Lake treatment plant (42 MGD, advanced secondary treatment of domestic and industrial wastewater, Shakopee, MN; final effluent DOC ¼ 7.49 mg/L, NO 3 ¼ 16.5 mg/L as N, pH ¼ 8.0), effluent from the Metro wastewater treatment plant (250 MGD, advanced secondary treatment of domestic and industrial wastewater, St. Paul, MN, DOC ¼ 8.12 mg/L, NO 3 ¼ 11.8 mg/L as N, pH ¼ 8.0), and Lake Josephine water (Roseville, MN, DOC ¼ 6.03 mg/L, NO 3 ¼ 0.4 mg/L as N, pH ¼ 8.0). To verify the roles of different photochemically produced reactive intermediates, quencher and sensitizer experiments were also performed. Solutions of 1% isopropyl alcohol were used to scavenge (hydroxyl) radicals. Isoprene, at a concentration of 0.1%, was added to selected experiments with sulfamethoxazole, as a scavenger of triplet excited states. Deoxygenation of solutions was also used to explore the role of triplet excited states. To verify susceptibility to reaction with triplet excited states, perinapthenone and 30 -methoxyacetophenone (Canonica et al., 1995) were used in the Milli-Q water as model triplet sensitizers, at a concentration of approximately 1 mM. The involvement of a triplet excited state in the degradation of trimethoprim was also examined using 40 mM trimethoprim and 2.5 mM cimetidine (which only reacts via singlet oxygenation; Latch et al., 2003) in 10 mM phosphate pH 8 buffered Milli-Q water solutions. Experiments quantifying the relative importance of direct and indirect photolysis of sulfamethoxazole and trimethoprim were conducted outdoors on August 3, 2009 in Minneapolis, MN, USA (w45 N latitude). These experiments involved comparing the degradation rates of trimethoprim, sulfamethoxazole, and pCBA in Milli-Q water, effluent from the Blue Lake plant, and solutions containing 1 mM potassium nitrate and 1 mg/L octanol (Fulkerson Brekken and Brezonik, 1988). pCBA was used as a hydroxyl radical probe to quantify hydroxyl radical steady state concentrations. Photolyses were performed on individual compounds in duplicate for each water sample/set of conditions.
wastewater effluent + isoprene
-1.0 wastewater effluent + IPA
-1.5 waste water effluent
-2.0 deoxygenated wastewater effluent
-2.5 0.0
0.5
Results and discussion
3.1.
Sulfamethoxazole
As shown in Fig. 1, sulfamethoxazole is susceptible to photolysis in wastewater effluent from the Blue Lake
1.5
2.0
2.5
3.0
3.5
Time (h) Fig. 1 e Photolysis of sulfamethoxazole in Blue Lake wastewater effluent with and without quenchers for photochemically produced reactive intermediates. Isopropanol (IPA) is a radical quencher. Isoprene is a quencher of excited triplet states, and deoxygenation removes the triplet quencher, oxygen.
treatment plant. Adding a radical quencher (IPA) suppressed the reaction rate, indicating that reaction with photogenerated radicals (an indirect photolysis process) also occurred. Addition of isoprene suppressed the reaction rate, and an increase in the reaction rate was observed in the absence of oxygen (Fig. 1; Table 1). Oxygen (a ground state triplet because of its unpaired electrons) is a quencher of triplet excited states, and thus its removal decreases the total
Table 1 e Photolysis rate constants (kobs) for sulfamethoxazole and trimethoprim. Matrix Sulfamethoxazole Wastewater effluent
Ultrapurified water
Lake water
Trimethoprim Wastewater effluent
Ultrapurified water
3.
1.0
Lake water
Quencher/alterationa
kobs (h1)
None Isopropyl alcohol Deoxygenation Isoprene None Isopropyl alcohol Deoxygenation None Deoxygenation
0.68 0.43 1.37 0.32 0.40 0.40 0.66 0.43 0.63
0.07b 0.04 0.29 0.07 0.01 0.006 0.10 0.02 0.23
None Isopropyl alcohol Deoxygenation None Deoxygenation None
0.18 0.016 0.23 0.03 0.36 0.06
0.01 0.019 0.01 0.01 0.03 0.02
a Isopropyl alcohol (IPA) is a free radical quencher, deoxygenation removes a triplet quencher (and thus increases reactive triplet lifetimes), isoprene is a triplet quencher. b Errors are 95% confidence intervals.
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quenching rate of triplets in the system. These results taken together provide strong evidence for the involvement of triplet excited species in the indirect photolysis of sulfamethoxazole. Such effects have previously been used to substantiate a role for triplet excited organic matter in the degradation of pharmaceuticals (Werner et al., 2005). Photolysis in Milli-Q water in the presence of 1 mM perinapthenone led to rapid loss of sulfamethoxazole (45 12 h1; all reported errors are 95% confidence intervals), indicating that sulfamethoxazole reacts with triplet excited states. (While perinapthenone is also a singlet oxygen sensitizer, sulfamethoxazole has a relatively small singlet oxygenation rate constant; Boreen et al., 2004). Because the IPA and isoprene quenchers did not completely suppress reaction, direct photolysis is also important in the wastewater matrix. The reaction of sulfamethoxazole via indirect photolysis was unexpected, given that our previous results (Boreen et al., 2004) in Milli-Q and natural (Lake Josephine) water had indicated that direct photolysis was the primary process. Thus, experiments in Milli-Q water and Lake Josephine water were repeated and compared to the results in wastewater effluent (Fig. 2; Table 1). Sulfamethoxazole was found to degrade with a rate constant of 0.40 0.01 h in pH 8 buffered Milli-Q water. Similar to previous results (Boreen et al., 2004), the rate constant in Lake Josephine water was identical, pointing to direct photolysis as the dominant process in natural waters (Table 1). Both of these rate constants, however, are significantly slower than that in the Blue Lake wastewater effluent (Table 1). Photolysis in the Metro Plant effluent gave similar results (0.61 0.05 h1) to the Blue Lake wastewater, verifying that indirect photolysis was an important loss process for sulfamethoxazole in both the wastewater effluents. The suppression of the sulfamethoxazole photolysis rate constant in wastewater effluent by either IPA or isoprene to values lower than that of unaltered wastewater (Table 1) indicates that radicals and triplet excited states are both
active as indirect photolysis process. As shown in Fig. 2, adding IPA to the Milli-Q water had no effect, and similar results with Lake Josephine water were seen previously (Boreen et al., 2004). Deoxygenation of both Milli-Q water and Lake Josephine water leads to an increase in the loss rate (Table 1, Fig. 2). The fact that the magnitude of the change is the same in both of these matrices, however, indicates that the excited state triplet NOM in the natural water is not serving as a photosensitizer. Rather, the direct photolysis of sulfamethoxazole proceeds through a triplet excited state, and the removal of oxygen increases the lifetime (and decreases the quenching) of this excited state (SterneVolmer quenching), allowing a greater fraction of the photo-excited molecules to be transformed (i.e., the quantum yield increases in the absence of oxygen). This explanation is consistent with previous findings that sulfamethoxazole is a singlet oxygen sensitizer (Zhou and Moore, 1997). The effect of deoxygenation further demonstrates that singlet oxygen is not important in the transformation of sulfamethoxazole. If singlet oxygen were the major reactive species responsible for the indirect photolysis, deoxygenating samples would dramatically decrease degradation rates, which was not observed.
3.2.
Trimethoprim
Like sulfamethoxazole, trimethoprim was photolyzed much more rapidly in Blue Lake wastewater effluent than in pH 8 buffered Milli-Q water or Lake Josephine water (Table 1; Fig. 3). Quenching with IPA dramatically lowered the rate constant in wastewater effluent, whereas deoxygenation led to a slight, but statistically significant, increase (Table 1; Fig. 3). The difference between the wastewater effluent and the natural water again indicates that the photosensitizing ability of the wastewater matrix is greater than that of the natural water. Deoxygenating the pH 8 buffered Milli-Q also dramatically increased the rate constant, indicating that the excited triplet state trimethoprim is effectively quenched by oxygen. This is
1.8 1.6
0.4 1.4
0.3 1.0
-1
kobs (h )
-1
ko b s ( h )
1.2
0.8 0.6
0.2
0.4
0.1 0.2
W
O no D
I,
LJ
2
I
o W
W
,n
+ W
D
O
2
A IP
W W
o
Fig. 2 e Comparison of observed first order rate constants for the photolysis of sulfamethoxazole in Blue Lake wastewater effluent (WW), Milli-Q water (DI), and Lake Josephine water (LJW). Results with quenchers (isoprene, isopropyl alcohol) and under deoxygenated conditions are also shown. Errors are 95% confidence intervals (n [ 1e3).
0.0
W
2 O
W LJ
,n W LJ
2 I,
D
D
I
+
no
O
IP A
I D
ne
2
re
O
op
o W
W
+
is
,n W W
W
W
+
W
W
IP A
0.0
Fig. 3 e Comparison of observed first order rate constants for the photolysis of trimethoprim in Blue Lake wastewater effluent (WW), Milli-Q water (DI), and Lake Josephine water (LJW). Results with isopropyl alcohol and under deoxygenated conditions are also shown. Errors are 95% confidence intervals (n [ 1e4).
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Table 2 e Quantum yields for direct photolysis of trimethoprim.a pH 5 8
Air saturated 4
6.2 10 1.2 103
Deoxygenated 7.9 103 7.0 102
a Quantum yields are for the simulated solar spectrum of the lamp. See Supplementary data.
borne out by the quantum yields measured for trimethoprim which are an order of magnitude higher in deoxygenated solutions (Table 2). The quantum yield reported here at pH 5 is similar to that previously reported in methanol (Dedola et al., 1999). The difference is likely due to the solvent. Trimethoprim has a pKa of 6.7 (Zhou and Moore, 1997) and the absorbance spectrum is influenced by pH (Supplementary Data). Thus, the quantum yield is pH, as well as oxygen, dependent. If trimethoprim in an excited triplet state is quenched by oxygen, singlet oxygen should be produced. This was tested by conducting experiments in pH 8 buffered Milli-Q water containing both trimethoprim (40 mM) and cimetidine (2.5 mM), a compound that is only susceptible to indirect photolysis via singlet oxygenation (Latch et al., 2003). In the presence of trimethoprim and oxygen, cimetidine degraded (first order rate constant of 0.49 0.04 h1 when sparged with oxygen and 0.21 0.05 h1 when sparged with air), whereas in deoxygenated solutions, trimethoprim degraded, but cimetidine loss was negligible, with a rate constant that was not statistically different than zero (0.04 0.09 h1). This indicates that trimethoprim is a singlet oxygen sensitizer, contrary to the findings of Zhou and Moore (1997). The more rapid reaction in deoxygenated wastewater could either be caused by a decrease in the rate of quenching of the trimethoprim itself or a decrease in the quenching rate of triplet excited effluent organic matter. Experiments performed with the triplet sensitizers perinapthenone and 30 -methoxyacetophenone in pH 8 buffered water gave the rate constants of 1.2 0.2 h1 and 0.12 0.01 h1 respectively, confirming the finding of Dedola et al. (1999) that trimethoprim is susceptible to reactions with triplet excited states. Again, the observed increase in rate upon deoxygenation rules out a major role for singlet oxygen in the photolysis of trimethoprim.
3.3.
Contribution of indirect photolysis processes
To quantify the fraction of reaction occurring via direct and indirect photolysis processes, a series of experiments were performed in sunlight in air saturated solutions. To determine the steady state concentration of hydroxyl radicals, pCBA was used in Blue Lake wastewater effluent and in an aqueous solution containing nitrate (a sensitizer for hydroxyl radical) and 1-octanol (to serve as a hydroxyl radical quencher). pCBA degrades exclusively by interaction with hydroxyl radicals, with a known second order rate constant of 5 109 M1 s1 (Buxton et al., 1988). When exposed to natural sunlight in a Blue Lake wastewater effluent solution, pCBA was found to degrade with a first order rate constant of 7.3 106 s1. By dividing the observed first order constant by the second order rate constant for reaction with hydroxyl radicals, the steady
state hydroxyl radical concentration was determined to be 1.5 1015 M, which is higher than normally reported for natural waters (Cooper et al., 1985), but reasonable given the nitrate concentration of the water (16.5 mg/L as N). The solution containing 1 mM nitrate (14 mg/L as N) and 1 mg/L octanol had a steady state hydroxyl radical concentration of 1.8 1015 M. For sulfamethoxazole, the overall rate constant in the sunlit wastewater effluent was 2.35 105 s1 (0.085 h1). Boreen et al. (2004) determined a second order rate constant of 5.8 109 M1 s1 for sulfamethoxazole reaction with hydroxyl radicals. Multiplying this rate constant by the steady state hydroxyl rate constant found in the effluent, gives a pseudofirst order reaction rate constant of 8.5 106 s1, which accounts for 36% of the total degradation of sulfamethoxazole. Direct photolysis of sulfamethoxazole in a parallel test tube containing pH 8 buffered Milli-Q water was found to proceed with a rate constant of 1.1 105 s1, which should be the same in Blue Lake effluent, ignoring the effect of screening by EfOM (calculated to be <10% of the incident light for wavelengths 290 nm). Thus, direct photolysis accounts for 48% of the degradation of sulfamethoxazole in the wastewater effluent. It is assumed that triplet excited EfOM is responsible for the remaining sulfamethoxazole degradation. This gives a pseudo-first order rate constant of 3.7 106 s1 for the reaction mediated by triplet excited states, which accounts for 16% of the degradation. This assumes singlet oxygenation is negligible, which is consistent with the laboratory findings in this work and previous studies (Boreen et al., 2004). The overall rate constant for trimethoprim was 1.62 105 s1 (0.058 h1) Trimethoprim was found by Dodd et al. (2006) to have a second order reaction rate constant with hydroxyl radicals of 6.9 109 M1 s1, giving a pseudo-first order rate constant of 1.0 105 s1 at the calculated steady state hydroxyl radical concentration. Hydroxyl radicals thus account for 62% of the observed degradation. The rate constant for direct photolysis was found to be 2.8 106 s1, which accounts for 18% of the degradation, and the remaining 20% of the degradation is attributed to trimethoprim interacting with triplet excited EfOM with a pseudo-first order rate constant of 3.3 106 s1. Again, this assumes no role for singlet oxygen, which is consistent with the laboratory results presented above. For the test tubes containing nitrate/octanol solution in water, the rate constant was predicted using the measured direct photolysis rate constant in pH 8 buffered Milli-Q water and the calculated pseudo-first order rate constant for reaction with hydroxyl radical based on the known second order rate constants and the steady state hydroxyl radical concentration determined with pCBA. These predictions were within a factor of two of the measured rate for sulfamethoxazole and within 15% for trimethoprim, indicating that parsing the reaction rate into direct and indirect photolysis pathways in this manner is reasonable. The data presented thus far point to triplet excited states and hydroxyl radicals as being important for the degradation of sulfamethoxazole in wastewater effluent that is exposed to light. A schematic for the hypothesized pathways of the degradation is given in Fig. 4. The proposed route for direct photolysis proceeds as follows: absorbed sunlight excites
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 8 0 e1 2 8 6
Fig. 4 e Hypothesized reaction scheme for the direct and indirect photolysis of sulfamethoxazole (SMX) and trimethoprim (TMP) in wastewater effluent.
sulfamethoxazole or trimethoprim to a singlet-excited state, which then undergoes intersystem crosses to a triplet excited state. From this point the molecule either reacts to form products or the dissolved oxygen that is present in the solution quenches the triplet excited state, returning the sulfamethoxazole or trimethoprim to the ground state. This hypothesis is consistent with the observation that the rate of photodegradation increases when dissolved oxygen is removed from Milli-Q water. For indirect photolysis there are two important possible degradation pathways. The first involves EfOM that is excited to a triplet state when it absorbs sunlight. This excited EfOM can then interact with the target to bring it directly to a triplet excited state via energy transfer, where again, it is either quenched by oxygen and returns to the ground state or proceeds to break apart to form products. Such energy transfer reactions are only possible if the triplet energy of the sulfamethoxazole or trimethoprim is lower than that of the triplet excited EfOM or the model sensitizers used in this work. We note, however, that perinapthenone (triplet energy 186 kJ mol1) reacted faster with trimethoprim than 30 methoxyacetophenone (triplet energy 303 kJ mol1). This indicates that electron transfer and/or H-atom abstraction reactions between the excited EfOM and sulfamethoxazole or trimethoprim may be responsible for the observed reactivity (Fig. 4). The second indirect photolysis pathway involves hydroxyl radicals that are produced by sunlight interacting with various dissolved species, principally nitrate, in the effluent water. The finding that indirect photolysis occurred in wastewater effluents (but not in Lake Josephine water) suggests that effluents from these wastewater treatment plants contain different indirect photolysis sensitizers than local natural waters (e.g., nitrate, organic carbon levels; see Section 2.3). The concentration of nitrate (a sensitizer for hydroxyl radicals) is higher in the effluent sample than in the Lake Josephine water sample (Section 2.3). The different composition of EfOM compared to NOM (Shon et al., 2006) may also result in different or higher energy triplet sensitizers being present. Those in EfOM may have triplet energies more conducive to reaction (via energy transfer or electron transfer/H-abstraction) with the compounds targeted in this study than does NOM. This suggests an inherent difference in the photochemistry of NOM
1285
and EfOM. Further work will need to be performed to evaluate seasonal variations in the photosensitizing ability of EfOM and NOM and the effects of pH on these processes. Similar results demonstrating the role of triplet excited states being dependent upon the organic matter source were recently found for sulfamethoxine (Guerard et al., 2009). A eutrophic water from an aquaculture pond containing autochthonous (i.e., microbially derived) organic matter was able to photosensitize the destruction of sulfamethoxine via formation of triplet excited states, but dissolved organic matter of allochthonous origin was unable to do so (Guerard et al., 2009). Because EfOM is also autochthonous, the results in this work further support that there is a difference in the photosensitizing ability of autochthonous and allochthonous organic matter. Nitrate and organic matter are both sensitizers of hydroxyl radical production. The differences in nitrate concentration are more likely to be responsible for the differences in radical production between the wastewater effluents and natural waters. The DOC levels are similar, but it cannot be ruled out that the EfOM is more efficient than NOM at producing hydroxyl radicals. This work also indicates that dissolved oxygen levels must also be considered when assessing the photolysis rates of sulfamethoxazole and trimethoprim.
4.
Conclusions
Both direct and indirect photolysis of sulfamethoxazole and trimethoprim occur in wastewater effluents exposed to sunlight. The indirect photolysis is attributable to the production of hydroxyl radicals and triplet excited state organic matter. Triplet excited state EfOM is able to react with sulfamethoxazole and trimethoprim, but the NOM present in a nearby surface water does not. Wastewater treatment wetlands/stabilizations ponds are environments in which photolysis of pharmaceuticals should be encouraged due to the presence of sensitizers not found (or found at lower concentrations) in receiving waters. According to the results of this work, the half lives of sulfamethoxazole and trimethoprim will be 1 h and 2.3 h, respectively, in the oxygenated photic zone of a midsummer sunlit wetland/stabilization pond. (Compared to 1.7 h for sulfamethoxazole and 24 h for trimethoprim in sunlit non-wastewater matrices). In wastewater impacted surface waters, EfOM and nitrate are likely to be more important sensitizers than NOM. In engineered systems, a photolysis step performed under deoxygenated conditions (e.g., post denitrification) will be advantageous if pharmaceutical removal is desired, because photolysis rates are enhanced (up to a factor of two) under such conditions.
Acknowledgements This work was funded by the Minnesota Environment and Natural Resources Trust Fund. Thanks to Peter Steen and
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Kristopher McNeill for valuable discussions. The reviewers are also thanked for their valuable input.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.10.005
references
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Quantification and molecular characterization of enteric viruses detected in effluents from two hospital wastewater treatment plants Tatiana Prado a,*, Dalton M. Silva c, Wilma C. Guilayn c, Tatiana L. Rose b, Ana Maria C. Gaspar a, Marize P. Miagostovich b a
Laboratory of Technological Development in Virology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), Av. Brazil 4.365, Manguinhos, CEP 21040-360, Rio de Janeiro RJ, Brazil b Laboratory of Comparative and Environmental Virology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro RJ, Brazil c Department of Sanitation and Environmental Health, Public Health National School, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro RJ, Brazil
article info
abstract
Article history:
Hospital wastewater has been described as an important source of spreading pathogenic
Received 10 May 2010
microorganisms in the environment. However, there are few studies reporting the pres-
Received in revised form
ence and concentrations of gastroenteric viruses and hepatitis A viruses in these envi-
2 September 2010
ronmental matrices. The aim of this study was to assess the contamination by viruses
Accepted 12 October 2010
responsible for acute gastroenteritis and hepatitis derived from hospital wastewater
Available online 20 October 2010
treatment plants (WWTPs). Rotavirus A (RV-A), human adenoviruses (HAdV), norovirus genogroup I and II (NoV GI/GII) and hepatitis A viruses (HAV) were detected and quantified
Keywords:
in sewage samples from two WWTPs located in Rio de Janeiro (Brazil) that operates
Hospital wastewater
different sewage treatments. WWTP-1 uses an Upflow Anaerobic Sludge Blanket (UASB
Upflow anaerobic sludge
reactor) and three serial anaerobic filters while WWTP-2 uses aerobic processes, activated
blanket (UASB)
sludge with extended aeration and final chlorination of the effluents. Viruses’ detection
Anaerobic filters
was investigated by using conventional PCR/RT-PCR, quantitative real-time PCR (qPCR) and
Activated sludge
partial sequencing of the genome of the viruses detected. Rate of viruses detection ranged
Enteric viruses
from 7% (NoV GI in WWTP-1) to 95% (RV-A in WWTP-2) and genome from all viruses were
Molecular characterization
detected. The most prevalent genotypes were RV-A SG I, HAdV species D and F, NoV GII/4 and HAV subgenotype IA. Mean values of viral loads (genome copies (GC)/ml) obtained in filtered effluents from anaerobic process was 1.9 103 (RV-A), 2.8 103 (HAdV) and 2.4 103 (NoV GII). For chlorinated effluents from activated sludge process, the mean values of viral loads (GC/ml) was 1.2 105 (RV-A), 1.4 103 (HAdV), 8.1 102 (NoV GII) and 2.8 104 (HAV). Data on viral detection in treated effluents of hospital WWTPs confirmed the potential for environmental contamination by viruses and could be useful to establish standards for policies on wastewater management. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel./fax: þ55 21 2562 1711. E-mail address: [email protected] (T. Prado). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.012
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1.
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Introduction
Several sources of sewage could contribute to the load of pollutants released in water bodies, particularly hospital sewage, as they carry a large amount of toxic substances and pathogenic microorganisms (Emmanuel et al., 2005, 2009; Prado et al., 2008). Wastewater treatment plants (WWTPs) have played an important role in microbiological reduction, minimizing the risks associated with pathogen circulation into the environment, although viruses still pose a challenge in wastewater treatment and disinfection. Enteric viruses transmitted by the fecal-oral route are often involved in infection resulted by the ingestion of contaminated food and water, although the global impact of water related disease is difficult to assess. Those viruses are shed in large quantities and disseminate widely in the environment representing a potential risk to human health, mainly due to their stability under adverse conditions (Bosch et al., 2008). In this context it was demonstrated the importance of studies on the efficiency of virus removal in WWTPs mainly in developing countries that often present high rates of morbidity or mortality associated to infectious diseases of fecal-oral transmission such as hepatitis and gastroenteritis (Parashar et al., 2006; Leite et al., 2008; Vitral et al., 2008). Viral detection, molecular characterization and removal efficiency of pathogenic viruses from WWTPs have been reported in Brazil (Villar et al., 2007; Barrella et al., 2009; Ferreira et al., 2009; Victoria et al., 2009), however none study specifically for hospital WWTPs. Studies on environmental contamination from hospital WWTP should be considered a priority since the major consume of water in these institutions results in a significant volume of wastewater carrying a high load of pathogenic viruses (Wang et al., 2005) and antimicrobial-resistant bacteria (Prado et al., 2008) as well as high levels of chemical substances such as pharmaceuticals, heavy metals, disinfectants, hormones, radionuclides and solvents (Emmanuel et al., 2005, 2009). Molecular methods of virus detection such as PCR assays have improved environmental virology surveys due to their sensitivity, specificity and ability to detect quickly a wide group of viruses in environmental samples (Bofill-Mas et al., 2006; De Paula et al., 2007; Girones et al., 2010). The aim of this study was to assess the environmental contamination by viruses responsible for acute gastroenteritis and hepatitis derived from hospital WWTPs. For purposes of
this study two hospital WWTPs using different sewage treatment processes were evaluated for the presence and quantification of rotavirus A (RV-A), human adenoviruses (HAdV), noroviruses (NoV) genogroup I and II (GI/GII) and hepatitis A viruses (HAV). It was used adsorption-elution method for virus concentration associated to molecular methods of genome amplification. Viruses molecular characterization was also used for genotyping and demonstration of the prevalent strains disseminated in the study area.
2.
Material and methods
2.1. Characterization of the sewage treatment plants and sampling A total of 34 samples were collected from two hospital WWTPs in the metropolitan area of Rio de Janeiro, Brazil over a period between 2005 and 2008 (section 3.2, Tables 3 and 4). While offering different characteristics of services, both hospitals have laboratories, rehabilitation and dialysis units, hospitalization, pediatrics, surgery, clinics, laundry, cafeteria and restaurants. Effluents from both WWTP are discharged in the municipal drainage system that flows across Guanabara Bay (WWTP-1) and empties into Jacarepagua Lagoon (WWTP-2), both eutrophic ecosystems located in the Metropolitan Region of Rio de Janeiro. WWTP-1 serves an average of 2.000 patients per month and has 800 regular employees. It uses an anaerobic treatment process (Upflow Anaerobic Sludge Blanket, UASB reactor) with a hydraulic retention time of 8 h, followed by post-treatment in three serial anaerobic filters (with crushed stone number four for fill support) with a hydraulic retention time of 4 h. The mean influent flow was of 2.54 l/s. Fourteen samples were collected from the following sampling points: influent (raw sewage), UASB reactor effluent and treated effluent (filtered effluent). WWTP-2 was built to serve 12.000 patients monthly but now serves an average of 22.000 patients per month. It uses an aerobic treatment process (activated sludge with extended aeration and disinfection of final effluent by chlorination) with a hydraulic retention time of 18h in the aeration tank, and a mean influent flow of 5.0 l/s. Twenty sewage samples were collected from influent (raw sewage), sedimentation tank effluent and final effluent after chlorination.
Table 1 e Viruses analyzed. PCR assay types, genomic regions of amplification on the genome, lengths of the amplicons and references. Viruses RV-A HAdV NoV (GI/GII) HAV
PCR assay
Region on the genome
Lengths of the amplicons
References
RT-PCR RT-qPCR Nested PCR qPCR Semi-nested RT-PCR Multiplex RT-PCR Nested RT-PCR RT-qPCR
VP6 NSP3 Hexon gene Hexon gene RdRp ORF-I-ORF-2 junction region VP1/2A junction region Non-coding region 5’(NC)
379 bp 86 bp 171 bp 139 bp 188 bp (GI) and 237 bp (GII) 85 bp (GI) and 98 bp (GII) 247 bp 83 bp
Iturriza-Go´mara et al. (2002) Zeng et al. (2008) Allard et al. (2001) Heim et al. (2003) Boxman et al. (2006) Pang et al. (2005) De Paula et al. (2002) De Paula et al. (2007)
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Table 2 e Mean physicochemical parameters obtained at each sampling point from wastewater treatment plants (WWTPs). WWTP 1
2
Stages of sewage treatment process RS (n ¼ 4) Ue (n ¼ 4) FE (n ¼ 4) Total removal (%) RS (n ¼ 6) ES (n ¼ 6) Chl.E (n ¼ 6) Total removal (%)
pH mean/SD
BOD5 mg/l mean SD
COD mg/l mean/SD
Ammonium mg/l mean/SD
7.0/0.09 7.0/0.05 6.9/0.31
40.4/12.08 7.7/7.5 4.0/5.47 (90.0) 95.9/4.59 17.5/2.14 13.7/4.31 (85.7)
157.0/57.98 61.7/10.22 28.4/14.37 (81.9) 285.6/75.06 52.8/8.42 70.5/33.92 (75.3)
9.7/3.29 12.1/7.39 3.1/3.60 (74.3) 8.3/3.01 1.5/1.55 1.3/1.10 (84.3)
7.0/0.05 6.1/0.22 6.2/0.15
RS ¼ raw sewage; Uc ¼ UASB effluent; FE ¼ filtered effluent; ES ¼ effluent from sedimentation tank; Chl.E ¼ chlorinated effluent; BOD ¼ biochemical oxygen demand; COD ¼ chemical oxygen demand; SD ¼ standard deviation.
At each sampling point, 2 l of sewage were collected in sterile plastic bottles, kept at 4 C and transported to the laboratory for immediate analysis.
reaction mixture was incubated in a thermal cycler (PTC-100 Programmable Thermal Controller; MJ Research, Inc., Watertown, MA) at 25 C for 5 min, 50 C for 60 min and 70 C for 20 min.
2.2.
2.6. Conventional (c) and quantitative (q)PCR protocols for viral detection
Physicochemical parameters
pH, BOD5 (biochemical oxygen demand over 5 days), COD (chemical oxygen demand) and ammonium (NeNH3) were evaluated. These physicochemical parameters were performed according to the Standard Methods for the Examination of Water and Wastewater (1998).
Viral RNA was extracted from 140 ml of the eluate to obtain a final volume of 60 ml, using the QIAmp Viral RNA kit (Qiagen, Inc., Valencia, CA) according to the manufacturer’s instructions.
PCR protocols for viruses’ detection and quantification, as well as the target region on the genome and lengths of the amplicons generated can be found in Table 1. To avoid false-positive results, quality control measures such as using separate rooms were adopted and each set of amplifications included negative and positive controls. cPCR was performed using a thermal cycler (PTC-100 Programmable Thermal Controller; MJ Research, Inc., Watertown, MA). PCR products were separated on 1.5% electrophoresis-grade agarose gel (GIBCO BRL, Life Technologies, Inc., Grand Island, NY) and stained with ethidium bromide (0.5 mg/ ml). Images were obtained using an image capture system (BioImaging Systems) and Labworks 4.0 software (UVP, Inc., Upland, CA). qPCR was carried out using an ABI PRISM 7500 Sequence Detection System (Applied Biosystems, CA, USA). For all viruses a standard curve (SC; 107 to 101 copies per reaction) was generated using tenfold serial dilutions of pCR2.1 vectors (Invitrogen, USA) containing the target region. The concentration of the primers and probes used in the qPCR reaction were described previously (Table 1). The qPCR reaction was performed in a final volume of 25 ml by using Universal PCR Master Mix (Applied Biosystems, CA, USA). Amplification data were collected and analyzed using Sequence Detection Software version 1.0 (Applied Biosystems, CA, USA). All reactions were performed in duplicate.
2.5.
2.7.
2.3.
Concentration method
The adsorption-elution method used a type-HA negatively charged membrane filter (Millipore Corporation, Bedford, MA, USA) of a 0.45-mm pore size, 142-mm of diameter and a vacuumpump filtration of samples, as described by Katayama et al. (2002). The sewage samples were pre-filtered through an AP 20 membrane (Millipore) (retention rate, 0.8e8 mm) before adding to the type-HA negatively charged membrane filter. Decontamination was done using a free chlorine solution (0.3 mM) for 15 min. Recirculation of distilled sterile water was done through the vacuum-pump system and was performed before each viral concentration procedure. The concentrated samples were stored at - 70 C until molecular analysis.
2.4.
Viral genomic extraction
Reverse transcription (RT) reaction
cDNA synthesis was carried out by RT using a random primer (PdN6; 50A260 units; Amersham Biosciences, Chalfont St Giles, Buckinghamshire, UK) for RV-A, NoV (GI/GII) and HAV. 2 ml of dimethyl sulfoxide (Sigma, St. Louis, MO) and 10 ml of RNA were mixed briefly, heated at 97 C for 7 min, and chilled on ice for 4 min. The components of the mixture and their final concentrations for a 50-ml RT reaction were as follows: 2.5 mM each deoxynucleoside triphosphate (GIBCO BRL, Life Technologies, Inc., Grand Island, NY), 1.5 mM MgCl2, 200U of Superscript III reverse transcriptase (Invitrogen), and 1 ml of PdN6. The RT
Nucleotide sequencing
cPCR products were purified with the QIAquick PCR Purification Kit (Qiagen) or QIAquick Gel Extraction Kit (Qiagen), according to the manufacturer’s instructions, and quantified by a 2.0% agarose gel electrophoresis with a Low DNA Mass Ladder (Invitrogen). PCR amplicons were sequenced in both directions with the Big Dye Terminator Cycle Sequencing Kit v.3.1 (Applied Biosystems, CA, USA) in an ABI Prism 3730 Genetic Analyzer (Applied Biosystems, CA, USA) as described by Otto et al. (2008), with the same primers used in the amplification reactions.
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Table 3 e Results of cPCR, qPCR (genome copies/ml) and molecular characterization obtained at each sampling point from anaerobic process (WWTP-1). Samples
Date (day/mo/yr)
Viral results RV-A
RS Ue FE RS Ue RS Ue FE RS Ue FE RS Ue FE n positive/n total n positive/n total
04/10/2005
08/05/2006 22/05/2006
05/06/2006
14/08/2006
(%) (%)b
() (þ) () () () () (þ) () (þ) () (þ) (þ) () () 5/14 (35.7) 7/14 (50.0%)
qPCR
Subgroup 3
2.08 10 5.5 103 () () () () 1.1 104 () 8.7 105 4.5 103 7.9 103 () () () 6/14 (42.8)
a
a
SG I SG I a
cPCR () () () () () () () () (þ) (þ) (þ) (þ) (þ) () 5/14 (35.7) 10/14 (71.0%)
NoV GII
qPCR
Specie
2
1.7 10 () () () () 2.1 103 2.1 103 4.4 103 () 6.0 101 2.2 103 2.3 104 2.9 103 4.6 103 9/14 (64.2)
D D D a a
cPCR () () () () () () () () () () (þ) () () () 1/14 (7.1) 4/14 (28.5%)
qPCR () () () () () () () () 1.8 103 1.1 103 9.6 103 () 3.2 101 () 4/14 (28.5)
RS ¼ Raw Sewage: Ue ¼ UASB effluent: FE ¼ Filtered effluent after post-treatment by three anaerobic filters: ND ¼ not determined; GenBank accession numbers: HM244916eHM244917 (RV-A); HM244884eHM244886 (HAdV); HM244881 (NoV); HM244903eHM244906 (HAV). a Not confirmed by nucleotide sequencing. b Total frequency of detection using cPCR and qPCR assays.
HAV Genotype
GII/4
cPCR () () () () () (þ) (þ) () (þ) () () (þ) () () 4/14 (28.5) 4/14 (28.5%)
qPCR ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND
Subgenotvpe
IA IB IA
IB
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cPCR
HAdV
Table 4 e Results of cPCR, qPCR (genome copies/mL) and molecular characterization obtained at each sampling point from activated sludge process (WWTP-2). Samples Date (day/mo/yr)
Viral results RV-A cPCR
6
(þ) 3.5 10 (þ) 2.9 108 () 5.9 103 (þ) 3.1 106 () () (þ) 8.06 106 (þ) 3.8 106 (þ) () (þ) 1.03 106 (þ) 2.9 106 (þ) 3.3 104 (þ) 9.6 106 (þ) 2.8 10s (þ) 7.1 105 (þ) 7.04 106 (þ) 6.2 106 (þ) 1.5 104 () 3.5 103 (þ) 1.5 106 (þ) 1.06 104 17/20 (85.0) 18/20 (90.0) 19/20 (95.0%)
Subgroup SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI SGI
cPCR
qPCR 3
(þ) 4.1 10 (þ) 2.1 103 (þ) () (þ) 7.9 102 () () (þ) 1.2 103 (þ) 4.3 103 (þ) 1.3 103 (þ) 1.9 103 (þ) () () 1.3 103 (þ) () (þ) 2.7 103 (þ) 6.09 103 (þ) 1.7 102 (þ) () (þ) () () () (þ) 1.9 103 () () 16/20 (80.0) 12/20 (60.0) 17/20 (85.0%)
NoV GII Specie/serotype D F/40 F/40 F/40 C C C F/41 F/41 F/41 D D D F/41 D F/41
cPCR
qPCR
() () () 7.4 103 () 9.7 102 () 1.1 104 () 2.3 103 () () (þ) 2.3 103 () () () () () () () 4.9 102 () () () () (þ) 2.1 103 () 1.2 103 () () () () () () () 7.6 102 () () 2/20 (10.0) 9.20 (45.0) 9/20 (45.0%)
HAV Genotype
GII/4
GII/4
cPCR
qPCR
() () () () () () () () () () () 1.3 105 (þ) 6.8 104 () 5.2 104 (þ) 1.5 104 (þ) 6.1 104 (þ) 5.7 104 (þ) 2.1 103 (þ) 7.5 103 (þ) 5.5 104 () 1.6 103 () 1.2 103 (þ) 5.9 103 () () () 2.2 103 (þ) () 9/20 (45.0) 13/20 (65.0) 14/20 (70.0%)
Subgenotvpe
IA IA IA IA IA IA IA
IA
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317 RS 01/07/2008 318 ES 319 RS 08/07/2008 320 ES 321 Chl.E 322 RS 15/07/2008 323 ES 324 Chl.E 334 RS 18/07/2008 335 ES 336 Chl.E 337 RS 22/07/2008 338 ES 339 Chl.E 340 RS 24/07/2008 341 ES 342 Chl.E 355 RS 29/07/2008 356 ES 357 Chl.E n positive/n total (%) n positive/n total (%)a
qPCR
HAdV
IA
RS ¼ Raw Sewage; ES ¼ effluent from sedimentation tank; Chl.E ¼ Chlorinated effluent. GenBank accession numbers: HM244918eHM244934 (RV-A); HM244887eHM244902 (HAdV); HM244882eHM244883 (NoV); HM244907eHM244915 (HAV). a Total frequency of detection using cPCR and qPCR assays.
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2.8.
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Sequence and phylogenetic analysis
Nucleotide (nt) sequences were edited and aligned with BioEdit Sequence Alignment Editor (Hall, 1999). The sequences were compared with their prototypes as well as with other sequences of the National Center for Biotechnology Information (NCBI/GenBank) (http://www.ncbi.nlm.nih.gov/). Phylogenetic trees were constructed using MEGA v.4.0 (Tamura et al., 2007) with the neighbor-joining method, using genetic distance corrected by the Kimura two-parameter model with 2.000 pseudoreplicates.
2.9.
Nucleotide sequence accession numbers
The Genbank accession numbers for the sequences obtained in this study are HM244916 to HM244934 (RV-A), HM244884 to HM244902 (HAdV), HM244881 to HM244883 (NoV) and HM244903 to HM244915 (HAV).
2.10.
Statistical analysis
For each type of virus analyzed, the total frequency of detection obtained in the same WWTP using conventional and/or quantitative PCR assays were compared by using chi-square test or Fisher’s exact test at a significance level of 0.05. The same procedure was carried out for viruses’ comparison between different WWTPs. The same statistical analysis was performed to determine the significant differences between cPCR or qPCR for each viral group found in the same WWTP. These analyses were made by use EPI InfoTM version 3.5.1 program, available in (http://www.cdc.gov/epiinfo/epiinfo.htm).
3.
Results
3.1.
Physicochemical parameters
The pH values obtained from both sedimentation tank and chlorinated effluent in WWTP-2 were slightly acidic. The BOD5/COD ratios in WWTP-1 and WWTP-2 were of 0.25 and 0.33, respectively, indicating that chemical substances could be present at relative high concentrations despite satisfactory removal efficiencies of BOD5, COD and ammonium (Table 2).
3.2.
Virus detection
Table 3 (WTTP-1) and 4 (WTTP-2) present the total detection frequency of each viral group using cPCR and qPCR. According to results, at least one virus was detected in 86% of samples in WTTP-1 and 100% in WWTP-2. HAdV was found the most prevalent in WWTP-1, followed by RV-A, HAV and NoV GII (Table 3). The differences in the total frequencies of viruses’ detection in WWTP-1 were significant between HAdV and NoV GII ( p ¼ 0.02, Chi-square) and HAdV and HAV ( p ¼ 0.02, Chisquare). In WWTP-2, RV-A was found the most prevalent virus, followed by HAdV, HAV and NoV GII (Table 4). Similarly to WWTP-1, the frequency of detection between RV-A and HAdV was not significant ( p ¼ 0.30, Fisher), but differences were significant between RV-A and NoV GII ( p ¼ 0.0005, Chi-
square), RV-A and HAV ( p ¼ 0.04, Fisher) and between HAdV and NoV GII ( p ¼ 0.008, Chi-square). NoV GI was identified by qPCR in one raw sewage sample (1/ 14 [7.0%]) from WWTP-1 and in two effluent samples (2/20 [10.0%]) from sedimentation tank in WWTP-2, where NoV GII was statically more frequent ( p ¼ 0.01, Chi-square) than NoV GI. The comparison of the viruses detection obtained from different WWTPs showed higher frequencies detection for all viruses in WWTP-2 than WWTP-1, but theses differences were statistically significant only for RV-A ( p ¼ 0.003, Fisher) and HAV ( p ¼ 0.01, Chi-square). In both WWTP no significant statistical differences were found between cPCR and qPCR detection frequencies for all viruses, except for NoV GII found in WWTP-2, where qPCR was more sensitive ( p ¼ 0.01, Chi-square). The mean values of viral loads (genome copies (GC)/ml) obtained from each stage of the sewage treatment processes are shown in Table 5. Viral loads were observed even after posttreatment by three anaerobic filters in WWTP-1 and after disinfection by chlorination in WWTP-2 for almost all viruses analyzed (Table 5). Higher viral loads were obtained for RV-A in all stages in both WWTPs compared with other viruses analyzed in these treatment systems. HAV presented the second-highest mean viral loads in all stages in WWTP-2 (Table 5). The mean values (GC/ml) of NoV GI were lower (1.4 101 GC/ ml) than other viruses analyzed in both WWTPs.
3.3.
Molecular characterization
Molecular characterization of the viruses detected was based on the nt sequence of cPCR products obtained according methods described previously (section 2.8). Nt sequences were compared to respective prototypes and other strains available in GenBank/NCBI in order to genotype strains as demonstrated at Tables 3 and 4. All RV strains sequenced were determined to belong to RV-A SG I with nt-identity ranging 92.6%e95.9% (WWTP-1, Table 3) and 94.1%e95.9% (WWTP-2, Table 4) with prototype strain (EF426124 GenBank accession number). For HAdV, the partial sequence of genome characterized 3 species D (WWTP-1, Table 3); 8 species F distinctly clustered (3 serotype 40 and 5 serotype 41), 5 species D and 3 species C (WWTP-2, Table 4). For specie F (serotype 40) the percentages of nt-identity ranged from 99.4% to 100.0% and for serotype 41 the nt-identity ranged from 92.6% to 95.4% with prototype strain (NC001454 GenBank accession number), for specie D (WWTP-1) and (WWTP-2) the nt-identity ranged from 98.8% to 99.4% and 96%e97.7%, respectively, with prototype strain (NC002067 GenBank accession number) and for specie C, the nt-identity ranged 98.3%e98.8% with prototype strain (NC001405 Genbank accession number). HAdV specie C (322 e WWTP-2) ranged 100.0% nt-identity with adenovirus type 2 (EU334498 e GenBank accession number) obtained from children with acute respiratory disease in Brazil. All NoV strains sequenced (Tables 3 and 4) clustered with GII/4 genotype, ranging 91.1% of nt-identity with GII/4 prototype (X76716 e Bristol - GenBank accession number). NoV strain sequenced from WWTP-1 (Table 3) obtained a high homology (98.2% nt-identity) with NoV GII/4 (AB447453 e GenBank accession number) isolated from clinical sample in
2
RS ¼ raw sewage; Ue ¼ UASB effluent; FE ¼ filtered effluent: ES ¼ effluent from sedimentation tank; Chl.E ¼ chlorinated effluent; SD ¼ standard deviation; ND ¼ not determined: WWTP-1 ¼ anaerobic process; WWTP-2 ¼ activated sludge process.
ND ND ND 2.1 104/4.8 104 1.9 104/3.0 104 2.8 104/2.8 104 103/1.0 103/1.3 103/2.1 103/1.5 103/1.5 103/2.3 5.0 1.0 2.8 1.0 1.6 1.4 3 3 3 4 5 3 103/3.8 103/4.5 103/3.9 106/4.0 107/1.3 105/2.8 2 3 1 7 7 4 RS (n ¼ 5) Ue (n ¼ 5) FE (n ¼ 4) RS (n ¼ 7) ES (n ¼ 7) ChI.E (n ¼ 6) 1
n positive (n ¼ total samples)
1.7 4.2 1.9 4.1 8.3 1.2
Mean/SD
105 103 103 106 108 105
n positive
Mean/SD
104 103 103 103 103 103
1 2 1 2 4 3
3.6 2.2 2.4 3.1 3.0 8.1
102/8.0 102/4.8 103/4.8 102/5.3 103/4.3 102/1.0
102 102 103 102 103 103
ND ND ND 4 5 4
Mean/SD Mean/SD n positive
NoV GII HAdV RV-A Stages of sewage treatment processes WWTPs
Table 5 e Arithmetic mean value (genome copies/mL) obtained from different stages of sewage treatment processes using real time PCR.
n positive
HAV
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Japan, 2006, that appointed the circulation of new variants of NoV during that period. For HAV, 2 strains from WWTP-1 belonged to subgenotype IA (94.6% and 94.0%) nt-identity with prototype strain (HAS15e X15464 GenBank accession number), and two strains were characterized as subgenotype IB (Table 3), ranging from 91% to 92.2% of nt-identity with respective prototype strain (HM-175 e M14707 GenBank accession number). In WWTP-2, all strains sequenced fall into subgenotype IA with percentages of ntidentity ranging from 94.6% to 95.2% with prototype strain HAS-15.
4.
Discussion
4.1.
Hospital WWTPs and viral detection
The load of pathogenic viruses found in effluents from WWTPs used in healthcare facilities are not well known yet as well as its potential to eliminate these agents. The pollution of water bodies and dissemination of pathogens in the environment could be minimized by suitable alternative sewage treatments. However, the construction, maintenance and operation of hospital WWTPs can mean additional costs to health systems meaning therefore that such systems should be more economically viable. Anaerobic processes for wastewater treatment are encouraged in Brazil, and a system of alternative treatment (UASB reactor with post-treatment by three anaerobic filters) was designed to treat sewage from a hospital in Rio de Janeiro. However, UASB reactors with post-treatment by three anaerobic filters arranged in series are not commonly reported and data on the effectiveness of such treatment in the removal of organic matter and pathogenic microorganisms should be validated. A previous study on the use of sewage samples from the same WWTP-1 evaluated the presence of multidrug-resistant Enterobacteriaceae to antibiotics and demonstrated the potential for spread of multidrug-resistant bacteria in the environment (Prado et al., 2008). In this study the potential of environmental contamination by organic matter and enteric viruses present in treated effluents from hospital WWTP-1 (anaerobic process) was compared with viruses’ detection found in treated effluents from another hospital WWTP-2 (activated sludge process). Regarding analysis of physicochemical parameters it was demonstrated that concentrations of BOD5 and COD found during the stages of sewage treatment processes were lower when compared to data observed in other studies (Rezaee et al., 2005; Emmanuel et al., 2005, 2009). In both WWTPs the COD concentrations were higher than BOD5. At BOD5/COD < 5, persistent chemical substances could be present in these effluents and delay biological treatment processes (Emmanuel et al., 2005). However, drugs, disinfectants and other chemical substances possibly present in hospital wastewater did not seem to affect the systems’ performance. The high viral load found in effluents from both WWTPs indicates that these hospital wastewaters contain great loads of enteric viruses, mainly RV-A and HAV found in WWTP-2. The excessive number of patients at this hospital and higher inflow of wastewater seems to have influenced the results.
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Mean viral loads of HAV found in hospital wastewaters in WWTP-2 were approximately 2 log higher than mean values of HAV quantified in wastewaters of an urban sewage treatment plant in Rio de Janeiro, Brazil (Villar et al., 2007). The viral concentrations detected in treated effluents from UASB reactor corroborated for data about the limited effectiveness of the system in the removal of pathogenic microorganisms (Ottoson et al., 2006; Prado et al., 2008). Post-treatment with three anaerobic filters is relatively inexpensive to build, operate, and maintain as a complementary treatment for removal of organic matter. However, high viral loads were detected even after post-treatment with anaerobic filters, suggesting that these treatment processes do not have a good performance to eliminate pathogenic viruses. Membrane bioreactor (MBR) processes, have been suggested as better alternatives for removal of pathogenic microorganisms, including some viruses (Ottoson et al., 2006; Zhang and Farahbakhsh, 2007). However, high installation and maintenance costs may limit their use in developing countries, particularly considering hospital settings. Results also suggest that chlorination of the effluents in WWTP-2 do not enable a satisfactory removal of viruses. Resistance of viruses in chlorinated effluents from WWTPs has been reported (Tyrrell et al., 1995; Carducci et al., 2008; Petrinca et al., 2009) and could be due to the presence of organic matter in effluents (Petrinca et al., 2009), which could facilitates the aggregation of virus. Some factors could be attributed to disinfection of viruses using chlorination: chlorine concentration x contact time (CT values), temperature (Lim et al., 2010), ionic strength and pH (Cromeans et al., 2010). Also, viral inactivation can be variable according to intrinsic characteristics of each type of virus and among different environmental samples (Espinosa et al., 2008; Cromeans et al., 2010). Moreover, Lim et al. (2010) appointed that a great proportion of viruses detected by PCR after disinfection with chlorine could be related to the lengths of the amplicons (short templates) used in the amplification reactions by PCR, which underestimates viral inactivation. A controversial study demonstrated that virus infectivity was correlated with the persistence of viruses’ genetic material detected by PCR after chlorination in water samples, suggesting that molecular techniques would be suitable for detecting viruses in water (Espinosa et al., 2008).
4.2. Methods for detecting enteric viruses in hospital wastewater The method that uses a type-HA negatively charged membrane filter (Katayama et al., 2002) seems appropriate for concentrating enteric viruses in hospital sewage samples. However, the pre-filtration of sewage samples (2 l) through an AP 20 membrane filter (Millipore) retained part of the suspended solids in which some viruses could be aggregated. There is a hypothesis that viral concentration obtained in this study, mainly in raw sewage samples would be probably higher than the observed. Katayama et al. (2008) evaluated the effectiveness of this concentration method for detecting poliovirus type 1 (PV-1) in sewage samples from WWTPs and found mean recovery yields of 23.0% for PV-1 recovered from
100 mL of raw sewage, 80.0% and 65.0% for PV-1 recovered in 1000 mL of secondary treated effluents and treated effluents, respectively. The evaluation of recovery yields of viruses using different concentration methods should be important for studies of viral removal efficiency in WWTPs. This study has evaluated the applicability of molecular methods to detect gastroenteric viruses and HAV in hospital sewage samples. No statistically significant difference was found between cPCR and qPCR for detecting viruses, except with NoV GII. The improvement of qPCR over cPCR for detecting NoV in sewage samples was also found by Victoria et al. (2009). The lengths of the amplicons obtained by qPCR (very short template) could explain a better performance when using this method in relation to cPCR used for amplifying the polymerase region of NoV. Similar results were not observed for RV detection, where the length of the amplicons obtained by qPCR was found lower than the one obtained by cPCR. Studies have demonstrated that different PCR protocols and primer sets may result in varying detection and quantification results (Bofill-Mas et al., 2006; Ferreira et al., 2009; Victoria et al., 2009). Moreover, qPCR is not always more sensitive than conventional PCR (Bastien et al., 2008), and an ongoing assessment of these methods are needed for detecting different types of viruses in environmental samples. Generally, PCRs have been considered as useful tools in environmental virology studies, especially due their specificity and sensitivity to detect a few viral genomic copies in several environmental matrices (Girones et al., 2010). Molecular methods can also be used to detect viruses that are not traditionally cultivable in cell cultures, such as human norovirus (NoV), and fastidious viruses such as rotavirus (RV-A) and human hepatitis A virus (HAV). Some studies have demonstrated that there are a great proportion of viral genomes detected by PCR in environmental samples that corresponds to infectious viral particles (Espinosa et al., 2008; Barrella et al., 2009). Moreover, viral concentration methods could play an important role in the recovery of intact virions versus naked genomes. Haramoto et al. (2007a) demonstrated that the adsorption-elution method using a type-HA negatively charged membrane filter for viral concentration in water samples is appropriate for detecting intact viral particles predominantly. Methods that combine features of cell culture and molecular methods for a rapid, sensitive detection of infective virus particles detected in water samples have been developed (Cantera et al., 2010) and is found promising to be used in the future.
4.3.
Molecular characterization
RV is the leading cause of acute gastroenteritis in children worldwide (Parashar et al., 2006). Viral genome detection in environmental samples could contribute to the characterization of RV-A load in several geographic settings, mainly for monitoring prevalent genotypes after the introduction of the rotavirus national vaccination program, in March 2006 (Leite et al., 2008). In this study, RV-A detection in two hospital WWTPs showed high frequencies of detection and viral loads, suggesting that these viruses remain disseminated widely in our
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 2 8 7 e1 2 9 7
region. Higher prevalence of RV-A in relation to other gastroenteric viruses in environmental samples has been reported in Brazil (Miagostovich et al., 2008; Ferreira et al., 2009). Molecular characterization based on partial amplification of VP6 gene demonstrated that the circulation of RV-A SG I differed from results obtained from RV-A detected in sewage samples, in Rio de Janeiro 2005, in which all RV-A strains belonged to SG II (Ferreira et al., 2009). VP6 is a trimeric protein that interacts with the inner-layer protein VP2 and with the two outer-layer proteins VP7 and VP4 (Iturriza Go´mara et al., 2002). Previous study provided evidence on independent segregation of the genes encoding VP7 and VP4 in reassortant rotavirus strains, where G1 P[8] strains are associated with a VP6 of SG II, while G2 P[4] strains are associated with a VP6 of SG I (Iturriza Go´mara et al., 2002). In fact, in last years there has been a remarkable reemergence of G2 P[4] RV-A in Brazil, but it seems to reflect a continental phenomenon (Leite et al., 2008) that is not associated necessarily with vaccination. Further epidemiological studies should be conducted to look for additional changes in the post-vaccination era. HAdV is also one of the main causes associated with different clinical syndromes including gastroenteritis, respiratory diseases, conjunctivitis, hemorrhagic cystitis and exanthema (Ishiko et al., 2008). HAdV was the most prevalent pathogen detected in WWTP-1 and the second most prevalent in WWTP-2. None statistically significant difference was obtained between detection frequencies of effluents from both WWTPs, indicating that this type of virus would be regularly excreted in these environments. The variety of species and serotypes that cause several diseases could contribute for wide dissemination of HAdV in the environment. Interestingly, specie F (enteric serotypes 40 and 41) was not detected in WWTP-1, as expected in environmental samples (Haramoto et al., 2007b), but it was detected in 50% of HAdV strains sequenced from WWTP-2. The prevalence of enteric serotype (40 and 41) of HAdV had been reported in acute gastroenteritis cases in hospitalized children from Rio de Janeiro and Salvador, Brazil (Pereira-Filho et al., 2007). HAdV species C and D, that include serotypes associated frequently with respiratory tract infections and nosocomial keratoconjunctivitis cases (Ishiko et al., 2008) were detected in 50% of samples analyzed from WWTP-2 indicating that the prevalence of species can vary according to the type of environment evaluated. NoV is transmitted mainly through contaminated water or food and has been related in outbreaks that occur frequently in schools, hospital, restaurants, among others (Tan and Jiang, 2007). The low detection of NoV GI found in this study is in accordance with other studies that reports lower frequencies of this genogroup when compared to GII in clinical and sewage samples in Rio de Janeiro, Brazil (Victoria et al., 2007, 2009). The clustering of NoV strains obtained in this study with NoV GII/4 prototypes corroborates with data that demonstrates that this genotype is the most prevalent worldwide (Siebenga et al., 2007). Economic development combined with improvement of sanitation services, mainly drinking and piped water supply, has contributed to a shift from a high to medium endemicity of HAV infection in Brazil (Vitral et al., 2008). Although all
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forms of viral hepatitis are considered diseases of compulsory notification, data on the incidence of HAV in the country are still incomplete due to insufficient information on notified cases and the fact that sometimes the aetiology of the infection is not investigated fully in the country (Vitral et al., 2008). In fact, HAV outbreaks have been occurring in Brazilian communities, considering water as the source of infection (De Paula et al., 2002, 2007), mainly in locations with less sanitation (Silva et al., 2007) showing a high detection of HAV obtained from Brazilian environmental samples (De Paula et al., 2007; Villar et al., 2007). The most of human HAV strains have been found belonging to genotype I, in which subgenotype IA predominates over subgenotype IB in South American countries (Costa-Mattioli et al., 2001), corroborating with data obtained in this study. However, the co-circulation of IA and IB subgenotypes in clinical and environmental samples in Brazil have also been identified as well as reported in this study carried out with hospital wastewaters samples in 2006, providing important information on the circulating genotypes of HAV strains in Brazil (De Paula et al., 2002, 2007; Villar et al., 2007).
5.
Conclusion
(1) Results showed that both WWTPs are not suitable systems for removal of gastroenteric viruses and HAV present in hospital wastewaters. (2) Hospital wastewaters can be contaminated by high load of enteric viruses, but the frequencies of detection and quantification results could be variable according to virus’ type and effluents coming from different health care centers. (3) RV-A could be considered the main responsible for acute gastroenteritis cases in the periods analyzed. (4) The prevalence of viral genotypes in hospital sewage samples demonstrated that environmental and molecular approach could provide viruses’ distribution, mainly in the absence of an accurate clinical diagnostic. Viral circulation pattern into the environment could be influenced by each geographic region and the epidemiological community profile. Other species of HAdV, associated generally with respiratory tract diseases and keratoconjunctivites cases could be easily detected in hospital wastewaters. (5) This is the first study concerning the detection, quantification and molecular characterization of several types of gastroenteric viruses and HAV in effluents from different hospital WWTPs carried out in Brazil. Studies on the performance of current WWTP processes for removing pathogenic microorganisms should be encouraged to support the sanitary authorities to improve policies on wastewater management.
Acknowledgements This work was financially sponsored by the Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq), Brazil and by CGVAM/Ministry of Health, Brazil. The authors thank the staff of PDTIS DNA Sequence Platform at FIOCRUZ (RPT01A) for technical support in sequencing reactions.
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Quantitative and qualitative analyses of methanogenic community development in high-rate anaerobic bioreactors Katarzyna Bialek a,b, Jaai Kim c, Changsoo Lee c, Gavin Collins b, The´re`se Mahony a,b, Vincent O’Flaherty a,b,* a
Microbial Ecology Laboratory, School of Natural Sciences and Environmental Change Institute, National University of Ireland, Galway, University Road, Galway, Ireland b Microbial Ecophysiology Research Group, Microbiology, School of Natural Sciences and Environmental Change Institute, National University of Ireland, Galway, University Road, Galway, Ireland c School of Civil & Environmental Engineering, Nanyang Technological University, 50, Nanyang Avenue, Singapore 639798
article info
abstract
Article history:
Methanogenic community structure and population dynamics were investigated in two
Received 18 August 2010
anaerobic reactors treating a dairy wastewater, an Inverted Fluidized Bed (IFB) and
Received in revised form
Expanded Granular Sludge Bed (EGSB). A combination of real-time PCR, denaturing
11 October 2010
gradient gel electrophoresis and statistical techniques was employed. Distinct methano-
Accepted 12 October 2010
genic communities developed in the IFB and EGSB reactors reflecting step-wise reductions
Available online 30 October 2010
in the applied hydraulic retention time from 72 to 12 h during the 200-day trial. The aceticlastic family Methanosarcinaceae was only detected in the IFB and the order
Keywords:
Methanomicrobiales was also much more abundant in this reactor, while the aceticlastic
Anaerobic digestion
family Methanosaetaceae was more abundant in the EGSB. The hydrogenotrophic order,
Methanogens
Methanobacteriales, predominated in both reactors under all applied operational conditions.
Real-time PCR
Non-metric multidimensional scaling (NMS) and moving-window analyses, based on
DGGE
absolute and relative abundance quantification data, demonstrated that the methanogenic
Non-metric multidimensional scaling
communities developed in a different manner in the IFB, compared to the EGSB reactor. In
(NMS)
our study, relative abundance-based quantification by NMS and moving-window analysis
Moving-window analysis
appeared to be a valuable molecular approach that was more applicable to reflect the changes in the anaerobic digestion process than approaches based either on qualitative analysis, or solely on absolute quantification of the various methanogenic groups. The overall results and findings provided a comparative, quantitative and qualitative insight into anaerobic digestion processes, which could be helpful for better future reactor design and process control. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Dairy wastewater is a complex substrate composed of easily degradable carbohydrates, mainly lactose, and bioavailable
proteins and lipids (Angelidaki et al., 1999; Fang and Yu, 2000; Yu and Fang, 2000). The chemical oxygen demand (COD) concentration of dairy effluents varies significantly, but since dairy waste streams are usually warm and strong, anaerobic
* Corresponding author. Microbial Ecophysiology Research Group, Microbiology, School of Natural Sciences and Environmental Change Institute, National University of Ireland, Galway, University Road, Galway, Ireland. Tel.: þ353 91 49 3734; fax: þ353 91 49 4598. E-mail address: [email protected] (V. O’Flaherty). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.010
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digestion (AD) is often an ideal treatment option (Wheatley, 1990). Indeed, AD is an advantageous treatment option for several reasons including: (1) no requirement for aeration, (2) low volumes of excess sludge, and (3) smaller footprints and lower land requirements than aerobic treatment processes (Demirel et al., 2005). The characteristics of the effluents arising from the production of various dairy products, such as milk, butter, yoghurt, ice-cream, desserts and cheese, are also variable depending on production systems, methods and operations used (Vidal et al., 2000). The diversity and complexity of dairy waste streams implies that different anaerobic treatment applications are required to optimally enhance the process efficiency and economic feasibility of AD treatment. Engineering optimized reactor configurations is among the most widely studied approach to improve AD of high-strength effluents (Alvarado-Lassman et al., 2008). However, although several anaerobic digesters with new configurations exist, there is still little information on how the microbial communities underpinning the different reactors (i.e., an Expanded Granular Sludge Bed (EGSB) and an Inverted Fluidized Bed (IFB) bioreactor) behave and respond. EGSB systems use self-immobilized granular sludge, whereas IFB reactors rely on inert carrier materials supporting attached growth of active biomass. The EGSB is a variant of the upflow anaerobic sludge blanket (UASB) concept, and in this reactor type, the up-flow liquid velocity (usually recommended to be >4 m h1) is the key operating feature, which expands the sludge bed and maximizes sludge-wastewater contact (Seghezzo et al., 1998). The IFB bioreactor has been identified as a new, promising design for AD (Garcia-Bernet et al., 1998). The novelty of this configuration arises from the use of floatable particles with a specific density lower than the liquid, such that the particles are fluidized downward (Garcia-Calderon et al., 1998). Due to the large specific area of support particles available for biomass retention, this technology offers advantages in the treatment of highstrength effluents by using reduced spaces and shorter hydraulic retention times (Alvarado-Lassman et al., 2008). The liquid and the produced biogas are flowing in opposite directions, which help for bed expansion (Arnaiz et al., 2003). Therefore, the down-flow (or inverse) configuration reduces energy requirements, because of the low fluidization velocities (Garcia-Calderon et al., 1998) when compared to up-flow systems. The links between changes in microbial community and perturbations in anaerobic digesters are not well understood and there may even be changes in community without apparent changes in performance. There is a need for more comprehensive studies on this topic, which can be done by aid of high throughput molecular tools (Talbot et al., 2008). Culture-free molecular techniques, particularly based on 16S rRNA genes, have been successfully applied to numerous microbial ecology studies, and helped us to link microbial community structure and dynamics to process performance (Fernandez et al., 2008; Lee et al., 2008). Many studies concerning anaerobic reactors have focused only on qualitative techniques, such as DGGE (Muyzer et al., 1993), and thus quantitative population dynamics of anaerobic bioreactors are still in its infancy. In particular, there is no information
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regarding quantitative comparison of different types of anaerobic reactors during transitional changes. Besides the community diversity and composition, the quantitative changes in microbial communities represent a significant factor affecting process performance (Wittebolle et al., 2005). Quantitative information about population structure can be therefore very useful in diagnosing problems with process performance or comparing anaerobic digesters. Acidogenic bacteria and the methanogen are the two major groups underpinning AD. However, methanogenesis is usually a rate-limiting step and requires effective control for successful operation of most AD systems (Yang et al., 2003; Yu et al., 2005, 2006). Five methanogenic groups: three hydrogenotrophic orders and two aceticlastic families are considered to cover most methanogens in anaerobic digesters (Yu et al., 2005; Lee et al., 2009). Consequently in the present study, the methanogenic community structure of the EGSB and the IFB bioreactors was quantitatively investigated using five methanogenic order or family-specific primer and probe sets employing real-time PCR. Quantitative community shifts were visualized using Non-metric multidimensional scaling (NMS) technique, based on real-time PCR data. Methanogenic community dynamics associated with operational changes were monitored using moving-window analysis (Wittebolle et al., 2008). Additionally changes in archaeal community structure in both reactors were examined using denaturing gradient gel electrophoresis (DGGE). The obtained microbial information was linked with the variations in process performance and operating conditions (i.e., hydraulic retention time (HRT)), in the IFB and EGSB reactors tested.
2.
Materials and methods
2.1.
Reactor operation
Lab-scale EGSB (Fig. 1a) and IFB (Fig. 1b) reactors (3.6-l working volume each) were continuously operated, at 37 C, for 200 days. Extendosphere (Sphere One, Chattanooga, Tennessee, USA) light mineral material composed mostly of silica and traces of aluminum, density of 0.69 g/cm3, was used as a carrier material for the IFB reactor. Particle size distribution analyses of virgin carrier material were performed using Mastersizer (Malvern Instruments) to determine particle distribution and percentage of each fraction. The range of particle size distribution was broad, from 73.6 mm to 2000 mm, but the majority of particles were in the range of 194e236 mm. The seed sludge used to inoculate both reactors was obtained from a full-scale internal circulation (IC) anaerobic digester located at the Carbery Milk Products (Ballineen, Co Cork, Ireland). The volatile solids (VS) concentration used to inoculate bioreactors was 60 g/l and granular seed sludge was crushed prior to inoculation. Both reactors were fed with a synthetic dairy wastewater (4 g COD/l) buffered with NaHCO3 and fortified with macro- (10 ml/l) and micro- (1 ml/l) nutrients (Shelton and Tiedje, 1984; Arnaiz et al., 2003). The applied hydraulic retention time (HRT) was decreased in a stepwise manner from 72 to 12 h during the operation of both reactors.
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Fig. 1 e Laboratory scale anaerobic reactors: a) Expanded Granular Sludge Bed (EGSB) and b) Inverted Fluidized Bed (IFB).
2.2.
DNA extraction
Total genomic DNA was extracted from seed sludge and biomass obtained from the reactors at various points during the reactor trial (Fig. 2). All biomass samples (50 ml) were mechanically disrupted by manual grinding with a pestle and mortar and diluted 10-fold with deionised and distilled water (DDW). Cells from each 1 ml sample were harvested by centrifugation at 13,000 rpm for 5 min, followed by decantation of the supernatant. The residual pellet was washed with 1 ml of DDW, and centrifuged again in the same manner to ensure a maximal removal of residual medium. After two washing cycles, pellet was resuspended in 1 ml of DDW. Total DNA in the suspension was extracted using an automated nucleic acid extractor (Magtration System 6 GC, PSS, Chiba, Japan). Purified DNA was eluted with 100 ml of
Fig. 2 e Chemical oxygen demand (COD) removal efficiencies of IFB and EGSB reactors. Arrows indicate biomass sampling points.
TriseHCl buffer (pH 8.0) and stored at 20 C for further analyses. DNA extraction was performed in duplicate.
2.3.
QPCR
Real-time PCR (QPCR) analysis was performed using a LightCycler 480 instrument (Roche, Mannheim, Germany) using three methanogenic order-specific primer and probe sets: Methanobacteriales (MBT), Methanomicrobiales (MMB), Methanococcales (MCC) and two methanogenic family-specific primer and probe sets: Methanosarcinaceae (Msc), Methanosaetaceae (Mst) as described previously (Yu et al., 2005; Lee et al., 2009). Each reaction mixture of 20 ml was prepared using the LightCycler 480 Probe Master kit (Roche Diagnostics): 2 ml of PCR-grade water, 1 ml of each primers (final concentration 500 nM), 1 ml of the TaqMan probe (final concentration 200 nM), 10 ml of 2 LightCycler 480 Probes Master, and 5 ml of template DNA. Amplification was performed in a two-step thermal cycling procedure: predenaturation for 10 min at 94 C followed by 40 cycles of 10 s at 94 C and 30 s at 60 C. All DNA templates were analyzed in duplicate. Quantitative standard curves were constructed as previously described (Yu et al., 2006) using the representative strains corresponding to each primer and probe sets targeting the following methanogenic groups: MBT (Methanobacterium formicicum M.o.H. (DSM 863) and Methanobrevibacter arboriphilicus DH1 (DSM 1536)); MMB (Methanospirillum hungatei JF1 (DSM 864) and Methanomicrobium mobile BP (DSM 1539)); MCC (Methanococcus vannielii SB (DSM 1224) and Methanococcus voltae PS (DSM 1537)); Msc (Methanosarcina acetivorans C2A (DSM 2834), Methanosarcina barkeri MS (DSM 800), Methanosarcina mazei Go1 (DSM 3647)); Mst (Methanosaeta concilli GP6 (DSM 3671)). For each standard solution, a 10-fold serial dilution series of 101e109 copies/ml was generated and analyzed by real-time PCR in duplicate with its corresponding primer/probe set.
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2.4.
Statistical analysis
Non-metric multidimensional scaling (NMS) was performed, based on the real-time PCR results, to visualize the methanogenic community shifts during the operation. Two separate matrices, based on the absolute quantity and the relative abundance of each target methanogenic group, respectively, were analyzed employing Sørensen distance measure in the PC-ORD software ver. 5.0 (McCune and Grace, 2002). The NMS plot mirrors the relationships between the community profiles by closely locating the communities with high similarity. Moving-window analysis was also carried out based on the absolute quantity and relative abundance matrices to monitor the variations in methanogenic community composition associated with decreases in the applied HRT in the two bioreactors. Moving-window analysis was previously demonstrated to be a valuable tool for monitoring microbial community dynamics (Wittebolle et al., 2008). The community similarity between two consecutive phases with different HRTs was calculated using PC-ORD software ver. 5.0 (McCune and Grace, 2002) and used as the indicator of community variation in response to the corresponding HRT change.
2.5.
Archaeal DGGE
Archaeal 16S rRNA genes were amplified by PCR using the primers ARC 787F and ARC 1059R (Takai and Horikoshi, 2000). To stabilize the melting behavior of the PCR products, a 40-bp GC-clamp was attached at the 50 -end of the forward primer (Muyzer et al., 1993). Touchdown PCR was conducted using thermal cycler G-Storm (Gene Technologies Ltd., Essex UK) and the following protocol was applied: initial denaturation at 94 C for 10 min; 20 cycles of denaturation at 94 C for 1 min, annealing at 65 Ce55 C (reducing 0.5 C per cycle) and elongation at 72 C for 1 min; followed by 20 cycles at 94 C for 1 min, 55 C for 1 min, 72 C for 1 min and final elongation at 72 C for 30 min (Janse et al., 2004). DGGE was performed using a D-Code system (BioRad Hercules, CA). Fifteen ml of the PCR product were loaded onto 8% acrylamide gel containing a 40e65% denaturant gradient (100% denaturant contained 7 M urea and 40% (v/v) formamide). After electrophoresis, the DGGE gel was stained with ethidium bromide and distained for 20 min, respectively. Gel image was captured using a UV transillumination camera. Bands of interest were cut directly from the gel using a sterile razor blade and eluted in 40 ml of DDW. Two ml of eluted DNA solution were further amplified using the ARC 787F and ARC 1059R primers, without the GC clamp. The PCR products were gel-purified and cloned into pGEM-T Easy vector (Promega, Madison, WI). The cloned gene fragments were sequenced using T7 primer and compared against the Ribosomal Database Project (RDP) database. Sequencing alignment and phylogenetic analyses were performed using MEGA 4 software (Tamura et al., 2007). All nucleotide sequence data reported in this study were deposited in the GenBank database under accession numbers GQ429188eGQ429206.
2.6.
Analytical methods
Biogas and effluent from both reactors were routinely sampled to analyze methane content and residual chemical
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oxygen demand (COD) concentration according to Standard Methods (APHA, 1998). All analyses were performed in duplicate. Analysis of effluent volatile fatty acids (VFA), by heated (85 C) and agitated headspace, were performed in a Varian Saturn 2000 GC/MS system, with CombiPAL autosampler (Varian Inc., Walnut Creek, CA). Separation was carried out on a Varian Capillary column, CP-WAX 58 (FFAP) CB (25 m length 0.32 mm i.d. 0.2 mm film thickness, Varian). The injector volume was 2 ml and the injector temperature was 250 C. The carrier gas was helium and the flow rate was 1 ml/min. The temperature program was as follows: 50 C (20 s) to 110 C (20 s) at a rate of 2 C/min; from 110 C to 200 C (20 s) at a rate of 20 C/min. The MS-detector was operated in the scan mode in the range of 40e150 m/z at a temperature of 210 C. Identification of VFAs was achieved by matching chromatographic retention times and spectra of standard compounds (acetic-, butyric-, iso-butyric-, propionic-, valericand iso-valeric acid). Calibration curves of standard VFAs were conducted and used for relative concentration of VFAs in effluent headspace samples and then expressed in mg/l.
3.
Results and discussion
3.1.
Process performance
The start-up of the IFB reactor was prolonged and perturbed, with large fluctuations in COD removal efficiency (CODre) and effluent VFA concentration, whereas the EGSB performance remained relatively constant throughout the initial period of operation (Figs. 2 and 3). The high initial effluent VFA concentration corresponded to poor COD removal efficiency by the IFB reactor (Fig. 2). After 2 months of operation, the IFB reactor stabilized, with a marked decrease in VFA concentration and significant improvement in COD removal. From day 60 to 160 of the trial, both the EGSB and IFB reactors performed very similarly in terms of COD removal (>80%; Fig. 2), effluent VFA concentration (<80 mg/l; Fig. 3) and >60% biogas methane content (data not shown) at an applied HRT of 72 h. With further decreases in the applied HRT from 72 down to 18 h (Fig. 2), the two reactors exhibited comparable performance with no significant deterioration recorded (Fig. 2). A further decrease in HRT to 12 h, however, caused a significant performance drop (i.e., decrease in CODre and increase in residual VFA concentration) in the IFB (Figs. 2 and 3), indicating that a loading rate threshold may have been reached. On the other hand, the EGSB showed no marked change in performance and maintained a similar level of treatment efficiency, even at the shortest HRT tested (Figs. 2 and 3).
3.2.
QPCR of methanogenic communities
In contrast to the conventional end-point detection PCR, quantitative real-time PCR (QPCR) technology based on the detection of fluorescence during amplification of target DNA (Higuchi et al., 1993) has better sensitivity and reproducibility than conventional PCR and can be easily used in studies requiring a large number of samples (Talbot et al., 2008). TaqMan QPCR primers and probe sets for the methanogenic orders:
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Fig. 3 e Volatile fatty acids (VFA presented as sum of: acetic-, butyric-, iso-butyric-, propionic-, valeric- and isovaleric acid); acetic acid and propionic acid concentrations in IFB and EGSB reactors.
Methanobacteriales, Methanomicrobiales and the two families: Methanosarcinaceae and Methanosaetaceae; demonstrated satisfactory specificity to allow quantitative comparison between the two bioreactors to be made and for correlation of the molecular data with process performance changes. In our study, the EGSB and IFB bioreactors displayed a noticeable disparity in terms of the quantitative composition of the methanogenic community by real-time PCR (Fig. 4). The order Methanobacteriales (MBT) was commonly the most dominant methanogen group in both reactors (i.e., 63e86% in the IFB and 52e71% in the EGSB), in terms of 16S rRNA gene concentration, during the trial. The 16S rRNA gene concentration of Methanobacteriales, in both reactors, remained stable at approximately 108 copies/ml, regardless of changes in operating conditions, which were characterized by decrease in HRT from 72 to 12 h (Fig. 4). The fact that Methanobacteriales were predominant throughout the trial, in both reactors, at all HRT’s, although it was not dominant group in the seed, was an
interesting finding. It is not clear whether high initial 16S rRNA gene concentration in the seed (i.e., 1.4 109 copies/ml), influent composition or other factors had a stimulating effect on growth of this group e but this should now be investigated. The hydrogenotrophic order Methanomicrobiales (MMB) was detected in both the EGSB and IFB reactors but its quantitative dynamics differed in each reactor configuration. Although the initial 16S rRNA gene concentration of Methanomicrobiales in the seed sludge was approximately 107 copies/ml in both reactors, the MMB were much more abundant in the IFB reactor during the trial (18e90-fold greater than EGSB; Fig. 4). The 16S rRNA gene concentration of Methanomicrobiales in the IFB biomass, during the operational period when the applied HRT was reduced from 72 to 18 h, stabilized between 106 and 107 copies/ml (Fig. 4). Following a further decrease in HRT to 12 h, however, the concentration of Methanomicrobiales significantly increased to 6 107 copies/ml. The Methanomicrobiales population was much less abundant in the EGSB reactor during the operational period corresponding to the HRT reduction from 72 to 18 h (1.2e2.2 105 copies/ml; Fig. 4). A slight increase in the 16S rRNA gene concentration of 6.7 105 copies/ml was, however, recorded following the reduction in HRT to 12 h (Fig. 4). In both reactors, therefore, the HRT change from 18 to 12 h seemed to evoke an increase in the abundance of this hydrogenotrophic group, which may be due to higher organic loading rate (OLR), or some morphological or biokinetic traits of the group. It is generally accepted that reducing the applied HRT, at a constant influent concentration (as was in case of this study at 4 g COD/l), will increase the OLR (Mahmoud et al., 2003). The applied HRT change from 18 to 12 h resulted in OLR increase of 50% (5.3 g COD/l/day at 18 h HRT to 8 g COD/l/ day at 12 h HRT), which had a direct and positive influence on the abundance of Methanomicrobiales. Although our results differ from those observed by Rinco´n et al. (2008), who reported a stable methanogenic community of Archaea, at every OLR tested, the archaeal community in their study was represented only by the genus Methanosaeta. Hypothetically, an increased abundance of Methanomicrobiales might be correlated with more diverse bacterial communities, and Rinco´n et al. (2008) did observe higher number of bacterial phylotypes at increased OLR; but this was not determined during our study.
Fig. 4 e Absolute quantification of methanogenic communities in the IFB and the EGSB bioreactors. Methanogenic groups: MBT (Methanobacteriales), MMB (Methanomicrobiales), Msc (Methanosarcinaceae), Mst (Methanosaetaceae).
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The aceticlastic family, Methanosaetaceae (Mst), was the most abundant group in the seed biomass and 16S rRNA gene concentration of this group was detected at 2 109 copies/ml (Fig. 4). During reactor operation between 72 and 18 h HRT, the 16S rRNA gene concentration of Methanosaetaceae in the IFB was 3.0e4.3 107 copies/ml (Fig. 4). On the other hand, Methanosaetaceae was about >2.6 fold more abundant in the EGSB (16S rRNA gene concentration of 1.0e1.5 108 copies/ml; Fig. 4). An increase in the 16S rRNA gene levels was observed, following a decrease in HRT to 12 h, to 8.3 107 copies/ml and 3.2 108 copies/ml in IFB and EGSB reactors, respectively. Previous studies have revealed the importance of Methanosaeta spp. in determining the development of granules in EGSB reactors (Liu et al., 2002; Collins et al., 2003). Methanosaeta spp. rods appear to provide a network, within the granule, to which other bacteria become associated. It is generally accepted that abundant Methanosaeta spp. improve granulation and result in more stable reactor performance (Liu et al., 2002). The predominance of Methanosaeta in our EGSB reactor (>2.6 fold higher population compared to the IFB), therefore, could be associated with more stable reactor performance and lower inreactor VFA concentrations, particularly during the first two months of the trial (Figs. 2 and 3). It has been reported that Methanosarcina spp. have higher maximum growth rates on acetate than Methanosaeta spp., but that the minimum threshold for acetate utilization by Methanosaeta spp. is 5e10 times lower than for Methanosarcina spp. (Zinder, 1990; Jetten et al., 1992). These kinetic data indicate that a selection for granules in anaerobic systems dominated by Methanosaeta spp. should be favored by low steady-state acetate concentration. Additionally, a decrease in HRT from 72 to 12 h seemed to enhance the granulation of initially crushed biomass in the EGSB reactor, which was particularly prominent after transition from 18 to 12 h HRT (visual examination). It has also been reported previously that increases in up-flow liquid velocity and reduction in applied HRT have a stimulating influence on granulation (Alphenaar et al., 1993). The appearance of the family Methanosarcina in anaerobic digesters, by contrast, has been associated with high inreactor acetate concentrations, accompanied by process deterioration (Collins et al., 2003; O’Reilly et al., 2009). VFA and acetic acid concentration data from the EGSB and IFB reactors apparently confirmed this observation (Fig. 3). Throughout the 200-day trial, the VFA concentration in EGSB reactor effluent was relatively constant, not exceeding 80 mg/l in the transitional periods associated with HRT decreases. Low VFA and therefore, low acetate concentrations, in the EGSB, especially during start-up, did not favor growth of Methanosacrcina and this tendency was maintained until the end of trial. On the other hand, the high initial VFA concentration (210e320 mg/l) where acetate constitutes 36e76% (80e220 mg/ l) apparently stimulated the growth of Methanosacrina in the IFB reactor. In addition, it should be noted that elevated concentrations of propionate (60e160 mg/l; 22e56% of all VFA; Fig. 3) in the IFB reactor effluent, during the start-up period may have played a role in stimulating the growth of propionate-oxidizing syntrophic bacteria (Zheng et al., 2006). Hypothetically, the presence of propionate-oxidizing syntrophic consortia could be linked with the more dynamic population of Methanomicrobiales, which was observed in the IFB
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system (Fig. 4). Higher levels of Methanomicrobiales have previously been reported (Zheng et al., 2006) in a reactor fed a mixture of glucose and propionate to enhance the growth of propionate-oxidizing syntrophic consortia. An interesting observation was that, although Methanosaetaceae was the dominant group in the seed sludge, once the reactors were started up, the Methanobacteriales become the predominant group (Fig. 4). The dominance of Methanosaetaceae in the original (seed) sludge is unsurprising and has been previously demonstrated in other studies (Raskin et al., 1994; McHugh et al., 2003; Sawayama et al., 2006), but the predominance of Methanobacteriales, in both reactors, at all HRT’s as recorded in this study, is unusual and has not, to our knowledge been reported previously in high-rate anaerobic sludge reactors. Quantitative PCR results demonstrated that the aceticlastic family Methanosarcinaceae (Msc) was only detected in the IFB reactor (Fig. 4). The initial 16S rRNA gene concentration of Methanosarcinaceae in the seed sludge, 1.8 105 copies/ml, markedly increased during the IFB reactor trial (to 3.5 106 copies/ml; Fig. 4). In case of the EGSB biomass, the Methanosarcinaceae 16S rRNA gene concentration was under the real-time PCR reaction detection limit (i.e., 1.6 104 copies/ml) throughout the trial. It is possible that Methanosarcinaceae were out-competed by Methanosaetaceae, due to the competitive growth relationship between both families for acetate, and that Methanosarcinaceae were washed out from EGSB system (Yu et al., 2006). As mentioned earlier, low acetate concentrations, as observed in the EGSB reactor, create an unfavorable environment for Methanosarcinaceae, which tend to predominate at higher acetate concentrations as identified in the IFB reactor. The order Methanococcales (MCC) was not detected in either of the reactors, presumably since organisms from this group require high-salt conditions for they growth (0.3e9.4% (w/v) NaCl), which are not normally found in anaerobic reactors (Boone et al., 2001).
3.3. Statistical analyses of the quantitative shifts in methanogenic communities Based on 16S rRNA gene concentration data, two different matrices were created: an absolute quantity-matrix and relative abundance matrix. The absolute quantity-matrix was based on the amount of rDNA detected by QPCR assay for each order-specific sets (MBT and MMB) and family-specific sets (Msc and Mst). The relative abundance matrix was created using the ratio between the rDNA concentration detected by the QPCR assay quantified for each order/family-specific sets (MBT, MMB, Msc and Mst) and the total rDNA concentration of methanogens detected in the sample. NMS analysis avoids the assumption of linear relationships among variables and it is reported to be the most generally effective ordination method for ecological community data (McCune and Grace, 2002). In both NMS plots (Fig. 5a and b), the cumulative r2 represented by the axes was >0.9, the final stress value was <2.5, and instability was <104, indicating that our results met the criteria for an excellent representation (McCune and Grace, 2002). Based on output from the absolute quantity-matrix a wide shift was visualized, by NMS, between the methanogenic
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Fig. 5 e Non-metric multidimensional scaling (NMS) analysis based on a) absolute quantity and b) relative abundance of target methanogenic groups measured by real-time PCR.
communities present in biomass samples taken from the EGSB during the 18 h HRT and 12 h HRT operational periods (Fig. 5a). This change in community abundance corresponded to a 2.3fold increase in the absolute numbers of rDNA of all methanogens, detected by QPCR, between these two consecutive time points. In particular, an increase in the absolute values of the predominant group, the Methanobacteriales, could possibly have some influence on the NMS output, indicating that highlyweighted effect of dominant population (especially with respect to evenness) might be a drawback in using an absolute matrix. It has been reported previously that all distance measures (including Sørensen distance, which was used in this study) lose sensitivity with increasing environmental distance (McCune and Grace, 2002). That would imply that the higher the absolute number, the greater the difference even a small change will make, although it is well known that Sørensen distance is not greatly influenced by outliers or elevated values and loses sensitivity over a distance about half the length of the environmental gradient (McCune and Grace, 2002). On the other hand, methanogenic communities from the IFB, taken at the same time points, were closely located on the NMS output, based on the absolute quantity-matrix (Fig. 5a), indicating the lesser change in the absolute numbers of rDNA of all methanogens detected by QPCR in the IFB reactor (i.e. a 1.5-fold increase). The absolute numbers of rDNA of all methanogens, detected by QPCR, were almost twice greater in the EGSB between the samples taken during the 18e12 h HRT operational periods (3.9 108 copies/ml to 9.0 108 copies/ml), when comparing to IFB (2.6 108 copies/ml to 4.0 108 copies/ml), possibly meaning that the greater the number is, the bigger the difference that will be visualized on the NMS plot based on absolute numbers. By contrast, NMS output based on the relative abundance matrix, showed a remarkable shift in community structure between the same biomass samples taken from the IFB reactor during the 18 h and 12 h HRT periods (Fig. 5b). This was paralleled
by the rapid increase in Methanomicrobiales, Methanosarcinaceae and Methanosaetaceae (10-fold increase in the MMB, 4.6-fold increase in the Msc and 2-fold increase in the Mst) during the corresponding period (Fig. 4). Interestingly, the relative abundance matrix did not show any great shift in the target microbial populations in the samples simultaneously taken from EGSB reactor e the obverse of the situation when output was based on the absolute quantification matrix. This result was consistent with the much smaller increases in the size of the populations, recorded during this period (2.4-fold increase in MBT, 3-fold increase in MMB and 2-fold increase in Mst). Moving-window analysis was also applied to investigate the dynamic shifts in community compositions of the IFB and EGSB reactors. It was previously demonstrated to be a useful tool to visualize large differences in microbial community dynamics between two reactors, which were inoculated with the same sludge (Wittebolle et al., 2008). Two approaches, again based on absolute quantity and relative abundance matrices as in case of NMS analysis, were employed to visualize shifts in quantitative community composition (Fig. 6a and b). Moving window output, based on the absolute quantitymatrix, demonstrated that the variation in each reactor was 20% during operation within the HRT range of 72e18 h. A much greater (40%) change in the EGSB community was detected in the reactor biomass, however, following reduction in the applied HRT from 18 to 12 h, far larger than the 20% change, which was recorded for the IFB reactor at the same time (Fig. 6a). On the other hand, and supporting the findings based on NMS, the relative abundance-based analysis exhibited that the EGSB community was relatively stable with <20% variation throughout the trial (5% within the HRT range of 48e12 h), whereas the IFB community showed a marked change of >20%, after the decrease in HRT to 12 h (Fig. 6b). These results clearly demonstrated that the community changes derived from the two different matrices were significantly different (Fig. 5a and b, Fig. 6 a and b). Given the
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Fig. 6 e Moving-window analysis based on a) absolute quantity and b) relative abundance of target methanogenic groups measured by real-time PCR.
performance of the IFB reactor between the 18 and 12 h HRT periods, however, with a decrease in COD removal efficiency (Fig. 2) and a corresponding increase in effluent VFA concentrations (Fig. 3), the moving-window analysis based on the relative abundance of target methanogenic groups appeared to better reflect the performance changes in our systems. Additionally, results from moving-window analysis of relative abundance correspond well to the relative abundance NMS matrix, where the community profiles of IFB biomass, sampled during the 18 h HRT period were most widely dispersed and distantly located from biomass sampled during the 12 h period.
The selection of appropriate tools for analysis of molecular data from anaerobic bioreactor (or other environmental samples) is important so that useful information on the relationship between community structure and process performance can be generated. This study suggests that the outputs derived from a relative abundance matrix could possibly avoid potentially distorted outputs, due to the influence of one dominant variable as presented, for example, by the absolute quantity-matrix relationship between EGSB biomass sampled during the 18 h and 12 h HRT periods. The relative abundancebased method, although based on reliable absolute quantification, possibly has a more meaningful correlation to process
Fig. 7 e Denaturing gradient gel electrophoresis (DGGE) profiles of temporal archaeal 16S rRNA genes from the IFB and EGSB reactors indicating the excised bands used for sequencing and phylogenetic analysis. Phylogenetic affiliation to order or family level: MBT (Methanobacteriales), MMB (Methanomicrobiales), Mst (Methanosaetaceae), Msc (Methanosarcinaceae).
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performance and might be useful, therefore, in diagnosing and/or assessing anaerobic digesters, especially in systems (such as the retained biomass systems used in this study) operated under reasonably constant conditions with no drastic changes in total microbial mass. When dealing with a system with a high fluctuation of active biomass (e.g. suspended biomass continuously stirred tank reactors), the absolute quantity-matrix may be more applicable e this suggestion should be investigated further.
3.4.
Archaeal DGGE and phylogenetic analysis
In order to link the Order and Family data from QPCR with basic information on diversity and phylogeny of the methanogens in the IFB and EGSB biomass, community fingerprinting was carried out using DGGE. This approach has been used widely in environmental microbiology to study diversity and relative abundance shifts of microbial populations in complex systems, including anaerobic bioreactors (Lee et al., 2008; Fernandez et al., 2008; Diaz et al., 2006). Extracted DNA samples were analyzed to investigate changes in methanogenic community structure, with respect to the HRT changes, in the reactors tested. A total of 19 bands, designated AD 1e19,
were visually detected (Fig. 7) and aseptically excised from the gel for subsequent sequencing analyses. The phylogenetic affiliations of the band sequences were determined by comparing against the GenBank database (Table 1). AD 6, 11, 13, 15, 16, 17 and 18 were dominant in both EGSB and IFB reactors, and they were all affiliated to the order Methanobacteriales (MBT). Four of them (i.e., AD 6, 11, 13 and 18) were closely related (>97% sequence similarity) to Methanobacterium species with 98.9e100% similarities. AD18, affiliated to Methanobacterium beijingense, was previously isolated from an EGSB bioreactor treating synthetic glucose wastewater (O’Reilly et al., 2009).Methanobacterium formicicum (AD 6, 11, 13), was previously observed in anaerobic sludge (Collins et al., 2003). The other three bands (i.e., AD 15, 16, 17), were not closely related to any known species with <90% similarities. Methanomicrobiales (MMB) related bands (i.e., AD 1, 2, 3, 9 and 14) were detected only in the IFB reactor. Four of them (i.e., AD 1, 2, 9 and 14) were commonly closely related to Methanoculleus species: Methanoculleus bourgensis, Methanoculleus palmolei, Methanoculleus marisnigri with 98.2e98.5% similarities and were previously reported in sludge samples from EGSB (Collins et al., 2003), while AD 3 was closely related
Table 1 e Phylogenetic affiliation of the 16S rRNA gene sequences from DGGE bands. Band AD1
AD2 AD3 AD4 AD5 AD6
AD7 AD8 AD9 AD10 AD11
AD12 AD13 AD14 AD15
AD16
AD17
AD18 AD19
Nearest species and taxon
Phylogenetic affiliation to order or family level
Similarity (%)
Methanoculleus bourgensis Methanoculleus palmolei Methanoculleus marisnigri Methanoculleus bourgensis Methanospirillum hungatei Methanosaeta concilii Methanosaeta concilii Methanobacterium formicicum Methanobacterium palustre Methanobacterium subterraneum Methanosaeta concilii M.ethanosaeta concilii Methanoculleus bourgensis M.ethanosaeta concilii M.ethanobacterium formicicum Methanobacterium palustre Methanobacterium subterraneum Methanosarcina mazei Methanosarcina lacustris Methanobacterium formicicum Methanobacterium palustre Methanoculleus bourgensis Uncultured archaeon PL-10D12 Methanothermobacter thermautotrophicus Methanothermobacter wolfeii Uncultured archaeon PL-10D12 Methanothermobacter thermautotrophicus Methanothermobacter wolfeii Uncultured archaeon PL-10D12 Methanothermobacter thermautotrophicus Methanothermobacter wolfeii Methanobacterium beijingense Methanosarcina lacustris Methanosarcina mazei
Methanomicrobiales Methanomicrobiales Methanomicrobiales Methanomicrobiales Methanomicrobiales Methanosaetaceae Methanosaetaceae Methanobacteriales Methanobacteriales Methanobacteriales Methanosaetaceae Methanosaetaceae Methanomicrobiales Methanosaetaceae Methanobacteriales Methanobacteriales Methanobacteriales Methanosarcinaceae Methanosarcinaceae Methanobacteriales Methanobacteriales Methanomicrobiales
98.2 98.2 98.2 98.9 98.2 99.6 99.6 99.3 99.3 99.3 99.6 99.6 98.2 99.6 98.9 98.9 98.9 100.0 100.0 100.0 100.0 98.5 100.0 85.6 85.6 99.6 85.2 85.2 98.9 84.5 84.5 100.0 99.6 99.6
Methanobacteriales Methanosarcinaceae Methanosarcinaceae
Accession no. AB065298 Y16382 AF028693 FJ155848 CP000254 X16932 X16932 AF169245 AF093061 DQ649330 X16932 X16932 FJ155848 X16932 AF169245 AF093061 DQ649330 AB065295 AY260431 AF169245 AF093061 FJ155848 AY570673 AB020530 DQ657904 AY570673 AB020530 DQ657904 AY570673 AB020530 DQ657904 AY350742 AF432127 AJ012095
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to Methanospirillum hungatei (98.2%), also formerly observed in anaerobic sludge (Fernandez et al., 2008; O’Reilly et al., 2009). Interestingly, abundant MMB populations were mainly detected by DGGE in the IFB reactor following the transition to a 12 h HRT (apart from band AD14 which was also visible at 18 h HRT). This corresponded very well with the increased 16S rRNA gene concentration detected by QPCR in 12 h HRT IFB biomass sample. It has been previously reported that some Methanomicrobiales, especially M. bourgensis, M. palmolei and M. marisnigri require acetate as a growth factor, or are greatly stimulated by this organic compound (Ollivier et al., 1986; Maestrojuan et al., 1990; Zellner et al., 1998). Sequences AD12 and 19 were closely related to Methanosarcina mazei and Methanosarcina lacustris with 99.6e100% similarity (Table 1). M. mazei has been frequently observed in anaerobic digestion systems (Diaz et al., 2006) and is able to utilize a variety of substrates, including acetate, H2/CO2, methanol and methylamines (Boone et al., 2001). All Methanosaetaceae-related bands (i.e., AD 4, 5, 7, 8 and 10) were apparent in both reactors and closely related to Methanosaeta concilii with 99.6% similarity. Although DGGE is a qualitative method, stronger band intensity observed in samples from EGSB suggested predominance of M. concilii-like organisms in this reactor (Fig. 7). As stated above, the majority of detected Methanomicrobiales populations were related to Methanoculleus species. It is suggested, therefore, that the dominance of MMB in IFB reactor was directly related to the higher in-reactor acetate concentrations, which enhanced the specific growth rate of those organisms. The absence of MMB-related bands in EGSB samples (although detected by QPCR) can be explained by the much greater concentration of MMB in the IFB (18e90fold higher during the trial) and relatively lower sensitivity of DGGE (Talbot et al., 2008), compared to QPCR.
4.
Conclusion
Our study showed that quantitative methanogenic community composition data, expressed both as absolute and relative abundances, can provide valuable and comparative information on the condition of anaerobic wastewater treatment systems. NMS and moving-window analysis, based on relative abundance matrices, were apparently more relevant for linkage of methanogenic population shifts and reactor performance during steady-state conditions in this specific study. Distinctive community development profiles were recorded in the IFB and EGSB reactors, which were influenced by variations in reactor performance and in-reactor VFA concentrations during the trial.
Acknowledgements This research was carried out with the financial support of Science Foundation Ireland, through a Charles Parsons Energy Research Award (BioGen: 06/CP/E006), and the European Commission Sixth Framework Programme, through a Marie Curie Transfer of Knowledge Host Development Fellowship (MicroGen MTKD-CT-2006-042802). The award of an Embark
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Scholarship to K. Bialek by the Irish Research Council for Science, Engineering and Technology is also gratefully acknowledged. Valuable help and scientific discussions with Alma Siggins are highly appreciated.
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Metabolic modeling of mixed substrate uptake for polyhydroxyalkanoate (PHA) production Yang Jiang, Marit Hebly, Robbert Kleerebezem, Gerard Muyzer, Mark C.M. van Loosdrecht* Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
article info
abstract
Article history:
Polyhydroxyalkanoate (PHA) production by mixed microbial communities can be estab-
Received 25 May 2010
lished in a two-stage process, consisting of a microbial enrichment step and a PHA accu-
Received in revised form
mulation step. In this study, a mathematical model was constructed for evaluating the
23 September 2010
influence of the carbon substrate composition on both steps of the PHA production process.
Accepted 12 October 2010
Experiments were conducted with acetate, propionate, and acetate propionate mixtures.
Available online 20 October 2010
Microbial community analysis demonstrated that despite the changes in substrate
Keywords:
ments. A metabolic network model was established to investigate the processes observed.
Mixed culture
The model based analysis indicated that adaptation of the acetate and propionate uptake
Mixed substrate
rate as a function of acetate and propionate concentrations in the substrate during culti-
Metabolic model
vation occurred. The monomer composition of the PHA produced was found to be directly
Copolymers
related to the composition of the substrate. Propionate induced mainly polyhydroxy-
Plasticicumulans acidivorans
valerate (PHV) production whereas only polyhydroxybutyrate (PHB) was produced on
composition the dominant microorganism was Plasticicumulans acidivorans in all experi-
acetate. Accumulation experiments with acetate-propionate mixtures yielded PHB/PHV mixtures in ratios directly related to the acetate and propionate uptake rate. The model developed can be used as a useful tool to predict the PHA composition as a function of the substrate composition for acetateepropionate mixtures. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Polyhydroxyalkanoates (PHAs) are biopolymers produced by many different bacteria as an intracellular carbon and energy reserve material (Steinbu¨chel and Valentin, 1995). They attract considerable attention as alternative for petroleumbased plastics because they are biodegradable and made from renewable resources (Braunegg et al., 1998). Industrial production of PHAs generally uses pure cultures of natural or genetically modified PHA producing bacteria. The price of PHAs is much higher compared to petro-chemical based plastic (Choi and Lee, 1999). Potentially, a reduction of the costs of PHA production can be established by using a non-
axenic culture and waste organic carbon as raw material. Agriculture is responsible for 95% global water usage, implying we should recycle organic materials from agricultural sources as much as possible. The products from fermented agro-residues can be used as the substrate of PHA synthesis (Kleerebezem and van Loosdrecht, 2007; Reis et al., 2003). For waste based production of PHA, a two-step process has been proposed, consisting of (1) enrichment of a PHA producing mixed culture with a feast and famine regime in an open sequencing batch reactor (SBR) and (2) PHA production in a fed-batch reactor under growth limited condition. This process when fed with acetate has been reported to accumulate up to 89 wt% cellular PHB content (Johnson et al., 2009a),
* Corresponding author. Tel.: þ31 15 278 1091; fax: þ31 15 278 2355. E-mail addresses: [email protected] (Y. Jiang), [email protected] (M. Hebly), [email protected] (R. Kleerebezem), G.Muijzer@ tudelft.nl (G. Muyzer), [email protected] (M.C.M. van Loosdrecht). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.009
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Nomenclature Ci CPHA CSRT PHA Cs CX CSRT X EM ESS fPHA;X fPHB fPHB,X fPHV,X fPr k KNH3 KS Ki mS mATP mPHA MwX MwPHA PHA% qS qfamine PHA qfeast S
concentration of the inhibitor [Cmol/L] Concentration of PHA [Cmol/L] Concentration of PHA calculated from SRT [Cmol/L] concentration of substrate [Cmol/L] concentration of active biomass [Cmol/L] concentration of active biomass calculated from SRT [Cmol/L] error between model and measurements error of model deviating from steady state fraction of PHA on active biomass [Cmol/Cmol] fraction of PHB over total PHA [Cmol/Cmol] fraction of PHB on active biomass [Cmol/Cmol] fraction of PHV on active biomass [Cmol/Cmol] fraction of propionate uptake to total carbon uptake rate constant of PHA degradation [(Cmol/Cmol)1/3/h] half-saturation constant for ammonia [mol/L] half-saturation constant for substrate [Cmol/L] half-saturation constant for inhibitor [Cmol/L] biomass specific substrate requirement for maintenance [Cmol/Cmol/h] biomass specific ATP requirement for maintenance [mol/Cmol/h] biomass specific PHA requirement for maintenance [Cmol/Cmol/h] molecular weight of active biomass per one mole of carbon (incl. ash) ¼ 25.1 g/Cmol molecular weight of PHA per one mole of carbon (depends on the PHA composition) [g/Cmol] PHA content of the biomass biomass specific substrate uptake rate [Cmol/ Cmol/h] biomass specific PHA degradation rate in the famine phase [Cmol/Cmol/h] biomass specific substrate uptake rate in the feast phase [Cmol/Cmol/h]
which is similar to the values reported for genetically modified Escherichia coli strains (Slater et al., 1988). A general metabolic model for both enrichment experiments in SBR and accumulation experiment in fed-batch with acetate as substrate has been constructed (Johnson et al., 2009b). This model has shown excellent correlation with experimental data. For full scale applications, however, it is aimed to use fermented organic wastewater. Although acetate is usually among the main fermentation products, significant concentrations of propionate, butyrate, lactate and ethanol have been reported as well in pre-fermented substrates (Bengtsson et al., 2008; Temudo et al., 2007). It is therefore of interest to study the impact of other fatty acids and mixtures thereof on the PHA production process. As a result of using propionate as substrate, a copolymer is normally synthesized consisting of 3-hydroxybutyrate (HB) and 3-hydroxyvalerate (HV) monomers (Lemos et al., 2006). The polymer properties of the homopolymer HB, as generated from acetate as sole substrate, are stiff and brittle.
qmax s SSrelEi t famine Yi;j feast Yi;j
Yi;j a d mfamine mfeast mmax
maximum biomass specific substrate uptake rate [Cmol/Cmol/h] sum of squared relative errors between measurements and model for compound i Model time [min] stoichiometric yield of compound i on compound j in the famine phase [Cmol/Cmol] stoichiometric yield of compound i on compound j in the feast phase [Cmol/Cmol] yield of compound i on compound j [Cmol/Cmol] exponent of PHA inhibition term efficiency of oxidative phosphorylation [mol ATP/mol NADH2] biomass specific growth rate in the famine phase [1/h] biomass specific growth rate in the feast phase [1/h] maximum biomass specific growth rate in the feast phase [1/h]
Abbreviations DO dissolved oxygen FISH fluorescence in situ hybridization GAOs glycogen accumulating organisms HB hydroxybutyrate HRT hydraulic retention time HV hydroxyvalerate NaAc sodium acetate NaPr sodium propionate PAOs phosphate accumulating organisms PHA polyhydroxyalkanoate PHB polyhydroxybutyrate PHH polyhydroxyhexanoate PHMV polyhydroxymethylvalerate PHV polyhydroxyvalerate SBR sequencing batch reactor SRT solid retention time TSS total suspended solid X biomass
The copolymer, poly(hydroxybutyrate-hydroxyvalerate) (PHBV), has properties more similar to polypropylene and is therefore more interesting. Until now, relatively few models for propionate and/or acetate usage are available. Dias et al. (2008) describes a model for copolymers production on mixtures of acetate and propionate, only focusing on the feast phase. Oehmen et al. (2005, 2006, 2007) developed anaerobic and aerobic models to simulate the behavior of polyphosphateaccumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs) with propionate as substrate. This paper aims to extend the existing metabolic flux based models for PHB production to describe the production of copolymers from propionate as sole substrate, or mixtures of acetate and propionate. The kinetic and stoichiometric properties of a microbial community using different substrates are evaluated. Extensive analysis of the data allows predicting the behavior of the process and the composition of the products by this model.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 0 9 e1 3 2 1
2.
Materials and methods
2.1.
Enrichment culture in SBR
2L working volume double-jacket glass bioreactors (Applikon, The Netherlands) were used to enrich PHA producing cultures. Four SBR runs were conducted with four different acetate/ propionate ratios (100/0, 75/25, 50/50 and 0/100, Cmole based). The reactors were operated as SBRs under non-axenic conditions. Each SBR cycle consisted of four phases: (1) a 7 min start phase, (2) a 10 min influent phase used to supply fresh medium, (3) a 683 min reaction phase, and (4) a 20 min effluent phase used to remove effluent from the reactor. The solid retention time (SRT) and the hydraulic retention time (HRT) were both maintained at one day. The end of an operational cycle was immediately followed by the start phase of the next cycle. Each enrichment culture was inoculated with biomass from a SBR operated in the same conditions with acetate as sole substrate for more than three years (Johnson et al., 2009a). A steady state was considered to be obtained when stable values were obtained for the length of the feast phase, total suspended solids (TSS) concentration and the ammonium concentration at the end of the cycle for at least five subsequent SBR cycles. The biomass for fed-batch accumulation experiments was harvested from SBR after steady state was achieved. The general reactor setup and biocontroller systems were described by Johnson et al. (2009a).
2.2.
PHA production in fed-batch reactor
In order to evaluate the maximum PHA production, a fed-batch protocol was applied. 1L of biomass from the enrichment SBRs was mixed with 1 L of carbon substrate and ammonium free medium and introduced in the reactor (same composition as for SBR, but no carbon source and NH4Cl). To avoid substrate inhibition, a fed-batch approach was used to supply substrate to the system. The initial substrate concentration was set to 30 mM (acetate, propionate, or a mixture of acetate and propionate with desired ratio). Additional substrate was supplied from a 1.5 M substrate stock solution (acetic acid, propionic acid, or acetic/propionic acid mixture) in a fed-batch mode using the pH-controller. Growth was restricted to the first few hours in these experiments as no nitrogen source was supplied and only a small amount of nitrogen source remained from the previous SBR cycle. The progress of the experiments was monitored via online (DO, pH, acid and base dosage, offgas CO2 and O2) and offline (acetate, propionate, TSS, PHA, ammonium) measurements. A detailed description of the analytical procedures for online and offline measurements can be found elsewhere (Johnson et al., 2009b).
2.3.
Medium and cultivation methods
The medium for the SBR was dosed to the reactor from three separate bottles containing respectively carbon substrate, nutrients and dilution water. The medium for propionate-fed SBR enrichment contained: 131 mM NaPr as carbon substrate; 67.5 mM NH4Cl, 24.9 mM KH2PO4, 5.6 mM MgSO4, 7.2 mM KCl, 15 ml/L trace elements solution according to (Vishniac and
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Santer, 1957) in the nutrient source. In the other experiments on acetate or mixtures of acetate and propionate, the total amount of organic carbon-moles in the carbon source was kept constant and the nutrient source was the same as the propionate experiment. Allylthiourea (100 mg/L) was added through a 0.2 mm filter to the nutrients after autoclaving to prevent nitrification. In the influent phase of each batch cycle, 100 ml of carbon source, 100 ml of nutrient source and 800 ml of dilution water were mixed and pumped into the reactor. All reactors were operated at 30 C; the pH in the reactor was controlled to 7 0.05 by addition of 1 M HCl and 1 M NaOH.
2.4.
Fluorescence in situ hybridization (FISH)
FISH was performed to investigate the microbial community structure in all cultures. The oligonucleotide probes and procedures used were the same as described in Johnson et al. (2009a).
2.5.
Calculations
TSS was assumed to be composed of active biomass (X ) and PHA. The measured PHA content of the sludge was expressed as: PHA% ¼
PHB þ PHV 100% ðg=gÞ TSS
(1)
The active biomass concentration was calculated by subtracting the amount of PHA from TSS. PHA% Xðg=LÞ ¼ TSS 1 (2) 100 And the fraction of PHA over active biomass fPHA;X was expressed as: fPHB;X ¼
PHB% MwX ðCmol=CmolÞ 100 PHA% MwPHB
(3a)
fPHV;X ¼
PHV% MwX ðCmol=CmolÞ 100 PHA% MwPHV
(3b)
fPHA;X ¼ fPHB;X þ fPHV;X ðCmol=CmolÞ
(3c)
The molar weight of the active biomass includes the ash content.
3.
Metabolic model
3.1.
Metabolism and basic reactions
Dias et al. (2008) proposed a metabolic model for PHB and PHV production from a mixture of acetate and propionate. This model focused on biopolymer production and therefore described only the conversions during the feast phase. We based our model on the Dias-model but several extensions and modifications were made: 1. The metabolic reactions during the famine phase were added to the model structure, 2. The biomass synthesis from acetyl-CoA and propionyl-CoA were individually specified.
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HAc
HPr
R1
NADH2
R8
R9
ATP
R6
Biomass
R2
AcCoA
R3
PrCoA
R4
R5
R7
R10 PHB
R11 PHV
Biomass
Fig. 1 e A schematic representation of the PHA metabolism. Solid lines indicate the reactions involved in the basic anabolism. The dot lines represent the reaction for substrate uptake during the feast phase. The dashed lines state the PHA degradation reaction during the famine phase.
3. Maintenance ATP requirement (mATP ) was used as estimation parameter in the kinetics whereas the P/O ratio was fixed. 4. The TCA cycle was assumed inactive when propionate was the sole carbon source in the medium. A schematic overview of the metabolic reactions involved in the PHA production process is shown in Fig. 1 and a complete overview of the reaction stoichiometries are presented in Table 1. All reactions are expressed on a carbon-mole basis. Both acetate and propionate are assumed to be taken up by the cells by active transport, requiring one mole of ATP per mole of carbon source (Gottschalk, 1986). They are subsequently converted into acetyl-CoA and propionyl-CoA by consuming another mole of ATP per mole of substrate. The net reactions for acetate or propionate are denoted as R1 and R2 respectively (Dias et al., 2008). It was observed that the biomass consumed acetate and propionate simultaneously in this work. To calculate the stoichiometric yields on mixed substrate, the fraction of propionate uptake rate in the total substrate uptake rate is defined as fPr :
fPr ¼
R2 R1 þ R2
(4)
It has been reported that a fraction of the propionyl-CoA formed can be converted into acetyl-CoA via five different metabolic pathways (Lemos et al., 2006). The net material and electron balances are identical in these pathways. Only the electron carriers used and consequently the amount of energy generated differs between these pathways. We used the same conversion pathway from propionyl-CoA to acetyl-CoA as described by Dias et al. (2008). The net reaction for this conversion is shown as R3 (Table 1). Except PHB and PHV, PHMV (polyhydroxymethylvalerate) can theoretically be produced when using propionate as carbon source. The monomer of PHMV can be formed by coupling two propionyl-CoA units. However, no PHMV formation was observed in this study. PHB and PHV were therefore the only polymers taken into account for the current model. The net reactions for polymer formation are shown in R4 and R5 (Dias et al., 2008; Johnson et al., 2009b). As generally observed in this study, HB and HV monomeric units were polymerized simultaneously. The fraction of PHB in the total PHA fPHB is defined by the following equation: fPHB ¼
R4 R4 þ R5
(5)
Biomass formation was assumed to occur from both acetylCoA and propionyl-CoA. The energy requirements for biomass production from acetyl-CoA and propionyl-CoA were estimated as 2.16 (van Aalst van Leeuwen et al., 1997) and 1.38 mol ATP per C-mol of active biomass (Dias et al., 2008). The reactions are shown in R6 and R7 in Table 1. The active biomass formula used in the stoichiometric calculation was CH1.8O0.5N0.2 with a molecular weight of 25.1 g/Cmol, including 2% ash (Beun et al., 2002). It is not identifiable whether active biomass is formed from either acetyl-CoA or propionyl-CoA. The experimental data from Dias et al. (2008) were obtained under ammonia limiting condition where cell growth is negligible. Here we assumed that acetyl-CoA and propionyl-CoA use for active biomass synthesis were proportional to the flux through acetyl-CoA and propionyl-CoA. In other words, fPr and fPHB determine the ratio between the growth occurring on acetyl-CoA and propionyl-CoA during the feast and the famine phase respectively.
Table 1 e Reactions considered in the metabolic model on a carbon-mole base. d is the efficiency of oxidative phosphorylation.
1 2 3 4 5 6 7 8 9 10 11
Reaction
Stoichiometry
HAc uptake HPr uptake Conversion from PrCoA to AcCoA PHB production PHV production Growth on AcCoA Grwoth on PrCoA Catabolism Oxidative phosphorylation PHB consumption PHV consumption
1HAc þ 1ATP / 1AcCoA 1HPr þ 2/3ATP / 1PrCoA 1.5PrCoA / 1 AcCoA þ 0.5CO2 þ 1.5NADH2 1AcCoA þ 0.25NADH2 / 1PHB 0.4AcCoA þ 0.6PrCoA þ 0.2NADH2 / 1PHV 1.267AcCoA þ 0.2NH3 þ 2.16ATP / 1X þ 0.267CO2 þ 0.434NADH2 1.06PrCoA þ 0.2NH3 þ 1.38ATP / 1X þ 0.06CO2 þ 0.373NADH2 1AcCoA / 1CO2þ2NADH2 1NADH2 þ 0.5O2 / dATP 1PHB þ 0.25ATP / 1AcCoA þ 0.25 NADH2 1PHV þ 0.2ATP / 0.4AcCoA þ 0.6PrCoA þ 0.2NADH2
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Table 2 e Stoichiometric yields derived from the metabolic reactions and balances for the conserved moieties, expressed as a function of the efficiency of oxidative phosphorylation d. Feast phase Growth
feast YX;S
PHA production
2 13:9fPr þ 2:3fPr 57:8 ; 36d þ 6fPr þ 6dfPr 18 6d þ fPr þ dfPr 3 ¼ ; 6:3d 2:3fPr þ 6:5
Famine phase Growth
48fPr þ 9fPHB 3fPr fPHB 144 ; 240d þ 40fPr þ 40fPr d 120 120d þ 20fPr þ 20dfPr 60 ¼ ; 144d 9dfPHB
2 106fPHB 7fPHB þ 224 ; 6fPHB 288d þ 18dfPHB þ 24 1:5fPHB 72d þ 4:5dfPHB þ 6 ; ¼ 63d þ 23:4fPHB dfPHB þ 41:4
famine ¼ YCO 2 ;X
¼ YOfamine 2 ;X
250fPHB 90d þ 45dfPHB þ 474 ; 15fPHB 720d þ 45dfPHB þ 60
famine YN;X ¼ 0:2
YOfamine ¼ 1:2 0:075fPHB ; 2 ;PHA famine YATP;PHA
20fPr 24d þ 20dfPr þ 9dfPHB 60 ; 120d þ 20fPr þ 20dfPr 60
feast ¼ 1; YCO 2 ;S
fPr þ 1; 6 fPr dfPr ¼1 2d; 3 3
famine YX;PHA
Maintenance
feast YCO ¼ 2 ;PHA
YOfeast ¼ 2 ;S feast YATP;S
3d 33:4fPr 10dfPr þ 94:8 ; 60d þ 10fPr þ 10dfPr 30
feast ¼ 0:2 YN;X
YOfeast ¼ 2 ;PHA feast YPHA;S
Maintenance
feast ¼ YCO 2 ;X
¼ YOfeast 2 ;X
famine YCO ¼ 1; 2 ;PHA
¼ 0:05fPHB 2:4d þ 0:15fPHB d þ 0:2
R6 1 fPr ¼ R7 fPr
(6)
couples substrate uptake to growth, PHA production and maintenance through defined yields.
R6 fPHB ¼ R7 1 fPHB
(7)
¼ qfeast S
Acetyl-CoA is converted to CO2 through the tricarboxylic acid cycle (TCA, R8). The net reaction was described by van Aalst van Leeuwen et al. (1997). The amount of ATP generated by oxidizing one mole of NADH2 is expressed by the P/O ratio (d), which represents the efficiency of oxidative phosphorylation. This reaction is expressed in R9. Degradation of PHB and PHV to acetyl-CoA and propionylCoA require one mole of ATP per mole of building block. The net reactions are shown in R10 and R11. Like the assumption made during PHA production, PHB and PHV are assumed to be simultaneously degraded to produce acetyl-CoA and propionyl-CoA. R10 fPHB ¼ R11 1 fPHB
3.2.
(8)
Determination of overall stoichiometry
The stoichiometric yield equations on mixed substrate were calculated from the metabolic reaction stoichiometries and mass and charge balances using the symbolic solver from software MathCAD (Table 2). The conserved moieties, NADH2, ATP, acetyl-CoA and propionyl-CoA and the elements C, O, H, N were used to balance all the reactions.
3.2.1.
Feast phase
In the feast phase nine, lumped metabolic reactions (R1e9, Table 1) are active. Due to the four conserved moieties and three constraints (Eqs. (4)e(6)); the degree of freedom is reduced from nine rates to two rates. If two reaction rates are defined, the remaining reaction rates can be calculated through Herbert-Pirt type of relationship during the feast phase (Eq. (9)). This relation
mfeast qfeast PHA þ feast þ mS feast YX;S YPHA;S
3.2.2.
(9)
Famine phase
In the famine phase, only seven lumped metabolic reactions (R3 and R6e11, Table 1) are active. As described for the feast phase, the four conserved moieties and two constrains (Eqs. (7) and (8)) decrease the degree of freedom from seven rates to one rate. The HerbertePirt relation only contains PHA consumption, growth and maintenance during the famine phase (Eq. (10)). ¼ qfamine PHA
mfamine þ mS famine YX;PHA
(10)
All the stoichiometric maximum yields during both the feast phase and the famine phase are listed in Table 2.
3.3.
Kinetic model
3.3.1.
Feast phase
Two limiting cases can occur (Johnson et al., 2009b). At low PHA contents the substrate uptake rate proceeds at a maximum rate, limiting growth and PHA production. In this case, the substrate uptake can be expressed by regular saturation kinetics (eq. (3), Table 3) and the PHA synthesis rate (eq. (1), Table 3) can be regarded as a resultant from substrate uptake, minus substrate required for growth and maintenance purposes. When PHA reaches a certain concentration in the cell, the PHA synthesis rate and the growth rate are limiting substrate uptake. In this case, the PHA synthesis rate can be described using the product inhibition kinetics as previous established by Johnson et al. (2009b) (eq. (2), Table 3). Herewith the substrate uptake rate can be calculated as the sum from
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Table 3 e Model kinetics. Feast phase PHA production
With PHA inhibition
Substrate uptake With PHA inhibition
Growth
! 1 feast feast qS ðtÞ mfeast ðtÞ feast mS YPHA;S if qfeast PHA;1 qPHA;2 YX;S !a # " CS ðtÞ fPHA;X ðtÞ feast max feast if qfeast 1 qPHA;2 ðtÞ ¼ qPHA PHA;1 qPHA;2 max KS þ CS ðtÞ fPHA;X qfeast PHA;1 ðtÞ ¼
qS;1 ðtÞ ¼ qmax S
CS ðtÞ feast if qfeast PHA;1 qPHA;2 KS þ CS ðtÞ
1 1 feast feast qS;2 ðtÞ ¼ mfeast ðtÞ feast þ qfeast PHA feast þ mS if qPHA;1 qPHA;2 YX;S YPHA;S mfeast ðtÞ ¼ mmax
CNH3 ðtÞ CS ðtÞ KNH3 þ CNH3 ðtÞ KS þ CS ðtÞ
mATP feast YATP;S
(1)
(2) (3) (4) (5)
Maintenance
mS ¼
CO2 evolution
feast feast feast feast qfeast ðtÞYCO þ qfeast CO2 ðtÞ ¼ m PHA ðtÞYCO2 ;PHA þ mS YCO2 ;S 2 ;X
(7)
O2 uptake
feast feast feast qfeast ðtÞYOfeast þ qfeast O2 ðtÞ ¼ m PHA ðtÞYO2 ;PHA þ mS YO2 ;S 2 ;X
(8)
NH3 uptake
qfeast NH3 ðtÞ
(9)
Famine phase Growth PHA degradation
¼m
(6)
feast
feast ðtÞYNH 3 ;X
famine mfamine ðtÞ ¼ YX;PHA qfamine PHA ðtÞ mPHA
(10) (11)
Maintenance
2=3 qfamine PHA ðtÞ ¼ kfPHA;X ðtÞ mATP mPHA ¼ famine YATP;PHA
CO2 evolution
famine famine qfamine ðtÞ ¼ mfamine ðtÞYCO þ mS YCO CO2 2 ;X 2 ;PHA
(13)
O2 uptake
qfamine ðtÞ ¼ mfamine ðtÞYOfamine þ mS YOfamine O2 2 ;X 2 ;PHA
(14)
NH3 uptake
qfamine ðtÞ NH3
(15)
substrate utilization for PHA production, growth, and maintenance purposes according to the HerbertePirt equation (eq. (4), Table 3). All other rates are the same in both cases. The growth rate can be described by Monod-type relation with limitation by the substrate and ammonium (eq. (5), Table 3). The substrate based maintenance rate is related to the ATP based maintenance rate through the ATP yield on substrate (eq. (6), Table 3). All other conversion rates can be obtained by rearrangement of the HerbertePirt relation with the corresponding yield factors. The stoichiometric relations for carbon dioxide, oxygen and ammonium are included by eqs. (7)e(9) in Table 3.
3.3.2.
Famine phase
The HerbertePirt relation describes growth as function of PHA conversion and maintenance use (eq. (10), Table 3). It has been generally assumed that in the case of growth on a storage polymer, the PHA conversion is the rate limiting step (Beun et al., 2002; van Loosdrecht and Heijnen, 2002). The PHA degradation rate during the famine phase was suggested to depend on the PHA surface area and was therefore defined as a two-third order function of the cellular PHA concentration (Murnleitner et al., 1997) (eq. (11), Table 3). The consumption of PHA for maintenance purposes was derived from the ATP maintenance rate through the ATP yield on PHA (eq. (12), Table 3). The conversions of carbon dioxide, oxygen and ammonium are stoichiometrically derived from the HerbertPirt relation (eqs. (13)e(15), Table 3).
¼m
3.4.
famine
famine YNH 3 ;X
(12)
Model calibration
For each sampling time point (ti), the modeled data for each compound were compared with the measured data. The relative errors were calculated, squared and summed up as shown for the example of propionate in Eq. (11). SSrelEPr ¼
2 N measure X nPr ðti Þ nmodel ðti Þ Pr ðti Þ nmeasure Pr i¼1
(11)
All the measurements were treated equally without bias on the accuracy. The squared relative errors for different measurements were summed to the total error between measurements and model (Eq. (12)). EM ¼
X
SSrelEi
(12)
with i ¼ Ac, Pr, NH3, PHB, PHV, CO2, O2. In steady state the amount of solid (active biomass and PHA) that is produced during one operational cycle is equal to the amount of the solids removed at the end of the cycle. The SRT definition can be used to calculate the concentration change for active biomass and PHA in one cycle (Eqs. (13) and (14)): SRT CSRT X ðtend Þ ¼ CX ð0Þ SRT tcycle
(13)
SRT CSRT PHA ðtend Þ ¼ CPHA ð0Þ SRT tcycle
(14)
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The steady-state error was defined as Eq. (15). h i2 h i2 model model ðtend Þ þ CSRT ESS ¼ CSRT X ðtend Þ CX PHA ðtend Þ CPHA ðtend Þ
(15)
For the cultivation experiments therewith the total error is defined as the sum of EM and ESS. By minimizing this total error using the solver tool in Microsoft Excel, all the kinetic rates and concentrations of all relevant compounds were calculated. Similar to the method used by Johnson et al. (2009b), some parameters like half-saturation constant for acetate, propionate and ammonia, P/O ratio, initial CO2 evolution and O2 uptake were kept constant to compute all the rates and concentrations. Some other relevant parameters like, maintenance ATP requirement (mATP ), maximum ), the fraction of propionate uptake substrate uptake rate (qmax S rate in the total substrate uptake rate (fPr ), maximum growth rate (mmax ), maximum PHA production rate (qmax PHA ), the fraction of PHB in the total PHA (fPHB ), exponent of PHA inhibition term max ) were estimated by the (a) and maximum fraction of PHA (fPHA calibration procedure.
4.
Results
4.1.
Microbial diversity evaluation
The Plasticicumulans acidivorans dominated microbial community was found to be very well capable of degrading short chain fatty acids different from the acetate which it was originally grown on. Even though the uptake rate was initially much lower when the substrate was changed to propionate, it took less than five SRTs to reach a steady-state. All the experiments described here were conducted when the reactors had been operated stably for three weeks. The microbial community composition (Fig. 2) was proven to be stable and independent of the substrate composition by FISH analysis. In all operated SBRs, P. acidivorans was found as the only dominant bacterial species (over 80% of the total culture). The influence from changes in microbial community structure could therefore be excluded and all the differences in
Table 4 e The experimental data sets collected for analysis in this study.
SBR-I SBR-II SBR-III SBR-IV
Acetate/ Propionate ratioa
Cycle measurement
Fed-batch measurement
100/0 75/25 50/50 0/100
þ þ
þ þ þ þ
þ: The experiment preformed. : The experiment not performed. a Cmole based.
metabolic activity were the result of variation in the metabolic activity of a highly comparable microbial community.
4.2.
Measurement and model evaluations
As described in the materials and methods section, we operated four SBRs using four acetate/propionate ratios as substrate: 100/0, 75/25, 50/50 and 0/100 (Cmole based). The measured data obtained from six distinct experiments were analyzed in this study, including two data sets from cycle measurements as obtained from the SBR used for enrichments with only acetate or sole propionate and the other four data sets from PHA accumulation experiments in fed-batch reactors fed with all the tested acetate/propionate rations (Table 4). As an indication of the accuracy of the measurements, the carbon balances and the electron balances of the data were calculated (Table 5). The relative errors were small (less than 10%), indicating the measurements were adequate to be used as the basis for the modeling. The concentration profiles for the different experiments are given in Fig. 3. The experiments with acetate as sole substrate gave comparable results compared to the work described by Johnson et al. (2009b). Fig. 3 therefore displays only the performance of the experiments conducted with sole propionate or acetate/propionate mixtures. In the graph of the cycle
Fig. 2 e The fluorescence microscopic photographs of the mixed culture in SBR fed with different acetate and propionate ratio (left: 75/25, middle:/50/50, right: 0/100, Cmol based). The general 16S rRNA probe for Eubacteria was labeled with Cy3 (EUB338 mix, red) and the specific probe for P. acidivorans was labeled with fluorescein (UCB823, green). The yellow color indicates that both probes hybridized with the same bacteria (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Table 5 e Observed variables and estimated parameters from both cycle measurements and accumulation experiments. Unit
Ac-SBR
Ac-Batch
75%Ac þ 25%Pr-Batch
50%Ac þ 50%Pr-Batch
Pr-Batch
Pr-SBR
Measured data
C-balance e-balance PHAmax a
[%] [%] [wt %]
2.8 (4.8) 0.4 (4.4) 53.0
0.5 (1.8) 0.4 (6.7) 87.7
0.57 (4.2) 0.1 (5.0) 85.2
3.8 (2.7) 10.8 (7.0) 80.2
1.5 (0.8) 1.0 (0.7) 60.0
4.6 (5.0) 4.9 (4.8) 33.1
Model parameters
qmax S fPr qmax PHA fPHB mmax k mATP a fmax PHA,X
[Cmmol/Cmmol/h] [Cmol/Cmol] [Cmmol/Cmmol/h] [Cmol/Cmol] [Cmmol/Cmmol/h] [(Cmol/Cmol)1/3/h] [mmol/Cmmol/h] [e] [Cmol/Cmol]
3.20 0.00 1.95 1.00 0.05 0.25 0.013 2.0 N/A
2.00 0.00 1.25 1.00 0.09 N/A 0.000 1.7 8.3
1.66 0.23 1.01 0.77 0.08 N/A 0.000 3.3 6.9
1.45 0.38 0.93 0.53 0.06 N/A 0.000 8.6 4.9
0.65 1.00 0.35 0.12 0.12 N/A 0.227 1.0 1.8
0.94 1.00 0.43 0.08 0.27 0.16 0.057 1.6 N/A
Model based variables
YX,S YPHA,S qProCoA,AcCoA
[Cmol/Cmol] [Cmol/Cmol] [Cmmol/Cmmol/h]
0.02 0.61 0.00
0.04 0.61 0.00
0.05 0.61 0.12
0.04 0.64 0.17
0.19 0.43 0.24
0.32 0.42 0.26
Ac: acetate. Pr: propionate. S: substrate (carbon source). SBR: sequencing batch reactor. X: active biomass. N/A: not available. a In SBR, PHAmax indicates the PHA content at the end of feast phase.
Fig. 3 e Modeling results. Figure shows the results obtained cycle measurement fed with sole propionate (A) and accumulation experiments fed by a mixture of acetate and propionate with different ratio (B: 0/100, C: 50/50, D 75/25, Cmol based). Full symbols represent the experimental data and full lines indicate the modeling results. (*) acetate, (-)propionate, (3) ammonia, (:) active biomass, (A) PHB, (C) PHV, (e) oxygen and (D) carbon dioxide.
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measurement on sole propionate (Fig. 3, A), all measured concentrations are shown in the graph, except for CO2 and O2 concentrations that are presented as cumulative data calculated from off-gas concentrations. In the graphs for the accumulation experiments (Fig. 3, BeD), the substrate concentration shown is calculated by assuming that all substrate was supplied to the system at the beginning of the experiment. Fig. 4 shows the coefficient of determination (R2) between the experimental data and modeled data in all experiments, excluding experiments performed on sole acetate. The values of R2 were all very close to 1, indicating that the model could adequately describe the dynamic behavior of the different cultures during feast and famine periods of the cultivation experiments as well as PHA accumulation in the accumulation experiments in absence of ammonium.
4.3.
PHA content at the end of the feast phase during the cycle measurements and at the end of the accumulation experiments are listed in the top part of Table 5. In both the cycle experiment and the accumulation experiment, the maximum PHA content was about 20 wt % lower when propionate was the sole substrate. Stoichiometric and kinetic parameter values were estimated by calibration of the model with the experimental data. Key model parameters are listed in the middle part of Table 5. Some parameters are only functional in the famine phase and were therefore only estimated for the cycle measurements (e.g. the k-value describing the PHA degradation rate). Other parameters could only be estimated from the accumulation max . It was noticed that the qmax -values experiments, like fPHA;X S obtained from cycle measurements were higher than those estimated from the accumulation experiments with the same substrate. This is potentially due to substrate inhibition related to the higher initial substrate concentration in the accumulation experiments. Some clear trends can be observed: at the higher fractions of acetate in the substrate, , qmax higher values were found for qmax PHA and fPHB but a lower S
Observed variables and estimated parameters
Some measured experimental data, estimated parameters and model-based variables are shown in Table 5. The maximum PHA content was derived directly from the measurements. The
500
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R2=0.991
R2=0.969 Modeled Propionate [Cmmol]
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R2=0.996 80 Modeled PHV [Cmmol]
200 Modeled PHB [Cmmol]
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Fig. 4 e Modeling results. Figures show the comparison between the experimental data (full symbols) and modeled data (solid lines) from both cycle measurement and accumulation experiments. Totally, two carbon-sources (acetate and propionate) and two major products were analyzed. (-) The experimental data are from propionate SBR cycle measurement, (:) 100% propionate fed-batch experiment, (A) 50% acetate and 50% propionate fed-batch experiment and (C) 75% acetate and 25% propionate fed-batch experiment.
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value for fPr . The values found for mATP were low and had a marginal impact on the reaction stoichiometry. The biomass specific acetate uptake rate and propionate uptake rate can be calculated from the overall biomass specific substrate uptake ) and fPr . The maximum acetate uptake rate was rate (qmax S obtained from the experiment with acetate as sole substrate, whereas the maximum propionate uptake rate was obtained with propionate as sole substrate. In the mixed substrate experiments, both acetate and propionate uptake rates were lower than their maximum value. By increasing the fraction of propionate in the substrate, the acetate uptake rate decreased almost linearly. In contrast, the propionate uptake rate only showed a significant decrease when the acetate fraction in the substrate was 75% (Fig. 5). The yield of active biomass on substrate (YX;S ) and the yield of PHA on substrate (YPHA;S ) were calculated from the model parameters to define the carbon fractions used for storage and for growth on both substrates. The higher YX;S -values and lower YPHA;S -values obtained from propionate fed experiments indicated that more substrate was used for active biomass synthesis and less substrate was used for PHA production. One reaction, oxidizing propionyl-CoA to acetylCoA (R3, Fig. 1), only occurs when propionate is present in the substrate. It can be noticed this reaction rate calculated by the model increased with an increase of the propionate fraction in the substrate.
5.
Discussion
lower value, it still is the highest PHA-content with propionate as sole substrate reported till now (Dias et al., 2006; Lemos et al., 2008). The high accumulation capacity seems strongly related to the dominance of P. acidivorans in the microbial community. A P. acidivorans dominated microbial community was previously shown to have a superior PHB storing capacity on acetate (Johnson et al., 2009a). The reason for the lower maximum PHA content when propionate was used as sole substrate remains unclear. A explanation is that both PHA synthesis and degradation occur simultaneously (Ren et al., 2009). At an increasing PHA content, the PHA synthesis rate gets limited by product inhibition, whereas the PHA degradation rate increases. When the PHA production rate equals the PHA degradation rate, the maximum PHA content is reached. Our results demonstrated that the propionate uptake rate is significantly lower than the acetate uptake rate. This suggests that PHA synthesis equals PHA degradation at a lower PHA content when propionate is used as substrate. In the propionate fed SBR, a lower PHA content at the end of the feast phase was observed as well (Table 5). This is due to the lower substrate uptake rate and higher growth rate observed with propionate. The values of YX;S also indicated that more substrate (about 30%, Cmmol basis) was directed towards active biomass when the culture was fed with propionate only. In contrast, YPHA;S values were higher when acetate was the substrate (Table 5). Therefore, with respect to rates and maximal storage capacity, acetate is a more suitable substrate for PHA production than propionate.
5.1. Influence of the substrate composition on the PHA content
5.2. Influence of the substrate composition on polymer composition
The PHA production capacity of the highly similar mixed culture was strongly influenced by the composition of the substrates. The maximum PHA contents obtained from fedbatch experiments were consistently higher than 80 wt% when acetate was present in the substrate. The maximum PHA content from the fed-batch experiment with propionate as the sole substrate was only about 60 wt %. Despite this
The composition of the final polymers strongly depended on the composition of the substrate. The homopolymer PHB was usually observed when biomass was fed with acetate as sole substrate as described before by others (Dionisi et al., 2004; Lemos et al., 2006; Dias et al., 2008). When propionate was used as sole substrate, the composition of copolymer in this study varied slightly from previous studies. Both Lemos et al. (2006) and Dias et al. (2008) reported a mixed microbial culture enriched on propionate. The polymers composition these authors described was approximately 20 Cmol % HB and 80 Cmol % HV, compared to 11 Cmol % HB and 89 Cmol % HV observed in our work. Dionisi et al. (2004) enriched a mixed microbial culture on a mixture of acetate, propionate and lactate. When this culture was fed with propionate only, pure PHV was obtained. No PHMV was observed in this study. This can probably be attributed to the specific properties P. acidivorans that is dominating the microbial community described here. The PHA synthase of P. acidivorans belongs to class III (based on genome sequencing, data not shown). Class III PHA synthase preferably produces short chain length PHA, although some papers have reported that it also had a slight affinity to synthesize 3HA middle chain length PHA (e.g. Polyhydroxyhexanoate, PHH) (Yuan et al., 2001). It seems like that the PHA-synthase in P. acidivorans prefers to bind one molecule of acetyl-CoA with one molecule of acyl-CoA (e.g. acetylCoA, propionyl-CoA, butyryl-CoA). The synthase seems to lack
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Fig. 5 e Biomass specific substrate uptake rates as a function of the fraction acetate in the feed. The influence of substrate composition on the acetate uptake rate (A) and propionate uptake rate (:). The solid and dashed line indicates the trend of acetate and propionate uptake rate change.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 0 9 e1 3 2 1
the capacity to bind two propionyl-CoA molecules as required for PHMV production. Whether this is a general characteristic of class III PHA-synthase genes remains to be elucidated. Microorganisms related to PAOs or GAOs were reported to produce PHMV from propionate (Oehmen et al., 2005, 2006, 2007). These microbial enrichments were selected under alternating anaerobic and aerobic condition. PHAs are generated in the anaerobic period from fatty acids and aerobically stored glycogen. As opposed to our P. acidivorans dominated culture; two propionyl-CoA units can be combined to produce PHMV. Lemos et al. (2006) and Dias et al. (2008) reported PHMV production also with a mixed microbial culture enriched on propionate under aerobic condition. However, the dominant microorganism in their enrichments was reported as Amaricoccus sp. (Lemos et al., 2008), which is closely related to GAOs. Therefore, the PHMV producing capacity is strongly dependent on bacterial species.
5.3. Influence of the substrate composition on the substrate uptake rates The specific total substrate uptake rate decreased at an increasing fraction of propionate in the feed, and this decrease was mainly the result of an apparent decrease in the acetate uptake rate. Considering that the microbial community structures in all enrichment were highly similar, the microorganisms adapted their substrate uptake strategy to the new acetate/propionate ratio in the medium. It is an interesting observation, because the PHA composition is also closely related to the acetate and propionate uptake rate. Most of the PHB produced is derived from the consumed acetate while a small part (about 10%) originates from the consumed propionate. However, the mechanism by which microorganisms adapt their relative substrate uptake rate to the substrate composition remains unclear. Over-expression of specific transporters may play a role but other bottlenecks in the biochemical pathways cannot be excluded. The influence of the substrate composition on the biomass specific substrate uptake rate has been reported before (Dionisi et al., 2004; Lemos et al., 2006; Dias et al., 2008). All these authors investigated the response of biomass to the external substrate after a short-term substrate shift, while our experiments were conducted after prolonged cultivation on the modified substrate. A direct comparison of our results with these previous results is therefore difficult.
5.4.
Rate-limiting step during PHA accumulation
Reaction R3 (Fig. 1), converting propionyl-CoA to acetyl-CoA, has been suggested as rate-limiting step when using propionate as sole substrate (Dionisi et al., 2004; Lemos et al., 2006; Dias et al., 2008). The low reaction rate of R3 can limit the propionate uptake rate. In presence of acetate, acetyl-CoA can be produced directly from acetate uptake (R1), decreasing the need for converting propionyl-CoA to acetyl-CoA (R3). Consequently, the propionate uptake rate can be enhanced by acetate uptake if R3 is rate-limiting. This phenomenon has been observed by short-term substrate shift experiments conducted by Dionisi et al. (2004), Lemos et al. (2006) and Dias et al. (2008).
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In contrast, the long-term substrate shift experiments from this study suggest that in our case the biomass specific propionate uptake rate may be the rate-limiting. As described above, the need of R3 is less when acetate is present. Therefore, it can be expected that the PHV synthesis rate will be augmented by increasing the fraction of acetate in the substrate. In the mixed substrate experiment (50/50, Cmmol based), PHV was more efficiently synthesized compared to the sole propionate condition. However, when the proportion of acetate was further increased (75/25, Cmmol based), the PHV synthesis rate was unexpectedly decreased. This suggests that not R3 but the specific substrate uptake rates were determining the flux through the metabolic network, with a preference for acetate uptake over propionate.
5.5.
Maximum P/O ratio
The P/O ratio is a crucial parameter in the model and is used to provide insight in the efficiency of oxidative phosphorylation. Like in many metabolic modeling studies (Beun et al., 2000; Johnson et al., 2009b), we also used a fixed P/O ratio in our study. Usually it is not possible to identify the P/O ratio independently from the ATP need for maintenance: a high P/O ratio and a high maintenance ATP consumption can give a similar result as a low P/O ratio and a low maintenance ATP consumption (Johnson et al., 2009b; Lopez-Vazquez et al., 2009). When the metabolism is rather simple, there however exists a possibility to independently estimate the P/O ratio. For instance, Smolders et al. (1994) estimated the P/O ratio directly from macroscopic conversions from PAOs. The value of P/O ratio calculated in their study (d ¼ 1.85). In our case for the conversion of propionate to PHA with propionate as the sole substrate, also only a limited amount of biochemical reactions are involved. Stoichiometric analysis demonstrated that the TCA cycle is reversed if choosing a P/O ratio value exceeds two, suggesting there is a maximum P/O ratio value when biomass was fed with propionate only. Adequate reducing power can potentially be generated by converting propionyl-CoA to acetyl-CoA, reducing the requirement of TCA cycle to generate reducing power. Theoretically, TCA cycle has no need to be active when a maximal P/O ratio can be established. To investigate at which P/O ratio the TCA cycle is not needed for energy production, we assumed that the flux through the TCA cycle was negligible in accumulation experiments with propionate as sole substrate. In absence of a flux through the TCA cycle, the maximum P/O ratio can estimated from the fraction of PHB in the polymer (fPHB , Eq. (16)) d¼
4fPHB þ 16 17fPHB þ 8
(16)
This equation suggests that if only PHV is produced (fPHB ¼ 0), the maximum P/O ratio amounts 2. The average value of fPHB identified from experiments with propionate as sole substrate was approximately 10% (Table 5), corresponding to a maximum P/O ratio around 1.7. This P/O ratio is similar to the one calculated by Smolders et al. (1994). Considering the stable microbial community, an identical P/O ratio of 1.7 mmol ATP/ mmol NADH2 was assigned to all experiment in this study. However, it should be noted that the solid evidence for the absence of TCA cycle in sole propionate experiment under
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aerobic condition is still missing. Only Pijuan et al. (2008) found the contribution of NADH2 from TCA cycle was negligible when feeding PAOs with only propionate in anaerobic condition. A lower P/O ratio is required when there is a flux through the TCA cycle.
6.
Conclusions
In this study a metabolic model has been developed that adequately describes the dynamics of a microbial community producing PHA dominated by P. acidivorans. The model includes regulation for simultaneous acetate and propionate uptake, and the production of both PHV and PHB as a function of the acetate-propionate substrate mixture composition. Both cultivation experiments in SBRs and fed-batch experiments were modeled. The stoichiometric and kinetic parameters obtained from the experiments with different substrate mixtures can be used for predicting the polymer composition and rate of production.
Acknowledgements We thank Gert van der Steen for the analytical work. The investigation was supported by the Netherlands Organisation for Scientific Research (NWO) in the NWO-ACTS research programme B_BASIC and by the Foundation for Technical Sciences (STW).
references
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enriched by aerobic periodic feeding. Biotechnology and Bioengineering 85 (6), 569e579. Gottschalk, G., 1986. Bacterial Metabolism, second ed. SpringerVerlag, New York. Johnson, K., Jiang, Y., Kleerebezem, R., Muyzer, G., Van Loosdrecht, M.C.M., 2009a. Enrichment of a mixed bacterial culture with a high polyhydroxyalkanoate storage capacity. Biomacromolecules 10 (4), 670e676. Johnson, K., Kleerebezem, R., Van Loosdrecht, M.C.M., 2009b. Model-based data evaluation of polyhydroxybutyrate producing mixed microbial cultures in aerobic sequencing batch and fed-batch Reactors. Biotechnology and Bioengineering 104 (1), 50e67. Kleerebezem, R., van Loosdrecht, M.C.M., 2007. Mixed culture biotechnology for bioenergy production. Current Opinion in Biotechnology 18 (3), 207e212. Lemos, P.C., Levantesi, C., Serafim, L.S., Rossetti, S., Reis, M.A.M., Tandoi, V., 2008. Microbial characterisation of polyhydroxyalkanoates storing populations selected under different operating conditions using a cell-sorting RT-PCR approach. Applied Microbiology and Biotechnology 78 (2), 351e360. Lemos, P.C., Serafim, L.S., Reis, M.A.M., 2006. Synthesis of polyhydroxyalkanoates from different short-chain fatty acids by mixed cultures submitted to aerobic dynamic feeding. Journal of Biotechnology 122 (2), 226e238. Lopez-Vazquez, C.M., Oehmen, A., Hooijmans, C.M., Brdjanovic, D., Gijzen, H.J., Yuan, Z.G., van Loosdrecht, M.C. M., 2009. Modeling the PAO-GAO competition: effects of carbon source, pH and temperature. Water Research 43 (2), 450e462. Murnleitner, E., Kuba, T., van Loosdrecht, M.C.M., Heijnen, J.J., 1997. An integrated metabolic model for the aerobic and denitrifying biological phosphorus removal. Biotechnology and Bioengineering 54 (5), 434e450. Oehmen, A., Zeng, R.J., Keller, J., Yuan, Z.G., 2007. Modeling the aerobic metabolism of polyphosphate-accumulating organisms enriched with propionate as a carbon source. Water Environment Research 79 (13), 2477e2486. Oehmen, A., Zeng, R.J., Saunders, A.M., Blackall, L.L., Keller, J., Yuan, Z.G., 2006. Anaerobic and aerobic metabolism of glycogen-accumulating organisms selected with propionate as the sole carbon source. Microbiology 152 (9), 2767e2778. Oehmen, A., Zeng, R.J., Yuan, Z.G., Keller, J., 2005. Anaerobic metabolism of propionate by polyphosphate-accumulating organisms in enhanced biological phosphorus removal systems. Biotechnology and Bioengineering 91 (1), 43e53. Pijuan, M., Oehmen, A., Baeza, J.A., Casas, C., Yuan, Z., 2008. Characterizing the biochemical activity of full-scale enhanced biological phosphorus removal systems: a comparison with metabolic models. Biotechnology and Bioengineering 99 (1), 170e179. Reis, M.A.M., Serafim, L.S., Lemos, P.C., Ramos, A.M., Aguiar, F.R., van Loosdrecht, M.C.M., 2003. Production of polyhydroxyalkanoates by mixed microbial cultures. Bioprocess and Biosystems Engineering 25 (6), 377e385. Ren, Q., de Roo, G., Ruth, K., Witholt, B., Zinn, M., Thony-Meyer, L. , 2009. Simultaneous accumulation and degradation of polyhydroxyalkanoates: futile cycle or clever regulation? Biomacromolecules 10 (4), 916e922. Slater, S.C., Voige, W.H., Dennis, D.E., 1988. Cloning and expression in Escherichia coli of the Alcaligenes-eutrophus H16 poly-beta-hydroxybutyrate biosynthetic-pathway. Journal of Bacteriology 170 (10), 4431e4436. Smolders, G.J.F., Vandermeij, J., van Loosdrecht, M.C.M., Heijnen, J.J., 1994. Stoichiometric model of the aerobic metabolism of the biological phosphorus removal process. Biotechnology and Bioengineering 44 (7), 837e848.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 2 2 e1 3 2 8
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Ultraviolet absorption properties of suspended particulate matter in untreated surface waters Raymond E. Cantwell, Ron Hofmann* Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario, Canada M5S 1A4
article info
abstract
Article history:
Previous research has shown that wastewater disinfection using UV light can be impaired by
Received 14 June 2010
attenuation of the UV light as it passes through particles to reach embedded and protected
Received in revised form
microorganisms. This study determined that the UV absorption (at 254 nm) of particles
17 September 2010
present in 10 untreated surface waters was similar to the absorption of wastewater particles.
Accepted 13 October 2010
As such, it provides evidence that UV disinfection of surface waters during drinking water
Available online 21 October 2010
treatment may be impaired by the same mechanism if particles are present. The study also demonstrated that among the 10 untreated surface waters examined, there was no corre-
Keywords:
lation between the UV absorption (254 nm) of the solid particulate material, total organic
Ultraviolet
carbon, total suspended solids, turbidity, or UV absorbance (254) of the bulk water. ª 2010 Elsevier Ltd. All rights reserved.
Disinfection Absorption Particle Untreated surface water
1.
Introduction
As drinking water and wastewater disinfection using ultraviolet (UV) light becomes more popular, the range of water qualities being treated by UV is expanding. It is important to explore possible water quality limitations to its use. One such possible limitation is the presence of particulate matter in the water. Recent work by the authors has shown that particles in unfiltered surface waters of even good quality (e.g. turbidity <3 NTU) can limit the extent of UV inactivation of indigenous microorganisms (Cantwell and Hofmann, 2008; Templeton et al., 2009). Particles can impact UV disinfection in two general ways: (i) by absorbing or scattering the light (Christensen and Linden, 2003), which decreases the light penetration into the water column, thereby reducing the fluence, and (ii) by shielding microorganisms embedded in a particle from the light. This latter phenomenon was a source of concern when UV began to be widely implemented for wastewater disinfection, and
a number of studies were conducted to explore its significance (e.g. Qualls et al., 1983; Emerick et al., 2000; Jolis et al., 2001). The studies reported that the degree to which an embedded microorganism can be protected by its surrounding particle depends in part on the absorption of the particulate material at germicidal wavelengths. Loge et al. (1999) reported that the absorption of activated sludge particles ranged from 3300 to 569,000 cm1 for UV light at 254 nm. This information can be used to extrapolate the potential for a particle-embedded organism to survive wastewater UV disinfection given different particle sizes. Similar to the previous work performed in the context of wastewater treatment, the objective of this study was to characterize the UV absorption properties of particles found in drinking water sources to determine if shielding of embedded organisms from UV light is a plausible explanation for the previously-reported survival of organisms. An important aspect of this work is that the water samples that were evaluated were all from surface sources, and were untreated (with
* Corresponding author. Tel.: þ1 416 946 7508. E-mail address: [email protected] (R. Hofmann). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.020
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one exception being a wastewater effluent sample). This is in contrast to most municipal drinking water plants treating surface water in which UV treatment would typically be placed after coagulation and filtration. The reasoning for conducting the work on untreated water was (i) to specifically explore challenges to the application of UV for small, nonmunicipal water treatment systems where UV is sometimes applied to treat unfiltered or poorly treated surface water, or for the rare instances where UV treatment is being considered prior to filtration at municipal systems, and (ii) to obtain fundamental information about the UV absorption of particles in natural waters which could then be combined at a later stage with similar information representing treated water flocs to build an overall model of particle protection. The earlier study by Loge et al. (1999) determined absorption properties of the activated sludge particles by trapping the particles in an agar plug, and then inserting fiber optic sensors into the plug to measure absorption. Another method that has been used in the past, and that was used in the current study, is called the ‘quantitative filter technique’. This technique involves passing a water sample though a glass-fiber filter and placing the filter in a spectrophotometer, using a clean moist filter as a reference (Mitchell and Keifer, 1988; Bricaud and Stramski, 1990; Cleveland and Weidemann, 1993; Roesler, 1998; Duarte et al., 2000; Lohrenz, 2000). The resulting absorption is a bulk value representing the particles collected on that filter. Until now, this method has been reported exclusively for measurements of the absorption of visible light by aquatic particles and plankton. In this context, the absorption value for the particles in the water sample, ap(l) (cm1), is calculated using the following equation (Mitchell and Keifer, 1988; Cleveland and Weidemann, 1993; Duarte et al., 2000): A ap ðlÞ ¼ 2:3$ ODf ðlÞ ODf ð750Þ $ V$bðlÞ
(1)
where ODf(l) is the optical density of the suspended matter retained on the filter (unitless) at wavelength l (nanometers), A is the area of the filter (cm2), V is the volume of water filtered (cm3), and 2.3 is the factor to convert from base 10 to natural logarithms. Note that the optical density of the suspended matter at 750 nm, ODf(750), is subtracted from the ODf at 254 nm as a correction to eliminate scattered light from the estimate of absorbed light at 254 nm (explained more fully in Mitchell and Keifer, 1988). With the exception of b(l), which represents the path length amplification due to scatter, the parameters in Equation (1) are relatively straightforward to determine. Different approaches for estimating b(l) have been used in previous studies involving visible light, with values ranging from 2 to 6 (Kiefer and SooHoo, 1982; Roesler, 1998; Bricaud and Stramski, 1990), but there is no information available for UV light. A value of b ¼ 1 is assumed for this study for reasons explained in Section 2.4.
2.
Materials and methods
2.1.
Water sources
Secondary effluent from a municipal wastewater treatment plant was collected from the Ashbridges Bay Treatment Facility
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(Toronto, Canada). Surface water samples prior to any treatment were collected at water treatment plants (WTPs) across Canada and one in California. Samples were obtained from: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Grand River (Mannheim WTP), Waterloo, Canada Lake Ontario (Ajax WTP), Ajax, Canada Lake Webster, Bala, Canada Dried Meat Lake (Camrose WTP), Camrose, Canada North Saskatchewan River, Edmonton, Canada Ottawa River (Britannia WTP), Ottawa, Canada Otonabee River (Peterborough WTP) Peterborough, Canada Rideau River (Smiths Falls WTP), Smiths Falls, Canada St. Lawrence River (Cornwall WTP), Cornwall, Canada Pardee Reservoir (aqueduct influent), Pardee Center, Valley Springs, California, USA
2.2.
Quantitative filter technique
The UV absorption properties of the suspended particles were determined by passing water samples through woven glassfiber filters (GF/C) (Whatman International Ltd., UK) by vacuum filtration according to Standard Method 2540C (APHA, 2005). The filter pore size was 1.2 mm. This pore size was selected to separate particles that are large enough to harbor bacteria and protozoa (generally >1 mm) from particles that are presumably too small to be of significance in terms of protecting such organisms. UV absorption measurements were determined from an average of four measurements for different sections of the filter pad. The filters were placed against the integrating sphere component of the spectrophotometer using tweezers, and held in place by two clamps that normally hold the attachment that secures the cuvette. The particulate concentration was fairly evenly distributed over the filter cake as seen by eye. In addition, ODf measurements of four mutually exclusive quadrants of the filter always had coefficients of variation of 4% or less. The coefficients of variation for replicate measurements (i.e. with different filters) were always <8%. The water sample size, V, depended on suspended solids content (i.e. total suspended solids, TSS) and ranged from 250 mL to 3 L. Bricaud and Stramski (1990) reported that b(l) values vary significantly for ODf values <0.2 (unitless). Preliminary filtration trials found this amount of solids loading to appear by visual inspection to cover the filter pad without leading to longer filtration times. Therefore, the volume of water passed through the filter was such that the ODf was >0.2. For this study particulate matter was defined as material that was retained on the glass-fiber filter (i.e. >1.2 mm diameter). The optical density at 254 nm (ODf) of the particulate matter retained on the filter was determined immediately after filtration so drying of the filter pad would not impact light absorption measurements. The particle light absorption, ap(l) (cm1), provides an indication of the amount of light absorbed by particles per centimeter depth of sample (i.e. per centimeter of water and particles suspended in the original sample). It is valuable, however, to be able to relate this information to absorption of UV light per unit length of solids (instead of length of the water column). For this study, the absorption of UV light per unit length of solids was approximated by substituting solids volume for sample volume (i.e. V in Equation (1)). The solids volume was estimated using total suspended solids (TSS).
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Along with an appropriate value for the specific gravity of the particles, the TSS can be used to approximate the volume of particulate matter in a sample. As an example and using Equation (1), consider the wastewater sample where ODf (l ¼ 254) ¼ 0.46 (unitless) for retained particles after a 500 mL sample was passed through the filter. Using a filter area of 9.6 cm2 and assuming no scattered light (b(l) ¼ 1), an ap-sample value of 2.03 102 cm1 is calculated. This is the absorption of UV light due to solids contained in a 1 cm length of water sample. To calculate the ap-solids value for this sample with a measured TSS of 1.8 mg/L, it was assumed that the specific gravity of the particles in the water samples was 1.0 based on the observation that particles did not settle or rise in suspension when left overnight (a sensitivity analysis of the effect of assumed specific gravity is presented in the Results and Discussion section). The volume of particulate matter retained on the filter was therefore:
(2)
using this value for V in Equation (1), an ap-solids value of 11,000 cm1 is calculated at 254 nm. This is the absorption of UV light due to particles per cm of solid particulate matter. It is this ap-solids value that is of interest to this study since, as with the previous wastewater research, it allows one to estimate the ability of particles of different thickness (diameter) to protect embedded microorganisms from UV light.
2.3.
Analytical methods
Turbidity was measured in nephelometric turbidity units (NTU) using a Hach 2100N turbidimeter (Hach Company, Loveland, CO). The sample pH was measured using a VWR pH meter Model 8015 (VWR, Mississauga, ON). Total organic carbon (TOC) measurements were performed according to Standard Method 5310-D (APHA, 2005) using an O-I Corporation Model 1010 total organic carbon (TOC) analyzer (College Station, TX) calibrated with a potassium phthalate solution. TSS was measured according to Standard Methods 2540-D (APHA, 2005) using 47 mm diameter GF/C glass-fiber filters (Whatman International Ltd., UK). UV and visible light absorption data (l ¼ 200e550 nm) were collected with a spectrophotometer (model CE3055, Cecil Instruments, Cambridge, United Kingdom) equipped for both standard simple transmission and using a fixed 11 angle center mounted integrating sphere accessory (Labsphere, North Sutton, New Hampshire, USA) to allow for both conventional simple transmittance and integrating sphere measurements (Linden and Darby, 1998; Christensen and Linden, 2003; Mamane et al., 2006). Spectral absorption measurements were measured at 1 nm intervals. Prior to liquid sample measurements, the 1 cm quartz cuvette was zeroed with Milli-Q water. ODf absorption measurements were blanked with filters wetted with 10 mL of sample filtrate (Lohrenz, 2000).
2.4.
Determination of b(l)
No information in the literature is available for the path length amplification, b(l), when measuring light absorbance through
3.
Results and Discussion
3.1.
Absorption of UV light by suspended particles
Experiments were conducted using a wastewater secondary effluent sample and ten untreated surface water samples to determine the absorption of UV light at 254 nm by the suspended particles (i.e. ap-solids). The calculations for Equation (1) for surface waters were performed assuming b(254 nm) ¼ 1 and a solids specific gravity of 2.0 (1.0 for the wastewater secondary effluent sample). The specific gravity of 2.0 for the surface waters is an estimate within the commonly reported range of between 1.0 and 2.65 (Bhargava and Rajagopal, 1992; Kassem and Imran, 2001; Gray et al., 2003), and is based on observations that the majority of the visible particles in the samples quickly settled under quiescent conditions. A sensitivity analysis of b(l) and specific gravity on the resulting ap-solids is presented after the discussion of the data. Since the specific gravity of the particles will vary widely, no single value for specific gravity will be adequate for all the particles in the sample; however, an estimate allowed for consistent treatment of the samples. 100
Scattering Albedo (%)
1:8 mg solids L ¼ 9:0 104 cm3 1000 mg solids cm3 solids
0:5 L original sample
a layer of particles in the UV band, and it is not a parameter that could be measured directly in this study. Instead, the proportion of scattered UV and visible light was measured in the water samples using the integrating sphere spectrophotometer. The proportion of scattered light is expressed as the scattering albedo (Mamane et al., 2006). Representative scattering albedo data (l ¼ 200e550 nm) are shown in Fig. 1, and illustrate that the scatter depends on wavelength with albedo generally increasing with wavelength for l above w220 nm (all except the Otonabee River sample). Therefore, for most of the samples, the impact of scattering on path length amplification would be more significant in the visible light range (l 400e800) than in the UV wavelengths. This is important because a lower scattering albedo for UV wavelengths means any correction in Equation (1) for path length amplification at UV wavelengths will be less significant than for visible light where values of b(l) ¼ 2 are commonly reported, so a value of 1 was assumed in this study. A sensitivity analysis of the effect of b(l) on ap-solids is presented in Section 3.2.
Albedo at 254 nm:
80
Lake Ontario 12 % Grand River 38 % Rideau Canal 7% Otonabee River 30 %
60
40
20
0 200
300
400
500
Wavelength (nm)
Fig. 1 e UV and visible light scattering albedo of selected surface water samples.
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Table 1 e Particulate UV absorption and typical water quality parameters. Source Secondary Effluent Secondary Effluent Secondary Effluent Grand River Grand River Grand River Lake Ontario Lake Webster Driedmeat Lake N. Saskatchewan River Ottawa River Otonabee River Otonabee River Rideau Canal Rideau Canal St. Lawrence River St. Lawrence River St. Lawrence River Pardee Reservoir
Collection Date
Water Vol. (mL)
TSS (mg/L)
Turbidity (NTU)
Aqueous UVT (% cm1)
TOC (mg/L)
Scattering Albedo (%)
ODfilter
ap-solids (cm1)
Jan 26 Oct 20 Dec 12 Feb 1 Apr 19 Aug 21 Apr 21 Jul 17 Jul 13 Jul 26 Aug 1 Jun 26 July 26 Feb 7 Jun 05 Feb 7 Aug 14 Aug 28 Oct 3
500 500 500 500 100 500 1200 50 150 1500 500 1500 1000 250 1000 3000 1500 1000 1000
4.1 1.8 4.2 16.4 60.4 4.3 0.4 33 23.8 0.71 2.63 0.67 2.35 2.2 1.25 0.83 0.77 2.3 0.81
e 1.3 e 22.3 50 5.4 0.5 7.9 29.1 1.5 4.8 0.79 1.1 8.2 0.84 0.47 0.85 1.7 0.74
e 74.8 70.1 61.8 66.7 65.3 94.8 41.9 39.0 91.6 61.1 79.3 72.1 61.0 57.8 93.3 93.3 96.4 93.1
e e e e 5.6 5.8 2.3 5.7 15.9 2.1 6.7 5.1 5.8 e 7.3 2.1 2.6 2.7 1.5
e 15% 15% e 38% 7% 12% 8% 13% 25% 13% 30% 7% 4% 7% 6% 19% 0% 0%
0.70 0.46 1.04 2.07 1.10 0.43 0.22 1.60 1.01 0.54 0.36 0.54 0.38 0.54 1.10 0.33 0.51 0.51 0.56
7600 11,000 11,000 11,000 8000 8800 20,000 42,000 13,000 22,000 12,000 24,000 7200 44,000 38,000 5800 20,000 10,000 30,000
As shown in Table 1, the values of ap-solids for three samples of wastewater treatment plant secondary effluent collected at different times ranged from 7600 cm1 to 11,000 cm1. This range of values for ap-solids overlap with the UV absorption values of wastewater suspended solids reported by Loge et al. (1999), who determined the ap at 254 nm of trickling filter solids and activated sludge solids to be between 3300 cm1 and 539,000 cm1 based on an analysis of wastewater solids from six wastewater treatment facilities (three of the six values were between 3300 cm1 and 15,200 cm1). The ap of suspended particulate matter from the ten surface waters is also summarized in Table 1. The ap-solids values for surface water ranged from 5800 cm1 to 44,000 cm1. A plot of the typical aqueous water quality parameters (i.e. turbidity, bulk water UV transmittance at 254 nm, TOC, and TSS) against ap-solids is presented in Fig. 2. There was poor correlation (r2 <0.1) between ap-solids and typical aqueous water quality parameters. When the data presented is regressed against ap-solids, the coefficient of determination (r2) for turbidity, bulk
20 Turbidity TSS UVT TOC
90 80 70
18 16 14 12
60 50
10
40
8
30
6
20
4
10
2
0 0
10,000
20,000
30,000
40,000
TOC (mg/L)
Turbidity (ntu), UVT (%/cm), or TSS (mg/L)
100
0 50,000
UV254 Absorbance of Solids (/cm)
Fig. 2 e Turbidity, UVT, TSS and TOC versus UV absorption of solids.
water UV transmittance at 254 nm, TOC, and TSS were 0.072, 0.061, 0.007, and 0.009, respectively. The UV absorption (ap) reported in the literature for various materials occurring in aqueous environments is summarized in Table 2. Soils, microorganisms and extracellular polymeric substances reportedly have UV absorption values <1000 cm1, while wastewater and surface water solids have UV absorption >3000 cm1. Given that it has been shown that wastewater particles can protect microorganisms from UV light (e.g. Emerick et al., 2000; Jolis et al., 2001), these results suggest a similar phenomenon may account for some protection of microbes by solids in surface water. Another way to consider the data is in terms of light attenuation within particulate matter. For example, the ap-solids values of the surface water particles ranged between 5800 cm1 and 44,000 cm1. Using these absorption values, UV light would be attenuated by two orders of magnitude (i.e. 99%) across 0.5 and 3.4 mm of particulate matter, respectively (Calculation: path length ¼ absorbance for 2-log reduction O ap ¼ log10 (100/1) O 5800 cm1 ¼ 3.4 mm). If the composition of particulate matter is homogeneous, the average particle ‘size’ able to extinguish 99% of the UV light can be approximated. For an MS2 phage (0.025 mm in diameter) a particle diameter of only 1 mme7 mm may extinguish 99% of light for a phage embedded in the middle. A Cryptosporidium oocyst (4e6 mm) (Fayer et al., 2000) would need to be embedded within the center of a spherical particle of 7 mme13 mm in diameter. In contrast, Amoah et al. (2005) previously speculated that a surface water particle would likely need to be greater than 25 mm to provide shielding from UV light to an embedded Cryptosporidium oocyst or Giardia cyst. While other factors such as particle shape, particle porosity and the location of the embedded microorganism complicate this exercise, the current study suggests that surface water particles need not be much larger than 13 mm in diameter to impact the effectiveness of UV light for the control of protozoa and bacteria.
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Table 2 e Comparison of UV absorption values (ap) of various solids. Material type
ap (cm1)
99% attenuation depth (mm)
19 soil samples Microorganisms Organic extracellular polymeric substances Activated sludge wastewater solids Wastewater secondary effluent solids Surface water suspended solids
300e500a w1000b 250 3300e569,000 7600e11,000 5800e42,000
40e67 20 80 0.04e6 1.8e2.6 0.5e3.5
Reference Ciani et al. (2005) Jagger (1967) Scott et al. (2005) Loge et al. (1999) This study This study
a absorption of UV light at 275 nm instead of 254 nm. b derived from note in Jagger (1967) that w90% of UV light absorbed in 10 mm of cells.
3.2.
Sensitivity of ap-solids to b(l) and specific gravity
A sensitivity analysis was undertaken to determine the impact of error in the assumptions about b(l) and specific gravity (S.G.) on ap-solids. The initial assumption was that b(l) ¼ 1.0 and S.G. ¼ 1.0 for particles from secondary effluent and S.G. ¼ 2.0 for particles from surface water. The impact of a change in the parameters is illustrated using a sample collected from the Grand River (Table 1). Three values for each parameter, which would bracket the possible true values, were selected. b(l) values of 1, 2 and 4 were selected. b(l) ¼ 1 represents the minimum values (i.e. negligible scattering) and b(l) ¼ 4 represents an upper value for experiments using visible light (Bricaud and Stramski, 1990; Kishino et al., 1985). The impact of b(l) on ap-solids is presented in Table 3. The value of ap-solids is inversely proportional to b(l). Therefore, by assuming b(l) ¼ 1, the ap-solids value is an overestimate if scattering is significant (i.e. 8800 cm1). However, since the relative scatter for UV light is less than visible light for most samples (Fig. 1), the error in b(l) should be minimal. To study the sensitivity of ap-solids to specific gravity, a specific gravity of 4 was selected to represent particles twice as dense as the assumed average value earlier (i.e. S.G. ¼ 2). The impact of specific gravity on ap-solids is presented in Table 3. The value of ap-solids is proportional to specific gravity; therefore, denser particles will have higher ap-solids values. Many of the particles will have a specific gravity near 1 (i.e. they were observed not to settle after 8 h) and therefore have ap-solids values lower than calculated based on a specific gravity of 2. For example, for b(l) and specific gravity values of 1 and 2, respectively, the average ap-solids value was calculated to be 8800 cm1 (Table 3). The suspended solids in the sample that have a specific gravity <2 will have a true ap-solids value <8800 cm1. Also presented in Table 3 are the 99% (2-log) UV attenuation depths as affected by b(l) and specific gravity. Based on
the assumed values of b(l) and specific gravity of 1 and 2, respectively, the 99% attenuation depth is 2.3 mm. For most of the scenarios highlighted in Table 3 the 99% attenuation depth is less than 9 mm, suggesting that particles with radii in the order of 10 mm may offer significant protection to embedded microorganisms.
3.3.
Variability in ap-solids measurements
UV absorption (at 254 nm) (i.e. ap-solids in the current study) of activated sludge particles reported by Loge et al. (1999) spanned two orders of magnitude (3300 to 569,000 cm1). Similar variability in the UV absorption of surface water solids was expected, both among individual particles, and in terms of bulk particle absorption from samples collected at different times. Samples were collected from four surface waters for repeat measurements several months apart. The ap-solids for the Grand River and Rideau Canal samples showed consistency (<25% difference) even though there were considerable differences in other water quality parameters (Table 1). For example, two of three Grand River samples collected five months apart had TSS values of 4.3 and 60 mg/L and the ap-solids values were similar at 8800 and 8000 cm1, respectively. For the Rideau Canal samples, the absorption of the retained particles was 38,000 and 44,000 cm1, respectively, for samples with turbidity of 0.84 and 8.2 NTU. Conversely, the Otonabee River and St. Lawrence River samples exhibited greater variability in the UV absorption of the solids, with the highest value being approximately three times the lowest value (e.g. ap-solids values for three St. Lawrence samples ranged from 5800 cm1 to 20,000 cm1). That ap-solids seems to vary independently of turbidity reinforces the notion that the UV absorption of particulate matter in surface water is not correlated to other common dissolved water quality parameters.
Table 3 e Impact of b(l) and specific gravity on ap-solids and 2-log light attenuation. b(l)
1 2 4
S.G. ¼ 1
S.G. ¼ 2
S.G. ¼ 4
ap-solids (cm1)
99% attenuation depth (mm)
ap-solids (cm1)
99% attenuation depth (mm)
ap-solids (cm1)
99% attenuation depth (mm)
4400 2200 1100
4.5 9.1 18
8800 4400 2200
2.3 4.5 9.1
17,600 8800 4400
1.1 2.3 4.5
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 2 2 e1 3 2 8
The UV absorption by particles depends not only on particle composition but also on particle size, shape, and porosity. For example, a highly porous particle might not be very effective at attenuating UV light even though the particulate matter might be highly UV absorbing. In natural untreated water samples, variation in particle composition and structure will result in deviation from average solids UV absorption. Specifically, solids with a highly reflective component such as alumina (Mamane et al., 2006) or clay (Mamane et al., 2006; Templeton et al., 2005) may allow UV light to traverse a particle via a tortuous pore-pathway (since reflected light can still disinfect). In contrast, a particle composed of highly absorbent material such as iron oxides (Cairns et al., 1993; Weishaar et al., 2003; Templeton et al., 2006) or organic carbon (Bitton et al., 1972; Templeton et al., 2005; Mamane et al., 2006) may only allow penetration via direct pathways. Therefore, ap-solids only allows for general observation and comparison regarding the average UV absorption of suspended particles and does not allow this information to be directly extrapolated to UV light attenuation within particulate matter or to indicate the extent of interference with UV disinfection.
4.
Summary and conclusions
The UV absorption of suspended particulate matter in ten untreated surface waters ranged from 5800 cm1 to 22,000 cm1 and was not correlated to turbidity, TOC, TSS or UV absorption at 254 nm of the bulk water. These absorption values are similar to previously-reported measurements for particles in secondary wastewater effluent. Since it has been shown that UV absorbing wastewater particles can offer protection to embedded microorganisms from UV disinfection, the evidence generated in this study supports the theory that it is possible for untreated surface water particles to offer similar protection to embedded microorganisms by a similar mechanism. One goal of this work was to provide guidance specifically for small UV disinfection systems that are treating unfiltered surface water. The results showed the existence of particles that are sufficiently opaque to UV light to theoretically interfere with disinfection, even in “good quality” water (e.g. turbidity less than 2 NTU). In fact, there was no correlation observed between conventional water quality parameters (turbidity, TOC, TSS, UVT) and the presence of particles with a high UV absorbance. As such, practitioners may need to consider that all surface waters may contain particles that can shield microorganisms from UV light. Some jurisdictions (e.g. Ontario, Canada) already require that small UV systems treating surface water use an upstream filter to remove particles that can contain embedded bacteria and protozoa. The current research tends to support this requirement.
Acknowledgements The authors gratefully acknowledge the funding provided by the Canadian Water Network through the Integrated Disinfection Strategies Optimization Project.
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references
American Public Health Association; American Water Works Association; and Water Environment Federation, 2005. Standard Methods for the Examination of Water and Wastewater, 21st ed. APHA, AWWA, and WEF, Washington, DC. Amoah, K., Craik, S., Smith, D.W., Belosevic, M., 2005. Inactivation of Cryptosporidium oocysts and Giardia cysts by ultraviolet light in the presence of natural particulate matter. Journal of Water Supply Research and TechnologydAqua 54 (32), 165e178. Bhargava, D.S., Rajagopal, K., 1992. An integrated expression of settling velocity of particles in water. Water Research 26 (7), 1005e1008. Bitton, G., Henis, Y., Lahav, H., 1972. Effect of several clay mineral and humic acids on the survival of Klebsiella aerogenes exposed to ultraviolet irradiation. Applied Microbiology 23 (5), 870e874. Bricaud, A., Stramski, D., 1990. Spectral absorption coefficients of living phytoplankton and nonalgal biogenous matter: a comparison between the Peru upwelling area and the Sargasso Sea. Limnology and Oceanography 35 (3), 562e582. Cairns, W.L., Sakamoto, G., Comair, C.B., Gehr, R., 1993. Assessing UV Disinfection of a Physico-Chemical Effluent by Medium Pressure Lamps Using a Collimated Beam and Pilot Plant. Proceedings of the Water Environment Federation Specialty Conference on Planning, Design, and Operations of Effluent Disinfection Systems, Whippany, NJ, Water Environment Federation, Alexandria, VA. Cantwell, R., Hofmann, R., 2008. Inactivation of indigenous coliform bacteria in unfiltered surface water by ultraviolet light. Water Research 42 (10e11), 2729e2735. Christensen, J., Linden, K.G., 2003. How particles affect UV light in the UV disinfection of unfiltered drinking water. Journal of the American Water Works Association 95 (4), 179e189. Ciani, A., Goss, K., Schwarzenbach, R.P., 2005. Light penetration in soil and particulate minerals. European Journal of Soil Science 56 (5), 561e574. Cleveland, J.S., Weidemann, A.D., 1993. Quantifying absorption by aquatic particles: a multiple scattering correction for glassfiber filters. Limnology and Oceanography 38 (6), 1321e1327. Duarte, C.M., Agustı´, S., Kalff, J., 2000. Particulate light absorption and the prediction of phytoplankton biomass and planktonic metabolism in northeastern Spanish aquatic ecosystems. Canadian Journal of Fisheries and Aquatic Sciences 57 (1), 25e33. Emerick, R.W., Loge, F.J., Ginn, T., Darby, J.L., 2000. Modeling the inactivation of particle-associated coliform bacteria. Water Environment Research 72 (4), 432e438. Fayer, R., Morgan, U., Upton, S.J., 2000. Epidemiology of Cryptosporidium: transmission, detection and identification. International Journal for Parasitology 30 (12e13), 1305e1322. Gray, J.R., Melis, T.S., Patin˜o, E., Larsen, M.C., Topping, D.J., Rasmussen, P.P., Figueroa-Alamo, C., 2003. U.S. geological survey research on surrogate measurements for suspended sediment. In: Renard, Kenneth G., McElroy, Stephen A., Gburek, William J., Canfield, H.Evan, Scott, Russell L. (Eds.), Proc. of the 1st Interagency Conference on Research in Watersheds, pp. 95e100. Jagger, J.H., 1967. Introduction to Research in Ultraviolet Photobiology. Prentice-Hall, Englewood Cliffs, N.J. Jolis, D., Lam, C., Pitt, P., 2001. Particle effects on ultraviolet disinfection of coliform bacteria in recycled water. Water Environment Research 73 (2), 233e236. Kassem, A., Imran, J., 2001. Simulation of turbid underflows generated by the plunging of a river. Geology 29 (6), 655e658. Kiefer, D.A., SooHoo, J.B., 1982. Spectral absorption by marine particles of coastal waters of Baja California. Limnology and Oceanography 27 (3), 492e499.
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Kishino, M., Okami, N., Ichimura, S., 1985. Estimation of the spectral absorption coefficients of phytoplankton in the sea. Bulletin of Marine Science 37 (2), 634e642. Linden, K.G., Darby, J.L., 1998. Ultraviolet disinfection of marginal effluents: determining ultraviolet absorption and subsequent estimation of ultraviolet intensity. Water Environment Research 70 (2), 214e223. Loge, F.J., Emerick, R.W., Thompson, D.E., Nelson, D.C., Darby, J.L., 1999. Factors influencing ultraviolet disinfection performance. Part I: light penetration to wastewater particles. Water Environment Research 71 (3), 377e381. Lohrenz, S.E., 2000. A novel theoretical approach to correct for pathlength amplification and variable sampling loading in measurements of particulate spectral absorption by the quantitative filter technique. Journal of Plankton Research 22 (4), 639e657. Mamane, H., Ducoste, J.J., Linden, K.G., 2006. Effect of particles on ultraviolet light penetration in natural and engineered systems. Applied Optics 45 (8), 1844e1856. Mitchell, B.G., Keifer, A., 1988. Chlorophyll a specific absorption and fluorescence excitation spectra for lightlimited phytoplankton. Deep Sea Research Part A 35 (5), 639e663. Qualls, R.G., Flynn, M.P., Johnson, D., 1983. The role of suspended particles in ultraviolet disinfection. Journal Water Pollution Control Federation 55, 1280e1285.
Roesler, C.S., 1998. Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique. Limnology and Oceanography 43 (7), 1649e1660. Scott, H.E., Liss, S.N., Farnood, R.R., Allen, D.G., 2005. Ultraviolet disinfection of sequencing batch reactor effluent: a study of physiochemical properties of microbial floc and disinfection performance. Journal of Environmental Engineering and Science 4 (S1), S65eS74. Templeton, M.R., Andrews, R.C., Hofmann, R., 2005. Inactivation of particle-associated viral surrogates by ultraviolet light. Water Research 39 (15), 3487e3500. Templeton, M.R., Andrews, R.C., Hofmann, R., 2006. Impact of iron particles in groundwater on the UV inactivation of bacteriophages MS2 and T4. Journal of Applied Microbiology 101 (3), 732e741. Templeton, M.R., Cantwell, R., Quinn, C., Hofmann, R., Andrews, R. C., 2009. Pilot-scale assessment of the impacts of transient particulate water quality events on the UV disinfection if indigenous total coliform bacteria in drinking water. Journal of Water Supply: Research and TechnologydAqua 58 (1), 11e20. Weishaar, J.L., Aiken, G.R., Bergamaschi, B.A., Fram, M.S., Fujii, R., Mopper, K., 2003. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environmental Science and Technology 37 (20), 4702e4708.
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Bicarbonate-form anion exchange: Affinity, regeneration, and stoichiometry Christopher A. Rokicki, Treavor H. Boyer* Department of Environmental Engineering Sciences, University of Florida, P.O. Box 116450, Gainesville, FL 32611-6450, USA
article info
abstract
Article history:
Magnetic ion exchange (MIEX) is an effective process for removing dissolved organic
Received 10 June 2010
carbon (DOC) from natural waters, but its implementation has been limited due to
Received in revised form
production of waste sodium chloride solution (i.e., brine) from the regeneration process.
17 September 2010
Chloride is of concern because elevated concentrations can have adverse effects on engi-
Accepted 13 October 2010
neered and natural systems. The goal of this research was to explore the efficacy of using
Available online 21 October 2010
anion exchange resin with bicarbonate as the mobile counter ion, which would produce a non-chloride regeneration solution. It was found that bicarbonate-form MIEX resin had
Keywords:
a similar affinity as chloride-form MIEX resin for sulfate, nitrate, DOC, and ultraviolet-
Carbon dioxide
absorbing substances. Both bicarbonate-form and chloride-form MIEX resins showed the
Magnetic ion exchange
greatest removal efficiencies as fresh resin, and removal efficiency decreased with multiple
Natural organic matter
regeneration cycles. Nevertheless, sodium bicarbonate solution was as effective as sodium
Bicarbonate
chloride solution at regenerating MIEX resin. Regeneration of the bicarbonate-form MIEX
Nitrate
resin was illustrated by sparging carbon dioxide gas in a water/resin slurry. This regen-
Sulfate
eration process would eliminate the need for the addition of salts such as sodium chloride or sodium bicarbonate. The stoichiometry of the bicarbonate-form resin revealed that the bicarbonate was deprotonating within the resin matrix leading to a mixture of both carbonate and bicarbonate mobile counter ions. This work makes an important contribution to ion exchange applications for water treatment by evaluating the affinity, regeneration, and stoichiometry of bicarbonate-form anion exchange. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Magnetic ion exchange (MIEX) is an effective process for treating water that contains dissolved organic carbon (DOC) and other pollutants (Boyer and Singer, 2005; Boyer and Singer, 2006; Kitis et al., 2007; Mergen et al., 2008; Drikas et al., 2009; Hsu and Singer, 2010). MIEX resin and most conventional anion exchange resins use chloride as the mobile counter ion, and are referred to as chloride-form resin. Chloride is used as the mobile counter ion because it is relatively inert with respect to water chemistry reactions and the low cost of sodium chloride
salt for regeneration. Chloride, however, is not ideal in every situation and can have several adverse outcomes, such as enhanced corrosion of plumbing, detrimental effects on biological wastewater treatment processes, and increased salinity loading to surface waters. Research has shown that an increase in the chloride to sulfate mass ratio (CSMR) can increase lead corrosion in water distribution systems (Edwards and Triantafyllidou, 2007). Chlorideform anion exchange resin is expected to increase the CSMR due to release of chloride and uptake of sulfate, and thus has the potential to increase the corrosivity of treated water towards
* Corresponding author. Tel.: þ1 352 846 3351; fax: þ1 352 392 3076. E-mail address: [email protected] (T.H. Boyer). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.018
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lead. Chloride-form resin also presents potential problems related to the regeneration process, whereby a high ionic strength sodium chloride solution (i.e., brine) is used to regenerate the resin. Waste brine is often difficult to dispose of due to the detrimental effects of high salinity on biological wastewater treatment processes (Panswad and Anan, 1999). Furthermore, wastewater effluent that receives waste brine is a threat to receiving waters because the increased loading of salt can degrade water quality over time. Altering the mobile counter ion on anion exchange resin to a non-chloride ion has the potential to negate the problems associated with chloride-form anion exchange discussed above. Investigations into the performance of non-chloride anion exchange are limited, and focus mostly on hydroxideform resin and, to a lesser extent, bicarbonate-form resin (Ho¨ll and Kiehling, 1981). Bicarbonate is an attractive alternative to chloride because it is harmless to humans and ecosystems, even when present at elevated levels (Jelinek et al., 2004). Bicarbonate-form anion exchange resin is expected to have similar treatment efficiency as chloride-form resin based on the equal preference of chloride-form resin for bicarbonate and chloride (Boyer and Singer, 2008). This is supported by the work of Ho¨ll and Kiehling (1981), Matosic et al. (2000), and Jelinek et al. (2004), which shows bicarbonate-form anion exchange can effectively treat water for the removal of nitrate and sulfate. In addition, bicarbonate-form anion exchange has been shown to decrease the corrosivity of treated water (Takasaki and Yamada, 2007). This is also seen in the Langelier and LarsonSkold indices, which consider bicarbonate to inhibit corrosion in iron-containing water distribution systems (Larson, 1975). The improved treatment outcomes of bicarbonate-form anion exchange relative to chloride-form anion exchange are treated water with lower corrosivity and waste brine that does not add chloride to engineered or natural systems. Finally, as demonstrated by Ho¨ll and Kiehling (1981), bicarbonate-form resin has the potential to be regenerated by sparging water with carbon dioxide (CO2) gas, which would generate aqueous bicarbonate and would reduce the need for salts used during regeneration. Greenleaf and SenGupta (2009) demonstrated a similar idea in which CO2-sparged water generated hydrogen ions that were used to regenerate calcium-loaded cation exchange resin. The potential of CO2 sparging for regeneration of anion exchange resin can ultimately lead to a water treatment process that uses less chemicals and sequesters waste CO2 gas for a beneficial purpose (Greenleaf and SenGupta, 2009). Regeneration of anion exchange resin with CO2 gas also points to the synergies of colocating power generation and water treatment, both of which are major users of water and energy (Carrillo and Frei, 2009; Blackhurst et al., 2010). Despite the appeal of bicarbonate-form anion exchange, the depth of knowledge about non-chloride anion exchange is rather limited. There has been a growing interest in non-chloride anion exchange which has caused an increase in the number of pilot studies and investigations into the performance of bicarbonate-form MIEX treatment (Dahlke et al., 2007). Selectivity of resins in the chloride-form for various inorganic and organic anions is well established (Li and Sengupta, 1998; Tripp and Clifford, 2006; Tan and Kilduff, 2007). However, it is unknown if using non-chloride mobile counter ions will alter the preference of a resin for other target anions in the presence
of DOC. For example, there is a limited amount of research testing bicarbonate-form resin for nitrate removal (Jelinek et al., 2004; Matosic et al., 2000; Ho¨ll and Kiehling, 1981); there is limited published work testing bicarbonate-form resin for DOC removal (Dahlke et al., 2007). This is important because anion exchange, in particular MIEX, is increasingly being used for DOC removal (Warton et al., 2007; Neale and Schafer, 2009; Singer et al., 2009). Other major unknowns related to non-chloride anion exchange include treatment efficiency over multiple regeneration cycles; whether it is possible to convert an anion exchange resin to the bicarbonate-form by sparging CO2 gas in water, without the addition of chemicals; and the precise mechanism of bicarbonate-form anion exchange. The overall goal of this work was to increase the depth of knowledge pertaining to bicarbonate-from anion exchange. First, the affinity of non-chloride-form anion exchange resin was evaluated by using resins loaded with various mobile counter ions. The affinity was tested using two synthetic model waters. Both model waters had the same composition of chloride, bicarbonate, nitrate, and sulfate. One of the model waters also contained a natural organic matter (NOM) isolate. Second, the continued reuse of bicarbonate-form resin was investigated to determine the effectiveness of the regeneration process. Experiments were conducted using both the synthetic inorganic model water and the synthetic NOM-containing model water. Third, the use of CO2 gas alone as a means of producing bicarbonate-form resin was examined. Finally, the stoichiometry of bicarbonate-form anion exchange process was quantified to ensure that the process occurring was in fact ion exchange and not adsorption or complex formation.
2.
Experimental section
2.1.
Materials
MIEX resin from Orica Watercare was used in this work and is referred to as MIEX-Cl because chloride is the mobile counter ion. All data for MIEX-Cl resin is virgin resin as received from the manufacturer, which is the generally accepted practice with MIEX-Cl resin. MIEX-Cl resin was regenerated, as described in Section 2.2, to obtain resins with other mobile counter ions. The nomenclature used in this paper is MIEX-Z or R-Z, where R is the MIEX resin phase and Z is the mobile counter ion placed on the resin during regeneration. For example, MIEX-HCO3 refers to resin with bicarbonate as the mobile counter ion. MIEX resin was also saturated with a pre-determined mobile counter ion, to simulate exhaustion, and then regenerated with a different mobile counter ion. The nomenclature used for this resin is MIEX-(Y)-Z, where Y is the counter ion used to saturate the resin and Z is the counter ion used to regenerate the resin. For example, MIEX-(SO4)-HCO3 refers to resin that was saturated with sulfate and then regenerated with bicarbonate. A subscript 2 after MIEX is used to show that divalent anions occupy two sites on the resin (e.g., MIEX2-SO4). Two synthetic model waters were used in this work: (i) inorganic ions only and (ii) inorganic ions with NOM. The composition of the synthetic model waters is listed in Table 1. The model waters were prepared in deionized (DI) water. Chloride, bicarbonate, nitrate, and sulfate were added as
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 2 9 e1 3 3 7
Table 1 e Average measured composition of synthetic model waters used in ion exchange experiments. Species chloride (mg/L) bicarbonate (mg/L) nitrate (mg N/L) sulfate (mg/L) DOCa (mg C/L)
Inorganic model water
NOM-containing model water
15 140 28 46 na
14 95 27 46 4.1
a SUVA254 w 5.4 L/mg C/m; not applicable (na).
sodium salts and were ACS reagent-grade purity. The NOM was a freeze-dried sample that was isolated from the Santa Fe River (SFR), FL, USA by Davis and co-workers (Davis et al., 1999).
2.2.
Jar tests and resin regeneration
All experiments were conducted at ambient laboratory temperature and open to the atmosphere, unless noted otherwise. Jar tests were performed using a Phipps & Bird PB700 jar tester with 2 L square jars. Two liters of synthetic model water was used in all jar tests. MIEX resin was measured as the volume of wet settled resin in a graduated cylinder. MIEX resin doses of 1, 2, and 4 mL/L were tested. The jar tests were conducted at 100 rpm for 30 min and the resin was allowed to settle for 10 min before samples were taken from a spigot in the jar. Each MIEX resin (e.g., MIEX-Cl, MIEX-HCO3, MIEX-NO3, etc.) was tested in duplicate or triplicate doses using the synthetic inorganic model water. The complete jar tests for MIEX-HCO3 and MIEX-(SO4)-HCO3 resins, using the synthetic inorganic model water, were replicated. No replicates were tested for the MIEX resins using the synthetic NOM-containing model water because of a limited supply of NOM isolate. MIEX resin was regenerated to alter the mobile counter ion available for exchange on the resin. Sodium salts of chloride, bicarbonate, nitrate, and sulfate were added to DI water to prepare the regeneration solution. The equivalent capacity of MIEX-Cl resin, which was previously determined to be 0.52 milliequivalents (meq)/mL resin (Boyer and Singer, 2008), was used to calculate the concentration of mobile counter ion in the regeneration solution. Regeneration experiments were conducted using regeneration solutions that contained 10 and 100 times the equivalent capacity of the resin (i.e., 10 and 100). Apell and Boyer (2010) used a similar approach to investigate regeneration of magnetic cation exchange resin. As an example of a 10 regeneration solution, 4 mL of MIEX-Cl resin was regenerated in 1 L of salt solution with an equivalent strength of 20.8 meq/L, which required 20.8 mmol/L of bicarbonate to produce MIEX-HCO3 resin or 10.4 mmol/L of sulfate to produce MIEX2-SO4 resin. The resin was mixed in the regeneration solution using a magnetic stir plate and stir bar for 30 min, and settled for 10 min before the supernatant liquid was decanted. To ensure that there was no excess salt remaining in solution, the resin was washed with 1 L of DI water, by mixing 10 min, settling 10 min, and then decanting the supernatant liquid. The washing procedure was repeated a second time before the resin was stored in a closed container in DI water.
2.3.
1331
CO2 regeneration
CO2 gas (Instrument Grade, Airgas) was used as an alternative method of producing MIEX-HCO3 resin, given the fact that CO2 gas would generate carbonate species when bubbled in DI water (Mills and Urey, 1940; Benjamin, 2002). 500 mL of DI water was added to a 1000 mL beaker and placed on a magnetic stir plate with stir bar. A pH probe, tube from the compressed gas cylinder, and 20 mL of MIEX-Cl resin (i.e., 40 mL/L) were added to the DI water. The beaker was covered with parafilm to minimize atmospheric interference in the headspace of the beaker. Prior to mixing or sparging, a sample was taken from the beaker and served as the initial measurement. The mixing was started and CO2 gas was sparged so that there was vigorous bubbling within the vessel with a CO2 partial pressure equal to atmospheric pressure. A constant flow rate of gas was maintained using a single-stage regulator (Fisherbrand) set at a 140 kPa. At 5, 15, 30, and 60 min, the stirring and sparging were temporarily stopped, MIEX resin was allowed to settle for 30 s, and then a sample was taken before resuming mixing and sparging. The process was repeated with nitrogen (N2) gas (Industrial Grade, Airgas), as a control in place of CO2 gas, under identical experimental conditions. All experiments were conducted in duplicate. All samples were filtered and analyzed for chloride.
2.4.
Titration of NOM
The SFR NOM isolate was titrated to provide detailed data about the charge density as a function of pH. The procedure was based on work by Boyer and Singer (2008), which follows a similar procedure as Ritchie and Perdue (2003) in which potentiometric titration of NOM was shown to be in good agreement with previous approaches documented in the literature. A 50 mL stock solution of NOM in DI water was prepared so as to have a concentration of 300 mg NOM/L and 0.1 M KCl. The NOM stock solution was covered with parafilm to limit interference by the atmosphere, and purged with N2 gas (Industrial Grade, Airgas) for 30 min while being continuously stirred using a magnetic stir plate and stir bar. After 30 min, the initial pH was recorded and the NOM stock solution was titrated with 0.04 M NaOH in increments of 0.1 mL. After each addition of titrant, the solution was allowed 1 min to equilibrate before the pH was recorded and the titration step was repeated. The complete titration procedure was conducted in duplicate and the titration curves were averaged. The charge density was calculated using Eq. (1) as follows: NOM charge density ðmeq=g CÞ ¼
þ þ H þ Na OH DOC
(1)
The NOM concentration on a charge-basis (in meq/L) was calculated as the product of the charge density (meq/g C) and the mass concentration (g C/L).
2.5.
Analytical methods
The pH was measured with an Accumet AP71 pH meter with a pH/automatic temperature compensation probe that was calibrated using pH 4, 7, and 10 buffer solutions. After pH was measured, all samples were filtered through 0.45 mm nylon
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3.
Results and discussion
3.1.
Affinity
There is a range of potential problems associated with chloride-form anion exchange, as discussed in the Introduction. This is particularly relevant to MIEX treatment, because a majority of the previous research studying MIEX is based on MIEX resin with chloride as the mobile counter ion (i.e., MIEXCl) leaving a gap in the knowledge of alternative forms of MIEX resin. Furthermore, knowledge about the affinity of MIEX resin for inorganic anions and NOM is fundamental to modeling and designing the MIEX process (Boyer et al., 2010). Therefore, research is needed that investigates the performance of MIEX resin using non-chloride mobile counter ions. The following criteria were used to evaluate non-chloride anion exchange: (i) affinity for target contaminants, (ii) efficacy of regeneration, and (iii) ion exchange mechanism. These criteria were systematically evaluated by studying different forms of MIEX resin and two synthetic model waters. This section focuses on the selective removal of target contaminants. Preliminary jar tests were conducted with 1, 2, and 4 mL/L of MIEX-Cl resin using the synthetic inorganic model water (results not shown). A MIEX resin dose of 4 mL/L was chosen for all subsequent jar tests because this dose achieved adequate removal of anions while still maintaining competition among the various anions. Fig. 1 shows the results of jar tests conducted with 4 mL/L of different forms of MIEX resin using the synthetic inorganic model water. MIEX-HCO3 (100), MIEX-HCO3 (10), MIEX-NO3 (10), and MIEX2-SO4 (10) were
2 1.8
HCO3 -
Cl -
NO3 -
SO4
--
1.6 1.4 1.2 C/C0
membrane filters (Millipore) to ensure only dissolved species were analyzed and to prevent unsettled MIEX resin from interfering with analysis. Membrane filters were pre-rinsed with 500 mL of DI water and w20 mL of sample. DOC and dissolved inorganic carbon (DIC) were measured on a Shimadzu TOC-VCPH total organic carbon analyzer equipped with an ASI-V autosampler. DOC and DIC calibration standards were prepared in DI water using potassium hydrogen phthalate (Shimadzu Scientific Instruments, Inc.) and sodium carbonate/sodium bicarbonate (Shimadzu Scientific Instruments, Inc.), respectively. Bicarbonate concentrations were calculated by converting DIC into total carbonate and, if necessary, using alpha values for carbonic acid at the pH of the sample (Benjamin, 2002). Chloride, nitrate, and sulfate were measured on a Dionex ICS-3000 ion chromatograph equipped with IonPac AG22 guard column and AS22 analytical column using 4.5 mM Na2CO3/1.4 mM NaHCO3 eluent (AS22 eluent concentrate, Dionex Corporation) as described in Apell and Boyer (2010). Calibration standards for chloride, nitrate, and sulfate were prepared in DI water using sodium salts that were ACS reagent-grade purity. All DOC, DIC, chloride, nitrate, and sulfate measurements were made in duplicate and averaged. Each run on the TOC-VCPH and the ICS3000 was monitored using calibration check standards to ensure that the measured concentration was within 10% of the known value. Ultraviolet absorbance at 254 nm (UV254) was measured on a Hitachi U-2900 spectrophotometer in a 1 cm quartz cuvette. Specific UV254 absorbance (SUVA254) was calculated by dividing UV254 by DOC.
1 0.8 0.6 0.4 0.2 0 R-HCO3 (100x)
R-HCO3 (10x)
virgin R-Cl
R-NO3 (10x)
R2-SO4 (10x)
form of MIEX resin
Fig. 1 e Effect of mobile counter ion on affinity of MIEX resin using synthetic inorganic model water (virgin R-Cl: C/C0 [ 4.6 for ClL).
produced in their respective sodium salt solution as described in Section 2.2. MIEX-HCO3 (100) and MIEX-HCO3 (10) showed very similar removal of nitrate and sulfate, with MIEX-HCO3 having a greater affinity for sulfate over nitrate. The increase in chloride seen for MIEX-HCO3 (10) resin suggests that there was a small fraction of chloride mobile counter ions remaining on the resin; however, this did not appear to affect nitrate or sulfate removal. Both 100 and 10 MIEX-HCO3 resins showed similar removal of nitrate and sulfate as MIEX-Cl resin. The greater affinity of MIEX-Cl for sulfate over nitrate is in agreement with previous selectivity studies using MIEX-Cl resin and synthetic inorganic model waters (Boyer et al., 2008). The MIEX-NO3 and MIEX2-SO4 resins demonstrated the same affinity trends as MIEX-HCO3 and MIEX-Cl resins. For example, MIEX-NO3 resin showed substantial removal of sulfate and minimal removal of other anions, and MIEX2-SO4 resin showed <25% removal of all anions. Thus, Fig. 1 illustrates that bicarbonate-form anion exchange performs nearly ideatical to chloride-form anion exchange, and that all forms of MIEX resin exhibit the same affinity trend with sulfate > nitrate > bicarbonate w chloride. Fig. 2 evaluates the effect of NOM on the removal efficiencies of MIEX-HCO3 and MIEX-Cl resin. Bicarbonate-form MIEX resin was produced using a 10 regeneration solution. MIEX-HCO3 and MIEX-Cl correspond to the data in Fig. 1. MIEX-HCO3 (NOM) and MIEX-Cl (NOM) are from jar tests using the synthetic NOM-containing model water. The presence of NOM had a negligible effect on the removal of nitrate for MIEX-HCO3 and MIEX-Cl resin. Sulfate removal appeared to be slightly improved in the presence of NOM for both resins. This is likely an artifact of conducting single jar tests for the synthetic NOM-containing model water, and will be discussed in more detail in the next subsection evaluating regeneration. MIEX-Cl resin removed more DOC than MIEX-HCO3 resin (82% vs. 61%, respectively); however, both resins removed >90% of UV254-absorbing compounds. The preference of MIEX-Cl resin for the UV254-absorbing fraction of NOM, especially in high SUVA254 waters, has been reported in the literature (Singer and Bilyk, 2002; Boyer and Singer, 2005), and it appears that MIEX-HCO3 resin has a similar preference as MIEX-
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1
0.9
R-HCO3 (NOM)
0.8
NO3-, R-(SO4)-HCO3
SO4--, R-HCO3
SO4--, R-(SO4)-HCO3
0.5
C/C0
0.7
R-Cl (NOM)
0.6
NO3-, R-HCO3
0.8
R-Cl
0.7 C/C0
1
R-HCO3
0.9
0.6 0.5
0.4
0.4
0.3
0.3
0.2
0.2 0.1
0.1
0
0 NO3-
SO4--
DOC
UV254
anions in solution
Fig. 2 e Comparison of bicarbonate-form and chloride-form MIEX resin in the absence and presence of SFR NOM.
Cl resin for UV254-absorbing NOM. Fig. 2 also shows that both MIEX-HCO3 and MIEX-Cl resin have a similar affinity for sulfate and DOC. Previous research studying conventional chlorideform resin and MIEX-Cl resin discuss that sulfate and NOM are competitors for anion exchange sites and that NOM properties play a key role (Fu and Symons, 1990; Boyer et al., 2008). In summary, the affinity sequence for both MIEX-HCO3 and MIEX-Cl resin, in the presence of NOM, is UV254-absorbing substances > DOC w sulfate > nitrate > bicarbonate w chloride. Most importantly, the results in Figs. 1 and 2 clearly show that MIEX-HCO3 resin is a suitable alternative to MIEX-Cl resin in terms of affinity and removal efficiency.
3.2.
Regeneration
3.2.1.
Salt regeneration
Many studies have investigated the performance of fresh ion exchange resin (Li and Sengupta, 1998; Jelinek et al., 2004; Hsu and Singer, 2010), but only a few studies have investigated the regeneration of the same batch of resin to simulate real-world conditions of ion exchange treatment (Mergen et al., 2008; Apell and Boyer, 2010). It was shown in Section 3.1 that MIEX-HCO3 resin has a high affinity for sulfate. As a result, studies were conducted to evaluate whether sodium bicarbonate could regenerate MIEX resin that was saturated with sulfate. The regeneration procedure is described in Section 2.2. Briefly, virgin MIEX-Cl resin was saturated with a 10 sodium sulfate solution to produce MIEX2-SO4 resin and then regenerated with a 10 sodium bicarbonate solution to produce fresh MIEX-(SO4)-HCO3 resin. Similarly, virgin MIEX-Cl resin was regenerated with a 10 sodium bicarbonate solution to produce fresh MIEX-HCO3 resin. The term “fresh” is used because the resins had not been tested in synthetic model water. The fresh resins were tested following the jar test procedure using the synthetic inorganic model and then regenerated. The sequence of jar test and resin regeneration was repeated two additional times. The results are summarized in Fig. 3. The fresh MIEX-(SO4)-HCO3 resin removed less nitrate and sulfate than the fresh MIEX-HCO3 resin. This suggests that the regeneration procedure did not fully saturate
fresh resin 1 number of regenerations
2
Fig. 3 e Effect of multiple regeneration cycles on the removal efficiency of bicarbonate-form MIEX resin using synthetic inorganic model water. Legend shows anion removed from solution, form of MIEX resin.
the MIEX2-SO4 resin with bicarbonate, i.e., sulfate remained on the resin which resulted in less removal of aqueous nitrate and sulfate. However, MIEX-(SO4)-HCO3 and MIEX-HCO3 resins showed nearly identical removal of nitrate and sulfate after one regeneration cycle, and the performance remained similar after two regeneration cycles. These results provide quantitative data which demonstrate that sodium bicarbonate can effectively regenerate exhausted anion exchange resin. The effect of multiple regeneration cycles on MIEX resin and NOM removal was the next step in evaluating the efficacy of sodium bicarbonate regeneration. Fig. 4a and b shows the final normalized concentrations of inorganic anions and NOM after three regeneration cycles using MIEX-HCO3 and MIEX-Cl resin, respectively. Fresh MIEX-HCO3 resin was produced by regenerating virgin MIEX-Cl with a 10 sodium bicarbonate solution. The experimental procedure, i.e., jar tests and regeneration of resin, was analogous to the procedure corresponding to Fig. 3. Fresh MIEX-HCO3 resin showed greater removal of UV254, nitrate, and sulfate than regenerated MIEXHCO3 resin (Fig. 4a). After three regeneration cycles the order of removal for MIEX-HCO3 was UV254 > DOC > sulfate > nitrate. MIEX-Cl resin followed a similar trend as MIEX-HCO3 resin with respect to fresh resin showing greater removal efficiencies than regenerated resin, and regenerated MIEX-Cl showing the same order of NOM and inorganic anion removal. The consistent removal and affinity trends for NOM and inorganic anions indicate that sodium bicarbonate is as effective as sodium chloride at regenerating MIEX resin. Fig. 4a and b also shows that regenerated MIEX-HCO3 resin has a higher affinity for NOM and a lower affinity for inorganic anions than regenerated MIEX-Cl resin. This result illustrates that regeneration of anion exchange resin is critical for understanding the true behavior of the resin. Other researchers have also reported that regenerated anion exchange resin performs differently than fresh anion exchange resin (Apell and Boyer, 2010), thus underscoring the importance of evaluating ion exchange treatment over multiple regeneration cycles. In summary, the regeneration efficiency of MIEX-HCO3 resin was similar to MIEX-Cl resin with respect to removal of inorganic anions and
1334
1
UV254
0.9
DOC
0.8
SO4--
0.7
NO3-
0.6 C/C0
14
MIEX-HCO3
chloride in solution (mg/L)
a
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 2 9 e1 3 3 7
0.5 0.4 0.3 0.2 0.1
12 10 carbon dioxide
8
nitrogen
6 4 2 0
0 fresh resin
1
2
0
3
10
20
number of regenerations
b
1 0.9
DOC
0.8
SO4--
0.7
NO3-
50
60
form resin. Future research is needed that optimizes the operating conditions at which CO2 gas is sparged in water and investigates the regeneration of MIEX resin that is exhausted with sulfate, NOM, and other anions.
0.6 C/C0
40
Fig. 5 e Release of chloride from MIEX-Cl resin by CO2 gas sparging.
MIEX-CI
UV254
30
time of gas sparge (min)
0.5 0.4 0.3 0.2
3.3.
Stoichiometry
0.1 fresh resin
1
2
3
number of regenerations
Fig. 4 e Effect of multiple regeneration cycles on the removal efficiencies of (a) bicarbonate-form MIEX resin and (b) chloride-form MIEX resin using synthetic NOMcontaining model water.
NOM. This is significant because it is the first work to quantify that sodium bicarbonate can be used in place of sodium chloride to regenerate MIEX resin which has been partiallyexhausted with NOM.
3.2.2.
CO2 regeneration
Experiments were conducted to investigate the potential of producing MIEX-HCO3 resin by sparging CO2 gas in DI water that contained MIEX-Cl resin. Fig. 5 shows the aqueous chloride concentration vs. the amount of time gas (either CO2 or N2) was sparged in the water/resin slurry. Sparging CO2 gas resulted in release of chloride from the MIEX-Cl resin and an aqueous chloride concentration that was w6e7 times greater than the initial chloride concentration. This data suggests that bicarbonate species are exchanging with resin-phase chloride to produce MIEX-HCO3 resin. In contrast, sparging N2 gas resulted in no change in the aqueous chloride concentration relative to its initial concentration. The result for N2 gas was expected because N2 does not generate anions in water. Overall, while the generation of aqueous bicarbonate and carbonate species from carbonic acid is well-known, these results illustrate that sparging CO2 gas leads to generation of bicarbonate species as a mobile counter ion that can then exchange with resin-phase chloride and produce bicarbonate-
The stoichiometry of ion exchange was investigated for all forms of MIEX resin using the synthetic inorganic and NOMcontaining model waters. The approach used in this work to quantify ion exchange stoichiometry has been previously published and used successfully (Boyer and Singer, 2008; Boyer et al., 2008). The data were evaluated by plotting the total uptake of anions by the resin (in meq/L) against the total release of anions by the resin (in meq/L), where the anion released is the mobile counter ion. The data were expressed in meq/L to account for exchange of monovalent and divalent inorganic anions and polyanionic NOM. The NOM charge density, in meq/g C, was plotted over the pH range of the titration and is shown in Fig. 6, with calculations performed as described in Section 2.4. The change in concentrations of NOM and inorganic anions were compared to ensure that each 30 charge density (meq/g C)
0
SFR NOM
25 20 15 10 5 0 2
4
6
8
10
12
pH
Fig. 6 e Average charge density of SFR NOM derived from duplicate titrations.
1335
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 2 9 e1 3 3 7
y=x R-Cl R-NO3 R-HCO3 R2-SO4 R2-CO3 calc. R2-CO3
2.5 2 1.5 1 0.5
3
R-HCO3 (0) R-Cl (0)
anion uptake (meq/L)
anion uptake (meq/L)
3
2.5
R-HCO3 (1) R-Cl (1)
2
R-HCO3 (2) R-Cl (2)
1.5
R-HCO3 (3) R-Cl (3)
1 0.5
0 0
0.5
1 1.5 2 anion release (meq/L)
2.5
3
Fig. 7 e Stoichiometry of various forms of MIEX resin using synthetic inorganic model water. All results are experimental data except calc. R2-CO3, which was calculated using R-HCO3 data.
component contributed to the stoichiometry of the system. NOM contributed to w5% of the anion uptake on a charge basis. Fig. 7 shows the stoichiometry of various forms of MIEX resin using the synthetic inorganic model water, i.e., in the absence of NOM. The y ¼ x line shows the equivalent exchange of anions in solution for anions on the resin, and represents ideal ion exchange. The data points were collected from the various affinity and regeneration experiments (see Sections 3.1 and 3.2), which in the case of MIEX-Cl and MIEX-HCO3 resins generated multiple data points on the figure due to the multiple regeneration steps the resins were subjected to. Data for MIEXCl, MIEX-NO3, and MIEX2-SO4 resins fall along the y ¼ x line, and confirm the expected ion exchange stoichiometry. Data for MIEX-HCO3 resin are generally scattered above the y ¼ x line, which indicates that more chloride, nitrate, and sulfate ions were removed by the resin than bicarbonate ions were released by the resin. This was surprising because bicarbonate was expected to follow the same ion exchange stoichiometry as the other inorganic anions. A review of previous literature suggested that it was possible for multiprotic mobile counter ions, such as carbonic acid and arsenic acid species, to deprotonate within anion exchange resins (Kimura et al., 1982; Horng and Clifford, 1997). It is important to note that while regenerating the bicarbonateform resin (as described in Section 2.2), there was a decrease of w1e2 pH units during the DI water rinse step. Although the pH of the rinse water was not measured with every regeneration, in the samples measured there was a consistent decrease in pH. Thus, it was hypothesized that bicarbonate was deprotonating within the MIEX resin, and the exchange process for MIEX-HCO3 was a combination of bicarbonate and carbonate mobile counter ions exchanging with chloride, nitrate, and sulfate in solution. This hypothesis was evaluated both theoretically and experimentally. First, data for MIEX-HCO3 was recalculated assuming that the change in DIC in solution was due solely to carbonate release by the resin (see calc. R2-CO3 in Fig. 7). Second, MIEX2-CO3 resin (see R2-CO3 in Fig. 7) was generated in a solution of pH 11.5 under N2 atmosphere to prevent interference by CO2, and tested following the jar test
0 0
0.5
1
1.5
2
2.5
3
anion release (meq/L)
Fig. 8 e Effect of regeneration on the stoichiometry of bicarbonate-form and chloride-form MIEX resin using synthetic NOM-containing model water. Number of regeneration cycles is given by the number in parentheses.
procedure described previously. The calculated data for MIEX2CO3 generally fall below the y ¼ x line indicating that the equivalent concentration of mobile counter ions released from the resin (i.e., carbonate) is greater than the equivalent concentration of anions removed from solution, which is in agreement with bicarbonate and carbonate acting as the mobile counter ion. The experimental data point for MIEX2-CO3 falls on the y ¼ x line suggesting that carbonate is the mobile counter ion on the resin, and the carbonate does not become protonated in the resin. Thus, experimental and calculated data, in conjunction with the noted decrease in pH during regeneration, suggest that the true mobile counter ion within MIEX-HCO3 resin is a mixture of monovalent bicarbonate and divalent carbonate. Fig. 8 shows the stoichiometry of MIEXHCO3 and MIEX-Cl resins in the presence of NOM and as a function of the number of regeneration cycles corresponding to Fig. 4a and b. Although previous work has confirmed that removal of NOM by virgin MIEX-Cl resin is a stoichiometric processes (Boyer and Singer, 2008; Boyer et al., 2008), this is the first work to evaluate the stoichiometry of ion exchange in the presence of NOM as a function of the regeneration process. This is a very important consideration because MIEX resin is intended to be regenerated and reused multiple times. Data for fresh MIEX-HCO3 and virgin MIEX-Cl resin fall along the y ¼ x line, as expected from previous research (Boyer et al., 2008). Importantly, the data for regenerated MIEX-Cl and regenerated MIEX-HCO3 resin also fall along the y ¼ x line. This is not a contradiction to Fig. 7, rather it suggests that the bicarbonate mobile counter ion does not always deprotonate within the resin phase, and that further investigations are needed to fully identify the mechanisms associated with why and to what extent the deprotonation occurs. Fig. 8 does illustrate, however, that over the course of multiple regeneration cycles ion exchange remains the operative mechanism of NOM removal, thus confirming that MIEX resin is reuseable without permanent fouling. Although MIEX-HCO3 resin performed very similarly to MIEX-Cl resin with respect to affinity and regeneration, the stoichiometry results in Fig. 7 indicate that there is a difference
1336
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 2 9 e1 3 3 7
between the pore-scale chemistry of bicarbonate-form resin and chloride-form resin. For example, chloride mobile counter ions are not expected to be affected by variations in pH or the presence of cations, whereas bicarbonate mobile counter ions will be affected by variations in pH and the presence of divalent cations. Furthermore, the deprotonation of resin-phase bicarbonate to carbonate and the reaction of carbonate with calcium could form calcium carbonate minerals within the resin pore structure, which would foul the resin. Thus, additional research is needed before bicarbonate-form anion exchange can be implemented at the full-scale.
4.
Conclusions
The goal of this work was to evaluate the extent to which bicarbonate-form anion exchange is a viable alternative to chloride-form anion exchange. The motivation for this work was the potential adverse effects of chloride-form anion exchange, such as increased corrosion and excessive chloride loading to wastewater treatment plants and surface waters. The major conclusions of this work are: Fresh MIEX-HCO3 resin showed very similar removals of nitrate, sulfate, DOC, and UV254-absorbing substances as virgin MIEX-Cl resin. Fresh MIEX-HCO3 resin and virgin MIEX-Cl resin showed greater removal of inorganic anions and NOM than corresponding regenerated resins. Nevertheless, sodium bicarbonate had approximately the same level of regeneration efficiency as sodium chloride when both anions were used at a concentration 10 times the equivalent capacity of MIEX resin. The affinity sequence for MIEX resin was UV254-absorbing substances > DOC > sulfate > nitrate > bicarbonate w chloride. This affinity sequence was based on using bicarbonate-, chloride-, nitrate-, and sulfate-form MIEX resin, synthetic inorganic and NOM-containing model waters, and fresh and regenerated resin. Sparging CO2 gas in a water/MIEX-Cl resin slurry resulted in release of chloride from the resin, which is suggested to result from bicarbonate species acting as mobile counter ions. No release of chloride was observed when N2 gas was sparged in a water/MIEX-Cl resin slurry. Thus, the sparging of CO2 gas is a potential technique for regeneration of bicarbonate-form MIEX resin that does not require salt. The stoichiometry of chloride-, nitrate-, and sulfate-form MIEX resin followed ideal ion exchange behavior. In contrast, the stoichiometry of bicarbonate-form MIEX resin did not always follow predictable ion exchange. It appears that resin-phase bicarbonate is deprotonating and resulting in a mixture of bicarbonate and carbonate mobile counter ions. Bicarbonate- and chloride-form MIEX resin showed ion exchange stoichiometry for NOM removal over multiple regeneration cycles.
Acknowledgements The authors would like to thank Orica Watercare for providing MIEX resin and Dr. Joseph Delfino for providing SFR NOM
isolate. The comments of two anonymous reviewers provided helpful insights that improved the work. Partial funding for this project was provided by Occidental Chemical Research Award to THB.
references
Apell, J.N., Boyer, T.H., 2010. Combined ion exchange treatment for removal of dissolved organic matter and hardness. Water Research 44 (8), 2419e2430. Benjamin, M.M., 2002. Water Chemistry. McGraw-Hill, New York. Blackhurst, M., Hendrickson, C., Vidal, J.S.I., 2010. Direct and indirect water withdrawals for US industrial sectors. Environmental Science & Technology 44 (6), 2126e2130. Boyer, T.H., Singer, P.C., 2005. Bench-scale testing of a magnetic ion exchange resin for removal of disinfection by-product precursors. Water Research 39 (7), 1265e1276. Boyer, T.H., Singer, P.C., 2006. A pilot-scale evaluation of magnetic ion exchange treatment for removal of natural organic material and inorganic anions. Water Research 40 (15), 2865e2876. Boyer, T.H., Singer, P.C., 2008. Stoichiometry of removal of natural organic matter by ion exchange. Environmental Science & Technology 42 (2), 608e613. Boyer, T.H., Singer, P.C., Aiken, G.R., 2008. Removal of dissolved organic matter by anion exchange: effect of dissolved organic matter properties. Environmental Science & Technology 42 (19), 7431e7437. Boyer, T.H., Miller, C.T., Singer, P.C., 2010. Advances in modeling completely mixed flow reactors for ion exchange. Journal of Environmental Engineering ASCE. doi:10.1061/(ASCE)EE.19437870.0000241 Accepted March 3, 2010. Carrillo, A.M.R., Frei, C., 2009. Water: a key resource in energy production. Energy Policy 37 (11), 4303e4312. Dahlke, T., Mathes, P.A. and Adams, B. 2007. Treatment of Highly Polluted Paper and Pulp Effluent Using Combined Treatment Processes Including a Continuous Ion Exchange Process. Proceedings Water Reuse and Recycling, pp. 122e129. Davis, W.M., Erickson, C.L., Johnston, C.T., Delfino, J.J., Porter, J.E., 1999. Quantitative Fourier transform infrared spectroscopic investigation of humic substance functional group composition. Chemosphere 38 (12), 2913e2928. Drikas, M., Dixon, M., Morran, J., 2009. Removal of MIB and geosmin using granular activated carbon with and without MIEX pre-treatment. Water Research 43 (20), 5151e5159. Edwards, M., Triantafyllidou, S., 2007. Chloride-to-sulfate mass ratio and lead leaching to water. Journal American Water Works Association 99 (7), 96e109. Fu, P.L.K., Symons, J.M., 1990. Removing aquatic organic substances by anion exchange resins. Journal American Water Works Association 82 (10), 70e77. Greenleaf, J.E., SenGupta, A.K., 2009. Flue gas carbon dioxide sequestration during water softening with ion-exchange fibers. Journal of Environmental Engineering-ASCE 135 (6), 386e396. Ho¨ll, W., Kiehling, B., 1981. Regeneration of anion-exchange resins by calcium-carbonate and carbon-dioxide. Water Research 15 (8), 1027e1034. Horng, L.L., Clifford, D., 1997. The behavior of polyprotic anions in ion-exchange resins. Reactive & Functional Polymers 35 (1e2), 41e54. Hsu, S., Singer, P.C., 2010. Removal of bromide and natural organic matter by anion exchange. Water Research 44 (7), 2133e2140. Jelinek, L., Parschova, H., Matejka, Z., Paidar, M., Bouzek, K., 2004. A combination of ion exchange and electrochemical reduction
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for nitrate removal from drinking water e part I: nitrate removal using a selective anion exchanger in the bicarbonate form with reuse of the regenerant solution. Water Environment Research 76 (7), 2686e2690. Kimura, E., Sakonaka, A., Kodama, M., 1982. A carbonate receptor model by macromonocyclic polyamines and its physiological implications. Journal of the American Chemical Society 104 (18), 4984e4985. Kitis, M., Harman, B.I., Yigit, N.O., Beyhan, M., Nguyen, H., Adams, B., 2007. The removal of natural organic matter from selected Turkish source waters using magnetic ion exchange resin (MIEX (R)). Reactive & Functional Polymers 67 (12), 1495e1504. Larson, T.E., 1975. Corrosion of Domestic Waters. Illinois State. Water Survey Bulletin, Champaing, IL. Li, P., Sengupta, A.K., 1998. Genesis of selectivity and reversibility for sorption of synthetic aromatic anions onto polymeric sorbents. Environmental Science & Technology 32 (23), 3756e3766. Matosic, M., Mijatovic, I., Hodzic, E., 2000. Nitrate removal from drinking water using ion exchange e comparison of chloride and bicarbonate form of the resins. Chemical and Biochemical Engineering Quarterly 14 (4), 141e146. Mergen, M.R.D., Jefferson, B., Parsons, S.A., Jarvis, P., 2008. Magnetic ion-exchange resin treatment: impact of water type and resin use. Water Research 42 (8e9), 1977e1988. Mills, G.A., Urey, H.C., 1940. The kinetics of isotopic exchange between carbon dioxide, bicarbonate ion, carbonate ion and water. Journal of the American Chemical Society 62, 1019e1026.
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Neale, P.A., Schafer, A.I., 2009. Magnetic ion exchange: is there potential for international development? Desalination 248, 160e168. Panswad, T., Anan, C., 1999. Impact of high chloride wastewater on an anaerobic/anoxic/aerobic process with and without inoculation of chloride acclimated seeds. Water Research 33 (5), 1165e1172. Ritchie, J.D., Perdue, E.M., 2003. Proton-binding study of standard and reference fulvic acids, humic acids, and natural organic matter. Geochimica Et Cosmochimica Acta 67 (1), 85e96. Singer, P.C., Bilyk, K., 2002. Enhanced coagulation using a magnetic ion exchange resin. Water Research 36 (16), 4009e4022. Singer, P.C., Boyer, T., Holmquist, A., Morran, J., Bourke, M., 2009. Integrated analysis of NOM removal by magnetic ion exchange. Journal American Water Works Association 101 (1), 65e73. Takasaki, S., Yamada, Y., 2007. Effects of temperature and aggressive anions on corrosion of carbon steel in potable water. Corrosion Science 49 (1), 240e247. Tan, Y.R., Kilduff, J.E., 2007. Factors affecting selectivity during dissolved organic matter removal by anion-exchange resins. Water Research 41 (18), 4211e4221. Tripp, A.R., Clifford, D.A., 2006. Ion exchange for the remediation of perchlorate-contaminated drinking water. Journal American Water Works Association 98 (4), 105e114. Warton, B., Heitz, A., Zappia, L.R., Franzmann, P.D., Masters, D., Joll, C.A., Alessandrino, M., Allpike, B., O’Leary, B., Kagi, R.I., 2007. Magnetic ion exchange drinking water treatment in a large-scale facility. Journal American Water Works Association 99 (1), 89e101.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 3 8 e1 3 4 6
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Sorption of the cyanobacterial toxins cylindrospermopsin and anatoxin-a to sediments Sondra Klitzke a,*, Christine Beusch b, Jutta Fastner b a b
Federal Environment Agency, Section Drinking Water Treatment and Resource Protection, Schichauweg 58, D-12307 Berlin, Germany Federal Environment Agency, Section Drinking Water Treatment and Resource Protection, Corrensplatz 1, D-14195 Berlin, Germany
article info
abstract
Article history:
The occurrence of the cyanobacterial toxins anatoxin-a (ATX) and cylindrospermopsin
Received 21 July 2010
(CYN) in surface waters has been reported throughout the world. Beside degradation,
Received in revised form
sorption is an important pathway for toxin elimination if these resources are used for
8 October 2010
drinking water production via sediment passage. However, to date studies that systemati-
Accepted 14 October 2010
cally investigated sorption of these toxins onto sediments are lacking. Therefore, the aim of
Available online 21 October 2010
our work was (i) to determine the adsorption coefficients of ATX and CYN according to the Freundlich and Langmuir model for sediments of various textures and (ii) to derive sorption-
Keywords:
relevant sediment characteristics. We determined sorption parameters in air-dried samples
OECD guideline 106
of eight differently textured sediments using batch experiments. Results for both toxins
Cation bridging
showed best fits with the Langmuir model. Organic C proved to be the main sediment
Cation exchange
parameter determining CYN sorption. There was no or little CYN sorption on sandy and silty
Ionic strength
sediments (0e39 mg kg1), respectively, presumably due to charge repulsion from the
River bank filtration
negatively charged surfaces. Sorption of ATX (max. sorbent loading ranging from 47 to
Cyanotoxin
656 mg kg1) was much stronger than that of CYN (max. sorbent loading ranging from 0 to
Subsurface passage
361 mg kg1) and predominantly controlled by clay and to a minor degree also by organic C and silt. While ATX sorption to most sediments occurred mainly through cation exchange this mechanism played only a minor role in CYN sorption to organic C. Hence, high mobility for CYN and moderate mobility for ATX during sediment passage has to be expected. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Anatoxin-a (ATX) and cylindrospermopsin (CYN) are toxins produced by certain cyanobacteria (ATX: Edwards et al., 1992; Sivonen et al., 1989; CYN: Ohtani et al., 1992; Falconer, 2005; Preußel et al., 2006). They are known to have a range of effects on human health such as tissue damage (liver, lung, gut) and cell necrosis (CYN: Hawkins et al., 1997), as well as damage to the nervous and respiratory systems (ATX: Thomas et al., 1993).
Anatoxin-a is an alkaloid toxin with a molecular weight of 165 g mol1 (Fig. 1a). Despite its worldwide distribution it seems not to occur as frequently as microcystins and cylindrospermopsin (Osswald et al., 2007; Hedman et al., 2008). The screening of 78 German water bodies yielded maximum total ATX concentration (i.e. intracellular and extracellular) of up to 13.1 mg L1, but higher concentrations can be expected during bloom situations (Bumke-Vogt et al., 1999). The highest anatoxin-a concentration found was 1750 mg L1 (Hedman et al., 2008). Anatoxin-a was shown to decompose
Abbreviations: ATX, anatoxin-a; CYN, cylindrospermopsin; OC, organic carbon; CEC, cation exchange capacity. * Corresponding author. Tel.: þ49 30 8903 4247; fax: þ49 30 8903 4200. E-mail address: [email protected] (S. Klitzke). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.019
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2.
Material and methods
To determine adsorption isotherms batch experiments were conducted with sediments of varying texture as outlined in the procedure of the OECD guideline 106 (OECD, 1997).
Fig. 1 e Molecular structure of (a) Anatoxin-a and (b) Cylindrospermopsin.
under the influence of light and alkaline pH (Stevens and Krieger, 1991) and to adsorb on lake sediments (Rapala et al., 1994). Cylindrospermopsin is an alkaloid toxin with a cyclic guanidine moiety bridged to a hydroxymethyluracil group and a molecular weight of 415 g mol1 mol1 (Chiswell et al., 1999; Fig. 1b). Cylindrospermopsin has been detected in many water bodies throughout the world with concentrations in subtropical regions of Australia in the range of up to120 mg L1 (Shaw et al., 1999; McGregor and Fabbro, 2000) and in European freshwaters with maximum concentrations between 9 and 18 mg L1 (Bogialli et al., 2006a; Quesada et al., 2006; Ru¨cker et al., 2007). Due to its chemical stability and slow degradation (Chiswell et al., 1999) CYN shows a high persistence in many water bodies (Wormer et al., 2008). Sorption to sandy sediments with a content of fines of 1% and 4% was found to be negligible (Klitzke et al., 2010). If toxin contaminated surface waters are used as drinking water reservoirs, efficient elimination has to be ensured. Beside degradation, dilution and physical straining, sorption is an important process for contaminant removal during drinking water (pre-) treatment methods (Gru¨tzmacher et al., 2010) such as river bank filtration, artificial groundwater recharge, and slow sand filtration. The properties and structure of the toxins together with sediment texture determine their sorption capacity. While CYN is very hydrophilic and carries both a positive and a negative charge at neutral pH (Meriluoto and Spoof, 2008) ATX occurs as cation below pH 9.6 (pka: 9.6; Devlin et al., 1977). The adsorption of both organic zwitterions to soils and soil minerals (Carrasquillo et al., 2008) as well as the adsorption of organic cations onto clays (Narine and Guy, 1981) and sediments (Brown and Combs, 1985) are well documented phenomena. Burns et al. (2009) found strong sorption of the cyanobacterial toxin saxitoxin, which occurs as protonated ion below pH 8.2, to clays and sediments. As a cation bridging mechanism has been reported for other organic zwitterions to enhance sorption to organic matter (MacKay and Canterburry, 2005), we hypothesize higher CYN sorption in the presence of divalent cations such as Ca. In order to gain a better understanding of the role of toxin removal by adsorption during sediment passage the objective of this study was to (i) determine the adsorption coefficients of CYN and ATX according to the Freundlich and Langmuir model for sediments of various textures and to (ii) derive relevant sediment and hydrochemical characteristics which control CYN and ATX sorption.
2.1.
Materials
2.1.1.
Sediments
We investigated 10 sediments of hydromorphic character from ponds (KHW, SRW), streams (Newel), aquifers (GW, Mu¨ggel), slow sand filter ponds (UBA), the Gr-horizon of a gley soil (Kyll), and three types of soils (NM, Mergel, Organic mud). With the exception of the three soils their sampling locations showed saturated conditions for most of the time during the year. Their origin and hydromorphic condition are summarized in Table 1 in the supplementary material. The physical parameters of the sediments (grain size distribution, water loss at 105 C and loss on ignition at 550 C) are displayed in Table 1, several chemical parameters (i.e. organic carbon (OC) content, C/N-ratio, pHCaCl2 , potential cation exchange capacity (CECpot), and effective cation exchange capacity (CECeff)) are shown in Table 2.
2.1.2.
Chemicals
All solutions were made up in deionised water. For preparing a 0.01 M CaCl2 solution (ionic strength I ¼ 0.03 M) CaCl2 2H2O was used, the 0.03 M KCl solution (I ¼ 0.03 M) was produced with KCl (both chemicals were obtained from Merck, Germany). Stock solutions of ATX were prepared using anatoxin-a-fumarate (Tocris, United Kingdom). Pure cylindrospermopsin (purchased from Dr. A. Humpage, Australian Water Quality Centre, Salisbury, Australia) was quantified using the extinction coefficient published by Sano et al. (2008). All stock solutions were made up in 0.01 M CaCl2 solution and 0.03 M KCl solution, respectively.
2.2.
Methods
2.2.1.
Batch experiments
Prior to the main sorption experiment, preliminary experiments were conducted to identify appropriate (i) solid-solution
Table 1 e Physical sediment parameters (n.d.: not determined). Sediments
Clay Silt Sand Water Loss on [%] [%] [%] content [%] ignition [%]
UBA GW NM Mergel Organic mud Organic mud-1 Mu¨ggel KHW Kyll Newel SRW
1 2 3 2 n.d. 15 9 6 6 36 27
0 2 1 9 n.d. 51 17 14 46 52 46
99 96 96 89 n.d. 33 74 80 48 12 27
21.3 8.1 19.6 7.6 73.1 48.6 15.5 46.7 17.5 36.2 30.3
0.1 0.6 1.6 0.0 83.5 11.0 0.9 6.2 1.7 7.1 4.2
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 3 8 e1 3 4 6
Table 2 e Chemical sediment parameters. Sediment
OC [%]
C/N
pH
CECpot
CECeff
[mmolc kg1] UBA GW NM Mergel Organic mud Organic mud-1 Mu¨ggel KHW Kyll Newel SRW
0.71 0.04 0.68 0.07 44.5 4.85 1.07 3.70 1.86 3.53 1.05
35 7 19 10 14 17.1 87 20 54 12 7
7.5 6.0 6.5 6.5 6.5 5.4 7.7 5.6 7.2 7.6 6.9
20 10 30 20 890 200 70 110 90 270 180
20 10 30 20 710 180 70 80 80 270 180
ratios (i.e. mass sediment per volume solution) and (ii) equilibration times for each toxin. For all experiments, air-dried sediments (<2 mm) were weighed in 100 mL glass containers. To exclude decomposition of ATX by light all sample containers were wrapped in aluminium foil. As CYN is photochemically stable (Chiswell et al., 1999) an exclusion of light during the experiment was not necessary. All samples were pre-equilibrated over night (between 16 and 21 h) in 45 mL 0.01 M CaCl2 solution. After the addition of 5 mL of the respective toxin stock solution (concentration tenfold of the desired final concentration) they were equilibrated for 24 h on an end-over-end shaker (GFL 3040) at 20 rpm. Blank samples were prepared by adding 5 mL of deionised water (instead of the toxin solution) to sediment containing samples. Control samples were made up of each concentration level of the respective toxin in 0.01 M CaCl2 solution. Samples were left to stand for 10 min following equilibration in order to allow for the solid phase to settle. A solution aliquot was filtered over a 0.45 mm Nylon filter (Rotilabo P014.1, Carl Roth). Some sediments (such as the organic mud, Newel and SRW in the solidsolution-ratio of 1:1) have been centrifuged (Variofuge 3.0R, Heraeus Sepatech) for 10e30 min at 1880 g prior to filtration. The samples were stored at 18 C until analysis.
2.2.1.1. Determination of the solid-solution ratios. The OECD guideline suggests using a ratio where sorption is greater than 20% and preferably exceeds 50%. For substances with an expected high sorption rate it recommends wider ratios (up to 1:100) and for substances with expected low sorption rates narrower ratios (1:1) (OECD, 1997). Experiments with CYN were conducted in duplicate, with ATX in triplicate. 2.2.1.1.1. Anatoxin-a. The solid-solution ratios for ATX were chosen in accordance with studies of other cyanobacterial toxins (Miller et al., 2001, 2005). As we did not expect strong ATX sorption we selected the ratios 1:1, 1:5, and 1:12.5. These ratios were tested on four different sediments in order to cover a broad range of sediment texture: KHW (moderately organic), SRW (clayey), GW (sandy), and Mergel (silty). After pre-equilibration, 5 mL of ATX stock solution (c(ATX) ¼ 300 mg L1) were added to the sample to yield a final concentration of 30 mg L1. Results showed that all sediments were in the suggested range above 20% using the solid-solution ratio
of 1:5 (Fig. 1 in the supplementary material). As this ratio would also allow for a comparison with sorption data for nodularin and microcystin-LR (Miller et al., 2005) it was chosen for all subsequent experiments.
2.2.1.1.2. Cylindrospermopsin. As CYN sorption on sand was low (Klitzke et al., 2010) we did not expect high CYN sorption on the selected sediments. As results of preliminary experiments (data not shown) with the sediments Newel (strongly clayey), Kyll (silty), and Organic mud (strongly organic) in a solid-solution-ratio of 1:1 (1:3.5 with the organic mud due to its very high absorption of water) CYN showed sorption between 60 and 80%, a solid-solution ratio of 1:5 was tested on the following sediments: Kyll, Newel, KHW, Organic mud, GW, SRW. After pre-equilibration, 5 mL of CYN stock solution (c(CYN) ¼ 150 mg L1) was added to the sample to yield a final concentration of 15 mg L1. As most of the investigated sediments showed sorption above the suggested margin of 20% (Fig. 2 in the supplementary material) and for reasons of comparison with ATX we selected a solid-solution ratio of 1:5 for all following experiments. 2.2.1.2. Determination of equilibration times. To determine sorption kinetics, samples were equilibrated for 0, 2, 4, 6, 8, 14 (ATX) and 15 (CYN), 24, and 48 h. 2.2.1.2.1. Anatoxin-a. The same sediments and solution concentrations were used as in the previously described experiments. Experiments were conducted in triplicate for each time investigated. Sorption equilibrium was obtained for three out of four tested sediments after approximately 14 h (Fig. 3 in the supplementary material). For reasons of practicability, we chose a shaking time of 16 h.
2.2.1.2.2. Cylindrospermopsin. For CYN, a sediment with high (SRW, clayey) and a sediment with low sorption (NM, sandy) were tested. Solution concentrations were kept as previously described. Experiments were conducted in duplicate for each time investigated. Sorption equilibrium was obtained after approximately 15 h (Fig. 4 in the supplementary material). For reasons of practicability and to have comparable experimental condition with ATX, we chose a shaking time of 16 h.
2.2.1.3. Main sorption experiment. According to the results obtained from two preliminary experiments (see supplementary material Figs. 1e4), we conducted the main sorption experiment at a solid-solution ratio of 1:5 and an equilibration time of 16 h. We investigated the sorption capacity of eight different sediments in triplicate. As CYN proved stable in contact with virgin (i.e. sediments that did not have any previous contact with the toxin) sediment for approximately 20 days (Klitzke et al., 2010) no CYN degradation had to be expected during the sorption experiment. To exclude possible microbial degradation of ATX during the time of the experiment preliminary batch sorption experiments in sterile and non-sterile samples of selected sediments (UBA, Mu¨ggel, SRW, Mergel; data not shown) were conducted with conditions being comparable to those of the main sorption experiment. Results showed degradation to be
1341
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 3 8 e1 3 4 6
negligible with the exception of the Mu¨ggel sediment where degradation amounted to 8%.
2.2.1.3.1. Anatoxin-a. To determine the sorption capacity of ATX we tested the following sediments: UBA, GW, Mergel, Organic mud, Mu¨ggel, KHW, Kyll, and SRW. Anatoxin-a was chosen to be tested in concentration ranges which are relevant in field samples (for instance Bumke-Vogt et al., 1999), i.e. between 0, 1, 5, 10, 20, and 30 mg L1.
2.2.1.3.2. Cylindrospermopsin. To determine the sorption capacity of CYN we tested the following sediments: UBA, NM, Mergel, Organic mud, Mu¨ggel, KHW, and SRW. Cylindrospermopsin occurrence in European freshwaters was reported in similar concentration ranges as ATX (9e18 mg L1; Bogialli et al., 2006a; Quesada et al., 2006; Ru¨cker et al., 2007). Therefore, and for reasons of comparability, we used the same solution concentrations as for ATX (0, 1, 5, 10, 20, and 30 mg L1). In order to account for CYN sorption on organic matter caused by cation bridging through the Ca2þ ion in the solvent, we investigated CYN sorption in the presence of a monovalent ion (Kþ). This experiment was conducted with the sediment “organic mud-1”, which was sampled at the same location as the above mentioned organic mud but at a later stage and is hence denoted “organic mud-1”. We used 0.01 M CaCl2 and 0.03 M KCl solution (with the ionic strength of both solutions amounting to I ¼ 0.03 M) and CYN concentrations of 0, 1, 5, and 20 mg L1 in each case.
2.2.2.
Toxin analyses
All samples were kept frozen at 18 C and filtered over a 0.2 mm PVDF membrane (Waters, Germany) prior to analysis. The analyses of CYN and ATX were performed by LCeMS/MS using an Agilent 1100 series HPLC system (Agilent Technologies, Waldbronn, Germany) coupled to an API 4000 triple quadrupole mass spectrometer (Applied Biosystems/MDS Sciex, Framingham, MA) equipped with a turbo-ionspray interface. The cylindrospermopsin analysis has been described in detail previously (Fastner et al., 2007). The samples of the preliminary experiment to determine the solid-solution-ratio were analysed by an enzyme-linked immunosorbent assay (ELISA; Abraxis, no. 522011). For the determination of ATX aqueous samples were separated using a 3.5 mm, 2.1 100 mm, C 18 column (XTerra, Waters, Germany) at 30 C. The mobile phase consisted of 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B). The following gradient program was applied with a flow rate of 0.2 ml min1: 100% A for 1 min, ramped to 50% B over 6 min, then to 70% B in 1 min, held for 2 min and equilibrated at 100% A for 10 min. The injection volume was 10 mL. The mass spectrometer was operated in the positive ion, selected reaction monitoring (SRM) mode monitoring the following transitions for the identification of anatoxin-a: m/z 166.1 [M þ H]þ to 149, m/z 166.1/131, m/z 166.1/91 and m/z 166.1/43 (Bogialli et al., 2006b). The transition m/z 166.1/43 was used for quantification of anatoxin-a. Anatoxin-a standard was purchased from Tocris (UK), and was analysed in line with the unknowns (one calibration curve after 20 unknowns). The detection limit was 0.01 mg L1.
2.2.3.
Data analysis
2.2.3.1. Cation exchange capacity. The potential and effective cation exchange capacity (CEC) have been estimated as a function of grain size distribution, OC content, and pH. Details on the calculation are provided in the supplementary material. 2.2.3.2. Calculation of sorption isotherms. The calculation of the sorbed amount of toxin is described in the supplementary material. Sorption isotherms according to Freundlich (Eq. (1.9)) and Langmuir (Eq. (2.0)) were generated using the computer programme ORIGIN Version 7. In addition, linear sorption isotherms according to Henry (Eq. (2.1)) were established. Cs ¼ Kf Cnw
(1.9) 1
Cs ¼ toxin concentration in the sediment [mg kg ], Kf ¼ Freundlich sorption coefficient [L kg1], Cw ¼ concentration at equilibrium in the supernatant [mg L1], and n ¼ Freundlich exponent. Cs ¼
Kl qmax Cw 1 þ Kl Cw
(2.0)
Cs ¼ toxin concentration in the sediment [mg kg1], Kl ¼ Langmuir sorption coefficient [L mg1], qmax ¼ maximum sorbent loading [mg kg1], and Cw ¼ toxin concentration at equilibrium in the supernatant [mg L1]. Cs ¼ Kd Cw
(2.1) 1
Cs ¼ toxin concentration in the sediment [mg kg ], Kd ¼ linear distribution coefficient [L kg1], and Cw ¼ toxin concentration at equilibrium in the supernatant [mg L1]. The Langmuir, Freundlich and Henry models are purely empirical models, which allow for the quantification of sorption in order to compare the sorption capacity of different sediments. However, they do not allow for a derivation of any sorption mechanisms. Hence, we conducted statistical analysis in order to determine the most sorption-relevant parameters.
2.2.3.3. Statistical analysis. For the lowest Cs-value with Cw ¼ 1 mg L1 and the highest Cs-value with Cw ¼ 30 mg L1, linear and bivariate regression analyses according to Pearson were established by using the computer programme ORIGIN Version 7. Details are reported in the supplementary material.
3.
Results and discussion
3.1.
Sorption of ATX
Anatoxin-a shows weakest sorption to sandy sediments such as UBA (99% sand, Fig. 2), Mergel (89% sand), and GW (96% sand) and highest sorption to clay-rich (SRW, 27% clay) and organic-rich (organic mud 44.5% OC; Fig. 2) sediments (Table 3). Even if we take 8% microbial degradation into account (see chapter 2.2.1.3), we cannot explain the high sorption onto the Mu¨ggel sediment (9% clay, 1% OC), which
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Table 4 e Statistically significant linear and bivariate regression equations for ATX at Cw [ 1 mg LL1 and Cw [ 30 mg LL1, with Cs being the toxin concentration in the sediment [mg kgL1]. Sediment parameters in brackets denote their fraction in sediment composition. Levels of significance are denoted as follows: *significant ( p < 0,05), **highly significant ( p < 0,01), ***highest significant ( p < 0.001).
Fig. 2 e Anatoxin-a sorption on an organic-rich sediment (organic mud) and a sandy sediment (UBA).
exceeds the qmax value of SRW. Sorption of ATX was mostly best described by the non-linear model according to Langmuir, whose sorption parameters of the analysed sediments are summarized in Table 3 (parameters of the linear Henry model are shown in Table 2 of the supplementary information). The result for the Freundlich exponent of n < 1 (Table 3) suggests limited availability of sorption sites at higher concentrations. While at lower concentrations sorption is linear, at higher concentrations sorption decreases due to the saturation of specific binding sites. Statistically significant results of the linear and bivariate regressions are listed in Table 4. At both investigated concentrations (1 mg L1 and 30 mg L1), clay proved to be the sediment component with the highest impact on ATX sorption. Similar findings were observed for the cyanobacterial toxin nodularin in a batch study by Miller et al. (2005). At high concentrations of 30 mg L1, ATX sorption onto clay is enhanced with increasing pH (Table 4). This observation can be explained by an increasing negative surface charge of clay with increasing pH leading to stronger electrostatic attraction of the ATX cation. The second important parameter affecting ATX sorption was the organic
Table 3 e Summary of the sorption coefficients according to Freundlich and Langmuir for ATX sorption, listed according to the sorbed amount. Grey shaded fields indicate the sorption model with the highest correlation coefficient (listed in the table). Sediment
Freundlich Kf [L kg1]
Mu¨ggel Organic mud SRW KHW Kyll GW Mergel UBA
24.45 12.28 6.64 3.53 5.57 4.83 2.39 1.42
n 0.93 0.87 0.85 0.89 0.84 0.71 0.81 0.75
Langmuir Kl [L mg1]
qmax [mg kg1]
0.050 0.017 0.016 0.009 0.019 0.032 0.028 0.023
561 656 346 329 254 112 107 47
R2
0.970 0.997 0.991 0.981 0.820 0.874 0.896 0.615
Cw ¼ 1 mg L1 Cs ¼ 0.3 (clay)* þ 0.48 Cs ¼ 0.02 (sand)* 0.39** Cs ¼ 0.06 (OC)* þ ¼ 1.58 Cs ¼ 0.037 (silt)* 0.573 (pH) þ 4.722* Cs ¼ 0.548 (sand)** 0.548 (pH)** þ 7.295** Cs ¼ 0.005 (CECeff)** þ 1.329**
R2 0.857** 0.749* 0.684* 0.896* 0.982** 0.827**
Cw ¼ 30 mg L1 Cs ¼ 16.82 (clay)* þ 2.46 Cs ¼ 1.26 (silt)* þ 37.29* Cs ¼ 0.94 (sand)* þ 130.7*** Cs ¼ 1.98 (OC)* þ 59.18 Cs ¼ 14.801 (clay)* 9.097 (OC) þ 15.153 Cs ¼ 15.951 (clay)* þ 12.821 (pH)* 80.08 Cs ¼ 1.458 (silt)** 17.659 (pH)* þ 149.993* Cs ¼ 4.909 (OC) 0.883 (sand)* þ 120.429** Cs ¼ 1.057 (sand)** 16.233 (pH)* þ 246.343** Cs ¼ 0.145 (CECeff)* þ 51.042**
R2 0.850** 0.774* 0.829* 0.602* 0.960 0.886 0.952* 0.878* 0.974** 0.756**
carbon content (Table 4). However, with the slope of the regression equation amounting to only 12.5% and 20% of the slope values of clay, it only plays a minor role. Interestingly, when organic C occurs combined with clay, organic C would impede ATX sorption. The observed ATX adsorption onto clay is in line with results reported by Narine and Guy (1981) who found organocations to adsorb onto negatively charged clays. They propose electrostatic interactions as the main sorption mechanism. In addition, they observed ionic strength to be a very sensitive parameter in the sorption of organocations onto clays, with increasing solution ionic strength displacing organocations from the exchange site. Besides, diffusion of the relatively small ATX molecule into swellable interlayers of a three-layer clay mineral where it would replace interlayer cations may also be possible. The latter mechanism has already been reported for the considerably larger antibiotic oxytetracycline (molecular weight: 460 g mol1; Kulshrestha et al., 2004). Organic matter promotes sorption of the cationic ATX molecule due to the availability of negatively charged sites (McBride, 1994). However, the lower slopes of the corresponding regression curves suggest organic C to contribute fewer ATX-specific adsorption sites than clay. This may also explain why in the presence of clay, organic matter would reduce ATX adsorption. As clay clearly seems to be the main sorption parameter for ATX, additional sorption of organic matter onto clay would obstruct adsorption sites and hence decrease ATX adsorption. This phenomenon has already been observed by Pateiro-Moure et al. (2009) who investigated the sorption of organocations onto clay in the presence and absence of organic matter. They found organic matter to occlude adsorption sites for organocations if clay is the main factor controlling organocation adsorption.
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Sand was found to even have a slightly negative effect on sorption (Table 4). This was already observed for the sorption of two other cyanobacterial toxins nodularin and microcystin and explained by (i) the smaller surface area of sand as compared to clay and (ii) the relatively small amount of reactive functional groups relevant to sorption on sand (Miller et al., 2005). The strong linear correlations at both concentrations between the sorbed amount of ATX and CECeff (Table 4) as well as the high correlations between qmax and CECeff (R2 ¼ 0.847**, with ** indicating a high level of significance (i.e. p < 0.01)). and Kf and CECeff (R2 ¼ 0.858**) suggest sorption to occur through cation exchange as a main mechanism. Similar findings are reported for the cationic cyanobacterial toxin saxitoxin in deionised water with a high linear correlation between Kf and CEC (R2 ¼ 0.96; Burns et al., 2009).
3.2.
Sorption of CYN
While CYN sorption to organic matter (Organic mud, 44.5% OC, Fig. 3) was very high, sorption to silty-sandy sediments such as Kyll, KHW, and NM (Fig. 3) was low (Table 5). Although sediment texture of Mu¨ggel was comparable to KHW (Table 1), sorption onto Mu¨ggel was much more pronounced. According to the Langmuir model, CYN showed moderate sorption onto clayey-sandy silt SRW. There was no sorption on strongly sandy sediments (UBA, Mergel; Table 3). Similarly to ATX, for most of the sediments, CYN sorption was best described by the non-linear model according to Langmuir, whose sorption parameters of the analysed sediments are summarized in Table 5 (parameters of the Henry model are displayed in Table 2 of the supplementary information). There are only few parameters which control CYN sorption (Table 6). The clay content does not have any effect on CYN sorption at all. Sand only contributes little to CYN sorption at higher CYN concentrations. However, this effect is diminished with increasing pH. The organic C content has the strongest impact on CYN sorption. The high correlation between OC and pH is obtained for the concentration of Cw ¼ 30 mg L1 (Table 6). Sorption on OC
Table 5 e Summary of the sorption coefficients according to Freundlich and Langmuir for CYN sorption, listed according to the sorbed amount. Grey shaded fields indicate the sorption model with the highest correlation coefficient (listed in the table). Sediment
Freundlich Kf [L kg1]
Organic mud Mu¨ggel SRW Kyll KHW NM UBA Mergel
n
7.5 0.73 5.2 0.85 0.6 0.95 0.5 0.99 1.1 0.74 1.0 0.76 No sorption No sorption
Langmuir Kl [L mg1]
R2
qmax [mg kg1]
0.014 360.5 0.022 206.2 0.005 123.6 0.016 39 0.031 27 0.043 19.4 No sorption No sorption
0.980 0.994 0.939 0.915 0.698 0.915
increases with increasing pH. This is due to a stronger deprotonation of carboxylic functional groups, allowing for increased cation exchange through the positively charged amine group (see below). At Cw ¼ 1 mg L1, pH has a negative effect on CYN sorption onto organic matter (Table 6), i.e. CYN sorption is highest at low pH. This suggests cation exchange to be of minor relevance at low concentrations. Instead, sorption increases at low pH when humic acid functional groups are fully protonated. Aristilde and Sposito (2010) suggest protonated groups to be ideal for the establishment of H-bondings between humic substances and polar functional groups of the organic zwitterion ciprofloxacin. Hence, we presume Hbonding to be the more dominant mechanism at low CYN concentration and low pH (see also below). Sorption of CYN onto organic mud-1 showed higher values (approx. 20% throughout the measured concentration range) in the presence of the monovalent Kþ ion than in the presence of the divalent Ca2þ ion (Fig. 4). This implies cation bridging between the negatively charged sulfur group and anionic functional groups between organic matter not to take place as this mechanism would have increased CYN sorption in the presence of Ca2þ. The data rather suggests CYN to sorb to
Table 6 e Statistically significant linear and bivariate regression equations for CYN at Cw [ 1 mg LL1 and Cw [ 30 mg LL1, with Cs being the toxin concentration in the sediment [mg kgL1]. Sediment parameters in brackets denote their fraction in sediment composition. Levels of significance are denoted as follows: *significant ( p < 0.05), **highly significant ( p < 0.01), ***highest significant ( p < 0.001).
Fig. 3 e Cylindrospermopsin sorption on an organic-rich sediment (organic mud) and a silty-sandy sediment (KHW).
Cw ¼ 1 mg L1 Cs ¼ 0.08 (silt)* þ 1.07** Cs ¼ 0.04 (OC)** þ 0.79** Cs ¼ 0.035 (OC)* 0.145 (pH) þ 1.741
R2 0.935* 0.945** 0.935*
Cw ¼ 30 mg L1 Cs ¼ 1.24 (OC)*** þ 7.95 Cs ¼ 0.105 (sand)* 3.32 (pH)* þ 38.481* Cs ¼ 1.246 (OC)** þ 1.351 (pH) 0.902
R2 0.994** 0.998* 0.994*
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mechanism: With increasing pH, sand surfaces become more negatively charged, leading to an increased repulsion of the anionic sulfur group. This also explains the observed high mobility in column experiments with sandy sediments of a previous study (Klitzke et al., 2010).
4. Sorption comparison and environmental relevance
Fig. 4 e Cylindrospermopsin sorption on (organic mud-1) in the presence of a monovalent (0.03 M KCl solution) and a divalent (0.01 M CaCl2 solution) cation.
a small degree (approx. 20%) via cation exchange, probably via the cationic amine group. Hence, in the presence of Ca2þ CYN is displaced from the exchange site due to sorption competition, resulting in lower CYN sorption (Fig. 4). A similar sorption mechanism was reported for the ciprofloxacin zwitterion: Vasudevan et al. (2009) found the molecule to be better suited for cation exchange via the cationic amine than for cation bridging via the anionic functional group. Interestingly, CYN is retained by organic matter at levels considerably lower than the cation exchange capacity. This observation together with the relatively low exchangeability of CYN by Ca2þ (approx. 20%) suggests not all negative sites on organic matter to be positionally available to bind with the positively charged amine group (Senesi, 1993) of CYN. Senesi (1993) proposes steric hindrance of organic matter functional groups to be the cause as it has already been presumed for other organocations. The large remaining fraction of CYN, which is not bound by cation exchange, is most likely retained on organic matter by a less specific sorption mechanism, for instance via hydrogen bonding (Senesi, 1993). Aristilde and Sposito (2010) found H-bonding to play an important role in the interactions of the zwitterion ciprofloxacin with natural organic matter. The combination of two different sorption mechanisms in CYN binding to organic matter raises the assumption of organic matter composition to be a crucial parameter in CYN retention. The comparably low sorption of CYN to sediments may be attributed to its high polarity and hence its strong tendency to remain in solution. The fact of clay not playing a significant role in CYN sorption may be explained by the molecular structure of the zwitterion: Attraction to negatively charged clay surfaces via the cationic amine was probably hindered by the repulsion of the anionic sulfur group. This kind of sorption mechanism of organic zwitterions on clay minerals has already been reported by Carrasquillo et al. (2008). The very low sorption on sand (Table 5) in combination with a negative effect of increasing pH (Table 6) suggests a similar
Our results showed much stronger sorption of ATX than CYN. Anatoxin-a sorption also exceeds the reported Kf-values of a batch study for nodularin and microcystin-LR (Miller et al., 2005). This difference in sorption may be explained by the structure of the three toxins: While nodularin and microcystin both carry negative charges (Miller et al., 2001), leading to increased repulsion from negatively charged mineral surfaces, ATX occurs as cation (Devlin et al., 1977) enhancing sorption to negatively charged sites. As Miller et al. (2005) conducted the study in a different concentration range (mg L1 as opposed to mg L1 in our experiments) we cannot exclude that the observed higher sorption of ATX in our study may also be attributed to the attachment of molecules to high specific sorption sites as opposed to a difference in sorption behaviour. The impact of clay and organic matter onto sorption of ATX and CYN is strongly controlled by toxin concentration (Tables 4 and 6) and this observation is in agreement with findings for microcystin-LR and nodularin (Miller et al., 2005). The possible strong sorption onto interlayer regions of clays and micro pores of humic material (as it may be postulated for ATX) may decrease the availability of organocations to microbes, leading to longer degradation times in sediments (McBride, 1994). The strong correlation between ATX sorption and CECeff suggests ATX sorption to be susceptible to changes in ionic strength. According to Narine and Guy (1981) this is the most important variable in clayeorganocation interactions. Brown and Combs (1985) found Ca, Mg, Na, and K ions to be the predominant sorption competitors of the organocation methylacridinium in sediments. This would suggest ATX to desorb easily from the exchange sites if these ions are available in the pore water. In addition, desorption would be enhanced if the ionic composition of the pore water changes either by (i) an increase in cation concentration or (ii) by an increase in cation valency (i.e. bivalent and trivalent cations). These aspects are crucial when assessing the risk of ATX breakthrough in sediments. As CYN is presumably predominantly sorbed via a non-specific sorption mechanism onto organic matter and only to a small degree via cation exchange, variations in the pore water chemistry would not result in such dramatic sorption changes. However, a desorption of CYN has to be expected if organic matter is being decomposed or transformed and CYN is hence released into solution. Thus, fluctuations in CYN (de)sorption from/to particulate organic matter occurring ubiquitously in natural waters may contribute to the observed high persistence in waters (Chiswell et al., 1999; Klitzke et al., 2010), as sorbed CYN is unavailable to microorganisms.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 3 8 e1 3 4 6
5.
Conclusions
Our results showed that sediment texture together with the chemical structure of the respective toxin are crucial parameters to predict the fate of ATX and CYN during sediment passage. Although clay and organic carbon content proved the most important parameter in ATX sorption to sediments, ATX is still retained by sandy porous media containing low fractions of fines. This suggests at least some ATX removal through sorption during water percolation through sandy sediments. Anatoxin-a is mainly sorbed by cation exchange mechanism, which makes it susceptible to desorption if hydrochemical conditions (especially pH and ionic strength) change. Cylindrospermopsin only demonstrated high removal on organic matter through presumably non-specific sorption. Cation bridging through Ca ions onto organic matter was not found to play a significant role in CYN sorption by organic matter. The generally low sorption of CYN reduces its residence time in sediments, shortening the time for microbial attack. Hence, high breakthrough has to be expected unless further environmental conditions are conducive to CYN degradation. Future studies should address CYN and ATX sorption as a function of pH and ionic strength, desorption from exchange sites and the role of organic matter composition in CYN retention.
Acknowledgements The authors would like to thank Silke Meier, Claudia Kuntz, and Gabriele Gericke for their support with batch experiments and LC-MS-MS measurements, respectively, Michael Facklam for analysis of sediment grain size distribution, Sabine Rautenberg for C/N-analysis, Gabriele Wessel for help with data analysis, Reimo Kindler and Jaane Kru¨ger for fruitful discussion and for proof-reading part of the manuscript. The funding provided by the Federal Ministry of Education and Research (BMBF), Veolia Water and the Berliner WasserBetriebe (BWB) under coordination of the KompetenzZentrum Wasser Berlin (KWB) is acknowledged.
Appendix. Supplementary material Supplementary data associated with this article can be found in online version at doi:10.1016/j.watres.2010.10.019.
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Rotating disk electrodes to assess river biofilm thickness and elasticity Ste´phanie Bouleˆtreau a,b,*, Jean-Yves Charcosset a,b, Jean Gamby d, Emilie Lyautey a,b, Sylvain Mastrorillo a,b, Fre´de´ric Aze´mar a,b, Fre´de´ric Moulin c, Bernard Tribollet d, Fre´de´ric Garabetian e a
Universite´ de Toulouse, UPS, INP, EcoLab (Laboratoire d’e´cologie fonctionnelle), 118 route de Narbonne, F-31062 Toulouse, France CNRS, EcoLab, F-31062 Toulouse, France c Institut de Me´canique des Fluides de Toulouse, UMR 5502, Alle´e du Professeur Camille Soula, 31400 Toulouse, France d Laboratoire Interfaces et Syste`mes Electrochimiques LISE, UPR 15 du CNRS, Universite´ Pierre et Marie Curie, 4 place Jussieu, 75252 Paris cedex 05, France e Universite´ de Bordeaux, EPOC-OASU, UMR 5805, Station Marine d’Arcachon, 2 rue du Professeur Jolyet, 33120 Arcachon, France b
article info
abstract
Article history:
The present study examined the relevance of an electrochemical method based on a rotating
Received 18 March 2010
disk electrode (RDE) to assess river biofilm thickness and elasticity. An in situ colonisation
Received in revised form
experiment in the River Garonne (France) in August 2009 sought to obtain natural river
26 September 2010
biofilms exhibiting differentiated architecture. A constricted pipe providing two contrasted
Accepted 14 October 2010
flow conditions (about 0.1 and 0.45 m s1 in inflow and constricted sections respectively) and
Available online 21 October 2010
containing 24 RDE was immersed in the river for 21 days. Biofilm thickness and elasticity were quantified using an electrochemical assay on 7 and 21 days old RDE-grown biofilms
Keywords:
(t7 and t21, respectively). Biofilm thickness was affected by colonisation length and flow
Epilithon
conditions and ranged from 36 15 mm (mean standard deviation, n ¼ 6) in the fast flow
Periphyton
section at t7 to 340 140 mm (n ¼ 3) in the slow flow section at t21. Comparing the electro-
Biofilm architecture
chemical signal to stereomicroscopic estimates of biofilms thickness indicated that the
Biofilm deformation
method consistently allowed (i) to detect early biofilm colonisation in the river and (ii) to
Voltammetry
measure biofilm thickness of up to a few hundred mm. Biofilm elasticity, i.e. biofilm squeeze
Electrochemistry
by hydrodynamic constraint, was significantly higher in the slow (1300 480 mm rpm1/2, n ¼ 8) than in the fast flow sections (790 350 mm rpm1/2, n ¼ 11). Diatom and bacterial density, and biofilm-covered RDE surface analyses (i) confirmed that microbial accrual resulted in biofilm formation on the RDE surface, and (ii) indicated that thickness and elasticity represent useful integrative parameters of biofilm architecture that could be measured on natural river assemblages using the proposed electrochemical method. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
River epilithic biofilms are complex microbial consortia of algae, bacteria and other micro- and meso-organisms that develop on
solid substrata (Lock, 1993). Embedded in a mucilage matrix of microbially generated biopolymers (EPS: extracellular polymeric substances), these aggregates have relatively high mechanical stability and cell density. River biofilm dynamics influences
* Corresponding author. Tel.: þ33 (0) 5 61 55 73 48; fax: þ33 (0) 5 61 55 60 96. E-mail address: [email protected] (S. Bouleˆtreau). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.016
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various instream processes such as primary production (Wetzel, 1975), river food web (Feminella and Hawkins, 1995), organic matter and nutrient cycling (Paul et al., 1991; Battin et al., 2003a; Teissier et al., 2007), and accumulation of contaminants such as pesticides (Dorigo et al., 2007) and toxic metals (Cheng et al., 2008; Thuy Dong et al., 2008). Biofilm architecture (e.g. thickness, cohesion) varies with community maturation and resistance to current velocity, both for monospecific biofilms (e.g. Mukherjee et al., 2008) or for complex river biofilms (Peterson, 1996). Architecture partly conditions biofilm functions affecting mass transfer between aggregates and bulk water, influencing for example the relative uptake of substrates differing in bioavailability (Battin et al., 2003b). In spite of its major interest, the in situ characterisation of biofilm architecture remains a challenge since tools are very scarce, inconvenient to use in the field and somewhat semiquantitative. Among architectural parameters, thickness is the most integrative and informative with respect to variation in key parameters including volume, wet weight, and number of species. However river biofilm thickness is rarely measured and studies often intentionally use biomass as an indirect estimation of thickness (Dodds et al., 1999). Several destructive (scanning electron microscopy, cryoembedding) and nondestructive (light microscopy, scanner with an image acquisition system, a laser triangulation sensor, confocal laserscanning microscopy and two-photon excitation microscopy) optical methods are available to measure biofilm thickness (Paramonova et al., 2007). They are ideal tools for biofilm monitoring at the micrometer scale spatial resolution. Investigations on bacterial biofilms are also oriented towards nanoscopic spatial arrangement using a combination of confocal laser-scanning microscopy and atomic force microscopy (Schmid et al., 2008). The main drawback for their application to river biofilm is the incompatibility between their observation scale and the centimetre or metre scale of biofilm development in rivers (e.g. on rock substrates such as pebbles). An optical method (Bakke and Olsson, 1986), periodically applied for river and estuarine biofilms (Sekar et al., 2002; Rao, 2003) determines biofilm thickness as the vertical sample displacement required to move the focal plane of the microscope from the waterebiofilm interface to the biofilmesubstratum interface. It is limited in that an estimate of the refractive index of the transparent film is required and it can only be applied to biofilm thinner than 100 mm (Paramonova et al., 2007). Herbert-Guillou et al. (1999) reported an electrochemical method based on the analysis of a tracer oxidation current on a rotating disk electrode (RDE) where biofilm has developed. This electrochemical technique was applied to detect very thin bacterial biofilms developed in sea and tap waters (Herbert-Guillou et al., 2000; Gamby et al., 2008). Beside thickness measurement, Herbert-Guillou et al. (2000) showed that the RDE method could be used to provide complementary information on biofilm functional properties relative to biofilm elasticity. The objectives of the present study were to (i) adapt the RDE method to estimate natural phototrophic biofilm thickness and elasticity and particularly, (ii) improve the biofilm elasticity parameter calculation, (iii) assess the relevance of thickness and biofilm elasticity measurements to differentiate contrasted river phototrophic biofilms and, (iv) prove the suitability of this
method for in situ experiments. As flow rate and biofilm maturation are proved to influence biofilm architecture (Peterson, 1996), we designed an experimental device to produce 7-day and 21-day-old biofilms in situ while varying the flow rate.
2.
Materials and methods
2.1.
Experimental design
2.1.1.
Biofilm production device
An experimental pipe device for biofilm production was designed and scaled to provide two contrasted current velocity conditions within the same pipe, so that all factors affecting biofilm dynamics other than flow could be considered similar. According to the volume continuity equation for an incompressible fluid, through a pipe constriction (from the section #1 of area A1 to the section #2 of area A2), (i) the fluid velocity increases and (ii) this increase in velocity (from v1 to v2) is set to the decrease in section area as follows: v2 =v1 ¼ A1 =A2 . The constricted pipe consisted in three main parts: an upstream first cylinder (section #1, slow flow) followed by a converging conical inlet (angle a1) and a second downstream cylindrical throat (section #2, fast flow) (Fig. 1.). The current velocity v1 was determined by the local river current velocity and followed river flow variations during the whole experiment. The current velocity v2 depends on v1 value and on the ratio between diameters ðF2 =F1 Þ. Diameter dimensions were chosen (i) to provide a quite easily handling structure, (ii) to ensure relatively homogeneous flow conditions in each section and (iii) to ensure a ratio v2 =v1 around 4. Inlet and throat diameters were set to 20 and 10 cm respectively. A diverging recovery part (angle a2) followed by a third cylindrical throat (section #3; diameter F3 ¼ F1 ) was added to the structure to ensure a straight exit stream. Convergence and divergence angles were chosen according to values minimising flow detachment and head loss in Venturi pipes: a1 ¼ 20 and a2 ¼ 14 . Numerous formulas are found to estimate the entrance length (le) of cylindrical ducts i.e. the position beyond which flow is fully developed (Anselmet et al., 2009). Application of such formulas to the present flow conditions yields values of le =Fe between 20 and 30 lead to too long pipe dimensions to be handled in the river. Entry and constricted section lengths were set to 3 and 4 times the diameter, the total length being therefore 186 cm. At RDE locations, viscous shear stress on the cylinder (and incidentally on biofilm) is around 10 times larger in the constricted than in the entry section, ensuring relative homogeneous and contrasted local flows at RDE surfaces. The constricted pipe was made of 3-mm thick Plexiglas to ensure light diffusion. Pipe sections for which diameter was smaller than F1 were surrounded with another 20-cm diameter Plexiglas pipe to form a single continuous pipe and decrease detachment of the external flow around the pipe. The additional sheath did not affect light penetration: irradiance in both sections of the pipe, as measured using a LI-COR Li100 quantameter at sunlight, exhibited similar values within a 10% range.
2.1.2.
Experimental procedure
Twelve RDE were incorporated at each downstream extremity of both sections of the apparatus (Fig. 1). The RDE were labelled
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 4 7 e1 3 5 7
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Fig. 1 e Photograph and schematic representation of the experimental pipe device. The position of the RDEs is indicated on the photograph by its labelling. SF: slow flow section; FF: fast flow section; 7: 7 days; 21: 21 days. Arrow shows current direction.
SF (for slow flow) or FF (for fast flow) according to which section they were located. In each section, the surface of 6 RDE per pipe side (right and left) was vertically positioned at the equator line to prevent particle sedimentation during the colonisation process. The RDE were positioned next to each other to ensure homogeneous environmental conditions between replicates. They were maintained in order to arise to the pipe internal surface with nylon cable gland allowing an easy recovery. The constricted pipe was immersed parallel to the water current at the bottom of the River Garonne at the study site of l’Aouach (01 180 0000 E; 43 230 0800 N). This site is a typical reach for biofilm development (Lyautey et al., 2005; Bouleˆtreau et al., 2006). During the low-water period (from July to October), the study river reach is characterised by a shallow (<1.5 m), wide (100 m), and unshaded bed. Water exhibits low turbidity (<30 NTU) and nutrient concentrations of about 10 mg P L1 of soluble reactive phosphorus, 1 mg N L1 of both ammonium and nitrates, and 1.5 mg C L1 of dissolved organic carbon. The constricted pipe was maintained on the river bottom in a zone where the riverbed was flat and homogeneous (boulder rocks), shallow (water depth around 50 cm) and current velocity was slow (around 0.1 m s1). The experiment was performed on August 2009 during a low-flow period to exploit the most stable current velocities as possible, and to enable biofilm accrual especially in the fast flow section. Data on daily mean flow were supplied by DIREN Midi-Pyre´ne´es (gauging station: Portet-sur-Garonne) and mean current velocity was measured at the pipe entry using an FLO-MATE portable flowmeter (Model 2000, Marsh-McBirney, USA). The device was immersed for 21 days, and six RDE per section were sampled after 7 (t7) and 21 (t21) days of colonisation. Replicate RDE were named as follows: SF or FF when
collected in the slow flow or fast flow section followed by 7 or 21 according to the sampling time, and followed by the replicate number; RDE SF7#3 stands for one of the RDE sampled in the slow flow section after 7 days of colonisation. Sampled RDE were kept in river water at 4 C in the dark during transport to the laboratory and measurements were performed within 5 h. At t7 the 12 sampled RDE were replaced by stainless-steel cylinders of similar diameter.
2.2.
Biofilm architecture measurements
2.2.1.
Electrochemical measurement theory
The method consists of measuring the steady-state diffusion current on the RDE interface at a fixed potential and at a fixed rotation speed U without biofilm (t0) and after biofilm development (t7 and t21). To impose this constant potential, a 3electrode-system immersed in an electrochemical cell filled with a tracer solution and connected to a potentiostat was used: (i) RDE, the working metallic electrode on which biofilm develops; (ii) the reference electrode that controls the potential of the working electrode and (iii) the counter electrode that closes the electrical circuit and the overall current goes through. Diffusion current results in the oxidation of a reduced species at the RDEeelectrolyte interface. Without biofilm, diffusion current depends directly on the diffusion boundary layer thickness at the RDEeelectrolyte interface. With RDE rotating at a constant rotation speed around its axis, the diffusion boundary layer thickness is maintained constant. Biofilm is considered as an inert porous layer with respect to mass transport since it contains more than 95% of water (Characklis, 1990). The biofilm is also considered as a layer of stagnant water on the RDE surface, and the slow convection
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existing inside the biofilm is neglected. The diffusion coefficient in biofilm was shown to be the same as the diffusion coefficient in water (L’Hostis et al., 1996), this property is extended for the thicker river biofilms under investigation in the present study. This layer adds to the hydrodynamic boundary layer one, inducing a decrease in diffusion current intensity.
2.2.2.
Electrochemical measurement setting
The RDE was made of a 5-mm diameter platinum cylinder (electrical conductor) coated with a Teflon cylinder (electrical insulator). The reference electrode was a saturated calomel electrode (SCE) (REF421, Radiometer Analytical, France). The counter electrode was a cylindrical grid of platinum immersed into the electrolyte solution that surrounded the working electrode. A 0.01M potassium ferrocyanide [Fe(CN)6]2 and ferricyanide [Fe(CN)6]3 solution was used as tracer in 1M KCl. Ferrocyanide oxidation current intensity was measured at 0 V/ SCE at which no water electrolysis and no oxygen reduction occur. Measurements were performed at 20 C. In the laboratory, the RDE was mounted on a motor axis plugged using mercury contacts and was rotated by a DC motor system. The motor speed was controlled with a servo system and measured using a tachometer. Prior to diffusion current measurements, the equilibrium potential of the ferrocynanide/ ferricyanide couple at the same concentration was measured between 0.240 and 0.236 V/SCE in accordance with the reference potential (0.237 V/SCE). Diffusion current was then measured at the potential 0 V/SCE for each RDE rotation speed between 100 and 1200 rpm by steps of 100 rpm. Rotation speed was limited to 1200 rpm to prevent biofilm erosion. Before t0 measurements, every RDE were polished using sandpaper (grade 1200) and cleaned with distilled water. After t7 and t21 measurements, each RDE was individually conditioned into river water until further analyses. Biofilm thickness d (mm) was calculated from diffusion current intensity measurements with ðiðtÞÞand without biofilm ðið0ÞÞ for each RDE rotation speed (U in rpm) as follows: h i d ¼ nFDC S iðtÞ1 ið0Þ1 10; 000
2.2.4.
Image acquisition and analysis
For RDE biofilm cover estimations, stereomicroscopy (Olympus SZX10, 24 magnification) images of the bare RDE (t0) and the wet colonised RDE (t7 or t21) surfaces were captured using an Olympus U-TV0.63XC camera (Olympus Corporation, Tokyo, Japan) as TIFF files (1600 by 1200 pixels) and imported in Photoshop CS3 (Adobe Photoshop v 10.0.1). No staining was performed. The image of the bare RDE surface was used as control. Binary images were generated by affecting the white color to the bare pixels and the black color to the colonised pixels. RDE biofilm cover (surface %) was determined on the platinum surface as the ratio of the surface area of black pixels to the total
Cell numeration
After electrochemical measurements, material on the RDE surface was removed with a sterile scalpel and placed into 1 mL of filter-sterilized (0.2 mm pore-size filter) river water and preserved for storage at 4 C with the addition of 100 mL of neutralized formaldehyde to the biofilm suspension. Biofilm suspension was sonicated in an ultrasonic bath (Elmasonic S900H, Elma, South Orange, NJ) at 37 kHz (15 min) and vortexed (15 min) according to Buesing and Gessner (2002). For bacterial counts, 500 mL aliquot of the appropriate cell suspension dilution was stained with 200 mL DAPI (0.01 mg mL1) and collected by filtration on 0.2 mm pore-size black polycarbonate filters (Nuclepore, Whatman, Maidstone, UK) according to Garabetian et al. (1999). Counts were carried out on an Olympus BH2 RLFA microscope at 1250 magnification and results were expressed as cell number per cm2. Diatom density in biofilm suspension was estimated directly (t7) or after 5-fold dilution (t21) using a Nageotte counting chamber, by counting the total number of diatoms in 30 fields (1.25 mL each, 0.5 mm depth), using light microscopy at 250 magnification (Olympus BH2 RLFA).
2.2.5. (1)
with n is the number of electrons, F the Faraday constant (96485 C mol1 or s A mol1), D the diffusion coefficient in both water and biofilm set to 6.8 106 cm2 s1 at 20 C according to Deslouis et al. (1980), C* the electroactive species concentration in the bulk solution (0.00001 mol cm3), and S the active RDE area (0.196 cm2).
2.2.3.
surface area (sum of white and black pixels) with Image J 1.37v (Wayne Rasband, National Institutes of Health, USA). For thickness estimation, stereomicroscopy (Leica MZ 12.5, 16 magnification) images of a side view of each colonised RDE standing in water were captured using a Leica DFC320 camera (Leica Microsystems DI Cambridge). Several focal planes corresponding to various cross sections ((x, z)-planes in a (x, y, z) coordinate system) were visible on the picture thanks to the setting of an appropriate depth of field. The projected image of the various focal planes was converted to binary image after biofilm pixels selection. The maximal biofilm height (maximal z-coordinate of the (y, z)-plane) on each abscissa of the image (x-axis) was measured automatically in pixels using Image J. Conversion from pixel to mm was performed using a line scale standard. This gives the mean maximal biofilm thickness (mean zmax) of the whole colonised RDE surface ((x, y)-plane).
Statistical analyses
Electrochemical parameters (biofilm thickness and elasticity) were deduced by statistical adjustment using Origin 8.1 SR1 (v8.1.13 88, OriginLab Corporation, Northampton, USA). Agreement between simulated and measured thickness was evaluated by X2 and R2 application. The non-parametric ManneWhitney U-test procedure was used to test for flow effects on biofilm thickness, biofilm elasticity, RDE biofilm cover, bacterial and diatom cell numbers. Correlation between biofilm architecture parameters was explored by using the Pearson r coefficient. All values are given as average standard deviation (SD). Statistical analyses were performed with SPSS 15.0 software for Windows, and were considered significant at p 0.05.
3.
Results
3.1.
Determination of biofilm thickness and elasticity
The reciprocal steady-state current intensity (mA1) was plotted against the reciprocal square root of the RDE rotation speed (rpm0.5) in the Koutecky-Levich coordinates in the
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Fig. 2. For each EDT, before (t0) or after biofilm colonisation (t7 or t21), the current increased with the RDE rotation speed according to the Levich law (Levich, 1962). For a given rotation speed, the current decreased with biofilm formation (t7 vs. t0 and t21 vs. t0). This decrease in the current intensity measured between t0 and t7 or t21 was significant and allowed thickness determination using equation (1) for 22 RDE over 24. Connecting issues were at the origin of the defects on 2 RDE (SF7#1 and FF21#16). For 22 RDE (and even the most colonised ones), minimal recorded current intensities (i.e. intensity measured at the minimal rotation speed of 100 rpm) were higher than several tens of mA suggesting that the measurement was relevant (see Appendix). The slope is higher for 21- than for 7-day-old biofilms, and for slow than for fast flow grown biofilms (Fig. 2). Biofilm thickness measured at each RDE rotation speed (U) was represented on Fig. 3. The relationship between thickness and rotation speed can be analysed by considering the following law: d¼
3.2.
1 1
Parameter values are resumed in the Table 1. The derivative of d vs. U may tend towards infinity when the rotation speed tends towards zero. This can result in a loss of accuracy on d0 yielding to unrealistic too large d0 for SF21#6, SF21#10 and SF21#12 parameter fits (as indicated using the infinity sign in Table 1). Such unrealistic values led us to exclude the corresponding RDE results. The poor agreement between measured and simulated thicknesses at high rotation speed for these RDE is likely to suggest that the law is not applicable under high rotation speeds for thick biofilms. Nevertheless weak X2 values confirmed good fit quality for 19 out of 22 RDE; the calculated d0 values are reliable and ranged from 16 mm after 7 days of colonisation to 500 mm after 21 days of colonisation. Electrochemically measured biofilm thicknesses were significantly correlated with stereomicroscopic estimates (Table 2). Electrochemical biofilm thickness estimates were 1.8-fold lower than stereomicroscopic estimates, ranging from 70 to 540 mm (Fig. 4).
In situ experimental settings
(2)
ðd0 Þ þKU
0:5
d0 (mm) is biofilm thickness at zero RDE rotation speed and, in other words, the theoretical biofilm thickness without any particular hydrodynamic constraint. The coefficient K (mm1 rpm1/2) relates the dependence of thickness with RDE rotation speed and was used to parametise biofilm elasticity as 1=K(mm rpm1/2).
The RDE supporting device was designed to be immersed into the river ensuring both in situ environmental variability (algal and bacterial inoculum, light, temperature, nutrient, etc.) and two contrasted flow conditions. Flow velocity level in the pipe was controlled by natural temporal hydraulic changes in the river. Other than days 5e6e7 when the daily mean flow peaked at 99 m3, the river experienced a period of quite stable and low Fast flow t7 vs t0
Slow flow t7 vs t0 10
25
FF7#13 FF0#13 FF7#15
20
SF7#3 SF0#3
-1
15
SF7#5 SF0#5 SF7#7 SF0#7 SF7#9
-1
SF0#9 SF7#11 SF0#11
10
8
intensity (mA)
intensity-1 (mA)-1
SF7#1 SF0#1
FF7#20 FF0#20 FF7#23
6
FF0#23 FF7#24
4
FF0#24
2
5
0 0.02
FF0#15 FF7#17 FF0#17
0.04
0.06
0.08
0.10
0 0.02
0.12
(electrode rotation speed)-1/2 (rpm)-1/2
0.04
0.06
0.08
0.10
Slow flow t21 vs t0
Fast flow t21 vs t0 10
25
FF21#14
SF21#2
10
FF0#14 FF21#18 FF0#18 FF21#19 FF0#19
6
FF21#21 FF0#21 FF21#22 FF0#22
-1
intensity (mA)
15
-1
intensity (mA)
-1
SF21#6 SF0#6 SF21#8 SF0#8 SF21#10 SF0#10 SF21#12 SF0#12
8
-1
SF0#2 SF21#4 SF0#4
20
4
2
5
0 0.02
0.12
(electrode rotation speed)-1/2 (rpm)-1/2
0.04
0.06
0.08
0.10
(electrode rotation speed)-1/2 (rpm)-1/2
0.12
0 0.02
0.04
0.06
0.08
0.10
0.12
(electrode rotation speed)-1/2 (rpm)-1/2
Fig. 2 e Inverse current intensity evolution with the electrode rotation speed measured on electrodes after different colonisation times (0 day, t0: closed symbols; 7 days, t7 and 21 days, t21: open symbols) in two flow sections (slow flow, SF and fast flow, FF) with the ferro-/ferricyanide tracer. Each symbol corresponds to one RDE.
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Slow flow t7
100
FF7#13 FF7#15 FF7#17 FF7#20 FF7#23 FF7#24
40
thickness (µm)
80
thickness (µm)
Fast flow t7
50
SF7#3 SF7#5 SF7#7 SF7#9 SF7#11
60
40
30
20
10
20
0
0 0
200
400
600
800
1000
0
1200
200
electrode rotation speed (rpm)
Slow flow t21
400
600
800
1000
1200
Fast flow t21
100 SF21#2 SF21#4 SF21#6 SF21#8 SF21#10 SF21#12
FF21#14 FF21#18 FF21#19 FF21#21 FF21#22
80
thickness (µm)
300
thickness (µm)
400
electrode rotation speed (rpm)
200
60
40
100 20
0
0 0
200
400
600
800
1000
1200
0
200
electrode rotation speed (rpm)
400
600
800
1000
1200
electrode rotation speed (rpm)
Fig. 3 e Thickness evolution with the electrode rotation speed measured on electrodes after two colonisation times (7 days, closed symbols and 21 days, open symbols) in two flow conditions (slow flow, SF and fast flow, FF) with the ferrocyanide tracer. Each symbol corresponds to one RDE.
flow (64 10 m3 s1) during the experiment, favouring biofilm development (data not shown). While measurement on day 7 highlighted the above mentioned 3-day period of hydraulic disturbance, other discrete measurements on days 0 and 21 in the slow flow section (i.e. inlet of the pipe) showed quite similar flow velocity values around 0.11 m s1 that correspond to a theoretical Reynolds number of 23,000 (Table 3). According to the device dimensions, flow velocity and Reynolds number in the fast flow section can be calculated from the former data to be around 0.46 m s1 and 46,000, respectively.
3.3.
Biofilm features
Diatom accrual contributed to biofilm formation on the RDE. Diatom density increased during colonisation with 27 103 and 102 103 individuals per cm2 in the slow flow section and with 8 103 and 33 103 individuals per cm2 in the fast flow section on average at t7 and t21 respectively (Fig. 5a). Consistently bacterial densities increased during colonisation reaching 32 106 and 27 106 cells per cm2 on average at t21 in the slow and fast flow sections, respectively (Fig. 5b.). Comparing the two sections, diatoms densities were significantly different, whereas bacterial densities were not. As expected, RDE biofilm cover significantly increased between t7 and t21 from 36 to
59% on average in the slow flow section and from 54 to 85% on average in the fast flow section (Fig. 5c.). Stereomicroscopic thickness significantly increased between t7 and t21 and significantly decreased from the slow to the fast flow section (Fig. 5d). Biofilm thickness significantly increased with time, means ranging from 100 to 340 mm in slow flow and from 36 to 72 mm in fast flow (Fig. 5e). Biofilm thickness was significantly affected by flow conditions at both sampling times. Significant (or quasi significant) changes in biofilm elasticity values ð1=KÞ occurred between t7 and t21 and between flow conditions (Fig. 5f.). Mean ð1=KÞ values were significantly higher in the slow (1300 mm rpm1/2) than in the fast flow section (790 mm rpm1/2) (ManneWhitney U-test, p ¼ 0.032). Electrochemical thickness measurements were significantly correlated with RDE biofilm cover, diatom and bacterial densities (Table 2). In addition, significant correlation was also observed between biofilm elasticity and other parameters except bacterial density.
4.
Discussion
Ecologists agree to consider thickness increase as the driving force of biofilm structural and functional properties (Sabater
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Table 1 e Results of parameter fits (minimisation Chisquare): parameter values (average ± square deviation) and fit quality (c2/degree of freedom; R2) for each RDE. c2 K (mm1 rpm1/2) d0 (mm) R2 dof Slow flow t7 SF7#3 SF7#5 SF7#7 SF7#9 SF7#11
87 2 65 1 193 13 49 1 90 1
0.00084 0.00116 0.00076 0.00103 0.00106
0.00001 0.00001 0.00002 0.00001 0.00001
0.13 0.04 1.52 0.03 0.04
0.9978 0.9987 0.9926 0.9986 0.9993
Fast flow t7 FF7#13 FF7#15 FF7#17 FF7#20 FF7#23 FF7#24
44 30 16 39 27 58
0.00222 0.00241 0.00124 0.00203 0.00205 0.00260
0.00002 0.00003 0.00002 0.00001 0.00003 0.00002
0.01 0.02 0.00 0.01 0.02 0.02
0.9995 0.9978 0.9970 0.9995 0.9973 0.9991
Slow flow t21 SF21#2 SF21#4 SF21#6 SF21#8 SF21#10 SF21#12
501 108 252 18 þNa 277 13 þNa þNa
0.00077 0.00071 0.00090 0.00042 0.00053 0.00044
0.00003 0.00002 0.00004 0.00001 0.00018 0.00002
5.81 1.70 58 1.43 1222 265
0.9869 0.9939 0.9328 0.9971 0.7460 0.9304
Fast flow t21 FF21#14 FF21#18 FF21#19 FF21#21 FF21#22
114 3 86 4 48 2 69 1 40 1
0.00084 0.00094 0.00076 0.00097 0.00099
0.00001 0.00003 0.00003 0.00001 0.00002
0.26 0.78 0.40 0.09 0.07
0.9973 0.9861 0.9780 0.9976 0.9949
1 1 0 0 0 1
Fig. 4 e Relationship between electrochemical and stereomicroscopic measurements of biofilm thickness.
a þN Indicates an unrealistic too large thickness value.
and Admiraal, 2005), but, studies on river biofilms suffer from a lack of available tools to characterise biofilm architecture. The present study intended to assess the ability of an electrochemical method based on rotating disk electrode to measure and evaluate two features of biofilm architecture: thickness and elasticity. Previously, the electrochemical method measured only very thin bacterial biofilms, between 0.9 and 3.5-mm thick in tap water (Gamby et al., 2008), and up to 10-mm thick in seawater (Herbert-Guillou et al., 1999). The use of 1 M KCl in the electrochemical assay could be expected to cause thickness underestimation due to EPS constriction (Frank and Belfort, 1997).
However, in their previous experiments, electrochemical estimates of biofilm thickness were validated by means of confocal laser-scanning microscopy (L’Hostis, 1996). In the present study, stereomicroscopy was used since the whole colonised RDE surface can be examined, and microbial counts can then further be done on fresh material since it does not require any previous processing such as staining, cryoembedding or cryosectioning. Stereomicroscopic measurements cannot provide absolute thickness values, but gave the upper limit of biofilm thickness range for each RDE. Nevertheless, the agreement between electrochemical measurements and stereomicroscopic estimates of biofilm thickness, 2-fold higher than the electrochemical one, confirmed the relevance of the electrochemical approach to usefully measure thicknesses ranging from a few mm to several hundreds of mm. The electrochemical method is suitable for studying biofilms containing not only prokaryotic but also eukaryotic microorganisms such as microphytobenthic algae, and particularly diatoms. Stacking of diatom cells, typically several 10 mm in size, would give a biofilm cluster of hundreds of mm in thicknesses. Our measurements are thus consistent with the expected thicknesses for such biofilms. The second parameter measurable by electrochemistry is biofilm elasticity. Initially Herbert-Guillou et al. (2000) found direct variation of bacterial biofilm thickness with electrode
Table 2 e Correlation values (Pearson r coefficient) between biofilm physiognomy parameters. Parameter d0 1/K Bacterial density Diatom density RDE biofilm cover Stereomicroscopic thickness
d0
1/K
Bacterial density
Diatom density
RDE biofilm cover
Stereomicroscopic thickness
1.000
0.615** 1.000
0.480* 0.428 1.000
0.764*** 0.696*** 0.533** 1.000
0.680*** 0.700*** 0.561** 0.714*** 1.000
0.833*** 0.781*** 0.646*** 0.755*** 0.822*** 1.000
Stars indicate the significance level (*p 0.05; **p 0.01; ***p 0.001).
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speed rotation, depending on biofilm development conditions. Therefore, they calculated biofilm deformation as the difference between electrochemical thickness at 100 rpm and thickness at a given rotation speed, and represented this latter as a function of electrode rotation speed. This simple relationship was not observed in the present study, probably because the studied biofilms contained algae and inorganic particles. Adapted from Foret (2006) that demonstrated the dependence of electrochemical thickness with kU0:5 in water circuit biofilms, an original parameterisation of biofilm elasticity resulting from the assessment of an empirical
Table 3 e Theoretical hydraulic characteristics in the slow and fast flow sections at t0 (first day), t7 (7 colonisation days) and t21 (21 colonisation days) estimated from measurements at the inlet of the pipe and pipe dimensions. t0
t7
t21
0.11 22,000 0.44 44,000
0.30 60,000 1.20 120,000
0.12 24,000 0.48 48,000
Parameter
-2
diatom density (ind. cm )
200x103
50x106
a
* p=0.010
t7 t21
b
t7 t21
40x106
-2
Fast flow
bacterial density (cells cm )
v (m s1) Re v (m s1) Re
Slow flow
150x103 * p=0.037
100x103
** p=0.004
50x103
NS p=0.423
30x106 NS p=0.749
20x106
** p=0.004
NS p=0.055
10x106
** p=0.004
0
0 Slow flow
c
600
NS p=0.055
t7 t21 * p=0.037
** p=0.004
80
Slow flow
stereomicroscopic thickness (µm)
RDE biofilm cover (surface %)
100
Fast flow
60 ** p=0.004
40
20
0
t21
500
** p=0.006
400
** p=0.004
300
** p=0.004
200
100
Fast flow
Slow flow
2000 t7
* p=0.011
t21 1/2
biofilm elasticity (µm rpm )
e
400
biofilm thickness (µm)
t7
** p=0.006
0 Slow flow
500
d
Fast flow
* p=0.025
300 * p=0.025
200 * p=0.028
100
0
1500
Fast flow
** p=0.006
f
t7 t21
NS p=0.053 NS p=0.053
1000 ** p=0.006
500
0 Slow flow
Fast flow
Slow flow
Fast flow
Fig. 5 e Effects of flow conditions (slow flow vs. fast flow) and colonisation time (t7, black vertical bar vs. t21, grey vertical bar) on diatom density (a), bacterial density (b), biofilm (electrochemical) thickness (c), elasticity (d), biofilm cover (e), and stereomicroscopic thickness (f).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 4 7 e1 3 5 7
relationship between biofilm thickness and RDE rotation speed U0:5 was proposed here. Resulting elasticity values, displaying a wide range of magnitude from about 400 to 2400 mm rpm1/2, express the magnitude of biofilm thickness variation due to increasing rotation speed and quantify the extent to which biofilm can be reduced by hydrodynamics constraint. The values cannot be compared to existing data, however. The in situ experiment was designed to compare core biological parameters to electrochemical parameters on natural river biofilms. As time is one of the main drivers of biofilm structuring, biofilms were sampled at two stages of biofilm accrual pattern, colonisation and maturation. Successional changes driven by changes in benthic microalgal species strategies result in temporal changes in biofilm structure (McCormick and Stevenson, 1991; Biggs et al., 1998; Wellnitz and Brader, 2003). Successional processes were also reported for river biofilm bacterial communities (Jackson et al., 2001; Lyautey et al., 2005; Lear et al., 2008). In the studied section of the River Garonne, biofilm bacterial richness proved to increase from 0 to 7 days, and decrease from 7 to 21 days (Lyautey et al., 2005), justifying the selected sampling times. The biofilm support material is known to influence biofilm community composition (Cattaneo and Amireault, 1992) and biofilms colonising RDE platinum may have exhibited distinctive taxonomic assemblages as compared to biofilms colonising river pebbles. An in-depth comparison of biofilm structure, biomass and composition between platinum and natural substrata is still to be performed, since no data on assemblage composition was recorded in the present study. Abundances of bacteria and diatoms were monitored, showing evidence of a microbial accrual on immersed RDE surfaces. Recovered densities were comparable to those previously observed in the River Garonne biofilms for diatoms, namely 105e107 individuals per cm2 (Eulin, 1997) and bacteria, about 107e108 cell per cm2 (Lyautey et al., 2010). Temporal evolution of microbial densities of RDE biofilms fitted with measured thickness enhancement. Interestingly, RDE biofilm cover increased with microbial densities and thickness suggesting that phototrophic river biofilms extend both horizontally and vertically in accordance with the typical model of biofilm development from isolated column forming clusters to connected mushrooms (Costerton et al., 1987). The proposed electrochemical assay was recommended to detect and survey fouling of man-made devices in marine and drinking waters (Herbert-Guillou et al., 1999; Gamby et al., 2008). It could also be used to evaluate the early dynamics of river biofilm e.g. the kinetics in the very early stage of colonisation in time course experiments or the patchiness of early accrual zones in microscale experiments. Another main driver of biofilm structuring is flow. The RDE supporting device was imagined on the pattern of one Venturi pipe immersed into the river ensuring both in situ environmental variability (algal and bacterial inoculum, light, temperature, nutrient, etc.) and two contrasted flow conditions. As intended, generated current velocities, 0.11 and 0.46 m s1, were in the velocity range that favours such biofilm development (Horner and Welch, 1981). Despite disturbed hydraulic conditions for a 3-day period, stable and low daily mean flows occurred during most of the experiment especially during the whole maturation period. During stable and low-flow periods, typical Reynolds numbers (23,000 and
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46,000) discriminated between optimal (Re near 22,000) and suboptimal biofilm growth conditions (Re > 40,000; Godillot et al., 2001). Consistently, higher diatom densities and biofilm thicknesses were found in the optimal flow section as compared to the other section. To our knowledge, only one study has quantified the effect of hydrodynamics on the thickness of stream microbial biofilms (Battin et al., 2003b): thicknesses deduced from confocal laser-scanning microscopy images of cryosections of biofilm were significantly higher for biofilms cultivated on ceramic coupons in the slow flow condition (0.065 m s1; Re ¼ 1869) than in the fast flow condition (0.23 m s1; Re ¼ 7559). The relationship between biofilm thickness and Reynolds number in the former and in the present study were consistent with Godillot et al. (2001) showing a maximum biofilm biomass for Re about 22,000. As for biofilm elasticity in the present study, biofilms produced in the slow flow section exhibited higher elasticity values than biofilms produced in the fast flow section. Most of the microorganisms that formed river biofilm biovolume are fitted with cellular structures maintaining cellular shape (e.g. bacterial cell walls, and diatom siliceous frustules). Biofilm elasticity most probably resulted rather from intercellular space reduction than from cell size constriction. Indeed, biofilm elasticity as defined in the present study might thus refer to voids (pores and channels) within biofilm and/or the looseness of cell adhesion in biofilm. Biofilm elasticity could thus fit with the sinuosity index of Battin et al. (2003b). The multiplication of pores or voids within biofilm contributes to enlarge biofilm surface area within biofilm and therefore facilitates biofilm e water interactions and advective solute transport (De Beer et al., 1996). Such mechanical property is well studied in biofilm models used to design and evaluate performance of biofilm reactors (e.g. Picioreanu et al., 1998). Biofilm elasticity as defined in the present study could be considered as an integrative parameter of biofilmewater interaction ability, in analogy with biofilm surface enlargement in studies of bacterial biofilms of industrial environments. For example, the reduction of biofilmewater interactions forming a barrier for advective solute transport could be an adaptative response of biofilm submitted to chemical stress. Indeed communities exposed to cadmium were primarily dominated by short stalked and ad-pressed diatom species whereas control communities were dominated by filamentous diatom species (Feurtet-Mazel et al., 2003). River biofilm architecture was also affected by chronic copper exposure through the growth of the chain-forming diatom Melosira varians changing from long filaments to short tufts (Barranguet et al., 2002). Such a qualitative observation might be quantified by measuring biofilm elasticity using the proposed electrochemical method. Further studies, addressing the relationship between biofilm architecture and the proposed measure of elasticity, might then allow to test whether biofilm physiognomic properties would reflect biofilm fitness at the community scale.
5.
Conclusion
The present study showed the suitability of an electrochemical method based on rotating disk electrode to assess
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river biofilm thickness up to 500 mm thick. Results extended the application domain of the method previously developed for tap water and seawater biofilms to complex biofilms mainly constructed by algae. The method reliably detected very thin biofilms, as well as measuring biofilm thickness of several hundred-mm. By analysing thickness evolution vs. electrode rotation speed, the electrochemical method can be used to calculate biofilm elasticity as an estimate of the extent to which biofilm is reduced by hydrodynamic constraint. This trait of biofilm architecture would relate to biofilmewater interactions. Very few studies have been conducted on the physical properties of river biofilms, due in part to technical difficulties associated with such complex biofilms. The electrochemical method developed here combined rotating disk electrodes which can be immersed directly in the river, and an electrochemical assay requiring only a few minutes. This nondestructive method is compatible with further analyses on the same sample e.g. bacterial or algal counts, pigment, or DNA extraction and analysis. Expanding the toolbox of biofilm characterisation techniques, the rotating disk electrode electrochemical method can be used to provide novel information on river biofilm architecture.
Acknowledgments This work stems from the project SurF “Surveillance des rivie`res par les biofilms” funded by CNRS PNIR “Biofilms”, Re´gion Midi-Pyre´ne´es and Re´gion Aquitaine (action interre´gionale Aquitaine & MidiPyre´ne´es). The authors are grateful to H. Bouillard and C. Portier (EPOC e Station Marine d’Arcachon) for pipe manufacturing, to F. Moyse and F. Santoul for field assistance and to E. Salvo for diatom counts. The authors wish to thank Dr. E. Topp for correcting English in the ms.
Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.10.016.
references
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Available at www.sciencedirect.com
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Evaluation of pepper mild mottle virus, human picobirnavirus and Torque teno virus as indicators of fecal contamination in river water Ibrahim Ahmed Hamza a,c,*, Lars Jurzik a, Klaus U¨berla b, Michael Wilhelm a a
Department of Hygiene, Social and Environmental Medicine, Ruhr-University Bochum, Universita¨tsstraße 150, 44801 Bochum, Germany Department of Molecular and Medical Virology, Ruhr-University Bochum, Germany c Environmental Virology Laboratory, Department of Water Pollution Research, National Research Centre, Cairo, Egypt b
article info
abstract
Article history:
A reliable indicator is needed to predict and reduce the risk of infection associated with fecal
Received 16 August 2010
contamination of surface water. Since Pepper mild mottle virus (PMMoV), human picobirna-
Received in revised form
viruses (hPBV) and Torque teno virus (TTV) have been detected at substantial levels in human
28 September 2010
feces, we explored whether detection of nucleic acids of these viruses is a suitable indicator of
Accepted 15 October 2010
fecal contamination in river water. From September 2008 to December 2009, water samples
Available online 23 October 2010
(n ¼ 111) were collected from the Ruhr and Rhine rivers and from the influents and effluents of a wastewater plant (n ¼ 12). Quantitative real time (RT-) PCR was used to determine the
Keywords:
abundance of PMMoV, hPBV, and TTV in comparison to human adenoviruses (HAdV) and
Virus contamination
human polyomaviruses (HPyV) that are frequently detected in surface water and were previ-
River water
ously proposed as indicators. While PMMoV was detected in all river water samples, the other
Sewage
viruses were detected less frequently. The concentration of the studied viruses in positive river
Indicators
water ranged from 5 101 to 1.07 106 genome equivalents per liter (gen.equ./l). All wastewater
qPCR
samples were positive for PMMoV, HAdV and HPyV, while TTV and hPBV were detected in 6/12 and 3/12 of samples, respectively. To determine if PMMoV is specific to human-derived fecal waste, fecal samples from human (n ¼ 20) and animal (n ¼ 53) were also tested. In contrast to the ubiquity of PMMoV in human feces (19/20) the virus was only detected at low concentration in a minority of the animal fecal samples tested (7/15 from chicken, 1/10 from Geese and 1/6 from cows). Therefore, in this setting TTV and hPBV do not seem to be suitable indicators of fecal contamination in water. Whereas, the high excretion level and dissemination of PMMoV in human sewage and river water suggest that PMMoV could be a promising indicator of fecal pollution in surface water. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Fecal contamination of water resources used for drinking or recreational purposes is a worldwide problem. The discharge from wastewater treatment plants into surface water is the
main source of water pollution. Viral concentrations in wastewater effluents vary according to the efficiency of the wastewater treatment process, geographical area, season, hygiene and sanitary conditions.
* Corresponding author. Department of Hygiene, Social and Environmental Medicine, Ruhr-University Bochum, Universita¨tsstraße 150, 44801 Bochum, Germany. Tel.: þ49 234 32 28931; fax: þ49 234 32 14199. E-mail addresses: [email protected], [email protected] (I.A. Hamza). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.021
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 5 8 e1 3 6 8
Historically, fecal coliform and enterococci are the most commonly used indicators to evaluate the quality of raw and finished water. It has been found that the detection of a bacterial indicator does not correlate with the presence of viruses (Baggi et al., 2001; Gerba and Rose, 1990; Hauri et al., 2005). Bacteriophages have also been proposed as indicators of fecal contamination (AWPRC, 1991; Leclerc et al., 2000; Tartera and Jofre, 1987). However, the presence of somatic coliphages (SOMCPH), F-specific RNA (F-RNA) bacteriophages, or phages of Bacteroides fragilis does not always correlate with human enteric viruses (Hot et al., 2003; Jiang and Chu, 2004). Besides, somatic coliphages cannot be expected to differentiate between human and animal fecal contamination. Accordingly, human enteric viruses have been proposed as indicators of wastewater contamination in aquatic environment, such as enteroviruses (Gantzer et al., 1998; Kopecka et al., 1993), rotaviruses (Miagostovich et al., 2008), adenoviruses (Pina et al., 1998; Puig et al., 1994) and human polyomaviruses (Bofill-Mas et al., 2000; Hamza et al., 2009; McQuaig et al., 2009). Since these human viruses consist of a diverse group of viruses, it is not yet feasible to examine the surface water for all these viruses. The selection of individual pathogens may also be misleading as each species can tolerate different environmental conditions and the presence of one may not indicate the presence of another. In addition, the presence of some viral pathogens (e.g. noroviruses, rotaviruses and enteroviruses) in the environment also depends on rates of infection and shedding within the host population and can follow seasonal patterns (Pusch et al., 2005; Sellwood et al., 1981), which may hinder their use as general indicators of contamination in surface water with human sewage. The primary aim of this study was to assess the utility of PMMoV, hPBV, and TTV as indicators of fecal contamination in river water. PMMoV belongs to the Tobamovirus genus which causes significant economic losses on infected pepper worldwide (Fauquet et al., 2005). The virus consists of a rod-shaped particle in which a positive sense linear single-stranded RNA is encapsidated. Recently, a metagenomic analysis revealed that food-derived plant viruses are actually the most abundant RNA viruses detected in human stool at concentrations up to 106 virion per mg of fecal material (Zhang et al., 2006). Since viruses of dietary origin presumably do not depend on human infection, their detection in surface water might be less dependent on seasonality and other temporal changes in viral circulation levels. Though PMMoV infect pepper, it does not affect tomato, eggplant and tobacco (Elizabeth et al., 2001), which are in the same family (Solanaceae). A number of pepper-based foods tested positive for PMMoV suggesting that food is a main source of PMMoV in human feces (Zhang et al., 2006). This was supported by Colson et al. (2010), who recovered PMMoV RNA sequences from 57% of food products containing pepper or spice, whereas the virus was not detected in food products free from Capsicum spp or spice. PMMoV was suggested before as an indicator of fecal pollution in marine water (Rosario et al., 2009). TTV is currently classified into the family Anelloviridae, with a non-enveloped, single-stranded, circular DNA virus of w 3.8 - Kb genomic length (Biagini, 2009). The virus has been found in serum, saliva, nasal, feces (Ross et al., 1999), river and
1359
wastewater samples and it was proposed previously as an indicator because of its excretion in feces and its remarkable environmental stability (Carducci et al., 2008; Diniz-Mendes et al., 2008; Haramoto et al., 2008). Picobirnaviruses (hPBV), bi- segmented double stranded RNA viruses, have been identified in a wide range of hosts including humans with or without gastroenteritis (Banyai et al., 2008; Masachessi et al., 2007). The selection of the hPBV for the present study was due to its high prevalence in wastewater samples (Symonds et al., 2009). As a result of the study, the high PMMoV load and its 100% detection frequency in human sewage and river water suggest that PMMoV is a promising indicator of fecal pollution in surface water.
2.
Materials and methods
2.1.
Sampling sites
Water samples (111 samples) were collected weekly between September 2008 and December 2009 (no samples were collected in October 2008) from five sampling sites along the river Ruhr and Rhine in the North Rhine Westphalia region (NRW), one of the most populated areas in Germany. The distance between the sampling sites and the next upstream wastewater treatment plant was approximately 1.5e9 km as described in our previous study (Jurzik et al., 2010). The physicochemical parameters including turbidity (measured by Portable 2100P turbidimeter; Hach Lange GmbH, Du¨sseldorf, Germany), conductivity (measured by LF 191 conductometer; Wissenschaftlich-Technische Werksta¨tten GmbH, Wellhelm, Germany), temperature, and pH value have been recorded for the collected river water samples. Additionally, twelve wastewater samples were collected separately from the influent and effluent of a sewage treatment plant based on conventional activated sludge that discharges the treated wastewater in Ruhr river.
2.2.
Detection of indicator bacteria
The following fecal indicator bacteria have been examined in the collected river water: Total coliforms (TC, n ¼ 104), Escherichia coli (E. coli, n ¼ 103), enterococci (EC, n ¼ 107), and Clostridium perfringens (C. perfringens, n ¼ 71). Analysis was performed immediately after sampling by membrane filtration method, following the method of the International Organization for Standardization (DIN EN ISO 9308-1, 2001; TrinkwV, 2001). Briefly, 100 ml of diluted or undiluted river water were filtered through 0.45 mm membrane filters (47 mm diameter, Millipore, Bedford, USA). For the detection and enumeration of TC and E. coli, the Lactose-TTC agar followed by oxidase and indol test were used, whereas Slanetz-Bartley and Esculin Azide agar were used for the enumeration and confirmation of EC. To detect C. perfringens, the filter membrane was placed onto a selective agar, M-CP (Merck, Darmstadt, Germany). After 24 h of incubation at 45.0 C, the yellow colonies were exposed to ammonium hydroxide fumes then colonies that turned red or dark pink were enumerated as C. perfringens.
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2.3. Concentration of environmental water samples for viral analyses Ten-liter river water samples were concentrated to approximately 3e4 ml using the virus adsorption elution method as previously described (Hamza et al., 2009). Briefly, negatively charged HA membrane (Millipore) with a 0.45 mm pore size and a 142-mm diameter was used for viral adsorption. For recovery of the adsorbed viruses, a non organic elution buffer (0.05 M KH2PO4, 1.0 M NaCl, 0.1% (v/v) Triton X-100, pH 9.2) was used. The eluate was further re-concentrated by overnight precipitation with 12.5% polyethylene glycol 6000 þ 2.5% NaCl at 4 C. The overall recovery rates of the concentration process and the assay detection limits have been estimated before for river water by using HAdV, HPyV, echovirus 11, norovirus, and 4X174 (Hamza et al., 2009). The same method was used for concentration of the effluent samples of the sewage treatment plant. The influent samples were used directly after low speed centrifugation at 3000 g for 5 min at 4 C. To determine the efficiency of wastewater concentration step, three effluent samples (2.5 l each) were autoclaved, spiked with HAdV 5 and 4X174 then concentrated as described for river water samples. The recovery rates ranged between 13 and 82% for HAdV and 11e26% for 4X174 (data not shown).
2.4.
Enumeration of somatic coliphages
Somatic coliphages were quantified (n ¼ 86) by using the double agar layer plaque test according to the standard method of the International Organization for Standardization, ISO 10705-2 (ISO, 2002). E. coli DSM 13127 and E. coli strain WG5 (DSM 12242) grown on modified Scholten’s broth (MSB) were used as host strains for the quantification of somatic coliphages in river water and sewage, respectively. Since a high concentration of bacteria was expected to be found in sewage samples, nalidixic acid-resistant (Nalr) E. coli strain WG5 (DSM 12242) was used as a host bacterium and the medium was supplemented with 60 mg/l nalidixic acid. One milliliter of exponentially growing host strain, 100 ml of the concentrated water sample and 2.5 ml of molten agar (Scholten’s modified semi-solid agar) were mixed then poured onto previously prepared modified Scholten’s agar plates. Plaques were counted within 3e5 h of incubation at 37 C and calculated as PFU/l.
2.5.
Viral nucleic acid extraction
Viral RNA and DNA were co-extracted from 200 ml of the concentrated virus suspension using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. Standard precautions to avoid contamination were taken.
2.6.
Detection of viruses by real time (RT-) PCR
To determine the concentration of viral nucleic acids, DNA standards were prepared for all viruses analyzed by PCR. The DNA standard for TTV was produced by using a synthesized oligo-DNA (SigmaeAldrich). In case of PMMoV and hPBV the DNA standards were prepared by purification of each amplicon using Genelute PCR clean-up kit (SigmaeAldrich). The
concentrations of the purified DNA were determined by using the Quant-iT dsDNA HS Assay (Invitrogen, Carlsbad, CA, USA). The DNA standards for HAdV and HPyV were produced by cloning PCR amplicons into the PCR 2.1 vector using the TA cloning strategy according to the manufacturer’s protocol (Invitrogen, Carlsbad, CA, USA). The plasmid was purified using QIAprep Spin Miniprep Kit (Qiagen, Hilden, Germany). The concentration of the purified plasmid DNA was determined by using the Quant-iT dsDNA HS Assay (Invitrogen, Carlsbad, CA, USA), the fluorescence measured using Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA). In order to allow the determinations of the efficiency and detection limit of the assay, a standard curve was created for each assay using 10fold serial dilution of the DNA standard. The sensitivity of the qPCR (copies/reaction) was calculated to be 5 for PMMoV, 1 for hPBV, 10 for HPyV, 5 for HAdV and 5 for TTV. It should also be noted that all detection limits reported are based on purified target DNA in pure water and not in an environmental water matrix. The sizes of the amplicons, primers, and probes used for the quantification assay are listed in Table 1. The new primers and probes were designed by Primer3 software (Rozen and Skaletsky, 2000). Primers and probe for TTV target the conserved, untranslated region (UTR) of the genome. They were modified from Diniz-Mendes et al. (2008) by using degenerated nucleotides for better detection of large range of TTV strains belonging to all genogroups. Primers of PMMoV were designed to target a well conserved 126 k gene, coding for a subunit of the RNA polymerase complex. The qPCR primers of HAdV, HPyV and hPBV target highly conserved regions of the respective virus families and were described previously (Biel et al., 2000; Heim et al., 2003; Rosen et al., 2000). The q(RT) PCR was conducted in a 20 ml reaction volume by using a Quantitect probe PCR kit (Qiagen), in which 5 ml DNA template, 0.25 mM each of forward and reverse primers, and 0.1 mM of TaqMan probe specific for HAdV, HPyV (JCPyV and BKPyV) or TTV were mixed. For quantification of PMMoV and hPBV the Quantitect probe one-step RT-PCR kit (Qiagen) containing the 0.25 mM each of forward and reverse primers and SYBR Green was used instead of TaqMan probe. Rotorgene 6000 cycler system (Corbett Research, Sydney, Australia) was used for the amplification, detection, and data analysis. The q (RT)PCR temperature conditions were optimized as follow: (i) HAdV; 95 C for 15 min, 45 cycles of 95 C for 15 s, 60 C for 1 min, (ii) HPyV; 95 C for 15 min, 50 cycles of 95 C for 15 s, 60 C for 1 min,(iii) TTV; 95 C for 15 min, 45 cycles of 95 C for 15 s, 60 C for 1 min, (iv) PMMoV; 48 C for 30 min for cDNA synthesis, 95 C for 15 min, 45 cycles of 58 C for 45 s, 72 C 15 s (for fluorescence acquisition), followed by melting curve analysis,(v) hPBV; 48 C for 30 min for cDNA synthesis, 95 C for 15 min, 45 cycles of 49 C for 45 s, 72 C for 15 s, 75 C for 5 s (for fluorescence acquisition), followed by melting curve analysis. The melting temperature was compared to that of the respective positive control (nuclease free water spiked with DNA standard). The expected product size was checked on a 2% agarose gel stained with ethidium bromide (0.5 mg/ml).
2.7.
q(RT-) PCR inhibition control
For all river water samples that tested negative for one of the viruses, the presence of PCR inhibitors were excluded
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Table 1 e Nucleotide sequences of primers and probes used in q(RT)PCR assay and DNA sequencing. Virus
Primer/probea
HAdV
AQ1 AQ2 AdeProbe PV-TMFOR PV-BACK PV-Probe TT1-174b TT2- 253c TTV-Probed T801 PicoB25 PicoB43c,e Ha-PMMV1 Ha-PMMV2 PM1602
HPyV
TTV
hPBV PMMoV
Sequence (50 e30 )
Amplicon (bp)
GCCACGGTGGGGTTTCTAAACTT GCCCCAGTGGTCTTACATGCACATC TGCACCAGACCCGGGCTCGGTACTCCGA TCTATTACTAAACACAGCTTGACT GGTGCCAACCTATGGAACAG TGGAAAGTCTTTAGGGTCTTCTACCTT CGGGTGCCGIAGGTGAGTTTA AGAGCCTTGCCCATRGCCC CCG WGC CCG AAT TGC CCC TTG GCTACGTCACTAACCACGTG TGG TGT GGA TGT TTC ART GYT GGT CGA ACT T GTG GCA GCA AAG GTA ATG GT ATT TGC TTC GGT AGG CCT CT TGT TTC GGA AAA GGC TCT TG
132
(Heim et al., 2003)
223
(Biel et al., 2000)
80
Modified (Diniz-Mendes et al., 2008)
200
Present study (Rosen et al., 2000)
80
Present study
References
Human adenoviruses (HAdV); human polyomaviruses (HPyV: JCPyV and BKPyV); Pepper mild mottle virus (PMMoV); human picobirnaviruses (hPBV); Torque teno virus (TTV). a All TaqMan probes were labeled with FAM (6-carboxy-fluorescein) at the 50 and with BHQ1 (Black Hole Quencher) at the 30 end. b Note that inosine (I) has base-pairing activity for C, A, G, and T. c R: G or A. d W: A or T. e Y: C or T.
by spiking the extracted nucleic acids with 102 to 103 gene copies of the respective DNA standards. Ct values of the PCR obtained from the spiked samples were then compared to those obtained from the same number of gene copies in RNase/DNase free water. Since the intra-assay variation was generally less than 1Ct, a D Ct of >1 was considered to indicate PCR inhibition. PCR inhibition was only observed for HPyV in three water samples. This inhibition was relieved by a 1:10 dilution of the samples prior to qPCR analysis.
2.8.
Integrated cell culture PCR
Integrated cell culture PCR (ICC-PCR) has been proposed as an alternative method for detection of viruses in environmental samples (Reynolds et al., 1996). A human lung adenocarcinoma cell line A549 cells were seeded at w 105 cells per well in 6-well plates. Cells were incubated for 3e5 days until they produced confluent monolayer on growth DMEM medium, supplemented with 10% (v/v) heat-inactivated fetal calf serum (FCS) and 100 units penicillin G sulphate/100 mg/ml streptomycin sulphate (Pen/Strep, Sigma). The cells were incubated for 120 min at 37 C with a 1:1 dilution of the concentrated river water samples in DMEM medium or inoculated with PBS only as a negative control. After the adsorption step, the inocula were removed, the cells were washed three times with PBS and 2.5 ml maintenance medium was added. Cells were incubated at 37 C in an atmosphere containing 5% CO2 for 3e5 day, and then cell lysates were used for viral DNA extraction. HAdV copy numbers were determined by qPCR, and the values were compared to those obtained from the inoculum. A more than 10-fold increase of HAdV DNA copy numbers was considered to indicate the presence of infectious adenovirus.
2.9. water
Stability of human viruses and PMMoV in river
Incubation experiments were executed to determine how long PMMoV, HAdV, HPyV and TTV could be detected by q(RT)PCR in river water. River water sample was spiked with PMMoV (concentrated from river water), HAdV-5 (propagated in 293Tcell and purified by ultracentrifugation), HPyV (recovered from urine sample of an infected person) and TTV (recovered from serum of an infected person). The seeded water sample was then divided into six aliquots of 1 l and stored at 4 C and 25 C. Representative samples (50 ml) were removed from each bottle on days 0, 1, 3, 6, 10, 21 and assayed for the later viruses after PEG-6000 precipitation as described above. The decrease in viral concentrations was calculated using the ratio logNt/N0 where N0 is the concentration at time 0 and Nt is the concentration at time t. Human picobirnavirus was not included in this experiment because its concentration at N0 was very low.
2.10.
Detection of PMMoV in human and animal feces
In order to determine if PMMoV is specific to human-derived fecal waste, a total of 20 human fecal samples were collected anonymously from healthy adults. Fifty three animal fecal samples were also tested for PMMoV (Table 3). These included: cows (from animal farm in Ennepetal, Germany), horses (from animal farm in Ennepetal, Germany), sheep (living along Kemnader lake, Bochum, Germany), chicken (domestic chicken from Haltern am See, Germany), and waterfowl (ducks and geese from Bochum, Germany). All fecal materials were extracted by using QIAamp viral RNA Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. QRT-PCR of PMMoV was performed as described above for water samples.
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Table 2 e A nonparametric Spearman rank order correlation coefficient (r) with a two-tailed P value for cross-correlations between viruses, bacteria and physicochemical parameters; correlations are significant at p < 0.01. Variable
HPyV
HAdV N p HPyV N p PMMoV N p hPBV N p TTV N p SOMCPH N p
0.52 111 <0.0001
PMMoV hPBV TTV SOMCPH E. coli TC 0.42 108 <0.0001 0.55 108 <0.0001
106
108
106
108
103
105
103
86
EC
C. perfringens Turbidity Conductivity
103
104 107
71
71
75
103
0.31 104 107 0.0014
71
71
75
100
101 104
68
69
86
98
99
102
67
86
100
101 104
56
57
0.43 86 0.0006 0.35 86 0.001
60
Temp
pH
103
103 0.27 103 0.0060
0.33 72 0.0053
0.40 103 <0.0001 0.29 100 0.0037
0.43 67 0.0003
71
98
0.31 98 0.0016
68
68
72
100
100
53
53
57
0.32 86 <0.002
86
100
Human adenoviruses (HAdV); human polyomaviruses (HPyV); Pepper mild mottle virus (PMMoV); human picobirnaviruses (hPBV); Torque teno virus (TTV); somatic coliphages (SOMCPH); total coliforms (TC); Escherichia coli (E. coli); Enterococci (EC); Clostridium perfringens (C. perfringens). Only the significant values are presented.
2.11.
Sequence analysis
To check the specificity of the newly designed primers, some arbitrarily chosen samples were sequenced for TTV (n ¼ 5) and PMMoV (n ¼ 8). TTV amplicons (w253 bp) were amplified by using T801 and TT2 primers, while PMMoV amplicons (w319bp) were amplified by using the newly designed primers PM1602 and Ha-PMMV2 (Table 1). Moreover, DNA sequence analysis was performed for some HAdV (n ¼ 10) isolated on A549 by using AQ1 and AQ2 primers (Table 1). All sequences were compared to the data posted in GenBank by using the BLAST algorithm. DNA sequences obtained in the present study were assigned GenBank accession numbers GU722336eGU722358.
2.12.
Statistical analysis
The concentrations of the detected viruses and bacteria were expressed as median values and ranges (ranges are only for positive samples). Data comparisons were performed by using a nonparametric Spearman rank order correlation coefficient with a two tailed P value, a p < 0.01 was considered to be significant. Furthermore, the receiver operating characteristic (ROC) curve was used to compare sensitivity (true positive rate) and specificity (false positive rate) of tested parameters to predict the contamination by HAdV or HPyV. Accuracy is measured by the area under the ROC curve (AUC); an area of one is optimal. All data analysis was done using the Statistica software version 9.
3.
Results
3.1. Detection and quantification of viruses in river samples In order to assess the utility of PMMoV, hPBV, and TTV as indicators of fecal contamination in river water, water
samples were collected from Ruhr and Rhine rivers. Viral nucleic acids were extracted from all samples and tested for PMMoV, hPBV, TTV, HAdV and HPyV by quantitative (RT) PCR. PMMoV showed the highest prevalence in river water samples with all examined water samples (n ¼ 108) being tested positive (Fig. 1), followed by HAdV (97.3%, n ¼ 111). Water samples were highly contaminated with PMMoV, with levels from 3.0 103e1.1 106 gen.equ./l (Fig. 1). The high PMMoV load in river water was extremely beneficial, it allowed to detect PMMoV by direct PEG-6000 precipitation from 50 ml river water samples (n ¼ 10) without concentration of large volumes of water, while similar attempts to detect the other viruses failed (data not shown).The concentrations of the detected HAdV ranged between 9.1 101e5.6 104 gen.equ./l, which relatively similar to that of HPyV (5.0 101e3.8 104) and hPBV (4.0 102e2.9 104). In comparison to the other detected viruses, TTV and SOMCPH showed the lowest levels with 5.6 101e1.1 103 gen.equ./l and 1.0e1.9 103 PFU/l; respectively. No clear seasonal variation was observed in the occurrence of the viruses under study, also no significant difference between the sampling sites has been found (data not shown). Spiking the extracted nucleic acids with defined amounts of viral target DNA excluded false negative results owing to carry-over of PCR inhibitors from the water samples (see material and methods for details).
3.2. Detection and quantification of viruses in wastewater samples The collected wastewater samples were tested for the same viruses described above. All influent samples were positive for HAdV, HPyV, PMMoV, and SOMCPH, while only 3/12 were positive for hPBV and 6/12 were positive for TTV (Fig. 2). PMMoV showed the highest viral concentration in the influent samples (1.9 108-9.6 108 gen.equ./l), followed by HPyV (5.7 107e5.7108), HAdV (1.0 107e1.7 108 gen.equ./l), and
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 5 8 e1 3 6 8
Table 3 e Detection rate and median concentrations (minmax) of PMMoV in human and animal fecal samples. Source of sample Human Cows Horses Sheep Ducks Geese Chicken
No. of Positive samples 20 6 6 10 6 10 15
19 1 ND ND ND 1 7
PMMoV (gen.equ./mg) Median (min-max) 1.93 105 (3.8 1029.8 106) 2.1 102 ND ND ND 9.0 101 7.3 102 (2.4 1021.3 103)
ND, not detected; the level of PMMoV was below the detection limits of the assay.
SOMCPH (4.2 105e2.5 106). The effluent samples showed high prevalence for PMMoV, HAdV and HPyV, as all effluent samples were positive and no PCR inhibitors were observed for sewage samples. There was variation in the treatment reduction as roughly estimated from the difference between viral concentrations in the influent and the effluent samples. The reduction efficiency ranged between 1.7e3 log10 for SOMCPH, 1.7e2.3 log10 for HAdV, 1.7e3.7 log10 for PMMoV, 0.7e2.4 log10 for hPBV, 2.6e3.5 log10 for TTV and 3e4.5 log10 for HPyV (Fig. 2).
3.3.
Infectivity assay of HAdV qPCR- positive samples
To explore whether the river water indeed contains infectious virions, representative river water samples containing more than 3.0 103 gen.equ./l of HAdV were incubated with A549 cells. A more than 10-fold increase in HAdV DNA copy numbers during the first 3e5 days of culture was taken as evidence for the presence of infectious virus. Although 4 out of 60 samples were cytotoxic, 19 out of the remaining 56 (33.9%) contained infectious HAdV. However, no correlation was observed between the HAdV genome copy number in the water samples and their infectivity. After 3e5 days few
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samples induced cytopathic effect (CPE), although negative HAdV-PCR results were obtained. These samples were considered to be negative for infectious HAdV, but could harbor other viruses that can replicate in A549 cells.
3.4. water
Stability of human viruses and PMMoV in river
To get an estimation of the relative stability of the different viruses analyzed, river water samples were spiked with PMMoV, TTV, HAdV and HPyV and incubated for up to 21 days. Virions were concentrated and viral nucleic acids extracted at various time points prior to quantitative PCR analyses. Since free viral nucleic acids are expected to be unstable, a decline in the concentration of viral nucleic acids during the incubation period should indicate the breaking up of the viral particles. PMMoV showed higher stability in river water than the previously proposed viral indicators (Fig. 3A and B). After 21 days of incubation, a 1.1 log10 reduction of PMMoV concentration was observed at 25 C, compared to 3.0 log10 for TTV, 3.7 log10 for HAdV and 4.2 log10 for HPyV. As expected, virions seemed to be more stable at 4 C (Fig. 3(B)).
3.5.
Sequence analyses of PCR amplicons
To confirm the specificity of the newly designed PCR assays for PMMoV and TTV, amplicons of arbitrarily selected samples positive for the two viruses were sequenced. All analyzed samples contained the expected sequences confirming the specificity of the PCR. Nucleotide sequences of PMMoV isolates showed 88e99% identity to each other and 94e100% to the reference sequences deposited in GenBank. The sequence identity obtained between TTV sequences was 77e94% to each other and 84e100% to the already deposited data in GenBank. Moreover, 10 samples that were positive for HAdV in the infectivity assay were also sequenced. The similarity among HAdV sequences was 84e100% to each other and 94e100% to HAdV sequences present in GenBank. The sequence comparisons also indicated that infectious HAdV strains from species A, C, and F were present in the aquatic environment.
3.6.
Identification of PMMoV in human and animal feces
PMMoV was abundant in human fecal samples (19/20) at levels up to 9.8 106 gen.equ./mg. Whereas, the virus was only detected in a minority of the tested animal fecal samples (Table 3) with the highest detection rate in chickens (7/15) at concentrations up to 1.3 103 gen.equ./mg.
Fig. 1 e Box-whiskers plot (min-max) median concentration of viral (gen.equ./l; PFU/l) and bacterial (CFU/l) indicators in positive river water samples. The values in parentheses represent the detection rate. Human adenoviruses (HAdV); human polyomaviruses (HPyV); Pepper mild mottle virus (PMMoV); human picobirnaviruses (hPBV); Torque teno virus (TTV); somatic coliphages (SOMCPH); total coliforms (TC); Escherichia coli (E. coli); Enterococci (EC); Clostridium perfringens (C. perfringens).
3.7. Bacteriological and physicochemical parameters of river water samples To get an overall assessment of the quality of the water samples, bacteriological and Physicochemical parameters were also characterized. Total coliform (TC) bacteria showed the highest median concentration followed by Escherichia coli (E. coli), enterococci (EC) and Clostridium perfringens (C. perfringens) as shown in Fig. 1. All samples were positive for TC bacteria. The detection rates of the other bacterial parameters
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wastewater samples (Figs. 1 and 2). Similarly, fecal bacterial indicators showed high detection rate in river water.
4.1.
Fig. 2 e Box-whiskers plot (min-max) of median concentration of viruses (gen.equ./l; PFU/l)in positive raw and treated wastewater samples. The values in parentheses represent the detection rate. Human adenoviruses (HAdV); human polyomaviruses (HPyV); Pepper mild mottle virus (PMMoV); human picobirnaviruses (hPBV); Torque teno virus (TTV); somatic coliphages (SOMCPH).
were 99% for E. coli, 97% for EC, 87.3% for C. perfringens (Fig. 1). The concentrations ranged between 1101 and 1.8 104 CFU/ 100 ml for both E. coli and TC, 1.0e7.8 103 CFU/100 ml for enterococci, and 1.0e5.8 103 CFU/100 ml for C. perfringens. Physicochemical parameters including turbidity, conductivity, temperature, and pH value were measured directly after water collection. The turbidity varied between 0.3 and 39 (Mean SD; 6.9 8.2) nephelometric turbidity units (NTU), while the conductivity range was 45e9220 mS/cm2 (Mean SD; 785.2 1122.0). Water temperature varied during the study period between 1 and 25 C , (Mean SD; 11.5 6.4) and pH values between 5.9 and 8.7 (Mean SD; 7.6 0.4) were recorded.
3.8.
Correlation among the examined parameters
A nonparametric Spearman rank order correlation coefficient (r) with a two tailed P value was calculated for cross-correlations between different bacteria indicator parameters, turbidity, conductivity, and the examined viruses (Table 2). Furthermore, ROC curve was used to assess the overall discriminatory ability of PMMoV, TTV and hPBV to predict the presence of HAdV and HPyV in river water. The results showed that only ROC curve from the levels of PMMoV could produce sufficient AUC to predict the presence of HAdV (AUC ¼ 0.7) and HPyV (AUC ¼ 0.79). A threshold value of PMMoV concentration (gen.equ./l) was also determined to provide a desired trade-off in sensitivity and specificity for best prediction of HAdV (PMMoV > 23,000; sensitivity ¼ 80%; specificity 25%) and HPyV (PMMoV > 32,000; sensitivity ¼ 83%; specificity ¼ 64%). SOMCPH had to be excluded from this test because of its low sample size.
4.
Discussion
Here the utility of PMMoV, TTV and hPBV as indicators of fecal contamination in river water was assessed. High prevalence of PMMoV, HAdV, HPyV and SOMCPH was found in river and
Pepper mild mottle virus
To our knowledge this is the first study demonstrating the abundance of PMMoV in river water and its association with the presence of human pathogenic viruses such as HAdV and HPyV. The high prevalence of PMMoV in human sewage is consistent with the data obtained previously in USA (Rosario et al., 2009). Therefore, the virus could have a worldwide prevalence in sewage water. Further support for using PMMoV as indicator of fecal contamination in river water comes from its high prevalence without detectable seasonal variation. This was expected, since PMMoV is assumed to be dietary in origin and independent of human infection. The detection of PMMoV in 100% of raw human sewage and final effluent samples is consistent with the recent findings by Rosario et al. (2009) and supports the concept of using this virus as an indicator of fecal contamination in surface water. The virus was suggested previously as an indicator of fecal pollution in coastal environments (Rosario et al., 2009). Since PMMoV particles seem to be more stable than virions of HAdV and HPyV, PMMoV might be a conservative indicator of fecal contamination of surface water, but it might be less suitable for the discrimination between recent and old fecal pollution in river water as demonstrated in Fig. 3A and B. Consequently, a high cut-off value was obtained by using ROC curve analysis to predict the presence of HAdV and HPyV. The higher stability of PMMoV compared to the human viruses can be attributed to its capsid structure (Fauquet et al., 2005).
4.1.1. Specificity of PMMoV in relation to the source of contamination To understand the specificity of the PMMoV in relation to the source of contamination, different fecal samples from human, cows, horses, sheep, chickens, ducks, and geese were examined for the presence of PMMoV RNA. Given the high detection rate (95%, n ¼ 20) and concentrations (up to 9.8 106 gen.equ./ mg) of PMMoV in human fecal samples, it is clear that the source of PMMoV in surface water is likely due to human origin. This finding is consistent with the data obtained by Zhang and co-workers, who observed high prevalence of PMMoV (66.7%, n ¼ 18) in human fecal material from San Diego in North America and Singapore in Southeast Asia, suggesting that this plant virus is prevalent in the human population (Zhang et al., 2006). Whereas, lower detection rate (7.2%, n ¼ 304) of PMMoV was found in stool samples from adult patients who received care in public hospitals, in France (Colson et al., 2010). One potential explanation for the difference in PMMoV prevalence in the study conducted by Zhang et al. (2006) and Colson et al. (2010) is that Colson and his group studied samples from hospital patients, who might have different diets. In contrast, lower concentrations (2.4 102e1.3 103 gen.equ./mg) of PMMoV were detected in some fecal samples (7/15) from chickens. It should be noted that chickens under study were domestic chickens that was kept as pets and sometimes eat vegetables including pepper. One limitation of this experiment is the small sample size. However, results are
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 5 8 e1 3 6 8
1365
river water compared to the other tested viruses. In particular, TTV showed 2.6e3.5 log10 removal efficiency by wastewater treatment (Fig. 2). Since only 12 wastewater samples have been tested, these values may not be representative of actual removal efficiency at the wastewater treatment plant. It has been reported before that about 1.58 e 2 log10 reduction of TTV could be achieved by wastewater treatment plant based on a conventional activated sludge (Haramoto et al., 2008). The low detection rate obtained for TTV in river water differs from the study conducted by Diniz eMendes and his group, they reported a high detection rate (48/52) of TTV in Brazil (DinizMendes et al., 2008). This remarkable difference in the detection rates might be due to the fact that the incidence of TTV in human population varies substantially in different areas (Bendinelli et al., 2001). However, our data agree with those of Verani et al. (2006), who detected TTV in only 25% (3/12) of examined river water samples in Italy. The data obtained in our study advise against using TTV as a sole indicator of fecal contamination in surface water.
4.3.
Fig. 3 e Stability of virions in river water during incubation at 4 C (A), and 25 C (B). Values were calculated as mean ± SD from three measurements including human adenovirus (HAdV), human polyomavirus(HPyV), Pepper mild mottle virus (PMMoV), and Torque teno virus (TTV). Human picobirnavirus was not included in this experiment because its concentration at N0 was very low.
in agreement with those of Rosario et al. (2009), who found that the majority of animals (e.g. pigs, horses, dogs, and cows) examined for PMMoV were negative and some samples only from chickens and seagulls were positive at concentrations between 5.2 102 to 2.2 104 and 5.84 102 to 9.55 102 gen.equ./mg, respectively.
4.2.
Torque teno virus
In this study, different strains of TTV were detected in 50% of tested river water samples at concentrations that ranged between 5.6 101e1.1 103 gen.equ./l. TTV was also detected in 50% of influent and effluent samples at lower concentrations, which could be the reason for the low detection rate in
Human picobirnaviruses
Human PBV showed low abundance in wastewater (25%) and river water samples (21.7%). Using the same qPCR primers the virus showed high abundance in all raw sewage (12 samples) collected from USA (Symonds et al., 2009). Many factors can lead to discordance between our findings and the data obtained by Symonds et al. (2009), such as the difference in the geographical area, concentration method, and processed water volume. In addition, the samples analyzed by Symonds et al. (2009) represent only a single time point in the USA so the detection rate and concentration of hPBV may vary at different times of the year. Whether the presence of hPBV in aquatic environment is specific to human fecal contamination or whether animals may act as a source for contamination is unclear, particularly porcine and human hPBV share crossing points in their evolution (Banyai et al., 2008; Symonds et al., 2009).
4.4.
Human adenoviruses and human polyomaviruses
HAdV and HPyV have been previously proposed as indicators of fecal contamination with human sewage and the data obtained in the present study support the same suggestion. Detection of HAdV and HPyV in aquatic environment is easy because DNA quantification is easier than RNA quantification. The high detection rates observed for HAdV could be attributed to establishment of latent infections with viral shedding for weeks. Similarly, HPyV are characterized by viral persistence in the kidney as judged by viral excretion in the urine that can show remarkably high levels of virus release (Kristina, 2002), causing the frequent detection in sewage and contaminated river water (Figs. 1 and 2). HAdV and HPyV are frequently detected in aquatic environment (AlbinanaGimenez et al., 2006; Bofill-Mas et al., 2006, 2001; Chapron et al., 2000; Girones et al., 1995; Hamza et al., 2009; McQuaig et al., 2009; Pina et al., 1998). In contrast to the high detection rate of HAdV by qPCR, only 19 out of 56 river water samples tested positive by ICC/qPCR. This difference might be due to the fact that PCR is able to
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detect infectious and non infectious form of pathogens. In addition, the infectivity assay might underestimate the number of infectious adenoviruses, since not all of them replicate in A549 cells.
applicability of this virus as indicator of viral contamination in surface water in Germany and other geographical areas and the correlation to human health risks.
4.5.
Author contributions
Somatic coliphages
Our results together with previous data demonstrated that SOMCPH could be used as indicator for the efficiency of water or wastewater treatment as they have been detected in all raw and treated wastewater (Figs. 1 and 2). Actually, the data concerning the use of somatic coliphages as indicator showed less concordance as has been observed elsewhere (Gantzer et al., 1998; Hot et al., 2003). In addition, detection of somatic coliphages in aquatic environment does not allow to discriminate between a contamination from a human and an animal fecal source (Havelaar et al., 1986).
IAH conceived, designed and conducted the experiments, analyzed the data and wrote the manuscript. LJ organized the ¨ and MW sampling and did the bacteriological analysis. KU provided tools, advice and consultation and participated significantly in editing the manuscript. All authors read and approved the final manuscript.
4.6.
All authors have no conflict of interests in relation to this work.
Correlations among the microbial parameters
PMMoV were significantly correlated with HPyV (R ¼ 0.55, p < 0001), and HAdV (R ¼ 0.42, p < 0.0001), supporting that the source of PMMoV contamination is mainly human origin, and reflecting the predictive value by using one of them as indicator for the presence of the others. Additionally, the lack of correlation between the concentration of HAdV and that of bacterial indicators suggests that these bacterial indicators are not suitable indicators of viral contamination in river water (Table 2). No direct correlation was observed between TTV or hPBV load and level of water pollution, as measured by bacteriological parameters and abundance of PMMoV, HAdV, HPyV and SOMCPH. This finding was in agreement with the study conducted by Diniz-Mendes et al. (2008), they could not find a correlation between the TTV load and level of water pollution. A limited association was also observed before between TTV and other human viruses (Verani et al., 2006). One the other hand, one explanation for the significant association between turbidity and the hPBV (R ¼ 0.43; p ¼ 0.0002), could be the re-dissemination of the virus from the sediments to river water, which could be indicative for remote or old contamination. Fernandez and his group (Fernandez-Molina et al., 2004) have documented the existence of association between conductivity factor and most of bacterial indicators in river water, whereas we found no correlation between the conductivity factor and the tested parameters except for PMMoV.
5.
Conclusion
Taken together, this research has shown that PMMoV could be a promising indicator of fecal contamination in river water, while TTV and hPBV are not appropriate for this purpose. Although PMMoV is consistently found at high concentrations in human feces and sewage, the virus showed less frequent detection in examined animal feces. Therefore, humans are most likely to be the source of PMMoV in surface water. Further research is required both to demonstrate the effective
Conflict of interests
Acknowledgments Ibrahim A Hamza received a PhD fellowship from the Ministry of Higher Education, Egypt. Authors would like to thank Dr. Refaat Sabry for his valuable comments on data analysis. Special thanks to Ulrike Bandow for her technical assistance in bacteriological analysis. We are thankful to Elke Uhlenbrock, Sabine Wagener, Gregor Perna and Rosi Bohr for collecting the samples.
references
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Exploring 17a-ethinylestradiol removal, mineralization, and bioincorporation in engineered bioreactors Taewoo Yi a, Susan Mackintosh b, Diana S. Aga b, Willie F. Harper, Jr.c,* a
Department of Environmental Science and Engineering, Ewha Womans University, Seoul 120-750, South Korea Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA c Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA b
article info
abstract
Article history:
This research investigated removal, mineralization, and bioincorporation of 17a-ethiny-
Received 3 June 2010
lestradiol (EE2) in membrane bioreactors and conventional bioreactors. When the influent
Received in revised form
EE2 concentration was >50 mg/L, the membrane bioreactor (MBR) biomass removed more
12 October 2010
EE2 than conventional bioreactor (CBR) biomass in continuous tests, likely because the
Accepted 17 October 2010
sorption partitioning coefficients are higher for MBR biomass. Microautoradiography was
Available online 23 October 2010
carried out to investigate the distribution of EE2 within the aggregates retrieved from the bioreactors, and the results revealed concentration gradients present within the floc.
Keywords:
Experiments using radiolabeled 14C-EE2 experiments (done with 24.5 mg/L EE2) showed that
Pharmaceuticals
EE2 removal rates and the amount of EE2 mineralized were similar in MBRs and CBRs. Direct
Sorption
measurements and bioenergetic estimates suggest that EE2-related carbon is probably
Wastewater
incorporated into active biomass, despite the fact that EE2 was added at a concentration
Bioreactors
that was much lower than that of the primary growth substrates.
microautoradioaugraphy
1.
Introduction
There is continuing interest in removal of 17a-ethinylestradiol (EE2), a key emerging contaminant that has been detected in surface waters and groundwater (Kolpin et al., 2002). EE2 has been linked to developmental anomalies in wildlife (such as feminized male fish) (Parkkonen et al., 2000). These negative effects appear to occur at very low concentrations (i.e. <10 ng/L) (e.g. Rodgers-Gray et al., 2000), making it important to avoid discharging EE2 into water bodies. Because EE2 is primarily introduced into domestic wastewater via urine and medical waste, wastewater treatment processes are important barriers for preventing the proliferation of EE2 into the aquatic environment. In order to improve removal of EE2 in wastewater treatment plants, we must improve our understanding of how EE2 is removed and mineralized in wastewater treatment systems. * Corresponding author. Tel.: þ1 412 624 9548; fax: þ1 412 624 0135. E-mail address: [email protected] (W.F. Harper Jr.). 0043-1354/$ e see front matter Published by Elsevier Ltd. doi:10.1016/j.watres.2010.10.022
Published by Elsevier Ltd.
The activated sludge process is the most commonly used method for treatment of domestic wastewater, hence removal of EE2 by activated sludge biomass has received considerable attention. Sorption of EE2 to activated sludge biomass is thermodynamically favorable (Xu et al., 2008), the equilibrium partitioning coefficients (expressed as log(Kd)) are generally between 2.3 and 2.7 (Clara et al., 2004; Ternes et al., 2004; Yi et al., 2006). The available data on Kd allow practitioners to model sorption in activated sludge processes. Biodegradation, on the other hand, is an area where consensus is still developing. A significant number of reports now suggest that nitrifying sludges (i.e. those that include autotrophic nitrifiers and heterotrophs) can co-metabolically remove EE2 (Yi and Harper, 2007; Shi et al., 2004). However, the involvement of nitrifiers has been directly contested by Gaulke et al. (2008), who found that the slow-growing autotrophs were not
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involved in EE2 degradation and that EE2 removal was being carried out by heterotrophs only. There are other areas that need to be clarified. First, there remains uncertainty regarding the suitability of membrane bioreactors (MBRs) for EE2 degradation. Full scale MBR systems discharge lower levels of EE2 than conventional bioreactors (CBRs) (e.g. Joss et al., 2004, Clara et al., 2005), but this is likely because MBR partitioning coefficients and mixed liquor suspended solids MLSS concentrations are higher (Xu et al., 2008, Yi et al., 2006, Cirja et al., 2007). MBRs are also typically operated at longer solids retention times SRTs than CBRs. It is not clear that MBRs biodegrade and/or mineralize more EE2 than CBRs, and it is not clear that MBR and CBR biomass, operated under similar growth conditions, mineralize EE2 at different rates. For example, Joss et al., 2004 conducted batch experiments with sludge retrieved from a MBR (operated at SRT ¼ 30 days) and CBR (operated at SRT 11 days), and they found that EE2 removal was faster in the MBR test than in CBR test; these results are in conflict with those of Gaulke et al. (2009), who performed a similar experiment and found virtually identical removal rates for MBR and CBR biomass. Neither of these reports discussed EE2 transformation to CO2. Overall, it is possible that EE2 is biodegraded at similar rates when MBR and CBR biomass are maintained under similar growth conditions, but more evidence is needed to address this issue. Second, EE2 is commonly believed to be co-metabolically degraded, but there has been no previous effort to quantitatively address the use of EE2 (and it’s metabolites) as growth substrates. There is a need to collect more information about the fraction of EE2 that is ultimately incorporated into active biomass, which can (in principle) be connected to the mineralized EE2 fraction using classic bioenergetics (McCarty, 1969; Metcalf and Eddy, 2003). This information can be used to better determine how EE2 is biochemically processed. The objectives of this work are #1) to investigate the removal of EE2 from an MBR and CBR over a range of EE2 concentrations, and #2) to assess EE2 mineralization and biomass incorporation for MBR and CBR biomass maintained under identical conditions.
2.
Methodology and materials
2.1.
Experimental overview
We operated two 4 L continuous flow bioreactors, a membrane bioreactor (MBR) and a conventional bioreactor (CBR). These bioreactors were originally seeded with mixed liquor from the McKeesport Water Treatment Facility in McKeesport, PA. These two reactors were both aerobic and were operated continuously for 240 days. EE2 was dissolved in water and feed into the bioreactors at various concentrations (i.e. 50 mg/ Le500 mg/L). A lower concentration (24.5 mg/L) was separately fed as 14C-EE2 into smaller (500 mL) MBR/CBR bioreactors seeded with sludge from the larger (4 L) units. We used these smaller bioreactors to minimize the generation of hazardous waste and to remove the possible effects caused by biofilm present on the submerged membrane module. The amount of 14 C-EE2 mineralized was determined using CO2 traps and the biomass-associated 14C levels were both measured and
predicted (as described below). Water and biomass samples were retrieved and analyzed for EE2 and EE2-related metabolites. We determined partitioning coefficients for MBR and CBR biomass to confirm previous findings. We studied the biomass properties, including the sludge hydrophobicity and aggregate size distribution, and we retrieved crude enzyme extracts to investigate enzymatic EE2 removal.
2.2.
Bioreactors
The MBR and CBR had a hydraulic retention time (HRT) of 12 h and SRT of 20 days. These bioreactors were originally seeded with mixed liquor from the McKeesport Water Treatment Facility in McKeesport, PA. The flow of influent and effluent was controlled by peristaltic pumps, and the pH was controlled between 7.0 and 8.0. The SRT was controlled by manually wasting of mixed liquor directly from the bioreactor. The pH was maintained with an automated controller (pH/ ORP Controller, EUTECH Instruments Pte Ltd, Singapore) and pH electrode (Thermo Orion Glass pH electrode, Orion Research, INC. Beverly, MA). The composition of the influent feed (as mg/L total influent concentration) consisted of acetate (440 mg/L as COD), NH3-N/L (100), CaCl2.2H2O (5.3), FeCl3.6H2O (3), CoCl2.6H2O (0.3), ZnCl2 (0.31), CuCl2.2H2O (0.09), H3BO3 (0.03), MgSO4.7H2O (30), MnSO4.7H2O (0.85), Na2MoO4.2H2O (0.12), KH2PO4 (54), mg/L K2HPO4 (136). The influent concentration of EE2 ranged from 50 mg/L to 500 mg/L. The MBR had an ultrafiltration membrane (ZeeWeedÒ-1 (ZW-1) Zenon Environmental) with a 0.047 m2 surface area, 0.04 mm pore size, and an operating flux of approximately 7 L/m2/h. The pressure drop across the membrane was monitored daily, and was typically 1.2 psi.
2.3.
Batch sorption experiments
Sorption experiments were conducted using 200 mL biomass samples taken from the CBR and MBR. Sorption of non-radiolabeled EE2 onto the biomass was determined by adding EE2 into glass bottles and mixing with sludge. To prevent biodegradation, sodium azide was added at 0.2% w/w (200 mg/ L); this concentration inhibited biodegradation but did not cause cell lysis as confirmed previously (Xu et al., 2008). Samples were mixed on an orbital shaker at 200 rpm, and additional control bottles were included without biomass to make sure that EE2 was not being lost to the glassware. Samples were taken after 4 h; preliminary kinetic tests indicated that equilibrium was reached in this time. The samples were centrifuged at 1500g for 5 min, filtered (0.2-mm Teflon filter), and then analyzed for EE2 using HPLC. Biomass-bound EE2 was determined by mass balance. Additional batch experiments were conducted to allow for biodegradation; in these experiments, sodium azide was not added.
2.4.
Microautoradiography
Before we started adding EE2 into the MBR and CBR (parent bioreactors), we conducted microautoradiography to observe the intrafloc distribution of 14C-EE2. Biomass was exposed to 500 mg/L of 14C-EE2, and samples were taken after 4 h and immediately transferred to a centrifuge tube. We carried out
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 1 3 6 9 e1 3 7 7
a series of washing steps to remove excess soluble radioactive substrate, we transferred one drop of sample to each of several standard microscope slides, and these slides were dried at ambient temperature. For microautoradiography, we used LM1 emulsion, Kodak D19 developer (40 g/L of deionized water), a stop solution of 0.5% (vol/vol) acetic acid, and a fixation solution of 30% (wt/vol) sodium thiosulfate. The exposure time was one day. The full details of the microautoradiographic procedure are included in the supplemental information.
2.5.
14
C-EE2 experiments
These studies were conducted with 14C-labeled ethinylestradiol (14C-EE2) (99% pure; American Radiolabeled Chemicals, St. Louis, MO) at room temperature. The 14C-EE2 experiments were performed to determine the fate of 14C-EE2 and related compounds in MBR and CBR biomass. Biomass (400 mL) was retrieved from the parent bioreactors and seeded into 500 mL beakers, configured as semi-continuous bioreactors (i.e. the bioreactors were fed continuously, but reactor volume was discharged only during sampling periods). We used this configuration in order to maintain active growth. A CO2 trap containing 500 mL 1 N KOH was setup and one additional back-up trap in the same size was installed after the 14CO2 trap to ensure good trapping efficiency and high recovery of applied radioactivity. At the beginning of the experiment, each bioreactor was spiked with 14C-EE2 so that the initial concentration was 24.5 mg/L. The influent feed was the same as those used for the parent reactors. The bioreactors were aerated continuously, and the pH was controlled between 7.0 and 8.0. We retrieved water and biomass samples after 1, 24, and 48 h of operation, at which point the volume of sample was exhausted and the test stopped. This test was repeated three times.
2.6.
Bioenergetic predictions
To compliment the 14C experiments described above, we used 14 C-EE2 mineralization data to predict the amount of EE2 that should be incorporated into active biomass. The theoretical underpinning for this lays in classical thermodynamic concepts (McCarty, 1969; Metcalf and Eddy, 2003). There are two assumptions that are invoked here. First, we assume that biodegraded 14C-EE2 is divided into two fractions; a fraction that is mineralized and another that is used for construction of active biomass. We further assume that the mineralization is connected to energy yielding heterotrophic metabolism. Using these assumptions, we calculated the fraction of EE2 used for energy (fe) and the fraction used for synthesis (fs) of biologically formed volatile suspended solids (VSS) (fe ¼ 0.33 and fs ¼ 0.67, see Note 2 in supplemental information for details). We then determined the amount of 14C-EE2 incorporated into active biomass as follows: qb ¼ qm/fe Ytrue, where qb is the mass of EE2 incorporated into biomass (ng EE2/mg VSS), qm is the mass of EE2 mineralized (ng EE2/mg VSS), and Ytrue (mg VSS/mg EE2 removed) is the true microbial yield (0.47 mg VSS-C derived from EE2-C/mg EE2 COD biodegraded, see Notes 2 and 3 in supplemental information for additional details and a calculation example).
2.7.
1371
Radioactivity detection
Radioactivity determination was performed for biomass, aqueous, and 14CO2 trap samples. Filter papers containing biomass were transferred to test tubes, extracted with 15 mL of HPLC-grade methanol (Honeywell B & J, Muskegon, MI), and sonicated for 30 min. After sonication a 200-uL aliquot from each test tube was supplemented with 5 mL of liquid scintillant (Ecoscint, National Diagnostics, Atlanta, GA) and radioactivity was counted. For each aqueous sample radioactivity was obtained by adding a 200-uL aliquot to 5 mL of liquid scintillant, and for 14CO2 trap samples a 200-uL aliquot was combined with 1 mL of water and 10 mL of scintillant. Radioactivity was counted using a Tricarb 1600TR liquid scintillation counter (PerkinElmer Waltham, MA) in duplicate for all experiments.
2.8. Crude enzyme extraction, assays and protein content Crude enzyme extraction was performed using biomass from MBR and CBR. The biomass was harvested by centrifugation at 5000g for 10 min and resuspended in 10 mM sodium phosphate buffer (pH 7.4). This procedure was repeated three times to ensure complete removal of electron donor. The resuspended pellet was sonicated for 10 min at 40% amplitude in an ice bath using Fisher Scientific Sonic Dismembrator (model 550, maximum power of 500 W at a frequency of 20 kHz). The particulate fraction of the sonicated product was separated by centrifugation at 10000g for 30 min at 4 C. The pellet was reconstituted in 10 mM sodium phosphate buffer (pH 7.4) supplemented with 1% dodecyl-b-Dmaltoside and mixed in orbital shaker for 1 h at 4 C. Cell free extracts were obtained by centrifuging the lysed cell mixture at 10,000g for 30 min at 40 C. These cell free extracts were used for two assays. The removal of 14C-EE2 was investigated by adding the cell free extracts to 10 mM sodium phosphate buffer and 1 mM 14C-EE2. AMO activity was assessed by adding the cell free extracts to 10 mM Tris-HCl, electron donors (0.5 mM NADH, 0.6 units diaphorase and 0.5 mM duroquinone), and 15 mg/L of ammonia-N. Control tests were always performed in the same way without the enzyme extract; these controls confirmed the absence of abiotic transformation. The protein content of crude extracts was determined by the method of Bradford (1976) using bovine serum albumin as a standard.
2.9.
Oxygenase activity
Oxygenase activity was measured by monitoring the conversion of indole to indigo by whole cell suspensions as described previously (Woo et al., 2000). Briefly, cells were centrifuged at 12,000g for 10 min at room temperature and resuspended in 1 phosphate buffered saline solution (PBS; NaCl 7.6 g/L, NaH2PO4 0.38 g/L; Na2HPO4 0.97 g/L, pH 7.5). The assay was performed at 37 C for 60 min and was initiated by the addition of indole (100 mM). Cell associated indigo was extracted by resuspending cells in dimethyl-formamide (DMF) and centrifuging at 12,000g for 5 min. The rate of indigo production was determined by measuring OD610 nm over time and converting
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optical density values using the extinction coefficient for indigo (3 ¼ 15900 L/mol-cm) (O’Connor and Hartman, 1998). Specific rates of indigo production were obtained by normalizing rates using the protein concentration of the cell mixture.
2.10.
Analytical methods
Suspended solids, soluble chemical oxygen demand, and soluble ammonia-N were determined on aqueous samples according to Standard Methods (APHA, 1992). Samples were filtered using 0.45 mm glass microfiber filters (934-AH Whatman). Particle size distribution and total specific surface area were determined on 15 mL samples from the MBR and CBR sludges utilizing a Horiba LA-920 laser scattering particle size distribution analyzer (Delta Analytical Instruments, North Huntington, PA). The measurement range for this instrument is 0.02e2000 microns. The concentrations of EE2 were determined using HPLC (HewlettePackard, HP 1100). The system consisted of a degasser (G1322A), a Quaternary pump (G1311A), an ALS auto-sampler (G1313A), a Colcomp column oven (G1316A) and Variable Wavelength UVeVIS Detector (G1314A). A Hypersil ODS C18, (125 46 mm, 5 mm) (Thermo Fischer Scientific, Waltham, MA) column was used. The HPLC operating conditions were as follows; UV detector wavelength set at 197 nm, isocratic mobile phase containing acetonitrile:water (40:60, v/v) at a constant flow rate of 1 mL/min. The total runtime of the HPLC analysis was 10 min. The limit of detection was 20 mg/L EE2. Analysis of water samples by liquid chromatography/mass spectrometry (LC/MS) and LC/radiochromatographic detection was carried out as follows. Lyophilized samples were reconstituted in 45 mL of HPLC-grade methanol (Honeywell B & J, Muskegon, MI) and extracted for analysis by liquid chromatography/radiochromatographic detection and liquid chromatography/mass spectrometry (LC/MS). Since all samples contained 14C-labeled EE2 analysis was performed using an Agilent 1100 HPLC equipped with an on-line radiochromatographic detector (IN/US Systems, Inc., Tampa, FL) as described previously (Skotnicka-Pitak et al., 2009). This radiochromatographic detector uses a flow through cell with a volume of 0.5 mL and a 3:1 scintillation fluid: eluent ratio (Ecoscint, National Diagnostics, Atlanta, GA). Separation was accomplished using a C8 column (Agilent ZORBAX C8 5 um, 4.6 250 mm) with a gradient elution starting at a solvent composition of 30/70 (v:v) ACN: H2O with 50 mM ammonium acetate and changing to 100% ACN over a 30-min run with a constant flow rate of 0.4 mL/min. Once the retention times of the radioactive peaks were identified an aliquot of the sample was re-injected into the LC column with the eluate being split between the radioactive detector and a triple quadrupole mass spectrometer (Agilent 6410 MSD). The splitter was put in place to ensure that the LC/MS data corresponded with the radioactive peaks. All LC conditions were the same although H2O was used where H2O with 50 mM ammonium acetate was used beforehand. LC/MS analysis was carried out under full scan monitoring of ions from m/z 100 to 600 in electrospray ionization (ESI) negative mode. The triple quadrupole mass spectrometer was equipped with nitrogen as a drying gas at a temperature of 350 C and flow rate of 12 L/min. The capillary spray voltage used was 4000 V and the nebulizer pressure was set at 35 Psi. Use of LC/MS in conjunction with a radioactive
detector allows for identification of the m/z ratios for specific EE2-related metabolites.
3.
Results and discussion
3.1.
Removal of aqueous phase EE2
Fig. 1 shows the soluble effluent concentration of EE2 from the MBR and CBR over a wide range of influent EE2 levels. The MBR effluent EE2 was lower than that of the CBR under most operating conditions. For the first 40 operating days the influent EE2 concentration was 500 mg/L, and the average effluent EE2 levels for the MBR and CBR were 122 mg/L and 350 mg/L respectively (the corresponding removal rates were 2.1 and 0.83 mg soluble EE2/min respectively). From day 41 to day 60, the influent EE2 concentration was 300 mg/L, and during this period the average effluent EE2 levels for the MBR and CBR were 100 and 192 mg/L respectively. The influent EE2 concentration was 500 mg/L from day 60e95, and average effluent EE2 levels for the MBR and CBR were 196 and 332 mg/L respectively. For five days 50 mg/L was added to both bioreactors, and the effluent EE2 levels were similar (24 and 16 mg/L for the MBR and CBR respectively). No EE2 was introduced into the bioreactors between days 101 and 119, but when EE2 feeding resumed, the MBR continued to produce lower effluent EE2 levels than the CBR between days 121e139 and days 151e180. When 50 mg/L was added into the bioreactor, the effluent EE2 levels were similar between MBR and CBR. Overall, these data support the notion that the MBR removes EE2 with higher efficiency, although the differences in removal efficiency appear negligible when 50 mg/L of EE2 is introduced. The results also show that the CBR effluent TSS levels where dramatically higher than that of the MBR (59 vs. 6.2 mg/L TSS). Because of the difference in the effluent TSS levels, the differences in total (particulate and soluble) EE2 removal are even greater than shown by Fig. 1 because EE2 partitions readily to suspended solids. We used the partition coefficients to estimate that the effluent TSS of the MBR and CBR contained (on average) 0.5 and 11 mg/L of EE2 respectively.
3.2.
Biomass properties
EE2 partitioning coefficients were determined for both the MBR and CBR biomass using sorption experiments carried out on inactivated biomass. The MBR Kd of 0.64 L/g was higher than the 0.52 L/g for CBR, confirming that the MBR biomass is a more favorable sorbent than CBR biomass (Yi et al., 2006, Xu et al., 2008) (Fig. 2). The MBR biomass had a mean particle size of 53 mm while the CBR particles had a mean particle size of 152 mm. In additions, MBR biomass was more hydrophobic (approx. 59%) than the CBR biomass (approx. 28%) (Table 1). These results are consistent with what has been reported previously, and explain the differences in the partitioning coefficients as well as the overall performance that are observed in Fig. 1. Interestingly, the crude enzyme extractions, oxygenase, and AMO assays suggested that enzymatic activity for the two systems was similar (Table 1). Thus, the differences in the performance observed in Fig. 1 can be explained
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Influent conc. of EE2
CBR
MBR
700 Mixedliquor suspended solids MBR aver = 1602 mg/L CBR aver = 1243 mg/L
600
Effluent TSS MBR aver = 6.2 mg/L CBR aver = 59 mg/L
Ammonia-N and COD removal wasapproximately 90% for the CBR and MBR
EE2 concentration (ug/L)
500
400
300
200
100
0 0
20
40
60
80
100
120
140
160
180
Day of operation (days)
Fig. 1 e Soluble effluent ethinylestradiol concentrations.
by the physical aggregate properties, not by differences in measured enzymatic activity.
3.3.
Microautoradiography
Fig. 3 shows microautoradiography images for MBR and CBR floc incubated in the presence of 500 mg/L of 14C-EE2. The CBR image shows that 14C-EE2 (shown as black granules) is mostly associated with the outer edge of the floc, with some black
granules shown within the floc interior. The CBR image shows large portions of floc that have a relatively low concentration of EE2. The MBR image shows the presence of 14C-EE2 throughout the floc structure, and the difference between the high intensity areas (i.e. those areas with lots of black dots) and the low intensity areas were not as discernable. It appears that 14C-EE2 partitions to MBR biomass more uniformly; in principle, this may help explain differences in the measured partitioning coefficients observed in Fig. 2. The floc distribution may also
Fig. 2 e Sorption isotherms.
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Table 1 e Biomass characteristics.
Unit MBR CBR
Hydrophobicity
Mean particle size
Enzymatic EE2 removala
Oxygenase activity
AMO activitya
% 59 29 (n ¼ 3) 28 18 (n ¼ 3)
mm 53 4 (n ¼ 2) 152 11 (n ¼ 2)
mg EE2/g protein-hr 60 25 (n ¼ 3) 59 16 (n ¼ 3)
mg indigo/g protein-hr 11.7 1.2 (n ¼ 3) 9 3 (n ¼ 3)
mg N/g MLSS-hr 10.3 0.8 (n ¼ 3) 9.1 0.3 (n ¼ 3)
a Obtained with crude extracts.
impact the bioavailability of 14C-EE2 by making the parent compound available to bacterial cells located throughout the aggregate. The differences observed in Fig. 3 may be more relevant for bioreactors receiving very high EE2 concentrations (such as those used in research or in the treatment of pharmaceutical wastewater). Microautoradioaugraphy was also conducted at 50 mg/L of 14C-EE2; black granules were visible near the surface of both MBR and CBR biomass, but no differences in the floc distribution were observed (see Fig. 1A, supplemental information).
3.4.
14
C-EE2 experiments
Fig. 4 shows the aqueous and biomass phase radioactivity associated with the first of three 48-h experiments. The figure also showed the predicted biomass-associated radioactivity calculated from the amounts of 14C-EE2 that were mineralized. The rates of aqueous phase removal, biomass-association, and mineralization were similar in both bioreactors within the first hour, but the performance of the bioreactors differed during the remainder of the experiment. The final aqueous phase level in the MBR experiment was 1.2 nCi/mL, while the CBR it was 5 nCi/mL. MBR mineralization reached 50% (i.e. 50% of the influent 14C-EE2 was mineralized), while that of the CBR reached 44%. The biomass-associated 14C levels increased more quickly in the MBR, rising to 271 ng/mg after 24 h and leveling off at 235 ng/mg after 48 h. The CBR biomass-associated 14C level rose more slowly and was 128 ng/mg after 48 h. The bioenergetic model predicted the biomass-associated levels reasonably well for both bioreactors, which suggests that EE2-related carbon was incorporated into active biomass. The
Fig. 3 e Microautoradiography images of
14
second 14C-EE2 experiment showed that removal from the aqueous phase was identical for both bioreactors (see Fig. 2A, supplemental information). The initial concentration decreased from 8.3 to 5.2 ng/mL within 1 h, and the final (48 h) concentration was 4.1 ng/mL. The extent of 14C-EE2 mineralization in both bioreactors was similar, resulting in 39% and 43% for the CBR and MBR, respectively, after 48 h. The bioenergetic model estimated that approximately 90 (MBR) and 80 (CBR) nCi/ mg are incorporated into the biomass. The MBR prediction is approximately 85% of the measured value (106 nCi/mg), but the predicted CBR biomass 14C content is only 53% of the measured value. This suggests that some of the 14C may have been adsorbed to the biomass of both bioreactors. The third experiment also revealed similar levels and rates of 14C removal from the aqueous phase, producing a final 14C level of 3.5 nCi/mL for both the MBR and CBR biomass (see Fig. 3A, supplemental information). The measured levels of mineralization were 53% and 56% for the MBR and CBR respectively. The bioenergetic model predicted approximately 130 and 139 nCi/mg of biomass-associated 14C for the MBR and CBR respectively; this agreed well with the measured value for the MBR but it underestimated the measured value for the CBR.
3.5.
Metabolites
During the experiments with 14C-EE2, a variety of EE2-related metabolites were produced as observed in the radiochromatograms. Lyophilized samples from the 14C CBR and MBR experiments were reconstituted with the LC mobile phase for analysis by LC/radiochromatography and LC/MS. Fig. 5 shows the radiochromatograms of these concentrated samples with peaks
C-EE2 sorption to biomass. (a) CBR particle; (b) MBR particle.
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Fig. 4 e Aqueous and biomass-associated radioactivity.
corresponding to the radiolabeled parent EE2 and metabolites that were formed during the 14C biodegradation experiment. Once the retention times of the radioactive peaks were identified an aliquot of the sample was re-injected into the LC column with the eluate being split between the radioactive detector and the LC/MS. The splitter was put in place to ensure that the LC/MS data corresponded with the radioactive peaks. LC/MS analysis was carried out under full scan monitoring of ions from m/z 100 to 600 in electrospray ionization (ESI) negative mode. Analysis of the same extracts by e ESI LC/MS revealed that within 1 h, two different metabolites with m/z ratios of 311 and 375 (corresponding to [M-H]- ) respectively were produced in the systems. The intensities of these peaks were significantly higher than the background peaks, and these peaks were not present in the blank sample. After 24 h, additional metabolites were present in the MBR (m/z of 385, 313, 309, 340) but only one additional compound (m/z of 309) was detected in the CBR. After 48 h, the chromatographic profiles of the metabolites produced the CBR were the same as the profile observed in the MBR, and both reactors contained metabolites with the same m/z ratios (i.e. m/ z of 385,313,309,340). Again, all these m/z peaks were unique to the EE2-spiked samples and were not present in the control samples. These results indicate that both systems produced metabolites quickly (i.e. within 1 h), and that similar metabolites were produced in both systems after 48 h. The identification of these metabolites is beyond the scope of this paper.
3.6.
Significance
This study informs the question of whether MBRs are better than CBRs for removing EE2. MBRs remove aqueous phase EE2 more efficiently at higher concentrations (i.e. >50 mg/L), where the differences in the aggregate properties impact performance. The MBR captures more EE2 onto the biomass
solids, where the distribution of EE2 within the floc is more uniform than it is within CBR floc particles. At lower concentrations (i.e. <50 mg/L), removal from the aqueous phase is similar for both systems, and no differences in intrafloc distribution are observed. This work strongly suggests that the membrane bioreactor configuration does not offer improved EE2 removal for treatment of municipal wastewaters, which receive very low concentrations of EE2 (e.g. ng/L). Further, the fate of low-level EE2 is similar for MBR and CBR biomass; the amount of EE2 mineralized by each system was similar in two or three 14C-EE2 experiments, and the metabolites detected from the MBR and CBR biomass were the same. It appears that MBRs and CBRs can be expected to remove low mg/L or ng/L levels EE2 with similar effectiveness under comparable operating conditions. Much of the current thinking related to EE2 removal assumes that EE2 is being cometabolized, because EE2 is present in concentrations that are much lower than the primary carbonaceous substrates. In the 14C-EE2 experiments discussed here, EE2 was mineralized, even though it was introduced at 24.5 mg/L, much lower than the concentrations of ammonia and acetate introduced into the bioreactors. This indicates that EE2-related metabolites are used as growth substrates even when EE2 is present at concentrations that are much lower than that of the primary growth substrates. The EE2-related metabolites detected in this study may be co-metabolically produced, but the resulting byproducts can be used for the production of energy and new biomass. This is a key finding because it informs future efforts to predict the fate of EE2 during biological wastewater treatment. The biomass-associated 14C levels agree with the general trend suggested by the bioenergetic model, indicating that a fraction of the EE2-related carbon was incorporated into active biomass. It is also likely that a fraction of the biomass-associated C14 is possibly adsorbed or entrapped within the aggregate matrix.
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Fig. 5 e Radiochromatograms of EE2 and metabolites (m/z labeled) generated from conventional bioreactor (CBR) and membrane bioreactor (MBR), (A) after 1 h, (B) after 24 h, and (C) after 48 h. The metabolites shown are produced biologically, except for m/z [ 340, which is likely both formed by way of an abiotic nitration reaction (Gaulke et al., 2008).
Biodegradation of EE2 appears to involve a combination of cometabolic and substrate utilization processes. The water quality community is concerned about the presence of EE2 in wastewaters, and as such, future efforts should continue to advance the understanding of EE2 metabolism in mixed culture. Ultimately, accurately predicting EE2 fate, the identity of metabolites, and the resulting toxicity will require
more data showing EE2 mineralization, incorporation, and metabolite production at even lower (e.g. ng/L) concentrations and in the presence of other microconstituents. There is also a need to further clarify the relative roles of nitrifiers and heterotrophs. These efforts, combined with mass transfer and toxicological modeling, should lead to the design and operational innovations needed to minimize risks to the environment.
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4.
Conclusions
The conclusions from this study are as follows: The membrane bioreactor (MBR) removed more EE2 than conventional bioreactor (CBR) over a range of influent EE2 concentrations >50 mg/L. The differences in process performance were likely due to the differences in the aggregate properties (e.g. hydrophobicity, particle size) and not enzymatic activity. The MBR and CBR biomass mineralized EE2 at similar rates when the influent EE2 concentration was 24.5 mg/L. Direct measurements and model estimates suggest that EE2 is adsorbed, mineralized, and incorporated into active biomass. Radiochromatograms show that MBR and CBR biomass produce EE2 metabolites quickly (i.e. within 1 h), and that similar metabolites were produced over the course of 48 h.
Acknowledgements This work is based upon work supported by the National Science Foundation (NSF Grant No. BES-0827417, BES-0827412, and BES054359). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. Co-author Susan Mackintosh acknowledges support from NSF IGERT fellowship program (NSF Grant No. 0654305). The authors thank William Barr (PhD student, University of Pittsburgh) and Lin Wang (PhD student, University of Pittsburgh) for experimental assistance and General Electric Water and Process Technologies (Oakville, Ontario, Canada) for providing the membrane filter modules. We thank Dr. Wendell Khunjar (Postdoctoral researcher, Columbia University) for advice and feedback. The authors also thank the anonymous reviewers for their helpful suggestions.
Appendix. Supplementary information Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.10.022.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Assessing the public health risk of microbial intrusion events in distribution systems: Conceptual model, available data, and challenges Marie-Claude Besner a,*,1, Miche`le Pre´vost b, Stig Regli a a
USEPA, Office of Ground Water and Drinking Water, Mail code 4607m, 1200 Pennsylvania Avenue, NW, Washington DC 20460, USA Ecole Polytechnique de Montreal, NSERC Industrial Chair on Drinking Water, Civil, Geological and Mining Engineering, CP 6079, Succ. centre-ville, Montreal, Quebec, Canada H3C 3A7 b
article info
abstract
Article history:
Low and negative pressure events in drinking water distribution systems have the
Received 10 June 2010
potential to result in intrusion of pathogenic microorganisms if an external source of
Received in revised form
contamination is present (e.g., nearby leaking sewer main) and there is a pathway for
26 October 2010
contaminant entry (e.g., leaks in drinking water main). While the public health risk asso-
Accepted 31 October 2010
ciated with such events is not well understood, quantitative microbial risk assessment can
Available online 5 November 2010
be used to estimate such risk. A conceptual model is provided and the state of knowledge, current assumptions, and challenges associated with the conceptual model parameters are
Keywords:
presented. This review provides a characterization of the causes, magnitudes, durations
Intrusion
and frequencies of low/negative pressure events; pathways for pathogen entry; pathogen
Pressure
occurrence in external sources of contamination; volumes of water that may enter through
Drinking water distribution system
the different pathways; fate and transport of pathogens from the pathways of entry to
Microbial contamination
customer taps; pathogen exposure to populations consuming the drinking water; and risk
Transient analysis
associated with pathogen exposure.
Public health risk
ª 2010 Elsevier Ltd. All rights reserved.
Contents 1. 2. 3. 4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962 Definition of intrusion events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962 Public health risk and low pressure events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965 Assessing the impact of microbial intrusion events on public health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966 4.1. Characterization of causes, magnitudes, durations and frequencies of low/negative pressure events . . . . . . . . 966 4.2. Characterization of pathways for contaminant entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 968 4.3. Characterization of occurrence of pathogens that may enter the distribution system through the pathways of the intruded water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969
* Corresponding author. Tel.: þ1 514 340 4711/5223; fax: þ1 514 340 5918. E-mail addresses: [email protected], [email protected] (M.-C. Besner), [email protected] (M. Pre´vost), [email protected] (S. Regli). 1 Permanent address: Ecole Polytechnique de Montreal, NSERC Industrial Chair on Drinking Water, Civil, Geological and Mining Engineering, CP 6079, Succ. centre-ville, Montreal, Quebec, Canada H3C 3A7. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.035
962
5.
1.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 6 1 e9 7 9
4.4. Characterization of volumes of water that may enter through the different pathways . . . . . . . . . . . . . . . . . . . . . . 4.5. Characterization of the fate and transport of pathogens from the pathways of entry to customer taps . . . . . . . 4.6. Characterization of pathogen exposure to populations consuming the drinking water . . . . . . . . . . . . . . . . . . . . . . 4.7. Characterization of risk associated with pathogen exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8. Existing QMRA models assessing the impact of intrusion events in distribution systems . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Introduction
Drinking water distribution systems are vulnerable to external contaminant entry if there is a loss of physical/hydraulic integrity. In their 2006 report on risk assessment and reduction for distribution systems, the Committee on Public Water Supply Distribution Systems of the National Research Council (NRC, 2006) defined a loss of physical integrity as when the system no longer acts as a barrier that prevents external contamination from deteriorating the internal, drinking water supply. Associated pathways of contamination include water main breaks/repair sites, uncovered reservoirs or covered storage tanks with structural deficiencies, and cross-connections with no, inappropriately installed, or inadequately maintained backflow prevention devices. Hydraulic integrity was defined as the capacity to maintain desirable water flow, water pressure, and water age in a distribution system, taking into account potable water delivery and fire flow conditions. The maintenance of adequate water pressure in a distribution system is a key element of hydraulic integrity and a loss of pressure represents a breach that could result in either backflow (from cross-connections) or contaminant intrusion through pipe leaks and other types of orifices (deflections at flexible couplings, leaking joints, and deteriorating seals (Kirmeyer et al., 2001)). Contamination from intrusion will be the main topic of this review. Distribution systems most vulnerable to intrusion events are those with intermittent water supply, most commonly found in developing countries. These systems are characterized by inadequate levels of water pressure for hours or days very often coupled with a combination of integrity problems including high leakage rates, non-standard connections to water mains, cross-connections, inadequate disinfection residuals, and poor sanitation practices (Lee and Schwab, 2005). The risk for contamination is high under such conditions and several reports of waterborne disease outbreaks and increased rates of gastrointestinal illness are available for such systems (Swerdlow et al., 1992; Semenza et al., 1998; Mermin et al., 1999; Yassin et al., 2006). In developed countries, the practice of intermittent supply is usually not encountered and distribution systems are normally delivering water at sufficient pressure on a continuous basis. However, adverse pressure conditions may still take place. These are generally transient in nature, with typical durations in the range of seconds to minutes and are generally associated with sudden pump shutdowns (Gullick et al., 2004,
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2005; Hooper et al., 2006; Besner et al., 2010). However, sustained pressure losses can also result from system failures such as main breaks (under free-flowing conditions) and subsequent repair site isolation, planned repairs (Besner et al., 2007) or from extreme rare events such as the U.S. Northeast blackout of August 14, 2003 where approximately 50 million people were without power and boil water advisories needed to be issued by some water utilities (CBSNews, 2003). Situations where sustained pressure losses occur are generally controlled by the installation of temporary distribution systems in case of major construction work/repair or application of mitigation strategies such as super-chlorination, boil water advisories, and non-consumption advisories prior to return to service. However, maintenance activities (and other unidentified causes) may trigger localized low pressure conditions in a distribution system for smaller but still significant durations (more than a few minutes) (Besner et al., 2010) and not be subjected to the same controls. The public health impact of possible intrusion events associated with this latter type of low/negative pressure events and the transient ones resulting from system operation is difficult to assess at this time. For the last decade or so, awareness regarding the effects of low pressure events on microbial water quality in distribution systems has increased. Field pressure monitoring and investigation of intrusion pathways have been conducted. Transient analysis has been applied to full-scale distribution systems to not only predict peak positive/negative pressures for system design, but to also identify critical locations for transient low pressures, and to predict intrusion volumes. Hydraulic analysis is used to model fate and transport of microbial contaminants, and quantitative microbial risk assessment (QMRA) can be applied for estimating potential public health risks associated with low pressure events. This paper seeks to review and discuss the available information within the context of a conceptual model, developed by the authors, for the estimation of public health risk resulting from intrusion events. The state of knowledge, current assumptions, and challenges associated with the conceptual model parameters will be presented.
2.
Definition of intrusion events
Over the years, researchers have provided definitions of intrusion events. Back in 2001, Kirmeyer et al. used the term “intrusion” in a broad sense to cover all potential pathogen routes of
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entry into a distribution system (storage facilities, main installation and repair sites, cross-connections and during transitory contamination events). The transitory contamination events were defined by the occurrence of negative or low pressure allowing untreated water to backflow into a distribution main through leakage points, submerged air valves, cross-connections, faulty seals or joints. Lindley and Buchberger (2002) also used the term “intrusion” to indicate contamination from all distribution system deficiencies and introduced three susceptibility conditions to be met for an accidental intrusion to take place (1) adverse pressure gradient, (2) intrusion pathway, and (3) contaminant source. LeChevallier et al. (2002) were the first authors to clearly differentiate between contamination from intrusion and cross-connections. Although they defined intrusion as a specialized backflow situation (to illustrate the reverse flow phenomenon), they removed the term “cross-connections” from the list of potential entry points associated with intrusion events (referring to the flow of non-potable water into mains through leakage points, submerged air valves, faulty seals or other openings). Gullick et al. (2004) and NRC (2006) adopted a similar definition and Friedman et al. (2004) and Fleming et al. (2006) specified that intrusion could occur when the external pressure from water surrounding a main exceeds the water pressure inside the main. Finally, Hooper et al. (2006) adapted the three susceptibility conditions to the specific case of microbial intrusion as: (1) the occurrence of pressure drops or transients, (2) the presence of pathogens in the soil and water surrounding drinking water pipes, and (3) poor structural integrity of pipes, specifying that all of these three condition must be present for water quality to be adversely affected by pressure drops. Based on this evolution of the term, it should be established that “intrusion” is to be associated with contamination due to adverse pressure conditions and physical infrastructure gaps and should not be used in the context of contamination from cross-connections or at main installation/ repair sites. The main driver for an intrusion event to occur is the failure to maintain an adequate pressure in the distribution system. Low pressures are usually considered as pressures below 20 psi (14 m) (gauge) as distribution systems should be operated at pressure greater than 20 psi under all conditions of flow (Great Lakes Upper Mississippi River Board of State Public Health and Environmental Managers, 2007). Atmospheric pressure corresponds to a gauge pressure value of 0 and negative pressures are when gauge pressures are below 0, creating a suction effect inside the water main. The lowest possible pressure measurement in a pipe would be a gauge value of minus atmospheric pressure, the value of which is variable depending on elevation and atmospheric conditions (this is also called the absolute zero). However, before the pressure reaches this value, it will start to evaporate and cause cavitation (i.e. water column separation) and the vapour pressure of water is function of temperature. For example, for a standard atmospheric pressure of 14.7 psi (10.3 m) and a temperature of 20 C, the vapour pressure value of water is 0.3 psi (0.2 m) (in absolute pressure) and the lowest gauge pressure would be 14.3 psi (10.1 m) before cavitation occurs. Two main types of adverse pressure conditions may take place as summarized below (with additional details provided in Section 4.1):
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(a) Transient low/negative pressure events: Pressure transients (or water hammer or surge event as commonly referred to) are caused by abrupt changes in the velocity of water and it can be assumed that all distribution systems experience, to various degrees, rapid pressure fluctuations that may result in low or negative transient pressure events. These are short duration events that may typically last from a few milliseconds to a few minutes and may be caused by distribution system planned operation (pumps on and off), or unplanned events such as service interruptions (power outages), and sudden changes in demand (rapid opening/closing of hydrant, main rupture). One of the first occurrences of low pressure transients to be reported in the literature was by Walski and Lutes (1994). More recently, monitoring of distribution systems with high-speed pressure transient data loggers (recording between 1 and 20 pressure values per second) has been conducted. Low (0 < p < 20 psi) or negative pressure events have been recorded in several full-scale water systems (Kirmeyer et al., 2001; Gullick et al., 2004, 2005; Hooper et al., 2006; Besner et al., 2010). In these studies, sensors were usually positioned at sites vulnerable to low pressures to increase the likelihood of recording such events. (b) Sustained low/negative pressure events: These events typically have longer durations (in the range of minutes to hours) and are often associated with distribution system operation and maintenance activities. Main breaks are a possible cause of sustained distribution system depressurization and a wide range of situations may be encountered (Fig. 1). On one end, complete interruption of the water supply may occur when rare events (e.g., earthquakes) take place, and catastrophic conditions such as rupture of drinking water and sewer mains are experienced (Geldreich, 1996). Although critical in nature, such situations are not directly addressed here, as we are trying to understand the potential health risks associated with smaller scale events occurring on a more frequent basis. For large-scale construction/repair/replacement work and for main breaks resulting in some depressurization, sustained low pressures may take place outside of the isolated area, representing the greatest concern for intrusion (Fig. 1). While mitigation strategies are generally applied prior to returning in service the isolated mains, adjacent area may not necessarily be subjected to the same controls. When system closure lasts for more than 24 h, water utilities may identify low pressure areas and apply mitigation strategies. Main breaks with depressurization, that are repaired within a couple of hours are probably the most critical for the occurrence of sustained low/negative pressures (and potential intrusion and subsequent population exposure). Depending upon a system’s characteristics and configuration, and the break orifice size, pipe diameter, and available pressure, some pipe breaks may lead to low pressure and water outages in some network area because of: (a) increased demand induced under freeflowing pipe break conditions (before isolation of the affected area), (b) modified hydraulic conditions induced by the isolation of the affected area, or (c) increased demand induced by flushing the repaired main once the repair is completed (Fig. 2). An example of this last case,
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System closure <<24 hours
System closure >24 hours
Large scale emergency
Large scale construction/repair /replacement
Main break with depressurization
Pipe leak repaired under pressurized conditions
Catastrophic s it u ati o n (e.g. earthquake)
P l an n ed o r emergency construction/repair/ replacement work
Isolatio n of broken main
Pressure reduction in sec ti on of main to be repaired
1. Sustained low pressure possible in DS area adjacent to repair
1. Sustained low pressure possible in DS area adjacent to repair
2. Temporary DS provided for isolated area
2. Transient low pressure possible in DS area adjacent to repair
No water provided
3. Superchlorination of isolated area prior to return to service
1. Transient low pressure possible 2 . Flushing of quasiisolated area
3 . Flushing (+ chlorination) of isolated area
Fig. 1 e Main breaks and pressure fluctuations.
Low/negative pressure created by increased flow at break site Duration: until break is isolated (valves closed)
Q Closed valve Hydrant
A
Pipe break (flowing to atmosphere)
Low pressure could be created by flushing at repair site
Possible that some area of DS could have lower pressure during repair
No or low pressure during repair Q
B
Pipe break is isolated and repaired
C.
Pipe is flushed after repair
Fig. 2 e Situations where pipe breaks can result in low/negative pressure events.
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illustrated in Fig. 3, is based on field work conducted by several of the authors where pressure measurements were collected in a full-scale distribution system at a site outside of the isolated area for a main repair. Flushing was conducted to clean the main after repair and low pressures (<20 psi) were measured for the first 17 min of a flushing period of approximately 2 h. Based on this, it can also be assumed that large-scale flushing programs conducted by water utilities (conventional or unidirectional) could also lead to the same type of sustained events under some circumstances. Similarly, increased water flows created by large fires (when a pump is installed to a hydrant) are also likely to create sustained low pressure conditions in distribution systems. The use of pumps by fire brigades has led to cases where water mains were not able to supply the required flows (IAFC and NFPA, 2009) and may be the cause of transient pressure events as well. Sustained low and negative pressures were reported in a Canadian distribution system in association with the planned repair of a transmission main (Besner et al., 2007). While there are few reports available from the scientific literature of sustained low/negative pressures, evidence of such events are commonly found in newspaper articles describing water shortages due to main breaks (The Tech, 2005; USA Today, 2008).
3.
Public health risk and low pressure events
Adverse health effects associated with low pressure events in distribution systems have been observed in two European epidemiology studies. However, these study designs did not allow the differentiation of the possible contamination sources: intrusion due to low pressure occurrence, contamination from pipe repair procedures or possible contamination from cross-connections. In England, a case-control study of sporadic cryptosporidiosis showed a strong association
Fig. 3 e Pressure measurements during flushing of a repaired main at a location outside of the isolated area (refer to case (c) on Fig. 2).
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between self-reported diarrhea and reported low water pressure at the faucet (Hunter et al., 2005). The authors concluded that up to 15% of the gastrointestinal illness (GI) may be associated with burst water mains and pressure loss events, although the study was not specifically designed to test the hypothesis that low water pressure events were associated with self-reported diarrhea. The epidemiological study conducted in Norway showed that low pressure episodes, defined as incidents where a part of the water distribution network was closed off due to main breaks or maintenance work (either planned or unplanned) with presumed loss of water pressure in the distribution system, caused an increased risk of GI illness among water recipients (Nygard et al., 2007). Based on 88 low pressure episodes taking place in seven distribution systems, 12.7% of the exposed households reported GI illness compared with 8% in the unexposed households during a 1-week period after the episode. For both European studies, concentrations of residual disinfectant in the systems were not given. However, chlorine residual levels in the participating distribution systems in Norway were likely to be low or even nonexistent (Berger, 2008 e Personal communication). Recognized intrusion events leading to outbreaks are rare and are probably the type of contamination event that is less well documented. Reasons for this could include the difficulties associated with identifying this specific mechanism as the single cause of contamination, or that such a pathway is not a significant source of contamination, one that would mainly contribute to the endemic level of illness related to consumption of drinking water. However, this remains to be evaluated. In New York, a Giardia intestinalis outbreak was recently reported in a trailer park and was related to a power outage which created a negative pressure condition in the distribution system. The exact contamination pathway could not be identified, but contaminated water entered the system either through a cross-connection inside a mobile home or a leaking underground pipe near sewer crossings. Six persons became ill (Blackburn et al., 2004). The Escherichia coli O157:H7 waterborne disease outbreak in Cabool (Mo) in 1990 could also in part be related to intrusion of contamination into the distribution system, especially through submerged meters (Geldreich et al., 1992). This outbreak was associated with the occurrence of two large water main breaks and the replacement of 45 failed meters, some of which were found to be submerged under water. Before the outbreak, no disinfectant was added to the water supply. Endemic illness associated with consumption of drinking water has been investigated through household intervention studies (Payment et al., 1991, 1997; Hellard et al., 2001; Colford et al., 2005). However, only one of these studies provides information on the potential role of distribution system on incidence of disease by differentiating between treatment and distribution effects (Payment et al., 1997). This non-blinded household intervention epidemiological study was conducted in a Canadian distribution system. The authors observed that the population drinking tap water (from the distribution system) had an excess of highly credible gastrointestinal illnesses (HCGI) between 14 and 40% (corresponding to 0.66e0.70 cases per person-year) compared to population drinking bottled plant water and purified bottled water, while
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there was no significant difference in HCGI rates between those last two groups (0.6 and 0.58 cases per person-year, respectively). During the study, water quality standards were met and the distribution system was found to be in compliance for both coliforms and residual chlorine. Messner et al. (2006) subsequently used the HCGI rates derived from the studies by Payment and colleagues to develop a national estimate of acute gastrointestinal illnesses (AGI) associated with drinking water in the United-States and to partition this estimate between source/treatment and distribution. The fraction of AGI attributable to drinking water was estimated at 8.5% and the incidence of AGI illness was estimated at 0.06 cases per person-year (95% CI: 0.02e0.12). The incidence of AGI illness due to distribution system deficiencies was estimated at 0.03 cases per person-year (95% CI: 0.003e0.09). AGI attributable risk from distribution systems has also been estimated as part of a community-wide intervention with UV disinfection in groundwater systems that do not use residual disinfection (The Wisconsin WAHTER Study) (Borchardt et al., 2009). These authors estimated this risk at 0.018e0.075 illness per person-year. They also detected viruses in tap water in 24% of 1204 samples collected in household taps. Assuming that the UV disinfection applied at the wells is effective, and because the participating communities do not use residual disinfection, the distribution system can be identified as the main entry point for those viruses. These studies and waterborne disease outbreak reports provide some insight into the potential public health risks associated with contamination events in distribution systems. However, the most likely pathways for contamination (intrusion during low pressure, main repair procedures, or backflow from cross-connections) in the reported events are not clearly identified and the associated conclusions on health outcomes are subject to the limitations of the methodologies used (nonblinded sampling, self-reporting). In its review of the Payment epidemiological studies (Payment et al., 1991, 1997), the National Academies’ Water Science and Technology Board Committee concluded that low disinfectant residuals and a vulnerability of the distribution system to pressure transients (suggesting intrusion as a possible mechanism of contamination) could account for the observed GI illnesses in the population consuming tap water from the studied distribution system (NRC, 2006). We support the hypothesis that intrusion events are a contributor to the endemic level of illness associated with drinking water consumption but that the fraction of this contribution is unknown. More targeted research (and information collection) is needed to more fully understand the issue of contaminant intrusion in the context of adverse pressure conditions and physical infrastructure gaps.
4. Assessing the impact of microbial intrusion events on public health A conceptual model focusing on an approach for quantifying the risks from pathogens entering the distribution system from intrusion events, their fate and transport through the distribution system to customer taps, and the associated risks that pertain to the consuming populations is illustrated in Fig. 4. The conceptual model entails: a) a characterization of
the causes, magnitudes, durations and frequencies of low/ negative pressure events; b) a characterization of pathways for pathogen entry; c) a characterization of pathogen occurrence that may enter the distribution system through the pathways of the intruded water; d) a characterization of volumes of water that may enter through the different pathways; e) a characterization of the fate and transport of pathogens from the pathways of entry to customer taps; f) a characterization of pathogen exposure to populations consuming the drinking water; and g) a characterization of risk associated with pathogen exposure. This paper will elucidate on the parameters that could be used for making the above characterizations, the level of information currently available to inform the values of these parameters, and challenges in addressing data limitations (either through obtaining additional information or making placeholder assumptions). We will focus primarily on categories aef in this paper since dose-response relationships for various pathogens are already available (Haas and Eisenberg, 2001; McBride et al., 2002; Teunis et al., 2008a) and the challenges with developing QMRA for intrusion contamination events in distribution systems are much greater for the factors contributing to exposure than for interpreting the risks from such exposure. The conceptual model presented here is used to clearly illustrate the concepts that need to be considered in developing a QMRA model for a distribution system application. A major aspiration for developing this risk assessment model is to provide a plausible means for informing the potential magnitude of public health risk associated with intrusion events for individual systems over time (e.g., within one year), and among systems having different characteristics. We also envision the risk assessment model being used to inform which risk factors are of greatest concern and how might such concerns be mitigated. Another use of the model would be to identify what missing information is most influential for informing the realization of these objectives, thereby informing further research and information collection priorities. A brief review of the actual state of knowledge for each of the conceptual model components and associated parameters is presented. Current assumptions and challenges in performing this type of analysis are also discussed.
4.1. Characterization of causes, magnitudes, durations and frequencies of low/negative pressure events Low or negative pressure events may be triggered by a number of causes, some of which may be taking place on a routine basis in a distribution system. An extensive list is provided by Kirmeyer et al. (2001) and includes: air-valve slam, altitude valve closure, check valve slam, malfunctioning air release/ vacuum valves, malfunctioning pressure relief valves, booster pump startup and shut down, uncontrolled pump startup and shut down, pump trip due to a power failure, break in a pipeline, feed tank draining, surge tank draining, losing an overhead storage tank, opening and closing of fire hydrants and valves, flushing operations, resonance, and sudden changes in demand. While these are mostly associated with the occurrence of transient pressure events, some like pipe breaks could also trigger low pressure events with longer durations. Based on data collected in the 1990s, Kirmeyer et al. (2001)
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Presence of a Pathway for Contaminant Entry
Cause of Failure
Magnitude/Duration of Failure
P resence of Contaminated Water Outside of Pipes
Failure to Maintain Adequate Pressure
Frequency of Failure
Outside Distribution System
External Contaminant Introduction
Intrusion Flow Rate
Duration
Concentration in Contamination Source
Locati on(s)
Within Distribution System
In-pipe Concentration (Dilution)
Transport
Customer Exposure and Response
Population Impacted and Estimated Risk
Hydraulic Disturbance Results in Internal Contaminant Released from Biofilm/Scale Sloughing
Interaction with Other Constituents (in Bulk Water/ at Pipe Wall)
- Inactivation - Die-off - Accumulation in biofilm/ pipe scales - Deposition
Coincidence of Water Use and Passage of Contaminated Water
Dose-Response
Ingested Volume
Customer Tap Water Concentration
Other Uses
Fig. 4 e Conceptual model of health risk associated with microbial intrusion events.
estimated that 237,000 water main repairs were performed annually in the U.S. based on national statistics on occurrence of water main breaks (AWWA and AWWARF, 1992) and the total miles of mains (Kirmeyer et al., 1994). The report by the National Research Council in 2006 (NRC, 2006) referred to an industry average of 100e300 breaks per 1000 miles of pipes per year (from Damodaran et al., 2005) and a national average of 240e270 breaks per 1000 miles. Grigg (2009) recently reported estimations of 250,000 to 300,000 main breaks per year for utilities in the U.S. With the aging and deteriorating state of the infrastructure, the number of main breaks is likely to increase in the future. An outstanding issue is the distribution of magnitude and duration of low or negative pressure events resulting from these main breaks. Field pressure studies conducted by several researchers show that sudden shutdown of pumps is the cause of transient negative pressure events that is most often reported (Gullick et al., 2004, 2005; Hooper et al., 2006; Besner et al., 2010). Power failures and intentional (pump stoppage or startup tests) circumstances were associated with these sudden shutdowns. Data on frequencies of power failures may therefore be valuable to estimate the possible frequency of low pressure events in distribution systems (although not all power outages may result in low pressure events). One distribution system investigated by Gullick et al. (2005) experienced 20 power outages over a 17-month period with 3 low pressure
(<20 psi) events measured in the system. Data on frequency of power failures mostly come from water utility surveys ranging from 1 to 20 power outages per water utility per year (Kirmeyer et al., 2001; Fleming and LeChevallier, 2008). Pressure reduction events were also compiled by ABPA (2000) from 60 U.S. water utilities. Routine system flushing and main breaks were the two main causes of pressure reduction events triggering 37% and 20% of the reported events respectively. These data do not indicate if pressure values below 20 psi were measured, only that a reduction in pressure took place. Magnitude and duration of negative pressure events have been compiled from two sets of field pressure data (based on the use of high-speed pressure transient data loggers) with a significant number of measured negative pressure events. Gullick et al. (2004) performed high-speed pressure monitoring at 43 sites in 8 distribution systems for a total of 4640 days of pressure data and recorded negative pressures at 21 monitoring locations. The second data set is from Besner et al. (2007) and Besner et al. (2010), with high-speed pressure monitoring data from 19 sites in one distribution system, for a total of 4104 days of pressure data and 36 negative pressure events. All the negative pressure events in the Gullick et al. study lasted less than 165 s (w3 min), and 18 out of 21 events were related to the shutdown of main pumps. Operation of water cannons (booster pumps for a sudden high demand)
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Table 1 e Causes and durations of negative pressure events measured by Besner et al. (2007, 2010). Cause of negative pressure event Unknown Power failure at WTP Closure and repair of transmission main (affected population under preventive boil water order) Hydrant use by construction crew (uncertain) Repair in DS area Hydrant out of service
was associated with 2 events and one had an unknown cause. In comparison, only 55% of the negative pressure events from the Besner data set had durations below 3 min. This data set included a wider range of possible causes for the negative pressures recorded, which could explain some of the longer durations measured (Table 1). While it is known that the pressure loggers were located outside of the isolated area for the repair of the transmission main, it is not known if this was the case when regular repairs of water mains were associated with negative pressure events. Some of these repairs led to the longest durations of negative pressures (greater than 4 h). Interestingly, the shorter events from the Gullick data set led to negative pressure events of higher magnitude as illustrated in Fig. 5. Approximately 50% of the events from the Gullick data set had a minimum pressure below 7 psi while this was the lowest value measured from the Besner data set. Even if these minimum pressures did not necessarily last for the complete duration of the negative pressure event, they still provide insights on the intensity of the recorded events. These two data sets raise questions about the importance of the negative pressure magnitudes versus their duration. Is the volume of intruded water in a distribution system likely to be greater when a low negative pressure is experienced for a long duration (Besner data set) or when a high negative pressure is experienced for a short time (Gullick data set)? In such context, magnitude and duration of low/negative pressures could be combined into a new metric for informing relative intrusion potential across systems. Having further clarity on the effect of each parameter could inform the potential value of such a metric. Further analysis of these two parameters is provided in Section 4.4.
4.2. entry
Duration of negative pressure events (seconds) 13, 15, 45, 1710 15, 30, 45, 60 15, 57, 58, 60, 195, 206, 390, 401, 420, 425, 456, 896, 2265 225, 360 1230, 2085, 15,975, 19,905 2745
leaking pipes (Environment Canada, 2010). While such estimates give an idea of water volumes lost, it does not provide any information on the number, location, and size of pipe leaks that can be found in a distribution system. The number and location of pipe leaks are specific to each distribution system, but information about actual sizes of pipe leaks is available from utility surveys. Based on seven water utility data, Kirmeyer et al. (2001) reported circular leak (tiny punctures through pipe wall) diameters from 3 mm (1/8 in) to 100 mm (4 in), circumferential leak width (along perimeter) of 3 mm (1/ 8 in) to 100 mm (4 in) and longitudinal leaks (along length) width of 3e150 mm (1/8e6 in) by 0.9e6 m (3e20 ft) long. Leaking water mains located below the water table are certainly more vulnerable to intrusion as the height of groundwater provides an external head that may become greater than the internal system pressure when a low pressure event occurs. Data on fraction of distribution system pipe length located below the water table are scarce. A survey from Kirmeyer et al. (2001) indicated that some systems could have up to 15e30% of their total length below the water table. Depending upon the time of year, the height of the water table may vary, affecting different distribution system areas. The height of water above a pipe could be quite significant, especially in systems located in areas subject to seasonal frost where it is often necessary to bury water mains to depths well below 2 m (Sepehr and Goodrich, 1994). It is worth noting that for leaking pipes located above the water table, the leak will provide a constant
Characterization of pathways for contaminant
Possible pathways for pathogen intrusion include those related to pipeline structural integrity: pipe fracture, deflections at flexible couplings, leaking joints, and deteriorating seals (Kirmeyer et al., 2001). Aging drinking water infrastructure exacerbates the creation of potential pathways for contaminant intrusion. Water losses between 8 and 24% of the water supplied (water produced e water billed/consumed) have been estimated for developed countries based on 1991 data (WHO, 2001) and higher values are now commonly reported. The fraction of water strictly lost through pipe leaks is not always available but in Canada, it is estimated that up to 30% of the total water entering supply-line systems is lost to
Fig. 5 e Duration and magnitude of negative pressure events recorded by Besner et al. (2007, 2010) and Gullick et al. (2004).
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feed of water into the soil, and local conditions around the leak may thus be similar to that of pipes under the water table. Submerged air-vacuum valves and meters that are located in flooded vaults or pits are also possible pathways for the introduction of microorganisms. High coliform failure rate in a UK water distribution system was associated with a submerged air-valve, located downstream of a pumping station, where sub-atmospheric pressures were measured during surges (McMath and Casey, 2000). The authors observed that the vault was flooded with dirty water during wet weather. Results of a field inspection of 45 air-valve vaults in a Canadian distribution system by Besner et al. (2010) showed that stagnant water could be found in 30 out of 45 vaults, with 10 of these vaults having the air-valve completely submerged under water. In a survey of 26 water utilities, 12 utilities indicated that the number of flooded valve vaults in their system could vary between 0 and 80% of total vaults (Kirmeyer et al., 2001). The outlet opening of air-vacuum valves (Fig. 6(A)), designed to allow air to enter the pipe when negative pressure occurs, may vary between 12 mm (1/2 in.) and 600 mm (24 in.) (APCO Valve and Primer Corporation, 2000). Under negative pressure conditions, and if submerged under water, these large orifices will allow for water (instead of air) to be introduced into the main, making this a significant pathway of entry if external contamination is present.
4.3. Characterization of occurrence of pathogens that may enter the distribution system through the pathways of the intruded water Contaminated soil and groundwater surrounding water mains may contain mixtures of pathogenic microorganisms (bacteria, protozoa, viruses) from leaking sewer mains in close proximity of drinking water mains. In six US distribution systems, Karim
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et al. (2003) collected soil and water samples at sites immediately exterior to drinking water pipelines during pipe repairs. These authors detected indicator microorganisms and enteric viruses (detected by cell-culture and reverse transcriptionpolymerase chain reaction (RT-PCR)) in more than 50% of the 65 samples examined. Out of 32 sites investigated, 18 (56.2%) were positive by either cell-culture or RT-PCR for one of the three viruses tested (Enterovirus, Norwalk virus, and Hepatitis A virus). A similar investigation by Besner et al. (2008) in two Canadian distribution systems showed that although some microbial indicators of fecal contamination were detected, their frequency of detection was lower and no virus were detected by cell-culture (molecular detection methods were not used). The condition of the sewage system infrastructures (U.S. water/ wastewater infrastructures were awarded a grade of D- by ASCE (2009)), the distance between the sewer mains and drinking water mains, the type of soil and degree of soil saturation are likely to influence the transport and detection of pathogens. While the distance between the drinking water pipes and sewer pipes was unavailable for the study by Karim et al. (2003), only one repair site out of 17 had both the drinking water and sewer mains visible in the trench in the investigation by Besner et al. (2008). This is similar to the survey results from Kirmeyer et al. (2001), who reported that 20 utilities out of 26 indicated that usually fewer than 5% of the sewer and drinking water mains are located in the same trench. On the other end, Nygard et al. (2007) who observed an increased risk of GI illness associated with main breaks and maintenance work, reported that in Norway, sewer lines are often located in the same ditch as water pipelines, but no further details were provided. More data on pathogen type, concentration, and viability outside drinking water mains are therefore needed. Microbial characterization of the water found in 30 flooded air-valve vaults was performed by Besner et al. (2010). The
Fig. 6 e (A) Combination air-valve (air-release and air-vacuum valve) in a dry vault; (B) Flooded vault; (C) Same flooded vault showing upper part of combination air-valve after some water was pumped from the vault.
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frequency of detection of microbial indicator of fecal contamination was higher from this pathway than from the soil/trench water samples from the same Canadian distribution system. More than 60% of the collected samples contained E. coli, C. perfringens, enterococci and Bacteroidales fecal bacteria but no enteric viruses were detected either by cell-culture or RT-PCR.
4.4. Characterization of volumes of water that may enter through the different pathways Once all the conditions for intrusion are met (at one or more locations in a distribution system), some volume of contaminated water is introduced into the system. Evidence of intrusion due to transient negative pressure events has been obtained from pilot-scale experimentation (Boyd et al., 2004a, 2004b). Based on a chemical tracer method (mass balance calculations using cesium), Boyd et al. (2004a, 2004b) showed that for a very short negative pressure event (approximately 1 s) with a magnitude of 12 psi, an average volume of 11 mL could enter a 3 mm (1/8 in) diameter orifice (small pipe leak) located below a “column of water” of 0.9 m (3 ft) (used to simulate the external pressure head). For a leak size of 6 mm (1/4 in) in diameter, the average intrusion volume reached 71 mL. Under the same conditions, the use of a volumetric method (video recordings of water fluctuations in the observation column) led to volume estimates of 47 and 119 mL and theoretical estimates (using the orifice equation discussed below) led to intrusion volumes of 62 and 227 mL for the 3 mm and 6 mm orifice sizes respectively. Difference in estimates between the laboratory methods (chemical tracer and volumetric) was attributed to dilution of cesium as it intrudes into the water main during each negative pressure wave. Difference between the volumetric method and the theoretical estimates was attributed to laboratory error related to inaccurate manual operation of a ball-valve in the test rig. These results show that per site of intrusion (per orifice), the volume of intruded water can be relatively small under the conditions tested (a 1-s negative pressure event). However, in a real distribution system, the number of locations having an internal pipe pressure lower than the external pressure, the magnitude of the pressure difference, the number and size of the orifices, the soil interaction, and the duration of the event are all factors that will influence the total volume introduced. For low/negative pressure events of short duration, it is possible to estimate intrusion volumes by using surge models (or transient analysis). These models are used to hydraulically simulate transient pressure events and then calculate the volume of intrusion at locations where the external pressure head becomes higher than the internal pressure head for some duration. Recently, commercial software has been used to estimate intrusion volumes from measured and/or modeled negative pressure events in full-scale distribution systems (Fleming and LeChevallier, 2008; Ebacher et al., 2009) with the goal of using these volumes to ultimately derive population exposure. The assumptions that are currently associated with the computation of intrusion volumes based on transient analysis results include: (i) The accurate modeling of negative pressure events in order to obtain realistic intrusion volumes. Only few comparisons of
negative field pressure measurements to modeled pressure profiles obtained from transient analysis are available and among those, several authors have noted an overestimation of the computed downsurges by the transient model (McInnis and Karney, 1995; Friedman et al., 2004; Fleming et al., 2006; Ebacher et al., 2010). This can result in a possible overestimation of the system area affected by low/negative pressure and of the external volume of water introduced. Several factors may explain the lower energy dissipation in numerical models and are detailed in Ebacher et al. (2010): the modeled nodal demands and distribution of demands at time of event, the estimation of pressure wave speeds (which depend on pipe diameter, pipe material, wall thickness, and restraint, fluid density, elasticity, and air and solids content [Wylie and Streeter, 1993]-many of which are unknown), the estimation of friction coefficients, the level of model skeletonization (surge analysis requires a detailed model, since the transient response is highly sensitive to system-specific characteristics [Jung et al., 2007]), and the use of the standard steady-state friction model. Transient analysis is more complex than extended-period simulation (EPS) modeling and water utilities should be aware that even if their EPS model is well calibrated, this will not necessarily be the case for the derived transient model. When using transient analysis, it is therefore highly desirable to have field pressure data (collected using high-speed pressure transient data loggers) to compare to the model results and reduce the model uncertainty for hydraulic conditions. (ii) The use of the orifice equation for calculation of the intrusion flow rate: pffiffiffiffiffiffiffiffiffiffiffiffiffi (1) Qintrusion ¼ CD A 2gDH where Qintrusion ¼ intrusion flow rate (m3/s), CD ¼ coefficient of discharge (unit less), A ¼ orifice area (m2), g ¼ gravitational acceleration (m/sec2), and DH ¼ difference between external pipe pressure head and internal pipe pressure head (m). The square root relationship is valid to estimate intrusion volumes through circular orifices, but may not necessarily yield accurate estimates for all shapes and sizes of orifices from pipe leaks (longitudinal/circumferential cracks). Investigation of the pressure-leakage relationship (under positive pressure conditions in pipes) by several researchers has shown that the exponent value may differ from the theoretical value of 0.5 as the effective leak area may, in some cases, be pressuredependent (Lambert, 2001; Greyvenstein and van Zyl, 2007). Pipe material behavior, resulting in the expansion of orifice size with pressure, appears as the main factor explaining the variation in exponent values. The behavior of leaks under negative pressure conditions has not yet been investigated. It should be recognized that soil characteristics are not taken into account when using the orifice equation which computes flow in the absence of soil (as if a leaking pipe was only surrounded by water and no soil). Flow in saturated porous media is likely to be influenced by soil characteristics such as permeability (and particle size), interaction of soil particle with the orifice, and type of flow in soil (laminar/turbulent) (Van Zyl and Clayton, 2007). These authors investigated the role of soil hydraulics on the pressure-leakage relationship
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(under positive pressure conditions in pipes) and concluded that the interaction between the external soil and the leaking pipe is complex and not fully understood at this time. This is likely to be the same for intrusion (or reverse leakage) conditions. Research work is currently being conducted at the University of Sheffield (U.K.) to investigate the potential for contaminant ingress into distribution systems by direct measurements using a laboratory facility (EPSRC, 2009). The full-scale experimental set-up is designed to include the pipe surrounding ground conditions and a contaminant flow field (leaking sewer). Results will be used to develop a new ingress model enabling quantification of potential intrusion volumes based on realistic conditions. Meanwhile, the current use of the orifice equation, with its square root relationship, indicates that the intrusion flow rate is not that sensitive to the pressure difference between the outside and inside of a main. As the intrusion volume is obtained by the product of the flow rate and the duration of the event, the latter becomes quite important, as illustrated in the simple example in Fig. 7. Although the intrusion flow rate is lower for a low intensity negative pressure event (small ΔH ), the longer duration results in a higher volume being intruded (assuming a constant flow rate over the whole duration). This would theoretically indicate that the distribution system studied by Besner et al. (2007, 2010), characterized by low intensity/longer duration negative pressure events (Fig. 5), would be prone to larger intrusion volumes than the distribution systems investigated by Gullick et al. (2004) if all conditions for intrusion were present and equal in both sets of systems. The current use of the orifice equation indicates that mitigation strategies aiming at limiting the duration of low/ negative pressures would contribute to limit potential exposure to contaminated water in distribution systems. (iii) The use of the same orifice diameter value and the same external pressure head value, for all orifices. In a real distribution system, leaks (of different shapes and sizes) will generally be located along pipes, with some sections of pipes having more leaks than others due to pipe material, soil conditions, etc. However, when using hydraulic models, leak orifices are not assigned to pipes but rather to pipe junctions (or nodes). In order to compute intrusion volumes, commercial surge models assign orifices of equal sizes to each node of the system and the diameter of these orifices is computed based on the leakage rate provided by the user (e.g., 20% of total water produced lost through leakage is translated into a unique orifice diameter value to be applied at all nodes included in the distribution system model). Even if a water utility knew that some area of its distribution system had significantly more leaks than other areas, it is not possible at this time to assign different orifice sizes to different nodes or to remove orifices from groups of nodes when using commercial software. The same principle applies to the external pressure head parameter (best illustrated as the height of the water table above a pipe, set equal to or greater than 0 m), which is specified by the modeler although this value is usually not known. The same value is used for all orifices (leaks at all nodes), which may significantly bias the representation of field conditions
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since only some fraction of pipe length may be located below the water table depending upon factors such as pipe elevation or time of year. The use of a unique low external head (¼ 0 m) may underestimate the intrusion volumes whereas higher external head values may lead to overestimation of these volumes. These modeling assumptions may be useful for computing worst-case scenarios since it is assumed that for all nodes where the specified external pressure head is higher than the internal pressure head, there is a pathway (leak) and an external contaminant resulting in intrusion. However, when specifically trying to assess the possible intrusion volumes associated with measured transient low/negative pressure events (and subsequent population exposure), these assumptions should be highlighted.
4.5. Characterization of the fate and transport of pathogens from the pathways of entry to customer taps Because of the nature of the potential sources of contamination (leaking sewer main, accumulated water in flooded vaults), intrusion events are most likely to result in the introduction of a mixture of microorganisms (sewage, street runoff, etc.). However, fate and transport analysis and subsequent risk analysis are usually conducted for a single type of pathogenic microorganism at a time as microbial contaminants have specific reaction kinetics and dose-response relationships. Consequently, only a subset of the potential pathogens introduced from a source is considered in this type of analysis. Selection of the pathogen to be modeled will be based (i) on the likelihood of finding it, versus other pathogens, in possible sources of contamination (soil/groundwater/flooded vaults), and (ii) on the availability of dose-response and morbidity data to estimate risk of infection or illness. Microorganisms of interest for estimating the potential health risks associated with intrusion events are listed in Table 2. These pathogens have different frequencies of occurrence in possible sources of contamination (leaking sewer, street runoff, etc.) and diverse resistance to residual disinfectants (free chlorine, chloramines), which will affect their capacity to remain viable in a distribution system. The possible range of microorganism concentrations in different sources of contamination can be derived from existing literature data (if available). Although microbial characterization data are available for soil and water surrounding water mains (Karim et al., 2003; Besner et al., 2008), these are mainly for indicator microorganisms and not for pathogenic microorganisms, which are used to conduct risk assessment (virus data are too few at this time to establish meaningful occurrence). Consequently, the use of sewage as an outside source of contamination is likely to represent a worstcase situation. In reality, different types of soil and transport mechanisms (advection and dispersion, that are influenced by the effects of filtration, adsorption, desorption, growth, decay, and sedimentation) (Abu-Ashour et al., 1994) are likely to attenuate the concentration of microorganisms found next to water mains. In fact, the level of pathogens found in intrusion pathways may be more typical of untreated river water than wastewater based on indicator microorganism concentrations
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Fig. 7 e Estimation of intrusion volumes associated with negative pressure events of short duration/high intensity (Site 1) versus long duration/low intensity (Site 2).
measured next to water mains and in flooded vaults (Besner et al., 2010). For the estimation of population exposure from microbial contamination associated with low/negative pressure events, EPS hydraulic/water quality simulations are used to simulate the fate and transport of the selected pathogenic microorganism, from its point(s) of entry up to consumer’s taps. Required inputs for simulation include the starting time of the event, the specific site(s) of intrusion (model node(s)), the contaminant mass rate (number of microorganisms entering the system per minute), and the duration of intrusion. The contaminant mass rate is obtained by multiplying the intrusion flow rate (volume/time) by the pathogen concentration in the source of contamination (microorganism/volume). Software that model the hydraulics and water quality in a distribution system, such as EPANET (Rossman, 2000), can be used for the fate and transport analysis. However, conventional water quality software are usually single-specie models, that can only track the movement and fate of a single nonreactive or reactive component. Mechanisms such as the interaction of microorganisms with the disinfectant residual
or accumulation of intruded microorganisms in pipe biofilm or scale cannot be modeled using such a tool. A single-specie model can be used to estimate worst-case exposure scenario where no inactivation takes place (Klosterman et al., 2009). For example, it can be adequate to simulate the transport of Cryptosporidium as it can be considered that even if a chlorine residual is present in the distribution system, it will barely have any effect on Cryptosporidium inactivation (Korich et al., 1990; Betancourt and Rose, 2004). However, the same is not true for the microorganisms that are sensitive to chlorination. Shang et al. (2008) provided upgrades to the EPANET model in order to simulate the fate and transport of multiple dissolved/ suspended constituents in distribution system, in both the bulk flow and at the pipe wall. Epanet-MSX (Multi-Species eXtension) has been released in 2007 and can be downloaded from the EPA website (http://www.epa.gov/NRMRL/wswrd/ dw/epanet.html). Of interest for this framework is the capacity of modeling the interaction between the modeled pathogen and any type of residual disinfectant, based on the mathematical expressions governing the reaction dynamics provided by the modeler. However, estimation of the necessary
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Table 2 e Microorganisms of interest for estimating the potential health risks associated with intrusion events. Microorganism Cryptosporidium
Giardia
Norovirus
E. coli O157:H7 (enterohaemorragic (EHEC) E. coli)
Campylobacter jejuni
Rationale for selectiona Cause self-limiting diarrhoea (but can be life threatening in immunocompromised people) Involved in several waterborne outbreaks Infectivity of Cryptosporidium oocysts is relatively high (ingestion of fewer than 10 oocysts can lead to infection) Hunter et al. (2005) found a relationship between cryptosporidiosis and low pressure events in distribution systems in the UK Cause diarrhoea and intestinal malabsorption Identified as the cause of waterborne outbreaks for over 30 years, at some point, Giardia was the most commonly identified cause of waterborne outbreaks in the USA. Ingestion of fewer than 10 cysts constitutes a meaningful risk of infection Giardia outbreak was reported in a trailer park (Blackburn et al., 2004); was related to a power outage which created a negative pressure condition in the distribution system Cause acute viral gastroenteritis and vomiting Today considered to be the most common cause of gastroenteritis in the western world regarding the number of outbreaks and people affected High attack rates in outbreaks indicate that the infecting dose is low. The average probability of infection for a single Norwalk virus particle is estimated to be close to 0.5, exceeding that reported for any other virus studied to date (Teunis et al., 2008a) Cause diarrhoea and some cases may develop haemolytic uraemic syndrome Infectivity of EHEC strains substantially higher than that of the other strains (as few as 100 EHEC organisms can cause infection) Identified as etiological agent of the waterborne outbreak due to DS deficiencies (main repair, sewage intrusion) in Cabool (Geldreich et al., 1992) (and other waterborne outbreaks such as Walkerton (Hrudey et al., 2003)) One of the most important cause of acute gastroenteritis worldwide Contaminated drinking water supplies have been identified as a significant source of outbreaks of campylobacteriosis. Was identified in the Walkerton outbreak (Hrudey et al., 2003) Relatively high infectivity compared with other bacterial pathogens (as few as 1000 organisms can cause infection)
Commentsb Oocysts are extremely resistant to chlorination (Korich et al., 1990; Betancourt and Rose, 2004)
Giardia is more resistant than enteric bacteria to free chlorine but not as resistant as Cryptosporidium (Betancourt and Rose, 2004)
Viruses are more resistant to inactivation than bacterial cells but are still easily inactivated by free chlorine. They are much more resistant to chloramines (USEPA, 1999)
E. coli strains are sensitive to chlorination (USEPA, 2001)
Campylobacter is sensitive to chlorination (USEPA, 2001)
a If not otherwise indicated, information provided is from WHO, 2004. b In all cases, if a chloramine residual disinfectant is used, greater exposure times will be needed to obtain similar inactivation as monochloramine is considered a weak biocide in comparison to free chlorine (USEPA, 2001).
parameters related to the kinetic models is necessary and may necessitate laboratory studies in some cases. Modeling frameworks to simulate the action of a disinfectant residual on microbial contamination in distribution system have been developed by Warn Betanzo et al. (2008) and Propato and Uber
(2004). In addition to the interactions between intruded microorganisms and disinfectant residuals, other types of interactions such as attachment/detachment of pathogens in biofilm could also play a significant role in the possible population exposure. Recent work on the development of a multi-
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specie biofilm pathogen attachment/detachment model has been conducted by Schrottenbaum et al. (2009) and uncertainty regarding parameter estimation requires further investigation. As noted by Uber (2010), the mathematical formulation of these models (microorganism inactivation by disinfectant residual, biofilm, etc.) can be solved using Epanet-MSX but the software does not provide any assurance that these models are valid. Laboratory and field scale experimentation will be needed to support the development and testing of these water quality process models. Some assumptions for the hydraulic simulation of contaminant dilution (at entry points), mixing, and transport in distribution system also need to be highlighted: (i) Dilution of entering contaminant to obtain the microbial concentration in the pipe at the entry point: The location where intrusion occurs defines the pipe flow rate into which the contaminant external concentration will be diluted when entering the system. The resulting contaminant concentration in the pipe is usually obtained by dividing the number of microorganisms introduced (external concentration volume introduced) by the volume of water flowing in the pipe (for the duration of the intrusion event) under steady-state flow conditions. For example, at one location in a distribution system where the steady-state flow rate is 15 L/min, an intrusion event lasting 2 min with an introduction of 1 L of sewage having a concentration of 10 Cryptosporidium oocysts/L, would result in the introduction of 10 oocysts. These oocysts will be “diluted” in 30 L (volume of water that flowed during the intrusion duration) and the in-pipe concentration will be 0.3 oocysts/L. While this is an adequate methodology for simulating a deliberate intrusion (pumping a contaminant in a flowing distribution system), this may be less suitable for cases where intrusion takes place due to changes in pressure. These pressure variations are likely to induce changes in both magnitude and direction of flows and the use of steady-state flow values for estimating dilution may not be appropriate. Inaccurate estimation of the in-pipe contaminant concentration may result. (ii) Mixing at pipe junctions: conventional hydraulic software considers that the mixing of fluid at pipe junctions is complete and instantaneous. The concentration of a substance in the water leaving the junction is the flowweighted sum of the concentrations from the inflowing pipes (Rossman, 2000). However, computational and experimental investigations have shown that this assumption may generate considerable errors (van Bloemen Waanders et al., 2005; Romero-Gomez et al., 2006; Austin et al., 2008). A water quality solver suitable for large-scale network simulations using an assumption of non-perfect mixing at pipe junctions has been developed by Choi et al. (2008) and has been validated with laboratory results (Song et al., 2009). Results of water quality simulations with the software, named “AZRED”, have been compared to results obtained with EPANET. Some investigators observed differences in contaminant transport in the distribution system (Choi et al., 2008; Romero-Gomez et al., 2008), significant enough to result in different schemes for sensor network design for early
warning detection systems (Romero-Gomez et al., 2008). However, in some other cases, application of AZRED only led to small differences in contaminant transport that were not significant (LeChevallier et al., 2009). Although large distribution systems that are highly looped may see increased effects from imperfect mixing conditions, such findings bring uncertainty about the current modeling of contaminant transport in distribution systems assuming perfect mixing at nodes. (iii) Transport of contaminant: conventional hydraulic software uses the one-dimensional advection-reaction model for transport of constituents. This translates into having a constituent traveling at the same velocity as the bulk fluid. Such an assumption is valid for turbulent flow conditions, as the deviations from the mean velocity (representing the axial dispersion) are relatively small (Boulos et al., 2004). The contribution of dispersion to axial spreading can therefore be neglected under most circumstances in water distribution systems. However, when flow velocities are low (laminar flow conditions), as in dead-end pipes, the dispersion mechanism may become significant. As advective transport models are not able to account for dispersive transport, it is likely that modeled constituent concentrations will be incorrectly estimated in areas of distribution systems with laminar flow conditions (Axworthy and Karney, 1996). To illustrate this, one can imagine that under advective transport conditions, a small concentrated bolus of pathogens is transported in the pipe with the bulk fluid (plug flow conditions). The likelihood for someone to consume this contaminated water will be much lower than if an equivalent amount of pathogens is distributed in a bolus that is spread over time, taking into account the dispersion mechanism. This is likely to lead to a greater population exposure but with individuals being exposed to lower microorganism concentrations. However, for highly infectious agents such as norovirus, exposure to only one organism may lead to a significant probability of infection (Teunis et al., 2008a). This is likely to be critical in the estimation of population exposure following an intrusion event, especially for the highly infectious microorganisms. Work on 2-D advective-dispersive model of constituent transport in water pipes has been conducted over the years but findings have not yet been integrated to commonly used hydraulic network solvers (Tzatchkov et al., 2009). Estimation of dispersion coefficients to better represent the solute transport is the subject of ongoing research (Romero-Gomez et al., 2009). Lee and Buchberger (2001) also claim that in order to get a better prediction of constituent concentrations, simulation models should also take into account the unsteady intermittent flow prevailing in dead-end pipes (in response to customer demands), in addition to laminar flow conditions. In an EPS model, the demands at each node are usually represented by a 24-h pattern with constant demand values over 1-h time steps. In reality, demands are highly variable and usually much shorter than the 1-h time period, especially in residential areas (Buchberger and Wells, 1996). Considering smaller
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temporal aggregations for demands (1-min and 10-min compared to 1-h), preliminary results from Yang and Bocelli (2009) showed that consideration of demand variability is also likely to impact distribution system hydraulics and transport. For transient contamination, as we are dealing with short duration events, the actual consideration of constant demands over 1-h periods may therefore be misleading. A review covering the impact of both dispersion and demand modeling on water quality evaluation is available from Blokker et al. (2008). Although several mechanisms take place inside the pipes of a distribution system, it is recognized that it is not possible to (i) model them all, and (ii) account for all water system-specific characteristics and associated uncertainties. However, one should be aware of the assumptions underlying the hydraulic and water quality modeling used to simulate the fate and transport of contaminants. The end product of this analysis is a time-varying concentration profile of the simulated pathogen in the bulk water at each node over the duration of the simulation.
4.6. Characterization of pathogen exposure to populations consuming the drinking water For an individual, exposure to contaminated water will be dependent upon the likelihood that this person will open its tap at the time of the passage of the contamination in the distribution system. It is true that delayed exposure could also occur if pathogenic microorganisms become attached to the pipe biofilm, remain protected, grow, and are later released if changes in hydraulic conditions take place (however, our current capacity to model this is rather limited at this time). Depending upon the type of pathogens, different water use scenarios (ingestion, inhalation or dermal contact) may lead to a person’s exposure to contaminated water. In the case of pathogen intrusion in distribution system, ingestion of drinking water is generally assumed to be the main contributor to exposure. A common rule of thumb for domestic water usage in the United-States is to assume that approximately 400 L/day of water are used per person while the average per capita daily quantity of tap water ingested is approximately 1 L (USEPA, 2004). The small fraction of water used for ingestion further decreases the likelihood of being directly exposed to contaminated water. As pathogen concentrations vary in time, the timing of exposure and the volume ingested is likely to play a significant role in determining the possible impact of an intrusion event, especially for short duration events (Davis and Janke, 2008). Estimation of population exposure over 24-h periods is based on the output of the fate and transport analysis, showing temporal profiles of microbial concentrations at every node in a distribution system. Different ingestion models have been used to estimate the amount of contaminated water that an individual can be exposed to combining ingestion volumes (typically 1 or 2 L/person-day) and timing of ingestion. The latter has been expressed as a constant consumption during an event (Propato and Uber, 2004), or proportional to the rate of water withdrawal from the
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network (demand) (Janke et al., 2006; Murray et al., 2006), which may not necessarily reflect the ingestion pattern (compared to all other water uses). Using fixed ingestion times (such as typical starting times of meals) is probably more realistic than the previous models in replicating the ingestion behaviour of the population. However, due to the infrequent and potentially short durations of exposure from intrusion events, the use of probabilistic models for ingestion timing and volume is even more realistic and should be considered (Davis and Janke, 2008). Davis and Janke (2009) developed a probabilistic model for timing of ingestion based on data from the American Time Use Survey to inform more realistic exposures. Recent information regarding the frequency, timing and volume of drinking water consumed at specific times during the day is also now available from a US nationwide water consumption survey (Barraj et al., 2009) and could help reduce modeling uncertainties.
4.7. Characterization of risk associated with pathogen exposure The final step in a QMRA analysis is to link the results of population exposure to health risk through the use of doseresponse models. These are mathematical functions used to derive the probability of a particular adverse health effect (infection, illness) based on the dose to which individuals or populations are exposed (Haas et al., 1999). Dose-response relationships have been developed for a variety of microorganisms as summarized by Haas and Eisenberg (2001) and McBride et al. (2002) along with more recent work from several authors (Teunis et al., 2005, 2008a, 2008b; Armstrong and Haas, 2008; Bollaerts et al., 2008; USEPA, 2008). Assumptions are associated with the use of dose-response models (such as extrapolation to low doses, strains of pathogens used in studies, etc.) but these are not discussed here as we consider that the challenges associated with the development of QMRA for intrusion contamination events in distribution systems are much greater for the factors contributing to exposure than for interpreting the risks from such exposure.
4.8. Existing QMRA models assessing the impact of intrusion events in distribution systems To the knowledge of the authors, two QMRA frameworks specifically designed to investigate the public health impacts of transient intrusion events in distribution systems have been developed (McInnis, 2004; Teunis et al., 2010). Although each of these models has its own set of assumptions, they basically follow the same principles as the ones described here and use transient analysis, fate and transport, and risk modeling outputs to derive their estimates. Teunis et al. (2010) concluded that the duration of the pressure event was the main driver for the risk of viral infection (determining the duration of virus intrusion and the probability of coincidence of virus peak and tap water intake), but also mention that many of the assumptions in the model could be improved as better information becomes available. McInnis (2004) recognized the inherent difficulty in obtaining all the required data needed for the procedure and used his model to specifically assess the relative
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reduction in the risk of receptor infection (compared to a basevalue) from the application of various mitigation strategies.
5.
Conclusions
Assessing the potential health risks associated with intrusion events in a distribution system is a complex process. The conceptual model presented here provided the main building blocks needed to develop a QMRA model for intrusion events in distribution systems. The current state of knowledge for both the input parameters to the model and the tools available to estimate population exposure were presented. When probabilities of infection associated with intrusion events are reported, it is important that interested stakeholders be aware of the assumptions and uncertainties currently associated with this type of risk assessment. Intrusion events can only take place if adverse pressure conditions are occurring, an outside source of contamination is present, and there is a pathway for external contamination entry. Population exposure is dependent upon several factors, among which are the quantity of pathogens entering the system, and the concentrations of pathogens reaching consumers taps. The duration of intrusion is certainly a key factor influencing exposure along with the likelihood that someone will withdraw water from its tap (to ingest it) at the same time as the passage of the contaminant. Public health risks associated with intrusion events can be estimated but such estimation is currently based on several assumptions. This review showed that opportunities for research are numerous for both the model input parameters and the tools used to estimate exposure.
Disclaimer The views expressed in this article are those of the individual authors and do not necessarily reflect the views and policies of the U.S. Environmental Protection Agency.
Acknowledgements This research was supported in part by a post-doctoral fellowship from the Natural Sciences and Engineering Research Council of Canada and by an appointment to the Research Participation Program at the Office of Ground Water and Drinking Water administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Environmental Protection Agency.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
A combined approach for a better understanding of wastewater treatment plants operation: Statistical analysis of monitoring database and sludge physico-chemical characterization A.C. Avella a,*, T. Go¨rner a, J. Yvon a, P. Chappe b, P. Guinot-Thomas b, Ph. de Donato a a
Laboratoire Environnement et Mine´ralurgie, Nancy-Universite´, LEM UMR CNRS INPL 7569, 15 avenue du Charmois, 54500 Vandœuvre-les-Nancy, France b Ge´nie Biologique Agro-Alimentaire, Nancy-Universite´, IUT, Le Montet, 54601 Villers-les-Nancy Cedex, France
article info
abstract
Article history:
Biological wastewater treatment plants (WWTP) are complex systems to assess. Many
Received 19 May 2010
parameters are recorded daily in WWTP to monitor and control the treatment process,
Received in revised form
providing huge amounts of registered data. A combined approach of extracting information
15 September 2010
from the WWTP databases by statistical methods and from the sludge physico-chemical
Accepted 19 September 2010
characterization was used here for a better understanding of the WWTP operation. The
Available online 29 September 2010
monitored parameters were analysed by multivariate statistical methods: Principal Components Analysis and multiple partial linear regression. The WWTP operational
Keywords:
conditions determine the sludge characteristics. The bacterial activity of the sludge in terms
Multivariate statistical analysis
of extra-cellular polymeric substances (EPS) production was assessed using size exclusion
Extra-cellular polymeric substances
chromatography and the internal structure of sludge flocs was observed by confocal laser
(EPS)
scanning microscopy. The diagnosis of three paper mill WWTP enabled the identification of
Activated sludge characterization
an important EPS production, the presence of the nitrification process and the presence of
Sludge settling
PO3 4 nutrient in WWTP-A. These three main characteristics of WWTP-A were related with a systematically good sludge settling. In WWTP-B and C with bad settling, the bacterial activity was weak. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Many different processes happen simultaneously in wastewater treatment plants (WWTP) leading to the difficulty of understanding the whole system. Physical operations are grouped together with chemical and biological processes to remove wastewater pollution. Moreover, the nature of influents is continuously changing over time, leading to an important variability of the system. The overall process itself
evolves over time and biomass must adapt to different conditions. The performance of the biological treatment depends on the pollution degradation by the biomass and on the separation of the biomass from the treated water, the sludge settling. Several parameters are constantly recorded in WWTP to monitor the performance of the process over time, in order to detect any special events and to control the effluent quality. The huge amount of registered data requires proper techniques to extract useful information.
* Corresponding author. Tel.: þ33 383596266; fax: þ33 383596285. E-mail address: [email protected] (A.C. Avella). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.028
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Multivariate statistical analysis methods such as Principal Components Analysis (PCA) and Partial Least Squares (PLS) are frequently used to analyse quite large databases of quantitative variables/observations and to compress them in order to extract relevant information (Aguado and Rosen, 2008; Kourti et al., 1996). These methods provide a diagnosis of the process through the established relationships among the measured data. The relationships were used to build the empirical models for estimating one or more properties of the system (Wise and Gallagher, 1996). The analysis of industrial databases using PCA and PLS methods is promising for industrial applications because they treat data in a truly multivariate manner (Kourti et al., 1995). In the case of the wastewater treatment process, statistical methods of data analysis have been used for different purposes. For example, Morad et al. (2007) identified the measured collinear parameters and the independent parameters using the stepwise regression method. They showed that among 22 parameters only the measurement of 12 parameters was strictly necessary and the remaining 10 could be calculated using linear relationships. Mujunen et al. (1998) used the PLS method with cross-validation to describe WWTP effluent quality parameters. Diluted sludge volume index (DSVI), chemical oxygen demand (COD) reduction and total nitrogen concentration could be explained by more than 65% of their variation as functions of some process parameters. In an activated sludge sequencing batch reactor, Ng et al. (2000) proposed a step by step approach for the identification of the pertinent measured parameters influencing the sludge volume index (SVI). A 2nd degree polynomial equation was found to be the best model to describe the SVI. Yoo et al. (2003) predicted the reduction of COD in a full scale WWTP using multivariate statistical methods. Firstly, the PCA method was employed to reduce the dimensionality and to remove the collinearity of the data. Then, a fuzzy model was used to build a nonlinear model. Many improvements of the PCA method have been proposed for the application in the wastewater treatment process. For example, Lennox and Rosen (2002) developed an adaptive multiscale PCA for a wide range of changes observed in the full scale WWTP data. Lee et al. (2004) used the kernel functions in the PCA method to capture the nonlinear relationships among the WWTP process variables. The data recorded by the WWTP operators contain an important information characterizing each system. The sludge bacterial activity depends on multiple operational conditions in the WWTP. The sludge in the biological tank of the WWTP is a complex dynamic biological structure which is composed of micro flora (microbial consortia) and micro fauna (mainly protozoa and metazoa). The population naturally produces the extra-cellular polymeric substances (EPS) which form with bivalent cations a network where microorganisms are embedded. Shedding of cell surface material, cell lysis and adsorption from the environment are also sources of EPS. The EPS keep the microbial aggregates together with adsorbed pollutants, nutrients and minerals in a three-dimensional matrix determining the physico-chemical and biological properties of biofilms (Wingender et al., 1999). The EPS are the main sludge component composed of up to 75e90% of polysaccharide and protein and of small amounts of lipids, DNA and RNA (Wingender et al., 1999). They can be present as pure or mixed components
with various functional groups and their molecular sizes can range from a few hundred to several hundred thousand Daltons. The EPS production is rather constant and typical for a given WWTP in steady state working conditions (Garnier et al., 2005). The role of EPS in sludge has often been studied, although exactly how EPS work has not yet been clearly established and studies are sometimes controversial (Liu and Fang, 2003). Nevertheless, it is known that EPS play an important role in several stages of the wastewater treatment process: from sludge flocculation and settling to sludge dewatering. Frequent disturbances in wastewater treatment are damageable to the process due to the direct impact on bacterial activity. This paper presents the study of three paper mill WWTPs applying the activated sludge treatment, but with different technologies and loads. We chose to follow-up three WWTPs (A, B and C) for several months because a very good settling was systematically observed in WWTP-A, while in WWTP-B and C the sludge settling was poor. We tried to relate the operational conditions with the sludge characteristics. Our approach consisted in extracting pertinent information from the WWTP databases by statistical methods and combining it with the sludge physico-chemical characterization by laboratory analysis. The database of each WWTP has been recorded by operators to meet the legislative requests and to control the process, monitored parameters being chosen arbitrarily. Each plant was independent therefore the available data were not homogeneous. The multiple monitored parameters were analysed by multivariate statistical methods: Principal Components Analysis (PCA) and multiple partial linear regression. The EPS polymers are directly involved in floc structuration and consequently in sludge settling. The bacterial activity was followed mainly via the analysis of EPS using size exclusion chromatography and analysis of protein and polysaccharide concentration by UV-Vis spectroscopy. The internal structure of sludge flocs was observed by confocal laser scanning microscopy.
2.
Materials and methods
2.1.
Monitoring of WWTP parameters
The recorded parameters of the three paper mill WWTPs (WWTP-A, WWTP-B and WWTP-C) are listed in Table 1. Beyond the different sludge settling in each WWTP, the WWTP-B shows the particularity of having two parallel treatment lines with different configurations fed by the same inlet wastewater quality but the wastewater outlet quality is different.
2.2.
Multivariate statistical analysis
Two statistical methods were applied to analyse the WWTP database: a) PCA by Pearson’s coefficient (linear combinations) (Mackiewicz and Ratajczak, 1993), and b) Multiple regression. Multiple regressions were restricted to linear or polynomial expressions of 2nd degree. For all calculations, the XLSTAT software (Addinsoft Company) was used.
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Table 1 e Measured parameters in different WWTPs. Process stage
WWTP-A 3
Influent
Flow [m /d] MLSS [mg/L]
Inlet to secondary treatment
COD [mg/L] MLSS [mg/L]
Secondary treatment
pH Temperature [ C] NO 3 [mg/L] NH3eN [mg/L] PO3 4 [mg/L] MLVSS [mg/L] SSV [mL/L] MLSS [mg/L]
Effluent
WWTP-B 3
WWTP-C 3
Flow [m /d] MLSS [mg/L] COD [mg/L] BOD5 [mg/L] Flow [m3/d] MLSS [mg/L] COD [mg/L] BOD5 [mg/L] TKN-N [mg/L] Total phosphorus [mg P/L] MLVSS [mg/L] Recycle sludge flow [m3/d] Recycle sludge MLVSS [mg/L]
Flow [m /d] MLSS [mg/L] COD [mg/L]
Flow [m3/d] MLSS [mg/L] COD [mg/L]
MLSS [mg/L] COD [mg/L] Temperature [ C]
MLSS [mg/L] COD [mg/L]
MLVSS [mg/L] Recycle sludge MLVSS [mg/L] DO [mg/L] DSSV [mL/L]
Settled sludge volume (SSV); dissolved settled sludge volume (DSSV); mixed liquor volatile suspended solids (MLVSS); mixed liquor suspended solids (MLSS); total kjeldahl nitrogen (TKN-N); dissolved oxygen concentration (DO).
All parameters measured by WWTPs operators over several months were used for analysis by PCA. PCA is a projection method (Lebart et al., 1979) which enables the visualization of the correlations among parameters in a correlation circle. Only parameters far from the biplot center, which are statistically well explained in the system, were considered here. The parameters that are close to each other are in normal correlation, those related to others by a 180 rotation are inversely correlated, whereas the parameters related by a 90 rotation are independent. The parameters close to the center are independent of those well explained in the circle. The correlations were calculated according to the Pearson and Spearman coefficients. Since the two methods provided similar results, Pearson’s coefficient was retained for the data analysis.
2.3.
Physico-chemical analysis of activated sludge
2.3.1.
Sampling
(MLVSS), the mixed liquor suspended solids (MLSS) and fixed solids were measured by 2540 G method, the settled sludge volume (SSV) and the sludge volume index (SVI) were determined by 2710 C and 2710 D methods. Floc size distribution was measured by a Malvern Mastersizer S particle size analyser which enables the measurement of particles within the range of 0.05e900 mm by Fraunhoffer diffusion of 630 nm light. For the analyse of each sample, its own supernatant was used as a liquid phase. 5e10 mL of sampled sludge was used for measurement.
2.3.3.
EPS analysis
Two types of samples were analysed: i) the EPS bound in the flocs and ii) the soluble matter. Bound EPS were obtained after EPS extraction which was performed according to Frolund et al. (1996) with modification: extraction time of 4 h instead of 1 h.
2.3.3.1. High pressure size exclusion chromatography analysis. Activated sludge samples were collected once a month in each WWTP during several months. The sludge was sampled in the zone of the best turbulence in the aeration tank. We checked that several samplings produced similar results. Two or three analyses were systematically carried out on each sludge sample. The sampled activated sludge was centrifuged (15 min at 4500 g) to separate the biomass (settled pellets) from the supernatant. The soluble matter present in the supernatants was directly analysed by chromatography for total polymeric substances content and by classical UV-Vis method for protein and polysaccharide concentration. The settled biomass (bound fraction) was submitted, before analysis, to EPS extraction.
2.3.2.
Activated sludge analysis
The standard methods (APHA et al., 2005) were used for sludge characterization: the mixed liquor volatile suspended solids
Soluble substances and extracted EPS were analysed by high pressure size exclusion chromatography (HPSEC) to estimate the molecular sizes of the present chemical species. The area of peaks can be considered in approximation as representative of EPS concentration. The separation of EPS samples was carried out on a Hewlett Packard 1100 series chromatograph. A Zorbax Bio series column (GF-250, 25 cm 9.4 mm, Agilent Technologies, France) with thermostatic control at 25 C. The chromatographic column elution volumes were calibrated by the main EPS constituents, protein and polysaccharide, as described elsewhere (Avella et al., 2010).
2.3.3.2. Total protein and total polysaccharide analysis by UV-Vis spectroscopy. For a total protein and total polysaccharide content, the classical UV-Vis spectroscopy methods were applied. Protein determination was carried out with the total protein Kit; Micro Lowry (Lowry et al., 1951)
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with Peterson’s modification (SigmaeAldrich, France) using bovine serum albumin as a standard for calibration from 20 to 200 mg/L, absorbance was measured at 750 nm. Polysaccharides were determined according to the classical Dubois protocol (Dubois et al., 1956) at 485 nm, calibration was done with glucose for concentrations ranging from 10 to 100 mg/L. All samples were measured in duplicate.
2.3.4.
Optical analysis by confocal microscopy
20 mL of the sludge was put in a test tube. After several hours of settling, the sludge was fixed by 2.5% glutaraldehyde (v/v) for 4 h and then stored at 4 C. Before microscopic analysis, the sludge was soaked in acridine orange buffered solution (22 mM acridine orange, 5 mM EDTA, 0.15 M NaCl, 0.1 M phosphateecitrate buffer pH 6; SigmaeAldrich) for 20 min at room temperature then the sludge was washed with ultra pure water to remove excess dye. The acridine orange dye stains bacteria nucleic acid and extra-cellular polymeric substances (Wirtanen et al., 2001). The characterization of 3D sludge structure was performed by Confocal Laser Scanning Microscopy (CLSM). The inverted light microscope (NIKON TE 2000 U) was equipped with a confocal head (Radiance 2100 Rainbow, Biorad). A Nikon CFI Plan Fluor Apo 20 X (NA 0.7 WD 1 mm) was used to explore sludge samples. Excitation beams at 457 and 488 nm were provided by the argon ion laser, 4 lines selectable. The following signals were separated and collected: (1) green fluorescence emission of cell DNA was recorded in the frequencies range of 530e560 nm (excitation at 488 nm), (2) fluorescence emission of EPS was detected over 600 nm (excitation at 547 nm), and (3) backscattered diffuse reflectance in the range of 480e490 nm was used for imaging reflecting matter (e.g. particles). For each channel (i.e. frequencies range), Z-series images (512 512 pixel2) at a resolution of 0.674 mm per pixels and a vertical resolution up to 2.23 mm were carried out using LaserSharp2000 software (Bio-Rad cell science division). Prior to quantitative image analysis, deconvolution of three-dimensional image stacks was applied to mitigate the distortion created by microscope optics and to enhance automatic detection of the edges of a structure. 3D captured fluorescence images were processed using the AutoQuant X v1.4.1 (MediaCybernetics) software. Representing images from all WWTP sludges were binarized using MacBiophotonics ImageJ software (NIH, Bethesda, Maryland, USA) in order to quantify respective contribution of cells, EPS and reflecting matters in sludge flocs. The results are presented in percentage of the contribution of disjointed components to the total sludge floc surface. The Z-projection of each channel stacks was also considered.
3.
Results
In the first step, the statistical treatment of monitoring WWTP database was performed to identify the relationships among technologic parameters. Then, they were related to the information obtained from the sludge physico-chemical characterization for a better understanding of phenomena occurring in the WWTP.
3.1.
Multivariate statistical analysis
3.1.1.
Principal component analysis (PCA)
In order to remove outlying data from the recorded WWTP database, we have examined the distribution of observations (measured parameters at different dates) in a two-dimensional map. The data were analysed by PCA to determine the correlations among measured parameters. The three first PCs axes were considered in this study, and we present in Fig. 1 only the first two axes (PC1 & PC2) which explain the highest variability of the data. Because all measured parameters in each WWTP were used for analysis, the explained variability obtained by the two axes (PC1 & PC2) was relatively reduced. The general tendency of the data was verified by applying partial linear regression among parameters and by analysing the third axis (PC1 & PC3) of correlations (not presented here). The third axis provided an additional explanation of data up to 12%. Only correlations showing a significant coefficient according to the level of significance were considered. In Fig. 1, one correlation circle is presented for WWTP-A and WWTP-C; and two correlation circles for WWTP-B. The WWTP-B has two process lines working in parallel and each line was analysed independently. The presence of positive or negative correlations or absence of correlations gave information about phenomena occurring in each WWTP.
3.1.1.1. WWTP-A. The following positive correlations can be seen in WWTP-A (Fig. 1): Inlet MLSS to the aeration tank (parameter 4) was correlated with inlet COD (parameter 3): This means that, the most important oxygen consuming pollutant in the inlet water was MLSS (rather than soluble matter). Biomass in the aeration tank (MLVSS, parameter 10) was correlated with SSV (parameter 12). This is a normal, expected relationship: the biomass has a settling ability and the biomass increase enhances the settled sludge volume. Inverse correlations were also found in Fig. 1: Biomass (MLVSS, parameter 10) and SSV (parameter 12) were inversely correlated to NO 3 concentration in the aeration tank (parameter 7): an increase in biomass and in SSV occurred simultaneously with a decrease in NO 3 concentration. This indicates the balance between nitrification and denitrification processes. Some conditions could promote the nitrification process in WWTP-A: i) the longer sludge retention time (50 15 days) which would allow an adequate growth of nitrifying bacteria, ii) sufficient concentration of dissolved oxygen in the aeration tank and iii) probably a complete BOD5 mineralization. It is known that the denitrifying bacteria growth rate is higher than nitrifying, which would explain the biomass and SSV increase when the denitrifying process happens. Independence between some parameters was also observed in Fig. 1: Inlet COD to the aeration tank (parameter 3) was independent of phosphates concentration (parameter 9 expressed
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Fig. 1 e Correlation circles of measured parameters in WWTPs obtained by PCA (PC1 & PC2). 3 as PO3 4 ) in the tank: the PO4 fluctuations were not correlated with those of the inlet COD.
3.1.1.2. WWTP-B. First, we present the common relationships found in both process lines and then the particular characteristics of each line. The common relationships show on Fig. 1 are: The influent MLSS (parameter 2) were positively correlated with influent COD (parameter 3) which indicates, as in the previous plant, that MLSS in the influent was an important O2 consumer. Since the influent MLSS was
not correlated with influent BOD5 (parameter 4), it means that an important fraction of MLSS was non biodegradable. Inlet COD (parameter 7) to the aeration tank was positively correlated with effluent COD (parameter 16). This means that COD removal was only partial: a fraction of inlet COD not removed in biological treatment was found in the effluent. In association with the previously described relations, this persistent matter would correspond to the non biodegradable fraction. The increase of the influent BOD5 to WWTP (parameter 4) was related to the increase in inlet BOD5 to the aeration tank
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(parameter 8). This normal, expected relationship showed a partial BOD5 removal in primary treatment (primary clarifier and pH equalization tank). Inlet BOD5 to the aeration tank (parameter 8) and biomass concentration (MLVSS, parameter 11) were related together: the biomass seemed to use the inlet biodegradable carbon as a principal energy source. This indicates that the major biological reaction was the BOD5 mineralization while ammonium transformation played a minor role in the biological process. Inlet TKN-N to aeration tank (parameter 9) and inlet total phosphorus (parameter 10, noted P) were positively correlated. It was probably related to the nutrients addition. Independence relation was observed in Fig. 1: The biomass (MLVSS, parameter 11) and inlet COD to the aeration tank (parameter 7) were independent. This suggests that a significant fraction of COD was non biodegradable matter and did not contribute to bacteria growth. As seen previously, the biomass was mainly sustained by inlet BOD5 content.
MLSS and COD evolved together along the treatment process. It was the case in WWTP influent (MLSS parameter 2 and COD parameter 3) and also in the effluent (parameters 11 and 12). However, the fluctuations of MLSS in the effluent of WWTP-C (parameter 11) cannot be explained by those in the influent (parameter 2) because influent MLSS and effluent MLSS were independent for the same day measurements. The same independence can be observed for COD. This could be explained by either: i) the biomass coped with the inlet fluctuations thus the effects on effluent were not observed at the same day, or ii) the influent MLSS and COD were removed in the process but their presence in effluent had other origins. The biomass in aeration tank (MLVSS, parameter 6) evolved with inlet COD and inlet MLSS. That means that suspended organic matter present in the inlet wastewater was involved in the biomass growth. Biomass (parameter 6) seemed to influence MLSS effluent (parameter 11), they are positively correlated. Biomass was probably not completely retained in the clarifier and some release would contribute to MLSS in the effluent.
3.1.2. The two process lines were supplied by inlet wastewater of identical quality, however the inlet flows were different because the geometry and volumes of the two aeration tanks differed. The aeration systems were also different (turbine aerators in line 1, while submerged diffused air in line 2). The particular characteristics of each line identified by statistical analysis were as follows: - Biomass content in aeration tank and sludge purge were higher in line 1 than in line 2 (biomass1 > biomass2 and sludge purge flow1 > sludge purge flow2) - MLVSS in recycled activated sludge was higher in line 2 than in line 1 (MLVSSrecycle 2 > MLVSSrecycle 1). That led to a sludge accumulation in the second clarifier which seemed to influence the solid/liquid separation: line 2 provided a better quality effluent with lower MLSS and COD than in line 1. The explanation of this phenomenon can be attributed to the important sludge column formed in the second clarifier that stimulated the flocs interaction/attraction. This argument is supported also by statistical analysis where effluent MLSS (parameter 15) and COD (parameter 16) were positively correlated in line 1 (effluent MLSS contributes significantly to COD). In line 2 the effluent MLSS (parameter 15) and COD (parameter 16) were independent. As mentioned above, the presence of a high sludge column in line 2 created conditions that breakdown the relation MLSS-COD. MLSS were better aggregated and were retained with better efficiency leading to the output effluent of better quality.
3.1.1.3. WWTP-C. It is necessary to note that very important fluctuations of recorded measured values were observed in WWTP-C and it was not possible to describe the process using one year of recorded data. Therefore, the analysis presented here was performed only on a three months database (from May to July). Fig. 1 shows the following positive correlations:
Multiple regression
Once the correlations among parameters were established, it was possible to describe by numerical expressions: the SSV (related to settling quality), and the effluent MLSS and COD (related to effluent quality). Considering the complexity and the multiple contributions of various parameters, only simple types of regressions were examined: linear and polynomial expressions of 2nd degree. The accuracy of numerical expressions to describe the parameters was estimated by the regression coefficient R2 relating the estimated values to the measured ones.
3.1.2.1. WWTP-A. A linear expression based on 12 process parameters (not shown), allows the description of 80% of SSV variance. However it is interesting to notice that only two parameters contribute to 70% of SSV explanation (Fig. 2a). In concentration has an important expression (1), the PO3 4 influence on settling: an increase in PO3 4 was involved in the decrease of SSV, signifying a better settling performance. Actually, in WWTP-A, an external addition of H3PO4 into the aeration tank (to reach a PO3 4 concentration from 0.5 to 2 mg/ L) seemed to improve the sludge settling. SSV ¼ 42 þ 0:2 MLVSS 10 CPO3 4
(1)
Fig. 2 (a) shows the evolution over time of the measured SSV and the SSV calculated by Eq. (1). A good agreement is obtained, the calculated values exhibit the same tendency as the measured values. On the other hand, MLSS in the effluent could be described neither by linear nor by polynomial expression of the 2nd degree. Indeed, MLSS (parameter 11 in correlation circle) was not well defined by PCA method since its projection was close to the center; it means that others parameters, not measured in the process, clearly affected it.
3.1.2.2. WWTP-B. SSV values were not recorded in WWTP-B. Thus, regressions will be focussed on parameters that measure the effluent quality. The numerical description
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a
b
c
Fig. 2 e Evolution over time of the measured parameters and of the calculated parameters. Relationship between measured and calculated parameters. a) WWTP-A, b) and c) WWTP-B.
differs for both process lines: the first line seems to be more simple to describe (Eqs. (2) and (3) where just a linear expression provides an acceptable description) than the second line (Eqs. (4) and (5) with 2nd degree polynomial expression). Among twelve parameters measured in the process, we selected the most significant parameters
according to the coefficient of correlation R2 to explain the MLSS and COD in the effluent. We considered that the regression coefficients R2 of about 0.5e0.6 (Fig. 2b and c) were acceptable to describe the multivariate system where the parameters have individual and multiple impacts on the biological process and on the effluent quality.
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Line 1:
MLSSeffluent ¼ 42:3 þ 1 103 flowinfluent 0:03BOD5 influent þ 6:7 103 flowinlet aeration tank 1:8 102 CODinlet aeration tank 4:8 103 MLSSrecycle sludge
CODeffluent ¼ 121:7 þ 8:4 103 MLSSinfluent 6 102 BOD5 influent þ 9:3 103 flowinlet aeration tank þ4:5 102 CODinlet aeration tank 6:2 103 MLSSrecycle sludge
(2)
(3)
Line 2:
MLSSeffluent ¼ 70 þ 0:016BOD5 influent þ 1:2 102 CODinlet aeration tank 1:2 102 BOD5 inlet aeration tank 0:7TKNinlet aeration tank 2:5 102 MLVSSaeration tank þ 6:6 106 BOD25 influent 6:3 106 COD2inlet aeration tank
(4)
2:8 105 BOD25 inlet aeration tank þ 2:3 102 TKN2inlet aeration tank þ 2:7 106 MLVSS2aeration tank
CODeffluent ¼ 194 þ 2:2 102 CODinfluent þ 0:5CODinlet aeration tank 0:2MLVSSaeration tank þ0:29flowrecycle sludge 1:9 106 COD2influent 1:9 104 COD2inlet aeration tank 5
þ2:3 10
MLVSS2aeration tank
4
3:5 10
(5)
2 flowrecycle sludge
The calculated MLSS and COD values (following Eqs. (2)e(5)) and the measured values are presented in Fig. 2b and c for each process line: the equations described satisfactory the parameters variations over time.
3.1.2.3. WWTP-C. The numerical descriptions of the sludge settling and of effluent quality parameters were rather complex in WWTP-C; neither linear nor polynomial expressions were satisfactory. Acceptable regression coefficients R2 (R2 w0.5) were obtained, only when including all measured parameters which does not have any practical interest. The choice of relevant parameters was not possible due probably to the high multiple dependency of the system or to the lack of the relevant measured parameters.
3.2.
Physico-chemical analysis of activated sludge
3.2.1.
EPS analysis
To have more information about bacterial activity, the sampled sludge from each WWTP aeration tank were submitted to the analysis of bound EPS in the flocs and of soluble EPS in the bulk solution. The chromatography analysis and the protein and polysaccharide quantification results presented in this section were normalized per gram of biomass. In our previous studies (Garnier et al., 2005), we have observed that in WWTP steady state working conditions, the EPS production reflecting the bacterial activity, was constant and typical for a given WWTP (in terms of EPS macromolecules size repartition and EPS production per gram of biomass). In this study, we also found that each WWTP showed a regular typical chromatographic shape.
3.2.1.1. Bound EPS. Whatever was the WWTP, the chromatograms of bound EPS (Fig. 3 iel) present a profile with three peaks. The macromolecular sizes of each peak were estimated by calibration as follows: the peak N 1:
polysaccharide w788000 Da or proteins w700000 Da; the peak N 2: polysaccharide w11800 Da or proteins w44300 Da; and the peak N 3: polysaccharide w180 Da or proteins: 5700 Da. It can be seen that the EPS concentration was much higher in WWTP-A compared to B and C plants. The total protein and polysaccharide content in the sludge was more important in WWTP-A than in the others plants. With the exception of WWTP-C, proteins were predominant in bound EPS (Fig. 3 mep), in the WWTP-C, the polysaccharide content was slightly higher than that of protein.
3.2.1.2. Soluble matter. The chromatographic profiles of soluble matter were simpler than those of bound EPS (not shown here). All chromatograms showed a single peak with the same group of oligomers corresponding to polysaccharide w180 Da and proteins: 5700 Da. The concentration of soluble matter was slightly higher in WWTP-A than in the B and C plants. Total protein and polysaccharide analysis showed also that only in WWTP-C the contribution of polysaccharide was higher than that of the protein (not shown here), as was observed in bound fraction. 3.2.2.
Settling properties
To describe the sludge settling, the SSV and SVI parameters were measured (Fig. 3 aed). The SSV was considered a better indicator for describing the sludge settleability because the SVI parameter can mask a bad settling when an important amount of biomass is present. The SSV values were systematically smaller than 200 mL/L, indicating a good settling in WWTP-A while a poor settling was observed in WWTP-B and WWTP-C (SSV w800 mL/L). The mean values of the floc sizes for each WWTP are in Fig. 3. Except WWTP-B line 2 (floc size 60e100 mm), the mean floc sizes were similar in WWTP-A, WWTP-B line 1 and WWTP-C (close to 50 mm), therefore no relation with settling
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properties could be established. The larger floc size in WWTPB line 2 could be explained by the physico-chemical attraction forces generated by a higher sludge column in the clarifier 2 retaining better the MLSS, this information was obtained through the statistical analysis (Section 3.1.1.2).
3.2.3.
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Optical analysis of the sludge by confocal microscopy
The image analysis method described in Section 2.3.4, was used to map sludge components for multiple flocs. The contributions of cells, biopolymers and reflective matter (associated with mineral matter) to floc structure were then quantified and
Fig. 3 e Analysis of activated sludge and extra-cellular polymeric substances characteristics.
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plotted. Representative structures of all specimens of activated sludge are in Fig. 3 (eeh). Histograms present the results in terms of percentage of each contribution to the total floc surface. This percentage corresponds to the minimum value because it was assumed for calculation that components are disjointed (no elements in common). To assess the reproducibility, the quantification was performed at three levels of image stacks (top, middle and bottom) and on the Z-projection image. Whatever was the approach, the results exhibited a similar tendency. The results were remarkably consistent relative to origin of sludge. A careful attention has been paid to reproducible preparation and image acquisition. Activated sludge from WWTP-A appeared to have systematically well defined structure (Fig. 3 e) compared to two other WWTPs. Moreover, the contribution of floc components (cells, EPS and reflective matter) was very different in different sludge origins. The ratio of EPS (displaying hydrophobic features) to cells was more important in the sludge from WWTP-A (r ¼ 4.5) compared to the sludge from other sources (in WWTP-B ¼ 0.1e0.2 and in WWTPC ¼ 0.4). It is necessary to notice that in WWTP-A, the cell presence in flocs was low. This indicates a high bacterial activity because a small quantity of cells attained a significant EPS production in the WWTP-A flocs. The EPS matrix seems to contribute to the floc aggregation and to better settling properties. The high cell proportion in the flocs of WWTP-B and WWTP-C and a lower EPS content indicate a low bacterial activity resulting in poorly structured flocs. The analysis of the reflective matter (associated with mineral matter) showed that in WWTP-A the mineral matter was involved in the shape structure of flocs following the sludge silhouette, whereas in the two other plants the mineral matter was scattered.
4.
Discussion
The wastewater treatment must comply with all changes in operational conditions and each WWTP is a unique entity with a specific technology. In this complex context, the combined approach of statistical analysis of monitored data and physico-chemical characterization of the sludge provided an interesting information about principal characteristics and phenomena in each WWTP and some general trends could be outlined. The difference of the WWTP sludge settleability was related to the more or less intense bacterial activity in terms of the EPS production by the biomass. The differences of effluent quality in line 1 and line 2 (in WWTP-B), both lines were supplied with the same wastewater quality, presented an interesting scientific question to understand it. In WWTP-A with a systematically good settling several points can be underlined: 1) A significant EPS production is involved in a good sludge structure cohesion and consequently in a good sludge settling. It was reported that the quantity of EPS in the sludge had a significant positive correlation with good settling (Sponza, 2003). Moreover, not only the quantity but also the quality of EPS and the availability of cations (Caþ2 and Mgþ2) to bind the EPS together affect the flocculating
ability of flocs (Wile´n et al., 2003). However the EPS concentration should not be excessive (Liao et al. (2001). An excessive EPS presence can induce a stronger electrostatic repulsion between negatively charged floc components, which contributes to a settling deterioration as described by the DLVO theory (Zita and Hermansson, 1994). In WWTPA studied here, the high EPS concentration seems to be within an optimum range to contribute to the sludge structuration. It remained practically constant per gram of biomass during all seasons. 2) The PO3 4 nutrient content was one of the two main parameters in the multivariate system affecting positively the sludge settling (Eq. (1)). It was reported that the biomass density and the settleability increased with polyphosphate content (Schuler and Jang, 2007). On the other hand, the bacterial concentration stress of Nostocoida limicola III at low PO3 4 provoked bulking in the paper mill WWTP (Richard and Cummins, 1997). Our results showed the same phenomcontent in the biological tank enon: the increase of PO3 4 enhanced the sludge settling. The PO3 4 as a biological nutrient is likely involved in more important production of the EPS, and consequently in a more structured flocs with better settling. 3) The nitrification process occurring in the aeration tank could improve the sludge settling. The NO 3 concentration in the aeration tank appeared as the third influential parameter in SSV regression (equation not shown here). According to Hartley (2008), the nitrificationedenitrification process governs the sludge settleability, the best settling coinciding with the best nitrogen removal. The author proposed a simple and successful empirical model describing the SVI behaviour. Some interesting points about WWTP-B are worth discussing in spite of poor sludge settling. The most remarkable aspect was the excessive biomass quantity in the aeration tanks 1 and 2 (high MLVSS values compared to A and C plants). The floc structure with high amount of cells was scattered in smaller units. The statistical analysis revealed that the biomass consumed the organic matter (measured by BOD5) as its exclusive energy source for growth. This suggests that carbonaceous mineralization (BOD5 removal) occurs without other parallel nutrient removal: nitrification seemed not be reached even when the sludge retention time was around 40 days. It appeared also that the significant fraction of non biodegradable matter in the influent did not seem toxic (probably hardly degradable lignine) and therefore it did not impact the biomass behaviour. Often the presence of toxics is accompanied by a high EPS production (Aquino and Stuckey, 2004; Avella et al., 2010) which was not observed here. A very low EPS content in flocs was detrimental to a good settling, indeed the EPS with hydrophobic patterns are involved in intraflocs interactions (Urbain et al., 1993). The particularity of the WWTP-C (as in WWTP-B) was also a very low EPS production and low EPS content involved in flocs. Moreover the polysaccharide-type EPS were prevailing in the WWTP-C contrarily to A and B plants where the protein EPS were predominant. According to Liu and Fang (2003) bacteria tend to convert excess of the nutrients into polysaccharide-type EPS at low sludge retention time. Indeed, in the WWTP-C, the sludge retention time was <3 days. Liao et al. (2001) reported also that a short sludge retention time led to a decrease of sludge
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hydrophobicity resulting in a poor sludge flocculation with presence of a high amount of pin flocs. The poor settling in WWTP-C is very probably related to a mean bacterial activity which is related to the operational parameters. The impossibility to obtain in the WWTP-C simple correlations of monitored data indicates the non steady state operation of the plant or the necessity to re-examine the relevance of monitored data.
5.
Conclusion
The request for identifying parameters responsible for good or bad performance of the wastewater treatment is a real challenge for a wastewater treatment community. However the task is vast because each WWTP is a unique entity depending on a multitude of operational parameters. The presented study showed that the combined approach of statistical analysis of the WWTP database and the sludge characterization allowed a better understanding of the whole system, the resulting diagnosis of WWTP operation can be used in the future for the process optimisation. We identified the statistically important technologic data in each plant and the sludge features contributing to a good sludge settling. In the WWTP-A with a high EPS production by cells, the statistical analysis showed the importance of the nitrification nutrients, leading to the process and the presence of PO3 4 bacterial consortium life with a well structured sludge and good settling. Besides the understanding of phenomena in the WWTPs studied, which was our main objective, the numerical description of the sludge settling and effluents quality in terms of SSV and of effluent MLSS and COD by simple empirical expressions could serve as a first approach for the modelling of a wastewater treatment process in any given WWTP.
Acknowledgement The authors thank C. Mustin (LIMOS UMR 7137, Nancy University) for helpful advice on image analysis and its technical contribution to the CLSM acquisition of 3D fluorescence data.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Feature selection methods for characterizing and classifying adaptive Sustainable Flood Retention Basins Qinli Yang a, Junming Shao b, Miklas Scholz a,c,*, Claudia Plant d,e a
Institute for Infrastructure and Environment, School of Engineering, The University of Edinburgh, William Rankine Building, Mayfield Road, The King’s Buildings, Edinburgh EH9 3JL, Scotland, United Kingdom b Institute for Computer Science, University of Munich, 80937 Munich, Germany c Civil Engineering Research Group, School of Computing, Science and Engineering, The University of Salford, Newton Building, Salford M5 4WT, England, United Kingdom d Department of Scientific Computing, Florida State University, Tallahassee, FL, USA e Klinikum Rechts der Isar der Technischen Universita¨t Mu¨nchen, Munich, Germany
article info
abstract
Article history:
The European Union’s Flood Directive 2007/60/EC requires member states to produce flood
Received 27 June 2010
risk maps for all river basins and coastal areas at risk of flooding by 2013. As a result, flood
Received in revised form
risk assessments have become an urgent challenge requiring a range of rapid and effective
30 September 2010
tools and approaches. The Sustainable Flood Retention Basin (SFRB) concept has evolved to
Accepted 9 October 2010
provide a rapid assessment technique for impoundments, which have a pre-defined or
Available online 16 October 2010
potential role in flood defense and diffuse pollution control. A previous version of the SFRB survey method developed by the co-author Scholz in 2006 recommends gathering of over
Keywords:
40 variables to characterize an SFRB. Collecting all these variables is relatively time-
Sustainable flood risk management
consuming and more importantly, these variables are often correlated with each other.
Flood control
Therefore, the objective is to explore the correlation among these variables and find the
Classification
most important variables to represent an SFRB. Three feature selection techniques
Information gain
(Information Gain, Mutual Information and Relief) were applied on the SFRB data set to
Mutual information
identify the importance of the variables in terms of classification accuracy. Four bench-
Relief
mark classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and Naı¨ve Bayes) were subsequently used to verify the effectiveness of the classification with the selected variables and automatically identify the optimal number of variables. Experimental results indicate that our proposed approach provides a simple, rapid and effective framework for variable selection and SFRB classification. Only nine important variables are sufficient to accurately classify SFRB. Finally, six typical cases were studied to verify the performance of the identified nine variables on different SFRB types. The findings provide a rapid scientific tool for SFRB assessment in practice. Moreover, the generic value of this tool allows also for its wide application in other areas. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The European Union (EU) has responded to an increase in the perceived severity of flooding across the 25 member states by
introducing the Flood Directive 2007/60/EC on the assessment and management of flood risks, which came into force on 26 November 2007 (European Union, 2007). The EU member states are required to define the river basins, which will be used as
* Corresponding author. Tel.: þ44 131 6506780; fax: þ44 131 6506554. E-mail addresses: [email protected] (Q. Yang), [email protected] (J. Shao), [email protected] (M. Scholz), [email protected] (C. Plant). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.006
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the basis for catchment management of flood risk. Areas at risk of pluvial or coastal flooding are to be identified by 2011, with the areas at risk of flooding mapped by 2013. Sustainable flood risk management plans will focus on prevention, protection and preparedness, and are to be prepared by 2015. A sustainable (urban) drainage system (SUDS) can be regarded as one type of adaptive structure to mitigate flood risk and diffuse pollution on a local scale. Indeed, general binding rules, introduced under the Water Framework Directive (EC/2000/60) for Scotland, require all new developments to include SUDS in the design as a precondition (Scottish Environment Protection Agency, 2010). The authors propose that SUDS and barriers need to be supplemented by more innovative approaches such as adaptive SFRB (Scholz, 2007) and the use of existing infrastructure to ensure effective sustainable flood risk management planning. An SFRB is defined as an impoundment or integrated wetland (Scholz, 2006), which has a pre-defined or potential role in flood defense and diffuse pollution control that can be accomplished cost effectively through best management practice, achieving sustainable flood risk management and enhancing sustainable drainage, pollution reduction, biodiversity, green space, and recreational opportunities for society. The word sustainable in SFRB means capable of being maintained at a steady level without exhausting natural resources, harming the environment and causing severe ecological damage (McMinn et al., 2010). Natural and constructed retention basins keep runoff for subsequent release, thus avoiding downstream flooding problems, but some basins such as wetlands do perform other tangible albeit less ‘visible’ roles including diffuse pollution control and infiltration for groundwater recharge. The functions of retention basins are therefore diverse. Moreover, one basin often has multiple and mixed functions. So, the classification of SFRB is required which allows for better understanding and communication of the status and functions of any basin. The SFRB concept supplements other wetland classification systems. Hydrological, geomorphological, chemical and biological factors were used in a hierarchical classification systems of wetland and deepwater habitats located in the United States of America. Five major wetland types (Lacustrine, Riverine, Palustrine, Marine and Estuarine) were generated. These were further subdivided into more specific categories (Cowardin et al., 1979). Furthermore, the Ramsar Classification of Wetland Types, which is based on physical and limnological characteristics, divides wetlands into three main categories (marine and coastal wetlands, inland wetlands and man-made wetlands) and 43 associated wetland types (Ramsar Convention Secretariat, 2006). These classification systems are complicated, and detailed data sets, which are frequently unavailable, are required. Moreover, these classifications do not show straight relationships between types and diverse functions of the water bodies. To appropriately meet the practical need to identify the main functions of the basins, six general SFRB types were proposed by an international group of civil engineers, landscape planners and environmental scientists (Scholz and Sadowski, 2009). They are defined as Hydraulic Flood Retention Basin (type 1), Traditional Flood Retention Basin (type 2), Sustainable Flood Retention Wetland (type 3), Aesthetic Flood Treatment Wetland (type 4), Integrated Flood Retention Wetland (type 5)
and Natural Flood Retention Wetland (type 6), as discussed below. The SFRB concept provides a holistic assessment of a water body or impoundment including its diffuse pollution control and ecological status, functional role, and flood control potential. In contrast to conventional assessment approaches, the SFRB assessment integrates hard engineering control variables including dam height and length with more holistic variables such as how highly engineered an SFRB appears, and whether the structure represents a significant barrier to aquatic or land animal passage (Scholz and Sadowski, 2009). The large range of information gathered aims to greatly assist in addressing multidisciplinary issues regarding the management and use of a water body. However, the use of all 40 variables (Table 1) characterizing water bodies may make the assessment system complicated, time-consuming and ambiguous. In part, the ambiguity is due to the amount of diverse information gathered to characterize each SFRB. Moreover, some of the variables have direct or indirect relationships with each other, which means that dependency (or correlation) is always hidden among these variables. The key scientific question is therefore as follows: how could somebody identify the most important variables and
Table 1 e Classification variables used for the assessment of Sustainable Flood Retention Basins. ID
Variable and unit
ID
Variable and unit Impermeable soil proportion (%) Seasonal influence (%) Site elevation (m) Vegetation cover (%)
1
Engineered (%)
21
2 3 4
Dam height (m) Dam length (m) Outlet arrangement and operation (%) Aquatic animal passage (%) Land animal passage (%) Floodplain elevation (m) Basin and channel connectivity (m) Wetness (%) Proportion of flow within channel (%) Mean flooding depth (m)
22 23 24
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
25 26 27 28 29 30 31
Typical wetness duration (d a1) Estimated flood duration (d a1) Basin bed gradient (%)
32
Mean basin flood velocity (cm s1) Wetted perimeter (m)
35
Maximum flood water volume (m3) Flood water surface area (m2) Mean annual rainfall (mm) Drainage (cm d1)
37
33 34
36
38 39 40
Algal cover in summer (%) Relative total pollution (%) Mean sediment depth (cm) Organic sediment proportion (%) Flotsam cover (%) Catchment size (km2) Urban catchment proportion (%) Arable catchment proportion (%) Pasture catchment proportion (%) Viniculture catchment proportion (%) Forest catchment proportion (%) Natural catchment proportion (%) Groundwater infiltration (%) Mean depth of the basin (m) Length of basin (m) Width of basin (m).
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subsequently remove the redundant ones to make SFRB characterization and classification more effective and efficient? To address this question, feature selection techniques were adapted to investigate the underlying relationships between the 40 variables and the six SFRB types to identify the importance of variables and the minimum number of variables essential for accurate characterization of SFRB. This approach will make SFRB assessment more rapid, efficient and cost effective, providing the EU member states with a rapid tool to implement the EU Flood Directive, and supporting engineers and planner regarding design, maintenance and management. Therefore, the key objectives of this paper are as follows: To investigate the relationships between different variables and identify the most relevant variables for SFRB classification; To remove the redundancy and dependence among variables so as to reduce dimensionality and save time, achieving the most efficient SFRB classification at minimal cost; To validate the effectiveness of the selected variables using different benchmark classifiers; and To verify the performance of the identified important variables by discussing representative case studies for each type of SFRB. The background to the feature selection methods is presented in Section 2. Variables characterizing SFRB and data acquisition are described in Section 3, while Section 4 gives a detailed overview of the methodology. Section 5 consists of the feature selection results and the evaluation of the approaches as well as the experimental validation for six representative case studies. Finally, conclusions are drawn in Section 6.
2.
Background
In many real-world classification problems, the relevant features (i.e. characterization variables) are often unknown prior to starting the investigation. A large number of features are therefore introduced in an attempt to better represent the underlying structure of a data set. Thus, many features, which are irrelevant and/or redundant with respect to a particular site, are recorded anyway, and subsequently analysed. Redundant and irrelevant features have adverse effects on data mining such as over-fitting and reduced classification accuracy, and are therefore undesirable. Effective feature transformation and feature selection techniques are often applied to reduce the dimensionality. The objective of feature transformation is to project the data set in a new feature space with fewer dimensions. Reducing the number of dimensions within the feature space typically results in increased accuracy of the classification. However, the extracted novel features are constructed form all original features. Scholz and Sadowski (2009) applied a principal component analysis (PCA), which is a classical and widely used feature transformation approach, on a German data set of SFRB for dimensional reduction. Although the loading plot showed the relationships between the characterization variables, it was unclear which variables were the most important ones, since
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coordinate rotation and scaling were performed in PCA application. The principal components are linear combinations involving all original features. This technique is not immune from distortion due to transformation, while due to simple scaling of some of the attributes, significant changes to the results can be noted. In contrast to feature transformation, feature selection does not alter the original representation of the variables, but merely select a subset of these. Feature selection techniques preserve the original semantics of the variables, and someone with expert knowledge of these variables can therefore interpret these accurately. The general concept is to identify the most relevant attributes for the concept at hand, thus facilitating assessment of the data. Feature selection therefore selects an optimal subset of the available variables, achieving the best classification performance with the fewest variables. Redundant and unimportant variables are thus excluded from the classification, leading to the optimal overall result. The reduction of feature space speeds up learning algorithms and leads to benefits in terms of classification accuracy and interpretability of the classification result. Identification of irrelevant variables ensures that these can be eliminated from the classification system, thus reducing the time and costs associated with data collection and analysis. In particular, feature selection helps to gain a deeper insight into the underlying data structure. It is therefore a powerful technique for reducing highly multi-dimensional data sets to manageable levels. Various feature selection methods have been studied comprehensively (Dash and Liu, 1997; Liu and Yu, 2005). Feature selection involves searching through various feature subsets and evaluating each of these subsets using appropriate criteria. The most popular search strategies are ‘greedy’ sequential searches through the feature space, either forward or backward. The evaluation criteria are roughly classified into filter and wrapper methods (Guyon and Elisseeff, 2003). A filter model (Liu and Setiono, 1996) examines the intrinsic properties of the data, such as the simple statistics computed from empirical distributions, to select and evaluate feature subsets without involving any classification algorithm. A wrapper method (Kohavi and John, 1997) requires a specified classifier and uses the wrappers performance as the evaluation criterion. It involves finding the optimal subsets of features, which achieve the highest classification accuracy. Feature selection techniques have been widely applied in many domains such as text categorization (Forman, 2003), image retrieval (Rui et al., 1999), magnetic resonance images classification, bioinformatics (Saeys et al., 2007), land cover classification for a semi-arid environment (Borak, 1999) and human gait recognition (Guo and Nixon, 2009).
3.
Data acquisition
The current assessment of any SFRB is a two stage process combining a desk study and a field visit during which 40 variables are assessed (McMinn et al., 2010). The desk study provides an estimate of most variables by searching for related information from websites, publications and digital databases in usually less than 20 min. The site visit typically
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requires 40 min per site during which parameters determined during the desk study are verified and a photographic record of any dam structure along with the inlets and outlets of the SFRB are collected. A guidance manual on how to determine the 40 variables characterizing water bodies including SFRB has been published by Scholz and Yang (in press). Data acquisition includes conventional hard engineering variables such as Dam Length and Dam Height, along with more holistic variables such as how Engineered the structure appears, Aquatic Animal Passage and Land Animal Passage (Scholz and Sadowski, 2009; McMinn et al., 2010). This combination of hard and soft variables readily lends itself to solving multidisciplinary problems such as sustainable water management. Catchment and land use details are typically obtained from 1:50,000 or 1:25,000 digital maps. Associated with the 40 SFRB variables, certainty values between 0 and 100%, indicating how confident the assessors are of the entries, are also recorded. Data with low confidence values were subsequently improved by further literature review or field visits.
4.
Methodology
4.1.
Feature selection
A total of 370 sites have been surveyed across the wider central Scotland area (Fig. 1). The database contains the 40 classification variables for each site along with a confidence level for each data point. Feature selection techniques were applied to extract the truly relevant information for classification from the large database. Feature transformation techniques extract novel features by constructing non-redundant (thus more
informative features) from all original features. This approach makes SFRB characterization and classification more effective and efficient. A small subset of key variables was identified. This allows accurate description and classification of the SFRB types. Therefore, feature transformation techniques are not so suitable for the purpose of this study. Feature selection algorithms have been designed for identifying the truly relevant key variables for classification tasks. Filter approaches generate a ranking of the features according to their relevance for classification. Established approaches include Information Gain (Hall and Holmes, 2003), Relief (Kira and Rendell, 1992), Minimal-redundancy-maximalrelevance (MRMR; Peng et al., 2005), Focus (Almuallim and Dietterich, 1991) and correlation-based feature selection (Hall and Smith, 1997). Wrapper approaches to feature selection evaluate the relevance of feature subsets using a classification algorithm. The following wrapper methods have been proposed: sequential forward selection and sequential backward elimination (Kittler, 1978) and plus q take-away r (Ferri et al., 1994). Wrapper approaches may lead to better classification results with certain classifiers since they use the classification accuracy itself to score the feature subsets. However, wrapper approaches are computationally very expensive. An exhaustive search for feature subsets is impossible on large high dimensional data. Different filter methods and classifiers were applied to obtain a comprehensive picture of the relevance of the features for SFRB classification. By combining the results of different filter feature selection algorithms and different classifiers, the corresponding results are not influenced by the special algorithmic characteristics of a single method. Among different evaluation criteria for filter feature selection, information-theoretic methods seem to be more comprehensively studied. The main reason is that
Fig. 1 e Map showing the study area, administrative boundaries and the 370 identified SFRB in the wider Central Scotland area (United Kingdom).
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information entropy offers a good measurement to quantify the uncertainty of a feature. The information entropy is intuitive, and generally results in good classifier performance, which is independent of the type of the classifier. Two established information-theoretic filter techniques (Information Gain and Mutual Information) were applied. Relief was also included in the analysis, because it is an effective evaluation criterion based on the local data distribution. These algorithms are described in detail below. The chosen feature selection approach comprises four phases (Fig. 2). First, the entire data set is split randomly into 10 folds. At each time, any nine folds are selected for training the feature selection and classification algorithms, while the remaining fold is used for testing. In the second phase, three popular feature selection algorithms (Information Gain, Mutual Information and Relief) are applied on the SFRB data set, obtaining three different feature sequences according to the priority of the degree of relevance for the SFRB types (Table 2). In the third phase, one uniform sequence of variables is determined. Since the feature rankings generated by the three feature selection algorithms are different, the sequence number of each variable in each feature sequence is counted and these are subsequently summed up. Then, the sum of their sequences is ranked to obtain the final and uniform order of the 40 variables. Finally, different numbers of the ranked features are passed to a classifier to further assess the effectiveness of the selected features by using the classification accuracy as the decision criteria. Four popular classifiers are used in this study: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), C4.5 Decision Tree (J48) and Naı¨ve Bayes (NB). A 10-fold cross-validation (Stone, 1974) is performed during the process of classification. Information Gain is employed as a relevance-criterion to measure the number of bits of information obtained for the class prediction by knowing the presence or absence of an attribute (Hall and Holmes, 2003). This technique is highly efficient to compute, and it is also not restricted to linear correlations, but captures arbitrary dependencies between features. For a given attribute A with respect to the class attribute C, the Information Gain is the reduction in uncertainty about the value of C when the value of A is known. The uncertainty about the class attribute of C is measured by its entropy H(C ). Therefore, the entropy of the class C before and after observing the attribute A is given by Eqs. (1) and (2).
Table 2 e Priority of selected variables based on three different feature selection algorithms (priority decreases from top to bottom). Variable
MI
Relief
IG
SN
Sum of SN
Final order
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
1 31 4 20 7 3 2 6 21 23 5 24 15 32 13 19 25 36 28 35 11 14 40 38 37 26 9 29 22 30 33 27 34 12 8 17 10 16 18 39
1 4 2 3 5 6 7 21 8 19 11 12 9 20 10 15 23 13 24 22 14 16 17 18 25 26 27 28 31 30 32 33 29 35 36 37 38 34 39 40
1 4 2 3 7 6 5 8 9 11 10 17 21 20 15 13 12 16 14 18 23 19 24 25 22 26 30 31 27 28 38 36 35 37 34 31 33 39 32 40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
3 13 14 7 23 20 17 52 49 63 42 63 49 62 44 78 71 83 48 32 30 74 48 54 66 78 88 77 61 87 95 84 100 106 87 85 95 92 117 103
1 4 2 3 7 6 5 21 20 11 15 19 23 9 13 8 24 29 14 10 12 25 17 22 28 16 26 18 32 36 30 35 27 38 31 37 33 40 34 39
MI, Mutual information; IG, Information gain; SN, Sequence number.
HðCÞ ¼
X
pðcÞlog2 pðcÞ
(1)
c˛C
where H(C ) is the entropy of the class attribute C, and p(c) is the probability mass function of the outcome c. HðCjAÞ ¼
X a˛A
Fig. 2 e Framework of the assessment approach.
pðaÞ
X
pðcjaÞlog2 pðcjaÞ
(2)
a˛C
where H(CjA) is the conditional entropy of C for a given A, p(c) is the probability mass function of the outcome c, and p(cja) is the conditional probability of c for a given a. Since the reduction amount of the entropy of the class after knowing the attribute A reflects the additional information about the class provided by the attribute, Information Gain captures the interestingness of variables for the class concept
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(Quinlan, 1993). Formally, the Information Gain I(C; A) is defined as shown in Eq. (3). IðC; AÞ ¼ HðCÞ HðCjAÞ
(3)
where I (C; A) is the information gain, H(C ) is the entropy of the class attribute C, and H(CjA) is the conditional entropy of C given A. Relief is a classical instance-based attribute selection scheme introduced by Kira and Rendell (1992), and enhanced by Kononenko (1994). The key idea of Relief is to estimate attributes according to how well their values distinguish among instances of different classes that are near each other (Kononenko, 1994). For that purpose, Relief for a given instance searches for its two nearest neighbours: one from the same class (called nearest hit H ) and the other from a different class (called nearest miss M ). For each attribute, it calculates the relevance scores and updates its value according to Eq. (4). WðAÞ ¼ WðAÞ diffðA; X; HÞ=m þ diffðA; X; MÞ=m
(4)
where W(A) represents the relevance scores for any attribute A, diff(A, X, H ) is the difference between the values of attribute A for the two instances X and H, and m is the number of instances sampled. This process is repeated for a user-specified number of instances m. The rationale is that a useful attribute should differentiate between instances from different classes and have the same value for instances from the same class (Hall and Holmes, 2003). To handle noise and multi-class data sets, Relief was later extended to ReliefF (Kononenko, 1994), which smoothes the influence of noise in the data by averaging the contribution of KNN from the same and opposite class of each sampled instance instead of the single nearest neighbour. Multi-class data sets are handled by finding nearest neighbours from each class, which is different from the current sampled instance and weighting their contributions by the prior probability of each class. It computes the difference between the values of attributes for two instances. Kononenko (1994) notes that the higher the value of m (i.e. the number of instances sampled), the more reliable ReliefF’s estimates are. For all experiments reported in this work, m and k were set at 250 and 10, respectively. In addition, the ReliefF is further written as Relief for simplification. As a measure of relevance and redundancy among features, Mutual Information of two random variables is a quantity that measures the mutual dependence of the two variables (Estevez et al., 2009). A heuristic minimal-redundancy-maximal-relevance (mRMR) framework can be used to select promising features for both continuous and discrete data sets (Peng et al., 2005). The maximal-relevance-criterion (Max-Relevance with class target) searches the attribute subset S, which approximates D(S, C ) in Eq. 5. ( D ¼ max
) 1 X IðAi ; CÞ jSj A ˛S
(5)
i
where D is the maximal mutual information between each attribute Ai and class C, S is the selected attribute subset, jSj is
the number of attributes in S, and I(Ai; C ) is the mutual information between attribute Ai and class C. It is likely that features selected according to Max-Relevance could have a rich redundancy; i.e. the dependency among these features could be large. When two features are highly dependant on each other, the respective classdiscriminative power does not change much, if one of them were removed. Therefore, the minimal-redundancy (MinRedundancy) condition is added to select mutually exclusive features (Eq. (6)). 9 8 < 1 X = I Ai ; Aj R ¼ min ; :jSj2 A ;A ˛S i j
(6)
where R is the minimal mutual information among selected attributes, S is the selected attribute subset, jSj is the number of attributes in S, and I(Ai, Aj) represents the mutual information between the two attributes Ai and Aj. The criterion combining the above two constraints is called minimal-redundancy-maximal-relevance (mRMR). Therefore, to optimize D and R simultaneously, the objective is to maximize the value of F (D, R) as shown in Eq. (7). F ¼ maxfD Rg
(7)
where F is defined as the operator, combining D and R, which represent the dependency and redundancy of a feature subset on the target class, respectively. Practically, an incremental search method is used to find the near-optimal features defined by F( ). This is done by optimizing the condition expressed in Eq. (8). " max
Aj ˛ASm1
I Aj ; C
1 X Sm1 I Aj ; Ai m1 A˛
# (8)
i
where A is the whole attribute, Sm1 is an attribute subset with m 1 attributes that have been obtained, I(Aj;C ) is the mutual information between the attribute Aj and the target class C, and I(Aj;Ai) is the mutual information between the two attributes Ai and Aj.
4.2.
Classification algorithms
Four benchmark classification algorithms were used to assess the effectiveness of the feature selection methods: Support Vector Machine (SVM), K-Nearest Neighbour (KNN), C4.5 Decision Tree (J48) and Naı¨ve Bayes (NB). SVM is a popular and promising tool for data classification. Its basic idea is to construct a separating hyperplane between the training instances of both classes. Among all possible hyperplanes, that one with the maximum margin between classes is selected (Chen and Lin, 2006). Given training vectors xk ˛ Rn (k ¼ 1, ., m) in two classes, and a vector of labels y ˛ Rm such that yk ˛ {1, 1}, then SVM solves a quadratic optimization problem (Eq. (9)). For any testing instance x, the decision function (predictor) has the form outlined in Eq. (10). m X 1 min uT u þ C 3k ; u;b;3 2 k¼1
subject to yk uT fðxk Þ þ b 1 3k ; 3k 0; k ¼ 1; .; m
ð9Þ
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where u is a normal vector, b is a scalar and 3k are non-negative variables, C is a penalty parameter on the training error, yk is the class label, b is a scalar, and 4( ) is a map function to transfer the training data into a higher dimensional space. f ðxÞ ¼ sgn uT fðxÞ þ b
(10)
where f(x) is the prediction function, sgn( ) is a symbol function, uT is the permutation of normal vector u, 4( ) is a map function, and b is a scalar. Practically, the Kernel function (Eq. (11)) is used to train the SVM. kðx; yÞ ¼ fðxÞ$fðyÞ
(11)
where the linear kernel function k(x, y) used in this study, and 4() is a map function. A simple k-nearest neighbour classification algorithm is used by setting k equal to three. The distance metric that has been used is the Pearson correlation coefficient. The k-nearest neighbour is a supervised learning algorithm based on instances (Aha et al., 1991). It simply stores the training data and postpones the generation until an instance must be classified. Given an instance, its k closest neighbours are found in terms of the Pearson correlation coefficient, and then its label value is determined by these k neighbours using the majority vote manner principle. In this study k ¼ 3. C4.5 is a statistical classification algorithm used to generate a top-down decision tree developed by Quinlan (1993). Each node of the tree is constructed by finding the best attribute of the data that most effectively splits its set of samples into subsets. The attribute with the highest normalized information gain is chosen to make the decision to split the data. The C4.5 algorithm then generates smaller sub-lists in a recursive way. Despite that only one feature is chosen at a time, this depends on previous results. J48 is an open source Java implementation of the C4.5 algorithm in the WEKA (http://www.cs.waikato.ac.nz/ml/weka) data mining tool. The applied Bayesian classifier is a simple probabilistic classifier based on Bayes’ theorem (from Bayesian statistics) with strong independence assumptions (Rish, 2001). Only the variances of the variables for each class need to be determined, and not the entire covariance matrix. Thus, an advantage of the Naı¨ve Bayes classifier is that it requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Due to the precise nature of the probability model, Naı¨ve Bayes classifiers can be trained very efficiently in a supervised learning setting. Feature selection of Mutual Information was implemented in Matlab. The Java implementations of Information Gain, Relief and the classifiers used in this paper are available in WEKA. All experiments have been performed on a workstation with 2.4 GHz CPU and 2.0 G RAM.
5.
Experimental results and discussion
5.1.
Experiments on feature selection
In the following sub-sections, the analytical results related to the feature selection of SFRB will be discussed. Important
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features will be identified and ranked by performing three feature selection algorithms. The corresponding classification accuracies will be obtained by using four different classifiers based on different numbers of selected variables. The whole data mining process will be validated by 10-fold-cross-validation. The selected nine significant variables will also be applied to six representative SFRB case studies (see Section 5.3), and their performances on different types will be discussed. With each feature selection approach, a list of 40 variables ordered in terms of their importance to SFRB classes (types) was obtained. The orders (sequences) of the variables were different for each feature selection method used. The findings of all three methods need to be compared with each other to finally achieve a comprehensive list to be used for classification. Therefore, for each variable, its sequence number in each method was obtained and then summed up. The lower the sum of sequence numbers gained, the more important the variable was. Consequently, the sum of sequence numbers was arranged in increasing order. Correspondingly, the associated variables were ranked according to decreasing importance from top to bottom. Finally, a list of 40 variables ranked according to their importance was obtained. Table 2 provides an overview of the rankings generated by the three feature selection algorithms as well as the final order. Findings show that after running three feature selection programmes, three lists of 40 variables each were displayed. For example, taking variable 4 (Outlet Arrangement and Operation), its consequence numbers were 3, 2 and 2 for the methods Mutual Information, Relief and Information Gain, respectively. Thus, the sum of the consequence numbers was 7. Compared with the sum of consequence numbers for other variables, 7 was the second smallest number. While in terms of importance, variable 4 ranked second in the final order. The last column in Table 2 shows that variable 1 (Engineered ) is the most important variable, variable 4 (Outlet Arrangement and Operation) is the second most important one and so on. Then, subsets of the ordered variables will be passed to classifiers. This simple method, however, assumes that all feature selection methods are equally important and valid for SFRB. The final order of the variables shows the priority of the variables’ importance on characterizing SFRB and distinguishing SFRB types (see Fig. 3 for example). In practice, the order of the key variables helps engineers, planners and practitioners to recognize to which variables they should pay high attention during SFRB design, management and maintenance. For instance, compared with Length of Basin and Width of Basin, Engineered should be particularly considered when engineers design a new SFRB site. If someone is designing a new SFRB of type 3 at any size, it is not necessary to construct high and long dams, which are usually associated with a SFRB of type 1. When planners decide to build a drinking water reservoir, they might consider Engineered, Dam Length, Dam Height and Outlet Arrangement and Operation as much more important than Aquatic Animal Passage and Land Animal Passage. For SFRB maintenance, practitioners should focus on how to maintain its outlet, dam structure and animal passage rather than how to maintain its groundwater infiltration or various catchment proportions. Acknowledgement of the importance of various variables reduces unnecessary costs and work effort.
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5.2.
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Classification results
After the final order of the 40 variables was determined, the next step was to assess the classification performance based on selected subsets of variables. Four classifiers were applied during this process. Each time, the SFRB data set was classified by one classifier, using different numbers of features (in order of decreasing priority) ranging from 1 to 40. Finally, the mean classification accuracy was compared between the four classifiers. Fig. 4 summarizes the classification performance of four classifiers based on the ranked features. Findings show that the
application of SVM, Naı¨ve Bayes and J48 classifiers leads to parallel patterns (from top to bottom: J48, Naı¨ve Bayes and SVM) and that the classification accuracy improves by increasing the number of variables from one to nine, but that the upwards trend reduced considerably afterwards until 35 variables were used. This indicates that about 35 variables are sufficient for the classification, but that even nine variables have comparable classification accuracy. Taking SVM classifiers for instance, the classification accuracy rose from 77.6 to 86.7% by increasing the number of variables from one to nine, and then increased to the highest value of 89.4% when 35 variables were used. After that, the accuracy became slightly lower at about 89.2%.
Fig. 3 e (a) Loch Lyon is a typical example of a Hydraulic Flood Retention Basin (Sustainable Flood Retention Basin Type 1); (b) Harperrig Reservoir is a typical example of a Traditional Flood Retention Basin (Sustainable Flood Retention Basin Type 2); (c) Dunfermline Eastern Expansion is a typical example of a Sustainable Flood Retention Wetland (Sustainable Flood Retention Basin Type 3); (d) Caw Burn Wetland is a typical example of an Aesthetic Flood Treatment Wetland (Sustainable Flood Retention Basin Type 4); (e) Lanark Loch is a typical example of an Integrated Flood Retention Wetland (Sustainable Flood Retention Basin Type 5); (f) Hare Myre is a typical example of a Natural Flood Retention Wetland (Sustainable Flood Retention Basin Type 6).
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86.7% for SVM and 87.1% for KNN. They have performed very well compared with the classification results of the total forty variables. The classification accuracy is only 3% lower for Naı¨ve Bayes, J48 and SVM. An improvement of 2.9 % for KNN has been noted. Therefore, considering the contribution to SFRB classification, the first nine variables are regarded as the most important ones; they are as follows: Engineered, Dam Height, Dam Length, Outlet Arrangement, and Operation Aquatic Animal Passage, Land Animal Passage, Floodplain Elevation, Impermeable Soil Proportion and Drainage.
0.95
Mean classification accuracy
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0.90
0.85
0.80
NB
J48
SVM
KNN
0.75 0
5
10
15
20
25
30
35
40
Number of variables Fig. 4 e Comparison of the mean classification accuracies among the four classifiers. NB, Naı¨ve Bayes; J48; C4.5 Decision Tree; SVM, Support Vector Machine; KNN, K-Nearest Neighbours.
The KNN classifier shows a different pattern from the above mentioned classifiers. The classification accuracy obtained the lowest classification value (81.4%) when only seven variables were used, but sharply achieved the highest classification accuracy of 87.1% when nine variables were applied. Afterwards, it dropped slightly with the increase of the number of variables. It is obvious that only using the first nine variables can achieve sufficient classification accuracy, while introducing more variables does not necessarily improve the classification performance much. On the other hand, the accuracy could become worse. More specifically, Table 3 provides the classification accuracy of four different classifiers based on the first nine variables and the total forty variables. For all classification results, the 95% confidence interval has also been provided in Table 3. The classification results based on the first nine variables are 89.5% for Naı¨ve Bayes, 91.3% for J48,
Table 3 e Classification results for four classifiers based on the first nine variables and a total of forty variables (95% confidence intervals). NB
J48
SVM
KNN
First 9 variables Accuracy 89.5% 91.3% 86.7% 87.1% confidence [85.76, 92.31] [87.89, 93.92] [82.78, 89.96] [83.07, 90.20] interval Total 40 variables Accuracy 92.5% 94.4% 89.2% 84.2% confidence [89.12, 94.82] [91.32, 96.36] [85.46, 92.08] [80.12, 87.79] interval NB, Naı¨ve Bayes; J48, C4.5 decision tree; SVM, support vector machine, KNN, K-nearest neighbour.
5.3. Experimental validation on six representative case studies Theoretically, the selected nine important variables, which led to the high accuracy of SFRB classification, were supposed to distinguish SFRB types well. It follows that the most important variables are supposed to have very different characteristics for different SFRB types. For validation purposes, the authors explored the underlying relationships between the identified nine variables and the six SFRB types (Table 4). One typical site for each SFRB type was studied to get insight into the behaviour of the selected variables for individual types. Fig. 3 shows pictures for the six representative cases. Summary statistics showing the characteristics and relationships between different SFRB types, their functions and the identified variables are presented in Table 4. The results verify that the six types of SFRB can be distinguished successfully, even if only the identified nine key variables are used. Findings clearly indicate, for example, that SFRB types 1 (predominantly used for hydraulic purpose) and 2 (mainly applied for drinking water supply) are highly engineered structures (98.6 0.9% and 69.8 10.1%, respectively) with high dams and advanced outlet arrangements such as spillways (97.7 1.0% and 69.5 11.0%, respectively), while type 6 is rather natural (5.7 3.9% for Engineered ) without a dam and designed outlets. Concerning SFRB types 1 and 2, the man-made and therefore highly engineered structures result in barriers for animals. Therefore, both Aquatic Animal Passage and Land Animal Passage are low. While for SFRB type 6 (mainly for the purpose of environmental protection) characterized by natural properties, the animal movement is not severely restricted (38.2 35.8 % and 71.0 10.3% for Aquatic Animal Passage and Land Animal Passage, respectively). SFRB types 3 (mainly used for sustainable drainage), 4 (predominantly applied for landscape enhancement) and 5 (mainly used for recreational activities) have similar characteristics such as low engineered structures; for example, low dams (about 3e5 m high) and poor outlet arrangements (potentially only weirs present). However, SFRB type 3 is much more distinctive from the other types due to its high values for Drainage (2.2 0.4 cm/d). Correspondingly, the Impermeable Soil Proportion for type 3 is relatively low. SFRB type 4 has slightly higher Drainage values compared to type 5. Furthermore, water bodies that are mainly used for wastewater treatment belong to type 4 while those used in parks or for other recreational activities are regarded as type 5. More specific characteristics associated with the nine key variables for the six representative case studies are discussed below.
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Table 4 e Summary statistics (mean ± standard deviation) characterizing the relationships between Sustainable Flood Retention Basin (SFRB) types, functions and the nine key variables. Key variables Engineered (%) Dam height (m) Dam length (m) Outlet arrangement and Operation (%) Aquatic animal passage (%) Land animal passage (%) Floodplain elevation (m) Drainage (cm/d) Impermeable soil proportion (%) Predominant functions
Case study examples Grid reference for case study
Type 1
Type 2
Type 3
Type 4
Type 5
Type 6
98.6 0.9 25.8 19.1 277.7 172.4 97.7 1.0 0.0 0.0 11.7 3.5 0.0 0.0 1.2 0.2 81.3 5.5
69.8 10.1 11.0 8.4 277.0 238.1 69.5 11.0 13.1 13.9 45.4 16.9 0.2 0.4 1.1 0.5 82.8 7.5
26.9 8.0 1.8 1.2 113.6 100.5 25.8 10.9 30.8 30.1 66.8 9.7 0.6 0.4 2.2 0.4 64.5 7.7
25.4 8.8 2.0 0.9 75.0 59.4 25.0 9.6 30.0 16.6 65.4 9.9 0.6 0.3 1.2 0.2 74.2 9.8
25.7 9.9 1.1 1.3 97.9 206.9 19.2 14.5 26.4 28.1 67.4 10.9 0.6 0.3 1.0 0.4 75.1 7.4
5.7 3.9 0.0 0.2 0.9 6.2 5.4 4.6 38.2 35.8 71.0 10.3 0.7 0.4 1.1 0.5 71.7 8.9
Hydraulic purpose
Drinking water supply
Sustainable drainage
Recreation
Environment protection
DEX wetland
Aesthetic landscape enhancement and sustainable drainage Caw Burn wetland
Lubreoch power station Lat 56 320 ; Long 4 350
Harperrig Reservoir Lat 55 500 ; Long 3 270
Lanark Loch
Hare Myre
Lat 56 040 ; Long 3 240
Lat 55 550 ; Long 3 290
Lat 55 400 ; Long 3 450
Lat 56 340 ; Long 3 200
DEX, Dunfermline Eastern Expansion.
The SFRB of type 1 are mainly hydro-electric power stations and current drinking water reservoirs. Generally, they are precisely designed, well maintained and automatically controlled. So, this kind of SFRB tends to obtain relatively high values for Engineered, Dam Height, Dam Length, Outlet Arrangement and Operation and Impermeable Soil Proportion, but very low scores for the variables Aquatic Animal Passage, Land Animal Passage, Drainage and Floodplain Elevation. For example, Lubreoch power station (Fig. 3a) is one typical SFRB type 1 case study. Being built for power generation, it gets a high score for Engineered (98%). In association, the Dam Height (39 m), Dam Length (530 m) and Outlet Arrangement and Operation (97%) also tend to be very high. Due to its steep concrete spillway and high dam structure, the passage of aquatic and land animals are severely hampered by the physical structures of this SFRB. For this reason, both Aquatic Animal Passage and Land Animal Passage obtained relatively low scores (close to 0). Since the power station is fully controlled and a large spillway releases extra water via an overflow, there is no clearly defined Floodplain Elevation. Water does not normally penetrate the dam or drains easily from the basin. So the Impermeable Soil Proportion for this site is relatively high at around 90% and the Drainage is fairly low at 1.3 cm/d. Comparatively, the dam and spillway tends to be of medium size for SFRB type 2. They generally have high values for Engineered (60e85%), Impermeable Soil Proportion (70e90%) mean values for Dam Height, Dam Length and Outlet Arrangement and Operation (50e75%), but low values for the Aquatic Animal Passage, Land Animal Passage and Floodplain Elevation. For example, as a drinking water supply reservoir with a dam (14 m high and 154 m long) and a spillway, Harperrig Reservoir (Fig. 3b) obtained 85% for Engineered and 75% for Outlet Arrangement and Operation. For the purpose of keeping water within the drinking water reservoir, the Impermeable Soil Proportion is as high as 90% and Drainage is as low as 0.5 cm/d. Due to the presence of a high dam structure and the lack of a fish ladder, the Aquatic Animal Passage and Land Animal Passage values are 10% and 50%, respectively. A spillway exists and has just been modified to optimize the flood protection purpose.
The mature Dunfermline Eastern Expansion (DEX) wetland (SFRB type 3; Fig. 3c) characterized by dense stands of reeds obtains a low value for the variable Engineered (35%) since there is no obvious dam or active control structure present. The outlet is a small weir with a width of 2.5 m. Several large stones are located in front of the weir to dissipate energy. Consequently, Outlet Arrangement and Operation and Aquatic Animal Passage were assigned 20 and 60%, respectively. Considering that the wetland system is located within a public park, this may result in less of a barrier for land animals. It follows that the wetland obtained 80% for Land Animal Passage. According to the topography of the DEX area, the Floodplain Elevation was estimated to be roughly 1 m. The Impermeable Soil Proportion was estimated to be 85%, since the area is predominantly characterized by clay soil of low permeability. Considering the high water retention potential within the wetland, the Drainage rate was estimated to be 2 cm/d. The Caw Burn wetland (SFRB type 4; Fig. 3d) is operated as an off-line structure treating the base flow and first foul flush of an urban area only. The wetland is fed by an abstraction pipe and overflows into a swale leading to the receiving watercourse. No engineered structures were constructed at the outlet of the wetland. A relatively low value of 20% was therefore assigned to the variable Engineered and 5% was assigned to Outlet Arrangement and Operation. The Caw Burn wetland is separated from the receiving watercourse by a shallow earth dam of 0.5 m height and 150 m width. However, the earth dam is overflowing at several locations due to its shallow nature during storm events. Since there are no engineered barriers for animals, this SFRB obtains a high score for both Aquatic Animal Passage (80%) and Land Animal Passage (80%). The Floodplain Elevation is around 0.1 m during the dry season. Considering the surrounding soil, which is dominated by old shale mining waste, the Impermeable Soil Proportion is estimated to be 70%. The Caw Burn keeps the wetland permanently wet. The Drainage rate was estimated to be as low as 0.1 cm/d. The value of Engineered assigned to Lanark Loch (SFRB type 5; Fig. 3e) was 25%. The dam height and length were 1 and
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20 m, respectively. The Outlet Arrangement and Operation was also low (15%) since only a small weir and a pipe exist near the outflow. Due to the low control of the outflow, the Aquatic Animal Passage was relatively high (40%). The variable Land Animal Passage also obtained a relatively high value (70%), because there are no obvious obstacles preventing land animals from roaming. Due to the flat topography of the basin area and the small capacity of the basin, the value for the Floodplain Elevation was also low. The estimated value of Impermeable Soil Proportion was 70% and Drainage was 0.8 cm/ d (based on an assessment of the soil present at the site). Water bodies belonging to SFRB type 6 are relative natural without high engineered structures such as dams, spillways or sluice gates. They usually obtain very low values for variables such as Engineered, Outlet Arrangement and Operation and Impermeable Soil Proportion. Take the Site of Specific Scientific Interest Hare Myre (Fig. 3f), for example, 2% was given to the variable Engineered. Accordingly, Outlet Arrangement and Operation also received a score as low as 3%. There is no dam present, so the variables Dam Length and Dam Height are not applicable but values of zero each were noted. Without the presence of an outflow, Aquatic Animal Passage was therefore also zero. Due to lack of obstacles around the wetland, Land Animal Passage is given a high value of 80%. Compared with drinking water reservoirs (usually SFRB types 1 or 2), there are no man-made structures to protect water from draining and infiltrating. Based on the observation of the soil of the basin, 50% and 1.5 cm/d were assigned to Impermeable Soil Proportion and Drainage, respectively. According to the topography of the catchment of the basin, the Floodplain Elevation was estimated at 0.5 m. The effectiveness of the selected nine key variables was successfully verified on six representative SFRB case studies. Reducing the number of the surveyed variables from forty to nine saves time, money and labour resources for the assessment of SFRB and achieves a rapid classification of SFRB. Furthermore, due to different types of SFRB having different performances regarding the nine classification variables, the rapid classification is beneficial in providing engineers, practitioners and land use planners with scientific support during design, maintenance and decision making. For example, the values of Engineered, Dam Height, Dam Length and Outlet Arrangement and Operation should be high when someone is designing a SFRB of type 1, but they should be low for SFRB of type 6. Regarding the latter, which should have low values for the variable Engineered, practitioners could consider how to improve the biological integrity of the SFRB rather than how to increase its water supply capability. For SFRB of type 4, authorities should focus on how to improve water quality but not flood reduction capability. If the variable Engineered for a very old drinking water reservoir decreases over time and its outlet does not work properly any more, planners might consider to assign a new SFRB status such as SFRB of type 5 used for public recreation. Furthermore, some drinking water reservoirs can also be used for flood control purposes by actively controlling their water levels. Therefore, with the aid of SFRB classification (knowing the status and functions of the basins) engineers’ design of new SFRB may better meet practical needs, authorities might gain an improved understanding of maintenance requirements, and planners may
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consider more comprehensively how to manage the development of future SFRB.
6.
Conclusions
The paper proposed feature selection techniques to identify the most relevant characteristic variables and remove any redundant variables. It indicated that Mutual Information, Information Gain and Relief performed well in identifying and ranking the most relevant SFRB classification variables. However, different feature selection algorithms generated different results regarding the importance of the 40 variables due to their different ranking strategies. A final list of priority variables was obtained by comparing and combining the feature selection methods with each other. The first nine variables were regarded as the most important ones for characterizing and classifying a SFRB. The findings should raise engineers, practitioners and planners’ attentions to the nine key variables during the process of SFRB design, maintenance and management. The classifiers SVM, KNN, NB and J48 were successfully applied to classify 370 SFRB by using different numbers of variables ranging from 1 to 40. The variables in the final list were ranked in decreasing order of importance. The results indicate that the first nine variables were sufficient for the four classifiers to achieve high classification accuracies. This was the optimal variable subset to classify SFRB. For the SVM, NB and J48 classifiers, there was no significant improvement of the classification accuracy by increasing the number of variables from nine upwards. In contrast, the classification accuracy even deceased for the KNN classifier with the number of variables ranging from 10 to 40. This indicates that introducing more variables might lead to more redundancy or dependency. Therefore, identification of the most relevant key variables plays a significant role in removing the redundant information, which allows SFRB assessment to be cost and time efficient. Using six typical case studies, it has been verified that the selected nine important variables have very different performances on individual SFRB types. The findings show that the rapid classification provides engineers, practitioners and land use planners with a useful scientific tool to manage different types of SFRB for purposes such as flood control, water supply, sustainable drainage, aesthetic landscape enhancement, recreation and nature protection. Feature selection and classification of SFRB would provide EU member states with a rapid tool to assess water bodies with flood risk to implement the EU flood directive. Moreover, the generic nature of the proposed tools allows their application also on other water and environmental data sets (e.g. river basin management and land cover classification). The proposed approach, which has been successfully tested in Scotland and Baden, can be extended and applied to other regions in the world with temperate or oceanic climate. However, local and regional variables such as Seasonal Influence, Typical Wetness Duration and Drainage might require adjustment. Moreover, it is recommended that national weather and mapping data should be used where available to decide on landscape and climatic variables.
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Acknowledgements The European Regional Development Fund Interreg IVB 2007e2013 North Sea Region Program funded the research project Sustainable Flood Retention Basins to Control Flooding and Diffuse Pollution, which is part of the British contribution to the Strategic Alliance for Water Management Actions consortium. The Deutscher Akademischer Austauschdienst has funded the collaboration with the Technische Universia¨t Mu¨nchen, Germany.
references
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