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WATER RESEARCH A Journal of the International Water Association

Editor-in-Chief Mark van Loosdrecht Delft University of Technology Department of Biochemical Engineering Julianalaan 67 2628 BC Delft The Netherlands Tel: +31 15 27 81618 E-mail:[email protected]

Editors J. Block Université H. Poincaré, Nancy I France David Dixon University of Melbourne Australia Hiroaki Furumai The University of Tokyo Japan Xiaodi Hao Beijing University of Civil Engineering and Architecture China Gregory Korshin University of Washington USA Anna Ledin Formas Sweden Eberhard Morgenroth Swiss Federal Institute of Aquatic Science and Technology (EAWAG) Switzerland W. Rauch University Innsbruck Austria Maria Reis Universidade Nova de Lisboa/FCT Portugal Hang-Shik Shin Korea Advanced Institute of Science and Technology Korea Thomas Ternes Bundesanstalt für Gewässerkunde Germany Stefan Wuertz Univ. of California, Davis USA

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Damien Batstone The University of Queensland Australia G-H. Chen The Hong Kong University of Science & Technology Hong Kong China Tom Curtis Univ. of Newcastle upon Tyne UK Ana Deletic Monash University USA Francis de los Reyes III North Carolina State University USA Rob Eldridge The University of Melbourne Australia Rosina Girones University of Barcelona Spain Stephen Gray Victoria University Australia Kate Grudpan Chiang Mai University Thailand E.E. Herricks University of Illinois - Urbana USA Peter Hillis United Utilities Plc UK H-Y. Hu Tsinghua University China P.M. Huck University of Waterloo Canada Bruce Jefferson Cranfield University UK Ulf Jeppsson Lund University Sweden Sergey Kalyuzhnyi Moscow State University Russian Federation Jaehong Kim Georgia Institute of Technology USA Jes La Cour Jansen Lund Institute of Technology Sweden G. Langergraber BOKU/Univ. of Natural Res. and Applied Life Scs. Austria S-L. Lo National Taiwan University Taiwan Dionisis Mantzavinos Technical University of Crete Greece

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 5 9 e2 3 7 4

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Prediction of biological integrity based on environmental similarity - Revealing the scale-dependant link between study area and top environmental predictors David Bedoya a,*, Elias S. Manolakos b, Vladimir Novotny a a

Civil & Environmental Engineering Department, Northeastern University, 400 Snell Engineering Center, 360 Huntington Avenue, Boston, MA 02115, USA b Department of Informatics & Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis, Ilissia, Athens 15784, Greece

article info

abstract

Article history:

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of

Received 20 July 2010

a water body because the biological community is an endpoint within natural systems.

Received in revised form

However, prediction of biological integrity using information from multi-parameter envi-

10 January 2011

ronmental observations is a challenging problem due to the hierarchical organization of

Accepted 11 January 2011

the natural environment, the existence of nonlinear inter-dependencies among variables

Available online 28 January 2011

as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observa-

Keywords:

tions at the state-scale in Ohio. Instream (chemical and physical quality) and offstream

Environmental stressors

parameters (regional and local upstream land uses, stream fragmentation, and point

Biological integrity

source density and intensity) are used for this purpose. The IBI predictions are obtained

Geographic scale

using the environmental site-similarity concept and following a simple to implement

Environmental similarity

leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated

Stressor and biological hierarchy

by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram ea hierarchical clustering tree of environmental observations- built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model’s over-predictions. Local land use conditions explained some of the model’s IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation.

* Corresponding author. Tel.: þ1617 314 7116; fax: þ1 617 3147115. E-mail addresses: [email protected] (D. Bedoya), [email protected] (E.S. Manolakos), [email protected] (V. Novotny). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.007

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The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Integrity has been defined as the ability of a water body to maintain “a balanced, integrated, adaptive community of organisms having a species composition, diversity and functional organisms comparable to that of a natural biota of the region” (Karr et al., 1986). Biological integrity of streams is usually measured with some version of a calibrated index. One of the most widely used indices in the United States is the Fish Index of Biological Integrity developed by Karr et al. (1986). Many public agencies have adopted it as a framework for deriving their own calibrated version at the state or regional scale (Ohio EPA, 1987; Bode, 1988; Roth et al., 1998; Lyons et al., 2001; Lyons, 2006). Karr’s IBI and subsequent versions and calibrations are based on a comparison of observed fish abundances and community composition against expected values in reference sites with similar environmental characteristics. The importance of IBI lies in its sensitivity to disturbances of different nature because the biological community is an endpoint in the ecological river system (Karr et al., 1986; Novotny, 2003). However, the identification of major sources of biological degradation is challenging because the natural habitat is organized as a nested hierarchy of environmental filters with different geographic scales, to which the biological community has adapted (Pickett et al., 1989). Consequently, the geographic scale at which biological integrity is evaluated is of great importance because the stressors identified as most significant to the biological community are those at the highest level in the hierarchy of environmental filters at that particular scale (Poff, 1997). Therefore, measures to improve biological integrity need to be approached in a holistic scaleadaptive manner in order to be effective. The impact of stressors should be viewed within the context of disturbances occurring at larger scales than the study region (i.e. background quality). The ecological hierarchy in the natural river system is composed of numerous instream and offstream environmental variables which are highly inter-twined and crosscorrelated (Novotny et al., 2005). Therefore, changes in one of them will most likely have a cascade effect that may translate into changes in the instream conditions affecting the biological community. For example, land use changes in the watershed will affect, among other variables, sediment and nutrient input which will, in turn, affect physical and chemical instream water quality. If enough exposure of living organisms occurs, these will be negatively affected because they are the system’s endpoint (Novotny et al., 2005).

The river system is organized a hierarchy of environmental characteristics and habitat conditions across multiple spatial scales (Frissell et al., 1986). An aquatic habitat is suitable for specific fauna when the different natural environmental filters at different spatial scales are overcome (Poff, 1997). Man-made modifications of any of these natural filters at any scale-level are stressors that will modify the pristine biological integrity of the site (Poff, 1997; Karr et al., 1986.) Large-scale variables -or environmental gradients- are those which produce a change in biological integrity of the system through their whole range of values within the study area (i.e. spatial scale of the study). These environmental parameters are usually the best integrity predictors at the selected scale. (Bedoya et al., in press; Lannert and Allan, 1999). On the other hand, smallscale variables have also an effect on particular sections of the area of study, but not on its entirety (Bedoya et al., in press; Lanmert and Allan, 1999). Therefore, identification of variables acting as gradients in a study area should be targeted as top priority for remediation purposes. Large-scale variables actually set the background biological integrity of a region and therefore, overall improvement of its biological integrity is always conditioned by them. Because of the numerous cross-correlated variables potentially affecting IBI and the non-linear variable-to-IBI relationships, development of effective predictive modeling methodologies able to exploit a large number of multi-dimensional environmental observations is paramount. Moreover, new methods to predict biological integrity should not be constrained by any pre-imposed conditions. The methodology we present in this research meets these two key requirements. IBI prediction is performed with a two-phase approach. The first phase consists of ranking variables based on their overall impact on the biological community at the scale of our study area. The second phase consists of a step-wise IBI prediction using environmental variables from different categories (e.g. instream habitat variables). The best predicting variables from each group of variables are then progressively combined in order to obtain an overall improved IBI predictive model.

2.

Methodology

2.1.

Data and study area

The research reported here was based on 429 observations within the state of Ohio. This dataset was extracted from a larger database compiled by the Ohio Environmental Protection Agency (EPA) during its Statewide Biological and

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Water Quality Monitoring and Assessment Program (Ohio EPA 2008). The original database made available to our research team consisted of 1848 observations out of which only 429 had information for all the environmental parameters (i.e. sites with no missing data). This research is based on these 429 complete observations. One observation corresponded to one or two visits to the corresponding site with a small time difference between visits (usually one to two weeks). During these site visits, grab samples for chemical water quality analyses were collected and an evaluation of habitat and biological qualities was performed. The data were collected between 1996 and 2000 by Ohio EPA. Most of the sites had at least two observations during this period, although some of them were evaluated just once. Most of the samples were collected in summer months (July through September) with very few (less than 20) in early October. By sampling in the same time period, potential IBI annual fluctuations were avoided to the maximum extent practicable. Sampling activities focused in summertime low flow periods when stress to aquatic biological communities is believed to be greatest (Ohio EPA, 2005). The distribution of the sampling sites across the state of Ohio is presented in Fig. 1.The state of Ohio follows a very systematic sampling strategy. Site selection within the watershed is driven by a stratification of the watershed based on a sequential, systematic halving of the drainage area, such that a census of all streams within the watershed down to a prescribed drainage area size are selected for sampling (Ohio EPA, 2005). Biological -fish IBI scores- as well as instream environmental parameters -chemical and physical quality- were complemented with offstream parameters obtained with a Geographic Information System (GIS). To our knowledge, all data were collected in base-flow conditions and extreme events (e.g. a spill) were not reported at any sampling site. For each observation site, biological integrity was measured using the fish IBI. In Ohio, this is a discrete score ranging from 12 (essentially no fish) to 60 (reference conditions). The IBI is calculated as the sum of 12 different metrics (each one an integer score ranging from 1 to 5) that describe the species

Fig. 1 e Distribution of sampling sites across the state of Ohio.

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richness and composition, the trophic composition, and the fish abundance and condition of the fish community (Karr et al., 1986; Ohio EPA, 1987). Instream variables consisted of water quality and habitat quality metrics (Table 2). Habitat parameters consisted of metrics from the Qualitative Habitat Evaluation Index (QHEI) (Rankin, 1989). The QHEI and its metrics are discrete scores with different ranges (see Table 2). The percentage of fine sediment in the river bed (embeddedness) was also available (this variable is not used as a QHEI metric itself, but as a penalizing factor for the QHEI’s substrate and channel quality metrics). The offstream environmental variables were grouped into three main categories: upstream land use, stream fragmentation, and point source density and intensity. In order to calculate the upstream land uses, each site’s watershed was delineated using a 30-m resolution Digital Elevation Map (DEM) with ArcGIS Spatial Analyst. Subsequently, the percentages of different upstream land use was calculated at two different scales: the regional scale, which included the whole upstream contributing catchment, and the local scale, which included only 2 miles upstream from the sampling site. Land use percentages were calculated for the whole upstream area as well as the 100- and 30-m buffers at both scales. These two buffer widths were selected based on literature values. A buffer width of 30 m is considered the minimum necessary to provide some benefit to the receiving water body such as temperature amelioration (Castelle et al., 1994). Moreover, 30 m was the maximum resolution of the DEM. A buffer with of 100 m was selected because this distance is considered sufficient to perform basic functions such as sediment removal, nutrient removal, and preservation of species diversity (Castelle et al., 1994). Land use percentages were calculated using the Thematic Raster Summary function within Hawth’s Analysis Tools for ArcGIS (Beyer, 2004). Land cover categories as defined in the 2001 National Land Cover Dataset (NLCD) were used (USGS, 2008b) and listed in Table 1. The Open Water (OW) land use category was only calculated for the regional- and local-scale whole catchment areas, not for the buffers. Drainage areas (DA) for each site were also calculated. The fragmentation and point source metrics (Table 2) were calculated using information from the National Hydrography Datasets (NHD) (USGS, 2008a). The ArcGIS Utility Network Analyst was used to trace upstream or downstream a specific site. Major dams (i.e. with DA  2.59 Km2) and point sources (major and minor waste water treatment plants and major industrial dischargers) were obtained from the National Inventory of Dams (USACE, 2005) and the Permit Compliance System database (USEPA, 2008) respectively. In the fragmentation metrics, downstream metrics such as downstream dam frequency in the main channel (DW_MainDf, see Table 2) or upstream metrics such as upstream dam frequency (U_Df, see Table 2) were considered indicators of site accessibility or habitat continuity from downstream or upstream points respectively. Metrics that combined upstream and downstream segments such as average dam frequency (Avg_Df, see Table 2) were considered indicators of habitat size. Sites were not segregated a priori based on ecoregions or stream size because the model should be able to separate clusters of sites with significant environmental differences. In other words, we wanted to “let the data speak”.

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Table 1 e Description, percentage quartiles, and individual IBI predicting power for the different NLCD land use categories in the Ohio database. Hay [ hay/pasture; ForD [ deciduous forest; ForM [ mixed forest; ForE [ evergreen forest; Shr [ shrub/scrub; WetH [ herbaceous wetlands; WetW [ woody wetlands; Herb [ herbaceous; Crop [ crops; Bar [ barren; DevH [ high intensity urban; DevM [ medium intensity urban; DevL [ low intensity urban; DevO [ open urban space; OW [ open water; Oth [ other land uses. Regional Drainage Area (RDA) Name

Quartiles

Regional 100-Meter Buffer (R100) R

2

Name

Quartiles

Regional 30-Meter Buffer (R30) 2

R

Regional Land Use in Contributing Area RDA_Hay 3.16e7.67e14.60 0.385a RDA_ForM 0.00e0.00e0.03 0.294b RDA_DevL 1.22e2.39e6.06 0.293a RDA_OW 0.10e0.25e0.60 0.292a RDA_ForD 5.29e9.45e18.60 0.281a RDA_WetH 0.00e0.02e0.09 0.261b RDA_Crop 40.49e60.84e75.58 0.257a RDA_DevM 0.24e0.64e1.64 0.253a RDA_ForE 0.01e0.08e0.25 0.243a RDA_Bar 0.00e0.01e0.05 0.239a RDA_Herb 0.35e0.89e1.38 0.225a RDA_DevH 0.07e0.26e0.75 0.223a RDA_WetW 0.00e0.04e0.20 0.223a RDA_Shr 0.00e0.00e0.03 0.221a RDA_DevO 5.23e6.25e9.76 0.212a RDA_Oth 0.00e0.00e0.00 0.012a

Regional Land Use in 100 m Buffer R100_Hay 2.97e8.20e13.60 0.322a R100_DevH 0.00e0.13e0.38 0.320a R100_DevO 5.26e6.84e10.33 0.300a R100_Herb 0.29e1.00e1.81 0.296a R100_ForD 8.22e16.06e30.56 0.292a R100_ForE 0.00e0.09e0.24 0.285a R100_DevM 0.10 -0.40 -0.87 0.275a R100_DevL 0.94e1.85e3.79 0.274a R100_WetW 0.00e0.13e0.58 0.270a R100_ForM 0.00e0.00e0.05 0.261a R100_WetH 0.00e0.03e0.20 0.236b R100_Crop 34.65e54.32e70.27 0.231a R100_Shr 0.00e0.00e0.05 0.223a R100_Bar 0.00e0.00e0.02 0.184b R100_Oth 0.00e0.00e0.00 0.012a

Local Drainage Area (LDA)

Local 100-Meter Buffer (L100)

Local Land Use in Contributing Area LDA_DevO 4.97e7.22e13.73 0.289a LDA_ForD 4.47e13.59e28.73 0.285b LDA_Hay 0.00 -5.51 - 12.92 0.214a LDA_OW 0.00e0.19e0.96 0.200b LDA_DevL 0.42e2.42e11.19 0.183a LDA_DevM 0.00e0.27e2.01 0.159a LDA_Crop 17.20 -44.71 -69.59 0.152a LDA_DevH 0.00e0.00e0.80 0.140b LDA_Herb 0.00e0.65e1.68 0.130a LDA_WetW 0.00e0.00e0.47 0.128a LDA_WetH 0.00e0.00e0.19 0.124b LDA_Shr 0.00e0.00e0.00 0.117a LDA_ForE 0.00e0.00e0.25 0.098a LDA_ForM 0.00e0.00e0.00 0.077a LDA_Bar 0.00e0.00e0.00 0.020a LDA_Oth 0.00e0.00e0.00 0.000

Local Land Use in 100 m Buffer L100_ForD 7.39e24.43e46.14 L100_DevL 0.30e2.06e6.99 L100_DevO 4.17e7.80e14.68 L100_Hay 0.00e3.97e10.85 L100_Crop 8.55e30.06e59.61 L100_Herb 0.00e0.34e1.84 L100_WetW 0.00e0.00e1.28 L100_DevM 0.00e0.00e1.78 L100_Shr 0.00e0.00e0.00 L100_ForE 0.00e0.00e0.15 L100_WetH 0.00e0.00e0.63 L100_DevH 0.00e0.00e0.16 L100_ForM 0.00e0.00e0.00 L100_Bar 0.00e0.00e0.00 L100_Oth 0.00e0.00e0.00

Name Regional R30_ForD R30_Hay R30_Herb R30_Crop R30_DevM R30_DevL R30_WetW R30_DevO R30_ForM R30_ForE R30_Shr R30_DevH R30_Bar R30_WetH R30_Oth

Quartiles

R2

Land Use in 30 m Buffer 9.82e20.90e38.01 0.368a 3.13e7.35e12.57 0.312a 0.23e1.00 -2.08 0.312b 30.69e50.23e64.82 0.304a 0.07e0.26 -0.62 0.259a 0.79e1.51e3.48 0.245a 0.00e0.26e0.95 0.242a 4.44e6.26e10.01 0.234a 0.00e0.00e0.04 0.232a 0.00 -0.05 -0.20 0.225a 0.00e0.00e0.02 0.208a 0.00e0.07e0.20 0.198a 0.00e0.00e0.01 0.182a 0.00e0.01e0.36 0.172b 0.00e0.00e0.00 0.012a

Local 30-Meter Buffer (L30)

0.335a 0.272b 0.208a 0.196a 0.190b 0.117b 0.113a 0.107b 0.069c 0.064a 0.051a 0.048b 0.042a 0.005b 0.000

Local Land Use in 30 m Buffer L30_ForD 7.33e29.34e53.65 L30_Crop 6.33e25.24e53.97 L30_DevL 0.00e1.20e6.59 L30_Hay 0.00e1.67e9.45 L30_DevO 3.00-6.42-15.23 L30_Herb 0.00e0.00e1.61 L30_DevM 0.00e0.00e1.12 L30_WetW 0.00e0.00e2.24 L30_WetH 0.00e0.00e2.24 L30_DevH 0.00e0.00e0.00 L30_ForE 0.00e0.00e0.00 L30_ForM 0.00e0.00e0.00 L30_Shr 0.00e0.00e0.00 L30_Bar 0.00e0.00e0.00 L30_Oth 0.00e0.00e0.00

0.334a 0.202a 0.186a 0.161a 0.158a 0.154b 0.100a 0.092a 0.087a 0.071c 0.066a 0.036c 0.032c 0.004b 0.000

a a ¼ best prediction at 423 branches. b b ¼ best prediction at 328 branches. c c ¼ best prediction at 233 branches.

2.2.

Environmental variables ranking

Environmental variables are divided into two categories: offstream and instream. The offstream category is composed of four groups: local and regional land use -in the whole upstream area and the 30- and 100-m buffers-, stream fragmentation, and point source density and intensity. The instream category is composed of two groups: water and habitat qualities. The individual predictive power of each environmental variable is initially estimated by obtaining the coefficient of determination [r2] of the observed (measured) IBI versus a calculated IBI prediction values, generated using a leaveone-out cross-validation approach detailed below. Let us assume that we are given a database of measurements represented as a matrix X of n observations (rows) by m environmental variables (columns). One element of that

matrix, i.e. one observation of variable v, (let’s call it Xi; v without loss of generality) is isolated and will be called the test or query site. The remaining n-1 observations of the same variable, namely the measurement (½X1; v; .Xn  1; v) in the same column of the data matrix, are organized in a “dendrogram” tree structure having ½X1; v; .Xn  1; v as leaves. This is done by applying agglomerative Hierarchical Clustering (HC) using the average linkage method and the standardized Euclidean as the distance metric (Jain et al., 1999). When the resulting dendrogram is “cut” at a certain distance from the root (more details on how the cut level is decided are given in the last paragraph of this section) several tree branches are emanating from the cut. Among them we identify the Most Similar Branch to the test site i when using variable v (to be called MSBi;v ) as the branch (overall branches Bk defined by the cut) for which the standardized Euclidean distance between

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Table 2 e Description and individual IBI predicting power for the water quality, habitat, point source, and stream fragmentation metrics. Name

Units

Description

R2

Name

Units

Description

Water Quality Parameters BOD

Habitat Parameters 0.12

b

Embed

0e4

Embeddedness

0.28a

Riffle Subs Pool Chan DA Rip Cover Grad Fragmentation Parameters SITE_Con*

0e8 0e20 0e12 0e20 Km2 0e10 0e20 0e10

Riffle and run quality Substrate quality Pool and glide quality Channel morphology score Drainage area Riparian and bank qualities Instream vegetal cover Gradient score

0.24a 0.23a 0.23a 0.21a 0.19c 0.17b 0.13a 0.09c

Fraction

Total connected length from observation site/ Total basin network length Downstream flooded area/Downstream main channel length Downstream dam storage/Main channel length

0.47a

0.09c 0.08c 0.07b 0.06b 0.04b 0.03a 0.02a 0.02c 0.02a

Cl

Biological Oxygen. Demand mg/L Total Kjeldahl Nitrogen mg/L Total Arsenic mg/L Ammonia as Nitrogen mg/L Nitrite as Nitrogen mg/L Total Magnesium mg/L Total Sulfate mmho/cm Conductivity mg/L Dissolved Oxygen mg/L Hardness (as CaCO3) mg/L Total Chloride

pH

S.U.

pH

0.01b Dfl_MainLen* m2/km

TSS

mg/L

0.01b Dsto_MLen*

m3/km

NO3

mg/L

Total Suspended Solids Nitrate as Nitrogen

0.01c DW_MainDf*

Km

Ca Cd Cu Fe Zn TP Pb

mg/L mg/L mg/L mg/L mg/L mg/L mg/L

Total Calcium Total Cadmium Total Copper Total Iron Total Zinc Total Phosphorus Total Lead

0.01b 0.01a 0.01c 0.01b 0.01b 0.00a 0.00a

TKN As NH4 NO2 Mg SO4 Cond DO Hard

mg/L

R2

0.02a

Avg_Df* Km U_Df Km UPS_Con Fraction Uflood_len m2/km UPS_Flooded Fraction UPS_stor_len m3/km UPS_stor_DA m3/Km2 Point Source Parameters PS_LTOT No./km PSDisch_LPS

m3/d/Km

PS_LPS

No./km

PSDisch_DA PSDisch_LT

m3/d/Km2 m3/d/Km

Flow_PS LPS-DA

% Km/Km2

0.46a 0.44b

Main channel downstream length/Number of 038a downstream dams Mean value between DW_MainDf and U_Df 0.26a Upstream network length/Number of upstream dams 0.25a Upstream connected length/Total upstream length 0.19a Upstream flooded area/Upstream network length 0.17a Upstream flooded area/Drainage area 0.17a Upstream dam storage/Upstream network length 0.16a Upstream dam storage/Drainage area 0.15a Number of upstream point sources/Upstream network length Upstream point source discharge flow/Distance from site to all upstream point sources Number of upstream point sources/Distance to all upstream point sources Upstream point source discharge flow/Drainage area Upstream point source discharge flow/Upstream network length % of upstream network carrying waste water Distance to all upstream point sources/Drainage area

0.26b 0.21b 0.21a 0.21b 0.20b 0.20b 0.18b

*Downstream parameters calculated up to the basin outlet All distances were calculated following stream network channels. a a ¼ best prediction at 423 branches. b b ¼ best prediction at 328 branches. c c ¼ best prediction at 233 branches.

the test site’s value (Xi; v) and the mean value of variable v over the branch leaves is minimized. See Eqs. (1) and (2) below for the formal definition, MSBi;v

Dki;v

n  o ¼ argk min Dki;v

dist½Xi; v; averageðXj; vÞ ¼ ; j˛Bk

(1)

(2)

whereDki;v is the standardized Euclidean distance between the test-site’s value Xi,v and the average {Xj,v} over the sites residing at the leaves of branch Bk. Note that depending on where the dendrogram is cut, a resulting branch may contain one or more observations. As “calculated IBI” prediction for site i based on variable v information we will use the mean IBI value of the

observations clustered in its corresponding Most Similar Branch (see Eq. (3)) IBICi;v ¼

  average IBIOj j˛MSBði; vÞ

;

(3)

where “observed IBI” (IBIOj ) is the measured IBI value for each site j of branch MSBi;v recorded in the database. The same procedure is repeated for every site i (keeping v fixed) leading to an n  1 column vector of predicted IBIs for all sites based on information for variable v alone, called IBICv . Then the same procedure is repeated for every variable v giving rise to an n  m matrix of IBI predictions,IBIc, having the same structure as the data matrix X. Finally three different dendrogram tree cuts are applied (leading to 233, 328, and 423 branches respectively) to obtain

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three different n  1 vectors of IBI predictions based on each variable v. Note that the distance from the root for each cut has been selected so that the number of resulting branches is approximately equal to 50%, 75%, or 100% of the total number of available complete observations in the database (see example in Fig. 2). Among the three vectors the one with maximal similarity (in terms of [r2]) to the observed (measured) IBIO v vector is considered as the “best” predictor of IBI using information of variable v alone, and this [r2] value is assigned as the score of variable v in the rank ordering of the environmental variables.

Fig. 2 e Example of a dendrogram built using an array of n-1 observations composed of v environmental variables (v is a one- or possibly multi-dimensional vector of selected environmental variables [v1,v2.,vm]). Test-sites (X1,v,X2,v,.Xi-1,v,Xi D 1,v,.Xn,v) correspond to the leaves of the tree. Three cuts (dashed lines) are determined (see text for details). Each cut generates branches (test site clusters). The Most Similar Branch (MSB) to the test site Xi,v, is determined (see text for details). The average measured IBI of the sites (leaves) belonging to the MSB is used as the predicted IBI for the test site. The same procedure is repeated for each test site and then for each cut. The predicted IBI values are compared to the measured IBI for all sites. The predictive value of variable v is assessed based on the jr2j of the fit of best predictive model (among the three models corresponding to the three different cuts).

2.3.

Step-wise IBI prediction

This step consists of obtaining progressively improved IBI predictions by combining variables from each group separately (see groups in Fig. 3). The “best” variables from each group are combined to find the “best” offstream and instream predictors following the order of variables specified in Fig. 3. Finally, the subset of best offstream and instream predictors are also combined in a similar manner to obtain the overall best set of predictors. The IBI prediction methodology remains the same as in step 2.2. However, in this case, a step-wise approach is followed. For each group, and following the group’s variable ranking obtained in Section 2.2, the best predicting variable is first selected (let’s call it v1 w.l.o.g.). Subsequently, the second best predicting variable in each group (to be called v2) is also selected to form an array of two-dimensional environmental

Final set of offstream and instream variables

Selected offstream variables

Selected LU variables

Selected regional LU variables

Selected PS variables

Selected instream variables

Selected fragmentation variables

Selected habitat variables

Selected water quality variables

Selected local LU variables

LU in regional catchment area

LU in local catchment area

LU in regional 30-meter buffer

LU in local 30-meter buffer

LU in regional 100-meter buffer

LU in local 100-meter buffer

Fig. 3 e Diagram showing the order in which the groups of variables are combined. Dark grey rectangles indicate instream variables. Light grey rectangles indicate offstream variables. White rectangles indicate final model with a mix of both types of variables.

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vectors ({X1,(v1,v2),X2,(v1,v2),.,Xn,(v1,v2)}). One at a time, a twodimensional test site is isolated from the rest n-1 two-dimensional observations (exactly as we did in Section 2.2). The testsite vector is presented again to the branches of the dendrogram built using the remaining n-1 two-dimensional vectors. The average IBI of the observations clustered in the most similar branch is selected as the test-site’s calculated IBI Eq. (3), but now with v ¼ (v1,v2). The selected dendrogram’s branch is the one that minimizes the standardized Euclidean distance between the test-site vector and the average environmental vector calculated from the observations located in the particular branch (Equations (1) and (2) but now with v ¼ (v1,v2)). If the two-variable model improves the previous prediction (increase in R2) at any one of the three selected cut levels of the dendrogram (i.e. using 233, 328, or 423 branches), the new variable is retained otherwise it is discarded. Subsequently, the third best predicting variable in each group (v3) is introduced to form an array of two ({X1,(v1,v3), or three ({X1,(v1,v2,v3),X2,(v1,v2,v3),., X2,(v1,v3),.,Xn,(v1,v3)}) Xn,(v1,v2,v3)}) dimensional observations (depending on the inclusion or exclusion of v2 in the previous step). Again, one at a time, a test site is separated from the rest of n-1 observations and associated to the most similar branch of the dendrogram calculated with the variables used in this step. Improvement in the IBI prediction relatively to the previously tested model results in the inclusion of the last variable in the best set so far, or exclusion otherwise. This procedure is repeated for each group until all variables have been considered for inclusion. At the end of this “greedy” procedure, the “best” combination of predicting variables from a particular group of variables is identified. Although this method does not guarantee to find the globally optimal IBI predictive model it does move step by step toward a model with improved performance as new variables are introduced and it is quite fast to implement. In this research, strongly cross-correlated variables were not eliminated because the model’s performance is not adversely affected when more variables are introduced. Since prediction with the environmental similarity concept is merely based on comparing site environmental vectors with the same vector elements, presence of cross-correlated variables will not affect the performance because the same variables are used for all prediction sites. Therefore, even marginal improvements can be accounted for without jeopardizing model performance. Furthermore, since variables are examined for inclusion in a sequential manner, keeping all variables “in the game” has also the advantage of not retiring prematurely a variable that although is highly correlated to a variable already added to the model may have a possible dependence to a third variable not yet examined for inclusion. Subsequently, the different groups of predictors are also progressively “merged” using only the “best” variables for each individual group resulted in the previous step. The stepwise IBI prediction methodology used when two groups of variables are combined is identical as before. Fig. 3 shows the order in which the groups of variables are merged.

predicted sites. A site was labeled as mispredicted if the calculated IBI fell beyond the 1.5  RMSE interval (where RMSE is the Root Mean Square Error of the IBI predictions for all the available observations in the dataset). Significant differences in water quality (for those sites affected by point sources), point source and fragmentation density and intensity, as well as local and regional land uses were tested using a Student t-test at the 95% confidence level.

3.

Results

3.1.

IBI predictions with offstream variables

3.1.1.

Land use

The top seven dominant land uses in our database were (in decreasing order of median percentage in the watershed [Table 1]): cropland (60.84%), deciduous forest (9.45%), hay/ pasture lands (7.67%), urban open space (6.25%), low intensity urban space (2.39%), herbaceous lands (0.89%), and medium intensity urban space (0.64%). All the remaining land uses had a median extent in the watershed smaller than 0.5%. The local land use sub-model (Local LU model in Fig. 4) was able to account for 49% of the total IBI variability. Results seemed to indicate that proximity to the stream is important because most of the selected variables in the group model were land uses within the buffer zones instead of the whole catchment area. The regional land use model (Regional LU model in Fig. 4) explained 58% of the total IBI variability. In this case, selected land uses alternated between percentages in the whole catchment and in the buffers. The first selected model variable -percentage of hay/pasture in the drainage area- had also the highest individual IBI prediction power of all local or regional land uses (Table 1). Approximately 95% of the group variability was explained with the top three group variables: hay/pasture in the drainage area and deciduous forest and urban open space in the 30- and 100-m buffers respectively. The other six model variables only accounted for the remaining 5% of the group’s variability. The subsequent merger of the regional and local land use models yielded almost identical results as the regional land use model with the exception of the last two variables (see Fig. 4). The overall land use model eAll LU model in Fig. 4accounted for 60% of the total IBI variability. The two selected local land uses (medium intensity urban lands within the local 30- and 100-m buffers) introduced marginal improvement (1.2% of the group’s variability).

3.1.2.

Impact from local stressors

Mispredicted observations were isolated and tested for statistically significant differences against the group of well-

Point source density and intensity

This sub-model ePoint Sources sub-model in Fig. 4- accounted for the smallest IBI variability of all the offstream sub-models. Upstream point source intensity (PS_LTOT) explained 26% of the overall variability and was the first and only metric selected in the step-wise algorithm.

3.1.3. 2.4.

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Stream fragmentation

Fragmentation density and intensity metrics explained 54% of the overall variability. River network connectivity at the basin scale (SITE_Con) explained 47% of the overall IBI variability (87% of the sub-model’s variability). This variable had the

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Local LU L100_ForD LDA_DevO L100_DevL L30_Crops L30_Hay L30_Herb L100_WetW L100_DevM L30_DevM LDA_ForE L30_DevH L30_ForM LDA_Bar

R2 0.335 0.363 0.363 0.373 0.392 0.427 0.458 0.465 0.472 0.476 0.480 0.484 0.492

Regional LU RDA_Hay R30_ForD R100_DevO RDA_ForD R100_DevL R30_WetW RDA_Herb RDA_WetW R100_Other

R2 0.385 0.509 0.546 0.558 0.562 0.569 0.570 0.572 0.577

Fragmentation SITE_Con DW_MainDf Avg_Df UPS_Con

R2 0.467 0.499 0.541 0.542

Point Sources PS_LTOT

R2 0.260

Water Quality BOD NO2 Cd

R2 0.116 0.124 0.130

Habitat Embed Riffle Subs Pool DA Cover

R2 0.281 0.326 0.403 0.431 0.442 0.491

All LU RDA_Hay R30_ForD R100_DevO RDA_ForD R100_DevL R30_WetW RDA_Herb RDA_WetW L100_DevM L30_DevM

R2 0.385 0.509 0.546 0.558 0.562 0.569 0.570 0.572 0.593 0.596

Offstream variables SITE_Con RDA_Hay DW_MainDf R30_ForD R100_DevO RDA_ForD R100_DevL R30_WetW

R2 0.467 0.512 0.535 0.537 0.563 0.592 0.596 0.597

Instream variables Embed Riffle Subs Pool DA Cover

R2 0.281 0.326 0.403 0.431 0.442 0.491

Overall SITE_Con RDA_Hay DW_MainDf R30_ForD R100_DevO RDA_ForD R100_DevL R30_WetW Riffle Cover

R2 0.469 0.512 0.535 0.537 0.563 0.592 0.596 0.597 0.605 0.606

Fig. 4 e Step-wise IBI predictions. R2 indicates the variability explained after adding a new variable to the model. All results were achieved using a hierarchical tree with 423 branches.

greatest IBI prediction capability of an individual variable overall. Downstream dam frequency, average dam frequency, and percentage of upstream connected network were other selected variables and accounted for the remaining 13% of the group’s variability.

predictions by the overall land use model, accounting for 60% of the total variability. This model used only eight variables instead of ten in the land use model.

3.2.

Predictions with instream variables

3.1.4.

3.2.1.

Instream habitat variables

Combination of best offstream variables

When the best offstream variables were combined (Fig. 3) only fragmentation and regional land use variables were selected (Offstream variables sub-model in Fig. 4). None of the local land uses or point source variables were selected. The best prediction of this sub-model model marginally improved

The instream habitat sub-model (which included drainage area) explained 49% of the overall IBI variability. Six variables were selected (Habitat sub-model in Fig. 4). The top four predictors, which accounted for 88% of the group’s variability, were directly or indirectly related to habitat’s substrate

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quality (i.e. embeddedness and substrate quality) or habitat variability (riffle and pool qualities). Drainage area (which was positively correlated to IBI) and instream cover explained the remaining IBI variability in the group.

3.2.2.

Water quality variables

The water quality variables group was clearly sorted in three main clusters. The first one was related to nutrient concentration, especially nitrogen (BOD, TKN, NO2, and NH4) which had the group’s highest prediction powers. Nitrate (NO3) and TP concentrations were not ranked among the top chemical IBI predictors, being TP the poorest predictor in the group. The IBI prediction with water quality parameters was the poorest of all. The group’s top two variables (BOD, NO2) were related to nutrient loading and explained 95% of the group’s variability. The remaining 5% was explained by cadmium concentrations.

3.2.3.

Combination of best instream variables

The final “Instream variables” model (Fig. 4) yielded the exact same results as the “Habitat” model. Therefore, the “Water Quality” model did not bring any new valuable information beyond the habitat variables.

3.3.

Final predictions

The final model eOverall model in Fig. 4- was composed of all the selected offstream variables and only two instream habitat parameters: riffle quality and instream vegetal cover. These two variables only accounted for 1.5% of the group’s variability. The final model accounted for 61% of the overall IBI variability, which was a very modest improvement from the “Offstream variables” model. IBI prediction plots for the “Offstream variables”, “Instream variables” and “Overall” models are presented in Fig. 5.

3.4.

Local environmental stressors

In the final “Overall” model, a total of 28 sites were above the 1.5  RMSE threshold, while 27 were below it (see Fig. 5). Among the over-predicted observations, two sites had either extremely high concentrations of copper and zinc or point source density. Since the influence of these two sites in the performance of the t-tests was evident, they were removed. The biological quality of these two sites was mostly set by their extremely degraded water quality. After removing sites with outlier local conditions, significant differences among over- and well-predicted observations were identified in the upstream river fragmentation as well as the land use-related metrics (Table 3). Over-predicted sites had more severe upstream fragmentation but also better land use at the regional scale, which was a likely cause of over-prediction. Local land use results were mixed. Over-predicted sites had larger percentages of forested areas in both, the local catchment and buffer areas but they also had larger percentages of hay and pasture lands in the local catchment area. Presence of hay pasture lands in the drainage area was identified by our model as the best IBI predictor and it is negatively correlated to IBI (Table 1). On the other hand, under-predicted sites (i.e. calculated IBI < observed IBI - 1.5  RMSE) had consistently significantly

Fig. 5 e IBI predictions with the best offstream variables (top), best instream variables (middle), and best variables overall (bottom). Dashed lines indicate perfect fit line (center) and ±1.5 3 RMSE (sides). Dot size is proportional to the number of hits that is indicated in the legend.

lower hardness and hardness-related parameter values in sites with upstream point sources, while sulfate concentration was higher. Under-predicted sites had better land use quality at the local and regional levels (more forested areas,

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Table 3 e List of variables with significant differences between over-predicted sites and sites with an IBI prediction within the ±1.5 3 RMSE range. Variable Name

Uflood_len UPS_Con UPS_stor_len Ups_stor_DA UPS_Con L30M_ForD L100M_ForD L100M_DevM LDA_ForD LDA_ForE LDA_Hay R30M_ForD R100M_ForD RDA_ForD

# of over-predicted sites/# of well-predicted sites 11/19 11/19 11/19 11/19 28/374 28/374 28/374 28/374 28/374 28/374 28/374 28/374 28/374 28/374

Type of sites

NPS NPS NPS NPS UF ALL ALL ALL ALL ALL ALL ALL ALL ALL

þ þ þ þ

Value in over-predicted sites (95% conf. interval)

Value in wellpredicted sites (95% conf. interval)

p

14.2  8.8 40.6  25.7 142.5  82.2 0.115  0.078 76.6  14.6 44.2  9.1 39.8  8.0 0.24  0.17 26.5  7.1 1.1  1.2 12.6  4.8 36.5  7.5 29.0  5.7 18.1  4.6

2.2  1.3 75.6  10.2 17.2  13.4 0.021  0.018 89.4  2.5 30.6  2.72 26.4  2.4 2.5  0.58 17.8  1.8 0.4  0.1 8.2  1.1 23.2  1.8 19.3  1.5 12.9  1.1

0.000 0.003 0.000 0.003 0.011 0.009 0.003 0.041 0.012 0.014 0.034 0.000 0.001 0.019

UF UF UF UF

NPS ¼ sites without point sources; UF ¼ sites with upstream fragmentation; ALL ¼ all sites.

less urban development and less crop lands). However, the density and severity of impoundments in the downstream section was greater (Table 4). Downstream fragmentation had great impact on IBI in the final model (Fig. 4).

4.

Discussion

This methodology proved to be very versatile and time-efficient when large, multi-parameter, environmental vectors are used for prediction of a target variable. The major advantageous difference with respect to more traditional approaches lies on the fact that the presented approach is able to allow easy, unbiased assessment of large, multi-dimensional

vectors composed of data of very different nature and measurement ranges such as concentrations of chemical compounds or discrete scores in the case of habitat quality. Because all environmental parameters are standardized prior to perform any IBI predictions, large site environmental vectors composed of parameters of very different nature and measurement range can be compared. Because all vector components are standardized a priori, each of them carries the same weight in the IBI prediction. Another big advantage over some commonly used, traditional prediction techniques such as regression is that model performance and speed is not affected by presence of highly cross-correlated variables since prediction is obtained by a mere comparison of site environmental parameters. Highly correlated variables do not affect

Table 4 e List of variables with significant differences between under-predicted sites and observations with a prediction within the ±1.5 3 RMSE range. Variable Name

Hard Mg SO4 Dsto_MLen L100_ForD L100_ForE L30_ForD L30_DevL L30_ForE LDA_ForD LDA_ForE R30_Crop R30_ForD R100M_ForD RDA_ForD

# of under/ well-predicted sites

Type of sites

11/213 11/213 11/213 22/331 27/374 27/374 27/374 27/374 27/374 27/374 27/374 27/374 27/374 27/374 27/374

PS PS PS DF ALL ALL ALL ALL ALL ALL ALL ALL ALL ALL ALL

Value in under-predicted sites (95% conf. interval) 247.0 20.9 135.2 1920.8 39.5 1.8 47.4 2.0 3.8 29.9 1.4 38.1 36.3 29.4 20.0

 42.1  4.7  15.5  711.9  8.5  2.0  9.9  1.7  5.8  7.8  1.3  1.3  7.2  5.9  4.7

DF ¼ sites with downstream fragmentation; PS ¼ sites with point sources; ALL ¼ all site.

Value in well-predicted sites (95% conf. interval) 313.8 28.2 64.1 1194.8 26.4 0.4 30.6 6.0 0.4 17.8 0.4 49.1 23.2 19.3 12.9

 13.8  1.6  25.1  157.5  2.4  0.2  2.7  1.0  0.2  1.8  0.1  2.5  1.8  1.5  1.1

p

0.033 0.046 0.042 0.025 0.005 0.002 0.002 0.038 0.000 0.000 0.000 0.025 0.000 0.000 0.002

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the outcome of the distance function because each parameter in the query site environmental vector is compared against the same exact environmental parameter in the rest of observations. Even though this methodology was not designed to “ordinate” environmental observations, the hierarchical tree used for prediction could be seen as a clustered distribution of similar sites, which is able to depict both explanatory (e.g. land use, water quality) and response variables (i.e. IBI) simultaneously. Traditional techniques such as Polar Ordination (PO) or NonMetric Multidimensional Scaling (NMDS) do not allow a simultaneous display of both explanatory variables (stressors) and response variables (IBI) on the same two-dimensional grid. Other widely used traditional ordination techniques such as Correspondence Analysis (CoA) allow a simultaneous display of variables as well (Giraudel and Lek, 2001). The site distribution using the presented methodology is obtained with no a priori relationships between the explanatory and response variables. Some multivariate ordination techniques such as Principal Component Analysis (PCA) or CoA assume linear relationships between the explanatory and the response variables which may not hold true in many cases, leading to well-known problems such as the horseshoe effect (PCA) or the arc effect (CoA) (Giraudel and Lek, 2001). The model confirmed biological integrity is the result of many inter-twined stressors of different nature acting at different scales. Out of the five main components of biological integrity (energy sources, water quality, habitat structure, flow regime, and biotic interactions) (Karr and Kerans, 1981; Karr et al., 1986; Karr, 1991), the first four were partially or fully represented in our database. At the study scale, only two groups of stressors were necessary to approximate the best variable combination for IBI prediction: regional land use and stream fragmentation at the basin-level. The relevance of these variables for IBI prediction was consistent with the geographic scale of the study, which had many sites scattered through a wide range of watersheds and within multiple basins. The relevance of the sampling strategy and geographic scale of the study area is paramount (Allan et al., 1997). At a specific scale, relevant variables in the highest possible level of the stressorresponse hierarchy reveal as best predictors of biological integrity. It has been proved that when IBI predictions are based on a wide array of observations from different watersheds and stream orders; regional scale variables will emerge as best predictors (Roth et al., 1996). Alternatively, if the study is based on similar types of observations with little regional environmental variability (e.g. same order streams in one watershed), more local variables will emerge as the most significant because the background regional quality for the group of sites is very homogeneous (Lammert and Allan, 1999).

4.1.

Land use

The model identified regional land use as one of the most important contributors to biological integrity at the statescale. Generally, the IBI prediction power of the dominant land uses was greater in the buffer zone than in the whole drainage area (Table 1). Hay/pasture and low intensity urban

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development were the only exceptions among dominant land uses. The effect of these on IBI was more evident in the whole drainage area, especially for hay/pasture. In Ohio, combinations of hay/pasture and deciduous forest (second and third most dominant land uses) were the most relevant to IBI. Surprisingly, the most abundant land use (i.e. cropland) was not part of the final model or the offstream variables sub-model. This was most likely due to negative, strong cross-correlations between the percentage of crops and deciduous forest. Agriculture and forest have been identified as important contributors to IBI variability (Roth et al., 1996; Wang et al., 1997; Wilson and Xenopoulos, 2008). A positive correlation between quality of fish assemblages and percentage of forested lands -which are negatively correlated to agriculture- has been reported. This correlation held true for both, the drainage area and the regional buffers (Wilson and Xenopoulos, 2008; Stewart et al., 2001). In most research efforts, different agricultural land uses such as cropland, range and pasture, orchards, or hay are usually merged into one category: agricultural lands (Anderson et al., 1976). In our research, agricultural land uses were not merged. The different sub-categories were kept as originally defined in the NLCD (USGS, 2008b). This revealed hay/pasture lands within the drainage area as a great predictor of biological integrity despite its smaller extent if compared to cropland (average cropland coverage equal to 56.1% versus 9.1% for hay/ pasture). Pasture and range lands in the drainage area have been associated with reduced vegetal cover, increased water temperature, nitrate, biomass concentrations, photosynthetic rates, and total suspended solids as well as increased fine sediment loading. A major shift in species composition of the macro-invertebrate community was also observed in areas with pasture lands (Quinn et al., 1997). The presence of rangeland is particularly harmful to aquatic fauna, especially in sites with poor riparian quality (Meador and Goldstein, 2003) and proved the most harmful to the aquatic community in the state of Ohio. Regional urban land uses played an overall smaller role on the integrity of Ohio streams. The dominant urban land uses (i.e. open space and low intensity development) were mostly relevant at the regional 100-m buffer (Fig. 4). This result agreed strongly with research negatively correlating urbanization along the stream buffers and stream integrity (Stewart et al., 2001; Wang et al., 2001; Morley and Karr, 2002). Urbanization seems to be significant at the local level as well. Medium intensity urbanization in the local buffers was the only local variable present in the final land use sub-model (see All LU model in Fig. 4). Medium intensity development was not a dominant land use in local buffers (2.21 and 1.76% in the 100and 30-m buffers respectively, versus 12.3 and 11.6%, and 5.9 and 5.51% of open space and low intensity urban lands respectively). Even though local open and low intensity developed lands were not selected in any model, this was most likely the consequence of strong correlation with their regional homologues (r ¼ 0.60 and 0.57 for open space and r ¼ 0.57 and 0.59 for low development in the 30- and 100-m buffers respectively). Nonetheless, new information introduced by the local medium intensity development could indicate that proximity of intense urbanization is an important factor to the site’s integrity (Wang et al., 2001; Morley and

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Karr, 2002). Around 10e12% of connected imperviousness is considered the threshold beyond which biological quality declines rapidly in watersheds with small or no riparian buffers (Schueler, 1994; Wang et al., 2000, 2001). Selection of medium intensity development in local buffers by our model may indicate that this threshold has been reached. Another minor contributor in the offstream and final models was the presence of woody wetlands in the 30-m regional corridor. Percentage of this land use in the regional 30-m buffer was present in the final model and its extent in the drainage area was selected in the land use model (Fig. 4). Even though little new variability was explained by this land use, its presence is remarkable because of its little extent (mean percentages equal to a 0.33, 0.70, and 1.07% in the drainage area, 100- and 30-m regional buffers respectively). Woody wetlands seemed to gain importance with proximity to the stream (its individual-based predictive power ranked 12th out of 16 land uses in the drainage area, 9th out of 15 land uses in the 100-m regional buffer, and 7th out of 15 land uses in the regional 30-m buffer). A similar result was reported by Richards et al. (1996), who linked forested wetlands (mean extent equal to 10% in the drainage area) with increased presence of woody debris and other channel characteristics such as bankfull depth. Wetlands regulate surface water flow and site’s hydrology (Mitsch and Gosselink, 1986). Their presence is associated with decreased sediment input, nutrients, temperature, ionic strength, and increased resilience to disturbances (Richards et al., 1996; Detenbeck et al., 2000). Of special importance is the presence of wetlands near the receiving water body as the model indicated (30-m buffer was selected over drainage area in the final model). A decrease in wetland-stream distance has been positively correlated to reduced levels of nutrients, ions, and bacteria. Wetland extent has been correlated to decreased lead and high color in downstream lakes. This was found to be especially true in areas with highly fragmented riparian corridors (Johnston et al., 1990; Detenbeck et al., 1993, 2000). In the final model, two regional land use variables, positively-correlated to IBI were selected as final predictors when in very close proximity to the stream (i.e. in the 30-m buffer). On the other hand, negatively-correlated, regional land use variables were usually selected for the whole drainage area or for the 100-m buffer. Even though a definite conclusion may not be inferred from this fact, it may be an indication that preserving watershed-wide natural continuity along a stream’s immediate lands may help improve or maintain biological integrity when development occurs beyond these limits.

4.2.

Fragmentation

The negative effects of stream fragmentation to aquatic species have been widely studied (Reyes-Gavilan et al., 1996; Morita and Yamamoto, 2002; Morita and Yokota, 2002). Stream fragmentation and anthropogenic flow regulation affects a large percentage of streams worldwide, especially in developed countries (Dynesius and Nilsson, 1994; Nilsson et al., 2005). Stream fragmentation by dams has serious consequences for the biological community, preventing fish from reaching upstream habitats and isolating trapped upstream populations. Decreased species richness and risk of

extinction of native fauna through demographic, environmental, and genetic stochasticity are some of the consequences fragmented populations face (Morita and Yamamoto, 2002). Moreover, physical barriers are not the only consequence of dams. Usually, hydrologic changes are also associated to impoundments. Alteration of the natural flow regime affects fauna by eliminating or modifying natural habitat conditions, which may generate a shift in species composition and, therefore, biological integrity (Poff and Allan, 1995; Richter et al., 1996; Poff et al., 1997; Fischer and Kummer, 2000; Freeman et al., 2001; Gilvear et al., 2002). In our research, some fragmentation metrics had the largest individual IBI predicting power overall. This was especially true with metrics that accounted for both, the upstream and downstream habitats or the downstream habitat only. These variables were able to explain around 40% of the total IBI variability by themselves. Upstream fragmentation metrics had far less prediction power and were only relevant in some sites as shown in Table 3. A potential explanation is that most of the available observations were located well inland and far from the basin outlet (average stream distance to basin outlet ¼ 284.3 Km, minimum distance ¼ 18.35 km, maximum distance ¼ 833 Km). This could have influenced the overall model results since most of the available habitat was located in the downstream section. The fact that most of the available habitat in the available observations was located in the downstream section may have generated strong correlations between overall fragmentation metrics (i.e. metrics including both, upstream and downstream sections) and downstream-only metrics. However, and as mentioned in previous sections, model performance and speed is not negatively impacted by introducing strongly cross-correlated variables. Irrespective of this caveat, the model still selected an overall fragmentation metrics as the most powerful IBI predictor (Table 2), which is a clear indication of the paramount importance of habitat size and continuity on aquatic ecosystems. The impact of a fragmented upstream network was demonstrated when comparing fragmentation levels between well- and mispredicted sites with fragmented upstream networks (Table 3). Statistical differences were identified among these. No statistical differences in the size of these sites’ drainage areas were found.

4.3.

Point sources and instream water quality

Even though most of the nutrient-related parameters were among the best water-quality IBI predictors, nitrate and TP concentrations were not ranked among them, being TP the poorest predictor in the group. A clear relationship between nitrate concentration and IBI has not been found in Ohio. Only concentrations beyond 3e4 mg/L had consistently negative effects on IBI Rankin et al. (1999). The poor prediction power of phosphorus concentrations could be attributed to high concentrations beyond the biomass limiting-nutrient condition (Rankin et al., 1999). The second cluster of variables was composed of ionic strength-related parameters (Mg, Hard, Cl, Cond, SO4). The third and last group was composed of metal concentrations (Zn, Cd, Fe, Cu, Pb) with the exception of arsenic which had the third highest individual predictive

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 5 9 e2 3 7 4

power of all available chemicals. Other variables such as DO, TSS, or pH had very low predictive power. The first two variables selected in the water Quality Model (BOD and NO2, Fig. 4) indicate that nutrient input is the main water quality contributor to biological degradation at the study scale. BOD has been identified as a significant source of degradation in Ohio streams (Dyer et al., 2000; Norton et al., 2000, 2002) and is an indication of highly eutrophic conditions. The higher predicting power of BOD and several nutrient-related parameters clearly indicate that eutrophication processes have a significant impact on IBI. The most significant impact of eutrophication on aquatic fauna occurs in the ultimate or “collapse” phase in which oxygen is depleted because it is used to fuel decomposition of massive amounts of decaying algae or phytoplankton. Concentrations of DO prior to the ultimate eutrophication phase fluctuate on a daily basis based on algae photosynthetic or respiration processes (Novotny, 2003). For this reason, for eutrophic systems that have not yet reached system collapse, DO may not always be a good predictor of biological integrity as the model identified. Even though samples were collected during summer months or in early Fall (period in which environmental conditions will be more favorable for algae blooms in Ohio), it is unlikely that all sites with an excess nutrient input were in the ultimate phase of eutrophication. The third selected variable in the model was cadmium concentration, which provided marginal improvement (see Fig. 4). Metal toxicity is indeed a powerful agent of biological degradation. However, it is only able to explain a significant part of the overall IBI variability at smaller scales such as the upper or lower parts of a watershed (Dyer et al., 2000). This is most likely a consequence of its highly localized nature (i.e. coming from point sources or legacy pollution). None of the chemical variables were included in the subsequent Instream variables model. Habitat and sometimes water quality -especially if related to nutrient input- are mostly driven by local and regional land uses. Therefore, in sites with severely impaired habitats (e.g. with a high level of fine sediment due to accelerated denudation processes), the most likely cause of poor water quality is non-point source pollution (i.e. chemicals attached to flushed particles in runoff). This could explain why water quality data did not provide any new information when merged with the habitat model at the study scale. Point source density and intensity only had a significant impact at the local scale as expected. When outliers were removed, significant differences were not identified between well- and mispredicted observations with reported point sources (therefore, not included in Table 3 or Table 4). Significant differences in water quality were only found in some under-predicted sites with respect to well-predicted sites. Lower hardness levels and higher sulfate concentrations were observed in under-predicted observations (Table 4). From the results, the overall effect of point source pollution on IBI is very small compared to other more ubiquitous stressors directly or indirectly related to land use at the scale of our study. Point sources are a significant factor if they have a significant presence in the area of study. For example, point source pollution has been identified as a significant negative factor in some studies based in only one basin or a portion of it (Dyer et al., 1998a, 2000). Another study based in the whole

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state of Ohio using only habitat and water quality data, concluded that water quality had a significant impact on IBI in specific clusters of sites only; but confirmed that the most relevant instream parameters at the state-scale were habitatrelated (Manolakos et al., 2007).

4.4.

Instream habitat

Instream habitat and drainage area were able to explain 49% of the overall IBI variability. Substrate-related metrics (i.e. embeddedness and substrate quality), stream variability (i.e. pool and riffle quality), and vegetal cover were the most relevant QHEI metrics. Habitat quality has been identified as the main instream source of IBI variability (Hall et al., 1996; Dyer et al., 1998a; Manolakos et al., 2007). Habitat quality is strongly driven by land use changes in the drainage area and may account for land use-related water quality information such as nutrient input. Our model confirmed this point and the Habitat model selected exactly the same variables as the Instream model (Fig. 4). Riffle and Cover qualities were selected in the Overall model but with very modest contributions to the final outcome. Stream variability, substrate quality, and/or instream cover have been identified as significant contributors to biotic quality in Ohio (Dyer et al., 1998a, 1998b; Yuan and Norton, 2004; Manolakos et al., 2007) and elsewhere (Minshall, 1984; Quinn and Hickey, 1990; Richards et al., 1993; Rabeni and Smale, 1995). Drainage area was positively correlated to IBI, which strongly agreed with the findings by Dyer et al. (1998a) in Ohio.

4.5.

Mispredictions due to local conditions

Two main sources of IBI overprediction due to local environmental conditions were identified. The first source consisted of higher levels of upstream fragmentation in sites with fragmented upstream networks (Table 3). The second source, which affected all observations, was local land use patterns not included in the final prediction model. Over-predicted observations had significantly higher percentages of forested areas in the drainage area and regional buffer corridor (Table 3). This contributed to high calculated IBI scores. The extent of forested land in the local catchment and buffer zones were also significantly higher in over-predicted sites while medium intensity urbanization in the local buffer was lower. These results were counter-intuitive given the lower observed IBI scores in sites with such good ‘land use quality’. However, these sites had significantly higher percentages of hay/pasture lands in the local catchment area. This land use was identified as the most detrimental to IBI and could explain the overpredictions (i.e. sites with very good regional characteristics, which were included in the final model, but significantly higher levels of pasture lands at the local scale, which was not included in the final model). Significant differences of pasture lands at the regional scale were not present in over-predicted sites. On the other hand, under-predicted sites had significantly better ‘land use quality’ at both scales as well. Therefore, exceptional local land use quality (i.e. significantly higher levels of forested areas combined with smaller percentages of urban and crop lands, see Table 4) is the most likely cause of

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IBI under-prediction. The final model didn’t include information from local land use variables.

5.

Conclusions

 We presented a highly versatile, predictive modeling methodology which has many potential applications in the environmental data analysis field. The new methodology is capable of dealing with a large number of observations with many associated attributes in a very time-efficient manner. Moreover, one of the main contributions of such methodology is that it does not rely on a-priori assumption on the relationship between the environmental variables and the prediction target. Many traditional exploratory techniques in the ecological modeling field make such kind of assumptions (e.g. canonical correspondence analysis is based on linear regressions between the explanatory and response variables). These two main features are of paramount importance because natural systems are composed of many inter-twined, cross-correlated variables with a highly non-linear inter-dependencies. Furthermore, the model performs well when discrete or crudely scaled data is used because it is based on assessing environmental similarities among sites with the same attributes. As a result, it allows using variables with different scoring criteria at once.  At the state-scale, regional land use and basin-level stream fragmentation are the main predictors of biotic integrity in Ohio. Habitat variables only contributed marginally to model improvement, while instream water quality and point source intensity and density were not able to improve the final model at all. Most of the information from instream water and habitat qualities is introduced into the model by regional land use, which acts as a surrogate variable.  We revealed the importance of local stressors which were not accounted for in the final model. Over-predictions mainly came from a combination of higher upstream fragmentation, extreme point source density and intensity, and high levels of hay/pasture in the local catchment area. under-predictions mainly came from extraordinary local land use quality which was not accounted for in the model.  If the 55 mispredicted sites eout of 429 observations- could be disregarded due to unique local conditions, the model would explain 86% of the overall IBI variability. Therefore, in our dataset local stressors accounted for an extra 25% (i.e.86%e61%) of the variability explained by land use and fragmentation metrics. The remaining 14% may be due to sampling errors, data quality issues, or natural randomness (for example, a site with BOD ¼ 24 mg/L; TKN ¼ 3.1 mg/L; TP ¼ 1.29 mg/L; Zn ¼ 180 mg/L; Cu ¼ 39 mg/L; Fe ¼ 19,700 mg/L; or NO2 ¼ 0.19 mg/L had one of the highest observed IBI scores (50)).  The results showed how water quality issues from point sources have a small overall impact on the biotic integrity in Ohio. This may indicate a successful control of point sources through the EPA’s NPDES Program, which have been top priority for U.S. surface waters since the Clean Water Act of 1972. These results do not indicate that water quality problems from point sources are not relevant anymore in

Ohio, but they have shifted from being a widespread issue to a local one at the state-scale.  Our model identified stream fragmentation and land use change - especially in the regional buffers- as the most important stressors to biological integrity. Habitat degradation and nutrient input are the most direct instream consequences from land use disturbances. Results suggest that in order to achieve the aimed physical, chemical, and biological integrity of the Nation’s waters, protection and enforcing policies have to refocus towards a more holistic view that goes beyond the traditional point source control. Ecosystem continuum must be kept and watershed- or basin-level- land use planning is necessary to attain such goals, especially in the most immediate lands of a water body. Stressors should be approached in a scale-down manner. This would guarantee that improvements at the local level have successful outcomes because the regional background conditions meet the minimum requirements to attain the targeted integrity.

Acknowledgement This research has been partially supported by the US EPA/NSF/ USDA STAR Watershed Program, Grant No. R83-0885-010 to Northeastern University, Boston, MA. The authors would like to express their gratitude towards Mr. Ed Rankin and Mr. Dennis Mishne from Ohio EPA and Mr. Scott Dyer and Ms. Charlotte White-Hull from The Procter & Gamble Company for their help with the environmental data and valuable advice.

references

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Carbon cycling in a zero-discharge mariculture system Kenneth Schneider a,b,*, Yonatan Sher a,1, Jonathan Erez b, Jaap van Rijn a a

The Hebrew University of Jerusalem, Faculty of Agricultural, Food and Environmental Quality Sciences, Department of Animal Sciences, P.O. Box 12, Rehovot 76100, Israel b The Hebrew University of Jerusalem, The Institute of Earth Sciences, Edmond Safra Campus, Givat Ram, Jerusalem 91904, Israel

article info

abstract

Article history:

Interest in mariculture systems will rise in the near future due to the decreased ability of

Received 16 September 2010

the ocean to supply the increasing demand for seafood. We present a trace study using

Received in revised form

stable carbon and nitrogen isotopes and chemical profiles of a zero-discharge mariculture

8 December 2010

system stocked with the gilthead seabream (Sparus aurata). Water quality maintenance in

Accepted 25 January 2011

the system is based on two biofiltration steps. Firstly, an aerobic treatment step comprising

Available online 21 February 2011

a trickling filter in which ammonia is oxidized to nitrate. Secondly, an anaerobic step comprised of a digestion basin and a fluidized bed reactor where excess organic matter and

Keywords:

nitrate are removed. Dissolved inorganic carbon and alkalinity values were higher in the

Zero-discharge mariculture

anaerobic loop than in the aerobic loop, in agreement with the main biological processes

Carbon stable isotopes

taking place in the two treatment steps. The d13C of the dissolved inorganic carbon (d13CDIC)

Alkalinity

was depleted in 13C in the anaerobic loop as compared to the aerobic loop by 2.5e3&. This

DIC

is in agreement with the higher dissolved inorganic carbon concentrations in the anaerobic loop and the low water retention time and the chemolithotrophic activity of the aerobic loop. The d13C and d15N of organic matter in the mariculture system indicated that fish fed solely on feed pellets. Compared to feed pellets and particulate organic matter, the sludge in the digestion basin was enriched in

15

N while d13C was not significantly different. This

latter finding points to an intensive microbial degradation of the organic matter taking place in the anaerobic treatment step of the system. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

The oceans supply of fish is stagnant and it is expected to decrease in the near future due to overfishing and biodiversity loss (Jackson et al., 2001; Worm et al., 2006). As such, it is expected that mariculture systems will have a significant role in the global fish supply because of the growing demand for fish (Tidwell and Allan, 2001). Mariculture systems in coastal areas (such as floating cages) or coastal, land-based ponds impose environmental concerns due to their contamination of

coastal waters with organic matter and nutrients (Wu, 1995). Such contamination brings about environmental alterations such as anoxic sediments that produce toxic H2S (Holmer and Kristensen, 1992), eutrophication, and a resulting decrease in biodiversity and biomass of the benthic communities (Mazzola et al., 1999; Karakassis et al., 2000). In the present study, a zero-discharge recirculation system, first developed for freshwater fish farming (van Rijn, 1996; Shnel et al., 2002) and later converted to a system for culture of marine fish (Gelfand et al., 2003) was examined.

* Corresponding author. Present address: Department of Global Ecology, Carnegie Institution, 260 Panama street, Stanford, CA 94305, USA. 1 Current address: The Jacob Blaustein Institute for Desert research, The Ben Gurion University of the Negev, Sede Boqer Campus, 84990, Israel. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.021

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Water quality in the system is maintained by recirculating the culture water through two main treatment loops: an aerobic loop consisting of a trickling filter (TF) used for oxidation of ammonia to nitrate by nitrifying bacteria (Chen et al., 2006; Eding et al., 2006) and an anaerobic loop, consisting of a digestion basin (DB) and a fluidized bed reactor (FBR), in which, among other processes, organic matter is digested and nitrate respired to elemental nitrogen (van Rijn et al., 2006). The high organic load in the DB creates a redox gradient that facilitates conditions not only for nitrate reduction but also for sulfate reduction to harmful sulfide. It was demonstrated that while in the DB some of the produced sulfide was reoxidized to sulfate by autotrophic denitrifiers (Sher et al., 2008), sulfide oxidation by these organisms was particularly evident in the FBR, which served as a final sulfide polishing stage before water from the anaerobic treatment step was returned back to the fish basin (Cytryn et al., 2005). Stable isotopes can be used to trace natural processes on the basis of their distribution as different biological processes result in different isotopic fractionation. Processes such as photosynthesis and chemosynthesis are associated with a large discrimination against 13C resulting in low d13C values of
11 to
35& depending on the process type and the enzymes associated with them (Guy et al., 1993; Goericke and Fry, 1994; Robinson et al., 2003). Contrary to other processes, carbon stable isotopes have little fractionation in the biological food web. It was observed that with each trophic level a slight increase in d13C of about 0.8  1.1& takes place; a phenomenon known as “you are what you eat 1&” (DeNiro and Epstein, 1978). As opposed to carbon, nitrogen isotopes have a larger fractionation with each trophic level and increases in d15N by about 3  2.6& have been reported (DeNiro and Epstein, 1981). The distinctive fractionation of carbon and nitrogen stable isotopes has been used as tracers in food web studies in natural systems and in engineered systems using mixing models based on mass balance consideration (Schroeder, 1983; Fry and Sherr, 1984). In this study we present a description of the carbon cycling in a zero-discharge mariculture system based on carbon and nitrogen stable isotopes and chemical parameter in the water and sludge phases of a zero-discharge mariculture system through measurement of changes in values of d13C and d15N and changes in concentrations of NH3, NO
3 , H2S, alkalinity, dissolved inorganic carbon (DIC), redox potential and dissolved oxygen (DO).

2.

Materials and methods

2.1.

General description of the mariculture system

a rate of 10 m3 h
1. In addition to the TF, the aerobic compartment comprised a foam fractionator (FF), which received water from the trickling filter basin (TFB). The particulate organic matter captured by the FF was discharged into the digestion basin (DB). This latter basin (volume: 5.4 m3) was part of the anaerobic treatment compartment. By gravitation, water from the bottom of the FB was led (0.8 m3 h
1) into this latter basin. Effluent water from the DB was collected in an intermediate collection basin (ICB) before being returned, by gravitation, to the TFB. Water from the ICB was recirculated (0.8 m3 h
1) through a fluidized bed reactor (FBR, volume:13 L) which effluent water was led through a swirl separator (SS) before being returned to the ICB. Sludge captured by the SS, was discharged into the DB. Previous studies on this and similar systems revealed that nitrification (aerobic treatment loop), digestion of organic matter together with nitrate and sulfate respiration (digestion basin), and microbial sulfide oxidation (FBR), were major processes affecting the overall water quality in the system (van Rijn et al., 1995).

2.2.

Sampling regime

2.2.1.

Sampling frequency and locations

The mariculture system was sampled on three separate occasions between July and December 2006. Samples were withdrawn from nine different locations within the system (Fig. 1): (1) fish basin (FB), (2) trickling filter collection basin (TFB), (3) effluent water from the trickling filter (TFout), (4) influent water to the trickling filter (TFin), (5) top layer (5 cm depth) of influent zone in the digestion basin (DBinT), (6) bottom layer (30 cm depth) of influent zone in the DB (DBinB), (7) top layer (5 cm depth) of effluent zone in the DB (DBoutT), (8) bottom layer (30 cm depth) of effluent zone in the DB (DBoutB), and (9) fluidized bed reactor (FBR).

2.2.2.

2.2.3. The intensive fish mariculture system was an enlarged version of the system previously described by Gelfand et al. (2003). The system (Fig. 1) comprised a fish basin (5 m3) stocked with the gilthead seabream (Sparus aurata) from which water was recirculated through aerobic and anaerobic treatment compartments. The aerobic compartment consisted of a trickling filter (TF) with a volume of 8 m3 and a surface area of 1920 m2. Surface water from the fish basin (FB) was recirculated through the aerobic compartment at

Sampling procedure

Water from each sampling point was collected in a 1.5 L plastic bottle and was initially filtered through cotton gauze with a mesh size of several mm for removal of agglomerated, floating sludge particles. Water for chemical and isotopic analysis was further filtered with a GF/F or GF/C before storage as described below. Water for DB chemical profiling of the DB was collected in 50 ml vials as described below (treatment of sludge samples). The vials were centrifuged immediately and the water and sludge where separated for different analysis. Water for measuring sulfide was transfer to 10 ml vial under nitrogen environment and the sulfide was fixed immediately with zinc acetate (Strocchi et al., 1992).

Treatment of water samples

Immediate after collection the pH, redox and inorganic
2
nitrogen species (NO
3 , NO2 and NH3) and sulfide (S ) were analyzed as described below. Samples for alkalinity, dissolved inorganic carbon (DIC) and carbon stable isotopes of DIC (d13CDIC) were stored in 60 mL brown glass bottles with gastight screws and refrigerated (4  C) until measurements. DIC and d13CDIC samples were poisoned with 0.6 mL (1% v:v) of saturated HgCl2 solution immediate after sampling. Particulate organic matter (POM) from a known water volume was

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A

Aerobic loop

Anaerobic loop

TFin

FB

DB

FF

FBR

TFout

ICB

S

TF

TFB

-

DBinT DBinB

Digestion Basin (DB)

Pump

B

DBin Inlet

DBout Outlet DBoutT DBoutB

Center

Sludge

Fig. 1 e Schematic design of the zero-discharge fish mariculture system (A). Detailed diagram of the digesting basin (B). Abbreviations are as follows: FB [ fish basin, TF [ trickling filter, TFB [ trickling filter basin, TFin [ influent water to the trickling filter, TFout [ effluent water from the trickling filter, FF [ foam fractionator, DB [ digestion basin, DBinT [ top layer (5 cm depth) of influent zone in the digestion basin, DBinB [ bottom layer (30 cm depth) of influent zone in the DB, DBoutT [ top layer (5 cm depth) of effluent zone in the DB, DBoutB [ bottom layer (30 cm depth) of effluent zone in the DB, FBR [ fluidized bed reactor, S [ swirl separator and ICB [ intermediate collection basin.

collected on combusted (450  C for 2 h) GF/F filters and dried at 30  C for about 48 h.

2.2.4.

Treatment of sludge samples

Sludge from the DB was sampled from three places (inlet, center and outlet) at 2e4 depths (12 cm apart) depending on the sludge depth. The samples were collected from different depths using a sampling device consisting of a 50 ml vial attached to a scaled pole. Samples were collected by lowering the closed vial to the desired depth and lifting the lid of the vials for a few seconds to allow filling of the vial. When possible, sludge was collected from the wall of the TFB. About 2e3 g of wet sludge was dried at 30  C for about 48 h.

2.3.

Chemical analysis

NH3 and NHþ 4 , referred to as total ammonia nitrogen (TAN), were determined by oxidation with salicylate-hypochlorite

method (Bower and Holm-Hansen, 1980). Nitrite was determined by reaction with sulfanilamide (Strickland and Parsons, 1972). Nitrate was measured according to the light absorption at two wave lengths 220 and 275 nm (APHA, 1998). When sulfide was present in the samples, samples were diluted with an HCl (0.1 N) solution in 1:1 proportion and nitrate was measured at an additional wave length of 250 nm accounting for the HS
remaining in the solution (Sher et al., 2008). Sulfide was determined by the methylene blue method (Cline, 1969). Total alkalinity was determined by titration with hydrochloric acid and calculated according to the Gran titration method (Grasshoff et al., 1983). Measurements of pH were conducted with a Radiometer Copenhagen pH meter (PHM92 Research pH Meter) and a Radiometer Copenhagen combination electrode (GK2401c). The electrode was calibrated using NBS scale standard buffers (Radiometer analytical) of 7.000 and 10.012. DO and temperature measurements at different sites at the mariculture system were conducted by means of an oxygen

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electrode combined with a temperature probe (OxyGuard, H01c Handy Gamma). Salinity was monitored with a refractometer (model: S-10E, Atago, Tokyo, Japan).

significance was set at a level of 5% or less. Statistica 6 software (StatSoft) was used for statistical calculations.

2.3.1.

3.

Stable isotopes

Stable isotopes are measured relative to an international standard (eq. (1)) in per mil (&).  d13 C or d15 Nð&Þ ¼

 Rsample
1  1000 Rstandard

3.1. In situ measurements within the mariculture system

(1)

Where d13C and d15N are the values measured for C and N, respectively and R is the ratio of the heavier isotope to the light isotope (13C/12C and 15N/14N). For each element, we used a commonly used international standard. For C, this standard is PDB (Pee Dee Belemnite-marine limestone) and for N, atmospheric air is used as the international standard.

2.3.2.

Results

3.1.1.

The inorganic carbon system

The DIC, alkalinity and the d13CDIC showed distinctive differences between the aerobic (TF-FB) and anaerobic (DB-FBR-FB) loops (Fig. 2). DIC in the aerobic loop was in the range of 2900e3900 mmol L
1 with an average of 3400  385 mmol L
1, and was lower than in the anaerobic loop which was in the range of 4200e5200 mmol L
1 with an average of

d13CDIC and DIC measurements

1-mL samples were injected into a 10-mL vial with a gas-tight screw filled with He gas at atmospheric pressure. Ten drops of H2PO4 (85%) were added and the vials were left for 24 h to equilibrate at 25  C in a temperature controlled sample tray (Finnigangasbench, Thermo Electro cooperation, USA). In each sample d13CDIC was measured eight times using an autosampler gas bench system connected online to an isotope ration mass spectrometer (IRMS; 252 mat, Finnegan) and the d13CDIC was averaged. The DIC was estimated from the first measured signal peak (mV) of each sample according to a calibration curve calculated from samples with a known DIC concentration, freshly prepared at each day of analysis.

2.3.3. POM, fish tissue, feed pellets and sludge d13C and d15N measurements

A

B

Dried sludge, freeze dried fish tissue and feed pellets were grained and samples weighing between 150 and 1100 mg, depending on their organic matter content, were analyzed. The filter upon POM sample were collected (as described above) was divided into quarters. The grounded material or a 1/4 of filter were wrapped in tin cups and measured using an element analyzer connected online to an IRMS (252 mat, Finnegan).

2.4.

Calculations

Carbon concentration in the POM (POMC) was calculated according to the following equation, POMC ¼

WCs  4 Vs

C

(2)

Where POMC is expressed in mg L
1, WCs is the carbon weight fraction of the organic matter on the filter measured by the mass spectrometer in mg and multiplied by 4 to account for using only 1/4 of the filtered material in the analysis, Vs is the water volume filtered. Nitrogen concentration in the POM (POMN) was calculated in the same manner as POMC by replacing WCs with WNs the weight of N in the filter as measured by the mass spectrometer in mg.

2.4.1.

Statistical analysis

Data are expressed as mean  SD. A comparison between treatments was performed using the ANOVA test. Statistical

Fig. 2 e DIC (A), d13CDIC (B) and alkalinity (C) measured at different locations of the mariculture system, abbreviations as in Fig. 1. The data is presented as mean ± SD.

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4630  296 mmol L
1. In the FBR, DIC was higher by about 1000 mmol L
1 (Fig. 2a). The high variation in the DIC is mainly due to a variation between sampling dates. Within each sampling session, the variation averaged 138  51 and 184  99 mmol L
1 for the aerobic and the anaerobic loops, respectively. The average DIC difference between the aerobic and anaerobic loops was statistically significant (ANOVA, p < 0.002). The d13CDIC (Fig. 2b, Table 1) showed 13C enriched values in the aerobic loop of
8.03  0.25& compared to values of
10.8  0.37& in the anaerobic loop. The average difference between the two loops was statistically significant (ANOVA, p < 0.005). Alkalinity in the aerobic loop ranged from 4080 to 4260 mmol L
1 with an average value of 4160  80 mmol L
1, significantly lower than the anaerobic loop, which fluctuated between 4900 and 5600 mmol L
1 with an average of 5300  350 mmol L
1 (Fig. 2c).

3.1.2.

A

POM

d13CPOM in all the 9 sampling sites of the system was very similar (Table 1) with an average of
23.05  1.08&,
23.29  1.43& and
23.16  1.28& for the aerobic, anaerobic and the all the stations combined, respectively. Carbon concentrations in POM (POMC) averaged 2.5  0.5 and 4.1  0.4 mg C L
1 in the aerobic loop and anaerobic loop, respectively. This difference was statistically significant (ANOVA, p < 0.03). d15NPOM in the TF was enriched in 15N with values of around 9& compared to the other sampling sites in the mariculture system where d15NPOM values were around 7.5& (Table 1). Nitrogen concentrations in the POM (POMN) were lower in the aerobic loop than in the anaerobic loop, 0.76  0.26 and 1.58  0.23 mg N L
1, respectively. This difference was statistically significant (ANOVA, p < 0.03). POM C/N ratios were significantly (ANOVA, p < 0.002) higher in the aerobic loop than in the anaerobic loop, 6  0.24 and 4.9  0.21, respectively.

3.1.3.

B

Digestion basin profiles

DO in the surface water of the DB decreased from the DBin to the DBout. A decrease in oxygen was also observed with depth (Fig. 3; a representative profile from the DB center measured on 6/11/2006). It was found that oxygen was totally consumed within the upper 5 cm of the sludge layer. These findings were

Fig. 3 e (A) A typical depth profile of DO (,) and Redox (C) in the DB center. (B) A typical depth profile of NOL 3 (B), S2L (:) and TAN (,) measured in the DB center.

Table 1 e The POM isotopic values (&) and the C and N concentrations (mg/L) measured in the water at the mariculture sampling stations, abbreviation as in Fig. 1. The results are expressed as average ± SD. Parameter d13CPOM SD d15NPOM SD CPOM SD NPOM SD C/NPOM SD

FB

TFB

TFout

TFin

DBinT

DBinB

DBoutT

DBoutB

FBR


23.03 1.52 7.63 2.23 2.31 1.53 0.52 0.34 5.97 1.69


22.96 1.16 9.03 2.32 2.71 2.39 0.60 0.52 6.50 2.35


23.18 1.03 8.58 2.50 2.72 2.13 0.52 0.45 5.84 1.21


23.03 0.83 9.20 2.49 2.32 1.85 0.51 0.38 5.77 1.02


23.35 1.04 7.42 3.64 3.90 1.97 0.95 0.25 4.88 0.38


23.16 0.92 7.47 3.13 4.24 2.63 0.79 0.55 4.94 0.34


23.10 2.01 7.51 4.33 3.68 2.26 0.87 0.35 4.82 0.17


23.59 2.09 7.23 3.52 4.58 3.35 1.14 0.59 5.00 0.39


23.11 2.01 7.57 3.31 3.87 3.17 0.93 0.57 4.93 0.24

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further validated by redox potential measurements showing a similar profile (Fig. 3A). A representative nutrient profile of the center of the DB (Fig. 3B) showed a nitrate decrease from the surface to the bottom with depletion at about 15 cm into the sludge. TAN and sulfide profiles showed an opposite trend as compared to nitrate.

3.1.4.

d13C and d15N values in sludge

13

d Csludge and d15Nsludge values were
22.5  0.9& and 9.2  1.3&, respectively. d13Csludge and d15Nsludge did not show any significant difference between the different areas within the DB and the organic matter derived from the wall of the TFB. The average values of the d13Csludge were very similar to that of the fish feed
22.2  3& (ANOVA, p < 0.67), while those of the d15Nsludge were 15N enriched compared to fish feed 6.2  0.5& (ANOVA, p < 0.0001). The C/N ratio of the sludge was slightly lower than that of fish feed 6  0.31 and 6.64  0.26, respectively (ANOVA, p < 0.01).

4.

Discussion

Significant differences in the inorganic carbonate system were found between the aerobic and anaerobic loops. Higher DIC and alkalinity, and 13C depleted d13CDIC values were measured in the anaerobic loop than in the aerobic loop (Fig. 2). Considering the main processes taking place in the treatment systems, we suggest that in the anaerobic loop 13C depletion in the DIC is caused by a CO2 release through respiratory processes and by an alkalinity increase through denitrification (van Rijn et al., 2006). In the aerobic loop, alkalinity is consumed due to nitrification (Chen et al., 2006; Eding et al., 2006) by chemolithoautotrophic microorganisms that assimilate 13C depleted CO2 from the water (Foesel et al., 2008; Sakata et al., 2008). This CO2 assimilation and, in addition, the intensive CO2 degassing taking place as a result of the low water retention time and the specific configuration of the trickling filters (Eding et al., 2006), may provide an explanation for the 13C enrichment in the aerobic treatment compartment. It seems, therefore, that the difference in alkalinity, CO2 uptake/release and the consequent difference in buffering capacity as well as the differences in filter configuration and retention time are responsible for the difference in DIC values between the two loops. Lower DIC values were measured in the TF components than in the fish basin. Within the aerobic loop, lowest DIC values were measured in the TFout. This is consistent with the chemolithoautotrophic utilization of CO2 and the consumption of alkalinity by the nitrification process within the TF. Based on the methodology used in this study as well as results from previous studies, which demonstrated oxidation of TAN to nitrate within the filter (van Rijn and Rivera, 1990; Gelfand et al., 2003), it might be concluded that autotrophic nitrification is a major process within this filter. Organic matter degradation in the DB is rapid. It was estimated that during one growth season (around one year) about 90% of the total organic matter added to the system and not utilized by fish, is digested (Fine, unpublished data; van Rijn et al., 1995; Gelfand et al., 2003; Neori et al., 2007). The d13CDIC in the anaerobic loop is 13C depleted compared to the aerobic loop by 2.5e3&. This finding is consistent with

the relative 13C depleted organic matter respired and mineralized in the DB. A bigger difference would be expected but, as previously noted, the difference in DIC between the aerobic and anaerobic loops is controlled by alkalinity and the water retention time in each of the loops. Both factors directly affect the efficiency of CO2 degassing, which is a dynamic process. Based on thermodynamic considerations, a significant kinetic discrimination against 13C is expected due to this process. The relative small difference between the loops may be explained by 13C depleted CO2 gas escaping from the DB and degassing during the water return to the aerobic loop due to the lower alkalinity and low water retention time there. It should be emphasized that, despite its static plug-flow mode of operation, large quantities of CO2 are released into the atmosphere in the DB since CO2 generation in this reactor is high. CO2 generation in the DB is high as feces and uneaten feed are all diverted to this basin. How much CO2 is released by digestion of the feces and uneaten feed can roughly be estimated by assuming that fish utilize around 50% of the carbon supplied with the feed (Neori et al., 2007). In this particular system, feed loads were as high as 4 kg per day thus, without accounting for uneaten feed, at least 1 kg of carbon was daily added to the DB. In the DB around 90% of the carbon is digested (Neori et al., 2007) which means that 0.9 kg carbon in the form of CO2 is produced daily. This daily added amount of carbon to the DB is equal to about 3e4 times the average amount of DIC in the DB, which can be calculated from DIC concentrations (Fig. 2) and the water volume of the DB. If no CO2 gas is released from the DB, it would be added to the DIC in the water. In such a case, the only exchange of DIC in the DB would be by water exchange. Because of the long water retention time in the DB (4.5 h), the d13CDIC would equilibrate to values similar to that of the organic material added to the DB i.e. w
22&, as opposed to measured values of w
10&. The carbonate system is probably in equilibrium between the DIC concentration determined by its alkalinity and the pCO2 in the atmosphere due to a relative long retention time of the DB. Based on these findings it seems likely, therefore, that the difference in d13CDIC between the aerobic and the anaerobic loops is controlled by the alkalinity differences. The fish in the mariculture are fed with pellets (48% protein, 20% fat) as the single external source of organic matter and feed, therefore, it is the source of most of the new organic matter in the system. The only other new source of carbon in the system is CO2 fixed by autotrophic bacteria, which in this particular system are mainly represented by nitrifying bacteria in the TF. Most of the organic matter produced in the TF is removed by the foam fractionator and is disposed in the DB, but quantitatively this source is highly insignificant. Feed pellets d13C and d15N values were
22.2  3& and 6.2  0.5&, respectively, while in the fish tissue these values were
20.4  1.2& and 10.8  0.6&, respectively, thus showing an increase in the isotopic values of about 2& and 4& in the d13C and d15N, respectively. These findings are consistent with previous studies, which point to an increase in d13C and d15N with each trophic level (DeNiro and Epstein, 1978; Fry and Sherr, 1984; Minagawa and Wada, 1984) and confirming that the fish in this system use feed pellets as their sole source of nutrition.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 7 5 e2 3 8 2

The input of organic matter from the fish basin to the DB as POM consists of undigested feed pellets and fish feces. This organic matter settles in the DB and serves as substrate for microbial respiratory processes mainly using nitrate and sulfate as electron acceptors as can be seen from the chemical and redox potential profiles in the DB (Fig. 3). The DB average d13Csludge and d15Nsludge values were
22.5  0.9& and 9.2  1.3&, respectively and the d13CPOM and d15NPOM were
23.3  1.4&, 7.4  3.3&, respectively. The carbon isotopes values in the DB sludge and POM were not significantly different from that of the pellets, while the nitrogen isotopes were 15N enriched compared to the pellets. The enrichment in the nitrogen isotopes may be a result of microbial decomposition (Fellerhoff et al., 2003), probably releasing 15N depleted ammonia. This possibility is further substantiated by the high TAN (Fig. 3B) in the bottom layers of the sludge column in agreement with previous work on a smaller scale system (Gelfand et al., 2003). It was shown by Cytryn et al. (2003), using DNA sequence analysis from the DB sludge, that a number of dominant microorganisms were affiliated with fermentative bacteria, Fusibacteria, Dethiosulfovibrio and members of the Bacteroidetes phylum. These fermentative bacteria are involved in the degradation of macromolecular compounds whereby secondary metabolites such as volatile fatty acids (VFA) are liberated (van Rijn et al., 1995). Under these conditions, liberated VFA were shown to undergo a rapid oxidation by bacterial respiration with mainly sulfate and nitrate as electron acceptors (Aboutboul et al., 1995; van Rijn et al., 1996, 1995).

5.

Conclusions

In this study, an approach, based on integrating stable isotopes with additional chemical analysis, was used to trace carbon in a zero-discharge mariculture system. It was shown that alkalinity values provide a clear indication for the main microbiological processes taking place in each of the system components. The carbon (DIC and POM) and nitrogen (POM) values show a consistent difference between the aerobic and anaerobic loops caused by a combination of differences in microbial processes and water retention time in these loops. Further studies are required to determine how d13C profiles, POM formation and different respiratory pathways affect the isotopic values in the system. Once such links are established, the technique of stable isotope tracing has the potential to diagnose changes in systems, such as examined in this study, by means of a few relatively simple measurements.

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Minagawa, M., Wada, E., 1984. Stepwise enrichment of 15N along food chains: further evidence and the relation between d15N and animal age. Geochimica et Cosmochimica Acta 48 (5), 1135e1140. Neori, A., Krom, M.D., van Rijn, J., 2007. Biogeochemical processes in intensive zero-effluent marine fish culture with recirculating aerobic and anaerobic biofilters. Journal of Experimental Marine Biology and Ecology 349 (2), 235e247. Robinson, J.J., Scott, K.M., Swanson, S.T., O’Leary, M.H., Horken, K., Tabita, F.R., Cavanaugh, C.M., 2003. Kinetic isotope effect and characterization of form II RubisCO from the chemoautotrophic endosymbionts of the hydrothermal vent tubeworm Riftia pachyptila. Limnological Oceanography 48 (1), 48e54. Sakata, S., Hayes, J.M., Rohmer, M., Hooper, A.B., Seemann, M., 2008. Stable carbon-isotopic compositions of lipids isolated from the ammonia-oxidizing chemoautotroph Nitrosomonas europaea. Organic Geochemistry 39 (12), 1725e1734. Schroeder, G.L., 1983. Sources of fish and prawn growth in polyculture ponds as indicated by dC analysis. Aquaculture 35, 29e42. Sher, Y., Schneider, K., Schwermer, C.U., van Rijn, J., 2008. Sulfideinduced nitrate reduction in the sludge of an anaerobic digester of a zero-discharge recirculating mariculture system. Water Research 42 (16), 4386e4392. Shnel, N., Barak, Y., Ezer, T., Dafni, Z., van Rijn, J., 2002. Design and performance of a zero-discharge tilapia recirculating system. Aquacultural Engineering 26 (3), 191e203. Strickland, J.D.H., Parsons, T.R., 1972. A practical handbook of seawater analysis. Fisheries Research Board of Canada, Ottowa. Strocchi, A., Furne, J.K., Levitt, M.D., 1992. A modification of the methylene blue method to measure bacterial sulfide production in feces. Journal of Microbiological Methods 15 (2), 75e82.

Tidwell, J.H., Allan, G.L., 2001. Fish as food: aquaculture’s contribution e ecological and economic impacts and contributions of fish farming and capture fisheries. EMBO Reports 2 (11), 958e963. van Rijn, J., 1996. The potential for integrated biological treatment systems in recirculating fish culture e a review. Aquaculture 139 (3e4), 181e201. van Rijn, J., Fonarev, N., Berkowitz, B., 1995. Anaerobic treatment of intensive fish culture effluents: digestion of fish feed and release of volatile fatty acids. Aquaculture 133 (1), 9e20. van Rijn, J., Rivera, G., 1990. Aerobic and anaerobic biofiltration in an aquaculture uniteNitrite accumulation as a result of nitrification and denitrification. Aquacultural Engineering 9 (4), 217e234. van Rijn, J., Tal, Y., Barak, Y., 1996. Influence of volatile fatty acids on nitrite accumulation by a Pseudomonas stutzeri strain isolated from a denitrifying fluidized bed reactor. Applied Environmental Microbiology 62 (7), 2615e2620. van Rijn, J., Tal, Y., Schreier, H.J., 2006. Denitrification in recirculating systems: theory and applications. Aquacultural Engineering 34 (3), 364e376. Worm, B., Barbier, E.B., Beaumont, N., Duffy, J.E., Folke, C., Halpern, B.S., Jackson, J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., Sala, E., Selkoe, K.A., Stachowicz, J.J., Watson, R., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787e790. Wu, R.S.S., 1995. The environmental impact of marine fish culture: towards a sustainable future. Marine Pollution Bulletin 31 (4e12), 159e166.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 8 3 e2 3 9 1

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Dechlorination of endocrine disrupting chemicals using Mg0/ZnCl2 bimetallic system Asfiya Begum, Sumit Kumar Gautam* The Energy and Resources Institute (TERI), 4th Main, II Cross, Domlur II Stage, Bangalore 560071, Karnataka, India

article info

abstract

Article history:

In the present study, Mg0/ZnCl2 bimetallic system was evaluated for its efficiency to

Received 15 September 2010

dechlorinate endosulfan and lindane in aqueous phase. Presence of acetone in the reaction

Received in revised form

mixture played an important role by increasing the solubilities of both pesticides and

18 January 2011

thereby accelerating its mass transfer. Water acetone ratio of 2:1 and 1:1 (v/v) was found

Accepted 21 January 2011

optimum for the dechlorination of endosulfan and lindane respectively. Presence of Hþ

Available online 31 January 2011

ions in the reaction mixture (50 ml ml1 of glacial acetic acid) accelerated the degradation efficiency of 30 ppm initial concentration of endosulfan (96% removal) and lindane (98%

Keywords:

removal) at Mg0/ZnCl2 dose of 5/1 mg ml1 within 30 min of reaction. Dechlorination

Endocrine

kinetics for endosulfan and lindane (10, 30 and 50 ppm initial concentration of each

disrupting

chemicals

(EDCs)

pesticide) with varying Mg0/ZnCl2 doses and the time course profiles of each pesticide were

Endosulfan

well fitted into the first order dechlorination reaction. The optimum observed rate constant (kobs’) values for endosulfan (0.2168, 0.1209 and 0.1614 min1 for 10, 30 and 50 ppm initial

Lindane Magnesium (Mg )

concentration respectively) and lindane (0.1746, 0.1968 and 0.2253 min1 for 10, 30 and

Zinc chloride (ZnCl2)

50 ppm initial concentration respectively) dechlorination were obtained when the reac-

0

tions were conducted with doses of 7.5/1 mg ml1 and 5/1 mg ml1 Mg0/ZnCl2 respectively. Endosulfan and lindane were completely dechlorinated into their hydrocarbon skeletons namely, Bicyclo [2,2,1] hepta 2-5 diene and Benzene respectively as revealed by GCMS analysis. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Endocrine disrupting chemicals (EDCs) are considered to be emerging contaminants, which means that they are either still unregulated or they are in the process of regularization. EDCs have been defined by the Organisation of Economic and Cooperative Development (OECD) as “an exogenous substance or mixture that alters the functions of the endocrine systems and consequently causes adverse health effects in an intact organism, or its progeny or (sub) populations” (Esplugas et al., 2007; McKinlay et al., 2008). A wide range of chemical compounds have been found to be capable of disrupting the

endocrine system. The EDCs include chlorinated pesticides (e.g. DDT, vinclozolin, TBT, atrazine, lindane, and endosulfan), persistent organochlorines and organohalogens (e.g. PCBs, dioxins, furans, and brominated fire retardants), alkyl phenols (e.g. nonylphenol and octylphenol), and heavy metals like cadmium, lead, mercury etc (Esplugas et al., 2007; Mediratta et al., 2008; Mertens, 2006; Usha and Harikrishnan, 2005; Weber et al., 2009). The presence of EDCs affect the environment and have been reported that they result in (1) pre-mature breakage of eggs of birds, fishes and turtles, (2) sex reversal like feminization of male fish, and (3) reproductive abnormalities in fishes, reptiles,

* Corresponding author. Tel.: þ91 80 25356590; fax: þ91 80 25356589. E-mail address: [email protected] (S.K. Gautam). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.017

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birds and mammals. These effects may also lead to extinction of these species (Esplugas et al., 2007; McKinlay et al., 2008). Two of the EDCs, Endosulfan and Lindane have been extensively used on a wide variety of crops, in warehouses, in public health (to control vector-borne diseases), and as wood preservatives. These compounds have been chemicals of choice as a result of their low cost, easy availability, applicability and more importantly their stability in the environment (Andreozzi et al., 1999; Gogate and Pandit, 2004). On the other hand, these pollutants are characterised by high chemical stability, lipophilic nature, hydrophobicity, accumulation in various compartments of earth (air, soil, and water), carcinogenic property, presence of chlorine atoms and have half-lives of many years that makes them recalcitrant and difficult to be completely mineralized by biological treatments (Andreozzi et al., 1999). In the last decade, extensive research has been carried out on the application of bimetallic systems to degrade chlorinated organic compounds. Bimetallic systems make use of two metals, one in the zero-valent form (with a high reduction potential like Mgþ2/Mg0, Feþ2/Fe0, etc.) to produce nascent hydrogen by anodic corrosion, and the other metal with a relatively high (positive) reduction potential (such as Agþ1/ Ag0, Pdþ4/Pd0) as a catalyst. Nascent hydrogen produced thus is intercalated by the catalyst to form a metal hydride (M  H), which reacts with the target substrate to reduce it into less recalcitrant compounds. The main advantage of bimetallic systems is the ability to conduct the reaction at ambient temperature and pressure without exclusion of atmospheric oxygen (Gautam and Suresh, 2006; Lin et al., 2004; Patel and Suresh, 2008; Simagina and Stoyanova, 2001). There are four major factors that influence the rates and extent of dechlorination by zero-valent metal systems: (i) ionization potential and E0 of the zero-valent metal; (ii) solubility of the metal hydroxide formed following corrosion of metal; (iii) availability of protons; and (iv) solubility of the target compound (Patel and Suresh, 2008; Wang et al., 2009). Based on the factors mentioned above, Mg0 offers distinct advantage as it has the high reduction potential (Mgþ2/Mg0 ¼ - 2.2 V) and it works in the presence of oxygen as compared to generally used iron. Further, the solubility of magnesium hydroxide is relatively high, which accelerates the corrosion of the metal. While the role of Zinc is to enhance the corrosion of primary metal and is a moderately strong reductant (0.76 V). It is being used because of its low cost and easy availability as compared to Ni, Pd, Cu etc. Based on the above mentioned distinct advantages of magnesium and zinc, Mg0/ZnCl2 bimetallic system was chosen to dechlorinate endosulfan and lindane in the present study. The specific objectives of the study were: 1. To study the effect of Hþ ion concentration on the dechlorination reactions 2. To optimize the solvent ratio (water: acetone) for the dechlorination reactions. 3. To study the dechlorination kinetics of Endosulfan and Lindane; and 4. To identify the intermediates/end products of dechlorination reaction and elucidate the dechlorination pathways for endosulfan and lindane dechlorination.

2.

Materials and methods

2.1.

Source of chemicals

Magnesium (Mg0) granules (w200 mesh), Endosulfan (6,7,8,9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-methano2,4,3-benzodioxathiepine-3-oxide), Lindane (1r,2R,3S,4r,5R,6S )1,2,3,4,5,6-hexachlorocyclohexane) were purchased from SigmaeAldrich Chemical Company (USA) and were >98% pure. Acetone, zinc chloride (ZnCl2), hydrochloric acid and glacial acetic acid were purchased from Merck Ltd. (India) and cyclohexane was purchased from Fisher Scientific (India). All the chemicals were of analytical grade. No pre-treatment was performed with the chemicals and was used as received. All the glasswares used were of “A” grade.

2.1.1.

Dechlorination reaction protocol

Separate experiments for the degradation of endosulfan and lindane were conducted in water: acetone (4 ml, 1:1, v/v) reaction phase in the absence and in the presence of acid (either 0.1 N hydrochloric acid or glacial acetic acid). An aliquot of ZnCl2 stock solution (0.2 g ml1) was added into reaction phase to attain the required final concentrations (1e5 mg ml1 for endosulfan dechlorination and about 1e2 mg ml1 for lindane dechlorination). An initial concentration of 30 ppm of endosulfan or lindane was added into the respective reaction phase from a 1000 ppm stock solution of each pesticide prepared separately in acetone. Reactions were initiated by the addition of Mg0 granules (1e25 mg ml1 for endosulfan experiment and 5e10 mg ml1for lindane experiment). Separate control experiments were carried out with either only Mg0 or ZnCl2 to evaluate the significance of each in the overall degradation of both the pesticides. Table 1 depict the contents of reaction mixtures for endosulfan and lindane dechlorination experiments. All the reactions were conducted in quadruplicate under atmospheric pressure with continuous shaking in a water bath maintained at 130 rpm at 30  C. Initial pH of the reaction mixture was recorded and pH was not maintained during the reaction course. No precautions were taken to exclude oxygen or reduce redox potential of the reaction phase. The entire reaction mixtures were sacrificed after 30 min of reaction, extracted twice using cyclohexane (total 8 ml) and 0.2 ml volume of the pooled hexane extracts were injected for GC-ECD analyses.

2.2. Solvent ratio optimisation for the dechlorination of endosulfan and lindane The solvent (water: acetone) ratio for the dechlorination of endosulfan was optimised using a reaction system consisting of ZnCl2 (2 mg ml1), Mg0 (10 mg ml1), and 30 ppm initial concentration of endosulfan while in the case of lindane the water: acetone ratio was optimized using a reaction system containing ZnCl2 (1 mg ml1), Mg0 (5 mg ml1), and 30 ppm initial concentration of lindane. In both the cases, the water: acetone ratios were varied from 1:1 to 19:1 (v/v) and a reaction time of 30 min was chosen to analyse the extent of disappearance of each pesticide.

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Table 1 e Dechlorination of endosulfan and lindane with/without hydrochloric acid or glacial acetic acid by varying concentrations of Mg0 and ZnCl2 (Reaction conditions: Reaction Time: 30 min; Water: Acetone; 1:1v/v). Mg0 (mg ml1)

ZnCl2 (mg ml1)

Glacial Acetic Acid (50 mL ml1)

Hydrochloric Acid (0.1N, 50 mL m1)

% Disappearance of pesticides

Initial Endosulfan Conc ¼ 30 ppm 1 10 2 10 3 10 4 25 7 5 8 5 9 5

2 2 2 5 1 1 1

* added * * * added *

* * added * * * added

89.54 99.3 93.86 96.75 57.06 95.89 59.56

Initial Lindane Conc ¼ 30 ppm 1 10 2 10 3 10 4 10 5 10 6 10 7 7.5 8 7.5 9 7.5 10 7.5 11 7.5 12 7.5 13 5 14 5 15 5 16 5 17 5 18 5

1 1 1 2 2 2 1 1 1 2 2 2 1 1 1 2 2 2

* added * * added * * added * * added * * added * * added *

* * added * * added * * added * * added * * added * * added

1.84 99.7 38.7 62.7 99.8 78.3 47.2 99.5 59.4 74.5 99.5 81.6 13.8 98.3 32.2 62.3 98.7 76.6

SNo

*- no acid added.

2.3. Kinetic study of endosulfan and lindane degradation using Mg0/ZnCl2 bimetallic system

2.4. GC-ECD (Gas ChromatographyeElectron Capture Detection) analyses

In the case of endosulfan, kinetic studies were conducted in water:acetone (2:1, v/v) phase to determine the rates and extent of dechlorination of 10, 30 and 50 ppm initial concentrations of endosulfan each as a function of: a) varying Mg0 (5.0, 7.5 and 10 mg ml1) concentrations at a fixed ZnCl2 concentration (1 mg ml1) and b) varying ZnCl2 concentrations (0.5, 1 and 1.5 mg ml1) at a fixed concentration (7.5 mg ml1) of Mg0 to establish the order of reaction and rate constant (kobs) values. In the case of lindane, the reactions were conducted at: a) varying Mg0 (1, 2.5 and 5 mg ml1) concentrations at a fixed ZnCl2 concentration (1 mg ml1) and b) varying ZnCl2 concentrations (0.5, 1 and 1.5 mg ml1) at a fixed concentration (5 mg ml1) of Mg0 with 10, 30 and 50 ppm initial concentrations of lindane. The corresponding control experiments were also conducted to determine the extent of degradation of the above mentioned pesticides, if any, using ZnCl2 or Mg0 alone under same conditions as the test samples. The entire reaction mixtures were sacrificed at chosen time points, extracted twice using cyclohexane (total 8 ml) and 0.2 ml or 1.0 ml volume of the pooled hexane extracts were analysed for residual pesticides, intermediates and end products using GC-ECD and GC-MS.

Analyses of extracted samples were done using a gas chromatograph equipped (Agilent, model no. 6890 N) with Ni63 electron capture detector (ECD). The column used was HP-5 capillary column of 0.32 mm ID, 0.25 mm film thickness and 30 m length. Injections were made in splitless mode using nitrogen as the carrier gas. The following temperature programming was used: initial oven temperature of 150 0C with hold time for 2 min, then ramped to 200  C at 6  C min1 with hold time of 2 min, again ramped to 250  C at 10 0C min1 with hold time of 2 min. Injector and detector temperatures were set at 200 and 290 0C respectively. The residual concentrations of endosulfan and lindane, partially chlorinated intermediates and end products were quantified from peak areas obtained through automated integration and also by comparison with known concentrations of the pure standard compounds.

2.5. GC-MS (gas chromatographyemass spectroscopy) analyses GC-MS analyses were carried out using TRACE GC ULTRA (Thermo make) equipped with MS (model DSQII). The column used for GC-MS analysis was TR-5 column of 0.25 mm I.D., 0.25 mm film thickness and 30 m length. 1 mL volume of

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samples was injected for analyses. He (Helium) was used as carrier gas. The temperature programming used was: Initial oven temperature of 40  C with the hold time of 1 min, ramped to 53  C at 1  C/min with hold time of 1 min, again ramped 53e60  C at 10  C/min with hold time of 1 min and finally ramped from 60 to 250  C with hold time of 1 min. Injector and detector temperatures were set at 200 and 300  C, respectively. The mass spectral data coupled with the systematic reduction in the retention times of the dechlorinated products (due to loss of chlorine atoms) allowed identification of the intermediates and end products with reasonable certainty. Wiley Registry (8e Mass Spectral Library) was used to identify the intermediate and end products.

In our study it was observed that the degradation efficiency was better when acetic acid (weak acid, pH range of 3.35e3.63) was added into the solution as against HCl (strong acid, pH range of 2.14e2.22), this may be due to very fast dissolution of Mg0 granules in the presence of strong acids which leads to immediate formation of nascent hydrogen which provides lesser time to Zn to capture it. While in case of weak acid, the dissolution of Mg granules is comparatively slow which provides sufficient time to catalyst Zn to capture produced nascent hydrogen to form hydrides. Based on the results discussed above, Mg0/ZnCl2 dose of 5/1 mg ml1 with 50 mL ml1 glacial acetic acid was chosen as optimum to conduct the experiments further.

3.

3.2. Solvent optimisation for the degradation of endosulfan and Lindane

Results and discussion

3.1. Catalyst and Hþ ion optimisation for the dechlorination of endosulfan and Lindane Table 1 compares extent of endosulfan and lindane (30 ppm initial concentration) dechlorination by varying Mg0/ZnCl2 concentrations in the presence and absence of acid (50 mL ml1 HCl or glacial acetic acid). 30 min of reaction time was chosen to carryout the optimisation studies in water: acetone (1:1 v/v) reaction phase. It can be observed from Table 1 that 90% disappearance of endosulfan at Mg0/ZnCl2 dose of 10/2 mg ml1 was achieved without the addition of any acid. The addition of 0.1 N HCl had a degradation efficiency of 94% while the addition of glacial acetic acid resulted in 99.3% removal of endosulfan. However on the reduction of Mg0/ZnCl2 dose to 5/1 mg ml1, 57% of endosulfan was degraded without the addition of acid. On the addition of 0.1 N HCl 60% removal of endosulfan was achieved. The addition of glacial acetic acid increased the degradation efficiency of endosulfan to 96%. In case of lindane, at Mg0/ZnCl2 dose to 10/1 mg ml1 about 2% removal of lindane was observed, which on the addition of 0.1 N HCl resulted in 39% degradation and the addition of glacial acetic acid had a degradation efficiency of 99.7%. However on reducing the Mg0/ZnCl2 dose to 5/1 mg ml1 about 14% lindane degradation efficiency was observed. While on the addition of 0.1 N HCl, 32% degradation of lindane was observed and about 98% degradation efficiency was observed on the addition of glacial acetic acid. Similar observations were also reported in the earlier studies conducted by Gautam and Suresh (2007); Mu et al., 2004; Patel and Suresh, 2008; Wang et al., 2009 and Xinhua et al., 2009 while studying the degradation of DDT using Mg0/Pd4þ; nitrobenzene using zero-valent metallic iron; pentachlorophenol using Mg0/K2PdCl6; hexachlorocyclohexane using zero-valent metallic iron and nitrochlorobenzene using Ni/Fe respectively where in all cases the presence of acid improved the dechlorination efficiency of metallic systems. This is attributed to the fact that addition of acid enhanced the rate of dechlorination of target compounds by: (a) facilitating fast corrosion of Mg0 and reduction of ZnCl2 (b) providing protons to generate nascent hydrogen and (c) delaying the creation of alkaline conditions in the reaction phase that may prevent passivation of metals which inturn may retard the dechlorination process.

Table 2 shows the effect of various water: solvent ratios on the degradation of 30 ppm initial concentration of each endosulfan and lindane in the presence of acetic acid. It is depicted in Table 2 that the presence of acetone had a positive influence on the extent of degradation. At water: acetone ratio of 1:1 (v/v), about 90% of endosulfan and about 98% of lindane was degraded within 30 min of reaction time. However at water: acetone ratio of 2:1, about 81% of endosulfan and 83% of lindane was degraded. Further, 73% of endosulfan and 78% lindane were degraded at water: acetone ratio of 4: 1. While at 9:1 water: acetone ratio, only 51% endosulfan and 73% of lindane could be degraded. However at 19:1 water: acetone ratio pesticides were not completely dissolved and a slightly milky solution appeared which was indicative that presence of acetone in the reaction mixture was very crucial. Overall the extent of endosulfan and lindane degradation decreases with the decreasing acetone ratio in the reaction mixture. The results are in accordance with the similar study conducted Gautam and Suresh, 2007 to dechlorinate DDT using Mg0/ K2PdCl6 bimetallic system where highest loss of DDT (84%) was obtained at 1:1 water: acetone ratio. The increase in water: acetone ratio led to a lowering of dechlorination efficiency. At 19:1 water: acetone ratio, least (40%) dechlorination efficiency was observed. In another study conducted by Patel and Suresh (2008) to dechlorinate pentachlorophenol (PCP) using Mg/Ag and Mg/Pd bimetallic systems, the effect of different co-solvents (acetone, methanol, ethanol, 1-propanol and no-solvent) on dechlorination efficiency of PCP was studied, and it was observed that maximum removal of PCP was achieved in the presence of acetone.

Table 2 e Effect of varying Water: Acetone ratio on the degradation of endosulfan and lindane (Initial Concentration of endosulfan/lindane: 30 ppm; Reaction Time: 30 min; Water: Acetone as indicated in figure). Water: Acetone Ratio (1:1) (2:1) (4:1) (9:1)

% degradation of endosulfan

% degradation of lindane

90.05 82.64 72.62 51.15

98.25 81.12 77.74 72.63

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30 Residua l Endo sulfa n co nc (ppm )

Comparison of these results with the present investigation reveals that decrease in degradation efficiency on reduction of acetone is because of the in-solubility of the target compound in solvent as it is a critical parameter which influences the mass transfer and hence the extent of degradation of target compounds. Thus water: acetone ratio of 2:1 was chosen to study the degradation kinetics of endosulfan and 1:1 ratio was chosen to carryout degradation kinetics of lindane.

3.3. Kinetic studies for the degradation of endosulfan and lindane using Mg0/ZnCl2 bimetallic system

MgZn: 10/1 mg ml-1 -0.1209x

y = 30e 20 15

2

R = 0.9142 10

R = 0.9663

2

R = 0.9241

5

-0.2168x

MgZn: 7.5/0.5 mg ml-1

y = 10e

MgZn: 7.5/1.5 mg ml-1

2

R = 0.9911

6

-0.1432x

y = 10e 2

R = 0.8565 -0.0797x y = 10e

4

2

R = 0.9409 2

10

20

30

Reaction time (min)

40

50

-0.1235x

-0.0909x

y = 10e 2

R = 0.9492

0

60

MgZn: 5/1 mg ml-1

y = 50e 2

MgZn: 10/1 mg ml-1

8

-0.0829x

y = 30e

Fig. 4. As shown in Fig. 4 Mg0/ZnCl2 dose of 7.5/1 mg ml 1 showed higher k obs’ values (0.2168, 0.1209, 0.1614 min1 for 10, 30 and 50 ppm initial concentration of endosulfan respectively) when compared with other Mg0/ZnCl2 doses (Fig. 4). The k obs’ values calculated at Mg0/ZnCl2 dose of 5/1 mg ml1 were 0.155, 0.1273 and 0.1235 min1 for 10, 30 and 50 ppm initial endosulfan concentration respectively indicating that the reduction of Mg0 dose had a diminishing effect on the rate of the reaction. Further, on increasing the Mg0 dose to 10 mg ml1; k obs’ values decreased to 0.1432, 0.0976 and 0.076 min1 for 10, 30 and 50 ppm initial endosulfan concentration respectively which indicated that excess of Mg0 in the reaction mixture also hindered the reaction rate. These rate constants could also be normalized by loading of the second metal (i.e., catalytic metal) under an assumption that the reaction rate is highly affected by the extent of primary metal surface coverage with a secondary metal (Choi and Kim, 2009). In our study, the reaction rate constants were normalized by the loading of the second metal (ZnCl2), assuming that the reactivity depends on the mass of the second metal. It was observed that when ZnCl2

Residual Endosulfan conc (ppm)

Residua l Endo uslfa n co nc (ppm )

R = 0.9711

-0.0371x

y = 30e 2

50

MgZn: 7.5/1 mg ml-1

2

MgZn: 7.5/1.5 mg ml-1 -0.0976x

y = 30e

Fig. 2 e Kinetic profile for the degradation of 30 ppm initial endosulfan concentration (Mg0 dose: 5e10 mg mlL1 and ZnCl2 dose: 0.5e1.5 mg mlL1 with water: acetone ratio of 2:1v/v and 50 mL mlL1 of glacial acetic acid).

MgZn: 5/1 mg ml-1

-0.155x

MgZn: 7.5/0.5 mg ml-1

2

R = 0.894

0

y = 10e

MgZn: 7.5/1 mg ml-1

2

R = 0.9676

25

0

Figs. 1, 2 and 3 shows the time course dechlorination profiles of 10, 30 and 50 ppm initial endosulfan concentration respectively as a function of time by various Mg0/ZnCl2 doses (5/1, 7.5/1, 10/1, 7.5/0.5, 7.5/1.5 mg ml1) in the presence of glacial acetic acid (50 mL ml1). As shown in Fig. 1, Mg0/ZnCl2 dose of 5/1 mg ml1 degraded 89% of 10 ppm initial concentration of endosulfan within 60 min of reaction. At 30 ppm and 50 ppm initial concentrations of endosulfan (Figs. 2 and 3 respectively), about 92% and 94% degradation was achieved within 60 min respectively. At Mg0/ZnCl2 dose of 7.5/ 1 mg ml1, 95%, 96%, and 97% degradation of 10, 30 and 50 ppm of initial endosulfan concentrations respectively was achieved within 60 min of reaction (Figs. 1e3). While 97%, 94% and 90% degradation of 10, 30, 50 ppm initial concentrations of endosulfan was recorded within 60 min of reaction time by Mg0/ZnCl2 dose of 10/1 mg ml1 (Figs. 1e3). As shown in Figs. 1e3, 10, 30 and 50 ppm initial concentrations of endosulfan at Mg0/ZnCl2 dose of 7.5/0.5 mg ml1 were degraded with the efficiency of 87%, 89% and 92% respectively. While at Mg0/ ZnCl2 dose of 7.5/1.5 mg ml1 about 90%, 92% and 96% of 10, 30 and 50 ppm initial endosulfan concentrations were degraded respectively within 60 min of reaction. The set of data presented in Figs. 1e3 could be fitted into the first order kinetics of the reaction and indicates that the rate of the reaction is dependent upon the initial concentration of endosulfan.. The observed rate constant (k obs’) values for the set of kinetics performed were calculated and are presented in

10

MgZn: 5/1 mg ml-1

-0.1273x

y = 30e

MgZn: 7.5/1 mg ml-1

R = 0.9154

MgZn: 10/1 mg ml-1

-0.1614x

40

y = 50e

MgZn: 7.5/0.5 mg ml-1

2

R = 0.9621

MgZn: 7.5/1.5 mg ml-1

-0.076x

30

y = 50e 2

R = 0.9696 -0.184x

y = 50e

20

2

R = 0.9117

-0.141x

y = 50e 2

10

R = 0.9675

0 0

5

10

15

20

25

30

Reaction time (min)

Fig. 1 e Kinetic profile for the degradation of 10 ppm initial endosulfan concentration (Mg0 dose: 5e10 mg mlL1 and ZnCl2 dose: 0.5e1.5 mg mlL1 with water: acetone ratio of 2:1v/v and 50 mL mlL1 of glacial acetic acid).

0

5

10 15 20 Reaction time (min)

25

30

Fig. 3 e Kinetic profile for the degradation of 50 ppm initial endosulfan concentration (Mg0 dose: 5e10 mg mlL1 and ZnCl2 dose: 0.5e1.5 mg mlL1 with water: acetone ratio of 2:1v/v and 50 mL mlL1 of glacial acetic acid).

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10 ppm

0.25

30 ppm

10

50 ppm

MgZn: 1/1 mg ml-1

-0.0252x

y = 10e

MgZn: 2.5/1 mg ml-1

2

R = 0.9678 8

0.15 0.1 0.05 0 (5/1)

(7.5/1)

(10/1)

(7.5/0.5)

(7.5/1.5)

Mg 0/ ZnCl2 (mg ml-1)

Fig. 4 e Observed rate constants for the degradation of endosulfan by varying Mg0/ZnCl2 dose.

Residua l Linda ne co nc (ppm )

k o bs' (m in -1)

0.2

MgZn: 5/1 mg ml-1 MgZn: 5/0.5 mg ml-1

-0.0821x

y = 10e

MgZn: 5/1.5 mg ml-1

2

R = 0.9651 6

-0.1746x

y = 10e 2

R = 0.9637 -0.0838x

y = 10e 2

4

R = 0.9042

-0.0364x

y = 10e 2

R = 0.936 2

0 0

dose was decreased to 0.5 mg ml1; lower k obs’ values were observed (0.0797, 0.0371 and 0.184 min1 for 10, 30 and 50 ppm initial endosulfan concentration respectively) while with the increase in ZnCl2 dose to 1.5 mg ml1; k obs’ values calculated were 0.0909, 0.0829 and 0.141min1 for 10, 30 and 50 ppm initial endosulfan concentration respectively. Thus from Fig. 4 it can be inferred that optimum degradation and rate constant values were achieved at Mg0/ZnCl2 dose of 7.5/1 mg ml1, thus this dose was chosen as optimum. No partially dechlorinated intermediates/end products of endosulfan degradation appeared in GC-ECD profiles. Hence the samples were analysed using GC-MS to identify the intermediates formed, if any and end products of endosulfan degradation. GC-MS analysis was carried out for the 1000 ppm initial endosulfan degradation using Mg0/ZnCl2 dose of 7.5/ 1 mg ml1. Water: acetone ratio of 2:1 was chosen and 50 mL ml1 of glacial acetic acid was added to the reaction mixture. Reaction mixtures were sacrificed at 10 min and 30 min reaction time and analysed using GC-MS. Elution profile of 10 min reaction time showed an abundant peak at 5.17 min. The molecular ion fragmentation of this peak matched with Bicyclo [2,2,1] hepta 2-5 diene using Wiley library. The structure of this compound was similar to dechlorinated endosulfan and this might have formed by the removal of all six chlorine atoms, two carbon atoms, three oxygen atoms and one sulphur atom during dechlorination reaction. The absence of any other partially dechlorinated intermediates suggests that nascent hydrogen attacks on all the six chlorine atoms simultaneously. Based on the GC MS analysis, the proposed mechanism of endosulfan degradation by Mg0/ZnCl2 system is elucidated in Fig. 5.

Cl

10

20

30

40

Figs. 6, 7 and 8 shows dechlorination kinetics of 10, 30 and 50 ppm initial lindane concentration respectively as a function of time and time course profile for lindane removal by various Mg0/ZnCl2 doses (1/1, 2.5/1, 5/1, 5/0.5, 5/1.5 mg ml1) in the presence of glacial acetic acid (50 mL ml1). As shown in Figs. 5e7, Mg0/ZnCl2 dose of 1/1 mg ml1 degraded 95%, 77% and 62% of 10, 30, and 50 ppm initial concentration of lindane respectively within 60 min of reaction. At Mg0/ZnCl2 dose of 2.5/1 mg ml1 about 72% of 10 ppm of initial lindane concentration was degraded within 60 min of reaction while 92% and 93% degradation of 30 and 50 ppm initial lindane concentrations were degraded in 60 min of reaction time. About 99% degradation of 10, 30, and 50 ppm initial concentrations of lindane was observed within 60 min of reaction by 5/1 mg ml1 dose of Mg0/ZnCl2. About 99% removal of 10 ppm initial concentration and w96% removal of 30 and 50 ppm initial concentration of lindane was achieved by Mg0/ZnCl2 dose of 5/0.5 mg ml1 (Figs. 6e8). At Mg0/ZnCl2 dose of 5/1 mg ml1, about 88% of 10 ppm lindane was degraded. However about 97% and 99% degradation was observed at 30 and 50 ppm of initial lindane concentration respectively. The set of data presented in Figs. 6e8 could be fitted into exponential curves thereby suggesting that lindane dechlorination reaction follows first order kinetics and indicates that the rate of the reaction is dependent upon the initial

Cl O

Cl

Cl

Endosulfan

Mg 0 / ZnCl2 S

O

H+

60

Fig. 6 e Kinetic profile for the degradation of 10 ppm initial lindane concentration (Mg0 dose: 1e5 mg mlL1 and ZnCl2 dose: 0.5e1.5 mg mlL1 water: acetone ratio of 1:1v/v and 50 mL mlL1 of glacial acetic acid).

Cl

Cl

50

Reaction time (min)

+

6 Cl - + 3 O + S+ 2 C

O

Bicyclo (2,2,1) hepta (2,5) diene

Fig. 5 e Proposed endosulfan degradation pathway using Mg0/ZnCl2 system.

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30

MgZn: 1/1 mg ml-1

-0.0479x

y = 30e

R = 0.9803

MgZn: 5/0.5 mg ml-1

y = 30e 2

MgZn: 5/1.5 mg ml-1

R = 0.9189 -0.1968x

y = 30e 2

R = 0.952

15

k o bs' ( m in-1)

-0.1004x

Residua l Linda ne co nc (ppm )

30 ppm

50 ppm

0.2

MgZn: 5/1 mg ml-1

25

20

10 ppm

0.25

MgZn: 2.5/1 mg ml-1

2

0.15 0.1 0.05

-0.1335x

y = 30e

0

2

10

R = 0.9926 -0.1294x

(1/1)

y = 30e

(2.5/1)

5

(5/1)

(5/0.5)

(5/1.5)

Mg 0/ ZnCl2 dose (mg ml-1)

2

R = 0.9091

Fig. 9 e Observed Rate Constant for the degradation of lindane by varying Mg0/ZnCl2 concentration.

0 0

10

20

30

40

50

60

Reaction time (min)

Fig. 7 e Kinetic profile for the degradation of 30 ppm initial lindane concentration (Mg0 dose: 1e5 mg mlL1 and ZnCl2 dose: 0.5e1.5 mg mlL1 water: acetone ratio of 1:1v/v and 50 mL mlL1 of glacial acetic acid).

concentration of lindane. The observed rate constant values (kobs’) for the set of kinetics performed were calculated and are presented in Fig. 9. It was observed that at Mg0/ZnCl2 dose of 1/1 mg ml 1 the kobs’ values were calculated to be 0.0252, 0.0479 and 0.0692 min1 for 10, 30 and 50 ppm initial lindane concentration. At Mg0/ZnCl2 dose of 2.5/1 mg ml 1 the kobs’ values for 10, 30 and 50 ppm initial lindane concentration were 0.0821, 0.1004 and 0.131 min1 respectively.. Further increase in Mg0 dose to 5 mg ml1 resulted in higher kobs’ values of 0.1746, 0.1968 and 0.2253 min1 for 10, 30 and 50 ppm initial lindane concentrations (Mg0/ZnCl2 dose of 5/1 mg ml 1). At Mg0/ZnCl2 dose of 5/0.5 mg ml 1 the kobs’ values were calculated to be 0.0838, 0.1335 and 0.0896 min1 for 10, 30 and 50 ppm initial lindane concentrations respectively. However, on increasing

50

-0.0692x

MgZn: 1/1 mg ml-1

y = 50e 2

MgZn: 2.5/1 mg ml-1

R = 0.8342

MgZn: 5/1 mg ml-1 -0.131x

Residua l Linda ne co nc (ppm )

40

y = 50e

MgZn: 5/0.5 mg ml-1

2

R = 0.926

MgZn: 5/1.5 mg ml-1 -0.2253x

y = 50e

30

2

R = 0.9112 -0.0896x

y = 50e 20

2

R = 0.9558 -0.2141x

y = 50e 2

R = 0.9887

the ZnCl2 dose (1.5 mg ml 1) decrease in kobs’ values (0.0364, 0.1294 and 0.2141 min1 was observed for 10, 30 and 50 ppm initial lindane concentrations respectively. Based on the above observations Mg0/ZnCl2 dose of 5/1 mg ml1 is proposed as optimum for the lindane dechlorination. The GC-ECD profiles of kinetics of lindane degradation did not show appearance of any other peak. Hence samples were analysed using GC MS to identify the intermediates formed, if any and end products formed of lindane dechlorination. GCMS analysis was carried out with initial lindane concentration of 1000 ppm, Mg0/ZnCl2 dose of 5/1 mg ml1, in the presence of glacial acetic acid (50 mL ml1) in reaction mixture comprising water: acetone (1:1 v/v). Reaction mixtures were sacrificed at 10 min and 30 min reaction time and analysed. Elution profile after 10 min reaction time showed the appearance of an abundant peak at 2.94 min. Based on the molecular ion fragmentation of this peak, it was identified as mono chlorobenzene probably formed by the removal of 5 chlorine atoms from lindane molecule. Further, the elution profile of 30 min reaction time did not show any peak at 2.94 min indicating the complete disappearance of mono chlorobenzene. However a new peak emerged at 2.64 min which was identified as benzene based on its molecular ion fragmentation profile. The formation of benzene and absence of any partially dechlorinated intermediate suggest the complete dechlorination of lindane into its hydrocarbon skeleton, benzene. Thus Fig. 10 elucidates the proposed mechanism of lindane degradation by Mg0/ZnCl2 system. Similar results have been also observed by Gautam and Suresh (2007) and Patel and Suresh, 2008 for the dechlorination of DDT and pentachlorophenol using Mg0/Pd4þ and Mg/Ag bimetallic systems respectively wherein second order kinetics was observed. However, results obtained by Lin et al. (2004);

10 Cl Cl

Cl

Mg0/ ZnCl2

0 0

5

10

15

20

25

30

Reaction time (min)

Fig. 8 e Kinetic profile for the degradation of 50 ppm initial lindane concentration (Mg0 dose: 1e5 mg mlL1 and ZnCl2 dose: 0.5e1.5 mg mlL1 water: acetone ratio of 1:1v/v and 50 mL mlL1 of glacial acetic acid).

Cl Cl

Lindane

Cl

Cl

H+

Mg0/ ZnCl2 H+

Monochlorobenzene

Benzene

Fig. 10 e Proposed lindane degradation pathway using Mg0/ZnCl2 system.

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Shih et al., 2009 and Zhang et al., 2009 reported pseudo-first order kinetics for the dechlorination of trichloroethylene, hexa chlorobenzene and 2,4- dichlorophenol respectively. First order kinetic reaction was observed in work carried out by Ghauch and Tuqan (2009) for the reductive destruction of dichlorophen using Pd, Ru and Ag. In the study conducted by Patel and Suresh (2007) dechlorination of pentachlorophenol was sequential with the formation of tri chlorophenol and tetra chlorophenol and finally phenol was identified as the end product. Xinhua et al., 2009 dechlorinated p-nitrochlorobenzene using Ni/Fe bimetallic systems and p-chloroaniline and aniline were identified as intermediate and end product respectively. In another study conducted by Shih et al. (2009) for dechlorination of hexachlorobenzene using Pd/Fe bimetallic particles, the intermediates identified were pentachlorobenzene, two isomers of tetra chlorobenzene and trichlorobenzene. Zhang et al., 2009 degraded 2,4-dichlorophenol using Ni/Fe nanoparticles and o-chlorophenol, p-chlorophenol were formed as intermediates followed by formation of phenol as end product. However, in consonance to the present study by Gautam and Suresh (2007) for the dechlorination of DDT using Mg0/Pd4þ bimetallic system no partially dechlorinated intermediates were reported and formation of its hydrocarbon skeleton, diphenyl ethane was observed in a single step.

4.

Conclusion

The following salient points emerged from the present study: 1. Mg0/ZnCl2 system was found to be an efficient system for dechlorination of endosulfan and lindane solubilized by the addition of acetone in the aqueous phase. 2. Water: acetone ratios of 2:1 and 1:1 were needed to completely dissolve endosulfan and lindane respectively. 3. Acetic acid being a weak acid facilitated slower dissolution of Mg0 granules which provided sufficient time to catalyst ZnCl2 to capture produced nascent hydrogen to form hydrides as against HCl which is a stronger acid. 4. The dechlorination reactions of endosulfan and lindane follow first order kinetics and the rate of reaction depends upon the initial concentration of target compound. 5. GC MS analyses reveals that Mg0/ZnCl2 system is efficient in complete dechlorination of endosulfan and lindane converting them into their hydrocarbon skeletons namely Bicyclo [2,2,1] hepta 2-5 diene and Benzene respectively. Authors conclude that Mg0/ZnCl2 bimetallic is a promising technology for the hydrodechlorination of environmentally problematic compounds viz. endosulfan and lindane. Also it would be worthwhile to evaluate Mg0/ZnCl2 reactive system for designing indigenous permeable barriers or reactors for contaminated water, ground water and wastewater effluent sites. However, the authors propose that before on field scaling up of Mg/Zn bimetallic system to remediate contaminated sites, a detailed study on the actual mechanism of catalysis is highly required.Surface properties of Mg0 should be charecterised as it plays the pivotal role in degradation

process. In-situ techniques such as EXAFS (EXAFS (Etended Xray Absorption Fine Structure) spectroscopy could be used study the chemical and structural nature of ZnCl2 on the Mg0 particles, before, during, and after reaction to understand the mechanism of catalysis. To the best of the author’s knowledge, no other study could be cited from open literature on the degradation of endosulfan and/or lindane using Mg0/ZnCl2 bimetallic systems.

Acknowledgements The authors would like to thank Department of Science and Technology (DST), Government of India for providing financial support to conduct this study. The authors would also like to thank Spectroscopy and Analytical Test Facility Lab, Indian Institute of Science (IISC), Bangalore, India for allowing us to use their GCeMS facility. Authors would also wish to extend their heartfelt thanks to Mr. K Johnson and Mr. Prakhar Agnihotri for their assistance extended during the project tenure.

references

Andreozzi, R., Caprio, V., Insola, A., Marotta, R., 1999. Advanced oxidation processes (AOP) for water purification and recovery. Catalysis Today 53, 51e59. Choi, J.H., Kim, Y.H., 2009. Reduction of 2,4,6-trichlorophenol with zero-valent zinc and catalyzed zinc. Journal of Hazardous Materials 166, 84e991. Esplugas, S., Bila, D.M., Krause, L.T., Dezotti, M., 2007. Ozonation and advanced oxidation technologies to remove endocrine disrupting chemicals (EDCs) and pharmaceuticals and personal care products (PPCPs) in water effluents. Journal of Hazardous Materials 149, 631e664. Gautam, S.K., Suresh, S., 2006. Dechlorination of DDT, DDD and DDE in soil (slurry) phase using magnesium/palladium system. Journal of Colloid and Interface Science 304, 144e151. Gautam, S.K., Suresh, S., 2007. Studies on dechlorination of DDT (1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane) using magnesium/palladium bimetallic system. Journal of Hazardous Materials B 139, 146e153. Ghauch, A., Tuqan, A., 2009. Reductive destruction and decontamination of aqueous solutions of chlorinated antimicrobial agent using bimetallic systems. Journal of Hazardous Materials 164, 665e674. Gogate, P.R., Pandit, A.B., 2004. A review of imperative technologies for wastewater treatment I: oxidation technologies at ambient conditions. Advances in Environmental Research 8, 501e551. Lin, C.J., Lo, S.L., Liou, Y.H., 2004. Dechlorination of trichloroethylene in aqueous solution by noble metalmodified iron. Journal of Hazardous Materials B116, 219e228. McKinlay, R., Plant, J.A., Voulvoulis, N., 2008. Endocrine disrupting pesticides: Implications for risk assessment. Environment International 34, 168e183. Mediratta, P.K., Tanwar, K., Reeta, K.H., Mathur, R., Banerjee, B.D., Singh, S., Sharma, K., 2008. Attenuation of the effect of lindane on immune responses and oxidative stress by Ocimum sanctum seed oil (OSSO) in rats. Indian Journal of Physiology & Pharmacology 52, 171e177. Mertens, I.R., 2006. Microbial monitoring and degradation of lindane in soil. Journal of Hazardous Materials 175, 680e687.

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Mu, Y., Yu, H.Q., Zheng, J., Zhang, S., Sheng, G., 2004. Reductive degradation of nitrobenzene in aqueous solution by zerovalent iron. Chemosphere 54, 789e794. Patel, U.D., Suresh, S., 2007. Dechlorination of chlorophenols using magnesiumepalladium bimetallic system. Journal of Hazardous Materials 147, 431e438. Patel, U.D., Suresh, S., 2008. Effects of solvent, pH, salts and resin fatty acids on the dechlorination of pentachlorophenol using magnesium-silver and magnesium-palladium bimetallic systems. Journal of Hazardous Materials 156, 308e316. Shih, Y.H., Chen, Y.C., Chen, M.Y., Tai, Y.T., Tso, C.P., 2009. Dechlorination of hexachlorobenzene using nanoscale Fe and nanoscale Pd/Fe bimetallic particles. Colloids and Surfaces A: Physicochemical and Engineering Aspects 332, 84e89. Simagina, V.I., Stoyanova, I.S., 2001. Hydrodechlorination of polychlorinated benzenes in the presence of a bimetallic catalyst in combination with a phase-transfer catalyst. Mandeleev Communications 11, 38e39.

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Usha, S., Harikrishnan, V.R., 2005. Endosulfan- Fact Sheet and Answers to Common Questions. IPEN Pesticide Working group secretariat. Wang, Z., Peng, P., Huang, W., 2009. Dechlorination of g- hexachlorocyclohexane by zero-valent metallic iron. Journal of Hazardous Materials 166, 992e997. Weber, J., Halsall, C.J., Muir, D., Teixeira, C., Small, J., Solomon, K., 2009. Endosulfan, a global pesticide: a review of its fate in the environment and occurrence in the Arctic. Science for Total Environment 408, 2966e2984. Xinhua, X.U., Jingjing, W., Jinghui, Z., Yanjum, W., Yong, L., 2009. Catalytic dechlorination of p-NCB in water by nanoscale Ni/Fe. Desalanisation 242, 346e354. Zhang, Z., Cissoko, N., Wo, J., Xu, X., 2009. Factors influencing the dechlorination of 2,4-dichlorophenol by 0Ni-Fe nanoparticles in the presence of humic acid. Journal of Hazardous Materials, 78e86.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 9 2 e2 4 0 0

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Exposure assessment for swimmers in bathing waters and swimming pools Franciska M. Schets*, Jack F. Schijven, Ana Maria de Roda Husman National Institute for Public Health and the Environment, Laboratory for Zoonoses and Environmental Microbiology, PO Box 1, 3720 BA Bilthoven, The Netherlands

article info

abstract

Article history:

Bathing water compliant with bathing water legislation may nevertheless contain patho-

Received 13 April 2010

gens to such a level that they pose unacceptable health risks for swimmers. Quantitative

Received in revised form

Microbiological Risk Assessment (QMRA) can provide a proper basis for protective

12 October 2010

measures, but the required data on swimmer exposure are currently limited or lacking. The

Accepted 29 January 2011

objective of this study was to collect exposure data for swimmers in fresh water, seawater

Available online 1 March 2011

and swimming pools, i.e. volume of water swallowed and frequency and duration of swimming events.

Keywords:

The study related to swimming in 2007, but since the summer of 2007 was wet and this

Recreational water

might have biased the results regarding surface water exposure, the study was repeated

Bathing water

relating to swimming in 2009, which had a dry and sunny summer. Exposure data were

Swimming pool

collected through questionnaires administered to approximately 19 000 persons repre-

Exposure

senting the general Dutch population.

QMRA

Questionnaires were completed by 8000 adults of whom 1924 additionally answered the questions for their eldest child (<15 years). The collected data did not differ significantly between 2007 and 2009. The frequency of swimming and the duration of swimming were different for men, women and children and between water types. Differences between men and women were small, but children behaved differently: they swam more often, stayed in the water longer, submerged their heads more often and swallowed more water. Swimming pools were visited most frequently (on average 13e24 times/year) with longest duration of swimming (on average 67e81 min). On average, fresh and seawater sites were visited 6e8 times/year and visits lasted 41e79 min. Dependent on the water type, men swallowed on average 27e34 ml per swimming event, women 18e23 ml, and children 31e51 ml. Data on exposure of swimmers to recreational waters could be obtained by using a questionnaire approach in combination with a test to measure mouthfuls of water for transformation of categorical data to numerical data of swallowed volumes of water. Previous assumptions on swimmer exposure were replaced with estimates of exposure parameters, thus reducing uncertainty in assessing the risk of infection with waterborne pathogens and enabling identification of risk groups. QMRA for Cryptosporidium and Giardia was demonstrated based on data from previous studies on the occurrence of these pathogens in recreational lakes and a swimming pool. ª 2011 Elsevier Ltd. All rights reserved.

Abbreviations: CI, Confidence Interval; QMRA, Quantitative Microbiological Risk Assessment. * Corresponding author. Tel.: þ31 30 274 3929; fax: þ31 30 274 4434. E-mail address: [email protected] (F.M. Schets). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.025

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 9 2 e2 4 0 0

1.

Introduction

As a result of poor microbiological water quality, exposure to bathing waters may pose health risks for swimmers. They may contract illnesses such as gastro-enteritis from infections with bacteria, viruses or parasites of fecal origin (WHO, 2003), but also skin disorders like cercarial dermatitis as a result of contact with the larvae of the parasite Trichobilharzia (Hora´k and Kola´
rova´, 2001), or otitis externa due to an infection with Pseudomonas aeruginosa (Van Asperen et al., 1995). Bathing water legislation is in place to protect swimmers from fecal contaminants, although guideline values are not based on pathogen concentrations but on fecal indicator levels (Anonymous, 2006). Previous studies have demonstrated that bathing water that complies with bathing water guideline values may contain pathogens (Graczyk et al., 2007; Schets et al., 2008), whether of fecal origin or not, and thus still pose an unacceptable health risk for swimmers. Management actions solely triggered by non-compliance with fecal indicator standards may therefore not effectively protect bathers. A Quantitative Microbiological Risk Assessment (QMRA) of bathing in surface water may provide insights that can be translated into effective protective measures (Ashbolt et al., 2010). QMRA requires information on the concentration of pathogens in the water, swimmer exposure to these pathogens and dose-response relations for different pathogens. In this regard, there is little information available on swimmer exposure, i.e. the amount of swallowed water and how much skin contact there is with water. WHO guidelines assume that 20e50 ml of water is swallowed per hour of swimming activity (WHO, 2003); however, these values are not supported by studies on water ingestion. Schijven and de Roda Husman (2006) estimated the exposure of occupational and sport divers and found that both groups swallowed about the same volume per dive in marine waters (9.8 vs. 9.0 ml), whereas sport divers swallowed more than occupational divers in fresh (recreational) waters (13.0 vs. 5.7 ml). Sport divers diving in swimming pools swallowed a mean volume of 20 ml per dive. A study of water ingestion during active swimming in a swimming pool showed that non-adult swimmers (18 years) swallowed far more water than adult swimmers during the 45 min of their exposure (37 vs. 16 ml) (Dufour et al., 2006). The authors suggested that these swallowed volumes of water may also apply to fresh water swimmers due to similar frequencies of head submersions and time spent in the water, but not to marine water swimmers because of different behavior. The study does, however, not provide data on exposure of swimmers to fresh and marine water to substantiate this suggestion. Recently, Stone et al. (2008) estimated water ingestion and exposure among surfers and reported an average of 171 ml swallowed per day. These studies provided valuable information, but data on water ingestion among swimmers in fresh and seawater are lacking and also, information on the duration and frequency of swimming events in recreational waters, sea or swimming pools is not available. The objective of this study was to collect data on exposure of swimmers to fresh water, seawater and swimming pools; exposure data encompass the volume of swallowed water and

2393

the frequency and duration of swimming events. In the Netherlands, swimming in surface water typically occurs during the official bathing season (May 1steOctober 1st), but mostly during the summer holidays (JulyeAugust). Water temperature in the North Sea ranges between approximately 10 and 20  C, with an average of 18  C; water temperatures in recreational lakes are highly variable depending on size and depth, but grossly range between 15 and 25  C. Public swimming pools, both indoor and outdoor, use drinking water as source water and are chlorine disinfected, as required by law (Anonymous, 2009). In indoor swimming pools the water temperature is 25e28  C, but in outdoor pools it is around 22e24  C. Many outdoor pools close during the winter season. The exposure data were used to estimate infection risks resulting from exposure to recreational waters and a swimming pool. To the latter, concentration data on Cryptosporidium oocysts and Giardia cysts in recreational waters (Schets et al., 2008) and a swimming pool (Schets et al., 2004) were used. In the two cited studies, infection risks were estimated in scenario’s using a range of set swallowed water volumes. In the current study, exposure data were collected by means of questionnaires. To evaluate the impact of different years characterized by different weather conditions, particularly during summer, exposure data were collected in 2007 and 2009. The hypothesis that 2009 responders would report more frequent and longer bathing in fresh and marine water than 2007 responders due to more favorable weather conditions during summer was tested.

2.

Materials and methods

2.1.

Data collection

Data on bathing water exposure were collected through administration of questionnaires to a group of approximately 60 000 to 75 000 inhabitants of the Netherlands representing the general Dutch population, hereafter referred to as the E-panel (Research institute Synovate BV, Amsterdam, the Netherlands). Members of the E-panel have given their consent for responding to questionnaires about various topics, in return for a small consideration. The questionnaires were administered via the internet and the respondents had access to the questions through a secured web link. E-panel members have also provided information on basic demographic characteristics such as age, gender, postal code, socioeconomic status and composition of family. The questionnaire on bathing water exposure included questions about frequency of bathing, duration of bathing, the amount of water swallowed during bathing, head submersion while bathing, whether or not bathing took place at sites that were designated as official European bathing sites, and general health and health complaints after bathing. The E-panel members were asked to provide answers for exposure to swimming pool water, fresh water and seawater separately. A questionnaire referring to bathing in 2007 was administered in December 2007eJanuary 2008, and a questionnaire referring to bathing in 2009 was administered in December 2009eJanuary 2010. Descriptive characteristics of the summers (JuneeAugust) of 2007 and 2009 were obtained from

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the Royal Netherlands Meteorological Institute (http://www. knmi.nl). E-panel members who completed the 2007 questionnaire were excluded from participation in 2009. People of 15 years of age or older answered the questions for themselves and if they had children between zero and 14 years of age, they also answered the questions for their eldest child. The goal was to get 4000 adult responses from the E-panel for each year. E-panel members were asked to report actual numbers of swimming events in fresh water, seawater or swimming pools. The time spent in the water could be reported in classes of minutes of water contact (0e30, 30e60, 60e120, 120e300 min) for each type of water. The participants were asked to report the volume of water they swallowed as an estimated number of mouthfuls in four classes: 1) no water or only a few drops, 2) one to two mouthfuls, 3) three to five mouthfuls, and, 4) six to eight mouthfuls. To provide a frame of reference it was indicated that the volume of one to two mouthfuls was comparable to the contents of a shot glass, that of three to five mouthfuls to the contents of a coffee cup and that of six to eight mouthfuls to the contents of a soda glass (Schijven and de Roda Husman, 2006).

2.2.

Data analysis

The data from the questionnaires were analyzed using Mathematica (version 7.0.1.0; Wolfram Research Inc., Champaign, IL, USA). For both years, responders were compared with the general Dutch population (CBS statistics Netherlands, http://statline.cbs.nl/statweb/) with respect to age and gender distribution in order to assess whether the study population was a good representative of the general Dutch population. Age and gender distributions for 2007 and 2009 were also mutually compared. Information on age and gender was compared by means of a chi square test. Swimming frequency ( f ) was assumed to follow a negative binomial distribution, duration of a swimming event (t) a lognormal distribution, and the swallowed volume of water (v) a gamma distribution. The entities f, t and v form the basic measures of exposure to bathing water. Correlations between f, t and v were tested by means of calculating Pearson’s correlation coefficients. Distribution parameters were estimated from the data using the method of maximum likelihood fitting and maximum likelihood ratio tests were applied to compare distributions between men, women and children (McCullagh and Nelder, 1989). The data from the questionnaires on the swallowed volume of water consisted of frequencies of the four volume classes. These volume classes were converted to actual volumes as follows. In total 119 persons (colleagues, friends and family of the authors within the same age range as the E-panel) took part in a test where they drank ten times from a glass of water. The full glass was weighed before starting the test, and subsequently after each mouthful. The mouthfuls were assumed to be gamma distributed and compared between women, men, girls and boys. Each volume class from the questionnaires was assumed to be discrete uniformly distributed, implying that, for example, in class two there is equal probability of swallowing one or two mouthfuls of

water. Actual volume distributions for the volume classes 2, 3 and 4 were then obtained by multiplying Monte Carlo samples from the gamma distributions from the test with Monte Carlo samples of the discrete uniform distributions of the swallowed numbers of mouthfuls that were reported in the questionnaires. Volume class 1 (zero to a few drops) was assumed to be a continuous uniform distribution of 0e5 ml. Swallowed volume distributions (v) were obtained by 10 000 times random sampling from the reported frequencies of the volume classes and multiplying these frequency distributions with the corresponding volume distributions. Finally, a gamma distribution was fitted to these randomly obtained volumes in order to obtain the distribution parameters r and l for use in QMRA and for comparison of the volume distributions for men, women and children and water type by likelihood ratio testing.

2.3.

Risk of infection

The swallowed volume data (v) were used in combination with concentrations of Cryptosporidium oocysts and Giardia cysts in a swimming pool in the Netherlands (Schets et al., 2004) and several recreational lakes in the vicinity of the city of Amsterdam, the Netherlands (Schets et al., 2008) in order to estimate infection risks. The raw data on the presence of Cryptosporidium and Giardia were available as counts of (oo)cysts and sample size. To the count data, negative binomial distributions with parameters r and l were fitted. The corresponding concentrations of Cryptosporidium and Giardia are gamma-distributed with parameters r and l (Teunis et al., 1999). Distribution data of the number of swallowed (oo)cysts per swimming event were obtained by multiplying Monte Carlo samples of the v-distributions and concentration distributions. Infection risks Pinf were estimated by applying the hypergeometric doseresponse relation (Teunis and Havelaar, 2000; Teunis et al., 2008) with beta-distributed dose-response parameters a and b for Cryptosporidium (Okhuysen et al., 2002; Teunis et al., 2002) and Giardia (Teunis et al., 1997): Pinf ðD; a; bÞ ¼ 1  1 F1 ða; a þ b; DÞ

(1)

where 1 F1 is a Kummer confluent hypergeometric function with parameters a and b and dose D ¼ Cn with concentration distribution C (numbers of pathogens per liter) and volume distribution n (liter).

3.

Results

3.1.

Descriptive parameters

In 2007, a total of 10 621 E-panel members had to be contacted to achieve the goal of 4000 completed questionnaires (response 38%); in 2009 this number was 8004 (response 50%). In both years, responders did not significantly ( p ¼ 0.01) differ from the general Dutch population with respect to age and gender distribution indicating that the study populations were a good representation of the general Dutch population. Moreover, age and gender distributions

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for the 2007 and 2009 study populations did not significantly differ ( p ¼ 0.01). Also, in both years, about half of the responders were non-swimmers, indicating that the questions were not only answered by people that took a special interest in swimming. Of the 8000 adult responders, 4123 were swimmers; a total of 1924 adults answered the questions for their eldest children, of which 1689 swam (Table 1). The majority of the swimmers visited a swimming pool, whereas about a quarter of the responders swam in fresh water or seawater. Almost half of the swimmers indicated that they had been swimming in more than one water type; two thirds of the swimmers swam at an official bathing site (Table 1). Both adults and children submerged their heads more often when they swam in a swimming pool than in fresh and marine water. Children tended to submerge their heads much more in fresh than in marine water (Table 1). About 90% of the adult swimmers stated that their general health was excellent or good, whereas about 80% of the non-swimming adults indicated this. Almost all parents

considered the health of their children, whether they had been swimming or not, excellent or good (Table 1). Of the adult swimmers, 320 (8%) filled in that they had had health complaints after swimming; 179 swimming children (11%) experienced health complaints after swimming. Twenty-five percent of the adults and 40% of the children with health complaints visited a physician; 90% of the adults and 92% of the children received treatment. In the Netherlands, the summers of 2007 and 2009 differed with respect to precipitation (202 mm vs. 189 mm), hours of sunshine (591 h vs. 729 h) and the number of days with average temperatures over 20  C (54 vs. 66) or 25  C (18 vs. 25).

3.2.

Exposure parameters

The quantitative exposure parameters frequency of bathing ( f ), duration of bathing (t) and swallowed volume (v) were independent variables, for both adults and children, in both 2007 and 2009 (0.05 < correlation coefficient < 0.3). Comparison of likelihood values of the distributions

Table 1 e Descriptive statistics of responders to inquiries. Adults (>15 years) 2007 number Total responders 4000 Swimmers 2149 Male 1068 Female 1081 Age 15e79 Non-swimmers 1851 Male 941 Female 910 Age 15e80 General health Swimmers Excellent 435 Good 1448 Mediocre 241 Poor 25 Non-swimmers Excellent 271 Good 1177 Mediocre 361 Poor 42 Water type used Swimming pool 1919 Fresh water 560 Seawater 605 Official bathing site Fresh water 386 Seawater 380 Head submersion Swimming pool 1053 Fresh water 256 Seawater 264 Health complaints after bathing 176 Swimming pool 118 Fresh water 39 Seawater 19

%

54 50 50 46 51 49

2009 number 4000 1974 969 1005 15e81 2026 1002 1024 15e98

Children (<15 years) %

49 49 51 51 50 50

2007 number 992 871 unknown unknown 0e14 121 unknown unknown 0e14

%

88

12

2009 number 932 818 405 413 0e14 114 52 62 0e14

%

88 50 50 12 46 54

20 67 11 1

427 1316 216 15

22 67 11 1

416 443 11 1

48 51 1 0

421 378 18 1

52 46 2 0

15 64 20 2

321 1217 429 59

16 60 21 3

68 51 2 0

56 42 2 0

48 63 3 0

42 55 3 0

89 28 26

1755 524 573

89 27 29

845 232 229

97 27 26

799 254 207

98 31 25

69 63

340 357

65 62

160 155

68 69

178 152

70 73

55 46 44

916 238 234

52 45 41

563 138 111

67 60 48

518 149 96

65 59 46

8 6 7 3

144 104 27 13

7 6 5 2

97 74 12 11

11 9 5 5

82 64 15 3

10 8 6 1

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describing the exposure parameters f, t and v for 2007 and for 2009 showed that the 2007 and 2009 data did not significantly differ. Therefore, the data for 2007 and 2009 were pooled, yielding distributions of exposure parameters based on the responses of 4123 adults and 1689 children that swam, either in 2007 or in 2009. The frequency of bathing varied from once to 365 times per year for adults and from once to 300 times per year for children. Negative binomial distributions described the frequency of bathing for men, women and children; comparison of likelihood values showed that there were significant differences between men, women and children and water types. Differences in bathing frequency between men and women in fresh and marine water were nevertheless small, whereas men, women as well as children swam much more often in swimming pools than in fresh water and the sea. For all water types, bathing frequency was highest for children (Table 2). Log-normal distributions described the duration of swimming events. Here, comparison of likelihood values also showed that there were significant differences between men, women and children and water types, albeit they were again small between men and women. All groups spent more time in the water during a swimming pool visit than while swimming in fresh or seawater (Table 2). The gamma distributions of the volumes of mouthfuls of water swallowed by either boys or girls did not significantly differ; therefore data for boys and girls could be pooled, yielding a distribution for children. On average, a mouthful of water swallowed by a child measured 25.0 ml (95% CI (Confidence Interval) 7.8e52.2; r ¼ 4.72, l ¼ 5.30). Differences between men and women were significant; on average men’s mouthful volume was 32.7 ml (95% CI 11.0e66.0; r ¼ 5.27, l ¼ 6.25) whereas women’s mouthfuls were on average 24.8 ml

(95% CI 12.0e42.1; r ¼ 10.27, l ¼ 2.42). Gamma distributions described the volume of water swallowed during a swimming event. Likelihood ratio testing showed that for men, women and children there was no significant difference between fresh and seawater. In general, children swallowed more water than adults, in all water types, whereas both adults and children swallowed more water in swimming pools than in fresh and seawater (Table 2).

3.3.

Risk assessment

The Cryptosporidium and Giardia concentrations in a Dutch swimming pool (Schets et al., 2004) and several Dutch recreational lakes (Schets et al., 2008) were described by gamma distributions (Table 3). In the swimming pool, the average Cryptosporidum concentration was 0.3 oocysts/L, whereas the average Giardia concentration was 0.025 cysts/L; for these (oo)cysts, viability data were not available and in risk assessment total (oo)cysts counts were used, assuming 100% viability. Average viable Cryptosporidium concentrations in recreational lakes ranged from 0.02 to 0.07 oocyst/L and average viable Giardia concentrations ranged from 0.01 to 0.14 cysts/L. In the swimming pool, the estimated infection risk for Cryptosporidium was about 1.103e2.103 whereas for Giardia it was about 1.105e3.105, per swimming event per person. Infection risks were higher for children than for men and women, who had comparable risks of infection (Table 4). Due to concentration differences in the recreational lakes, the estimated infection risks per swimming event per person differed, but generally ranged between 1.104 and 5.104 for Cryptosporidium and between 4.106 and 1.104 for Giardia. Children were at higher risk than adults due to the larger

Table 2 e Exposure parameters for swimmers in swimming pools, fresh water and seawater: frequency of swimming per year, duration of swimming and volume swallowed per swimming event. Adults

Children

Men a

Average 95% CI Frequency f (Negative binomial distribution, r, l) Swimming pool Fresh water Seawater Duration (min) t (Log-normal distribution, m, s) Swimming pool Fresh water Seawater Volume swallowed v (ml) (Gamma distribution, r, l) Swimming pool Fresh water Seawater a CI: Confidence Interval.

Women Distribution Average 95% CIa parameters r

l

Distribution Average 95% CIa Distribution parameters parameters r

l

r

l

13 7 6

0e54 0e25 0e22

0.83 1.2 1.4

0.06 0.15 0.18

16 7 6

0e65 0e23 0e19

0.84 1.3 1.5

0.05 0.17 0.21

24 8 7

0e91 0e25 0e24

1.0 1.3 1.5

0.04 0.14 0.17

68 54 45

19e180 7e200 6e160

m 4.1 3.6 3.5

s 0.57 0.85 0.85

67 54 41

19e170 6e220 4e180

m 4.0 3.5 3.2

s 0.55 0.94 0.94

81 79 65

24e200 12e270 8e240

m 4.2 4.1 3.8

s 0.55 0.80 0.80

34 27 27

0.022e170 0.016e140 0.016e140

r 0.48 0.45 0.45

l 71 60 60

23 18 18

0.033e110 0.022e86 0.022e90

r 0.52 0.51 0.51

l 45 35 35

51 37 31

0.62e200 0.14e170 0.08e140

r 0.81 0.64 0.58

l 63 58 55

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Table 3 e Gamma distributions of Cryptosporidium and Giardia concentrations in a swimming pool (Schets et al., 2004) and recreational lakes (Schets et al., 2008) in the Netherlands. Location

Cryptosporidium Average (n/L)

c

95%

Giardia

Distribution parameters

Swimming pool Recreational lakesb Lake 1 Lake 2 Lake 3 Lake 4 a b c d

95%

l

r a

Average (n/L)

c

Distribution parameters l

r

0.3

1.6

0.19

1.6

0.025

0.14

0.11

0.24

0.02 0.029 0.067 xd

0.059 0.066 0.38 xd

1.0 2.3 0.11 xd

0.02 0.013 0.58 xd

0.12 0.039 0.01 0.14

0.56 0.075 0.047 0.51

0.28 4.1 0.27 0.53

0.44 0.0096 0.037 0.26

No viability data available. Viable Cryptosporidium and Giardia. 95% of the concentrations lie below this value. No viable Cryptosporidium oocysts detected.

volume of water they swallow during swimming activities (Table 4).

4.

(2006) data support our findings, despite differences in approach, suggesting that using questionnaires in combination with a small sampling test may be as effective in assessing the volume of water swallowed during swimming activities as using cyanuric acid as a tracer. An additional advantage of the questionnaire approach is that it can be applied to a larger group of participants, which contributes to the reliability of the data. Dutch men and children of all ages swallowed more water per swimming event than Dutch sport and occupational divers per dive in all water types (Schijven and de Roda Husman, 2006), and less than surfers in Oregon, United States, swallowed per day while surfing (Stone et al., 2008). Female swimmers swallowed more water than sport and occupational divers in marine and fresh water, but approximately the same as sport divers in swimming pools. These results reflect different frequency and intensity of water contact and different behavior during exposure to water for swimmers, divers and surfers. The duration of water contact per swimming event or dive is comparable for sport divers and

Discussion

The average volume of water swallowed per minute by children during an average visit to a swimming pool of 81 min, is 0.63 ml/min; adult men swallowed 0.50 ml/min, and women 0.34 ml/min. These average volumes are in the same order of magnitude as those determined in the study of Dufour et al. (2006), where children swallowed 0.82 ml/min (i.e. 37 ml in 45 min) and adults 0.36 ml/min. However, Dufour et al. (2006) considered all individuals of 18 years of age and younger as children, whereas in the current study children were younger than 15 years. For comparison, our data were re-evaluated by using the childeadult definition of Dufour et al. (2006) which yielded average swallowed volumes for men, women and children identical to those obtained by using our own childeadult definition. Thus, the Dufour et al.

Table 4 e Risk of infection for Cryptosporidium and Giardia in a swimming pool and recreational lakes in the Netherlands. Swimming pool Cryptosporidium

Men Women Children

Giardia

Men Women Children

a 95% of the risks are below this value. b No Cryptosporidium oocysts detected.

Average 95%a Average 95%a Average 95%a Average 95%a Average 95%a Average 95%a

3

1.5.10 8.2.103 1.1.103 6.2.103 2.2.103 1.3.102 1.9.105 8.3.105 1.3.105 5.5.105 2.8.105 1.3.104

Recreational lakes Lake 1

Lake 2

Lake 3

Lake 4

4

4

4

xb xb xb xb xb xb 7.9.105 3.7.104 5.1.105 2.3.104 1.0.104 4.8.104

1.2.10 6.2.104 8.1.105 3.8.104 9.0.105 4.2.104 7.6.105 3.7.104 4.1.105 2.0.104 5.0.105 2.3.104

1.8.10 7.8.104 1.2.104 4.8.104 2.4.104 9.5.104 2.3.105 9.8.105 1.5.105 6.1.105 3.1.105 1.2.104

3.7.10 1.7.103 2.4.104 1.0.103 4.9.104 2.2.103 6.9.106 2.7.105 3.6.106 1.7.105 7.6.106 3.7.105

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swimmers, but longer for occupational divers (Schijven and de Roda Husman, 2006). The number of dives per year is highly variable, but the number of days with water contact for swimmers and divers is in the same order of magnitude. However, divers use either ordinary diving masks or full face masks that decrease or prevent the swallowing of water and swimmers do not use such protective gear. On average, water contact is more frequent for surfers than for swimmers (77 days/year (Stone et al., 2008) vs. 7 days/year (this study)), but this may reflect climatic differences between North Sea beaches and Oregon Pacific Coast beaches. Moreover, surfers are more likely to accidentally swallow gulps of water than swimmers as a result of their activities in the water e.g. when they catch waves or fall off their surfboards and submerge unexpectedly. The exposure parameters f, t and v do not largely differ between men and women, suggesting that they display similar swimming behavior, although men swallow more water per swimming event than women, in all water types. Children, however, behave very different: they swim more often, stay in the water longer and submerge their heads more often, and they swallow more water than adults. Children swim slightly more often than adults in fresh and seawater, but they visit swimming pools one-and-a-half to nearly twice as much. This may reflect behavior of a specific age group: over 80% of the children for which the questionnaires were completed were between four and fourteen years of age, which includes the children of 4e12 years of age that attend swimming classes during their primary school period, and the fraction of young adolescents (12e14 years) that visit swimming pools with friends for leisure activities. Whether this behavior in the Netherlands is similar to other parts of Europe or can be extrapolated to other parts of the world is yet unclear. The swallowed volume of water per swimming event may be person and water type specific and therefore not necessarily apply for the Netherlands only. However, frequency and duration of swimming may also be influenced by local factors such as climatic and cultural aspects. Despite more favorable weather conditions in the summer of 2009 compared to the summer of 2007, the hypothesis that 2009 responders would report more frequent and longer bathing in surface water was rejected; 2007 and 2009 data did not significantly differ, suggesting that the exposure parameters f, v, and t are independent of the observed summer weather fluctuations, and are thus potentially applicable for other parts of Europe. The methodology to estimate the swallowed volume of water, applied in the current study, is an alternative to measuring the concentration of cyanuric acid in urine samples from swimmers that is less laborious, easier to extend to larger groups of participants of all ages and independent of the water type studied. Moreover, finding a chemical tracer to measure the swallowed volume of water and applying this approach in natural waters is difficult. In the test that estimates the volume of a mouthful of water, however, people drink a glass of water and take controlled mouthfuls. Swallowing water during swimming activities may have a more accidental nature and as a result the volume of swallowed mouthfuls may be larger than estimated in this test. Nevertheless, the combination of this test and questionnaires

may be preferable due to its practical ease and ability to study large groups of subjects. A drawback of collecting exposure data through questionnaires with closed-end questions completed by bathers and non-bathers is that bias is easily introduced, difficult to identify and difficult to exclude (Craun et al., 2001; Fleisher and Kay, 2006). For answering the questions, study participants must recall their swimming activities in the past, i.e. days, weeks or months ago. The reliability of their answers totally depends on their memory of such events. Data may be obscured by poor remembrance resulting in choosing the wrong answer or picking ‘just an answer’ from the offered options. This recall bias may lead to misclassification of exposure in terms of frequency and duration of swimming events. Participants may have guessed the volume of water they swallowed and, for some, this guess may have been influenced by the perception that it is wise to swallow a limited amount of bathing water, whereas other may have overestimated the volume of water they swallowed. The large group of participants, equally male and female, in a broad range of ages discounts for spurious guesses and contributes to the reliability of the results. The fact that exposure data obtained from different groups of responders in 2007 and 2009 do not significantly differ supports the credibility of the results. Unambiguous formulation of the questions is to minimize the chance of receiving erroneous answers, whereas collecting exposure data immediately after swimming events or by asking a group of participants to keep a diary of their swimming behavior for a prolonged period, e.g. a year, are means to overcome poor memory. The questionnaire approach used in the current study may have rendered biased data, but nevertheless gave insight in swimming behavior of men, women and children in fresh water, seawater and swimming pools and provided data that were previously lacking. Based on the updated exposure data, and using the hypergeometric dose-response model for Cryptosporidium and Giardia (Teunis and Havelaar, 2000; Teunis et al., 2008), the risk of infection per swimming event with Cryptosporidium and Giardia during exposure to Dutch recreational lakes was estimated. Depending on the lake and the corresponding Cryptosporidium and Giardia concentrations, approximately on average one to five in 10 000 swimmers (0.01e0.05%) may become infected with Cryptosporidium, whereas approximately on average four to 100 in a million swimmers (0.0004e0.01%) may contract a Giardia infection. In the studied swimming pool, the risk of infection with Cryptosporidium was about tenfold higher than in the recreational lakes, whereas the risk of infection with Giardia was alternately higher, lower or equal. This swimming pool was dealing with a fecal contamination incident and filter malfunctioning during the time of sampling and data collection (Schets et al., 2004). It should be noted that in the studies of Schets et al. (2004, 2008) exponential dose-response relations were applied, using the dose-response parameters published by Teunis et al. (1996); the use of updated dose-response information in the present study yielded Cryptosporidium infection risks that were a factor 10e100 higher than those previously reported; Giardia infection risk were in the same order of magnitude (Schets et al., 2004, 2008).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 3 9 2 e2 4 0 0

Cryptosporidium and Giardia have been chosen to demonstrate the use of the updated exposure data in QMRA. However, the frequent reporting of skin disorders and ear conditions after contact with recreational waters (Schets et al. 2010) warrant the use of these exposure data for evaluation of risks of conditions caused by waterborne pathogens of non-fecal origin, such as Trichobilharzia and P. aeruginosa. Currently, such risk evaluations are limited by the lack of dose-response data.

5.

Conclusions

Major gain of this study is that previous assumptions on swimmer exposure in fresh and marine bathing waters were replaced with estimates of the exposure parameters frequency and duration of swimming events and volume of water swallowed, whereas existing data on the volumes of water swallowed by swimmers in swimming pools were confirmed, and extended with data on frequency and duration of swimming in such facilities. Replacing assumptions on bather exposure, with estimates of exposure parameters may reduce the uncertainty in assessing the risk of infection with waterborne pathogens. The swallowed volume of water appears different for men, women and children, but also in fresh water, seawater and swimming pools. This knowledge enables risk assessment for specific target groups, e.g. children during swimming classes. Moreover, the other measures of exposure, i.e. frequency and duration of swimming, do also differ for men, women and children and in different water types, and provide a basis for the identification of high risk populations under specific circumstances, e.g. due to their extended water contact and frequent head submersions, children may be more prone to contract otitis externa due Pseudomonas aeruginosa infections. Preventive measures may be targeted at specific risk groups.

Disclosure statement All authors confirm that there are no conflicts of interest and all authors have approved the final version of the manuscript.

Acknowledgments This research was performed by the order and for the account of the General Directorate for Environmental Protection, Ministry of Housing, Spatial Planning and the Environment, the Netherlands. The authors thank Arie Havelaar for critically reading of the manuscript.

references

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Degradation of soil-sorbed trichloroethylene by stabilized zero valent iron nanoparticles: Effects of sorption, surfactants, and natural organic matter Man Zhang a, Feng He a, Dongye Zhao a,*, Xiaodi Hao b a

Environmental Engineering Program, Department of Civil Engineering, 238 Harbert Engineering Center, Auburn University, Auburn, AL 36849, USA b The R & D Center for Sustainable Environmental Biotechnology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

article info

abstract

Article history:

Zero valent iron (ZVI) nanoparticles have been studied extensively for degradation of

Received 2 November 2010

chlorinated solvents in the aqueous phase, and have been tested for in-situ remediation of

Received in revised form

contaminated soil and groundwater. However, little is known about its effectiveness for

27 January 2011

degrading soil-sorbed contaminants. This work studied reductive dechlorination of

Accepted 31 January 2011

trichloroethylene (TCE) sorbed in two model soils (a potting soil and Smith Farm soil) using

Available online 2 March 2011

carboxymethyl cellulose (CMC) stabilized FeePd bimetallic nanoparticles. Effects of sorption, surfactants and dissolved organic matter (DOC) were determined through batch

Keywords:

kinetic experiments. While the nanoparticles can effectively degrade soil-sorbed TCE, the

Dechlorination

TCE degradation rate was strongly limited by desorption kinetics, especially for the potting

In-situ remediation

soil which has a higher organic matter content of 8.2%. Under otherwise identical condi-

Nanoparticles

tions, w44% of TCE sorbed in the potting soil was degraded in 30 h, compared to w82% for

Sorption

Smith Farm soil (organic matter content ¼ 0.7%). DOC from the potting soil was found to

Surfactant

inhibit TCE degradation. The presence of the extracted SOM at 40 ppm and 350 ppm as TOC

TCE

reduced the degradation rate by 34% and 67%, respectively. Four prototype surfactants

Zero valent iron

were tested for their effects on TCE desorption and degradation rates, including two anionic surfactants known as SDS (sodium dodecyl sulfate) and SDBS (sodium dodecyl benzene sulfonate), a cationic surfactant hexadecyltrimethylammonium (HDTMA) bromide, and a non-ionic surfactant Tween 80. All four surfactants were observed to enhance TCE desorption at concentrations below or above the critical micelle concentration (cmc), with the anionic surfactant SDS being most effective. Based on the pseudo-firstorder reaction rate law, the presence of 1cmc SDS increased the reaction rate by a factor of 2.5 when the nanoparticles were used for degrading TCE in a water solution. SDS was effective for enhancing degradation of TCE sorbed in Smith Farm soil, the presence of SDS at sub-cmc increased TCE degraded by w10%. However, effect of SDS on degradation of TCE in the potting soil was more complex. The presence of SDS at sub-cmc decreased TCE degradation by 5%, but increased degradation by 5% when SDS dosage was raised to 5cmc. The opposing effects were attributed to combined effects of SDS on TCE desorption and degradation, release of soil organic matter and nanoparticle aggregation. The findings strongly suggest that effect of soil sorption on the effectiveness of FeePd nanoparticles

* Corresponding author. Tel.: þ1 334 844 6277; fax: þ1 334 844 6290. E-mail address: [email protected] (D. Zhao). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.028

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must be taken into account in process design, and soil organic content plays an important role in the overall degradation rate and in the effectiveness of surfactant uses. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Over the past six decades or so, groundwater contamination of chlorinated solvents, such as trichloroethylene (TCE) and tetrachloroethylene (PCE), has been a major concern to public health and environmental safety (Bartseh et al., 1979; Lyne and McLachlan, 1949; McCarty, 1997; Wu and Schaum, 2000). The past extensive uses and discharges of chlorinated hydrocarbons have left a legacy of widespread groundwater contamination in the U.S. as well as other industrialized nations (Moran et al., 2007). By 2007, TCE was found at 301 of the 1689 National Priorities List sites and 391 of the 1444 ATSDR’s (Agency for Toxic Substances and Disease Registry) CEP (Completed Exposure Pathway) sites (ATSDR, 2007). Chlorinated hydrocarbons are often categorized as dense non-aqueous phase liquid (DNAPL) for their low aqueous solubility and greater density than water. Once released into the subsurface environment, TCE will distribute between soil and water. Because of its low solubility and high adsorbability to soil organic matter (SOM), a great fraction of TCE tends to be retained in the solid phase. Yet, soil-sorbed TCE can be slowly released back into groundwater, rendering TCE a long-term threat to the environment and human health (Burris et al., 1995; Mohammad and Kibbey, 2005). It is well known that soil sorption can inhibit or limit natural attenuation or bioavailability of TCE. In-situ dechlorination in the subsurface through injection of zero valent iron (ZVI) nanoparticles is an innovative technology. Since it was first proposed by Wang and Zhang (1997), it has attracted extensive interests from academia, industries, and government agencies. Extensive bench-scale and pilotscale studies have been carried out to demonstrate the technical effectiveness of this technology. For instances, ZVI particles have been found effective for rapid degradation of chlorinated solvents dissolved in water, such as chlorinated methanes, ethanes and ethenes, chlorinated aromatic compounds, and pesticides that are dissolved in water (Arnold and Robets, 2000; Joo and Zhao, 2008; Lien and Zhang, 2005; Lowry and Johnson, 2004; Matheson and Tratnyek, 1994; Schrick et al., 2002). Researchers noticed that such abiotic reductive dechlorination processes can be greatly enhanced in the presence of trace amounts (typically 0.1% of ZVI) of a metal catalyst such as Pd (Wang and Zhang, 1997; He and Zhao, 2008). To facilitate soil delivery and mobility of ZVI particles, various particle stabilization techniques have been explored (e.g. He and Zhao, 2005; Phenrat et al., 2008; Ponder et al., 2000, 2001; Saleh et al., 2007; Schrick et al., 2004). A number of field tests have been reported to test the effectiveness of the in-situ injection of ZVI nanoparticles. Elliott and Zhang (2001) reported that FeePd nanoparticles of 100e200 nm were able to remove TCE by 1.5e96.5% (depending on the spatial and temporal locality) at a contaminated site in a 4-week operation. Quinn et al. (2005) reported that ZVI particles emulsified

with a non-ionic surfactant sorbitan trioleate at a NASA site decreased TCE concentrations in soil by 87e99.5% in four of the 6 sampling borings in 90 days, and TCE concentration in groundwater was lowered by 57e100% at all depths in 5 months. However, the greatest technical obstacle for implementing this technology remains in the limited particle mobility in the subsurface. To improve soil mobility and deliverability of the ZVI nanoparticles, Zhao and co-workers have developed and extensively studied a class of soil-injectable ZVI nanoparticles that were prepared using low-cost and “green” water-soluble polysaccharides (starches and carboxymethyl celluloses or CMC) (He and Zhao, 2005, 2007; He et al., 2007, 2009). The stabilized nanoparticles displayed much improved soil deliverability and greater dechlorination reactivity compared to non-stabilized ZVI counterparts. He et al. (2010) successfully injected 0.2 g/L of CMC-stabilized FeePd nanoparticles (CMC ¼ 0.1 wt%, Pd/ Fe ¼ 0.1% (w/w)) into a secondary source zone of PCBs and chlorinated ethenes (TCE and PCE). They observed that effective dechlorination was taking place within two weeks, followed by a long-term (nearly two years) enhanced biotic dechlorination which was presumably boosted by the injected nanoparticles. However, due to the complexity of the subsurface environment, these pilot-tests have been largely limited to a “black-box” approach, i.e. the effectiveness has been quantified based on samples from limited monitoring wells without knowledge of detailed transport, distribution and reaction processes of the nanoparticles. Although sorption/desorption and diffusion are known to limit physical and biological availabilities of contaminants in porous media, the effect of soil sorption on the degradation effectiveness of ZVI nanoparticles has been lacking. While numerous studies have revealed the effectiveness of ZVI nanoparticles for degradation of chlorinated hydrocarbons in homogeneous systems, little is known on the effectiveness for degrading soil-sorbed contaminants. In addition, while dissolved organic matter (DOM) is known to interact with ZVI particles (Giasuddin et al., 2007; Tratnyek et al., 2001), effect of DOM on the nanoparticle stability and dechlorination effectiveness remains unknown. Lacking this critical information has often rendered results from various field tests inconclusive. Surfactant-enhanced desorption has been widely studied for enhanced dissolution and removal of soil-sorbed hydrophobic contaminants (Mayer et al., 2007; Park and Bielefeldt, 2005; West and Harwell, 1992). Due to their amphiphilic nature, surfactants are known to enhance the solubilization and mobilization of soil-sorbed contaminants. Cationic surfactant HDTMA was observed to enhance chlorinated solvent degradation with micro-sized ZVI powders at concentrations under its critical micelle concentration (cmc) due to increased TCE adsorption on ZVI nanoparticles (Shin et al., 2008). Note that in this work, the lower case cmc refers to critical micelle concentration and the upper case CMC to

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carboxymethyl cellulose. When the surfactant concentrations are above the cmc value, soil sorption of TCE decreased as a result of competitive TCE partitioning into the surfactant micelles (Ayoub et al., 2008; Loraine, 2001). Li et al. (2006) reported that amending granular ZVI particles with 2.5 mM HDTMA solution decreased TCE reduction by a factor of 2e3, and they attributed the inhibitive effect to the blockage of atomic hydrogen release from the surface of the ZVI particles. While various stabilizers (e.g. polyelectrolytes or polysaccharides) have been employed to enhance mobility and physical stability of ZVI nanoparticles, the combined effect of surfactants and these stabilizers is unknown. Also, while most of reported studies have focused on dechlorination in the aqueous phase, information has been lacking pertaining to effect of surfactants on ZVI’s degradation of soil-sorbed TCE. This research aimed to investigate effect of soil sorption on the effectiveness of CMC-stabilized ZVI nanoparticles and explore ways to overcome the sorption effect. The specific objectives were to: 1) investigate effect of soil sorption on the dechlorination extent and rate of CMCstabilized FeePd nanoparticles with two representative soils and using TCE as a model contaminant; 2) examine effect of DOM on the reactivity of the nanoparticles; and 3) test the effectiveness of various surfactants for enhanced degradation of soil-sorbed TCE.

2.

Materials and methods

2.1.

Chemicals

The following chemicals (analytical grade or higher) were used as received: ferrous sulfate heptahydrate (FeSO4$7H2O, Acros

Organics, Morris Plains, NJ, USA), sodium carboxymethyl cellulose (Na CMC or CMC, M.W. ¼ 90,000, Acros Organics), sodium borohydride (NaBH4, ICN Biomedicals, Aurora, OH, USA); sodium tetrachloropalladate(II) trihydrate (Na2PdCl4$3H2O, 99%, Strem Chemicals, New Buryport, MA, USA), sodium azide (NaN3, Fisher, Fairlawn, NJ, USA), hydrochloric acid (Fisher), methanol (Fisher), hexane (Fisher) and photometric grade trichloroethylene (Aldrich Chemical, Milwaukee, WI, USA). Four commercially available surfactants were tested for their effects on desorption and degradation of soil-sorbed TCE, including two anionic surfactants known as sodium dodecyl sulfate (SDS) with a purity of >98.5% (SigmaeAldrich, St. Louis, MO, USA) and sodium dodecyl benzene sulfonate (SDBS) with a purity of >88% (Acros Organics), a non-ionic surfactant Polyoxyethylene (20) sorbitan monooleate (Tween 80) (Aldrich) and a cationic surfactant hexadecyltrimethylammonium bromide (HDTMA) with a purity of w99% (Sigma). Table 1 provides relevant properties of these surfactants. Two soils were tested in this study. A potting soil (HYPONEX, OH, USA) purchased from a local Wal-Mart store (Auburn, AL, USA) was used to represent soils of relatively high organic content, whereas a top (0.4 m) loam soil obtained from a local farm (E.V. Smith Farm, Auburn, AL, USA) was used to represent soils lean of organic matter. The Smith Farm soil is designated as Lynchburg fine sandy loam (siliceous, semiactive, thermic Aeric Paleaquults). Before use, the soils were sieved through a standard sieve of 2 mm openings, and then washed with tap water to remove fine colloids and watersoluble compositions, which can adsorb significant amounts of TCE but hardly separable from water. The washed soils can be completely separated from water through centrifugation at 400 g-force. Finally the soils were air-dried at room temperature and stored for use. Soil analyses were performed by the

Table 1 e Selected properties of surfactants used in this study. Surfactants

Ionic property

Molecular formula

Critical micellar concentration (cmc), mM

Sodium dodecyl sulfate (SDS)

Anionic

NaC12H25SO4

8.2 (Filippi et al., 1999)

Sodium dodecyl benzene sulfonate (SDBS)

Anionic

C12H25C6H4SO3Na

1.5 (Zhang et al., 2006)

Polyoxyethylene (20) sorbitan monooleate (Tween 80)

Neutral

C64H124O26

0.012 (Yeom et al., 1995)

Hexadecyltrimethyle ammonium bromide (HDTMA)

Cationic

C16H33N(Br)(CH3)3

0.9 (Karapanagioti et al., 2005; Li, 2004)

Molecular structure

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Soil Testing Laboratory at Auburn University. The soil textural analysis was conducted following the bouyoucos hydrometer method. Soil pH was measured via the Reference Soil Test Methods for the Southern Region of the United States: Southern Cooperative Series Bulletin 289 (1983). The total organic carbon and sulfur were analyzed with an Elementar Vario Macro CNS Analyzer (Elementar, Hanau, Germany) at 140  F. The metal contents of the soils were analyzed with an Inductively Coupled Plasma Emission Spectroscopy (Varian Vista-MPX Axial Spectrometer, Varian, Walnut Creek, CA, USA) after acid digestion following EPA method 3230. Soil potassium, calcium, magnesium and sodium were determined by a Varian Vista-MPX Radial Spectrometer following the Melich I extraction. The pH at point of zero salt effect (PZSE) of the soil was determined following the potentiometric titration method (Marcano-Martinez and McBride, 1989). Table 2 gives salient physical and chemical properties of the soils. The hydrodynamic particle size and zeta potential of ZVI nanoparticles were determined by dynamic light scattering (DLS) (Zetasizer Nano ZS, Malvern Instruments Ltd, Malvern, Southborough, MA, USA) at 25  C. The resultant intensity data were then converted to the volume-weighted hydrodynamic diameter.

2.2.

TCE sorption tests

TCE sorption to the two soils was tested through batch isotherm tests. A series of TCE solutions at concentrations of 50, 100, 200, 300, 500 and 600 mg/L, respectively, were prepared by adding a known mass of TCE, delivered in a small volume of methanol, into deionized (DI) water. Total methanol content in the final solution was below 0.02% (v/v). To inhibit any possible biological activities during the sorption tests, 0.2 g/L of NaN3 was included in the solutions. Sorption tests were then initiated by adding 12 g of each of the soils in w63 mL of the respective TCE solution in 67 mL screw-capped glass vials sealed with PTFE-lined septa. Nearly zero-head space was maintained in the vials to avoid volatilization loss of TCE, and the mixtures were mixed on a rotator placed in an incubator at 21  1  C. Based on separate sorption kinetic tests, the mixtures were equilibrated for 1 week for the potting soil and 2 weeks for the Smith Farm soil to assure equilibrium. Upon equilibrium, the vials were centrifuged with a Fisher Marathon 21K/R Centrifuge (Fisher Scientific) at 400 g-force for 10 min. Then, 100 mL of the supernatant was withdrawn using a 100 mL gastight glass-syringe and transferred to 1 mL of hexane in a 2-mL GC vial. Upon phase separation, TCE in hexane was analyzed using an HP 6890GC (Hewlett Packard, Palo Alto, CA, USA) equipped with an electron capture detector (ECD) following the method by He and Zhao (2005).

Mass balance results showed that the overall recovery of TCE was always within 90e105%.

2.3.

Effects of surfactants on TCE desorption

To examine the physical availability of soil-sorbed TCE, desorption kinetic tests were carried out with the same batch reactors as in the isotherm tests and the soils that were preequilibrated with TCE. Upon centrifuging, about 93% of the supernatant was pipetted out and replaced with soil-amended water, which was prepared by mixing DI water and a TCE-free soil at the same soil to water ratio as in the sorption tests. The amendment ensures that the background compositions (e.g. dissolved SOM) during sorption and desorption remain identical. Again, 0.2 g/L of NaN3 was maintained to minimize biological activity. The vials were resealed and mixed on the rotator at 21  1  C. At selected times, the suspension was centrifuged and the supernatant was extracted by hexane and analyzed with GC-ECD following the same method as aforedescribed. The amount desorbed from the soils was obtained via mass balance calculations. To test effect of surfactants on the desorption rate, desorption kinetic tests were carried out in the presence of the four surfactants at initial concentrations of 1cmc and 5cmc values.

2.4.

Effects of surfactants on TCE degradation in water

CMC-stabilized ZVI nanoparticles were prepared at 0.1 g/L as Fe following the approach of aqueous phase reduction with borohydride as described in our previous study (He and Zhao, 2007). Trace amounts (0.1 wt% or 0.3 wt% of Fe) of Pd catalyst was added to the fresh ZVI particles by adding a known amount of Na2PdCl4 into the nanoparticle suspension. The addition of Pd was able to greatly enhance the dechlorination rate of TCE (He and Zhao, 2005, 2008). Batch degradation tests were carried out using 43 mL amber glass vials with open-top screw caps and PTFE-lined septa. To test the effect of each surfactant, 1 mL of a surfactant stock solution was added into the FeePd nanoparticle suspension to yield a desired concentration level. TCE degradation was then initiated by injecting 25 mL of a TCE stock solution, resulting in an initial TCE concentration of 10 mg/L for all cases. The mixture was then mixed on a rotator (50 rpm) at room temperature. At selected times, 100 mL of aqueous samples were taken, extracted with hexane, and analyzed via GC-ECD for TCE. Parallel control experiments were conducted with 0.2% CMC solution but without the nanoparticles. Mass balance analyses of TCE in the control tests indicated that the mass loss was <4% in all cases.

Table 2 e Physicochemical characteristics of potting soil and Smith Farm soil. Sample

K Mg P Al B Cu Fe Mn N Zn Taxonomy pH H2O OM,% S,% CEC* Meq/ Ca 100g ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm

potting soil Sandy loam 6.65 0.11 Smith farm loam 6.60 0.14 soil *CEC: Cation exchange capacity.

8.2 0.7

0.025 0.022

10.3 1.4

1834 172

153 17

365 49

42 3

77 34

0.4 <0.1

11 24

95 43

62 9

81 58

19 10

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Effects of surfactants on degradation of soil-sorbed

The same procedure was followed to pre-load TCE to the soils as in the sorption isotherm tests, except that no NaN3 was added. Based on our separate experimental results (data not shown), the presence of NaN3 inhibited the ZVI’s reducing power, and there was no significant biological degradation of TCE during the loading. The equilibration time was 1 week for the potting soil and 2 weeks for the Smith Farm soil. Based on the TCE desorption and solution phase TCE degradation tests, SDS displayed the greatest ability in enhancing both TCE desorption and TCE degradation. Consequently, SDS was investigated further for its effect on the degradation of soil-sorbed TCE. First, a stock suspension of FeePd nanoparticles (1.0 g/L as Fe, Pd ¼ 0.1 wt% of Fe) was prepared with 0.8 wt% Na CMC. The suspension was diluted with nitrogen-purged DI water and/or nitrogen-purged SDS solution. The resultant nanoparticle concentration was 0.3 g/L as Fe. The degradation tests were then initiated by replacing 93% (58 mL) of the supernatant in each of the 62.5 mL soil suspensions with the same volume of the nanoparticle suspension containing SDS at 1 or 5 times the cmc value. For comparison, the degradation tests were also conducted in the absence of SDS. The mixtures were mixed on the rotator at 21  1  C. At selected times, duplicated vials were centrifuged to separate soil and water, and TCE in the supernatant was extracted by hexane and analyzed via the GC-ECD method. To facilitate mass balance calculations, TCE remaining in soil was extracted using 50 mL methanol in a hot water bath for 48 h at 70  C twice consecutively. Control tests were carried out in parallel in the absence of the nanoparticles, which indicated that the TCE mass balance was within 88e110%.

2.6.

Effect of DOM on TCE degradation

To study effect of DOM on TCE degradation, batch kinetic tests were carried out in the 43 mL glass vials with a soil-amended background solution. The background solution was prepared by mixing 36 g of the potting soil with 63 mL DI water for 3 days, and then collecting the supernatant upon centrifuging. The TOC in the resultant soil-amended solution was 860 mg/L. Then, 28.7 mL of 0.45 g/L CMC-ZVI nanoparticle suspension was mixed with a nitrogen-purged mixture of the background solution and distilled water in a 43 mL glass vial, which resulted in a total Fe concentration of 0.3 g/L and a TOC concentration of 40 mg/L and 350 mg/L, respectively. The reaction was then initiated by injecting a TCE stock solution into the mixtures, which resulted in an initial TCE concentration of w100 mg/L. Control tests were conducted in the absence of the organic matter but under otherwise identical conditions. At selected times, samples (100 mL each) were taken from the vials and analyzed for TCE remaining in the systems via the GC-ECD method.

2.7. soils

Sorption of CMC-stabilized ZVI nanoparticles to

Batch tests were performed to examine the sorption behavior of the stabilized ZVI nanoparticles on the two soils. To be

consistent with the procedures in the TCE degradation tests, 12 g of a soil was first mixed with 63 mL DI water and aged for 1 week for the potting soil and 2 weeks for the Smith Farm soil. Subsequently, the nanoparticle sorption was initiated by replacing 93% of the supernatant in each vial with the same volume of the FeePd nanoparticle suspension (Fe ¼ 0.3 g/L). The vials were placed on a rotator placed in an incubator at 21  1  C. Adsorption of ZVI from the aqueous phase to the soils was followed in duplicate for up to 70 h. At selected times, duplicate vials were centrifuged at 400 g-force for 10 min. The centrifuging was able to remove soil and soilsorbed nanoparticles, but not CMC-stabilized free nanoparticles. Then, 1 mL of the supernatant was taken and digested with 4 mL of 12 M HCl to dissolve the suspended nanoparticles. The samples were then analyzed using a flame atomic-absorption spectrophotometer (AAS) (220FS, Varian, Palo Alto, CA). The ZVI nanoparticles in the solid phase were quantified based on mass balance calculations.

3.

Results and discussion

3.1.

TCE sorption and desorption

Fig. 1 shows TCE sorption isotherms for the two soils. The classical Langmuir model (Eq. (1)) was able to adequately interpret the non-linear isotherm data, q¼

bQc 1 þ bc

(1)

where q is the equilibrium uptake of TCE in soil (mg/g); C is TCE concentration in the aqueous phase (mg/L); Q is the Langmuir maximum capacity (mg/g); b is the Langmuir affinity constant (L/mg). As shown in Table 1, the SOM content for the potting soil was 8.2%, compared to only 0.7% for the Smith Farm soil. SOM has been well known to be the primary sink for hydrophobic organic contaminants (Chiou et al., 1979; Ong and Lion, 1991;

0.8 TCE sorbed in soil, mg TCE/g soil

2.5. TCE

0.6 potting soil Smith Farm soil Modeled Potting soil Modeled Smith Farm soil

0.4

0.2

0.0 0

100

200

300

400

500

600

TCE in aqueous phase at equilibrium,mg/L

Fig. 1 e Experimental (symbols) and Langmuir model fitted (lines) TCE sorption isotherms for a commercial potting soil (sandy loam) and a Smith Farm soil (loam). Data plotted as mean of duplicates, error bars indicate standard deviation from the mean.

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3.2.

TCE(in potting soil)=0.52mg/g TCE(in SF soil)=0.45mg/g C0=100mg/L (in water) Fe=0.3g/L

0.8

0.6

In potting soil In Smith Farm soil In water

0.4

0.2

0.0 0

10

20

30

40

50

Time, hour

Fig. 3 e Dechlorination of soil-sorbed and dissolved TCE with CMC-stabilized Fe/Pd nanoparticles with two model soils. Initial TCE in potting soil and Smith Farm soil was 0.52 mg/g and 0.45 mg/g, respectively; C0 [ 100 mg/L (initial TCE concentration in the solution only degradation tests); Fe [ 0.3 g/L; Pd/Fe [ 0.1 wt%; Na CMC [ 0.24 wt%. ZVI to TCE mass ratio [ 6.24: 17.4 and 5.4:17.4 and 1:3 for the three systems, respectively. Data plotted as mean of duplicates, error bars indicate standard deviation from the mean.

Degradation of soil-sorbed TCE and effect of DOM

Fig. 3 compares degradation kinetic data of TCE that was presorbed in the two soils and TCE dissolved in water solution. In the homogeneous system (i.e. in the absence of soil), complete TCE degradation was observed within 4 h. The degradation data can be interpreted using the pseudo-first-order reaction kinetics (He and Zhao, 2007; Liu et al., 2007), and the reaction rate constant was determined to be 1.64 h1. In contrast, only 44% of TCE sorbed in the potting soil was degraded in 30 h and w82% in the Smith Farm soil was degraded in 27 h. It is evident from Fig. 3 that the degradation rate and extent were severely

1.2

1.0

Mass remaining

1.0

TCEremaining/TCEinitial

Zhao et al., 2001). As expected, the potting soil offered a much higher sorption capacity and affinity with a Langmuir Q value of 0.94 mg/g and b of 0.0080 L/mg, compared to 0.32 mg/g and 0.0027 L/mg, respectively, for the Smith Farm soil. Fig. 2 shows desorption kinetic data of TCE in DI water. The TCE mass remaining in the soils was normalized to the mass of TCE initially loaded in the soils. Both soils displayed a rapid initial (<3 h) desorption rate followed by a slow release over the test period of 120 h. The observed desorption profile agreed with the commonly known biphasic process: rapid desorption from the easily-accessible sites followed by slow desorption associated with slow diffusion in the SOM and in the micropores (Pavlostathis and Mathavan, 1992; Pignatello and Xing, 1995; Sahoo and Smith, 1997). However, the extent of equilibrium desorption differed substantially for the two soils. Approximately 78% of TCE was desorbed in the first 24 h for the Smith Farm soil, compared to only 13% for the potting soil, indicating much limited availability of TCE in the higher SOM soil. At the end of desorption, the TCE distribution coefficient (KOC) based on soil organic carbon was determined to be 611 L/kg for the potting soil and 311 L/kg for the Smith Farm soil (organic matter¼1.72*organic carbon. Significant sorption hysteresis was evident for both soils by comparing the equilibrium sorption and desorption data (Figs. 1 and 2).

0.8 Smith Farm soil potting soil Control

0.6

0.4

0.2

0.0 0

50

100

150

200

Time, hour

Fig. 2 e Experimental data for desorption of TCE from potting soil and Smith Farm soil. Initial TCE in the potting soil and Smith Farm soil was 0.52 mg/g and 0.45 mg/g, respectively. Data plotted as mean of duplicates, error bars indicate standard deviation from the mean.

suppressed by soil sorption, especially for the soil of higher SOM content. Comparing the desorption (Fig. 2) and degradation data (Fig. 3) reveals that the degradation lowered the TCE remaining in the soil from 86% to 66% for the potting soil and from 22% to 18% for the Smith Farm soil. This is reasonable because the aqueous phase degradation of TCE resulted in an enhanced desorption driving force, i.e., the chemical potential difference between the soil and solution phases. However, the fact that in both cases a relatively large fraction of TCE remained in the soils confirmed that sorption of TCE in the solid phase greatly reduced the availability and the overall extent of TCE degradation by the nanoparticles. Given the relatively short reactive lifetime (days to a couple weeks) (He and Zhao, 2008; He et al., 2010) of the stabilized ZVI nanoparticles, rapid initial desorption rate is essential for making best use of the overall dechlorination potential of the nanoparticles. In addition to the sorption limitation, we found that soluble SOM, released from the soils can retard the ZVI’s dechlorination power. Fig. 4 shows the aqueous phase TCE degradation by the nanoparticles in the presence of various levels of SOM (measured as TOC) extracted from the potting soil. The observed pseudo-first-order rate constant was reduced from 1.22 h1 with no extracted SOM to 0.81 and 0.41 h1, respectively, when 40 mg/L and 350 mg/L of TOC were present. Similar retardation effects were reported by several researchers (Doong and Lai, 2005; Klausen et al., 2003; Tratnyek et al., 2001) on dechlorination effectiveness of granular or non-stabilized ZVI particles (which are typically present as micron-scale aggregates). For instances, Klausen et al. (2003) followed dechlorination of TCE using a granular ZVI for 100 days and observed that the presence of

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 0 1 e2 4 1 4

1.2 1.0 TOC=40ppm 0.3%Pd + 40ppm TOC TOC=350ppm No soil extracts

C/C0

0.8 0.6 0.4 0.2 0.0 0

2

4

6

8

10

Time, hour

Fig. 4 e Reductive dechlorination of TCE in water by CMCstabilized ZVI nanoparticles with or without soil extracts. Initial TCE concentration is 100 mg/L, Fe [ 0.3 g/L, Na CMC [ 0.24 wt%, Pd [ 0.1 wt% of Fe except for one case where Pd [ 0.3 wt% of Fe. Soil exudates were quantified as TOC in the aqueous phase. Data plotted as mean of duplicates, error bars indicate standard deviation from the mean.

2e20 mg/L humic acids or Great Dismal Swamp NOM reduced the pseudo-first-order rate constant by up to 50%. Doong and Lai (2005) reported that the presence of 50 mg/L humic acid decreased the normalized surface reaction rate constant of PCE dechlorination by palladized iron powders by a factor of 20. DOM can retard dechlorination in a number of ways. First, it may interfere with the particle stabilization of CMC, resulting in larger particles due to partial replacement of CMC molecules on the nanoparticle surface and/or direct coating of the DOM molecules on the nanoparticles. Based on dynamic light scattering (DLS) measurements, the mean particle size in the presence of extracted SOM from the potting soil (TOC ¼ 350 mg/ L) was 200 nm, compared to 155 nm when no extracted SOM was present. DOM is mainly composed of humic acid and fulvic acid, which are known to chelate with the iron oxides shell of the ZVI nanoparticles (Giasuddin et al., 2007). Such chelating effects will compete with the CMC molecules that are adsorbed on the particle surface, thereby diminishing the stabilizing effectiveness of CMC and resulting in the elevated particle agglomeration. The DOMenanoparticle interaction and the associated alteration of surface chemistry were evident from the change in the zeta potential. The presence of 350 mg/L TOC from the potting soil lowered the zeta potential of CMC-stabilized ZVI nanoparticles from 160 mV to 111 mV, which in turn lessened the electrostatic repulsion between the nanoparticles. The sorption of DOM and an increase in particle size would result in abatement of available reactive sites on the particle surface. The attachment of DOM molecules can also render the nanoparticles bulkier and less mobile, reducing the overall mass transfer rate. Second, the uptake of DOM molecules on the particle surface can form a mass transfer and electron transfer barrier that impedes the contact and reaction between TCE and the reactive sites of the nanoparticles. As a result, the

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dechlorination rates can be diminished (Giasuddin et al., 2007; Feng et al., 2008). The CMC molecules are macromolecules with a molecular weight of 90 k. Our prior work (He et al., 2007; He and Zhao, 2007) indicated that CMC can strongly complex with the nanoparticle surface, stabilizing the nanoparticles through concurrent steric exclusion and electrostatic repulsion. Yet, because of the large molecular structure of CMC, the layer of sorbed CMC molecules remains loosely structured, allowing for a relatively easy mass transfer of TCE. However, when DOM molecules, especially those with smaller M.W., are adsorbed, the attached molecular barrier becomes more compact, exerting more profound mass transfer resistance to TCE. Tratnyek et al. (2001) investigated the DOM sorption effect on ZVI’s TCE reduction kinetics, and reported that DOM could be sorbed more strongly to Fe0 than TCE, resulting in reduced TCE degradation. Klausen et al. (2003) also observed that the TCE degradation rate by granular iron in the presence of 2 mg/L (as TOC) DOM decreased by about 50% after 100 days. They attributed the reactivity drop to the stronger NOMeiron interaction that competitively excluded the weakly bonded TCEeiron surface complex. Third, DOM may compete with TCE for electron donors in the presence of FeePd. Zhu et al. (2008) reported evidence of NOM reduction in the presence of 0.83 g/L Pd/Fe particles. They observed that as ZVI was oxidized to Fe2þ, no appreciable H2 was detected in the system. Fourth, DOM may affect the catalytic activity of Pd. Although Pd was present at only 0.1% of Fe, Pd plays a pivotal role in accelerating the dechlorination process (He and Zhao, 2008). The formation of galvanic couples between the iron and palladium greatly accelerates the electron flow among metals and enhance the rate of reduction process thereby (Schrick et al., 2002). Pd may also act as a catalyst and promotes formation of the highly reactive atomic hydrogen which is essential for the reductive dechlorination process (Cwiertny et al., 2007). However, natural organic matter would attach to the surface of the catalyst and consequently reduce the degradation rate of TCE. Chaplin et al. (2006) and Ambonguilat et al. (2006) observed that the presence of NOM diminished nitrate reduction rate when Pd was used as a catalyst. To test the possible effect of DOM on the activity of Pd, TCE dechlorination rates were compared at two levels of Pd (Pd ¼ 0.1% and 0.3% of Fe) but at a fixed TOC concentration of 40 mg/L Fig. 4 shows that the pseudo-first-order reaction rate constant was quite comparable: 0.81 h1 at Pd ¼ 0.1 wt% Fe and 0.77 h1 at Pd ¼ 0.3 wt% Fe. This observation suggests that Pd was not the limiting factor in this experiment. Last, strong adsorption of CMC-stabilized ZVI nanoparticles was observed for both of the soils as shown in Fig. 5. Both soils exhibited strong uptake capacity for the CMCstabilized ZVI nanoparticles. While equilibrium uptake appeared nearly the same for the two soils, the potting soil displayed a faster sorption rate with an equilibrium time of w5 h, compared to 26 h for the Smith Farm soil. The faster kinetics of the potting soil indicated easier accessibility of the sorption sites. However, during the tests, the pH of the suspension was kept in the range of 7.9 and 8.5 for both cases. The PZSE of the potting soil and Smith Farm soil was determined to be 5.81 and 6.53, respectively. The surfaces for both soils were negatively charged. On the other hand, CMC with

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Fig. 5 e Sorption kinetics of 0.3 g/L CMC-stabilized ZVI nanoparticles onto potting soil and Smith Farm soil. The soil to ZVI suspension ratio is 12g: 63 mL for both potting soil and Smith Farm soil. The experimental pH was in the range of 7.9e8.5 for all cases.

a pKa value of 4.3 was fully dissociated, and thus, the surface of the CMC-coated nanoparticles was also negatively charged (as indicated by the negative zeta potential). Consequently, an unfavorable electrostatic condition was expected for sorption of the nanoparticles to the soils. He et al. (2007) identified that monodentate interaction between CMC and iron was the primary mechanism for binding CMC molecules to Fe nanoparticles, and the same type of interaction was also observed by Jones et al. (1998) for adsorption of polyacrylate onto hematite (a-Fe2O3). Therefore, the sorption of the nanoparticles to the soils is likely facilitated by monodentate interactions between CMC and the metals (e.g. Al and Fe) of the soils. Although the potting soil contained much higher SOM and metals (Table 2), the final equilibrium sorption capacity for the two soils was virtually the same. This can be attributed to the greater negative charge of the potting soil, which resulted in a stronger electrostatic repulsion against the negatively charged CMC-coated FeePd nanoparticles. It should be noted that the CMC molecules on the nanoparticles also serve as a steric barrier preventing direct binding of the soils with the core FeePd nanoparticles. In both cases, w71% of the nanoparticles were associated with the soils, which can diminish the mobility of the nanoparticles, and thus, the collision and reaction rates between the nanoparticles and TCE. Based on the sorption kinetic data, it can be inferred that the slower degradation of TCE after 5 h in Fig. 3 was actually facilitated by the soil-sorbed nanoparticles. For in-situ degradation, controlled mobility of the nanoparticles is of profound importance. The research findings here indicate that once delivered into the contaminant zone, most of the nanoparticles will be adsorbed on the soil matrices, forming a stationary reactive zone for degrading TCE.

3.3.

Effects of surfactants on TCE desorption

Fig. 6(a) and (b) show TCE desorption kinetic data for the potting soil in the presence of various surfactants at concentrations

equal to, or at w5 times the respective cmc values. In these tests, mass remaining is defined as the ratio of the total mass of TCE sorbed at time t to the initial mass of TCE in the soil. Control tests were performed to quantify any losses of TCE during the experiments and sample analysis. Mass balance results showed that the overall recovery of TCE was always within 90e105%. In all cases, the surfactants displayed various degrees of enhancement on TCE desorption, especially for the potting soil. For both soils, equilibrium state was achieved within 60 h. In the previous TCE desorption test with soilamended water (Fig. 2), only 14% of TCE was released from the potting soil after 100 h Fig. 6 shows that the presence of 1cmc of SDS, SDBS, Tween 80 and HDTMA increased the desorbed amount of TCE to 19%, 17%, 15% and 17%, respectively (Based on the t-tests, the differences between SDS and other surfactants are statistically significant with a p value of <0.003 at the 0.05 level of significance). When the surfactant concentration was raised to 5cmc, the amount of released TCE went up to 22%, 17%, 18% and 16% for SDS, SDBS, Tween 80 and HDTMA, respectively ( p value <0.01 at the 0.05 level of significance). Apparently, the anionic surfactant SDS outperformed the other surfactants in both cases, whereas no marked differences were evident for HTDMA, Tween 80 and SDBS. The presence of a surfactant can increase the solubility of TCE in the aqueous phase, thereby promoting the desorption process. On the other hand, a surfactant can be attached to soil, and thus, facilitate sorption of TCE via partitioning into the surfactant lamellae on the solid surface (Liu et al., 1992; MataSandoval et al., 2002; Yuan et al., 2007). At pH less than soil PZSE, the anionic surfactants (SDS and SDBS) could be sorbed through the electrical attraction with the positively charged þ groups such as NHþ 4 and OH2 of soil organic matter and soil Fe/ Mn oxides (Rodriguez-Cruz et al., 2005; Yang et al., 2006; Yuan et al., 2007). However, sorption of the anionic surfactants was unfavorable at the prevailing experimental pH of w8. Consequently, more of the surfactant molecules remain in the aqueous phase, resulting in greater TCE desorption. In contrast, the non-ionic surfactant Tween 80 and cationic surfactant HDTMA can be favorably adsorbed to the soil due to the electrostatic attraction (Deshpande et al., 1999; RodriguezCruz et al., 2005), which potentially creates a sink for retaining more TCE in the soils. Because of soil sorption, the actual concentration of each surfactant in the aqueous phase was expected to be below the initial 1cmc or 5cmc level. Consequently, no aqueous phase micelles were expected for the case of 1cmc. As a result, the increase of desorption was only modest (maximum 5% for SDS). Grasso et al. (2001) observed that desorption of polynuclear aromatic hydrocarbons was negligible when the concentration of a non-ionic surfactant, Alfonic 1412-7, was lower than its cmc level. When initial surfactant concentration was increased to w5cmc, the equilibrium surfactant concentration in the solution phase was measured to be well above the cmc value, and thus, stable micelles were expected to be formed, which in turn would solubilize more TCE (Mata-Sandoval et al., 2002; Yang et al., 2006; Yuan et al., 2007). However, the 5-folds increase in surfactant dosage only increased the TCE desorption by 4% (from 19% to 22%) for SDS and even less for the other surfactants (Fig. 6). This disproportionality lay in the fact that as the number of micelles in solution increased,

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 0 1 e2 4 1 4

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attachment of the surfactants on the soils also rose (Liu et al., 1992), which leveled off the solubility enhancement. Of the two anionic surfactants, SDS was more effective than SDBS for enhancing TCE desorption. This can be attributed to the differences in their molecular structures and the associated physical and chemical characteristics such as the hydrophilic lipophilic balance (HLB) value. HLB is a measure of the hydrophilicity or lipophilicity of a surfactant. A higher HLB value indicates a surfactant’s greater tendency to partition into the aqueous phase than in the oil phase. The HLB value was reported to be 40 for SDS and 11.7 for SDBS (Shen et al., 2007; Van Aken, 2003), suggesting greater hydrophilicity of SDS. The stronger hydration ability of SDS resulted in the greater TCE solubility. Li et al. (2007) reported that SDS could enhance solubility of biphenyl A more effectively than SDBS. Boving and Brusseau (2000) reported that SDS was more effective for solubilization and removal of TCE from porous media, than DOWFAX 8390, Hydroxypropyl-b-cyclo-dextrin and methyl-b-cyclo-dextrin. Fig. 6(c) shows TCE desorption rate from the Smith Farm soil in the presence of various surfactants at w1cmc (initial concentration). Unlike the potting soil, the presence of the surfactants did not show any significant enhancement of TCE desorption. In all cases, equilibrium was rapidly reached in only 20 min, where almost 80% of sorbed TCE was released, whereas the residual 20% TCE appears to be almost undesorbable. This observation indicates that surfactants are much less effective for enhancing TCE desorption from SOMdeficit soils. Sanchez-Camazano et al. (2000) reported that atrazine desorption with SDS was more effective for desorbing SOM-sorbed atrazine.

3.4. TCE degradation by ZVI nanoparticles in the presence of surfactants

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Fig. 6 e (a) Effect of representative surfactants at 13cmc on desorption of TCE from potting soil. (b) Effect of various surfactants at 53cmc on desorption of TCE from potting soil. (c) Effect of various surfactants at 13cmc on desorption of TCE from Smith Farm soil. Surfactant concentration is given as initial concentration. Initial concentration of TCE was 0.52 mg/g for potting soil and 0.45 mg/g for Smith Farm soil. Data plotted as mean of duplicates, and error bars represent standard deviation from mean of duplicates. Man: change CMC in the fig to 3cmc to be consistent e check all figures.

Surfactants are known to interact with ZVI particles, and thus, may affect the reactivity of the nanoparticles. Sayles et al. (1997) reported that the dechlorination rate of DDT, DDD and DDE by powdered ZVI was increased by w 1.8 times with a non-ionic surfactant Triton X-114 of 250 mg/L. Aqueous TCE degradation in the presence of various surfactants at a range of concentrations (from sub- to supracritical micelle concentrations) was examined in batch experiments with 0.1 g/L as Fe of CMC-stabilized ZVI nanoparticles. As shown in Fig. 7, surfactants can either enhance or inhibit TCE degradation, depending on type and concentration of a surfactant. To facilitate the rate comparison, the TCE degradation data were interpreted with the pseudo-first-order rate law. The reaction rate constants were obtained by nonlinear fitting of the rate model to the experimental kinetic data using the Sigma Plot 11 package. In all cases, the R2 (the coefficient of determination) was >0.980. In the absence of a surfactant, the observed rate constant was approximately 0.063 min1. Fig. 7a shows that the presence of 1cmc SDS increased the rate constant by a factor of w1.7 to 0.106 min1. However, when SDS concentration was increased further to 5 and 10cmc, the reaction rate constant was reduced to 0.087 min1 and 0.074 min1, respectively, but still higher than that when no surfactant was present. Interestingly, the presence of the other anionic surfactant (SDBS) at the

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concentration of 1cmc and 5cmc clearly inhibited the reductive dechlorination of TCE (Fig. 7b), whereas the nonionic Tween 80 at concentrations of 1cmc, 5cmc and 10cmc did not show any appreciable effect on the degradation rate (Fig. 7c). The degradation rate was also inhibited by the cationic surfactant HDTMA at 0.5cmc and 1cmc, where the rate constant was reduced to 0.031 min1 and 0.025 min1, respectively. Of the 4 surfactants, SDS appeared to be most promising to enhance degradation of soil-sorbed TCE for its ability to promote both desorption soil-sorbed TCE and accelerate aqueous phase dechlorination. The presence of CMC-coating on the surface of Pd/Fe nanoparticles rendered a negatively charged surface, as evidenced by the zeta potential of 160 mV. Consequently, the head groups of anionic surfactants were subjected to electrostatic repulsion from the particle surface. On the other hand, the amphiphilic nature of the surfactant tended to decrease the interfacial tension of the solution. As a result the tails of the surfactant molecules tended to be adsorbed to the particle surface, which favored the mass accumulation of TCE at the surface, and thus enhanced the TCE degradation rate. However, when the surfactant concentration was so high that more micelles are formed in the aqueous phase, the micelles would tend to hold TCE in the solution, resulting in reduced TCE accessibility to ZVI nanoparticles. Note that micelles may not be fully developed at lower surfactant dosages (e.g. 1cmc) due to sorption of the surfactants to the nanoparticles. However, TCE reduction was inhibited by the other anionic surfactant SDBS at 1cmc or higher. This can be attributed to the different molecular structures between SDS and SDBS. Although both surfactants carry the same sulfonic functional groups, SDBS contains a benzene ring at the head of the molecules, compared to the linear chain structures of SDS (Table 1). As a result, SDBS is more hydrophobic than SDS, which is consistent with the much lower HLB value of SDBS. Consequently, TCE associated with SDBS in the solution phase tends to be less available for the nanoparticles. In addition, SDBS as a TCE carrier is likely subject to greater mass transfer resistance due to the benzene ring, which hinders the delivery of TCE to the nanoparticle surface. Compared to SDS, Tween 80 has a 4.5 times greater M.W. and much bulkier molecular structure. As a result, the CMC molecules on the particle surface may exert greater barrier effect toward Tween 80 molecules, resulting in little enhancement in TCE delivery and degradation at concentrations less than 5cmc. When the surfactant dose was increased to 10cmc, more aqueous phase micelles were formed, leading to the elevated inhibition effect (Fig. 7c). The cationic surfactant displayed the most inhibitive effect on TCE degradation even at 1cmc dosage. The positively charged heads of HDTMA interact with the negative charges of CMC on the ZVI surface. This interaction can lead to two important consequences. First, the surfactant molecules were sorbed on the CMC layer in a tail-outward mode, which would not promote TCE delivery toward the nanoparticles. Second, neutralization of the surface negative charges tended to destabilize the ZVI nanoparticles, which reduced the reactive surface area. In the experiments, the zeta potential of the Fe-Pd nanoparticle with 0.5cmc, 1cmc and 5cmc of

HDTMA was measured to be 154 mV, 158 mV and 72 mV, respectively, compared to 160 mV when no HDTMA was present. The hydrodynamic diameter of the FeePd nanoparticles increased with the addition of HDTMA, from 214 nm with no surfactant to 228 nm with 0.5cmc, 318 nm with 1cmc and 585 nm with 5cmc of HDTMA. Earlier, Shin et al. (2008) studied TCE degradation using non-stabilized ZVI aggregates, and they observed that three cationic surfactants including CTAB were able to enhance TCE degradation at a concentration below the cmc level, while anionic and nonionic surfactants inhibited TCE dechlorination. The apparently contradicting results stem from the different surface properties of bare and CMC-coated ZVI particles, and reflect the important role of the CMC stabilizer. In the absence of a stabilizer, the bare ZVI particles were not only much larger (up to 0.15 mm), but with a nearly neutral surface (zeta potential ¼ w20 mV). When a cationic surfactant was added (e.g. cetylpyridinium chloride (CPC)), the surface charge was readily neutralized or reversed (e.g. zeta potential became wþ3 mV at 1cmc and wþ54 mV at 2cmc of CPC. This positive surface charge favored sorption of TCE to the nanoparticles due to its electrostatic interaction with the electronegative chloride group on TCE, and thus, boosting the reductive dechlorination process. It is worth noting that in the case of SDS, the maximum dechlorination rate was observed at the 1cmc. The similar tendency was also observed by Zhu et al. (2008), who studied dechlorination of 1, 2, 4-trichlorobenzene by bare FeePd nanoparticles. They claimed that the contact between the contaminant and FeePd was enhanced due to the augmented accumulation of the contaminant on the nanoparticles by the sorbed surfactants (Zhu et al., 2008). However, at elevated surfactant concentrations, more micelles would form in the solution and compete for TCE, resulting in a decreased rate of contaminant degradation. To enhance TCE mass transfer to ZVI particles, Zhan et al. (2009) associated CMC-stabilized FePd nanoparticles on colloidal carbon microspheres. While the hydrophobic carbon surface enhances TCE availability to the nanoparticles, the particles mobility is expected to be impeded in soil.

3.5.

Effect of SDS on degradation of soil-sorbed TCE

Effect of SDS was further tested on degradation of soil-sorbed TCE using CMC-stabilized FeePd nanoparticles. Fig. 8 shows total TCE remaining in the system as a function of reaction time. The experimental data displayed a two-stage rate profile, i.e., an initial rapid rate in the first 1 h followed by a slower dechlorination rate. This clearly staged rate profile agreed with the biphasic desorption profile (Fig. 2), revealing the profound limitation of desorption kinetics on the degradation rate of soil-sorbed TCE. For the potting soil (Fig. 8a), 44% of sorbed TCE was degraded within 40 h when no surfactant was added. When SDS was added at 1cmc (initial concentration), however, only 39% of TCE was degraded. When SDS dosage was raised to 5cmc, TCE degradation was increased to 49%. Evidently, SDS was able to enhance the rate and extent of TCE degradation in the soil only at a sufficiently high concentration (e.g. 5cmc), and the presence of 1cmc SDS actually inhibited the

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Fig. 7 e TCE degradation by CMC-stabilized ZVI nanoparticles in the aqueous phase in the presence of various initial concentrations of: (a). SDS, (b). SDBS, (c). Tween 80, and (d). HDTMA. Fe [ 0.1 g/L, Pd/Fe [ 0.1 wt%, Na CMC [ 0.2wt%, Initial TCE C0 [ 10 mg/L. Data plotted as mean of duplicates and error bars refer to standard deviation from the mean.

TCE degradation rate by 5%. For the Smith Farm soil (Fig. 8b), however, SDS appeared much more effective for enhancing TCE degradation. In this case, nearly 80% of soil-sorbed TCE was degraded within 8 h, and finally 83% of total TCE was removed after 47.5 h without surfactant addition. In contrast, when 1cmc SDS was added, about 90% of TCE was degraded in 28.5 h. The different surfactant effects on the two soils are attributed to their different SOM contents (Table 2). Given the fact that SDS enhanced both desorption of TCE from the potting soil (Fig. 6) and degradation of TCE (Fig. 7), it was somewhat counterintuitive for the observed inhibition of TCE degradation at 1cmc SDS (Fig. 8a). This inhibitive effect at the lower SDS dosage can be attributed to the inhibitive effect of the dissolved NOM, which resulted from the addition of SDS, as discussed above. In the presence of SDS, more organic matter in the soil phase was released into the solution phase. As measured, the TOC value for 1cmc SDS and 5cmc SDS solution was 422.5 mg/L and 2054.6 mg/L, respectively. After 1day mixing with potting soil at the same wateresoil ratio as TCE degradation test, TOC in the aqueous phase decreased to 262.2 mg/L for 1cmc SDS solution, mainly due to the SDS

sorption on soil. However, for 5cmc SDS solution, TOC in the aqueous phase increased to 2505.7 mg/L, which indicated the strong release of natural organic matter from soil matrix even though a part of SDS was adsorbed on the soil. For reference, only 43 mg/L of TOC was released from soil with DI water. Muroi et al. (2009) reported that the formation of humic substanceesurfactant complex at SDS below the cmc level and the solubilization of humic substances into the surfactant micelles at SDS above the cmc level would increase the concentration of humic acid in the aqueous phase. Otto et al. (2003) also pointed out that SDS and humic substances can interact strongly through strong hydrophobic interactions. Sorouradin et al. (1993) reported that SDS could effectively enhance desorption of humic substances from XAD resins. At 1cmc SDS, the increased release of NOM might compete with the desorbed TCE for the reactive sites on FeePd nanoparticles, resulting in the decline in the overall degradation rate and extent of TCE. It is also possible that at the lower SDS, more NOM was dissolved than TCE from the soil. When SDS dosage was further increased to 5cmc, SDS micelles would be expected, which would increase the partition of NOM into the hydrophobic interior of the SDS micelles.

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1) The degradation rate and extent of soil-sorbed TCE by CMCstabilized FeePd nanoparticles can be strongly limited by desorption kinetics, especially for soils of a higher organic matter content. 2) The presence of surfactants can impact TCE desorption and degradation with CMC-stabilized FeePd nanoparticles. However, this impact depends on the physiochemical properties of surfactants and soil characteristics (especially the SOM content). Anionic surfactant SDS was most effective for enhancing TCE desorption, and furthermore, was able to enhance TCE degradation in water at concentrations both at and above its critical micelle concentration. However cationic and non-ionic surfactants were found to inhibit the TCE degradation. For soil-sorbed TCE, the addition of SDS appears more beneficial for soil of lower SOM content. For SOM-rich soil, the higher dosages (e.g. 5cmc) of SDS are required to achieve enhanced degradation of TCE. 3) The presence of soil soluble organic matter can severely inhibit TCE degradation by FeePd nanoparticles. 4) Under batch mixing conditions, more than 71% of CMCstabilized FeePd nanoparticles became associated with the soil matrix, suggesting that after injected in the subsurface, the nanoparticles are very likely to become immobilized. Yet, even the immobilized FeePd nanoparticles remain reactive for dechlorination.

Time, hour

Fig. 8 e Effect of SDS on degradation rate and extent of TCE by CMC-stabilized ZVI nanoparticles in potting soil (a) and Smith Farm soil (b). The initial amount of TCE in potting soil and Smith Farm soil was 0.52 mg/g and 0.45 mg/g, respectively. Fe [ 0.3 g/L, Pd/Fe [ 0.1 wt%, Na CMC [ 0.24 wt%. Data plotted as mean of duplicates and error bars indicate standard deviation from the mean.

The findings in this work indicated that soil sorption of TCE and soluble soil organic matter in groundwater can impede the effectiveness of in-situ dechlorination by CMC-stabilized ZVI nanoparticles. Some surfactants such as SDS may aid in overcoming the mass transfer limitation. However, its effectiveness is dependent on factors such as soil type, SOM content and surfactant type and dosage.

Acknowledgments The outer micelle structure would mitigate the sorption of dissolved NOM onto the particles and thereby lessen the inhibitory effect of NOM on TCE degradation, and the overall degradation of TCE would be augmented. Sulfite and sulfide were known to poison the catalytic activity of Pd (Lowry and Reinhard, 2000). However, as indicated in Table 2, the total sulfur in the soil was very low for the soil and was not considered here. For the Smith From soil, no NOM was leached during the reaction. Consequently, the interference of NOM on the TCE degradation was negligible, allowing for better use of the positive effect of SDS on TCE degradation. In fact, the SDSenhanced degradation of TCE in the aqueous phase promotes further desorption of TCE, leading to the enhanced overall reaction rate. Therefore, the beneficiary effect of SDS is better utilized for soils lean of SOM than those rich in SOM.

The authors would like to gratefully acknowledge the partial financial support by US EPA through an STAR grant (GR832373) and an AAES Hatch Multistate grant.

references

Agency for Toxic substance and Disease Registry, 2007. ATSDR CEP site count report. http://www.atsdr.cdc.gov/cep/07cep. html (accessed 10.10). Ambonguilat, S., Gallard, H., Garron, A., Epron, F., Croue, J.P., 2006. Evaluation of the catalytic reduction of nitrate for the determination of dissolved organic nitrogen in natural waters. Water Res. 40 (4), 675e682. Arnold, W., Robets, A.L., 2000. Pathways and kinetics of chlorinated ethylene and chlorinated acetylene reaction with Fe(0) particles. Environ. Sci. Technol. 34 (9), 1794e1805.

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Characterisation and removal of recalcitrants in reverse osmosis concentrates from water reclamation plants Arseto Y. Bagastyo a, Jurg Keller a, Yvan Poussade b,c, Damien J. Batstone a,* a

Advanced Water Management Centre, The University of Queensland, St. Lucia, QLD 4072, Australia Veolia Water Australia, Level 1, 20 Wharf St, Brisbane, QLD, Australia c Water Secure, Level 2, 95 North Quay, QLD, Australia b

article info

abstract

Article history:

Water reclamation plants frequently utilise reverse osmosis (RO), generating a concen-

Received 1 September 2010

trated reject stream as a by-product. The concentrate stream contains salts, and dissolved

Received in revised form

organic compounds, which are recalcitrant to biological treatment, and may have an

28 January 2011

environmental impact due to colour and embedded nitrogen. In this study, we characterise

Accepted 31 January 2011

organic compounds in RO concentrates (ROC) and treated ROC (by coagulation, adsorption,

Available online 13 February 2011

and advanced oxidation) from two full-scale plants, assessing the diversity and treatability of colour and organic compounds containing nitrogen. One of the plants was from a coastal

Keywords:

catchment, while the other was inland. Stirred cell membrane fractionation was applied to

Reverse osmosis concentrates

fractionate the treated ROC, and untreated ROC along with chemical analysis (DOC, DON,

Organic fractionation

COD), colour, and fluorescence excitation-emission matrix (EEM) scans to characterise

Coagulation

changes within each fraction. In both streams, the largest fraction contained <1 kDa

Advanced UV/H2O2 oxidation

molecules which were small humic substances, fulvic acids and soluble microbial products (SMPs), as indicated by EEM. Under optimal treatment conditions, alum preferentially removed >10 kDa molecules, with 17e34% of organic compounds as COD. Iron coagulation affected a wider size range, with better removal of organics (41e49% as COD) at the same molar dosage. As with iron, adsorption reduced organics of a broader size range, including organic nitrogen (26e47%). Advanced oxidation (UV/H2O2) was superior for complete decolourisation and provided superior organics removal (50e55% as COD). ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Direct reclamation of secondary sewage effluent using combined microfiltration (MF) and reverse osmosis (RO) membranes is becoming a widespread solution for augmenting the water supply. However, while RO membranes can remove efficiently most of organic and inorganic material, these rejected compounds are concentrated in the RO concentrate (ROC) stream. ROC represents approximately 10e20% of the inflow, depending on the RO recovery ratio (Al-Rifai et al., 2007) and contains salts, dissolved nutrients

and recalcitrant chemicals that were not removed in biological wastewater treatment such as pharmaceuticals, pesticides, endocrine disruptors and other dissolved organics (Al-Rifai et al., 2007; Pehlivanoglu-Mantas and Sedlak, 2006; Westerhoff et al., 2009). Currently, the direct discharge of ROC to surface waters is a common practice for water treatment plants. However, disposal of ROC rich in nutrients and micropollutants may have a negative impact on the aquatic ecosystem, e.g. disturbing biogenesis of aquatic microorganisms (Bronk et al., 2006). There is also an increasing regulatory concern over the long-term impact of organically bound

* Corresponding author. Tel.: þ61 7 3346 9051; fax: þ61 7 3365 4726. E-mail address: [email protected] (D.J. Batstone). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.01.024

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nutrients. Management and treatment of ROC will likely be a key component for reducing the environmental impact of (waste)water reclamation. There is an overall lack of literature in characterisation of raw and treated ROC, including evaluation of effective technology for the removal of organics (especially organic nitrogen) contained in the concentrates (Lee and Lueptow, 2001). In general, treatment methods can be divided in two groups: high-energy oxidative methods (sonolysis, advanced oxidation, electrochemical oxidation), and those that consume chemicals (coagulation and adsorption). Dialynas et al. (2008) investigated different methods for the treatment of ROC produced during RO treatment of a membrane bioreactor municipal wastewater effluent. They reported up to 42% and 52% of dissolved organic carbon (DOC) removal during alum and ferric coagulation, respectively. Also, the activated carbon adsorption removed up to 91% of DOC for a dose of 5 g L1, while advanced oxidation processes (AOPs) including TiO2 photocatalysis, sonolysis and electrolytic oxidation achieved moderate mineralization efficiencies. Benner et al. (2008) investigated the removal of b-blockers in ROC obtained from RO treatment of wastewater, where ozone dosage of 5e10 mg L1 was able to partially remove propranolol and oxidise 70% of metoprolol in ROC which initial DOC was 46 mg L1. Electrochemical treatment is an attractive alternative for the ROC treatment due to its relatively high conductivity, but both operational and electrode costs currently limit its application (Dialynas et al., 2008; Perez et al., 2010). Although several treatment options of ROC stream have been evaluated, there is a lack of information on its characterisation and on the impact of the treatment on the specific ROC fractions. For example, dissolved organic nitrogen (DON) is one of the key components of ROC, and changes in recalcitrant nitrogenous organics should be studied during the treatment. Aluminium sulphate (alum) coagulation (Lee and Westerhoff, 2006) as well as UV/H2O2 advanced oxidation (Dwyer et al., 2008) showed substantial removal of DON from other wastewaters. Biological activated carbon and capacitive deionisation have been successfully applied for removal of DOC and total nitrogen (TN) from ROC (Ng et al., 2008). Through this process, 78% of DOC and 91% of TN were removed. The main research gaps addressed in this study are characterisation of the organic compounds that appear in both raw and treated ROC, and in particular, assessment of diversity and treatability of nitrogen containing organics. The diversity of organic material in ROC indicates that a multi-step characterisation method is required. This paper uses stirred cell membrane to fractionate treated and untreated ROC. The changes within each fraction was characterised by both physicochemical characterisation and fluorescence excitation-emission scans. This method has been increasingly developed as a monitoring tool used to identify organic compounds related to natural organic matter (NOM) (Henderson et al., 2009).

2.

Materials and methods

ROC samples were taken from two full-scale MF/RO plants in South East Queensland (Australia) at coastal area of Luggage

Point Water Reclamation Plant (WRP) and inland Bundamba advanced water treatment plant (AWTP). Being in a more sensitive environment, the ROC at Bundamba AWTP is actually treated for inorganic nutrients removal prior to being discharged into the Brisbane River. However, samples for this study were taken before any ROC treatment. These samples were characterised by stirred cell fractionation, excitationemission matrix (EEM) fluorescence spectroscopy, and analytical methods described below, and treated by coagulation (alum and iron), ion exchange, and AOP. Treated samples were then characterised by chemical analysis and EEM within each fraction.

2.1.

RO units and ROC sampling

The Luggage Point (LP) WRP produces 8.8 ML day1 of recycled water from a secondary effluent of biological nutrient removal (BNR) treatment using combined hollow fibre MF and RO spiral wound membrane modules. The RO recovery ratio is approximately 70% when operating at full capacity. Bundamba (BD) AWTP has two parallel MF/RO treatment trains which can deliver up to 66 ML day1 of recycled water using a mixture of BNR effluents from four wastewater treatment plants. The treatment train consists of pre-treatment (by ferric chloride coagulation and addition of monochloramine), main membrane processes (MF and 3-stage RO with 85% recovery), advanced UV/H2O2 oxidation, stabilisation and final disinfection. The conductivity in LP ROC was higher than in BD ROC. The higher conductivity of LP ROC is due to the seawater intrusion in the coastal catchment. ROC was taken in composite samples (three samples of continuous sampling at LP) and a grab sample from BD prior to each experiment. Immediately after sampling, fresh samples were filtered using a 0.20 mm sterile Millipore membrane filter unit, 45 mm diameter STERITOP-GP. For storage, the filtered samples were kept in a sterile container in the dark room at 4  C to minimise the microbial activity.

2.2.

Analysis of raw and treated ROC

2.2.1.

Analytical methods

Prior to analysis, all treated ROC samples were filtered using a 0.22 mm Millipore syringe-driven membrane unit to remove residual particulate solids. COD (mg L1) was analysed by a COD kit (Merck 1.14540.0001) for the range 10e150 mg L1, based on standard spectrophotometric method (APHA, 1998). DON (mgN L1) was calculated by subtracting ammonia-N value from total kjeldahl nitrogen (TKN). TKN was measured using Lachat QuickChem Method 10-107-06-2-D, while ammonia-N was determined using a Lachat Quickchem8000 flow injection analyser (FIA) applying Omnion FIA software according to Method 31-107-06-1-A (Dwyer et al., 2008). DOC (mgC L1) was calculated as the difference between total carbon (TC) and inorganic carbon (dissolved) that were determined on a TC analyser (Tekmar Dohrmann DC-190) by the standard high-temperature method (APHA, 1998; method 5310B). Colour (mgPtCo L1) was measured using a Cary50 spectrophotometer. A volume of 3 mL sample in an acryl cell

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 1 5 e2 4 2 7

(1 cm path length) was used to determine the absorbance at 475 nm wavelength and measure colour as mg platinumecobalt (PteCo) L1. A calibration PteCo standard was applied to measure the colour intensity. Ultraviolet absorbance (UVA) was a measure of absorbance at 254 nm, measured in a 1 cm path length quartz cell using a Cary50 spectrophotometer. Specific UVA (SUVA) at 254 nm was calculated by dividing UVA 254 nm with DOC value. EEM was determined using a fluorescence spectrophotometer (Hitachi F-2000), equipped with a 150 W xenon lamp. The filtered sample (3 mL) was scanned in a 1 cm quartz cell at excitation and emission ranges from 220 nm to 450 nm and from 220 nm to 600 nm, respectively. The slit was 10 nm for both excitation and emission. The measurement was performed at 25  C to avoid variation between samples. Both Raman and Rayleigh scatters were minimised by subtracting a pure water blank which was scanned prior to analysis of the samples. The samples were diluted to DOC < 1 mg L1 to avoid errors associated with inner filter effect (IFE) (Baker et al., 2004). A sample dilution up to 60 times was applied, depending on the DOC value (lower dilution for treated ROC). EEM were plotted to identify compounds based on specific peaks. Identification of organic compounds was based on peak characterised by Chen et al. (2003).

2.2.2.

Stirred cell fractionation

A single pressurised stirred cell apparatus (Millipore Amicon 8400) equipped with ultrafiltration (UF) membrane sizes of 10 kDa, 5 kDa, 3 kDa, 1 kDa, and 0.5 kDa (63.5 mm regenerated cellulose) was used. Membranes were pre-washed with MilliQ water. The method used was as for Dwyer et al. (2008). An initial sample volume of 300 mL was fractionated into several sizes sequentially from the highest size to the lowest, obtaining 50 mL concentrated above each membrane for further analytical measurement. A mass balance was used to calculate effective overall concentration within <0.5 kDa, 0.5e1 kDa, 1e3 kDa, 3e5 kDa, 5e10 kDa and >10 kDa according to in Dwyer et al. (2008).

2.3.

ROC treatment methods

2.3.1. Alum and iron coagulation, and MIEX adsorption testing Coagulation, flocculation and adsorption tests were conducted in a Phipps and Bird flocculator test apparatus (PB-700), equipped with six paddle-stirred 1 L glass beakers.

2.3.1.1. Alum coagulation. A 100 g L1 alum stock solution was made up of Al2(SO4)3.18H2O pure solid (Merck) dissolved in Milli-Q water. Six alum doses were tested in LP ROC treatment: 0.75, 1.5, 3, 4.5, 6 and 7.5 mM as Al, whereas in BD ROC treatment alum doses were: 0.15, 0.375, 0.75, 1.5 and 4.5 mM as Al. 1 L of ROC sample was placed in each beaker. Rapid mixing was done at 210 revolutions per min (rpm). Alum was then added using a pipette tip submerged, near the stirring paddle for effective mixing (Lee and Westerhoff, 2006). Rapid mixing was applied for 2 min and pH was simultaneously adjusted by dosing either NaOH or H2SO4 (to pH 3, 4, 5, and 6 for LP samples, and pH 4, 5, and 6 for BD samples) and using an Ionode pH sensor and a MiniChem receiver for pH monitoring.

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Slow mixing was applied at 40 rpm for 15 min before 45e60 min unmixed settling. After this settling period, supernatant was sampled for further analyses. Temperature was monitored continuously, and found to be 25  1  C.

2.3.1.2. Iron coagulation. A 97% FeCl3.6H2O reagent (Sigma Aldrich) was dissolved in Milli-Q water to a 10 g L1 iron stock solution. Iron coagulation experiments were performed the same way as for alum coagulation except with dose rates 0.74, 1.48, 2.22 and 2.96 mM as Fe3þ for LP samples (pH variations of pH 4, 5, 6, and 7), and lower doses of 0.37, 0.74, 1.48 and 2.22 mM as Fe3þ for BD samples (pH 4 to 6). 2.3.1.3. MIEX adsorption. The MIEX resin was provided in a slurry form consisting of 60% (v/v) resin (CAS number 398140-29-9) and less than 40% (v/v) of other non-hazardous materials (Orica Advanced Water Technologies Pty Ltd). The solids have a specific gravity of 1.2 and are insoluble in water. Adsorption was tested in the same stirred cell unit as for coagulation. A single mixing speed was applied at 150 rpm, evaluating resin doses of 5, 10, 15, and 20 mL L1 (mLslurry in 1 L sample). Temperature was maintained at around room temperature (25  C) without any pH adjustment. The resin was dosed using a pipette with a wide hollow space at the bottom of the pipette tip. The 25 mL samples were taken at 5, 10, 20, 30, and 40 min of mixing time. Thus the final volume after 40 min was 875 mL. Treated ROC was sampled from the side of the glass beaker, and the remaining resin was left to settle down during 5 min after the last sampling.

2.3.2.

Advanced oxidation (UV/H2O2)

The advanced oxidation reactor consisted of a U-shaped tube around a 253.7 nm low-pressure mercury lamp (UV-C, 60 W electrical, 50 Hz, 240 V). Volume in the reactor was 440 mL, out of which 380 mL was continuously illuminated. Fluid was continuously recirculated through the tubes from a reservoir using a peristaltic pump. The total volume of liquid in the reactor was 1 L (including tubing). The transparent Teflon type tubes (27 mm diameter, 77 cm length with 66.4 cm exposed length) were made of the activated fluoropolymer (AFP-840) and are chemically inert. Oxidation was conducted in batch, with the reservoir continuously stirred using a magnetic stirrer at 250 rpm, with a 1.6 L min1 recirculation flow. The photo-oxidation test was performed as follows. After adding 1 L of sample to the photoreactor system, 30% H2O2 (Univar) was added to final concentrations of 200, 400, 600, and 800 mg L1. The reactor was then operated for 210 min. The 25 mL samples were taken at 0, 15, 30, 45, 60, 75, 90, 120, 150, 180, and 210 min using a syringe. A specific amount of bovine catalyst (diluted C-100, SigmaeAldrich) was added to the sample to decompose the H2O2 excess (Dwyer et al., 2008). The pH was adjusted to pH 7  0.1 at each sampling time by adding 2 M H2SO4 or 1 M NaOH manually. Temperature was actively maintained at 25  1  C throughout each experiment by a submerged cooling coil in the reservoir. Photonic flux was determined using iodideeiodate actinometry (Goldstein and Rabani, 2008; Rahn et al., 2003), resulting in a value of 2.74  106 E (L s)1. This value was similar to a previous actinometry experiment applying ferrioxalate, i.e. 2.4  106 E(L s)1 (Dwyer et al., 2008), and

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comparable to the value achieved in an earlier study, i.e. 4.7  106 E (L s)1 (Dialynas et al., 2008). This confirms that the effective energy released by the UV light to the solution was around 1.29 W. The UV dose is expressed as kWh m3, representing applied power intensity per litre treated sample, i.e. 0, 0.3, 0.7, 1.0, 1.4, 1.8, 2.3, 3.1, 4.0, 4.9, and 5.9 kWh m3.

3.

Results

3.1.

ROC characterisation

3.1.1.

Basic characterisation

The composition of ROC sampled at LP and BD WRPs is summarised in Table 1. The concentrates were very similar in terms of constituents except for a higher conductivity and colour in LP ROC, and higher ammonia in BD ROC.

3.1.2.

Results of fractionation

Approximately half of the organics contributing to the measured COD was a fraction with molecular weights (MWs) < 1 kDa in both untreated ROC sources (Table 2). The remaining organics were distributed evenly amongst other size fractions (i.e. >1 kDa). Likewise, DON was represented predominantly by the fraction of <1 kDa in both sources, while DOC was relatively contributed at the equal percentage by the whole MW fractions. In particular, DON in LP ROC was dominated by the lowest molecular size range below 0.5 kDa with a shift to larger fractions (0.5e1 kDa) in BD ROC. Colour was mostly distributed in high to medium size range of organics (>0.5 kDa), indicating that the smaller fractions contribute less to colour. As indicated by EEM (Fig. 1), the organics were mainly identified as humic acid and fulvic acid like compounds. These major constituents were found in all the fractions of both ROC samples. Tryptophan-like compound and soluble microbial products (SMPs) were identified as secondary/ tertiary constituents, also found from the highest to the lowest fractions.

3.2.

Comparison of ROC treatment methods

The optimal conditions and results summary for each treatment method is reported in Table 3. Both alum and iron dose

Table 1 e Characterisation of two ROC sources. Source pH DOC Colour TKN Ammonia DON COD Conductivity

Unit mg L1 mg L1 PteCo mg L1 mg L1 mg L1 mg O2 L1 mS cm1

LP ROCa 7.8  42  228  6.2  0.3  5.3  147  12.76 

a Average of analyses (3 sampling times). b Average of analyses of 1 bulk sample.

0.2 4 50 0.4 0.1 0.6 5 0.01

Table 2 e Size fractions of both ROC sources. Fraction

LP ROC (%)

BD ROC (%)

DOC COD Colour DON DOC COD Colour DON >10 kDa 5e10 kDa 3e5 kDa 1e3 kDa 0.5e1 kDa <0.5 kDa

14 12 14 13 21 25

6 8 11 11 28 35

21 20 22 13 19 5

6 6 10 10 15 53

16 12 20 14 25 14

15 11 18 8 17 31

32 19 22 10 12 5

11 15 16 17 29 12

had an optimal dose of 1.5 mM with diminishing impact at higher doses on both ROC sources. For example, for the LP ROC the removal of COD and DON was only improved by 5e20% with 2e5 times higher doses than the optimum (Figs. 2 and 3). Conversely, lower alum dosage was effective at an appropriate pH (i.e. pH 5) for both concentrates. Iron appeared to be more effective than alum in the BD ROC particularly on decolourisation and COD removal, while in the LP ROC both coagulants gave similar impact with slightly higher DOC removal by alum dosing and COD removal by iron dosing. For adsorption, increased resin dosage gave marginal improvements in DOC removal for both ROC samples. On the contrary, COD removal was considerably enhanced when a higher dose was applied for the BD ROC (Fig. 2). A resin dose of 10 mL L1 was observed to be the optimal dose for the LP ROC, while 15 mL L1 was the optimal dose for the BD ROC. Similar decolourisation efficiencies (close to 80%) were achieved, while DON removal (47%) was higher for the LP ROC. A resin contact time of 20 min overall appeared to be the optimal point in both ROC sources, with shorter contact times (i.e. 5 min) needed for the DON removal in the BD ROC as shown in Fig. 3. The UV/H2O2 process was effective at 3.1 kWh m3 with an optimal H2O2 dose of 400 mg L1 (Figs. 2 and 3). The overall treatment performance was similar for both ROCs. Unlike in the case of LP ROC, higher H2O2 gave higher kinetic rate for the BD ROC but with similar results between 400 and 600 mg L1 of H2O2. The improvement in removal at H2O2 dosage higher than 400 mg L1 was less than 8% for COD and only 2% for DOC at the same power supplied to the sample of 3.1 kWh m3. Small amounts of DON were readily oxidised after applying 0.33 kWh m3, with a continuous but slower further decrease in DON after that point. Up to 16% and 20% of DON removal was achieved for the LP ROC and BD ROC, respectively. When higher power was applied to the sample, DON removal rate for the BD ROC was higher than for the LP ROC.

BD ROCb 8.1  62  101  13  5.6  7.8  168  7.30 

0.1 5 1 1 0.1 0.3 12 0.01

3.3.

Treatment impact on each size fraction

The impact of treatment within each size fraction is summarised in Table 4. For ROC treated by alum, size of most of the remaining organics and colour occurrence was below 3 kDa. Colour removal for the ROC fraction >1 kDa was >65% (Fig. 4a), indicating that the treatment is effective for removing high MW colour-causing compounds. There was a residual non-nitrogenous humic acid group, in the 5e10 kDa fraction (Fig. 4b), contributing to a peak in humic acid region

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Fig. 1 e EEM characterisation on (a) <0.5 kDa fraction of untreated LP ROC (b) >10 kDa fraction of untreated BD ROC.

(based on EEM). Iron appeared to better target large to medium size compounds such as humics, followed by smaller size compounds (Table 4). Colour was effectively removed in all size ranges (Fig. 4c and d). DOC and COD were noticeably removed in all size ranges, but better for large molecules. DON was reduced by 20% or more for the fraction of larger MW organics. For both alum and iron coagulation, the highest residual component after the treatment comprised of 0.5e1 kDa molecules. Adsorption by MIEX effectively removed colour in all size ranges, with more than 50% efficiency (Table 4). Furthermore, the COD and DOC removed mostly comprised larger MW organics (Fig. 4e), while up to 70% of DON removal was achieved for all ROC size fractions. For the BD ROC (Fig. 4f), DOC and DON removal were higher than 20% for the fraction >1 kDa, with high residual COD representing the compounds <5 kDa. In general, however, the most effective removal of organics and colour occurred in the larger and medium molecular size fraction (>1 kDa) (Fig. 4e and f). The remaining DON consisted mainly of organics with MW < 3 kDa (Fig. 4g) after the longest UV/H2O2 oxidation time, whereas the residual DOC represented mostly molecules

smaller than 1 kDa (Fig. 4h). The main part of the initial DON in the BD ROC was comprised of molecules with MW larger than 3 kDa. Less than 10% removal observed for this fraction (i.e. <3 kDa) suggests that the recalcitrant compounds were mainly in the smaller MW range.

3.4.

Removal of specific compounds according to EEM

The impact of each of the investigated treatments on the size fractions of ROCs is illustrated in Fig. 5, according to EEM. Alum mostly targets the removal of larger humic compounds (Fig. 5a). Other compounds in this size fraction were also removed by alum. Likewise, iron is effective in removing larger molecules. The SMPs, humic and fulvic acids were largely removed in the 5e10 kDa size fraction as shown in Fig. 5b. Adsorption by MIEX resin removes compounds from all size fractions. However, at lower doses than the optimum, larger compounds were progressively less targeted (Fig. 5c). This is in contrast to oxidation, where lower doses resulted in higher amounts in the lowest size range, probably partly due to generation of by-products (Fig. 5d).

Table 3 e Optimal conditions of treatment methods. Source/Treatment

LP Alum

BD Alum

LP Iron

BD Iron

LP MIEX

BD MIEX 1

LP AOP 1

BD AOP

1

Optimal dose

1.5 mM (Al) 1.5 mM (Al) 1.48 mM (Fe) 1.48 mM (Fe) 10 mLslurry L

15 mLslurry L

Optimal contact time (min) Optimal pH DOC removal (%) DON removal (%) COD removal (%) Colour removal (%)

n/a

n/a

n/a

n/a

20

20

400 mg L H2O2 400 mg L1 H2O2 3.1 kWh m3 UV 3.1 kWh m3 UV 120 120

pH 5 52 30 34 73

pH 5 25 22 17 45

pH 5 34 28 49 79

pH 5 38 35 41 74

n/a 24 47 28 78

n/a 43 26 35 77

n/a 38 32 55 98

n/a 40 27 50 98

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Fig. 2 e Effect of treatment methods on COD removal in LP ROC (left) and BD ROC (right). The error bars represent ninety-five percent confidence interval of three replicate samples.

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Fig. 3 e Effect of treatment methods on DON removal in LP ROC (left) and BD ROC (right). The error bars represent ninety-five percent confidence interval of three replicate samples.

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60% (>5 kDa) and >20% (>0.5 kDa)  80% (>3 kDa) and 60% (<3 kDa)

100% (all sizes)

80% or higher (>3 kDa), <15% (<3 kDa) 100% (all sizes)

80% or higher (>0.5 kDa)

400 mg L1 H2O2; 6 kWh m3 70% or higher (>1 kDa), <30% (<1 kDa) 30% (>10 kDa) and 60% or higher (<10 kDa) 60% or higher (all sizes) 10 mL L1 MIEX 40 min 40% or higher (>0.5 kDa) 50% (>5 kDa)

400 mg L1 H2O2; 6 kWh m3 80% or higher (>1 kDa)

LP AOP BD MIEX

BD AOP

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4.

Discussion

4.1.

ROC characterisation

In general, the main characteristics of the two streams were similar, apart from specific aspects related to the catchment. There was an additional non-coloured organic compound in BD ROC, possibly due to its inland status. The location may affect the feed water characterisation of the BD AWTP as the feed contains less salinity and more complex NOM from the vegetation life cycle. The BD plant also had a higher recovery ratio which leads to higher concentration of rejected organics in the ROC stream. The higher level of ammonia measured in the BD ROC is consistent with chloramination which is applied for bio-fouling control prior to RO treatment in the BD AWTP.

 80% (>0.5 kDa) and <60% (<0.5 kDa)

70% (>5 kDa), 40% (<1e5 kDa) and 10% (<0.5 kDa) 50% or higher (all sizes)

 80% (>0.5 kDa) and <60% (<0.5 kDa) 40% or higher (>3 kDa)

50% or higher (>3 kDa) and <20% (<3 kDa) 60% or higher (>0.5 kDa) DON removal

Colour removal

over 40% (>3 kDa)

DOC removal

COD removal

40% (all sizes); slight lower (3e5 kDa) 60% or higher (>1 kDa) and <40% (<1 kDa) 20% (all sizes) >70% (>10 kDa) and <40% (<10 kDa) >50% (>10 kDa) and 20% (<10 kDa) 40% or higher (>5 kDa)

>40% (all sizes); except 5% (0.5e1 kDa) 5% (0.5e1 kDa), 50% (other sizes) 40% or higher (>1 kDa), and 10% (<1 kDa) over 80% (>1 kDa), lowest (<0.5e1 kDa)

10 mL L1 MIEX 20 min 50% (>5 kDa), 20% (<5 kDa) 1.5 mM at pH 5 1.5 mM at pH 5 1.5 mM at pH 5

4.5 mM Al at pH 5 40% (>10 kDa) Fractionation

BD Alum LP Alum Source/ Treatment

Table 4 e Impact of treatment on each size fraction.

LP Iron

BD Iron

LP MIEX

4.2.

Optimal treatment conditions

As coagulation is pH dependent, the pH needs to be maintained at a specific optimum value allowing the coagulant to work effectively. Coagulant addition caused a large drop of pH from its original value, e.g. alum addition of 1.5 mM caused a pH of ROC dropped from pH 7.8 to around pH 4. Under the correct conditions of dosage and pH the coagulation process is optimal in terms of obtaining good flocs and effective organics removal (Jarvis et al., 2005). Coagulation is most effective when monomers formation like Al (OH)3 and Fe(OH)3 are maximised at optimal pH (Dominguez et al., 2007; Duan and Gregory, 2003). Such monomers are more effective to settle than other formations. Coagulation for both concentrates provided substantial, but not high removal of colour and organics (as COD, DOC, and DON), consistent with previous study indicating pH 5e6 for alum and more acidic pH 4.5e5.5 for iron as optimum point (Sharp et al., 2006b). In general, higher resin dose allows higher removal of colour and organic compounds. However, doses above 15 mL L1 did not significantly enhance the treatment performance (according to t-tests within the error bars). Increased resin dose shows a higher impact than prolonging the contact time. After 10e20 min of contact time organic compounds reached adsorption equilibrium, lowering its further removal. This optimum contact time was similar with previous study (Humbert et al., 2005). They reported that the process of DOC removal by MIEX reached maximum equilibrium at 10e15 min of contact time. Optimisation of advanced oxidation utilising UV/H2O2 includes both chemical dose and power consumption. The oxidant consumed by UV light to produce free radicals has an optimal point which yields high kinetics rate of oxidation. Further addition of oxidant above that point would slow down the oxidation kinetics as less reactive radicals would be formed. Knowing depletion of H2O2 during the experiment is essential to evaluate the efficiency of the process (Wang et al., 2000), including the need of an efficient power (energy) consumption.

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Fig. 4 e Fractions of treated organics and colour in LP ROC (left) and BD ROC (right); (a) 4.5 mM alum, (b) 1.5 mM alum, (c and d) 1.48 mM iron, (e) 10 mL LL1 MIEX at 20 min contact time, (f) 10 mL LL1 MIEX at 40 min contact time, (g and h) 40 mg LL1 H2O2 at 5.9 kWh mL3. The error bars represent ninety-five percent confidence interval of three replicate samples.

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Fig. 5 e EEM characterisation on the fractions of treated ROC; (a) >10 kDa treated LP ROC by 4.5 mM alum at pH 5, (b) 5e10 kDa treated BD ROC by 10 mL LL1 MIEX at 40 min contact time, (c) 5e10 kDa treated LP ROC by 1.48 mM iron at pH 5, and (d) <0.5 kDa treated BD ROC by 400 mg LL1 H2O2 at 5.9 kWh mL3. See text for identification and discussion of key features.

4.3. Comparison of treatment methods for removal of organics For both ROC samples, UV/H2O2 oxidation proved to be the most effective treatment, while alum coagulation being the least effective. The UV/H2O2 oxidation was able to largely remove organics and colour, including aromatic fraction of NOM as measured by SUVA at 254 nm wavelength (Fig. 6). COD removal indicates that most of organics were converted into oxidation products. However, complete mineralisation has not been achieved as indicated by a low DOC removal observed in the UV/H2O2 process. This low removal can also be caused by higher inorganic carbon and salinity in the ROC, which lowers the oxidation rate. The potential formation of the intermediate products due to the presence of inorganic carbon and salinity reduces the

oxidation rate of organics to the final products. Carbonate and bicarbonate ions are hydroxyl radical scavengers, affecting the reaction rate of hydroxyl radicals with organic compounds (Wang et al., 2000). A slightly lower COD removal was found for the BD ROC which potentially contains more recalcitrant compounds to oxidation, including the non-coloured DON. Alum performed poorly for COD removal as Al(OH)3 was too slow to initiate aggregation and form flocs from more stable and recalcitrant compounds. These recalcitrant compounds may include hydrophilic compounds as mentioned in Lee and Westerhoff (2006). However, it seemed that alum is effective for the removal of large MW colourcausing organics (Sharp et al., 2006a). Since LP ROC contains largely high colour compounds, alum was capable of removing large MW organics, indicated by high DOC removal.

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Fig. 6 e Effect of UV/H2O2 on Colour and SUVA254nm in LP ROC (left) and BD ROC (right).

According to the EEM, these large molecular size compounds are mostly contributed by hydrophobic acids. In general, iron is superior to alum because of larger floc sizes, allowing for a more rapid coagulation to occur. Dialynas et al. (2008) mentioned 0.5 mM iron and 2 mM alum as the optimum dosage to remove 52% and 42% of DOC in ROC, respectively, at the initial DOC of 12 mg L1. Iron was able to remove more non-coloured DON compounds, which could be NOM including sugars, proteins, amino acids, organic acids, and other recalcitrant organics which could not be removed using alum. This coagulant effectively performed two removal mechanisms: (i) charge neutralisation by binding metal-anion species to form metalehumic complex precipitates, and (ii) adsorption of organics on amorphous metal hydroxides. In the latter case iron flocs are a good absorbent for hydrophobic compounds, and particularly for humic compounds (Duan and Gregory, 2003). The efficiency of DOC and COD removal of MIEX is comparable to the one observed for iron coagulation of ROC. The remaining organics were likely small, neutral or positively charged compounds (Mergen et al., 2008). More adsorption of colour-causing compounds could be characterised as high MW hydrophobic substances in the LP ROC. These hydrophobic compounds likely contributed to colour, indicating the coloured compounds have a strong affinity for MIEX. This supports the findings that the initial higher removal of colour was not accompanied by the DOC removal. Integrity of the resin as compared to coagulant flocs allows higher mixing, which can increase the adsorption of organic matter (Slunjski

et al., 2002). Magnetised components (mostly polymerised iron oxide) within the resin structure are intended to allow agglomeration and gravitational sedimentation, separating the formed flocs effectively from treated water (Drikas et al., 2003). The poor DOC removal in LP ROC by MIEX was likely due to quick saturation caused by adsorption of large coloured hydrophobic acids (Mergen et al., 2008), and might be hampered by the higher total dissolved solids (Johnson and Singer, 2004; Shorrock and Drage, 2006). However, MIEX is reusable, and works effectively in a wider pH range, which represents an advantage over the conventional coagulants. AOP is the most expensive treatment method due to the high use of electricity and higher chemical cost of oxidant (estimated $AU 0.47 m3 ROC). As expected, the lowest estimated operating cost is coagulation ($AU 0.12e0.13 m3 ROC including sludge management, details in Supplementary material). Since MIEX can be applied in multiple loading treatments, this method allows more economic saving of resin and regeneration cost. This may reduces the regeneration cost up to 10e15 times operation in which its total cost is estimated to be $AU 0.15e0.20 m3 ROC.

4.4.

Effect on molecular weight distribution

Both MIEX and AOP methods were capable of removing compounds belonging to a broader range of size fractions and greater amount of organic fractions as previously reported (Boyer and Singer, 2006; Drikas et al., 2003; Dwyer and Lant, 2008) compared to coagulation. This suggests that AOP

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performed a non-selective oxidation of organics due to its production of strong oxidants (i.e. hydroxyl radicals), while MIEX has its active sites within the porous resin matrix to bond molecules more selectively, compared to AOP. Both methods also effectively removed colour intensity which was mainly contributed by large MW compounds. Higher size organics (i.e. >3 kDa) that contributed to the initial DON were largely eliminated in the MIEX and AOP treatments for both ROC samples. Smaller MW compounds of the initial DON were more difficult to remove. DON was poorly removed in MIEX and AOP because it consists mainly in smaller MW organics, which could also be neutral or positively charged compounds and minimally reactive to AOP. Moreover, MIEX had a low affinity to form agglomerates with smaller MW organic as there were more competitive large MW organics in the ROC. In the UV/H2O2 oxidation process, the residual organics were mainly in the smaller MW ranges and were probably the breakdown by-product of larger molecules, creating more biodegradable compounds (Dwyer and Lant, 2008; Kerc et al., 2004). This process appeared simultaneously with the inorganics formation and some gas production due to the oxidation to bicarbonate and nitrogen gas as mentioned in Dwyer et al. (2008). Complete decolourisation appeared for all molecular size fractions except for the organics with MW < 0.5 kDa in the BD ROC (which around 90% removal of colour). This also confirms that the smaller MW organic compounds are the most difficult to treat. Most humic acids and SMPs were well removed by the AOP, as can be seen from the EEM contour plots (Fig. 5). Alum had the highest efficiency in removing the organic fraction of the LP ROC with MW > 10 kDa. This is consistent with literature observation (Shon et al., 2006), which found that large molecules are treated most efficiently using coagulant methods. Alum was ineffective for the BD ROC with larger MW organics (i.e. <3 kDa), thus a more effective coagulant like iron should be used for removing the larger MW fraction. Alum and iron had DON removal of MW compounds >3 kDa in both ROC samples. However, iron coagulation was capable of removing a broader size range of organic compounds than the alum coagulation. The remaining organics were mainly in the lower MW range (<1 kDa). This indicates that iron initially removed larger MW compounds due to the hydrophobic domination on organic-metal precipitation (Sharp et al., 2006a).

5.

Conclusions

The two ROC samples were of similar characteristics with more non-coloured organic compound in BD ROC due to its inland catchment and the higher recovery ratio of the RO membranes. Most of the ROC organics were situated in the low MW range (<1 kDa), which were majorly identified by EEM scans as humic and fulvic acids like compounds including some SMPs. Optimum coagulations with alum and iron (i.e. 1.5 mM Al at pH 5 and 1.48 mM Fe at pH 5) were successful in removing large MW compounds of MW>10 kDa, with better performance and over a wider size range observed for the iron coagulation than the alum coagulation. Adsorption was also efficient in decreasing the initial DOC of both ROC samples

and a dose of 10e15 mL L1 of MIEX resin and contact time of 20 min were optimum for the removal of organics of all molecular sizes, including organic nitrogen compounds. Advanced oxidation (400 mg L1 H2O2 dose at 3.1 kWh m3 UV power) was the most efficient treatment, achieving a complete decolourisation and organics breakdown to smaller size NOM fraction (mainly <0.5 kDa), possibly leaving more biodegradable, residual organics. In general, the study confirms that lower MW fraction of the NOM, mainly organic nitrogen compounds, is the most difficult one to eliminate in all of the investigated treatments. A biological treatment can be potentially applied as a post treatment for the complete organic degradation.

Acknowledgements Arseto Bagastyo is a staff of Institut Teknologi Sepuluh Nopember in Surabaya, Indonesia who undertakes research study at The University of Queensland as an AusAID scholarship holder. This project was also supported by chair in Water Recycling funding. The authors would also like to thank Dr. Wolfgang Gernjak and Dr. Maria Jose Farre for their contributions to the study.

Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.01.024.

references

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Dominguez, J.R., Gonzalez, T., Garcia, H.M., Sanchez-Lavado, F., Heredia, J.B.D., 2007. Aluminium sulfate as coagulant for highly polluted cork processing wastewaters: removal of organic matter. Journal of Hazadous Materials 148, 15e21. Drikas, M., Chow, C.W.K., Cook, D., 2003. The impact of recalcitrant organic character on disinfection stability, trihalomethane formation and bacterial regrowth: an evaluation of magnetic ion exchange resin (MIEX) and alum coagulation. Journal of Water Supply: Research and Technology-AQUA 52 (7), 475e487. Duan, J., Gregory, J., 2003. Coagulation by hydrolysing metal salts. Advances in Colloid and Interface Science 100 (102), 475e502. Dwyer, J., Kavanagh, L., Lant, P., 2008. The degradation of dissolved organic nitrogen associated with melanoidin using a UV/H2O2 AOP. Chemosphere 71, 1745e1753. Dwyer, J., Lant, P., 2008. Biodegradability of DOC and DON for UV/H2O2 pre-treated melanoidin based wastewater. Biochemical Engineering Journal 42 (1), 47e54. Goldstein, S., Rabani, J., 2008. The ferrioxalate and iodideeiodate actinometers in the UV region. Journal of Photochemistry and Photobiology A: Chemistry 193, 50e55. Henderson, R.K., Baker, A., Murphy, K.R., Hambly, A., Stuetz, R.M., Khan, S.J., 2009. Fluorescence as a potential monitoring tool for recycled water systems: a review. Water Research 43 (4), 863e881. Humbert, H., Gallard, H., Suty, H., Croue, J.-P., 2005. Performance of selected anion exchange resins for the treatment of a high DOC content surface water. Water Research 39, 1699e1708. Jarvis, P., Jefferson, B., Parsons, S.A., 2005. How the natural organic matter to coagulant ratio impacts on floc structural properties. Environmental Science and Technology 39, 8919e8924. Johnson, C.J., Singer, P.C., 2004. Impact of a magnetic ion exchange resin on ozone demand and bromate formation during drinking water treatment. Water Research 38, 3738e3750. Kerc, A., Bekbolet, M., Saatci, A.M., 2004. Effects of oxidative treatment techniques on molecular size distribution of humic acids. Water Science and Technology 49 (4), 7e12. Lee, S., Lueptow, R.M., 2001. Membrane rejection of nitrogen compounds. Environmental Science and Technology 35, 3008e3018. Lee, W., Westerhoff, P., 2006. Dissolved organic nitrogen removal during water treatment by aluminium sulfate and cationic polymer coagulation. Water Research 40, 3767e3774.

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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, 1977e1988. Ng, H.Y., Lee, L.Y., Ong, S.L., Tao, G., Viawanath, B., Kekre, K., Lay, W., Seah, H., 2008. Treatment of RO brine-towards sustainable water reclamation practice. Water Science and Technology 58 (4), 931e936. Pehlivanoglu-Mantas, E., Sedlak, D.L., 2006. Wastewater-derived dissolved organic nitrogen: analytical methods, characterization, and effects e a review. Critical Reviews in Environmental Science and Technology 36 (3), 261e285. Perez, G., Fernandez-Alba, A.R., Urtiaga, A.M., Ortiz, I., 2010. Electro-oxidation of reverse osmosis concentrates generated in tertiary water treatment. Water Research. doi:10.1016/j. watres.2010.02.017. Rahn, R.O., Stefan, M.I., Bolton, J.R., Goren, E., Shaw, P.-S., Lykke, K.R., 2003. Quantum yield of the iodideeiodate chemical actinometer: dependence on wavelength and concentration. Photochemistry and Photobiology 78 (2), 146e152. Sharp, E.L., Jarvis, P., Parsons, S.A., Jefferson, B., 2006a. Impact of fractional character on the coagulation of NOM. Colloids and Surfaces A: Physicochemical and Engineering Aspects 286, 104e111. Sharp, E.L., Parsons, S.A., Jefferson, B., 2006b. Coagulation of NOM: linking character to treatment. Water Science and Technology 53 (7), 67e76. Shon, H.K., Vigneswaran, S., Snyder, S.A., 2006. Effluent organic matter (EfOM) in wastewater: constituents, effects, and treatment. Critical Reviews in Environmental Science and Technology 36, 327e374. Shorrock, K., Drage, B., 2006. A pilot plant evaluation of the magnetic ion exchange process for the removal of dissolved organic carbon at Draycote water treatment works. Water and Environment Journal 20, 65e70. Slunjski, M., Nguyen, H., Ballard, M., Elridge, R., Morran, J., Drikas, M., O’Leary, B., Smith, P., 2002. MIEX e good research commercialised. Water 29 (2), 42e47. Wang, G.-S., Hsieh, S.-T., Hong, C.-S., 2000. Destruction of humic acid in water by UV light-catalyzed oxidation with hydrogen peroxide. Water Research 34 (15), 3882e3887. Westerhoff, P., Moon, H., Minakata, D., Crittenden, J., 2009. Oxidation of organics in retentates from reverse osmosis wastewater reuse facilities. Water Research 43 (16), 3992e3998.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 2 8 e2 4 3 6

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

NIR-red reflectance-based algorithms for chlorophylla estimation in mesotrophic inland and coastal waters: Lake Kinneret case study Yosef Z. Yacobi a,*, Wesley J. Moses b,1, Semion Kaganovsky a, Benayahu Sulimani a, Bryan C. Leavitt b, Anatoly A. Gitelson b a b

Israel Oceanographic and Limnological Research, Kinneret Limnological Laboratory, P.O. Box 447, Migdal 14950, Israel CALMIT, School of Natural Resource Sciences, University of Nebraska-Lincoln, 3310 Holdredge St, Lincoln, NE 68583, USA

article info

abstract

Article history:

A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration

Received 25 November 2010

in turbid and productive waters. All are based on optical information in a few spectral

Received in revised form

bands in the red and near-infra-red regions of the electromagnetic spectrum. The wave-

23 January 2011

length locations in the models used were meticulously tuned to provide the highest

Accepted 3 February 2011

sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water.

Available online 2 March 2011

But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or

Keywords:

time of the year. In this study we tested the performance of NIR-red models in estimating

Near-infra-red

Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical

Remote sensing

for coastal and mesotrophic inland waters. The models with the same spectral bands as

Hyperspectral

MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chla concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations

MERIS

ranged from 4 to 21 mg m
3 during four field campaigns. A two-band model without reparameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5 mg m
3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Reflectance emerging from the water surface may be detected by remotely operated sensors and used for the estimation of the concentrations of chlorophyll-a (Chl-a) and other constituents dissolved and suspended in water. Water quality assessment using optical sensors mounted on space-borne satellites has been proved as a fruitful method for the estimation

of phytoplankton density and productivity in open seas over regional (Joint and Groom, 2000) and global (Field et al., 1998) scales. Estimation of water quality parameters by remotely operated sensors has also been applied in inland water bodies but to a lesser extent; the high variability of the composition of constituents in inland and coastal waters causes difficulties in reliable interpretation of the optical information contained in the light reflected from water surface. In open ocean waters, where

* Corresponding author. Tel.: þ972 4 672 1444; fax: þ972 4 672 4627. E-mail addresses: [email protected] (Y.Z. Yacobi), [email protected] (A.A. Gitelson). 1 Current address. Research Associate, The National Research Council at the Naval Research Laboratory, Washington, DC 20375, USA. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.002

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 2 8 e2 4 3 6

concentrations of non-algal particles and colored dissolved organic matter (CDOM) are closely correlated with phytoplankton density, Chl-a concentration is typically estimated on the basis of the reflectance in the blue and green portions of the electromagnetic spectrum (Gordon and Morel, 1983). In inland and coastal waters, most of which are turbid and productive, however, these spectral regions are of limited value in retrieving Chl-a concentration because the concentrations of non-algal particles and CDOM are uncorrelated with phytoplankton concentration, and they have strong overlapping absorption features in the blue spectral region, which makes the blue reflectance an unreliable indicator of the concentration of Chl-a. On the other hand, the optical information in the red and the near-infra-red (NIR) regions is reliable for estimating Chla concentration in turbid productive waters, as the absorption effects of non-algal particles and CDOM largely fade in those portions of the electromagnetic spectrum (e.g., Gitelson, 1992; Gons, 1999; Schalles, 2006). A variety of algorithms have been developed for retrieving Chl-a concentration in turbid productive waters based on the optical information in the red and NIR regions acquired at water level. These include the ratio of the reflectance peak near 700 nm in the NIR region to the reflectance at 670 nm in the red region (Chl-a absorption peak), the ratio R705/R670 (Gitelson, 1992; Dekker, 1993; Han and Rundquist, 1997), and the position of the NIR reflectance peak (Gitelson, 1992). Using vector analysis, Stumpf and Tyler (1988) showed that the ratio of reflectances in the NIR and the red bands of space-borne sensors, such as AVHRR (Advanced Very High Resolution Radiometer) and CZCS (Coastal Zone Color Scanner), can be used to identify phytoplankton blooms and estimate Chla concentrations above 10 mg m
3 in turbid estuaries. Gons (1999) used the ratio of reflectances at 704 nm and 672 nm as well as the reflectance at 775 nm to construct an algorithm for assessing a wide range of Chl-a concentrations. Gons et al. (2002, 2005) adapted this algorithm for use with MERIS (MEdium Resolution Imaging Spectrometer) satellite imagery by using reflectances at 708 nm, 665 nm, and 778 nm instead of the original bands and reported highly accurate estimates of Chl-a concentration retrieved from MERIS images. Close correlations between Chl-a concentrations and three-band combinations (Hoge et al., 1987; Yacobi et al., 1995; Pierson and Strombeck, 2000) and even a four-band combination (Le et al., 2009) in the red and NIR regions have been reported. Previous work showed that NIR-red algorithms based on a conceptual, semi-analytical model for pigment retrieval in optically deep media can provide accurate satellitederived estimates of pigment concentrations in turbid productive waters (Dall’Olmo et al., 2003; Dall’Olmo and Gitelson, 2005; Gitelson et al., 2008). The model was formulated as follows: Chl-a ¼ [R
1(l1)
R
1(l2)]  R(l3)

(1)

where R(l1), R(l2), and R(l3) are the reflectance values at wavelengths l1, l2, and l3, respectively. l1 is in a spectral region such that R(l1) is maximally sensitive to absorption by Chl-a. l2 is in a spectral region such that R(l2) is minimally sensitive to absorption by Chl-a and its sensitivity to absorption by other constituents is comparable to that of R(l1). l3 is

2429

located in a spectral region such that R(l3) is minimally affected by absorption due to any constituent, and is therefore used to account for the variability in scattering amongst water samples. For waters that do not have significant concentrations of non-algal particles and CDOM, the subtraction of R
1(l2) in the model may be omitted (Dall’Olmo and Gitelson, 2005), leading to a special case of two-band NIR-red model (Stumpf and Tyler, 1988): Chl-a f R
1(l1)  R(l3)

(2)

where l1 is in the red region while l3 in the NIR region beyond 730 nm. Another two-band model, which is different in its formulation from the previously mentioned two-band model is (Gitelson, 1992): Chl-a f R
1(l1)  R(l2)

(3)

where l1 is in the red region and l2 is in the region of the reflectance peak around 700e710 nm. The accuracy of algorithms, developed on the basis of Eqs. (1) and (3) and optical information acquired from water at surface level, was evaluated with spectral bands available on MERIS and proven to be a reliable tool for turbid productive waters with Chl-a concentrations in the range 2e83 mg m
3 (Gitelson et al., 2009). The goal of the current study was to test the applicability of the NIR-red algorithms, based on MERIS and MODIS (MODerate resolution Imaging Spectroradiometer) spectral bands, in an aquatic environment that is characterized by a range of Chl-a concentrations (less than 25 mg m
3) typical for coastal and mesotrophic inland waters.

2.

Material and methods

Data presented in the current study were acquired in four campaigns on Lake Kinneret, Israel, in May and June 2009, totaling 56 samplings. Lake Kinneret is a warm, monomictic lake with a surface area of 164 km2, average volume of 4100 mm3, and an average annual recharge of about 450 mm3. The mean and maximum depths are 23 m and 43 m, respectively, when the mean lake level is 209 m below sea level. The work was done in the pelagic region, where water depth was >10 m and the stations were unlikely to be influenced by bottom reflectance, considering the lake water transparency (Yacobi, 2006).

2.1.

Acquisition of data in the lake

Secchi depth was measured by the aid of a 25-cm white disk. Hyperspectral reflectance measurements were taken from a boat using two intercalibrated Ocean Optics USB2000 radiometers, each with a coupled 2048-element linear CCDarray detector. Data were collected in the range 400e900 nm with a sampling interval of 0.3 nm, spectral resolution of 1.5 nm, and signal-to-noise ratio above 250. Radiometer 1, equipped with a 25 field-of-view optical fiber, was pointed

2430

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downward to measure the below-surface upwelling radiance, Lup(l), at nadir. The tip of the optical fiber was kept just below the water surface by means of a 2-m long, hand-held dark pole. To simultaneously measure incident irradiance Einc(l), radiometer 2, connected to an optical fiber fitted with a cosine collector, was pointed upward and mounted on top of a 2.5 m tall mast. To match the transfer functions of the radiometers, intercalibration of the instruments was accomplished by measuring simultaneously the upwelling radiance Lcal(l) from a white Spectralon reflectance standard (from Labsphere, Inc., North Sutton, NH) with known reflectance Rcal(l), and the corresponding incident irradiance Ecal(l). The remote sensing reflectance at nadir was computed as:
 

Lup l Ecal ðlÞ t FðlÞ Rrs l ¼   100  Rcal l  2  Einc ðlÞ Lcal ðlÞ n p

(4)

where p is used to transform the irradiance reflectance into remote sensing reflectance, n is the refractive index of water relative to air (taken as equal to 1.33), F(l) is the spectral immersion factor computed after Ohde and Siegel (2003), and t is the water-to-air transmittance (taken as equal to 0.98). To simulate surface reflectances in satellite spectral bands, measured reflectances were averaged in the spectral bands of MODIS: band 13: 662e672 nm, and band 15: 743e753 nm, and MERIS: band 7: 660e670 nm, band 9: 703e713 nm, and band 10: 748e755.5 nm. The NIR-red models were used in the following forms: Two-Band MODIS NIR-red model based on (Eq. (2)) Chl-a f (Rband 13)
1  (Rband 15)

(5)

Three-band MERIS NIR-red model based on (Eq. (1)): Chl-a f [(Rband 7)
1
(Rband 9)
1]  (Rband10)

(6)

Two-Band MERIS NIR-red Model based on (Eq. (3)): Chl-a f (Rband 7)
1  (Rband 9)

2.2.

(7)

Laboratory analysis

Water samples collected at each station were processed in the laboratory within 1 h after collection. The samples were filtered onto glass-fiber filters (Whatman GF/F), extracted in 90% acetone, and left overnight at 4  C in the dark. Chla concentration was determined fluorometrically (HolmHansen et al., 1965), following clarification of the extract by centrifugation for 3 min at 1100 g. The concentration of total suspended solids (TSS) was determined by filtering a known volume of water sample onto GF/F filters and drying the filters for 24 h at 105  C. Organic matter content (OMC) of the particulate material was measured as the component lost by ignition, i.e., following combustion at 530  C; carbon content was assumed to be 50% of OMC (Eckert and Parparov, 2006).

3.

suspended particles. The temporal variation of Chl-a concentration in Lake Kinneret was far below the expected variation based on the multi-annual record of the lake (Yacobi, 2006). The decline of temporal variation is also paralleled by the decline of spatial variability of Chl-a concentration. Phytoplankton was dominated by mostly small species of cyanophytes, diatoms, chlorophytes, and dinoflagellates in varying proportions throughout the MayeJune period when the study was conducted. High phytoplankton density in the lake is mostly affiliated with the presence of the large dinoflagellate Peridinium gatunense, which displays a conspicuous patchy distribution, with a difference of two orders of magnitude between the lowest and highest densities. However, Peridinium did not develop dense populations in 2009 and small forms dominated phytoplankton. Chl-a showed a weakly linear correlation with TSS, with r2 ¼ 0.49 (n ¼ 31, p < 0.001). Secchi depth ranged from 1.7 to 3.9 m and was non-linearly correlated with Chl-a (r2 ¼ 0.57, n ¼ 56, p < 0.001). These relationships demonstrate that the samples were collected from a meso-eutrophic, case 2 water body.

3.1.

Prominent features of the reflectance spectra

All reflectance spectra collected in this study had a similar shape, although with wide variations in magnitude (Fig. 1). Reciprocal of reflectance, which is a proxy of absorption coefficient (Gordon et al., 1975), demonstrates the unique effects of optically active constituents (Fig. 2) and highlights the effect of water absorption at wavelengths longer than 600 nm, especially beyond 690 nm, where there is a rapid increase in absorption by water. Peaks and troughs can be clearly seen in the reciprocal reflectance spectra shown in Fig 2. Total absorption coefficient was high in the blue range of the spectrum with a conspicuous peak at 440 nm, which was followed by a smooth decline to a prominent trough in the green region, with minor changes in slope at 492 nm, 515 nm, and 551 nm. A minimum in absorption coefficient was seen in the green region around 560 nm. The minimum was followed by a mild increase until about 650 nm due to increase in water absorption (Fig. 2). The main features in the red region are a peak around 670 nm due to Chl-a absorption and a prominent minimum of the reciprocal reflectance around 700 nm.

Results

Chl-a concentration ranged from 4.6 to 20.8 mg m
3 and TSS from 3.3 to 5.6 g m
3. Organic matter comprised 62e85% of

Fig. 1 e Reflectance spectra acquired in Lake Kinneret from May throughout June 2009.

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2431

Fig. 2 e Reciprocal reflectance spectra acquired in Lake Kinneret from May throughout June 2009, and absorption of pure water (solid line, indicated by an arrow).

This minimum is caused by the combination of decreasing absorption by Chl-a and increasing absorption by water; it corresponds to a peak in reflectance (Fig. 1).

3.2.

Performance of the NIR-red models

The optimal spectral bands in the models (Eqs. (1) and (3)) might vary for water bodies with different optical properties, and an optimization technique should be employed in order to refine the choice of wavelengths l1, l2, and l3 (Dall’Olmo and Gitelson, 2005). To find the positions of optimal wavelengths for our data set, we calculated spectra of the root mean square error (RMSE) of Chl-a concentration estimation for l1, l2, and l3 using techniques suggested in Dall’Olmo and Gitelson (2005) e Fig. 3. The lowest RMSE for l2 was found around 702 nm whereas the lowest RMSE for l3 occurred in a wide range from 710 to 760 nm with minimal values near 713 nm (Fig. 3a). The lowest RMSE values for l1 appeared to occur in the range 660 to 670 nm with a definite minimum at 665 nm (Fig. 3b). A decrease in RMSE values for l1 was also observed in the range from of 420 to 490 nm, but the values were not as low as those observed in the red region. Two important points are: (i) the positions of optimal wavelengths coincided with those found for lakes in the Midwest U.S. and in the Chesapeake Bay (Dall’Olmo and Gitelson, 2005 and Gitelson et al., 2008, 2009) and (ii) all three MERIS spectral bands corresponded to spectral regions of minimal RMSE values (shadowed areas in Fig. 3 for MERIS bands 7, 9 and 10). Reciprocal reflectance at 665 nm, where absorption by Chla is maximal, was virtually independent of Chl-a concentration (Fig. 4a). It shows that other factors strongly affect the reflectance in this spectral region (e.g., scattering by suspended matter and absorption by CDOM among others). Subtraction of the reciprocal reflectance at 708 nm from the reciprocal reflectance at 665 nm, the first term in the threeband model (Eq. (1)), resulted in the removal of most of these
1 effects, as ðR
1 665
R708 Þ was positively correlated with Chla concentration, with r2 > 0.86 (Fig. 4b), though affected by
1 scattering by inorganic particles. Multiplication of ðR
1 665
R708 Þ

Fig. 3 e Spectra of root mean square error (RMSE) of the linear relationship between Chl-a concentration and 3band NIR-red model (Eq. (1)): (a) l2 is the wavelength where the reflectance is minimally sensitive to absorption by Chla and l3 is the wavelength where the reflectance is minimally sensitive to absorption by all suspended and dissolved constituents in water; (b) l1 is the wavelength where the reflectance is maximally sensitive to absorption by Chl-a. Shaded areas delineate the wavebands which correspond (from left to right) to MERIS bands 7, 9 and 10.

by the reflectance at 753 nm, as in the three-band model (Eq. (1)), significantly minimized the effect of scattering by inorganic particles and resulted in a closer relationship with Chl-a concentration, with r2 > 0.93 (Fig. 4c). A two-band model (Eq. (3)), widely used for Chl-a estimation (Gitelson, 1992), was also accurate (r2 > 0.94) (Fig. 4d). Thus, both the three-band (Eq. (1), Fig. 4c) and the two-band (Eq. (3), Fig. 4d) models had close relationships with Chl-a concentration, with high correlation coefficients. In all cases a non-linear regression produced a better fit than a linear regression, but the latter still yielded high values of correlation coefficient. The two-band MODIS NIR-red model (Eq. (5)), which was based on the approach of Stumpf and Tyler (1988), yielded a moderate correlation with Chl-a concentration (r2 ¼ 0.52, Fig. 5a). Due to the merely moderate correlation of the twoband MODIS NIR-red model with Chl-a concentrations and its general inability to accurately estimate low-to-moderate Chla concentrations (Moses et al., 2009a), no attempt was made to calibrate this model or do further assessment of its accuracy in estimating Chl-a concentration. The three-band (Eq. (6)) and two-band (Eq. (7)) NIR-red models with simulated MERIS bands yielded very high correlations with Chl-a concentration, with quadratic polynomial

2432

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Fig. 4 e Models with narrow spectral bands plotted versus Chl-a concentration: (a) Reciprocal reflectance at 665 nm, (b) The difference of reciprocal reflectances at 665 and 708 nm; (c) Three-band model based on the reflectance at 665, 708 and 753 nm; (d) Two-band model (Eq. (3)) with wavelengths 708 nm and 665 nm.

best fit functions (Fig. 5b, c). The linear relationships between the model values and Chl-a concentrations were as follows: Chl-a ¼ 80.167  (3-band model) þ 17.105

(8)

Chl-a ¼ 41.127  (2-band model)
23.484

(9)

The MERIS-based three-band and two-band NIR-red models were previously parameterized and calibrated in small and shallow lakes in Nebraska, USA, where Chl-a concentrations ranged from 2 to 200 mg m
3 (Gitelson et al., 2009). For Chl-a concentrations not surpassing 25 mg m
3, the relationships between the models and Chl-a concentration for the Nebraska lakes dataset were as follows: Chl ¼ 142.27  (3-band model) þ 19.516

(10)

Chl ¼ 45.535  (2-band model)
25.895

(11)

As can be seen, the coefficients of Eq. (9) for the two-band NIR-red model is similar to the model calibrated using data from Nebraska lakes, Eq. (11). We applied the two-band and three-band algorithms calibrated using Nebraska lakes data (Eqs. (10) and (11)) to the data collected in Lake Kinneret. The results of this validation test are presented in Fig. 6. For the two-band model, the relationship between the estimated (Chlest) and measured Chl (Chlmeas) concentrations was:

Chlest ¼ 0.985Chlmeas þ 0.6814

(12)

with the RMSE of the estimated Chl-a concentration less than 1.25 mg m
3 and the mean normalized bias below 5.5% (Fig. 6a). For the three-band model, the relationship was Chlpred ¼ 1.386Chlmeas
5.0202

(13)

with an RMSE of 2.61 mg m
3 and the mean normalized bias below 46% (Fig. 6b). While the two-band algorithm calibrated in Nebraska was very accurate in estimating Chl-a concentrations over the entire range in Lake Kinneret (Fig. 6c), the three-band algorithm was accurate only for Chl-a concentrations above 10 mg m
3 and exhibited a significant underestimation at lower Chl-a concentrations (Fig. 6d).

4.

Discussion

4.1.

Optical effect of water constituents

Seldom are pigment features as clearly evident in reflectance spectra as seen in our study. That is particularly the case in productive waters, where non-pigmented particles and CDOM mask the pigment signature in the blue range of the

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 2 8 e2 4 3 6

2433

Fig. 5 e NIR-Red models with simulated bands of the space-borne sensors plotted versus Chl-a concentration: (a) two-band MODIS NIR-red model (Eq. (5)); (b) two-band MERIS NIR-red model (Eq. (7)); (c) three-band MERIS NIR-red MERIS model (Eq. (6)).

electromagnetic spectrum (e.g., Dekker, 1993; Schalles, 2006). The troughs at 440 nm and 670 nm are formed by Chla absorption, and the prominent peak around 560 nm is an outcome of minimum absorption by all pigments. Although the optical signature of Chl-a around 670 nm is clearly identified in all but the most oligotrophic waters (Schalles, 2006), the appearance of a trough around 440 nm, is not common in spectra acquired in productive coastal and inland waters. The optical activity of detritus and CDOM declines exponentially from 400 nm towards longer wavelengths, but in productive water is often high enough to mask pigment absorption (Gege and Albert, 2006). The effect of the absorption of CDOM on reflectance is often a major factor that renders the blue range of the electromagnetic spectrum ineffective for use in estimating Chl-a concentration in productive waters. But, CDOM concentration in Lake Kinneret during the time period of the reported study was extremely small (absorption coefficient of filtrate passing through a 0.45 mm filter at 440 nm, was <0.06 m
1). Therefore, only detritus was the potential component to interfere with pigment absorption. Subtracting the concentration of organic matter (OM) harbored by phytoplankton from the total OM, we estimate that the concentration of non-algal OM during our study was, on average, less than 1.6 g m
3. Even if all non-algal OM is considered as detritus, we assume that the relatively low concentration was not high enough to mask the absorption signature of phytoplankton, and the small variation in detritus concentration was not sufficient to modify the impact of pigments on the reflectance spectra. The spectral feature at 514e515 nm is

probably the imprint of the absorption of fucoxanthin and peridinin, harbored by diatoms and dinoflagellates, respectively. These carotenoids have a maximum absorption in solution at around 470 nm and the assumption is that the shift in vivo is 40 nm (Bidigare et al., 1990). Thus, the impact of the presence of diatoms and dinoflagellates should peak at around 510 nm.

4.2. The optimal NIR-red model for estimating Chl-a concentration We tested several other NIR-red models, to which references were made in the Introduction section, on our dataset, and found that most of them can give reasonably accurate estimates of Chl-a concentration. However, the correlation coefficients for the regression between the estimates from the aforementioned models and measured Chl-a concentrations were mostly not higher than 0.6e0.7, which is significantly lower than those achieved for the MERIS-based two-band and three-band NIR-red models. An exception was the model suggested by Gons et al. (2002, 2005). The estimates from this model, which is based on the ratio R708/R665, were highly correlated with Chl-a (r2 > 0.93). However, in addition to the ratio R708/R665, Gons’ model involves reflectance in NIR region at 778 nm, which is in the region of very high water absorption and, thus, very low reflectance. Hence, while performing well for data taken with field spectrometers at water surface level, this model is quite susceptible to effects arising from low signal-to-noise ratio in the detector and residual effects from

2434

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Fig. 6 e Comparison of Chl-a concentration determined in laboratory and Chl-a concentration estimated using NIR-red models calibrated in Fremont Lakes, NE: (a) the two-band NIR-red model (Eq. 11), and (b) the three-band NIR-red model (Eq. 10), with the 1-by-1 line (dashed line) and the best fit function (solid line). The relative errors of Chl-a estimation by the (c) two-band and (d) three-band models are also shown.

atmospheric correction when applied to satellite data, whereas the two-band model (Eq. (3)) is not affected by these factors to the same degree. While the MERIS-based two-band NIR-red model gave consistently highly accurate estimates over the entire range of Chl-a concentrations in our dataset, the three-band NIR-red model yielded less accurate estimates for Chl-a concentrations less than 10 mg m
3. The three-band model, which involves the use of information acquired at l3 to remove the effects of particulate backscattering, is theoretically robust. However, it relies on the assumption of spectral uniformity of backscattering coefficient over the entire range of wavelengths (l1el3), thus, between 660 and 750 nm. Such an assumption may be invalid for inland waters (Gons, 1999; Oki and Yasuoka, 2002). Moreover, there are also documented instances of non-uniform backscattering in the visible and NIR regions and evidences that the pattern of this non-uniformity might vary across water bodies (Herlevi, 2002; Kutser et al., 2009; Aas et al., 2005). The effects of such non-uniformity in backscattering coefficient will have a higher impact on the accuracy of the three-band model at low Chl-a concentrations. Moreover, with the two-band model spanning a lower range of wavelengths than the three-band model, the effects of the spectral non-uniformity of backscattering will be less pronounced in the two-band model. We, therefore, postulate that the spectral non-uniformity of backscattering coefficient is a primary factor that caused the MERIS-based three-band NIR-red model to be less accurate than the MERIS-based

two-band NIR-red model at Chl-a concentrations lower than 10 mg m
3. Thus, the MERIS-based two-band NIR-red model seems to be the most optimal model for estimating low-tomoderate Chl-a concentrations in turbid productive waters such as Lake Kinneret.

5.

Conclusions

The reflectance spectra in the dataset were relatively uniform, i.e., the location of peaks and troughs showed only minor shifts with changes in Chl-a concentration. The MERIS-based NIR-red models had a consistent close correlation with Chl-a concentration. The rationale behind the waveband choice for the construction of NIR-red algorithms for turbid productive waters have been outlined in a recent review (Gitelson et al., 2011), and tested in several campaigns in different water bodies (Gitelson et al., 2008, 2009; Moses et al., 2009a). The MERIS-based NIR-red algorithms developed, parameterized, and calibrated for lakes in Nebraska, were reliable for estimating Chl-a concentration in Lake Kinneret. Similar results were obtained when the algorithms calibrated for lakes in Nebraska were used to estimate Chl-a concentrations in the Azov Sea, Russia, using actual MERIS data (Moses et al., 2009b) and when they were applied to reflectances simulated by the radiative transfer model, Hydrolight (Gilerson et al., 2010). This strongly suggests that the MERIS-based NIR-red algorithms, especially the two-band NIR-red algorithm, do not

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 4 2 8 e2 4 3 6

need to be re-parameterized for waters with varying biophysical characteristics, and have a strong potential for being applied universally for turbid productive waters around the globe. Our study shows the robustness of the MERIS-based NIR-red algorithms at low-to-moderate Chl-a concentrations, which are typical for mesotrophic waters around the globe. However, further tests need to be done to validate the universal applicability of these algorithms for inland and coastal waters.

Acknowledgements We would like to thank skipper Moti Diamant for his reliable partnership in the execution of our lake missions. We gratefully acknowledge the use of radiometers provided by the Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln. We acknowledge the contribution of three anonymous reviewers who provided constructive criticisms that helped to improve the clarity and quality of the presentation. This work was partially supported by the Lake Kinneret Extended Monitoring Program, funded by the Israeli Water Authority.

references

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water research 45 (2011) 2437

Available at www.sciencedirect.com

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Comment

Comment on “Adsorption mechanism of selenate and selenite on the binary oxide systems” by Y.T. Chan et al. [Water Research 43 (2009) 4412-4420] Yuh-Shan Ho* Department of Environmental Sciences, College of Environmental Science and Engineering, Peking University, Beijing 100871, People’s Republic of China

article info Article history: Received 4 November 2009 Accepted 14 April 2010 Available online 24 April 2010

Recently, Chan et al. (2009) published the paper entitled above. In section of 2.4. Adsorption kinetics, the authors noticed “the initial sorption rate” with Eq. (4) without any citations. In fact, Ho has presented a definition for the initial adsorption rate from the parameters of pseudo-second-order model (Ho, 1995; Ho et al., 1996). A modified initial adsorption rate equation has also been made in the following years because a mistake was included in the paper published in 1996 (Ho and McKay, 1998; Ho, 2006). In addition, authors cited Saha et al. (2004) for pseudo-second-order kinetic model. In fact there is nothing related the model in the reference. Accuracy of citations and quotations are very important for the transmission of scientific knowledge. I suggest that Chan et al. cite the original or the most frequently cited papers for the initial adsorption rate to have more accuracy and details of information about its expression.

references

Chan, Y.T., Kuan, W.H., Chen, T.Y., Wang, M.K., 2009. Adsorption mechanism of selenate and selenite on the binary oxide systems. Water Res. 43, 4412e4420. Ho, Y.S., 1995. Adsorption of heavy metals from waste streams by peat. Ph.D. thesis, The University of Birmingham, Birmingham, U.K. Ho, Y.S., 2006. Review of second-order models for adsorption systems. J. Hazard. Mater. 136, 681e689. Ho, Y.S., McKay, G., 1998. Sorption of dye from aqueous solution by peat. Chem. Eng. J. 70, 115e124. Ho, Y.S., Wase, D.A.J., Forster, C.F., 1996. Kinetic studies of competitive heavy metal adsorption by sphagnum moss peat. Environ. Technol. 17, 71e77. Saha, U.K., Liu, C., Kozak, L.M., Huang, P.M., 2004. Kinetics of selenite adsorption on hydroxyaluminum- and hydroxyaluminosilicateemontmorillonite complexes. Soil Sci. Soc. Am. J. 68 (4), 1197e1209.

DOI of original article: 10.1016/j.watres.2009.06.056. * Tel./fax: þ86 10 62751923. E-mail address: [email protected] 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.04.013