WATER RESEARCH A Journal of the International Water Association
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Fouling control mechanisms of demineralized water backwash: Reduction of charge screening and calcium bridging effects Sheng Li a,*, S.G.J. Heijman a, J.Q.J.C. Verberk a, Pierre Le Clech c, Jie Lu a, A.J.B. Kemperman b, G.L. Amy a,d, J.C. van Dijk a a
Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands Membrane Technology Group, Institute of Mechanics, Processes and Control Twente (IMPACT), Faculty of Science and Technology, University of Twente, P.O. Box 217, NL-7500 AE Enschede, The Netherlands c UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, The University of New South Wales, Sydney 2052, Australia d Water Desalination and Reuse Research Center, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia b
article info
abstract
Article history:
This paper investigates the impact of the ionic environment on the charge of colloidal
Received 19 April 2011
natural organic matter (NOM) and ultrafiltration (UF) membranes (charge screening effect)
Received in revised form
and the calcium adsorption/bridging on new and fouled membranes (calcium bridging
4 August 2011
effect) by measuring the zeta potentials of membranes and colloidal NOM. Fouling
Accepted 5 August 2011
experiments were conducted with natural water to determine whether the reduction of the
Available online 8 September 2011
charge screening effect and/or calcium bridging effect by backwashing with demineralized water can explain the observed reduction in fouling. Results show that the charge of both
Keywords:
membranes and NOM, as measured by the zeta potential, became more negative at a lower
Ultrafiltration
pH and a lower concentration of electrolytes, in particular, divalent electrolytes. In addi-
Fouling
tion, calcium also adsorbed onto the membranes, and consequently bridged colloidal NOM
Backwash water
and membranes via binding with functional groups. The charge screening effect could be
NOM
eliminated by flushing NOM and membranes with demineralized water, since a cation-free
Zeta potential
environment was established. However, only a limited amount of the calcium bridging connection was removed with demineralized water backwashes, so the calcium bridging effect mostly could not be eliminated. As demineralized water backwash was found to be effective in fouling control, it can be concluded that the reduction of the charge screening is the dominant mechanism for this. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Ultrafiltration (UF) is a proven technology in water treatment nowadays. It has developed rapidly in the past two decades
due to the progress of both membrane manufacturing and design and operation. UF is mainly applied in surface water treatment, filter backwash water treatment and as a pretreatment for reverse osmosis in desalination (Adham
* Corresponding author. Tel.: þ31 (0) 152784282; fax: þ31 (0) 152784918. E-mail address:
[email protected] (S. Li). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.08.004
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et al., 2004). However, fouling remains a major challenge in the operation of UF, especially in regard to colloidal NOM fouling. In general, a number of colloidal NOM fouling mechanisms may occur, such as adsorption, gel formation, and the interaction with multivalent cations. Adsorption is the physico-chemical interaction between solutes and the membrane and is considered a major fouling mechanism in water treatment (Scha¨fer, 2001). Gel formation was described as a symbiotic effect of salts and organics, and it appears as if gel formation or cake depositions are more important longterm problems. Multivalent ions have been reported to enhance adsorption and fouling in general (Cho et al., 2000; Jermann et al., 2007; Li and Elimelech, 2004; Li et al., 2009b). Possible interactions are bridging and charge neutralization between the membrane and organic molecules (both are usually negatively charged), chelation, complexation and aggregation (in the bulk solution and boundary layers), and coprecipitation of organic and inorganic components. Multivalent ions are believed to enhance colloidal NOM adsorption, but the effects depend on the nature of the organic molecules involved (Scha¨fer, 2001). Besides the effect of multivalent cations, colloidal NOM fouling is also influenced by ionic strength and pH as well. NOM compounds change from a linear to spherical form at increasing ionic strength (Hong and Elimelech, 1997; Song and Singh, 2005; van de Ven et al., 2008). The change in size is attributed to the neutralization of anionic carboxylic acid and phenolic groups by cations. Size of NOM molecules depends on ionic strength-the higher the ionic strength, the smaller the molecule (Jucker and Clark, 1994). Ghosh and Schnitzer (1980) reported molecules fully uncoiled at the low ionic strength of 1 mM NaCl; whereas, at 50e100 mM NaCl, the structure was fully coiled (Ghosh and Schnitzer, 1980). Some research showed that pH influences the charge of the organic molecules and a variation in charge affects a different repulsion between the functional groups (Hong and Elimelech, 1997; Wang et al., 2001). In order to control membrane fouling, different pretreatments such as powder activated carbon adsorption, lime softening, ion exchange, conventional media filtration and coagulation with inorganic and polymeric coagulant have been investigated (Abrahamse et al., 2008; Kweon and Lawler, 2004; Lee et al., 2009). All these pretreatments show different levels of effectiveness for the control of fouling. However, inline coagulation remains the most commonly used pretreatment for UF of surface water. Kabsch-Korbutowicz claimed that in-line coagulation resulted in better removal of NOM and less membrane fouling (Kabsch-Korbutowicz, 2006). However, the problem with in-line coagulation is that a large amount of backwash-derived waste sludge containing dosed coagulants is produced. Since the backwash waste sludge with coagulant has to be treated before its discharge (in especially Western Europe), this option creates additional cost for the membrane treatment plant. Panglisch et al. calculated the cost of the biggest UF treatment plant in the Germany and reported that the backwash waste sludge treatment can be up to 20% of the total cost of the whole plant (Panglisch et al., 2008). Besides inline coagulation, backwashing with demineralized water has recently been proven to be a good method to control UF fouling (Li et al., 2010a, 2009a, 2010b). The results the authors
obtained were from modules with different levels of surface area (from 0.07 to 2.4 m2), which all demonstrated the effectiveness of demineralized water backwashing on fouling control. With the combination of ion exchange pretreatment with demineralized water backwashing, UF membrane could be stably operated without irreversible fouling for 14 days (Li et al., 2009a). In the previous study on the influence of backwash water composition on fouling control (Li et al., 2009b), it became clear that the presence of monovalent and divalent cations in backwash water reduces the fouling control efficiency. Both the elimination of the charge screening effect and the breakdown of the calcium bridging effect are possible mechanisms to explain this improvement. However, there is no scientific evidence to show that both the elimination of the charge screening effect and the breakdown of the calcium bridging effect are really involved in this phenomenon. If both mechanisms are involved, it is also valuable to know which one is the dominant mechanism. The charge screening effect is related to the well known DLVO theory in colloidal chemistry, named after Derjaguin, Landau, Verwey and Overbeek. Although there are likely to be other forces (such as hydrophobic interactions) at play for the fouling reduction of demineralized water backwashing, in general this phenomenon seems behavior in a manner consistent with DLVO theory. Furthermore, the hydrophobic interactions between colloidal NOM and membranes are not very likely to be affected by varying backwash media, so it is not the focus in this study. Nevertheless, there is not a clear relationship describing the impact of electrolyte pH, concentration and valence on the charge of UF membranes and the corresponding consequences on colloidal NOM fouling control with demineralized water backwashing. Furthermore, there is also no study regarding the adsorption of calcium on UF membranes, its consequent bridging effect between colloidal NOM and membranes, and the possibility of the breakdown of Ca-bridging with demineralized water backwash. The objectives of this study are: firstly, to characterize the membrane charge under different electrolyte conditions; secondly, to investigate the calcium adsorption on membranes and its reversibility with demineralized water backwash by measuring the zeta potentials of membranes under different situations (e.g., before and after a continuous filtration with CaCl2 solution); lastly, to investigate the consistency of fouling behaviors for different water backwashing with characterization results based on the DLVO theory.
2.
DLVO theory and hypotheses
2.1.
DLVO theory
The DLVO theory describes the interaction forces between charged surfaces. In the framework of the DLVO theory, the interaction energy between two charged surfaces is composed of van der Waals and electrical double layer interaction. The potential energy between spherical compounds and flat membranes can be calculated with Derjaguin’s approximation via surface integration (Bhattacharjee and Elimelech, 1997). The integrated solutions for this specific application are described in the following sections.
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2.1.1.
van der Waals (VDW) interaction
AH a UVDW ¼ 6D
(1)
Where UVDW is the attractive van der Waals energy (J), AH is the Hamaker constant (J), a is the diameter of the spherical colloidal NOM compounds (m) and D is the distance between spherical compounds and the membrane surface (m).
2.1.2.
Electrical double layer (EDL) interaction " 2 2js jp KB T 2 1 þ expkD ln js þ j2p 2 1 expkD ne js þ j2p # þln 1 exp2kD
UEDL ¼ pε0 εr a
(2)
Where UEDL is the repulsive electrical double layer energy (J), ε0 is the vacuum permittivity (8.85 1012 C V1 m1), εr is the relative permittivity of the background solution (80 for water), y is charge number of counter ions to the considered surface, e is the elementary charge (1.60 1019 C), KB is Boltzmann constant (1.38 1023 J/K), T is the absolute temperature (K), js is the zeta potential of spherical colloidal NOM compounds (V), jp is the zeta potential of the membrane surface plate (V), l is the inverse Debye screening length (m1). Therefore, the total interaction energy between spherical colloidal NOM and membranes is: UDA ¼ UVDW þ UEDL
(3)
Where UDA is the total interaction energy (J), including attractive van der Waals energy and repulsive electrical double layer energy.
2.1.3.
Influence of electrolyte condition on zeta potential
As shown in Eq. (2), the electrical double layer repulsion is influenced by the zeta potential of membranes and colloidal NOM. Eq. (4) describes the potential of substances at a specific distance away from their surface. This potential is a function of inverse Debye screening length and distance away from the surface. The zeta potential is the potential of substances at the shear plane. Although different electrolytes give small changes at the location of the shear plane, the location of the shear plane is actually determined by surface roughness and counter ion size in electrolytes. The surface roughness of NOM and new and fouled membranes were kept constant in the experiments, and the ionic radii for ˚ ) and calcium (1 A ˚ ) are similar (Shannon, 1976). sodium (1.02 A Therefore, the location of the shear plane is considered to be constant here. j ¼ j0 expðkxÞ
(4)
Where j is the potential at a specific distance from the surface (V), j0 is the surface potential (V) and x is the specific distance (m). In another words, the zeta potential of considered compounds/membranes depends on the surface potential j0, and the inverse Debye screening length k. j0 depends on the amount of acidic functional content of the colloidal NOM and
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membranes. Since the inverse Debye screening length depends on ionic strength (as shown in Eq. (5)), the ion valence and concentration can influence the inverse Debye screening length, and consequently the zeta potential and interaction energy. The zeta potential becomes less negative with the increase in electrolyte concentration and valence (Koper, 2007). sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1000NA e2 2I k¼ ε0 εr KB T
(5)
Where NA is the Avogadro number (6.0 1023 mol1) and I is the ionic strength (Mol/L), ε0, εr, e and KB are identical as Eq. (2). The calculation of UVDW and UEDL is described in the following section. First of all, the values for NA, ε0, εr, e and KB are known constants as shown in Eq. (5). Ionic strength I can be determined based on the electrolytes used for zeta potential measurements, and then the corresponding l can be determined. UVDW and UEDL can then be determined with Eq. (1) and Eq. (2). The missing parameters for the calculation are y, T, AH, js, jp, D and a. The parameter y is the charge number of counter ions to the considered surfaces; it is 1 and 2 for monovalent and divalent electrolytes, respectively. T, js and jp were experimentally determined under specific electrolyte conditions corresponding to I. The Hamaker constant (AH) used in this study is 1.4 1020 J. That is because the size of colloidal and macromolecular NOM compounds is in nanoscale, and according to literature the Hamaker constant for organic carbon in water is around 1 1020 J (Petosa et al., 2010). Considering the mixed nature of surface water consisting of also relatively large colloidal compounds, 1.4 1020 J is applied here. The distance between the colloidal NOM compounds and the membrane (D) and the diameter of spherical colloidal NOM compounds (a) are two parameters that need to be assumed to complete the calculation of UVDW and UEDL. Because colloidal NOM compounds are generally compounds or particles which deposit on the membrane surface and/or block the membrane pores and thereby build up membrane fouling layer, the focus of the demineralized water backwash is the removal of colloidal NOM foulants with a diameter similar with the pore size of the membranes. Considering that the pore size of UF membranes is around 30 nm, the diameter of spherical NOM compounds (a) is assumed to be 30 nm. In terms of the distance D, if the colloidal NOM foulants are reversible with demineralized water backwashing, they probably are in the secondary energy minimum according to the DLVO theory. The exact value will be assumed in the later section based on the exact zeta potential of the two considered charged surfaces.
2.2.
Hypotheses for demineralized water backwash
Because the UF membranes used in this study and the NOM in surface water are (usually) negatively charged, the presence of cations in feed water can enhance UF fouling through two mechanisms: charge screening and calcium bridging effects. The charge screening effect involves both monovalent and divalent cations, and the strength of this effect relates with the DLVO theory. The higher electrolyte valence
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and concentration, the stronger the charge screening effect is. Consequently, the total interaction energy between NOM and membranes becomes more attractive. Therefore, NOM easily fouls the membranes. In addition to the charge screening effect, due to the double valences of calcium, it probably bridges the negatively charged NOM and membranes through bindings of acidic functional groups on NOM and membranes. In that case, the fouling of UF membranes can be enhanced as well. The complexation of natural organics with calcium may lead to stable complexes or aggregation according to Liao and Randtke (1986). In that case, the fouling is further enhanced after backwashes when the following colloidal NOM interacts with NOM foulants on the membranes via calcium. One possible mechanism of demineralized water backwashing is the reduction of the charge screening effect. According to the DLVO theory, the charge screening effect can be changed by varying the ionic environment. When the UF membranes are backwashed with demineralized water, the cation concentration near the membrane surface is reduced by the demineralized water. Consequently, the charge screening effects on colloidal NOM foulants and membranes are reduced and repulsion between NOM foulants and membranes is restored, improving the removal of foulants by backwashing. However, when the membranes are backwashed with UF permeate, the charge screening effects on foulants and membranes are maintained because the cation concentration in UF permeate is similar with that near the membrane surface. Therefore, there is no improvement on foulant removal. If the charge screening effect is the only mechanism behind demineralized water backwash, backwashing with two types of solutions having the same impact on interaction energy would show identical fouling control efficiency. Another possible mechanism of demineralized water backwashing is the breakdown of the calcium bridging effect. Calcium interacts with natural organics in two manners: site specific weak and strong bindings (Leenheer et al., 1989). Weak binding is present in all molecules and increases with carboxylic acid content and the structural arrangement of these groups, whereas strong binding is very specific to organic components and is not always present. Cabaniss and Shuman reported that about 50% of fulvic acid in a natural environment may be associated with calcium and magnesium, but that such associations may be broken down during extraction process (Cabaniss and Shuman, 1988a,b). By applying demineralized water backwashes, some of the calcium bridges may be broken down due to the difference of calcium concentration between demineralized water and the membrane surface. Since this concentration difference leads to a demand for equilibrium, in the case of weak bindings, calcium may be extracted and diffuse into the demineralized water. However, this difference in concentration is not expected to occur when the membrane is backwashed with UF permeate. According to the DLVO theory, if there is no calcium adsorption/bridging, the zeta potentials of membranes should be identical for measurements with the same electrolyte solution (the same strength of the charge screening effect). Therefore, the zeta potentials of a membrane can be
measured at three moments: 1) before and 2) after calcium adsorption, and 3) after demineralization of water backwash. If a less negative zeta potential is observed after calcium adsorption, it is caused by the calcium adsorption/bridging. If a more negative zeta potential is observed after demineralization of water backwash, the adsorbed calcium is removed by the backwash.
3.
Material and methods
3.1.
Characterization of NOM
A Nano Zetasizer from Malvern Company was used to assess the zeta potentials of organic matter components. This equipment is capable of measuring the zeta potentials of particles with a diameter between 3.8 nm and 100 mm; thus, colloidal NOM compounds can be measured. In principle, by applying an electric field, NOM compounds with a zeta potential will migrate toward the electrode of an opposite charge with a velocity proportional to the magnitude of the zeta potential. The velocity of the movement is measured using laser Doppler velocimetry (LDV), and converted into a zeta potential through calculations applying dispersant viscosity and the Smoluchowski theory. Raw surface water (Schie canal water in Delft, the Netherlands) was used to investigate the zeta potential of colloidal NOM compounds. At first, a filtration experiment with demineralized water backwashes was conducted and the backwash wastewater was collected during the experiment. Since demineralized water contains no ions, the backwash wastewater only consisted of rejected Schie water NOM. Afterward, the 500 ml of collected backwash wastewater from demineralized water backwashes was divided into 16 samples and adjusted to electrolyte conditions of 1e6 and 12e21 (Table 1). Furthermore, the organic composition of raw Schie water and its UF permeate was analyzed with LC-OCD. On the one hand, because KCl is a standard electrolyte used in SurPAAS, KCl was used to investigate the influence of pH on the zeta potentials of the NOM compounds. On the other hand, since the Naþ and Ca2þ are common cations found in natural surface water, NaCl and CaCl2 electrolyte solutions were used to investigate the influence of electrolyte concentrations and valences on the zeta potentials. All the measurements were conducted with one membrane to eliminate the variation of different membranes.
3.2.
Characterization of membranes
In this study, the zeta potentials of membrane were determined with a commercial analyzer, SurPAAS, from Anton Paar Company. This analyzer measures the streaming potential of target surfaces and then calculates the corresponding zeta potential using Eq. (6). The streaming potential equals DF/DP. By flushing the target surface with electrolyte solution, potential and pressure differences over the surface are created. The values of DF and DP were measured by electrodes and pressure meters installed at both ends of the target surface. Since the focus of this study is the interaction between the NOM compounds and active membrane surfaces,
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Table 1 e Different electrolyte conditions for zeta potential measurements of Schie water NOM and UF membranes. Electrolyte No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Table 2 e Conditions of zeta potential measurements regarding the impact of Ca adsorption.
pH
KCl (mMol/L)
NaCl (mMol/L)
CaCl2 (mMol/L)
4 5 6 7 8 9 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
1 1 1 1 1 1 1 5 10 25 50 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 5 10 25 50 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.5 1 2 5 10
Moment of zeta potential measurement 2 Moment of zeta potential measurement 3
Fouled membrane
Before a 2-h continuous filtration with 5 mMol/L CaCl2 After a 2-h continuous filtration with 5 mMol/L CaCl2 N/A
After a 2-h continuous fouling experiment and rinsing with demineralized water After a 2-h continuous filtration with 5 mMol/L CaCl2 After an 8-min demineralized water backwash
3.4. Fouling experiments based on the results of characterization 3.4.1.
the zeta potential along membrane fibers were determined, instead of the zeta potential through membrane. Df ε0 εr z ¼ DP h,EC
Moment of zeta potential measurement 1
New membrane
(6)
where, DF is the measured potential difference along membrane fibers (V), DP is the respective measured pressure difference along membrane fibers (Pa), z is the zeta potential of the measured surface (V), h is the dynamic viscosity of the solution (Pax s), EC is the conductivity of the solution (Sx m1). The influence of pH on the zeta potential was investigated at a constant electrolyte concentration for both new and fouled membranes (electrolyte 1e6). The new membranes were characterized under different KCl and CaCl2 concentrations (electrolyte 7e11 and 17e21). In order to confirm the similar effect of monovalent electrolytes on the zeta potential and demonstrate that results obtained from NaCl and KCl are comparable, characterization of the fouled membranes included NaCl, KCl and CaCl2 electrolytes (electrolyte 1e21). Two membranes were used in this section, one new and one fouled membrane. All the measurements were conducted with the same new or fouled membrane to eliminate the variation of different membranes. Measurements with monovalent electrolytes were conducted first and then with CaCl2 electrolytes.
Feed and backwash water
Raw surface water was taken from the Schie canal in Delft as feed water. The water was stored in a refrigerator at a temperature of 4 C without prefiltration. The water quality is shown in Table 3. Four types of backwash water were used: 1) UF permeate, 2) demineralized water with 2 mMol/L CaCl2, 3) demineralized water with 10 mMol/L NaCl and 4) demineralized water. The water quality is shown in Table 3. Ten mMol/L NaCl and 2 mMol/L CaCl2 were also used for backwashing, since these concentrations have shown similar interaction energy between NOM compounds and UF membranes (similar charge screening effect).
3.4.2.
Membrane module
Self-prepared membrane modules containing UFC M5 0.8 mm hollow fibers (X-FLOW Company) were used. Two 30 cm fibers were potted in an 8 mm PVC pipe (using a polyurethane potting resin), providing a surface area of 0.0015 m2 for each module. The characteristics of the membrane fibers are listed in Table 4. Each experiment was conducted with a new membrane.
3.4.3.
Ultrafiltration setup
The UF setup (Fig. 1) was designed for constant flux experiments. The constant feed and backwash flow were maintained during the experiments with a DUAL syringe pump system and one single syringe pump (New Era Pump Systems,
Table 3 e Water qualities of feed and backwash water. DOC (mg/L C)
3.3. Impact of calcium adsorption on zeta potentials of membranes Table 2 shows the different measuring moments of zeta potentials for a new membrane and a fouled membrane regarding the impact of calcium adsorption. All the zeta potentials of membranes were measured with 1 mMol/L KCl electrolyte solution.
Schie canal water (Feed) UF permeate 2 mMol/L CaCl2 10 mMol/L NaCl Demineralized water
pH
Naþ (mg/L)
Ca2þ (mg/L)
25
7.1
59
117
23 <0.1 <0.1 <0.1
7.0 6.9 6.9 7
57 N/A 229 <0.1
115 80 N/A <0.1
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Table 4 e Characteristics of membrane fibers provided by manufacturer. Membrane material
PES/PVP
Molecular weight cut-off Filtration mode Internal fiber diameter (mm) Length of module (cm) Surface area of module (m2) Module diameter (mm)
100 kDa Inside-out 0.8 35 0.0015 8
Inc.), respectively. Two solenoid valves (Burkert Fluid Control Systems) were applied to control the direction of the water flow. The syringe pumps and solenoid valves were controlled by a programmable logic controller (PLC), so the setup could be run automatically and continuously. As shown in Table 5, four operational phases were developed for this setup: 1) Forward flush 1 (feed side), 2) Filtration, 3) Forward flush 2 (permeate side) and 4) Backwash. Forward flush 2 (permeate side) was programmed to ensure that the membrane fibers were completely surrounded by demineralized water before a demineralized water backwash. Two digital pressure meters were used to measure the pressure of feed water during filtration and backwash. Since the pressures of the permeate and backwash waste stream were equal to atmospherical, the pressure exhibited by the two pressure meters was the transmembrane pressure (TMP). The TMP values were uploaded to the computer every 8 s.
3.4.4.
Filtration protocol
All experiments were carried out in a dead-end operation mode. Before each experiment, the setup was thoroughly flushed with demineralized water in both filtration and backwash modes to remove chemical residues and air in the system. Afterward, the setup was operated with demineralized water at a flux of 120 L/(h m2) for half an hour to determine the initial permeability of each membrane. Each fouling experiment consisted of 9 operational cycles. Each cycle was
composed of three phases: 1) Filtration at a flux of 120 L/(h m2) for 15 min, 2) Forward flush of permeate side for 30 s, 3) Backwashing at a double filtration flux for 1 min. Fouling experiments were conducted for the different types of backwash water shown in Table 3 to compare their fouling control efficiency.
4.
Results and discussion
4.1.
Zeta potentials of NOM
4.1.1.
Impact of pH
The zeta potentials of Schie water NOM as a function of pH are shown in Fig. 2. The zeta potentials of Schie water NOM became more negative with an increase in pH. That is (probably) because the carboxyl functional groups of NOM compounds were protonated by hydrogen ions at low pH values. The carboxyl functional groups are the negatively charged components of the NOM compounds. After protonation, the NOM compounds became less negatively charged. However, the concentration of hydrogen ions decreased with the increase in pH, leading to less protonation and consequently more negative zeta potentials of the NOM compounds. Furthermore, the zeta potential of the NOM at pH 3 was around 17 mV (not yet reaching its isoelectric point (IEP)); thus, this type of NOM is highly negatively charged. The zeta potentials are influenced by the structure of the NOM compounds. There might be high levels of negative functional groups in the Schie water NOM. Because the analyzed Schie water NOM is a mixture of different fractions of NOM, the reason for such a highly negative charge needs a further research. Fig. 3 shows the LC-OCD analysis of Schie canal water, and it contains biopolymers, big molecular weight humic substances, building blocks and low molecular weight humics, etc. After filtration, a certain amount of biopolymers, humic substances and low molecular weight substances was removed and these substances probably the foulants on the
Fig. 1 e Layout of ultrafiltration setup.
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Table 5 e Operational phases of UF setup.
Forward flush 1 (feed side) Filtration Forward flush 2 (permeate side) Backwash
Valve 1
Valve 2
Filtration pumps
Backwash pump
Close
Open
On
Off
Open Open
Close Close
On Off
Off On
Close
Open
Off
On
membranes. Some of biopolymers and humic substances containing a lot of carboxyl groups, and they might be the reason for the high negative charge of Schie water NOM.
4.1.2.
Impact of electrolyte concentration and valence
As shown in Fig. 4, the NOM compounds also became less negatively charged when the electrolyte concentration increased for both monovalent and divalent electrolyte solutions. That is because there were more cations available for negative charge screening when the electrolyte concentration increased. However, CaCl2 showed a much greater reduction of the membranes’ negative charge than NaCl did due to its stronger effect on ionic strength.
Fig. 2 e Zeta potentials of Schie water NOM at different pH values, measured with 1 mMol/L KCl electrolyte solution.
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At a similar ionic strength, both calcium and sodium electrolytes should show a similar zeta potential, such as 1.5 mMol/L CaCl2 and 5 mMol/L NaCl (as shown in Table 6). Consequently, a similar zeta potential for both situations should be expected, according to Eq. (4). However, it was not as expected; the measured zeta potential at 1 mMol/L CaCl2 was more close to the one at 5 mMol/L NaCl (Fig. 4). That is probably because a small amount of Ca2þ adsorbed onto the NOM during the zeta potential measurement (which took more than half an hour) via the acidic functional groups on membranes, reducing the surface potential of NOM (j0). I¼
1X Zi Mi 2 i
(7)
Where I is the ionic strength, Zi is the valence of ion type i and Mi is the mole concentration of ion type i in units of Mol/L.
4.2.
Zeta potentials of membranes
4.2.1.
Impact of pH
Fig. 5 shows the zeta potentials of new and fouled membranes at different pH values (all these zeta potentials were measured with 1 mMol/L KCl electrolyte solution). It is clear that the membranes used in this study are negatively charged at a neutral pH of 7. Furthermore, the zeta potentials of both new and fouled UF membranes become more negative with an increase in pH. At a low pH, the membrane was positively charged, while it became negatively charged when the pH was higher than 4.5; thus, the IEP for fouled membranes is pH 4.5. The influence of pH on the zeta potential (charge) of membranes is dramatic. However, the zeta potentials of new membranes were more negative than they were for fouled membranes with a fouling layer, but were still negative (around 14 mV), even at pH 4. The same type of membrane fibers were also used by van der Ven, and a constant negative streaming potential within the whole pH range was reported (van der Ven, 2008). The difference between the zeta potential of new and the fouling layer indicates that foulants reduce the negative
Fig. 3 e LC-OCD analysis of Schie canal water.
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Fig. 4 e Zeta potential of Schie water NOM as a function of electrolyte concentration for two types of electrolyte solution: NaCl and CaCl2 at pH 7.
charge of membranes, probably due to the adsorption of calcium and/or its combination with NOM during filtration.
4.2.2.
Impact of electrolyte concentration and valence
4.2.2.1. New membrane. The zeta potentials of a new membrane under different electrolyte conditions (electrolyte valence and concentration) are shown in Fig. 6. Zeta potentials of new membranes became less negative with the increase in cation concentration for both types of cation. In addition, divalent cations showed a much bigger influence on the reduction of the negative zeta potential than monovalent cations did. That is because of the higher charge screening efficiency of divalent cations. Similar to the NOM results, similar zeta potential of the membrane should occur at 1.5 mMol/L CaCl2 and 5 mMol/L NaCl as well according to the DLVO theory since they have a similar ionic strength (Table 6). However, a greater influence of calcium on the zeta potential was also observed. As explained in Section 4.1.2, calcium adsorption during measurement with calcium electrolytes is probably the reason for this. Although the impact of cations on NOM and new membranes was similar, the exact zeta potential values under the same electrolyte concentrations were different. That is because the zeta potential also depends on the surface potential of substances (as shown in Eq. (4)). Surface potential depends on the acidic functional content of substances. Since the amount of this content is probably different on membranes and NOM, their surface potentials are different as well. Consequently, zeta potentials are different under the same electrolyte conditions. 4.2.2.2. Fouled membrane. Fig. 7 shows the zeta potentials of fouled
UF
membranes
under
different
electrolyte
concentrations for three types of electrolyte solutions: KCl, NaCl and CaCl2 at a pH of 7. Although the zeta potentials are not identical as in the characterization of NOM and new membranes, the tendency of zeta potential variation under different conditions is similar to those of NOM and new membranes. According to the trend line, the membrane reached its IEP at a concentration around 8 mMol/L CaCl2. On the other hand, the membrane reached its IEP at concentrations of 40 and 45 mMol/L for KCl and NaCl electrolytes, respectively. KCl and NaCl electrolyte solutions have a similar trend line concerning the zeta potential as a function of their concentration. That is because both of them are monovalent ions, which have a similar effect on the electrical double layer on the membrane surface. The zeta potentials of fouled membranes were less negative than those of new membranes. That is probably because of the adsorption of calcium and NOM molecules on the membranes, reducing the zeta potential of membranes (Fig. 5). Because there are calcium ions and NOM compounds in surface water, calcium is expected to adsorb on membranes, making surface of membranes less negatively charged. By varying the distance between the colloidal NOM and membranes, a graph of interaction energy versus distance is obtained as Fig. 8. According to this figure, the secondary energy minimum is at a distance around 30 nm, so the distance between the colloidal NOM foulant and membranes is assumed to be this value. At this distance, interaction energy based on the zeta potentials of fouled membranes was calculated with Eqs. (1)e(3) and (5) for different electrolyte situations. For example, using 1 mMol/L CaCl2 electrolyte, the ionic strength is 0.0035 M (as shown in Table 6), and the corresponding inverse Debye screening length was calculated to be 1.77 108 m1 with Eq. (4).The measured zeta potentials of the NOM and membranes at 20 C with 1 mMol/L CaCl2 electrolyte were 18.5 and 5.5 mV, respectively (as shown in Figs. 4 and 6). Putting all experimentally determined values together with the known constants in Eqs. (1) and (2), UVDW and UEDL can be obtained, and consequently the total interaction energy (UDA) can be determined with Eq. (3). Therefore, the UDA for 1 mMol/ L CaCl2 is 19 1021 J (4.64 kT) with a UVDW of 2.33 1021 J and a UEDL of 21.4 1021 J. The corresponding interaction energy of different electrolyte situations is calculated and shown in Fig. 9. In this figure, positive interaction energy indicates the repulsive interaction between membranes and NOM compounds, while negative interaction energy reveals attractive interaction between them. The increase in both divalent and monovalent cation concentration reduces the repulsive interaction energy between membranes and NOM,
Table 6 e Ionic strengths of 1 and 1.5 mMol/L CaCl2 and 5 mMol/L NaCl electrolytes.
CaCl2 NaCl
Cation concentration (mMol/L)
Cation valence
Anion concentration (mMol/L)
Anion valence
Ionic strength (mMol/L)
1.0 1.5 5.0
2 2 1
2.0 3.0 5.0
1 1 1
3.0 4.5 5.0
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Fig. 5 e Zeta potentials of new and fouled membranes at different pH values, measured with 1 mMol/L KCl electrolyte solution.
and the interaction energy translates into attractive interaction at a specific concentration. The attractive interaction energy of both CaCl2 and NaCl solutions supports the argument about the enhancement of membrane fouling with a presence of cation in feed water (Hong and Elimelech, 1997; Li et al., 2009b). This specific concentration is different for divalent and monovalent cations, and the one for calcium is much lower than for sodium. In terms of NaCl electrolyte, the interaction energy for the colloidal compounds with diameters around 30 nm becomes repulsive when concentration is lower than 8 mMol/L NaCl. The interaction energies between NOM and membranes for both 2 mMol/L CaCl2 and 10 mMol/L NaCl solutions are attractive and at the same level (around 0.35 kT). When membranes are backwashed with demineralized water, the cation concentration near the membrane surface is almost zero. As seen in Fig. 9, the interaction energy between the membrane and the NOM is repulsive, enhancing the backwash efficiency. On the other hand, the interaction energy is still attractive if the backwash water is UF permeate because the cation concentration near the membrane surface does not change, containing about 3 mMol/L Caþ2 and 2.5 mMol/L Naþ. Since the hydraulically irreversible fouling cannot be completely prevented even with demineralized water
Fig. 6 e Zeta potential of a new UF membrane as a function of electrolyte concentration for two types of electrolyte solutions: KCl and CaCl2 at pH 7.
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Fig. 7 e Zeta potential of a fouled UF membrane as a function of electrolyte concentration for three types of electrolyte solution: KCl, NaCl and CaCl2 at pH 7.
backwashing, the fouling control of UF membranes by backwashing is more related to how to control the build up of hydraulically irreversible fouling on slightly fouled membrane as opposed to new membrane. Therefore, the results of the zeta potential characterization of fouled membranes and their corresponding calculated interaction energy were applied to the fouling experiments to assess whether the charge screening effect is the dominant mechanism behind demineralized water backwashing (Fig. 10).
4.3.
Adsorption of calcium on the UF membranes
The zeta potential of the membrane after calcium adsorption became significantly less negative, with a difference up to more than 4 mV (as shown in Fig. 11). The observed less negative zeta potentials demonstrated that a certain amount of calcium adsorbed on the membranes and consequently influenced the charge of membranes.
Fig. 8 e Variation of interaction energy between colloidal NOM compounds and membranes versus the distances between them.
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Regarding the effectiveness of the demineralized water backwashing on the fouling caused by calcium adsorption, Fig. 11 shows the zeta potential of fouled membrane under three conditions: 1) before calcium adsorption (freshly fouled membrane), 2) after calcium adsorption (fouled membrane after a 2-h filtration with 5 mMol/L CaCl2 solution), and 3) fouled membrane after a 2-h filtration with 5 mMol/L CaCl2 solution and an 8-min demineralized water backwash. Although the zeta potentials of the fouled membrane after a demineralized water backwash became slightly more negative than those values after the calcium adsorption, the zeta potential after demineralized water backwash was still obviously less negative than the original negative zeta potential of freshly fouled membrane. Therefore, it is clear that (at least) part of the adsorbed calcium could not be removed from the membrane even with demineralized water backwashing. This part of calcium makes the surface charge of a fouled membrane less negative, and consequently makes the membrane more easily fouled by the colloidal NOM again in the next filtration cycles. That is probably the reason why a certain amount of hydraulically irreversible fouling was observed even when demineralized water backwashing was applied in the Schie canal water filtration in the past (Li et al., 2010a, 2009b).
4.4.
Fouling experimental results
4.4.1.
Clean water permeability of membrane modules
All the self-prepared membrane modules were tested with demineralized water to determine their clean water permeability; the TMP of modules in this test were used as the TMP0 of modules (0.095 0.005 bar at a flux of 120 L/(h m2)). As shown in Fig. 12, demineralized water exhibits the lowest increase in TMP/TMP0 within 2.5 h. According to the characterization results of fouled membrane, similar interaction energy was calculated for the electrolyte solutions with 2 mMol/L Ca2þ or 10 mMol/L Naþ by applying Eqs. (1)e(3) (Fig. 9). If eliminating the charge screening effect is the
Fig. 9 e Calculated interaction energy between spherical colloidal NOM compounds and membrane surfaces at different electrolyte concentrations for both NaCl and CaCl2 solutions (the distance between NOM compounds and membranes, diameter of NOM compounds and the Hamaker constant (AH) are assumed to be 30 nm, 30 nm and 1.4 3 10L20 J, respectively).
Fig. 10 e Zeta potential of a new membrane as a function of pH for two situations: before and after 2-h calcium adsorption.
dominant mechanism of demineralized water backwashing, then backwashing with these two types of water should show a similar fouling control efficiency. In Fig. 12, the increase rates in TMP/TMP0 of these two backwash waters are higher than the demineralized water, showing the impact of cations in backwash water on fouling control. Although the control efficiency of 10 mMol/L Naþ is slightly higher than 2 mMol/L Ca2þ (a bit lower increase in TMP/TMP0), they are on a similar level. The higher fouling removal for 10 mMol/L Naþ was probably caused by the breakdown of a small amount of weak calcium bridging. Therefore, reduction of the charge screening effect plays a dominant role on the fouling removal of demineralized water backwashes. UF permeate showed the highest increase in TMP/TMP0 in Fig. 12, which is almost 6 times higher than demineralized water. That is because of the composition of the UF permeate. The UF permeate contained around 3 mMol/L Ca and 2.5 mMol/L Na, so it present a stronger potential for maintaining the charge screening effect during its backwash than any other backwash waters used in this study. The improvement of demineralized water backwash observed here corresponds to the previous findings of the authors (Abrahamse et al., 2008; Li et al., 2010a, 2010b, 2009b).
Fig. 11 e Zeta potential of a fouled membrane as a function of pH for three situations: before calcium adsorption experiment, after 2-h calcium adsorption experiment, and after calcium adsorption and 8-min demineralized water backwash.
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Fig. 12 e Normalized TMP as a function of time for four backwash waters: 1) Demineralized water, 2) Demineralized water with 2 mMol/L Ca2D, 3) Demineralized water with 10 mMol/L NaD, and 4) UF permeate (with 3 mMol/L Ca2D and 2.5 mMol/L NaD).
5.
Conclusions
The hypotheses of demineralized water backwashing were investigated in this study, including the charge screening and the calcium bridging effects. By determining the zeta potential of the membranes and the colloidal NOM compounds at different conditions, the impact of pH and electrolyte valence and concentration on their charge was assessed. Furthermore, the adsorption of calcium on the membranes and the NOM compounds was also illustrated. Results showed that a membrane became less negatively charged when the pH decreased and the concentration of electrolyte increased. That is because the negatively charged functional groups on the membrane surface were protonated by hydrogen ions at low pH values, and the negative charge of membranes were screened by more cations when the electrolyte concentration was high. Furthermore, divalent cation has a much stronger effect on the increase of membrane zeta potential than monovalent cations. Although similar tendencies were observed for new and fouled membranes at different conditions, the zeta potential of fouled membranes is less negative than of new membranes at the same electrolyte condition. That is probably because calcium ions and NOM molecules are adsorbed on the membrane surface. Calcium ions indeed adsorbed on either new or fouled membranes, and bridged NOM and membranes afterward. However, the interaction of calcium with fouled membranes is more substantial than with new membranes. Demineralized water backwashing showed the best fouling control, while the UF permeate showed the lowest foulants removal. The 2 mMol/L CaCl2 and 10 mMol/L NaCl solutions showed the same interaction energy between the NOM and membranes. Although 10 mMol/L NaCl solution displayed a slightly better fouling control, both solutions exhibited a similar level of fouling control efficiency. This indicates that the charge
screening effect played a dominant role in the membrane fouling and fouling control by demineralized water backwashing. The small difference of backwash efficiency between 2 mMol/L CaCl2 and 10 mMol/L NaCl was probably caused by the breakdown of weak calcium bridging connections. However, most of the fouling caused by calcium bridging is difficult to remove even with a demineralized water backwash.
Acknowledgments This research was funded by Senter Novem in the framework of the Innowator grants. Norit X-Flow B.V., the membrane manufacturer, the Netherlands, is gratefully acknowledged for providing UFC M5 0.8 membrane fibers. Evides. BV and Hatenboer-water. BV are appreciated for the cooperation in the DEMIFLUSH project.
references
Abrahamse, A.J., Lipreau, C., Li, S., Heijman, S.G.J., 2008. Removal of divalent cations reduces fouling of ultrafiltration membranes. Journal of Membrane Science 323 (1), 153e158. Adham, S., Chiu, K.P., Gramith, K., Do-Quang, Z., 2004. Development of an MF/UF Knowledge Base Orlando. Bhattacharjee, S., Elimelech, M., 1997. Surface element integration: a novel technique for evaluation of DLVO interaction between a particle and a flat plate. Journal of Colloid and Interface Science 193, 273e285. Cabaniss, S.E., Shuman, M.S., 1988a. Copper binding by dissolved organic matter: I. Suwannee River fulvic acid equilibria. Geochimica et Cosmochimica Acta 52 (1), 185e193. Cabaniss, S.E., Shuman, M.S., 1988b. Copper binding by dissolved organic matter: II. Variation in type and source of organic matter. Geochimica et Cosmochimica Acta 52 (1), 195e200.
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Cho, J., Amy, G., Pellegrino, J., 2000. Membrane filtration of natural organic matter: factors and mechanisms affecting rejection and flux decline with charged ultrafiltration (UF) membrane. Journal of Membrane Science 164 (1e2), 89e110. Ghosh, K., Schnitzer, M., 1980. Macromolecular structures of humic substances. Soil Science 129 (5), 266e276. Hong, S., Elimelech, M., 1997. Chemical and physical aspects of natural organic matter (NOM) fouling of nanofiltration membranes. Journal of Membrane Science 132 (2), 159e181. Jermann, D., Pronk, W., Meylan, S., Boller, M., 2007. Interplay of different NOM fouling mechanisms during ultrafiltration for drinking water production. Water Research 41 (8), 1713e1722. Jucker, C., Clark, M.M., 1994. Adsorption of aquatic humic substances on hydrophobic ultrafiltration membranes. Journal of Membrane Science 97, 37e52. Kabsch-Korbutowicz, M., 2006. Application of in-line coagulation/ ultrafiltration process in water treatment. Environment Protection Engineering 32 (2), 67e75. Koper, G.J.M., 2007. An Introduction to Interfacial Engineering. VSSD, Delft. Kweon, J.H., Lawler, D.F., 2004. Fouling mechanisms in the integrated system with softening and ultrafiltration. Water Research 38 (19), 4164e4172. Lee, B.B., Choo, K.H., Chang, D., Choi, S.J., 2009. Optimizing the coagulant dose to control membrane fouling in combined coagulation/ultrafiltration systems for textile wastewater reclamation. Chemical Engineering Journal 155 (1e2), 101e107. Leenheer, J.A., McKniht, D.M., Thurman, E.M. and MacCarthy, P., 1989. Structural components and proposed structural models of fulvic acid from the Suwannee River. Humic Substances in the Suwannee River: interactions, properties, and proposed structures, U.S. Geological Survey, Open-File Report 87-557, 331e360. Li, Q., Elimelech, M., 2004. Organic fouling and chemical cleaning of nanofiltration membranes: measurements and mechanisms. Environmental Science and Technology 38 (17), 4683e4693. Li, S., Heijman, S.G.J., Van Dijk, J.C., 2010a. A pilot-scale study of backwashing ultrafiltration membrane with demineralized water. Journal of Water Supply: Research and Technology e AQUA 59 (2e3), 128e133.
Li, S., Heijman, S.G.J., Verberk, J.Q.J.C., van Dijk, J.C., 2009a. An innovative treatment concept for future drinking water production: fluidized ion exchange-ultrafiltrationnanofiltration-granular activated carbon filtration. Drinking Water Engineering and Science 2, 41e47. Li, S., Heijman, S.G.J., Verberk, J.Q.J.C., van Dijk, J.C., 2010b. Influence of Ca and Na ions in backwash water on ultrafiltration fouling control. Desalination 250 (2), 861e864. Li, S., Heijman, S.G.J., Verberk, J.Q.J.C., Verliefde, A.R.D., Kemperman, A.J.B., van Dijk, J.C., Amy, G., 2009b. Impact of backwash water composition on ultrafiltration fouling control. Journal of Membrane Science 344 (1e2), 17e25. Liao, M.Y., Randtke, S.J., 1986. Predicting the removal of soluble organic contaminants by lime softening. Water Research 20 (1), 27e35. Panglisch, S., Dautzenberg, W., Holy, A., 2008. Drinking water treatment with combined coagulation ultrafiltration-long term experience with Germany’s largest plant. Water Science & Technology: Water SupplyeWSTWS 8 (4), 363e375. Petosa, A.R., Jaisi, D.P., Quevedo, I.R., Elemelech, M., Tufenkji, N., 2010. Aggregation and deposition of engineered nanoparticles in aquatic environments: role of physicochemical interactions. Environmental Science and Technology 44, 6532e6549. Scha¨fer, A.I., 2001. Natural Organics Removal Using Membranes: Principles, Performance and Cost. Technomic, Lancaster. Shannon, R.D., 1976. Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallographica Section A 32, 751e767. Song, L., Singh, G., 2005. Influence of various monovalent cations and calcium ion on the colloidal fouling potential. Journal of Colloid and Interface Science 289 (2), 479e487. van de Ven, W.J.C., van’t Sant, K.v., Pu¨nt, I.G.M., Zwijnenburg, A., Kemperman, A.J.B., van der Meer, W.G.J., Wessling, M., 2008. Hollow fiber dead-end ultrafiltration: influence of ionic environment on filtration of alginates. Journal of Membrane Science 308 (1e2), 218e229. van der Ven, W., 2008. Towards Optimal Saving in Membrane Operation. Twente University, Enschede. Wang, Y., Combe, C., Clark, M.M., 2001. The effects of pH and calcium on the diffusion coefficient of humic acid. Journal of Membrane Science 183 (1), 49e60.
W a t e r R e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 0 1 e6 3 0 7
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Using magnetic seeds to improve the aggregation and precipitation of nanoparticles from backside grinding wastewater Terng-Jou Wan a,*, Shu-Min Shen b, Sheng-Han Siao a, Chong-Fu Huang b, Chiung-Yi Cheng b a
Department of Safety, Health and Environmental Engineering, National Yunlin University of Science and Technology (NYUST), Yunlin 64002, Taiwan, ROC b Graduate School of Engineering Science and Technology, NYUST, Yunlin 64002, Taiwan, ROC
article info
abstract
Article history:
Backside grinding (BG) wastewater treatment typically requires large quantities of chem-
Received 29 December 2010
icals, i.e. polyaluminum chloride (PAC) coagulant and produces considerable amounts of
Received in revised form
sludge, increasing the loading and cost of subsequent sludge treatment and disposal
29 April 2011
processes. This study investigated the effects of the addition of magnetic seeds (FeO*Fe2O3)
Accepted 31 August 2011
of selected particle sizes and of optimized combinations of magnetic seeds and PAC on the
Available online 29 September 2011
aggregation of silica nanoparticles from BG wastewater and on the sedimentation time at
Keywords:
significantly reduced by the magnetic aggregation treatment. The dosage of PAC combined
Backside grinding (BG)
with 2.49 g L1 or 1.24 g L1 of magnetic seeds was reduced by 83% (from 60 to 10 mg L1)
Silica nanoparticles
compared to the conventional process of using only PAC as a coagulant. The turbidity of
Magnetic seeds
the BG wastewater, initially 1900e2500 NTU, could also be successfully decreased about to
Applied magnetic field
23 NTU by the addition of 3.74 g L1 magnetite (FeO*Fe2O3) only at pH 5 with an applied
Aggregation
magnetic field of 1000 G. Different coagulation conditions using magnetic seeds combined
various pH values (5e9). The results show that the turbidity of BG wastewater was
with coagulant resulted in different aggregation performances. The treatment performance was more effective by using two-stage dosing, in which magnetic seeds and PAC were added separately, than that with one-stage dosing, where the magnetic seeds and PAC were added simultaneously during rapid mixing. The two-stage dosing allowed for a reduction in the optimum dosage of magnetic seeds from 3.74 g L1 to 2.49 g L1 or 1.24 g L 1
without affecting performance when coupled with 0.01 g L1 of PAC coagulant. The
developed method effectively reduced the production of waste sludge. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Backside grinders (BG), as used in the semiconductor industry, produce turbid wastewater containing substantial quantities of fine abrasive particles. Current methods for the purification
of BG wastewater employ coagulation and sedimentation to remove fine particles. But these methods not only require large quantities of chemicals but also produce a considerable amount of sludge, increasing the cost of subsequent sludge treatment and disposal processes. The clarification of BG
* Corresponding author. Tel.: þ886 5342601x4484; fax: þ886 5312069. E-mail address:
[email protected] (T.-J. Wan). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.08.067
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wastewater using such methods is difficult because the suspended particles exhibit high stability and resistance to diffusion (Matteson et al., 1995; Touris et al., 2001). In general, semiconductor foundries use coagulants to aggregate wastewater nanoparticles, forming flocs to facilitate sedimentation prior to filtration (e.g., sand or membrane filtration). However, the lifespan of these filtration devices is short due to membrane pore blockage. Thus, the use of these membrane technologies is ineffective (Weigert et al., 1999; Yang and Tsai, 2006; Lin and Yang, 2004). There have been recent reports of the successful application of aggregation technologies employing magnetic seeds. Magnetic separation has attracted great attention because the magnetic force is a long-range attraction, and thus enhances the removal of waste nanoparticles. The main advantage of this technology is that it can treat a mass of wastewater in a very short period of time and produces no contaminants, and reduces the quantity of chemical waste sludge. As a result, it has been widely used in the textile industry, the field of biology and in environmental protection (Chin et al., 2006; Rocher et al., 2008; Lo et al., 2008; Pal and Alocilja, 2009). Higashitani et al. (1996) investigated the effects of magnetic fields on the stability of nonmagnetic colloidal particles and suggested that colloidal stability is influenced by magnetic fields through alteration of the structure of molecular water clusters and ions, either adsorbed on the particle surface or within the medium (Kney and Parsons, 2006). Raw water processing normally involves physicochemical procedures based on the coagulation and flocculation of suspended solids (SS) and colloids and the adsorption of soluble material onto solid substrates such as metal hydroxides (Bolto and Gregory, 2007). Using the jar test system, magnetic seeds have been shown to successfully bind and precipitate phosphate from wastewater; adjusting the pH and calcium concentration can improve the sedimentation efficiency (Karapinar et al., 2004, 2006). Magnetic composites can adsorb contaminants from aqueous or gaseous effluents and can then be easily separated from the medium by a simple magnetic process (Oliveira et al., 2004). Highly turbid raw water has been effectively treated using FeO*Fe2O3 magnetic particles, reducing the turbidity from 9600 to 20 NTU (Lo et al., 2008). In another study, magnetite nanoparticles were synthesized and used as seed particles. The turbidity of the CMP wastewater was reduced from 110 NTU to 7 NTU at a solution pH of 6 with no salt addition (Chin et al., 2006). The current study was divided into three stages. The first stage investigated magnetic seed formulations and their characterization. The second stage studied the extent of
magnetic seeding-induced aggregation in BG wastewater under different physical and chemical conditions, namely, magnetic seed dosage, pH, applied magnetic field, and the use of coagulants. In this stage, coagulants were added to increase the collision frequency and aggregation among the magnetic seeds and nanoparticles, thereby reducing the amount of magnetic seeds required for similar turbidity removal efficiency with the same sedimentation time. The third stage investigated the effect of the magnetic seeding aggregation technique on treatment efficiency.
2.
Materials and methods
2.1.
Samples
Wastewater samples were obtained from semiconductor companies. The wastewater from the silicon wafer backside grinding process is mainly composed of fine nanoparticles with highly stable silica particles (SiO2) and some trace metals that originate from rinsing silicon wafers with ultrapure water.
2.2.
Preparation and characterization of magnetic seeds
Various dosages of magnetic seeds were prepared by combining FeCl3$6H2O (ferric chloride, Shimakyu, Japan) and FeCl2$4H2O (ferrous chloride tetrahydrate, Hanawa, Japan), followed by titration with NaOH(aq) (see Table 1) with stirring at 300 rpm, at room temperature. The optimum molar ratio of Fe2þ to Fe3þ was taken as 2:3, as this ratio has been shown to produce a synergistic effect (Hu et al., 2005). The prepared mixture was black. Finally, the magnetic seeds were washed with deionized water. When the aggregation reaction was completed, there were still impurities remaining in the colloidal water suspension (pH ¼ 5.5 0.5). The dried magnetic seeds were analyzed for particle size and distribution by X-ray powder diffraction, and the results are shown in Fig. 1(a) and (b). The particle size of the magnetic seeds in the slurry was approximately 100e300 nm, the same size as reported by Chin et al. (2006). The diffraction angles of the magnetic seed crystals appeared at approximately 2q ¼ 30 , 35 , 43 , 57 and 63 . The chemical composition of the magnetic seeds was confirmed to be FeO*Fe2O3 by comparison of this diffraction pattern with previously reported patterns (Deng et al., 2003) found in the database of the Joint Committee on Powder Diffraction Standards (JCPDS). Finally, 100e300 nm
Table 1 e Preparation of different magnetic seed dosages. No. 1 2 3 4 5 6 7
FeCl2$4H2O (g L1)
FeCl3$6H2O (g L1)
NaOH (g L1)
Molar ratio of Fe2þ:Fe3þ
Theoretical dosages (g L1)
1.24 2.47 3.70 4.93 6.16 7.39 8.62
2.5 5.0 7.5 10.0 12.5 15.0 17.5
1.62 3.23 4.84 6.45 8.06 9.67 11.28
2:3 2:3 2:3 2:3 2:3 2:3 2:3
1.24 2.49 3.74 4.99 6.23 7.49 8.72
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Fig. 1 e Particle size, SEM and XRD analyses of magnetic seeds.
of magnetic seed crystals were examined using a SEM, that was matched by particle size analysis, as shown in Fig. 1(c).
2.3. Magnetic seed aggregation and sedimentation experiments The modified jar test (Cherng Huei, CG-8277) was used in this study. One liter of BG wastewater was mixed with an aliquot of magnetic slurry that contained 1.24e8.72 g magnetic seeds. NaOH (sodium hydroxide, Merck, Germany) and H2SO4 (sulfuric acid, Merck, Germany) of 0.1 M or 1 M were used to adjust the pH of the wastewater and magnetic seeds separately, to values varying from 5 to 9. The mixture was rapidly mixed at 100 rpm for 3 min and then slowly mixed at 30 rpm for 20 min. To ascertain the economic cost of chemical addition, the amounts of PAC (polyaluminum chloride, Nippon Shinyaku Co., Japan) added during the rapid mixing ranged from 0.01 to 0.06 g L1. After the jar test, a magnetic field was applied for 30 min during aggregate sedimentation. The residual turbidity of the supernatant was measured at 5 min time intervals. Aggregates of silica nanoparticles and magnetic seeds could be separated from aqueous streams by magnetic field strengths of 500, 1000, 1500, or 2000 G. The reactor was applied with magnetic field by surrounding the reactor with copper wire coils and was connecting it to a circulating water bath (TKS, RCB411, Taiwan) to maintained the temperature of reactor at 25 C.
2.4.
The reuse of magnetic seeds
In this study, we preferred to utilize the maximum capturing capacity of the magnetic seeds, thus magnetic seeds left in the
beaker were reused directly, and a new wastewater sample and 0.01 g L1 PAC were added into the beaker for the next cycle of wastewater treatment.
2.5.
Analytical methods
The zeta potential and particle size of the samples were measured using a Malvern Instruments Zetasizer (Nano-ZS, 3000HSA). Analytical procedures from the standard methods were adopted for the measurement of water quality. Suspended solids (SS) were measured by weighing the dried filter membrane after the filtration of the sample (Method 2540, B and D) (APHA, 1992). The crystallinity and orientation of the prepared magnetic seeds were investigated by XRD (D5000, SIEMENS). The surface morphology of the magnetic seeds was examined by SEM (JEOL 5410LV). Wastewater turbidity was measured using a Model 2100P Hach meter. Gauss was measured by Kanetrc Tesla meter TM-701. The trace metals were measured by ICP-AES (OPTIMA 5100DV, Perkin Elmer).
3.
Results and discussion
3.1.
Water quality of BG wastewater
The basic wastewater quality parameters, i.e., pH, SS, turbidity, conductivity, color and the particle size of the wastewater nanoparticles were determined. The pH of the wastewater was generally close to neutral (pH 6.3e8.1), and its color was dark brown (Table 2). The turbidity of wastewater ranged from 1900 to 2500 NTU. The particles in this wastewater were 310e450 nm in size. The results of ICP-AES
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Table 2 e BG wastewater quality parameters.
1600
pH SS (mg L1) Turbidity (NTU) Conductivity (mS cm1) Zeta potential (mV) Particle size (nm) Color
Range 6.3e8.1 150e250 1900e2500 46.1e56.4 24.5 to 34.5 310e450 Dark brown
5 min
1400 Residual turbidity (NTU)
Water quality parameters
analysis of wastewater samples (Table 3) varied greatly in elemental composition.
15 min
1200
30 min
1000 800 600 400 200 0
Effects of pH on magnetic seeding aggregation
Table 3 e Metal composition of BG wastewater.
Si Ca Mg Pb Zn Cu Fe
3.74
4.99
6.23
7.49
8.72
-1
The effect of pH on magnetic seeding aggregation was explored within the range of pH 5e9 (Fig. 3). The results showed that aggregation was most efficient in the pH range of 5e7, particularly at pH 5, i.e. under acidic conditions. Magnetic aggregation was likely affected by the surface charges which was measured by zeta potential (x) of both the magnetic seeds and the silica nanoparticles in wastewater,
Metals
2.49
Dosage of magnetic seeds (g L )
The original turbidity of the BG wastewater was approximately 1900e2500 NTU, and the turbidity was higher than the original turbidity of BG wastewater because of the addition of the magnetic seeds. This mixing could reduce the distances between the magnetic seeds and other nanoparticles in the wastewater. Aggregation was less effective when the dosage of magnetic seeds was small. A large dose of magnetic seeds provided for the most effective removal of particulates; however, at large doses, the seeds themselves became a contributing factor to solution turbidity. Thus, treatment costs would likely become prohibitive if large volumes of magnetic seed slurry were required for the effective removal of contaminants. Increasing the dosage of magnetic seeds from 3.74 g L1 to 4.99 g L1 or greater had no effect on the removal of turbidity in the wastewater. This result suggests that nanoparticles were remained stably suspended in the wastewater and that the turbidity could be reduced only slowly. Thus, we determined that 3.74 g L1 was the appropriate dosage of magnetic seeds for BG wastewater treatment (Fig. 2).
3.3.
1.24
Determination of the optimum dosage of magnetic
mg L1 232.1e258.6 31.9e35.9 3.4e7.2 0.6e0.8 2.1e3.2 0.3e0.6 2.5e11.9
Fig. 2 e Effect of magnetic seeds dosage on aggregation without application of a magnetic field for 5, 15 and 30 min.
which vary at different solution pH values, as shown in Fig. 4. The zeta potential of the magnetic seeds was positive within the pH range of 3e7, but in contrast acquired negative when pH greater than 9, while the zeta potential of the silica nanoparticles in wastewater was always negative within the pH range of 3e11. Thus, the magnetic seeding aggregation efficiency was poor at high pH (9e11) because zeta potential of the magnetic seeds and nanoparticles were both negative. In our study, the zero point of charge (pHzpc) was about 7.5; some literature reports a pHzpc of about 6.7, 6.4 and 6.0 (Chin et al., 2006; Lo et al., 2008; Chin and Fan, 2010). The pH values lower than 4.5 could not be adopted because the magnetic seeds would dissolve below this pH of 4.5 (also seen in Sun et al., 1998). Thus, the pH was within the range of 5e7, the silica nanoparticles would be attracted to the magnetic seeds because of their oppositely charged having electrostatic forces, obviously increasing the likelihood of successful aggregation, especially under particle sizes of several hund-
4000
Residual turbidity (NTU)
3.2. seeds
Sedimentation time = 5 min Sedimentation time = 10 min Sedimentation time = 15 min Sedimentation time = 20 min Sedimentation time = 25 min Sedimentation time = 30 min
3000
2000
1000
0
5
6
7
8
9
pH
Fig. 3 e Residual turbidity and sedimentation time after aggregation at various pH values (with 3.74 g LL1 of magnetic seeds and without applied magnetic field).
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Zeta potential (mV)
40 Nanoparticles in backside grinder wastewater Magnetic seeds
20
0
-20
-40
-60 3
5
7 pH
9
11
Fig. 4 e Effects of pH on the zeta potentials of the fine abrasive nanoparticles in BG wastewater and the magnetic seeds.
reds nanometers and at pH 5. This phenomenon is consistent with other previous literature results (Thomas et al., 1999; Chin et al., 2006).
3.4.
Effects of magnetic field strength on sedimentation
Waste nanoparticles can be easily separated from wastewater by magnetic force after they are seeded with oppositely charged magnetic seeds. Fig. 5 shows the residual turbidity of BG wastewater treated with magnetic seeds and settled in magnetic fields of various strengths. When the magnetic attraction was stronger, more waste nanoparticles were captured by magnetic sedimentation. Because the magnetic seed/waste nanoparticle aggregates settled in a magnetic field, there was improved magnetic aggregation between the suspended magnetite seeds and between the settling aggregates. Therefore, the sedimentation of aggregates was accelerated (Chin et al., 2006). The residual turbidity dropped to 70 NTU with an applied magnetic field of 500 G and under sedimentation time of
30 min. This residual turbidity was much lower than that without the applied magnetic field (195 NTU). The final turbidity was 23 and 17 NTU when the magnetic field was 1000 G and 2000 G, respectively. This result demonstrates that the sedimentation properties of the magnetic seeds were significantly affected by the applied magnetic field. For a given sedimentation time, there was no observed improvement in the residual turbidity of the wastewater when the applied magnetic field was set above 1000 G. Moreover, the magnetic field of 2000 G consumed more energy. Thus, subsequent experiments in this study employed a magnetic field of 1000 G. for the separation or sedimentation of the aggregates of magnetic seeds and silica nanoparticles. There was a slightly increase in turbidity between 1000 G and 1500 G, but we could also find that there are almost no different between them in the removal rate of turbidity.
3.5. Effects of different magnetic seed dosages coupled with the addition of coagulants on wastewater residual turbidity The use of magnetic seeds proved effective in decreasing the residual turbidity of BG wastewater. However, coagulants are less expensive than magnetic seeds, and the treatment of a large volume of wastewater using magnetic seeding aggregation would be unnecessarily expensive. Combining a coagulant (PAC) with the magnetic seeds slurry was investigated with the goal of significantly reducing the treatment cost. The effects of different coagulation conditions on residual turbidity are shown in Fig. 6. Two-stage dosing (adding magnetic seeds and PAC separately during the rapid mixing period, with an interval of 1.5 min between the 2 materials) yielded better removal efficiency than one-stage dosing (adding magnetic seeds and PAC simultaneously during the rapid mixing period). In the two-stage dosing, the magnetic seeds completely captured the silica nanoparticles due to their highly opposite zeta potential having electrostatic attraction, van der Waals forces or chemical affinity during the first stage dosing (Thomas et al., 1999; Chin et al., 2006; Lu et al., 2008). Then the PAC was mixed with nanoparticles 10000
Sedimentation time = 5 min Sedimentation time = 10 min Sedimentation time = 15 min Sedimentation time = 20 min Sedimentation time = 25 min Sedimentation time = 30 min
Residual turbidity (NTU)
300 250 200 150 100
Residual turbidity (NTU)
350
1000
100
10
Magnetic Magnetic Magnetic Magnetic
50 1
0
0
500
1000
1500
2000
Magnetic field (G)
Fig. 5 e Effects of strength of magnetic fields on residual turbidity (3.74 g LL1 of magnetic seeds at pH 5).
0
5
seeds seeds seeds seeds
1.24 g/L+PAC 0.01 g/L (one-stage) 1.24 g/L+PAC 0.01 g/L (two-stage) 2.49 g/L+PAC 0.01 g/L (one-stage) 2.49 g/L+PAC 0.01 g/L (two stage)
10 15 20 Sedimentation time (min)
25
30
Fig. 6 e Effects of the dosage of magnetic seeds on the residual turbidity when combined with PAC coagulant at 0.01 g LL1 (with a magnetic field of 1,000 G at pH 5).
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which had already been combined with magnetic seeds, and the flocs were formed by PAC during the second stage dosing. In addition, in the one-stage dosing, when adding magnetic seeds and PAC simultaneously, some silica nanoparticles would be surrounded by PAC, which separated magnetic seeds and silica nanoparticles to reduce the attraction and opportunities of collisions between silica nanoparticles and magnetic seeds. Thus, it was difficult to capture the silica nanoparticles and separate them by a magnetic field. When the magnetic seeds were added at doses of 2.49 g L1 and 1.24 g L1 (2/3 and 1/3, respectively, of the optimal dosage of 3.74 g L1) coupled with the addition of the PAC coagulant at the dose of 0.01 g L1, the residual turbidity decreased to 19 NTU and 14 NTU, respectively. These combination treatments achieved the same effect as adding magnetic seeds alone at the optimal dosage of 3.74 g L1 (Fig. 5) or by adding only PAC at 0.06 g L1 (Fig. 7). Remarkably, when the magnetic seed dosage was reduced to 1.24 g L1 (containing 0.01 g L1 PAC), the residual turbidity was approximately 14 NTU, which is very close to that of high-turbidity wastewater treated with 3.36 g L1 or more of magnetic seeds (20 NTU; Lo et al., 2008) and oxide-CMP wastewater treated by electrocoagulation (18 NTU; Chou et al., 2009).
3.6.
The reuse of magnetic seeds
Table 4 shows the removal rate of turbidity achieved with magnetic seeds recycled under 30 min of sedimentation time. The removal rate of turbidity could be increased to 99% when magnetic seeds at 1.24 g L1 are coupled with 0.01 g L1 PAC for the first time aggregation. The removal rate of turbidity greatly dropped to 70% for the second time aggregation, indicating that the capturing capacity of the magnetic seeds was saturated. Moreover, both the removal rate of turbidity went up to 99% when magnetic seeds at 2.49 g L1 are coupled with 0.01 g L1 PAC for the first or second time aggregation. For the third time aggregation, the removal rate decreased significantly to 87%. These results demonstrate that residual
Residual turbidity (NTU)
10000
Table 4 e The effects of the number of times reusing the magnetic seeds on the removal rate of turbidity. Removal rate of turbidity (%) 1
1.24 g L magnetic seeds þ 0.01 g L1 PAC 2.49 g L1 magnetic seeds þ 0.01 g L1 PAC
The number of recycling 1
2
3
99.6
70.2
e
99.1
99.6
87.4
turbidity and removal rate had been greatly affected by the dosage of magnetic seeds and the number of times we reused the magnetic seeds. However, reuse capability was limited if the magnetic seeds had not been regenerated or treated properly before being reused.
4.
Conclusions
The magnetic seeds adhered to the silica nanoparticles, indicating the effects of opposite charges, having electrostatic forces, and van der Waals forces or chemical affinity which held an attraction between them, especially under particle sizes of several hundreds nanometers. As the magnetic field was increased up to 1000 G, the turbidity removal efficiency could be improved. The residual turbidity was 179 NTU in the absence of a magnetic field application, and was 23 NTU with an applied magnetic field of 1000 G. However, there was no significant reduction in turbidity or sedimentation time for applied magnetic field strengths above 1000 G. The two-stage dosing of magnetic seeds and PAC coagulant had a higher removal efficiency of turbidity than that of one-stage dosing. Furthermore, with the addition of PAC at 0.01 g L1, the dosage of magnetic seeds could be reduced from 3.74 g L1 to 2.49 g L1 or 1.24 g L1 without adversely affecting the removal efficiency. In contrast, traditional methods require using a sixfold greater quantity of PAC to achieve the same effect. In conclusion, decreasing the dosage of magnetic seeds and incorporating PAC coagulant still produced an effective reduction in residual turbidity. Therefore, this result supports the feasibility of magnetic seeding aggregation as a viable technique for BG wastewater treatment.
1000
references
Without magnetic seeds Magnetic seeds 1.24 g/L
100
Magnetic seeds 2.49 g/L
10
1 0
0.01
0.02
0.03
0.04
0.05
0.06
-1
Dosage of PAC (g L )
Fig. 7 e Aggregation effects at different dosages of magnetic seeds and PAC (with a magnetic field of 1,000 G at pH 5 and a sedimentation time of 5 min).
APHA, 1992. Standard Method for The Examination of Water and Wastewater, 19th ed. APHA, WEF and AWWA, Washington, DC. Bolto, B., Gregory, J., 2007. Organic poly electrolytes in water treatment. Water Res. 41, 2301e2324. Chou, W.L., Wang, C.T., Chang, S.Y., 2009. Study of COD and turbidity removal from real oxide-CMP wastewater by iron electrocoagulation and the evalution of specific energy consumption. J. Hazard. Mater. 168, 1200e1207. Chin, C.J.M., Chen, P.W., Wang, L.J., 2006. Removal of nanoparticles from CMP wastewater by magnetic seeding aggregation. Chemosphere 63, 1809e1813. Chin, C.J.M., Fan, Z.G., 2010. Magnetic seeding aggregation of high turbid source water. J. Environ. Eng. Manage. 20, 145e150.
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Deng, Y., Wang, L., Yang, W., Fu, S., Elaı¨ssari, A., 2003. Preparation of magnetic polymeric via inverse microemulsion polymerization process. J. Magn. Magn. Mater. 257, 69e78. Higashitani, K., Oshitani, J., Ohmura, N., 1996. Effects of magnetic field on water investigated with fluorescent probes. Colloid. Surface. A 109, 167e173. Hu, C.Y., Lo, S.L., Li, C.M., Kuan, W.H., 2005. Treating chemical mechanical polishing (CMP) wastewater by electrocoagulation-flotation process with surfactant. J. Hazard. Mater 120, 15e20. Karapinar, N., Hoffmann, E., Hahn, H.H., 2004. Magnetite seeded sedimentation of phosphate. Water Res. 38, 3059e3066. Karapinar, N., Hoffmann, E., Hahn, H.H., 2006. P-recovery by secondary nucleation and growth of calcium phosphates on magnetite mineral. Water Res. 40, 1210e1216. Kney, A.D., Parsons, S.A., 2006. A spectrophotometer-based study of magnetic water treatment: assessment of ionic vs. surface mechanisms. Water Res. 40, 517e524. Lin, S.H., Yang, C.R., 2004. Chemical and physical treatments of chemical mechanical polishing wastewater from semiconductor fabrication. J. Hazard. Mater. 108, 103e109. Lo, S.L., Wang, Y.L., Hu, C.Y., 2008. High turbidity removal by magnetite particles. Res. J. Chem. Environ. 12, 40e45. Lu, Z., Dai, J., Song, X., Wang, G., Yang, W., 2008. Facile synthesis of Fe3O4/SiO2 composite nanoparticles from primary silica particles. Colloids Surfaces A 317, 450e456.
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Matteson, M.J., Dobson, R.L., Glenn Jr., R.W., Kukunoor, N.S., Waits, W.H., Clayfield, E.J., 1995. Electrocoagulation and separation of aqueous suspensions of ultrafine particles. Colloid. Surface. A 104, 101e109. Oliveira, L.C.A., Petkowicz, D.I., Smaniotto, A., Pergher, S.B.C., 2004. Magnetic zeolites: a new adsorbent for removal of metallic contaminants from water. Water Res. 38, 3699e3704. Pal, S., Alocilja, E.C., 2009. Electrically active polyaniline coated magnetic (EAPM) spores in food samples. Biosens. Bioelectron. 24, 1437e1444. Rocher, V., Siaugue, J.M., Cabuil, V., Bee, A., 2008. Removal of organic dyes by magnetic alginate beads. Water Res. 42, 1290e1298. Sun, Z.X., Su, F.W., Forsling, W., Samskog, P.O., 1998. Surface characteristics of magnetite in aqueous suspension. J. Colloid Interf. Sci. 197, 151e159. Thomas, D.N., Judd, S.J., Fawcett, N., 1999. Flucculation modeling: a review. Water Res. 33 (7), 1579e1592. Touris, C., DePaoli, D.W., Shor, J.T., Hu, M.Z.C., Ying, T.Y., 2001. Electrocoagulation for magnetic seeding of colloidal particles. Colloid. Surface. A 177, 233. Weigert, T., Altmann, J., Ripperger, S., 1999. Crossflow electrofiltration in pilot scale. J. Membrane Sci. 159, 253e262. Yang, G.C.C., Tsai, C.M., 2006. Performance evaluation of a simultaneous electrocoagulation and electrofiltration module for the treatment of Cu-CMP and oxide-CMP wastewaters. J. Membrane Sci. 286, 36e44.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 0 8 e6 3 2 0
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
An online method for estimation of degradable substrate and biomass in an aerated activated sludge process Marcus Hedega¨rd, Torsten Wik* Department of Signals and Systems, Chalmers University of Technology, SE-412 96 Go¨teborg, Sweden
article info
abstract
Article history:
The activated sludge process for degradation of organic matter is one of the main processes
Received 23 February 2011
commonly used in biological wastewater treatment, and aeration in that process stands for
Received in revised form
a large part of the energy consumed in a plant. Hence, there have been many attempts to
29 August 2011
improve the operation of the activated sludge process using mathematical models of the
Accepted 1 September 2011
process. The advanced models used has in general their origin in IWA (former IAWQ)
Available online 10 September 2011
activated sludge model no 1 (ASM1). Unfortunately, optimization w.r.t. discharge and economy is limited for municipal wastewater treatment plants because several of the most
Keywords:
important variables; heterotrophic biomass, readily biodegradable soluble substrate, and
Activated sludge control
slowly biodegradable substrate cannot be reliably measured online because of their
Extended Kalman filter
complexity hiding behind their notation. With the predenitrifying WWTP in Go¨teborg
Soft sensor
having post nitrification in trickling filters as an example, we resolve this problem by
Observer
deriving an observer that estimates these concentrations in the aerobic parts based on only the commonly available online measurements of oxygen, water flows, TSS concentration and supplied air. Based on control theory analysis and simulations it is concluded that estimation does not work for an activated sludge process with aeration in one stirred tank alone, but when the activated sludge process can be described by at least two tanks in series, with oxygen measurements in each tank, the estimates converge. A sensitivity analysis with respect to deviations in model parameters reveals that the derived estimator is also fairly robust to model errors. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
The activated sludge process (ASP) is probably the most common degrading process in large scale biological wastewater treatment, and the cost of aeration generally stands for a large part of the total operating costs for a plant. Optimized operation of this complex process is therefore highly motivated, though it requires a dynamic mathematical model of the process. The most widely used models for modeling of ASPs are the Activated Sludge Model NO.1 (ASM1) and its successors ASM2 and ASM3 (IWA, 2000). Unfortunately, these
models contain several concentrations that are highly difficult to reliably measure online. This is in particular true for some of the main variables, such as readily biodegradable dissolved substrate (SS), slowly biodegradable substrate (XS), and active biomass concentrations (XBH and XBA). Substrate analyzers have been available for many years now, but historically they have been considered too unreliable to be used for control (Olsson and Newell, 1999). The measurements closest to SS and XS are BOD-measurements, which are normally measured off-line in laboratories. However, there are a number of different approaches to make more rapid and also online
* Corresponding author. Tel.: þ46 31 772 51 46. E-mail addresses:
[email protected] (M. Hedega¨rd),
[email protected] (T. Wik). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.003
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Notation1 ASM ASP EKF BOD COD KLa Q Qin Qrec 1
Activated Sludge Model Activated Sludge Process Extended Kalman filter Biochemical oxygen demand Chemical oxygen demand Oxygen mass transfer function Total flow through the ASP Influent flow to the ASP Flow from the nitrifying trickling filters
Qx q R1 R2 TSS V WWTP Xx ^ X gBH
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Return sludge flow Air flow rate through diffussors in one tank Process noise covariance matrix Measurement noise covariance matrix Totally suspended solids Tank volume Wastewater treatment plant Sludge concentration in the return sludge flow Estimated sludge concentration in one tank fraction of the sludge being active heterotrophic biomass
The notation, meaning and units of the remaining variables are listed in Tables 1e4.
measurements (Liu and Mattiasson (2002). Examples are biosensors of biofilm type (having immobilized bacteria), respirometers and bioreactors with suspended bacteria, but also the early reported biofuel cell sensor (Karube et al., 1977) and methods based on heat release as introduced by Mattiasson et al. (1977). For specific conditions these methods work well but for the harsh and highly varying conditions in most municipal wastewater treatment plants there are problems that are hard to overcome for stable and accurate sensing. Filtering is required in order to measure the dissolved substrates, among which some may be slowly biodegradable. Hence, it is intrinsically difficult to distinguish between SS and XS, which can only partly be made by additional parallel measurements of unfiltered water. This implicates the traditional problem of lacking robustness for field service and intense maintenance (Liu and Mattiasson, 2002), though the instrument manufacturers are gradually improving their products in this aspect. In order to guarantee sensor stability though, the biosensors generally contain one or only a few bacterial species. Since different species have different kinetics for different types of substrates this means that accuracy is hard to achieve for wastewater where the composition varies. For plants receiving industrial as well as municipal wastewater such variations are further enhanced. Also for spectrometric methods (Spanjers and van Lier, 2006), the problem of varying composition is a problem hard to overcome, since they are based on experimental correlations. For the biosensors there is also an additional complication when there are substances in the water being toxic to the species used. According to the surveys presented by Olsson et al. (2005) for a large number of European wastewater treatment plants it was found that very few plants were equipped with BOD-sensors, and none of them were using them for feedback control. Totally Suspended Solids (TSS) measurements give indication of biomass concentration, and bacterial concentration can be estimated from respiration tests, though to the best of the authors’ knowledge, robust rapid online-sensing of the concentration of active bacteria is still troublesome, basically for the same reasons as for the BOD sensors. There are many possible ASP control variables in an optimization of its operation, such as the aeration flow rates, the aerated volume, the amount of external carbon, and the
return sludge flow. These variables are considered by Samuelsson et al. (2007), Chachuata et al. (2005) and Lindberg (1997), for example, using the ASM1 but not taking into account the actual measurement of substrates or biomass. If all relevant concentrations in the ASM1 were available online, this information should be possible to use for improving existing operation either by monitoring or by use of new control strategies. Suppose we have restrictions on ASP effluent dissolved organics in terms of discharge, or on influent SS to a post nitrifying unit, for example. Having reliable estimates of readily biodegradable substrate then opens up for a couple of different options: Avoiding excess aeration when effluent SS and XS are below desired maximum values. The oxygen transfer in the aeration is a nonlinear function of the air supply flow (saturation type), such that the higher the flow the less efficient the aeration is. At the same time the transfer is also affected by the actual oxygen concentration. An optimization of the air flow profile along the ASP is then possible under supervision, or control of SS. By reducing the sludge concentration, oxidation can be moved downstream provided we supervise effluent SS. This will make better use of the efficient parts of the aeration function and also decrease the load on the final settlers. A general process control remedy for absence of online measurements is to find a so called observer to estimate unmeasured variables based on a dynamic model and online measurements of other variables. Observers to estimate biodegradable substrate based on the ASM1 have previously been formulated by Benazzi et al. (2007) and Boulkroune et al. (2009). Both of these are for one aerobic reactor in the COST benchmark model (Copp, 2001; Alex et al., 1999) assuming constant and known bacterial concentration. In practice this is a highly restrictive assumption because of unreliable online-sensing of the concentrations of active heterotrophic bacteria alone is still not feasible. Benazzi et al. (2007) used an extended Kalman filter (EKF) but found by simulation that for the estimates to converge it was also necessary that at least the influent concentration of readily degradable substrate was measured as well. Boulkroune et al. (2009) used an LMI (Linear Matrix Inequality) based nonlinear observer also based on a simplified version of
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the ASM1. They lumped soluble and the particulate degradable substrate into one variable XDCO since their presumed measurement could not distinguish between them. They concluded that the system became unobservable if the influent XDCO concentration was unknown. However, if they used a daily mean substrate level based on lab analysis, their estimations could converge. The drawbacks of the above methods are that they rely on data for bacterial concentration and influent concentrations of biodegradable substrates. The latter method does not distinguish between soluble and particulate substrate, although this is very important since the effects and kinetics for the two are very different. Neither have the robustness to model and measurement errors been assessed. In this work we derive an extended Kalman filter for a predenitrifying process with post nitrification. It estimates all the discussed concentrations in the aerated parts of the ASP, including unknown influent concentrations of substrate and biomass to the aerated zones, based on measurements in two or three aerobic tanks. The estimated SS may also be preferable to use for control rather than measured BOD alone, since it reflects the process behavior w.r.t. the modeled variable actually used for the optimization. The success of this method, in the sense that no measurements of organic matter are needed, relies on the following: Instead of assuming the influent concentrations known, they are considered as stochastic processes and are estimated by the observer. By including more than one tank additional measurements are gained, and because of mass balances there is a coupling between the states, improving the information used by the observer. The concentration of heterotrophic biomass in the process can change rapidly as a result of changes in the process flows. To capture this, the biomass concentrations in the reactors are modeled as a product of the TSS concentration and a parameter gBH. The TSS concentration in the sludge recycle is measured and the fraction gBH of the sludge being heterotrophic biomass is treated as a slowly varying stochastic variable that is estimated by the observer. The sensitivity of the observer is investigated both with respect to measurement errors and model errors. It is found that the performance is robust to changes up to at least 10e20%, depending on the type of error.
2.
Fig. 1 e Model of the activated sludge process at Ryaverket.
observer model for the derivation of the observer (see Section 2.3).
2.1.
Simulation model
The part of the process that is included in the simulation model is within the dashed line in Fig. 1. The activated sludge process have nine zones (see Fig. 2a), where the two first ones (40% of the volume) are always anoxic, and the last 4 zones (also 40% of the volume) are always aerated. In the middle there are three zones, comprising 20% of the volume, which can be either anoxic and mixed, or aerated. Lithium tracer tests carried out on the sludge basin have indicated that it can be approximated by 8 ideally stirred tanks (Kjellstrand, 2006) as illustrated in Fig. 2b. This tank division is used in the modeling, with concentrations, inputs and parameters indexed according to the tank they belong to. Because the fluid dynamics is very rapid it is assumed that the water flow Q is the same through all tanks. In reality, the air flow rates through the diffusors can be individually controlled for each zone, while in the simulation model they are controlled per tank. The dissolved oxygen (DO) concentration is assumed to be measurable in all aerated tanks. The basic block in the simulation model is the model of one tank. For the full ASP model the consecutive tank models are linked by mass balances. In the simulation model we also include the mixing of the three influent flows as shown in Fig. 1.
a
Material and methods
As a basis for the observer derivation we consider the waste water treatment plant (WWTP) Ryaverket in Go¨teborg, Sweden, which is predenitrifying in activated sludge with post nitrification in nitrifying trickling filters (see Fig. 1). This plant has been described in more detail by Balmer et al. (1998), Persson et al. (2002), Wik et al. (2004) and Wilen et al. (2010). Two models are used in this work; one simulation model, which is basically the full ASM1 model that is supposed to describe the process realistically, and one highly reduced
b
Fig. 2 e Illustration of how the activated sludge basin is divided into zones and tanks.
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The model of tank i is given by the mass balances for each component, i.e. d Zi ¼ Di ðQÞðZi1 Zi Þ þ x qi ; Zi ; dt
(1)
where Zi is a vector of all concentrations considered (see Table 1) and Di ¼ Q/Vi is the dilution rate. x describes the reactions and kinetics in the ASM1 (IWA, 2000) and it also includes the liquid-gas mass transfer of oxygen from the diffusors: qO2 ¼ KL a qi ðSOsat SO Þg= m3 d
(2)
where SOsat is the oxygen saturation concentration. The oxygen transfer coefficient, KLa, increases with the air flow rate qi through the diffusors. Because of changes in for example bubble size it is often assigned the following expression (Olsson et al., 2005): KL aðqÞ ¼ k1 1 ek2 q
(3)
There are several methods to estimate KLa, but it should be noted that it varies with temperature and the kind of waste (Lindberg, 1997; Olsson and Newell, 1999). The ASM1 variables, given in Table 1, can be divided into three groups. The ones in the upper part of the table are the most important ones and stands for the dominating part of the reactions in this kind of ASP. The variables in the middle of the table enter the equations in expressions that involve a product with the concentration XBA of active autotrophic bacteria. XBA is very small due to the configuration of the plant, having post nitrification and a very short sludge age. This means that SNH, and XBA account for only a small portion of the reactions. SND and XND in the lower part of the table affect the variables in the upper part only via XBA. These variables are therefore omitted from the simulation model. The variables SI, XI, XP and SALK in the lower part of the table have no effect on the heterotrophic activity according to the ASM1, and are therefore also excluded.
Table 1 e Variables in ASM1. Symbol
Name
Dimension (COD) m3 (COD) m3 (COD) m3 (COD) m3 (COD) m3
SO SS XS XBH SNO
Oxygen Readily biodegradable substrate Slowly biodegradable substrate Active heterotrophic biomass Nitrate and nitrite nitrogen
g g g g g
XBA SNH
Active autotrophic biomass Ammonium
g (COD) m3 g (N) m3
SI XI XP
Soluble inert organic matter Particulate inert organic matter Particulate products arising from biomass decay Soluble biodegradable organic nitrogen Particulate biodegradable organic nitrogen Alkalinity
g (COD) m3 g (COD) m3 g (COD) m3
SND XND SALK
g (N) m3 g (N) m3 Molar units
Table 2 e ASM1 parameters for Ryaverket.
Stoichiometric parameters Heterotrophic yield (g (COD)/g (COD)) Autotrophic yield (g (COD)/g (COD)) Nitrogen fraction in biomass (g (N)/g (COD)) Nitrogen fraction in endogenous mass (g (N)/g (COD)) Fraction of biomass leading to particulate material Kinetic parameters for heterotrophs Maximum specific growth rate (1/day) Half saturation coefficient for heterotrophic biomass (g (COD)/m3) Oxygen half saturation coefficient ðgðO2 Þ=m3 Þ Nitrate half saturation constant (g (NO3N)/m3) Decay coefficient for heterotrophic biomass (1/day) Anoxic growth correction factor Kinetic parameters for autotrophs Maximum specific growth rate (1/day) Ammonium half saturation constant (g (NO3N)/m3) Oxygen half saturation constant (g (O2)/m3) Decay coefficient for autotrophic biomass (1/day) Hydrolysis parameters Maximum specific hydrolysis rate (1/day) Anoxic hydrolysis correction factor Half saturation coefficient for hydrolysis of XS
2.2.
Symbol
Value
YH YA ixB
0.666 0.15 0.068
ixP
0.068
fp
0.08
m ^H KS
3.2 5
KOH
0.2
KNO
1.0
bh
0.62
hg
0.8
m ^A
0.8
KNH
1.0
KOA
0.2
bA
0.04
kh
2.81
hh Kx
0.4 0.15
Simulation data
The presented simulation case is for 14 days, and corresponds to a dry summer period with a water temperature of 20 C, and 60% of the process aerated. The oxygen reference is set to 2 mgO2 l1. The parameters in the ASM1 (as implemented in GPS-X by Hydromantis Inc.) have been tuned by the plant staff to get agreement for daily averages over two years and are given in Table 1 for the temperature 20 C. Daily mean samples are analyzed for COD at the plant. This COD content has then been divided over the different organic compounds in the ASM1 according to a standard distribution determined experimentally at the plant. The remaining concentrations in the influent flows were chosen to have representative means for a summer period at the point m in Fig. 1. To get a realistic variation around the mean, the weather files from the COST benchmark project (Copp, 2001) were used. First the COST averages were removed and the remaining variations were multiplied by a common factor that was adjusted to get amplitudes that were considered representative for the plant. The dominating source of heterotrophs in the ASP is the return sludge flow Qx. There is also a relatively small concentration in the influent Qin, and the concentration is also
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Table 3 e Mean concentrations in Tank 5. Symbol SO SS XS XBH SNO XBA SNH
Mean
Standard deviation
0 14.5 225 1120 0.25 25.5 4.70
0 13.4 71 190 0.06 1.1 0.22
Unit mg mg mg mg mg mg mg
(COD) l1 (COD) l1 (COD) l1 (COD) l1 (N) l1 (COD) l1 (N) l1
affected by growth and decay in the ASP. In the return sludge flow Qx the concentration of heterotrophs is factorized as XBH ¼ XX $gBH ;
(4)
where Xx is the TSS concentration in Qx, and gBH (see Fig. 5f) is a slowly time varying parameter representing the ratio of the sludge being heterotrophs. The heterotrophic concentration in a reactor can still vary fast when the operators change the flows. To simulate this, Qrec (the flow from the trickling filters) and Qin varies stepwise synchronically between 3 and 4 m3/s, while Qx is constantly 3 m3/s, such that the flow Q through the ASP was constant at 10 m3/s, which is normal because of limited capacity in the final settlers (see Fig. 1). The means and standard deviations of the resulting influent concentrations to the aerated zones are presented in Table 3. Fig. 5(a), (d), and (e) show the concentrations SS, XS, and XBH in the same flow. All diffusors in the ASP are built up of the same kind of pipes. Therefore, if we ignore the interaction between the diffusors it is enough to estimate the KLa function parameters for one tank, and calculate the parameter for the other tanks based on this estimate using data on the number of pipes in each tank. If k1p and k2p are the diffusor parameters for one pipe and n is the number of pipes, KL aðqÞ ¼ k1p n 1 ek2p q=n and, hence, k1 ¼ k1pn and k2 ¼ k2p/n. For simplicity it is here assumed that the number of pipes in each tank are the same, and that the corresponding KLa functions therefore have identical parameters. The function parameters used in the simulations are given in Table 4 for 20 C, and have been estimated from experiments carried out on Tank 7 with the method described by Suescun et al. (1998), and the KLa temperature model described by Stenstrom and Gilbert (1981).
Table 4 e Basic parameters in the model. Parameter
Value
Unit
Comment The oxygen saturation concentration Volume of tank 5, 6, 7 and 8 KLa parameter in Equation (3) KLa parameter in Equation (3)
SOsat
8.73
g (O2)/l
V5, V6, V7, V8
3310
m3
k1
326
1/day
k2
1.9,104
day/m3 (air)
Fig. 3 e A blockscheme of the observer.
The oxygen concentration SOsat is assumed to be the one for pure water at 20 C (Riley and Skirrow, 1975) corrected with the correction factor (b factor) 0.95 (Stenstrom and Gilbert, 1981), and is given in Table 4, as are also the volumes of the tanks. The measurement noises of the DO sensors (see Fig. 2) are assumed to be white Gaussian and all with a standard deviation sO2 ¼ 0:2 mgO2 l1 .
2.3.
Observer model
The purpose of an observer is to estimate unmeasured, or improve measurements of dynamic variables (states) in the presence of measurement noise and process disturbances (Glad and Ljung, 2000). When a system is considered from a stationary point of view the determination of an unmeasured concentration relies on the solution of one or several algebraic equations giving the combination of measurements to be used. It may then be the case that there is no solution or the solution is too sensitive with respect to measurements or model errors. However, the unmeasured variable can still reveal itself in the transient behavior of the system, making it possible to be estimated with an observer. Because of the natural variations in the incoming water to a municipal WWTP the conditions are more or less transient all the time. The main principle for state estimation using an observer is that a dynamic model (the observer model), which describes the main dynamics using the estimated states x^, is fed the same inputs u as is affecting the real system, and then the modeled output y^ is compared to the measurements y (see Fig. 3). This difference is then used to correct the states in the model such that the estimation error ðx x^Þ is minimized. In our case, the inputs are the flows Q and Qx, the airflows through the diffusors (q6, q7 and q8), and the TSS concentration Xx in the sludge recycle flow. The observer model outputs are the estimated oxygen concentrations S^O:6 , S^O:7 , S^O:8 (to be compared with the corresponding measured ones), and the disturbances include all other variables affecting the process,
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i.e. variations in influent concentrations to the ASP, and deviations in the flows. The observer model derived here involves a subset of the simulation model for the last 3 downstream tanks combined with simplified mass balances for TSS in all tanks. To keep the complexity of the observer low, a model less detailed than the one used for simulation is desired and therefore some further simplifying assumptions are made. First, the sludge in the influent Qin and the net production of TSS sludge in the ASP is neglected such that the observer then handles the corresponding deviations as process ^ in each tank can then disturbances. The TSS concentration X be estimated from mass balances: d ^ QX ^1 XX X X1 ¼ D1 ðQÞ Q dt d ^ ^ i1 X ^ i Þ; i ¼ 2; 3; . 8 X ¼ Di ðQÞðX dt i
(5)
(6)
Contained in the TSS is the concentration XBH of heterotrophic bacteria, which directly affects the reaction rates. Also for the ASP tanks, this concentration is modeled as the product of the stochastic variable gBH and the TSS concentration. When the flows Qin, Qrec and consequently Q are changed there can be rapid changes in XBH and these are caught by the above mass balances. Because of changes in the operating conditions there will also be a much slower change in the fraction gBH of the sludge being active bacteria, and this is estimated by the observer. The observer model is simplified by removing the state equations for XBH:i. In the remaining ^i. mass balances XBH:i is substituted with gBH $X Next, since XBA is very small the nitrification is ignored, and XBA and SNH are removed from the model. Nitrate is also removed since SNO enters the equations in a product with KOH/ (KOHþSO), which (by definition) is small in an aerobic reactor. The full simplified state space model for Reactor i (aerobic) becomes d ^ i ; Zi ; gBH ; Zi ¼ Di ðQÞðZi1 Zi Þ þ x qi ; X dt where Z ¼ ½ SO
SS
(7)
XS T , and
realistically modeled based on physical relations and are instead modeled as random walk processes. In its simplest form, a random walk process is a variable r described by the equation d r ¼ wr ; dt
(8)
where wr is zero mean Gaussian white noise with a variance Rr. The size of Rr should describe how large the variation of r is compared to other random variables in a model. In the full observer model it is also possible to take into account that variations in r are correlated with other random variables. The resulting state vector x(t), the input vector u(t) and the process noise vector w(t) for the observer model are now x ¼ ½ SS:5 XS:5 gBH Z6 / Zk T ^ k T ^ 6 /X u ¼ ½ Q q6 / qk X T w ¼ ½ wSS wXS wg 0 / 0 The state equation in the observer model then becomes 3 2 0 7 6 7 6 0 7 6 7 6 7 6 0 d 7 6 7 þw (9) x¼6 ^ ; Z6 ; gBH 7 6 D6 ðQÞðZ5 Z6 Þ þ x q6 ; X dt 6 7 6 7 6 7 6 « 4 5 ^ Dk ðQÞðZk1 Zk Þ þ x qk ; Xk ; Zk ; gBH |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} aðx;uÞ
where the process noise covariance matrix R1 ¼ E[wwT] is chosen to be diagonal. The upper first three elements on the diagonal in R1 are the only nonzero elements, belonging to the random walk processes. These are design parameters that can be used to tune the convergence rate and the noise reduction of the observer. The measurement equation is T y ¼ ½ SO:6 / SO:k þv |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
(10)
cðx;uÞ
The covariance of the measurement noise v is R2 ¼ IsO2 , where I is the identity matrix with k5 rows. The complete observer model is now on a standard form
2
3 1 YH SS SO ^ gBH KL aðqÞðSOsat SO Þ X m ^H 6 7 YH KS þ SS KOH þ SO 6 7 6 7 6 7 SS SO XS SO 6 1 7 ^ ^ Z; gBH Þ ¼ 6 m 7: X gBH þ kh ^ xðq; X; ^ 6 YH H KS þ SS KOH þ SO K þ S O7 KX þ XS =ðX gBH Þ OH 6 7 6 7 6 7 X S 4 5 S O ^ gBH kh 1 fp bh X ^ K þ S O KX þ XS =ðX gBH Þ OH
We want to study the properties of the observer depending on the number of oxygen measurements that are used, and therefore a parameterized observer model is derived. A simplified model including Tank 6 to Tank k, k˛{6,7,8} is formed by connecting the corresponding tank models by mass balances. This model then contains the unmeasured inputs of SS and XS to the first aerobic reactor, and also the unknown fraction gBH of heterotrophs. None of these inputs can be
(
d x ¼ aðx; uÞ þ w dt y ¼ cðx; uÞ þ v
2.4.
(11)
The extended Kalman filter
Special kinds of observers are filters for stochastic systems in which statistical properties of disturbances acting on the
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system are taken into account in the observer synthesis. An example is the Kalman filter for linear systems with white Gaussian disturbances, which is optimal in the sense that it minimizes the variance of the estimation error. The Extended Kalman Filter (EKF) is a widely used filter for nonlinear systems, and it is based on a linearization of the nonlinear system around its actual estimate. This filter is in general not optimal, but if the system fulfills some observability properties and disturbances are small and Gaussian, it is locally a good approximation of the optimal filter. The EKF is in general not a globally converging observer (Besanc¸on, 2007). The reason is simply that the linear approximation of the model (11) may be a very poor approximation if the estimated state x^ is far from the true state x. The EKF algorithm for a system on the form (11) is given by x^ ð0Þ ¼ x^ 0
Initialization :
Pð0Þ ¼ P0 ;
Estimate update :
d x^ ¼ aðx^ ; uÞ þ K½y cðx^ ; uÞ dt
Error covariance update :
d P ¼ Aðx^ ÞP þ PT Aðx^ Þ þ R1 dt ^ PCðx^ ÞT R1 2 Cðx ÞP
Kalman gain :
K ¼ PCðx^ ÞT R1 2
Jacobians
Aðx^ Þ ¼
vaðx; uÞ vx x¼^x
CðxÞ ¼
vcðx; uÞ vx x¼^x
(12)
which can be derived by a first order approximation of the equations describing the optimal filter (Lewis, 1986). If the system is linear, the equations simplifies to the Kalman filter for linear systems. x^0 is the initial guess or estimate of the state x(0), and P0 ¼ E½ðx^0 xð0ÞÞðx^0 xð0ÞÞT is the covariance of the initial guess, which should reflect the quality of the initial guess. The actual implementation of the continuous time EKF can be made using, for example, Runge Kutta methods (Lewis, 1986). Fig. 4 illustrates the total observer solution in this application.
2.5.
Observability conditions
Observability conditions should express that there is indeed a possibility that the purpose of the observer can be fulfilled, i.e. to recover the state vector x of the system based on measured or known inputs u and measured outputs y. For nonlinear systems observability can be difficult to determine and the more complex and larger the number of equations there are in the observer model, the more difficult it is. Further, observability for nonlinear systems depends on the input, while for linear time invariant systems (LTIs) the property is global in both states and inputs. If the system is globally observable, this holds independently of the initial guess, which is in general too difficult to determine for nonlinear systems. Local weak observability is easier to investigate and it means that states can be distinguished from their neighbors, suggesting that a locally convergent observer can be designed, i.e. an observer for which the estimates converge toward the true states if the initial guess x^ð0Þ is sufficiently close to the real state x(0). The observability rank condition (Besanc¸on, 2007) can be used to determine local weak observability for nonlinear systems. To evaluate this condition for a general input and a general state can be difficult due to equation complexity. However, it is feasible to determine the rank condition for specified inputs and states, which gives an indication of the observability of the system. Note that even if observability can be verified for a nonlinear system, this does not mean that the chosen observer design will be convergent, since the design might not be optimal. For linear time invariant (LTI) systems, observability is a general concept, and if it is fulfilled for a system a global observer with tunable convergence can be designed. A weaker condition is detectability, which implies that a global observer design is still possible, but that the convergence rate is not tunable for all states. Because an EKF is based on a linearization of the observer model, observability of the linearized model is an important indication of the potential performance of the EKF. Observability or detectability of the linearization of a nonlinear observer model will further on be referred to as Jacobian observability or Jacobian detectability. Methods to evaluate observability
Fig. 4 e Illustration of the observer solution.
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and detectability for linear systems (or linearized systems) are given by Lewis (1986) and Glad and Ljung (2000), for example.
3.
Simulation
Both the simulation model described in Section 2.1 and the filter (12) based on the observer model (11) were implemented using state space functions in Matlab and Simulink. For the filter, the initial guess x^ð0Þ ¼ x^0 of the states was taken as 1.6
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time the correct value, except for the oxygen concentrations that were assumed to be known. The initial covariance P0 was chosen to be a diagonal matrix with ð0:6x^0 Þ2 on the diagonal. The process noise covariance matrix R1 was manually tuned to R1 ¼ diagð5,105 ; 7,106 ; 0:6; 0; .; 0Þ which gave a fair balance between convergence rate and noise reduction for the different states. Three different observers have been investigated and compared, that is when the observer is based on outputs from 1, 2 or 3 tanks (i.e. k ¼ 6, 7 or 8). Under what conditions the
Fig. 5 e Estimated (noisy) and true variables (smooth).
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different observers worked was investigated both by simulation and observability analysis.
3.1.
Simulations
The observer for one tank was found to be divergent. The result for the EKF:s based on measurements in two and three tanks are indistinguishable, and therefore only the results for three tanks are presented in Fig. 5. As seen, the estimates converge rapidly from the erroneous initial guess and the ^S ^ BH , and X estimation error is small. The offset, especially in X are mainly due to the neglected nitrification in the derivation of the observer model. Many of the parameters in the model are temperature dependent, and the observer for three tanks (and the simulation model) was also evaluated with parameter values for the typical winter temperature 10 C, with equally good results as those in Fig. 5. The benefit of the observer for three tanks is seen when different simulation data are used. In general, the higher the excitation of the dynamics the more information about the unmeasured variables is contained in the data. For the filter for two tanks to be convergent, a variation in the actual concentrations is necessary, otherwise the estimates may diverge. One large peak a day, though, seems to be a sufficient excitation for filter convergence. When all input concentrations and flows are set to constants the estimates diverged. A typical simulation result for this case is illustrated in Fig. 6a. For three tanks, however, the convergence was found to be unconditional, in the sense that the estimates converged for all investigated inputs and parameter combinations, and the corresponding simulation results for constant inputs for this filter is illustrated in Fig. 6b.
based on this model is divergent. The observer model for two tanks was found to be locally weakly observable for all points, and it is likely that this condition holds for any reasonable state and input. This indicates that a converging observer design should exist. However, this is not contradicting that the corresponding filter is dependent on variations in the actual concentrations for convergence, since the EKF is not optimal. More important for an EKF design is Jacobian detectability, which was not fulfilled for any of the points for this filter. For the observer model for three tanks, however, weak observability as well as Jacobian observability could be confirmed for all points motivating the observed unconditional convergence of the corresponding EKF.
3.3.
Next we consider model errors and in particular errors in the parameters used by the observer. The results are presented for the observer based on three tanks and to limit the number of combinations to investigate, the sensitivity has not been investigated for all parameters. The parameters fP and bh only enter the equations for the XS concentrations in the observer model. These are mainly driven by mass balances, and therefore errors in the parameters fP and bh have been omitted. Neither are errors in the parameters KOH and YH considered. The former since the Monod expression SO/ (SOþKOH) is close to saturated for the set point oxygen concentration, and the latter since it is a stoichiometric coefficient fairly well known, and because it enters the equations similarly to mH and gBH. Let q 0 ¼ ½ k1
3.2.
Observability
The state vector of the model (11) was sampled once an hour during one day for the data in the simulation case study. Local weak observability, Jacobian observability and the weaker condition Jacobian detectability (see Section 2.5) were then investigated for all 24 state vectors and inputs. The results are summarized in Table 5. The model based on one tank did not fulfill any of the conditions, which may explain that the EKF
Simulation with parameter errors
k2
SOsat
kh
KS
KX
m ^ H
be the vector of parameters in the simulation model and q the corresponding vector for the observer model (12) and let J ¼ ½ SS:5
SS:6
SS:7
SS:8
XS:5
XBH5
be the vector concentrations estimated by the EKF. Sensitivities of the estimates of XS, and XBH are only presented for one tank, since these are mainly driven by mass balances.
Fig. 6 e Simulation with actual quantities set to constants.
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Table 5 e Observability of the observer models, and convergence of the corresponding filters. Measurements in
Observability rank condition
Jacobian detectability
Jacobian observability
Filter convergence
Not satisfied Satisfied Satisfied
Not satisfied Not satisfied Satisfied
Not satisfied Not satisfied Satisfied
Divergent Conditional Unconditional
1 tank 2 tanks 3 tanks
Denote the estimate of J when q ¼ q0 by ^J and let ^Ji;j ðqj Þ be the estimate of Ji as a function of the j:th parameter in q (i.e. qj) with all other parameters in the observer equal to those in q0. The mean relative error in the estimation of concentration number i as a function of the relative error Dj ¼ (qjq0:j)/qj in parameter j is
eEKFi;j Dj ¼
Z14 ^Ji;j Dj þ 1 q0:j ^Ji dt 2
Z14
: j^Ji jdt
2
The integration is started 2 days after the startup of the filter to remove the effects of the initialization. The sensitivity of the mean relative estimation error in variable i with respect to parameter j is determined as follows ki;j ¼
veEKFi;j vD j
Dj ¼0
dDj veEKFi;j ¼ 1= dqj vqj q ¼q j
0:j
0 1 Z14 B j^Ji;j qj ^Ji jdtC B C C vB B C ¼ q0:j $ B 2 C Z14 C vqj B B C @ A j^Ji jdt 2
qj ¼q0:j
Z14
j^Ji;j 1:1q0:j ^Ji j þ ^Ji;j 0:9q0:j ^Ji dt
z2
Z14 0:2
and the oxygen consumption much. The effects of parameter errors are interpreted by the observer (through its model) as changes in the process rates. Consequently, small effects of the errors will cause relatively large changes in the estimated concentrations if they are far higher than the saturation coefficient KS. In the last tank, on the other hand, the situation is the opposite: SS is often below the saturation coefficient (c.f. Fig. 5c) and therefore relatively small changes in SS affect the response significantly. Consequently, parameter changes will result in smaller changes in the estimate ðS^S:8 Þ and therefore a low sensitivity to model errors. Kalman filters use a combination of measurements and a dynamic model to produce the estimates. In fact, the two covariance matrices R1 and R2 can be seen as tools for tuning which (model or measurement) to put the most emphasis on. Large R1 relative to R2 (usually in powers of 10) implies more trust on measurements and small R1 implies more trust on the model. Due to the optimality properties of the linear Kalman filter, the EKF also ”knows” that the estimate is better the lower the concentration is. It will therefore rely more on the model the higher upstream the concentration is estimated and thus, the parameter sensitivity should generally increase then. This holds for all the investigated parameters except for KS, because for that specific parameter this is counteracted by the fact that the higher the concentration the less sensitive the Monod expression is to changes in KS. As an illustration simulation results for q ¼ 0.9q0 (all parameters reduced 10%) are shown in Fig. 7. From Table 6 and extensive simulations of this kind we conclude that the SS estimates are in general relatively little affected by the errors, while XS and XBH are more sensitive to parameter errors.
hk^i;j : j^Ji jdt
2
The calculated values of k^i;j are given in Table 6, from which we can determine the approximate mean estimation error
4.
Discussion
When the observer model is based on outputs from at least two tanks, the EKF is convergent for the simulation data,
e^EKFi;j Dj ¼ k^i;j Dj : As an example, the mean error in S^S:5 due to a 30% error in KS is approximately 0.3,0.16 ¼ 4.8%. From Table 6 we conclude that the estimates of XS are more sensitive than the estimates of SS to errors in most of the parameters. For SS the sensitivity decreases consistently from tank 5 to tank 8 for most parameters. The reason is due to the saturation properties of the Monod kinetics; the lower the concentration the stronger the coupling is between SS and the corresponding process rate. In tank 5 and 6, for example, the Monod expression is close to saturated during periods (cf. Fig. 5a and b) and then changes in SS do not affect the aerobic growth
Table 6 e Relative sensitivity ðk^i;j Þ of estimate errors w.r.t model parameters. ^ S:5 ^ BH:5 S^S:6 S^S:7 S^S:8 X X Parameter S^S:5 k1 k2 SOsat kh KS Kx mH
1.18 0.82 1.54 0.31 0.16 0.09 0.15
0.49 0.69 0.67 0.28 0.38 0.09 0.13
0.02 0.53 0.07 0.16 0.61 0.05 0.08
0.03 0.49 0.003 0.15 0.64 0.05 0.07
1.06 1.28 1.40 2.36 0.36 0.94 1.36
0.98 0.51 1.25 0.08 0.24 0.03 1.04
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Fig. 7 e Estimated (noisy) and true variables (smooth) when the observer has used q [ 0.9q0. reacts fast to changes in the input concentrations, and gives only a small bias in the estimates due to the neglected nitrification in the observer model. The convergence of the filter for two tanks is, however, dependent on variations in the actual concentrations, which can be related to the corresponding observer model not being Jacobian detectable (see Section 2.5). The reason why this filter is convergent at all is due to the way the biomass concentrations are modeled; as a product between a slowly varying parameter gBH and the TSS concentration. The property that convergence is a property of
immeasurable quantities we consider too restrictive in an observer design, and it is recommended that the EKF is implemented for systems with at least three tanks. The observer model for two tanks is, however, most likely locally weakly observable for any reasonable state and input, which means that there may exist a different observer design, for this case, that is unconditionally convergent. An example of a convergent observer design for the case when a nonlinear observer model is not Jacobian detectable can be found in (Xia and Zeitz, 1997).
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The observer sensitivity to parameter errors has been evaluated, and it has been shown that it is robust against reasonable errors. Fortunately, the estimation of the perhaps most important variable, effluent readily biodegradable substrate, is the least sensitive. The developed observer solution is for a process with post nitrification. An extension of the method to include nitrification in the observer model is straight forward, but has not been considered in detail. There are advantages of being able to ignore the nitrification and denitrification in the observer design because otherwise we would get 5 or 6 state equations (adding SNH, SNO and SALK if used in the rate expression for autotrophic growth) instead of 3 for each tank. We would also get one more unknown input, the influent concentration of autotrophs, which we could factorize with a variable gAH as for the heterotrophs. Including nitrification would likely not make it harder to achieve a convergent EKF provided we instrument the ASP accordingly with nitrate sensors, ammonium sensors and, if required, alkalinity electrodes. These measurements together with the oxygen sensors should reflect sufficient information about the unknown input concentration of autotrophic bacteria. Neither has the denitrifying part of the ASP been considered in the observer model. Including also the denitrifying part of an ASP implies an increase in the number of tanks as well (for this particular plant the observer model would comprise 8 tanks instead of 3) and hence increase the dimension of the EKF accordingly. Computationally, though, it would become quite a lot more demanding (increases with approximately the cube of the dimension) and so would also the investigation of the observability and the sensibility analysis.
5.
Conclusions
An observer based on an extended Kalman filter for online estimation of biodegradable substrates and biomass in the activated sludge process has been developed. It uses only standard online measurements at a WWTP, i.e. oxygen concentration, water flows, airflows and TSS, making it more feasible to apply than previously presented solutions. The success relies on (i) the introduction of a stochastic slowly time varying parameter gBH representing the ratio of the sludge being heterotrophic biomass, (ii) letting influent substrate concentrations also be stochastically varying states, and (iii) use of the mass balance couplings between variables when having several tanks in series. The filter has been shown to be robust against reasonable parameter errors and measurement noise.
Acknowledgments ˚ sa The authors wish to thank Glen Nivert, Maria Neth and A Nilsson at GRYAAB in Go¨teborg for valuable discussions and for providing the required plant data.
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references
Alex, J., Beteau, J., Copp, J., Hellinga, C., Jeppsson, U., MarsiliLibelli, S., Pons, M., Spanjers, H., Vanhooren, H., August 31-September 3 1999. Benchmark for evaluating control strategies in wastewater treatment plants. In: European Control Conference Karlsruhe, Germany. Balmer, P., Ekfjorden, L., Lumley, D., Mattsson, A., 1998. Upgrading for nitrogen removal under severe site restrictions. Water Science and Technology 37 (9), 185e192. Benazzi, F., Gernaey, K., Jeppsson, U., Katebi, R., 2007. On-line estimation and detection of abnormal substrate concentrations in wwtp:s using a software sensor: a benchmark study. Environmental Technology 28, 871e882. Besanc¸on, G., 2007. Nonlinear Observers and Applications. Springer, New York. Boulkroune, B., Darouach, M., Zasadzinski, M., Gill, S., Fiorelli, D., 2009. A nonlinear observer design for an activated sludge wastewater treatment process. Journal of Process Control 19, 1558e1565. Chachuata, B., Rocheb, N., Latifia, M., 2005. Optimal aeration control of industrial alternating activated sludge plants. Biochemical Engineering Journal 23, 277e289. Copp, J.B., 2001. The COST Simulation Benchmark: Description and Simulator Manual. COST Action 624 and COST Action 682. http://www.ensic.inpl-nancy.fr/COSTWWTP/. Glad, T., Ljung, L., 2000. Control Theory Multivariable and Nonlinear Methods. Taylor and Francis, London. IWA task group on mathematical modelling for design and operation of biological wastewater treatment, 2000. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Publishing, UK. Karube, I., Matsunaga, T., Mitsuda, S., Suzuki, S., 1977. A microbial electrode BOD sensor. Biotechnology and Bioengineering 19 (10), 1535e1547. Kjellstrand, R., 2006. Hydraulic behaviour in an activated sludge tank -from tracer test through hydraulic modelling to fullscale implementation. Ph.D. thesis, Chalmers University of Technology, SE-412 96 Go¨teborg, Sweden, ISSN 1652-943X, http://hdl.handle.net/2320/1737. Lewis, F., 1986. Optimal Estimation with an Introduction to Stochastic Control Theory. Wiley Interscience, Atlanta. Lindberg, C., 1997. Control and estimation strategies applied to the activated sludge process. Ph.D. thesis, Uppsala University, Department of Materials Science, http://user.it.uu.se/bc/ WWT/cfl/phd.pdf. Liu, J., Mattiasson, B., 2002. Microbial BOD sensors for wastewater analysis. Water Research 36, 3786e3802. Mattiasson, B., Larsson, P., Mosbach, K., 1977. The microbe thermistor. Nature 268 (5620), 519e520. Olsson, G., Newell, B., 1999. Wastewater Treatment Systems, Modeling, Diagnosis and Control. IWA Publishing, London. Olsson, G., Nielsen, M., Yuan, Z., Lynggaard-Jensen, A., Steyer, J., 2005. Instrumentation, Control and Automation in Wastewater Systems. IWA Publishing, London. No. 15 in Scientific and Technical Report. Persson, F., Wik, T., Srensson, F., Hermansson, M., 2002. Distribution and activity of ammonia oxidizing bacteria in a large full-scale trickling filter. Water Research 36, 1439e1448. Riley, J.P., Skirrow, G., 1975. Chemical Oceanography, second ed., vol. 4. Academic Press, London. Samuelsson, P., Halvarsson, B., Carlsson, B., 2007. Cost-efficient operation of a denitrifying activated sludge process. Water Research 41, 2325e2332.
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Spanjers, H., van Lier, J., 2006. Instrumentation in anaerobic treatment - research and practise. Water Science and Technology 53 (4e5), 63e76. Stenstrom, M., Gilbert, R.G., 1981. Effects of alpha, beta and theta factor upon the design, specification and operation of aeration systems. Water Research 15, 643e654. Suescun, J., Irizar, I., Ostolaza, X., Ayesa, E., 1998. Dissolved oxygen control and simultaneous estimation of oxygen uptake rate in activated-sludge plants. Water Environment Research 70, 316e322.
Wik, T., Olsson, D., Lumley, D., 2004. Model Based Control of External Carbon Dose Rate in a Full-scale Predenitrification System. Water Intelligence Online. IWA Publishing. www. iwaponline.com. Wilen, B., Lumley, D., Mattsson, A., Mino, T., 2010. Dynamics in flocculation and settling properties studied at a full-scale activated sludge plant. Water Environment Research 82 (2), 155e168. Xia, X., Zeitz, M., 1997. On nonlinear continuous observers. International Journal of Control 66 (6), 943e954.
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Escherichia coli virulence genes profile of surface waters as an indicator of water quality N. Masters a, A. Wiegand a, W. Ahmed a,b, M. Katouli a,* a b
Faculty of Science, Health and Education, University of the Sunshine Coast, Maroochydore DC, Queensland 4558, Australia CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Brisbane, Queensland 4067, Australia
article info
abstract
Article history:
We compared the presence of 58 known virulence genes (VGs) associated with Escherichia
Received 12 April 2011
coli strains causing intestinal (InPEC) and extra-intestinal (ExPEC) infections in three
Received in revised form
estuarine, four brackish and 13 freshwater sites during the dry and wet seasons. The most
17 August 2011
common VGs observed in water samples during the dry season belonged to ExPEC (traT;
Accepted 5 September 2011
80% and ompA; 70%) whilst east1 (70%) gene was the most common among InPEC. More
Available online 21 September 2011
types of VGs were observed in water samples during wet season and included those found among InPEC (e.g. eaeA; 100%; fyuA, 90%; paa, 65%; cdt, 60%; and stx2, 60%) and ExPEC (e.g.
Keywords:
iroNE.coli, 90%; iss, 90% and kpsMTII, 80%). Eight VGs were found exclusively in the wet
Escherichia coli
season, of which four were found in all three water types indicating their association with
Virulence factors
storm-water run off. The number of VGs associated with ExPEC were significantly (P < 0.05)
Surface water
higher in only brackish and estuarine waters during the wet season compared to the dry
Indicator of water quality
season. There was no correlation between the number of E. coli and the presence of VGs in any of the water types in both seasons but we found similarities in VG profiles of sites with similar land uses. Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Human enteric pathogens such as Escherichia coli O157:H7 (Ibekwe and Grieve, 2003), Salmonella spp. (Savichtcheva et al., 2007), Cryptosporidium spp. (Ho¨rman et al., 2004) and enteric viruses (Haramoto et al., 2005) have been found in environmental waters as a result of faecal pollution from point and non-point sources (O’Shea and Field, 1992; Aslan-Yilmaz et al., 2004; Ahmed et al., 2005a). Due to the complexities associated with attempting to detect all possible pathogens in a water source, faecal indicator bacteria such as enterococci and E. coli have long been used by water industries to assess the microbiological quality of environmental and drinking waters. However recent studies suggest a limited relationship between faecal indicator bacteria and their ability to
accurately predict the presence of pathogens, especially protozoans and enteric viruses (Ho¨rman et al., 2004; McQuaig et al., 2006). In addition, it has been reported that faecal indicator bacteria such as E. coli can be present in the environment in the absence of faecal pollution and regrowth is possible under suitable conditions (Desmarais et al., 2002 Power et al., 2005). E. coli strains are normal inhabitants in the gut of warmblooded animals including humans (Hart et al., 1993). Whilst most E. coli strains in the gut are non-pathogenic commensals, certain strains may carry a combination of virulence genes (VGs) which enable them to cause intestinal infections such as diarrhoea or haemolytic colitis, or to cause extra-intestinal infections such as neonatal meningitis, nosocomial septicaemia, haemolytic uraemic syndrome, urinary tract and surgical
* Corresponding author. Tel: þ61 7 54304528, fax: þ61 7 54302887. E-mail address:
[email protected] (M. Katouli). 0043-1354/$ e see front matter Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.018
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site infections (Falagas and Gorbach, 1995; Johnson and Stell, 2000). Pathogenic E. coli strains can be classified into intestinal (InPEC) and extra-intestinal (ExPEC) on the basis of their virulence factors and clinical symptoms (Kaper et al., 2004). InPEC can be further classified into enterotoxigenic (ETEC), enteropathogenic (EPEC), enterohemorrhagic (EHEC), enteroinvasive (EIEC), and enteroaggregative (EaggEC) E. coli and diffusely adherent E. coli (DAEC) (Nataro and Kaper, 1998). InPEC can be further classified into enterotoxigenic (ETEC), enteropathogenic (EPEC), enterohemorrhagic (EHEC), enteroinvasive (EIEC), and enteroaggregative (EaggEC) E. coli and diffusely adherent E. coli (DAEC) (Nataro and Kaper, 1998; Kaper et al., 2004; Ishii et al., 2007). Common reservoirs of ETEC and EPEC include ruminants, porcine, other domesticated animals including goats, dogs, cats and humans (Levine, 1987; Nataro and Kaper, 1998; Djordjevic et al., 2004). EHEC have been isolated from various ruminants, primarily cattle (Paton and Paton, 1998). The principal reservoir for EIEC, EaggEC and DAEC are humans (Levine, 1987; Kaper et al., 2004). ExPEC can further be classified into uropathogenic E. coli (UPEC), meningitis neonatal E. coli (MNEC) and avian pathogenic E. coli (APEC); the two former pathotypes are commonly isolated from humans, APEC are associated with avian infections and have been isolated from poultry (Johnson and Stell, 2000; Kaper et al., 2004). It has to be noted that the possession of a single or multiple VGs does not necessarily indicate that a strain is pathogenic unless that strain has the appropriate combination of VGs to cause disease in a specific host (Gilmore and Ferretti, 2003). Faeces from domestic and wild animals as well as humans may contain high numbers of E. coli strains harbouring one or more VGs (Ishii et al., 2007). Runoff from agricultural lands and sewer overflows may also contribute pathogenic E. coli strains containing these VGs into environmental waters. Pollution of surface waters with pathogenic strains of E. coli has been implicated in an increased number of disease outbreaks and consequent deaths (Feldman et al., 2002; Olsen et al., 2002). However, only a few studies have investigated the presence of E. coli strains carrying VGs in environmental waters (Martins et al., 1992; Lauber et al., 2003; Chern et al., 2004; Hamelin et al., 2006; Ahmed et al., 2007; Ram et al., 2007; Hamilton et al., 2010), and not all of them to the extent reported in this study. The objectives of this study were to a) observe the prevalence of E. coli VGs in waters with contrasted land use and various fecal sources and b) to compare these VG profiles between sites to define the sources of fecal pollutions. Furthermore, correlations between the presence of VGs and the number of E. coli were also investigated.
2.
Materials and methods
2.1.
Sampling sites
Water samples were collected from three estuaries (EW1eEW3) with salinity ranging between 21 and 34 PSU, four rivers with brackish water (BW1eBW4) salinity range between 3 and 14 PSU and 13 creeks with fresh water (FW1eFW13) (salinity < 1 PSU) in the Sunshine Coast region located
approximately 100 km north of Brisbane, the capital city of Queensland, Australia. Fig. 1 shows the location of all sampling sites and their catchment area. In all, 40 water samples were collected from 20 sites during the dry season of 2009 (n ¼ 20) and wet season of 2010 (n ¼ 20). Table 1 shows the location, land uses and potential sources of faecal pollution of each sampling site. These sites were selected after consultation with local council and a community water quality monitoring group, who identified these sites as being suspected of having poor water quality and or previously found to have high numbers of coliforms. All samples were collected during low tide. Samples were collected during the dry season when the study areas received either no rainfall for more than 1 month or not more than 2 mm for at least 15 days prior to sampling. In contrast, samples were collected during the wet season when the sampling sites had received more than 100 mm rainfall two days prior to sampling. From each site, grab water samples were collected in 5 L sterile bottles from 30 cm below the water surface and transported on ice to the laboratory where they were processed within 6 h of collection.
2.2.
Enumeration of E. coli
The membrane filtration method was used to process all water samples. Serial dilutions of samples were filtered through 0.45 mm pore size nitrocellulose membranes (Millipore, Tokyo, Japan), and placed on chromocult coliform agar (Merck, Germany) plates and incubated at 35 C for 2 h to revive cells and then 44.5 C for 22 h.
2.3.
DNA extraction
For DNA extraction a 1 L replicate of each sample was filtered through 0.45 mm pore size membranes (Millipore), and E. coli were grown on chromocult coliform agar (Merck, Germany). After incubation for 35 C for 2 h to revive cells and then 44.5 C for 22 h, filters were transferred to sterile flasks containing 50 ml of tryptic soy broth (TSB) (Oxoid, London, UK), and incubated at 44.5 C for another 24 h. This enrichment step was included to ensure low numbers of E. coli containing VGs genes can also be identified. DNA was extracted from 2 ml culture using DNeasy blood and tissue kit (Qiagen) according to the manufacture instructions, and the resulting DNA extracts were stored at 20 C until use.
2.4.
PCR detection of virulence genes
Using a combination of multiplex and single PCR, a total of 58 VGs associated with E. coli strains causing intestinal and extraintestinal infections were selected for this study according to Chapman et al. (2006). Primers used for PCR detection of these VGs, the size of the PCR products for corresponding targets and PCR cycling conditions have been previously reported (Johnson and Stell, 2000; Chapman et al., 2006). For each PCR experiment, corresponding positive DNA, and negative control (sterile water) were included. The PCR amplification was performed using the Eppendorf Master Cycler (Eppendorf, Germany). To detect the amplified product, up to 5e8 ml aliquot of the PCR product was visualized by electrophoresis through a 2% agarose gel (Progen, Australia) in 0.6 TBE buffer and ethidium
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Fig. 1 e Map of sunshine coast region with location of sampling sites; EW1eEW3 indicates the estuarine water sites, BW1eBW4 indicates the brackish water sites, FW1eFW13 indicates the freshwater sites. Black lines indicate individual catchment areas.
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bromide. Identification of the bands was established by comparison of the band sizes with molecular weight markers of 100 bp and 3 kb ladder (Gene-Works, Australia). Samples were considered to be positive for a specific VG when the visible band was the same size as that of the positive control DNA. To minimize PCR contamination, DNA extraction, PCR set up, and gel electrophoresis were performed in isolated rooms. To identify false positive results filtered milliQ water was used as a negative control in all experiments.
2.5.
Data analyses
Virulence genes were initially categorized based on their functions and/or their (e.g. adhesion genes, toxin genes etc.) and/or different pathotypes (Table 2). Two separate analyses were undertaken in order to determine the impact of (i) season and (ii) water salinity on the presence or absence of genes within each gene category and also within each E. coli pathotype. Numbers and/or proportions of genes that tested positive in samples from comparative categories (e.g.: dry vs. wet) were compared using Z-tests. The proportions of genes observed across sets of data were compared using a Z-test with the null hypothesis being that the proportions of positive observed genes during the dry and wet seasons were not significantly different. Since the sampling locations were the same in both the dry and the wet seasons, a direct comparison between the presence and absence of individual genes at each location in each season could be made. The absolute difference between two corresponding sets of a particular gene category was defined
as the total number of instances, in which each gene was present in a sampling location during the dry season and absent in the wet season, or vice-versa. A linear regression analysis was applied to investigate the degree of correlation between the number of indicator bacteria and the number of VGs observed at each site. A VG fingerprint was developed for each water sample by giving a value of 1 to each gene found in a water sample and a value of 0 for their absence. Using the PhPlate software version 4.0, similarities among VG profiles were measured after pair wise comparison of the VG fingerprint of each water sample and the obtained similarity matrix was clustered according to the un-weighted pair-group method using arithmetic averages (UPGMA) to yield a dendrogram. In the dendrogram each line represents VG profile of a water sample and they are connected to each other at the similarity level shown. The student t-test was used to compare the significance of difference between the number of E. coli and the observed VGs numbers in samples collected during dry and wet seasons and between water types.
3.
Results
The number of E. coli detected in water samples collected from the estuarine sites (EW1eEW3) during the dry and wet conditions ranged from 2 to 24 CFU/100 mL and 260 to 1600 CFU/100 mL, respectively (Table 3). For the brackish water sites (BW1eBW4), E. coli numbers ranged from 1 to 40 CFU/100 mL (dry conditions) and 100 to 5000 CFU/100 mL
Table 1 e List of the 20 sampling sites chosen for this study and their location, water characteristics and the suspected sources of faecal contamination impacting each site. Sampling sites
Location
Land use
Suspected sources of faecal pollution
Estuarine water EW1 EW2 EW3
Golden Beachb Currimundib Mooloolabab
Urban Urban Urban
Urban run off, dogs, coastal birds Sewer overflows, waterfowls, dogs, coastal birds Urban run off, dogs, coastal birds
Brackish water BW1 BW2 BW3 BW4
Coolumb Bli Blib Nambour/Bli Blib Boreen Pointb
Urban Peri-urban Peri-urban Urban
Urban run off dogs, coastal birds Sewer overflows, wild animals Septic systems, cattle, horses, wild animals Septic systems, waterfowls, wild animals
Freshwater FW1a FW2 FW3a FW4a FW5 FW6 FW7 FW8 FW9 FW10 FW11 FW12 FW13
Alexandra Beachb Eumundi Noosab Sunshine Beachb Mooloolahb Eudlo Nambourb Yandina Yandina North arm (east) North arm (west) Eumundi/North arm Kin Kin
Urban Pasture Urban Urban Pasture Peri-urban Urban Peri-urban Pasture Pasture Pasture Pasture Peri-urban
Urban run off, dogs, coastal birds Septic systems, cattle, horses Urban run off, coastal birds Urban run off, dogs, coastal birds Septic systems, cattle, horses, wild animals Septic systems, cattle, horses, wild animals Urban run off, cattle, horses, wild animals Septic systems, cattle, horses, wild animals Septic systems, cattle, horses, wild animals Septic systems, cattle, horses, wild animals Septic systems, cattle, horses, wild animals Septic systems, cattle, horses, wild animals Septic systems, cattle, horses, wild animals
a Samples were collected from storm water outlets draining into the estuarine waters. b Sampling site is used for swimming, recreational and social activities.
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Table 2 e List of 58 virulence genes screened in this study, their categories based on their function and E. coli pathotypes carrying these genes. E. coli pathotypes
Virulence genes categories Adhesins
Toxins
Capsule synthesis
ExPEC
papAH fimH papEF bmaE sfa/focDE papG allele II & III nfaE papC focG papG allele II papG allele III Afa/draBC sfaS
hlyA cvaC cdtB cnf1 UNIVcnf cdta
kpsMTIII kpsMT K1 rfc kpsMT II kpsMT K5 ireA
DAEC
aah (orfA) aidA AIDA-I (orfB) aidA AIDAc
EHEC
iha eaeAa saa
exhAa stx2 stx1
ETEC
fasA faeG fanC fedA F41
eltA estI estII
EPEC
paa eaeAa bfpA
exhAa cdta
EaggEC
Siderophores
Invasins
Non-categorised virulence genes
fyuA iutA iroNE.coli
ibeA
PAI traT ompT iss yjaA TSPE4C2
chuA
east1
EIEC
ipaH
a Indicates genes shared by more than one E. coli pathotype.
(wet conditions), respectively (Table 3). For the freshwater sites, these values were 2 to >5000 (dry conditions) and 28 to 5000 (wet conditions), respectively (Table 3). Generally the numbers of E. coli in the estuarine and brackish waters were higher during the wet conditions than those collected during dry conditions, however due to the low number of samples collected from each water type and variations within samples it was not possible to calculate the significance of these findings. This was more pronounced among fresh water samples which showed a higher variability between different sites, with 6 sites having a higher number of E. coli in the dry conditions compared to wet conditions (Table 3).
3.1.
Prevalence of E. coli VGs
During the dry season 18 (90%) samples were positive for multiple VGs ranging from 4 to 22 genes per water sample (Table 4). Only freshwater sites were positive for >20 VGs during dry season. No VG was detected in samples collected in brackish waters designated as BW3 and BW4 during the dry season. The main source of faecal contamination of these sites was septic systems. Our previous study in this region has
identified farm animals and septic systems as potential sources of VGs in these waterways. However, recent upgrading of septic systems within the regional area could be one of the reasons why no VGs were detected at these sites. Alternatively, it could be that E. coli strains found in these sites did not carry any VGs, this is supported by the low number of E. coli strains isolated from these sites (Table 3). In total, 39 (67%) of the 58 VGs tested were detected in all water samples collected during this season (Table 4). The most commonly observed VGs were fimH (85%), chuA (85%), TSPE4C.2 (85%), traT (80%), OmpT (70%), east1 (70%), fyuA (65%), and ibeA (60%) and all except fimH which is commonly found in all E.coli strains and chuA whch is commonly seen in EHEC pathotype, the rest are common among EXPEC strains (Fig. 2). Among the toxin genes tested, east1 (70%) was the most prevalent gene observed followed by cdt (40%), cvaC (35%), stx1 (25%), stx2 (25%) and estI (5%). The remaining toxin genes hlyA, cdtB, cnf1, UNIVcnf, exhA, eltA, estII could not be detected in all water samples collected during dry season. During the wet season, all water samples were positive for multiple VGs ranging from 7 to 30 genes per water sample. Of the 58 VGs tested, 44 (76%) genes were observed in water
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Table 3 e Number of E. coli in water samples collected during the dry and wet conditions and the corresponding number of virulence genes detected by PCR in DNA extracted from 2 mL aliquots of enriched cultures obtained from each sampling site. Sampling sites
Number of E. coli (CFU/100 mL) a
Dry conditions
Number of positive observations of virulence genes b
Wet conditions
Dry conditionsa
Wet conditionsb
Estuarine water EW1 EW2 EW3 Mean SD
10 2 24 12 11.1
260 1600 420 760 731.8
12 14 6 10.7 4.2c
21 29 27 25.7 4.2c
Brackish water BW1 BW2 BW3 BW4 Mean SD
<1 22 40 38 33.3 9.9
100 >5000 860 270 410 398.9
4 17 0 0 5.3 8.1d
26 19 26 16 21.8 5.1d
Freshwater FW1 FW2 FW3 FW4 FW5 FW6 FW7 FW8 FW9 FW10 FW11 FW12 FW13 Mean SD
2 260 660 2500 920 290 >5000 30 76 120 >5000 150 140 468 730.6
>5000 610 80 160 2400 >5000 520 140 100 28 140 370 100 422.5 683.2
4 20 17 14 21 22 19 5 12 12 15 19 20 15.4 5.8
17 17 21 19 27 7 17 25 24 17 17 21 19 19.1 5
a study area received no rainfall 15 days prior sampling. b study area received > 100 mm rainfall 2 days prior sampling. c,d P < 0.05 for the number of VGs observations between water samples in dry and wet seasons.
samples collected during the wet conditions (Table 4). Except the fimH gene which was observed in all samples, the most commonly observed VGs belonged to both ExPEC e.g. iroNE.coli, iss, fyuA, TSPE4C.2, yjaA, kpsMTII, PAI, traT, ompT and IPEC e.g. stx2, chuA, saa (found in EHEC pathotype), F41 (found in ETEC pathotype), east1 (found in EAggEC pathotype), and paa and eaeA (in EPEC pathotype). Among the toxin genes detected, east1 (95%) was most prevalent gene followed by cdt (60%), stx2 (60%), ehxA (45%), cvaC (40%), cdtB (30%) estI, eltA and UNIVcnf (10%), cnf1 and stx1 (5%) (Fig. 2). There was no positive observations of VGs associated with EIEC throughout the study and was therefore omitted from subsequent analysis. Comparison between the observed VGs in samples collected during the dry and wet seasons is given in Fig. 2. Whilst the overall numbers of VGs observed in water samples collected during the wet season was significantly (P < 0.05) higher than those collected during the dry season, their prevalence was higher in fresh water sites FW2, FW6, FW7 and FW13 during the dry season than their corresponding sites during the wet season (Table 3). While some sites (i.e. FW7) had an exceptionally high number of bacteria during the dry season (Table 3) which would suggest an equally proportional number of VGs, other sites had lower numbers of E. coli and yet had a higher number of VGs (i.e. FW9) during the wet season indicating that the presence of VGs in these samples
were not proportional to the number of E. coli strains. This could be partly explained by the diverse sources of faecal pollution, rather than hydrological conditions, within the proximal areas of those sampling sites. VGs were grouped according to their association with different E. coli pathotype and further classified based on functional characteristics of the gene into toxin genes (Tox), adhesion genes (Adhes), capsule genes (Cap), iron acquisition genes (Iron), invasion genes (Inv) and non-classified (NC) VGs (Fig. 3). This enabled us to identify the prevalence of different pathotypes of E. coli in the water samples. We observed a significant (P < 0.05) difference in the presence of genes belonging to pathotypes EHEC, ETEC and EPEC pathotypes in the freshwater samples during the dry and wet seasons (Fig. 3A). The brackish (Fig. 3B) and estuarine (Fig. 3C) water samples on the other hand contained a significantly (P < 0.05) higher number of genes belonging to ExPEC, EHEC, EPEC and EaggEC during the wet season indicating that the brackish and estuarine sites may have similar sources of fecal pollutions. Overall, there was a significantly (P < 0.05) higher number of positive gene observations at all three water types and for all pathotypes (except DAEC) during the wet season compared to the dry season (Fig. 3D). Grouping of VGs based on their functional characteristics showed striking differences in each water type during the dry
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Table 4 e Virulence genes observed (indicated by C) in at least one DNA extract of 2 mL enriched sample of fresh (FW), brackish (BW) and estuarine (EW) sampling sites in samples collected during dry and wet season. Boxed area denote virulence genes found only in the dry season samples while, grey area denotes virulence genes found only in the wet season samples.
and wet seasons (Fig. 4). There was a significant (P < 0.05) difference between the number of VGs observed during the dry and the wet season for all water types (Fig. 4). However, fresh water samples shared a high number of VGs in both season as opposed to brackish and estuarine waters which contained a high number of VGs unique to the wet season (Fig. 4). This could be explained by continuous input from specific source(s) such as domestic and native animals at these sites. Brackish and estuarine waters, in contrast had higher numbers of suspected pathotypes in the wet season (Fig. 3) resulting in higher number of VGs that are normally associated with pathotypes EHEC, ExPEC, EPEC and EaggEC.
3.2. Correlation between the numbers of E. coli and the presence of virulence genes A linear regression analysis was applied to calculate any possible correlation between the number of E. coli and the presence of VGs during the dry and wet season. It was found that the number of E. coli in water samples did not correlate to the number of positive VG observations during the dry (P > 0.9612) or the wet (P > 0.2751) seasons (Fig. 5).
3.3.
Comparison of E. coli virulence gene profiles
Similarity among the VG profiles of water samples was measured and corresponded to the land use and characteristics of each sampling site to ascertain any possible correlation between the nature of the land use and its characteristics with VG contents of water at that site. The results indicated that some sites (e.g. FW5 and FW6) with similar land uses had high
similarity during the both seasons (Fig. 6), whilst other sites such as FW7 and FW8 with similar land uses had a high similarity to each other only during the wet season (Fig. 6). Similar results were found with other sites during the wet seasons (e.g. FW9 and FW12 and FW11 and FW13) (Fig. 6). All sites with a similarity coefficient above 70% in the dry season and above 80% in the wet season were all fresh water with similar adjacent land uses (Fig. 6). Further analysis of available data on land use of these sites indicated that they were semirural locations with various agricultural uses and native animal habitats. The increased similarity during the wet season however could be due to the increased diversity of E. coli sources with equally diverse VGs as a result of the local run off during wet weather events.
4.
Discussion
As expected the numbers of E. coli in the water samples collected during the dry season were much lower than those found during the wet season, this is largely due to the run off generated from heavy rainfall events transporting a high number of bacteria from various point and non-point sources to the waterways (O’Shea and Field, 1992). However, this was not the case for freshwaters where several sites showed a higher number of E. coli during the dry season. E. coli is known to have short survival rates in saline water and because of this they may not be an indicator of faecal pollution for the estuarine and brackish waters (Anderson et al., 1983). In our study, freshwater sites with a high number of E. coli showed to be located in pasture or peri-urban catchments
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with multiple sources of faecal pollution such as defective septic systems and faecal materials from cattle, horses and wild animals. A previous study has also reported the presence of high numbers of faecal indicator bacteria originating from defective septic systems and grazing animals in these sites (Ahmed et al., 2005a, b). The presence of strains with virulence characteristics similar to EPEC (Lauber et al., 2003), ETEC (Chern et al., 2004),
EHEC (Chern et al., 2004), and ExPEC (Hamelin et al., 2006) has been previously reported in the freshwaters. A more recent study also indicates the presence of potential EPEC strains in estuarine waters (Hamilton et al., 2010). In our study a high number of E.coli VGs were detected in samples from the estuarine and brackish waters during both dry and wet seasons suggesting the persistence of pathogenic E. coli strains in these waters. Alternatively this may indicate a continuous
Observed VGs across all sampling sites (%) 0
40
60
80
100
*
*
* * DRY *
WET
* * *
*
* * * * *
ns
Side roph o re s
Virulence Genes (VGs)
papAH fimH Adhesins papEF bmaE sfa/focDE papG allete II&III papC focG papG allele II papG allete III Afa/draBC aidA AIDAc iha eaeA saa faeG fanC F41 paa cvaC cdtB Toxins UNIVcnf cdt exhA stx2 stx1 eltA east1 kpsMT III kpsMT K1 Capsule rfc Synthesis kpsMT II kpsMT K5 ireA fyuA Siderophores iutA iroN e coli ibeA Invasins Non categorised VGs traT ompT iss yjaA TSPEC4C2 chuA
20
* * * *
Fig. 2 e The overall percentage of positive gene observations in all water samples during wet and dry season. * indicate significance of difference ranging from P < 0.05 to P < 0.001 between the number of observations, calculated using a Z-test.
Fig. 3 e The percentage of positive gene observations for each pathotype in the different water types; fresh (A), brackish (B), estuarine (C) and overall (D). Virulence genes that belonged to more than one pathotype were grouped with all associated pathotypes. * indicate significance of difference ranging from P < 0.05 to P < 0.001 between the number of observations of VGs belonging to each pathotype, calculated using a Z-test.
Fig. 4 e The percentage of virulence genes (VGs) classified based on functional characteristics during the dry season (top half of each graph) and the wet season (bottom half of each graph): for each water type; fresh (A), brackish (B), Estuarine (C) and -VGs unique to the dry season, -VGs unique to the wet season, -VGs commonly found during dry and Overall (D). wet season. Tox; toxin genes, Adhes; adhesion genes, Cap; capsule genes, Iron; iron acquisition genes, Inv; invasion genes, NC-VG; non-classified virulence genes. * indicate significance (P < 0.05) of difference for overall percentage of VGs found during the dry and wet season, calculated using a paired t-test.
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Fig. 5 e The correlation between the numbers of E. coli during the dry and wet Cseasons and the corresponding total number of positive virulence gene observations at each sampling site.
input of these bacteria from a common source in the water or a combination of both. The fact that VGs belonging to ExPEC pathotype were more prevalent in these waters was of interest and may indicate a high potential health risk of such waters as the number of ExPEC VGs in E. coli is proportional to its pathogenic potential (Picard et al., 1999). A number of samples were also positive for toxin genes belonging to intestinal pathogenic bacteria. The frequency of detecting these toxin genes was higher during the wet season than the dry season especially in the freshwater sites which are normally surrounded by animal farm such as ruminants and swine that are known to harbour these VGs (Djordjevic et al., 2004; Gyles, 2007; Ishii et al., 2007). Interestingly a recent study in this region has also identified farm animals and septic systems as potential sources of these VGs in waterways (Ahmed et al., 2007). The fact that almost all water samples containing stx1 and/or stx2 also harboured eaeA gene has to be emphasised. This gene is required for the full expression of virulence by EHEC strains (Borelin et al., 1999) and is most commonly found in ruminant and avian guts (Hamelin et al., 2007). The prevalence of E. coli isolates harbouring VGs in environmental waters is reported to be low ranging from 0.9% to 10% (Chern et al., 2004; Lauber et al., 2003; Martins et al., 1992).
Fig. 6 e Dendrograms showing similarity among the virulence gene (VG) profiles of all water sites during the dry (left dendrogram) and wet (right dendrogram) seasons. Sites with high similarity during both seasons have been highlighted with grey. Sites with low similarity during the dry season but high similarity in their VG profiles have been identified with box. indicates highly similar sites during dry season but no similarity during the wet season.
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Therefore, a large number of isolates need to be screened in order to detect VGs. In view of this, in this study, E. coli isolates were concentrated from large volume (1 L) of water samples followed by an enrichment step so that PCR detection sensitivity can be increased as shown before (Ahmed et al., 2009; Savichtcheva et al., 2007; Scott et al., 2005). Three freshwater sites (FW7, FW8, and FW12) were positive for LT1 and STa genes. The presence of ST and LT enterotoxins which are commonly associated with ETEC strains have been reported by other workers in surface waters (Obi et al., 2004; Begum et al., 2005) and is thought to be originated from swine and humans with diarrhoea. The toxin gene east1 was highly prevalent in water samples from the estuarine, brackish and freshwater sites during both the dry and wet season. This gene is commonly found in ruminants and swine (Yamamoto and Nakazawa, 1997) and are, in some cases, responsible for waterborne diseases in humans (Hedberg et al., 1997; Yatsuyanagi et al., 2003). E. coli has long been used as one of the primary faecal indicator bacteria due to the previous assumption that it has limited survival ability within the environment however recent studies suggest that some lineages of E. coli have adapted and naturalized within tropical, subtropical and even temperate environments (Ishii and Sadowsky, 2008; Walk et al., 2007) and as such could be the pertinent reason for E. coli blooms especially within freshwaters. Data from Hamilton et al. (2010), and the present study suggests E. coli strains harbouring clinically significant VGs may persist in the estuarine environment despite the fact that E. coli is not generally monitored to assess faecal pollution in the estuarine waters. In fact, there are few studies that have correlated the presence of known VGs with the number of E. coli in all water types. The present study supports the idea that at least some clinically significant strains of E. coli may persist in the waterways surviving both the dry and wet season and this may explain why some sites with very low numbers of E. coli showed the presence of a high number VGs. The presence of these genes could not totally be attributed to storm run off in wet seasons or fresh waters as they were also found during the dry season and in all water types. Several sites in this study had lower numbers of E. coli than those required by national and international water quality guidelines for fresh and marine waters (USEPA, 1986; ANZECC, 2000) but contained a high proportion of VGs. Contrary to these, there were sites that far exceeded the existing guidelines for the number of E. coli for recreational waters and yet found to harbour no VGs. Whilst these data outline the limited correlation between E. coli numbers and VGs, they also highlight the potential of surface water with low and accepted level of E. coli to cause infections. In this study we aimed to determine the viability of E. coli VG profiles as a potential indicator of water quality. The results suggest there is a varied distribution of VGs within the catchment. Whilst some VGs were specific to the wet season, suggesting their affiliation to storm-water run off, some genes were specific to water types and were found at specific sampling locations suggesting their association to localised faecal contamination. We also found that fresh water sites were less populated, and mainly constituted of agricultural, semi-rural areas with pockets of natural habitat as opposed to
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the brackish and estuarine sites. The latter’s were all found to be located within medium to high density residential areas. This may indicate that the fresh water sites were probably contaminated with animal faecal E. coli whilst the other two water types were primarily impacted on by urban run off. The fact that Figs. 3 and 4 showed a much higher similarity between the VG profile of brackish and estuarine water samples supports this observation. In this study we used a similarity coefficient between the VG profiles of each sampling site and found a much higher similarity among sites that had common sources of contamination indicating the possible use of such method for analysis of water sites based on their VG profile. This however was more pronounced in samples collected during the wet season. There were also sites that showed to be distantly dissimilar to each other during the dry season but had a high similarity in their VG profile during the wet season suggesting that the VG profiling of E. coli in a water site especially after a storm run off can give a better picture of the E. coli flora of the site with respect to its animals and human land uses pattern. In conclusion, a number of water samples collected during the dry and wet conditions were positive for multiple E. coli VGs which indicate the presence of potential pathogenic E. coli in these waters. However, the percentage of E. coli isolates harbouring these VGs is not reported in this study, and should be addressed in future studies to provide a better understanding of the potential health risk of such waters. We also suggest that VG profiling of surface waters can be used as a tool to indicate water quality and should be used in conjunction with enumeration of E. coli bacteria in water samples.
Acknowledgements We are grateful to Dr Toni Chapman from Elizabeth MacArthur Agricultural Institute, Industry and Investment of NSW, Australia and Miss Nubia Ramos from the University of the Sunshine Coast for providing the positive control strains. We also thank Susie Chapman from SEQ catchments and Graham Webb from the Sunshine Regional Council for their advice on site selection.
references
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Spatial and temporal effects of olive mill wastewaters to stream macroinvertebrates and aquatic ecosystems status Ioannis Karaouzas a,*, Nikolaos T. Skoulikidis a, Urania Giannakou b, Triantafyllos A. Albanis c a
Institute of Inland Waters, Hellenic Centre for Marine Research, 46.7 km Athens-Sounio Av., 19013 Anavissos, Attica, Greece Department of Fisheries Technology and Aquacultures, Technological Educational Institute of Thessaloniki, N. Miltiadi 1, 63200 N. Moudania, Greece c Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece b
article info
abstract
Article history:
Olive mill wastewater (OMW) is one of the major and most challenging organic pollutants
Received 5 March 2011
in olive oil production countries. However, the knowledge about the in-situ effects of olive
Received in revised form
mill wastewaters to lotic ecosystems and their benthic organisms is very limited. To
20 August 2011
resolve this, eight sampling sites were selected upstream and downstream the outflow of
Accepted 6 September 2011
several olive mills to assess the spatial and temporal effects of OMW to stream macro-
Available online 17 September 2011
invertebrates and to ecological status of stream ecosystems. Biotic (macroinvertebrates) and abiotic (physicochemical, hydromorphological) data were monitored for two years
Keywords:
thus following the biennial cycle of olive growth and production and hydrological variation
Olive mill wastewater
(droughtewet years). The results of this study revealed the spatial and temporal structural
Streams
deterioration of the aquatic community due to OMW pollution with consequent reduction
Pollution
of the river capacity for reducing the effects of polluting substances through internal
Macroinvertebrates
mechanisms of self-purification. OMW, even highly diluted, had dramatic impacts on the
Ecological status
aquatic fauna and to the ecological status of the receiving stream ecosystems. The organic load of the wastewater expressed as BOD5, COD and TSS, substrate contamination (sewage bacteria) and distance from the mill outlet, were the most important factors affecting macroinvertebrate assemblages while the typology (i.e. slope, altitude) and hydrology of the stream site (i.e. mountainouselowland) and the intensity and volume of the wastewater were the most important determinants of self-purification processes. As OMW are usually being discharged in small size streams that are not considered in the Water Framework Directive 2000/60/EC, there is a need for including such systems into monitoring and assessment schemes as they may significantly contribute to the pollution load of the river basin. Furthermore, guidelines to manage these wastes through technologies that minimise their environmental impact and lead to a sustainable use of resources are critical. ª 2011 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ30 22910 76391; fax: þ30 22910 76419. E-mail addresses:
[email protected] (I. Karaouzas),
[email protected] (N.T. Skoulikidis),
[email protected] (U. Giannakou), talbanis@ cc.uoi.gr (T.A. Albanis). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.014
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1.
Introduction
Olive mill wastewater (OMW), is one of the major and most challenging organic pollutants in olive oil production countries (Paraskeva and Diamadopoulos, 2006; Kapellakis et al., 2006). OMW is the turbid liquid waste by-product generated during the extraction of olive oil, where huge quantities of organic wastes are produced within a short time period. The operation of olive mills is seasonal and usually lasts 3e5 months (NovembereMarch) and is estimated that more than 30 106 m3 of OMW are produced annually in the Mediterranean region (Hamdi, 1993; D’Annibale et al., 2004). Despite the global spread of the olive tree, 95% of the production of olive oil (which yields about 2.5 million tonne olive oil per year), comes from the Mediterranean countries with Spain, Italy and Greece being the largest producers. The milling process of olives generates about 50% of wastewater, 30% of solid residues and 20% of olive oil. Typical OMW composition by weight is 83e94% water, 4e16% organic compounds and 0.4e2.5% mineral salts (Davies et al., 2004). The wastewater arising from the milling process amounts to 0.5e1.5 m3 per 1 ton of olives, depending on the process method (Vlyssides et al., 1998; Alburquerque et al., 2004). OMW is easily fermentable and its characteristics are variable depending on the method of extraction, type of olive variety, soil and climatic conditions and cultivation methods. The high pollution property of OMW is attributed to its extremely high organic load (BOD5: 25e100 g/L; COD: 45e220 g/L) and high content of phenolic compounds (Vlyssides et al., 1998; De Marco et al., 2007), and to its significant concentrations of magnesium, potassium and phosphate salts (Arienzo and Capasso, 2000). In addition, it contains many organic compounds such as lipids, sugars, organic acids, tannins, pectins and lignins contributing to its organic load (Vlyssides et al., 1998; Davies et al., 2004). Although disposal of untreated OMW in aquatic compartments is not allowed in Greece, it is estimated that approximately 1.5 million tons of OMW are disposed of every year in rivers, streams, lakes and even in the sea (Kapellakis et al., 2006). The effective treatment of OMW requires expensive and advanced technologies that most olive mills lack. The usual treatment and disposal practice followed in Greece involves neutralization with lime and disposal in evaporation ponds/lagoons. Disposal of OMW causes significant environmental pollution with unforeseeable effects on the quality of soil, surface and ground water (Fiorentino et al., 2003; Mekki et al., 2008) and poses a serious risk to aquatic and terrestrial biota and subsequently to the health of corresponding ecosystems. The toxic effects of OMW and its polyphenolic fraction to aquatic organisms (Paixao et al., 1999; Fiorentino et al., 2003; Rouvalis et al., 2004; Karaouzas et al., 2010), on bacteria and yeast (Yesilada and Sam, 1998) and on seed germination (DellaGreca et al., 2001) are well documented. Moreover, OMW has been shown to affect the physical and chemical properties of the soil and its microbial community (Rinaldi et al., 2003; Kotsou et al., 2004; Mekki et al., 2006) while several studies have evidenced its phytotoxic effects and antimicrobial activity (Aggelis et al., 2003). Finally, OMW can be toxic to
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anaerobic bacteria which may inhibit conventional secondary and anaerobic treatments in municipal treatment plants (Venieri et al., 2010). However, evaluation of the adverse effects of OMW to aquatic organisms has been mainly derived from singlespecies laboratory tests and thus effects on aquatic fauna populations and communities remain relatively unknown. The present research aims in contributing to the knowledge of the impacts of OMW pollution to stream ecosystems. Specifically, the objectives of this study are to: (a) assess the spatial and temporal effects of OMW to stream macroinvertebrate abundance, composition and assemblage structure, (b) compare and contrast the effects of OMW pollution in different stream types (mountainous and lowland streams), and (c) evaluate the impacts of OMW to the ecological status of stream ecosystems. Since olives strongly follow a biennial cycle of growth and production (Ben-Gal et al., 2010), a twoyear biomonitoring campaign was conducted in order to assess spatial and temporal responses of stream fauna to high and low OMW yield years. Furthermore, two different hydrologic years (wet and dry year) were covered during the twoyear monitoring, thus allowing evaluation of hydrologic regime variation to OMW pollution intensity and effects.
2.
Methods and materials
2.1.
Study area
For the purposes of this study, the Evrotas River Basin was selected where 79 olive oil mills are operating throughout the basin. The river belongs to a mid-altitude Mediterranean basin, located in southeastern Peloponnese (Prefecture of Laconia) (Fig. 1). The river drains a total area of 2418 km2 and discharges into the Laconic Gulf after crossing 90 km of valley basin. The basin expands between the mountain ranges of Taygetos (2407 m) and Parnon (1940 m), where numerous intermittent and ephemeral streams, discharge into the main course. The Evrotas basin has a typical Mediterranean climate with mild and cool winters and prolonged hot and dry summers with an average annual temperature of 16 C and a mean annual precipitation of 803 mm (2000e08). The majority of rainfall occurs during the months of October through March; highest rainfall being in December and lowest in June. The most important point pollution sources of the Evrotas River Basin include municipal wastewaters and agro-industrial wastewaters from olive oil mills, orange fruit juice processing units, and dairy and meat processing units. Significant diffuse or otherwise non-point source pollution in Evrotas basin includes the widespread use of fertilizers and pesticides. The total area of arable land of Evrotas Basin is estimated to 912 km2 (38% of the basin), where olive and orange trees dominate, followed by cereals, corn, vegetables and vines. For the purposes of this study 8 sampling sites were selected along 4 streams that receive OMW (Fig. 1). Two streams are perennial streams (sites 1, 2 and 7, 8) flowing through mountainous and semi-mountainous areas and may dry out only during intense and prolonged droughts. The remainders are intermittent streams (sites 3, 4 and 5, 6) that flow through semi-
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Fig. 1 e Sampling sites of the Evrotas River basin.
mountainous and agricultural areas. Sampling sites were established upstream and downstream the outflow of olive oil mills in order to compare biotic and abiotic parameters. Biotic (macroinvertebrates) and abiotic parameters were sampled at a monthly basis from November 2006 to May 2008. Samples were not collected when sites were dry or during the drought period when flow was at minimum (JulyeOctober) to exclude hydrological stress on fauna community. Upstream sites that are near - reference sites (i.e. no pollution or minimum disturbance) were 1, 3, 6 and 8, whereas downstream sites 2, 4, 5 and 7 receive directly untreated olive mill wastewaters during the olive harvesting period.
2.2.
Macroinvertebrate sampling
The collection of macroinvertebrates was performed with the STAR-AQEM methodology (AQEM Consortium, 2002) upstream and downstream the outlet of the olive mills. The STAR-AQEM sampling method is based on a multi-habitat scheme designed for sampling major habitats proportionally according to their presence within a sampling reach. A sample consists of 20 ‘replicates’ taken from all microhabitat types at the sampling site with a share of at least 5% coverage, which must be distributed according to the share of microhabitats. Benthic macroinvertebrates were collected using a rectangular hand
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net of 0.25 m 0.25 m with a mesh size of 500 mm nytex screen. Each of the 20 replicates was taken by positioning the net and disturbing the substrate in an area that equals the square of the frame width upstream of the net (25 25 cm). Thus, a total of 1.25 m2 (25 25 20 replicates) was sampled for each sampling site. All macroinvertebrates were identified at family level and where possible at genus or species level. However, family level was used for statistical analysis in order to have a homogeneous data set.
2.3.
Environmental data
A total of 50 environmental variables were recorded aiming to give a detailed display of river and floodplain hydromorphology including local scale characteristics (instream and riparian habitat composition, current velocity, water chemistry, etc.) and catchment-scale characteristics (land cover at floodplain and catchment level, geology, altitude, etc.). At each stream site current velocity, water temperature, pH, conductivity and dissolved oxygen were measured in-situ. Water samples were collected, preserved in cooling conditions (3e4 C), filtered upon arrival in the laboratory and analysed 2 for major ions (Ca2þ, Mg2þ, Naþ, Kþ, HCO3, CO2 3 , Cl , SO4 ), þ 3 , NO , NH , total nitrogen, PO silicate and nutrients (NO 3 2 4 4 and total phosphorous). In addition, total phenol concentration was analysed by means of the FolineCiocalteau colorimetric method using gallic acid as standard. Absorbance was determined at 765 nm.
2.4.
Data analysis
The STAR_ICMi multimetric index (Buffagni et al., 2007) was used to evaluate the biological status of the selected streams, which has been calibrated for Greek running waters through the Water Framework Directive (WFD) Intercalibration Exercise (EU, 2007). For the evaluation and classification of the physicochemical status, the Nutrient Classification System (Skoulikidis et al., 2006; Skoulikidis, 2008) was used that includes two indices; the organic pollution index (NeNO 2, dissolved oxygen and BOD ) and the chemical polluNeNHþ 4 5 3 tion index (NeNO 3 , PePO4 , total P and total phenols). For the ecological status classification, the worst quality element (biological and physicochemical) was considered (REFCOND, 2003). Principal component analysis (PCA) was used to ordinate abiotic data among polluted and reference or minimally polluted sites during the 2 year sampling period. Prior to PCA, abiotic variables were log transformed and normalized (Clarke and Warwick, 1994). To test for spatiotemporal differences in macroinvertebrate assemblages among sites, analysis of similarities (ANOSIM) was used while SIMPER analysis was used to detect those species that contribute to macroinvertebrate community variation among sites and periods. ANOSIM has been widely used for testing hypotheses about spatial differences and temporal changes in assemblages (Chapman and Underwood, 1999). ANOSIM generates a value of R which ranges between 1 and þ1, a value of zero representing the null hypothesis (no difference among a set of samples). When R is near to 0, spatial or temporal differences between sites are indistinguishable whereas when R is near to
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1, then sites do contain different types and/or numbers of organisms, or that these have changed from one time to another. Non metric multidimensional scaling (NMDS) was used as an ordination procedure to illustrate differences among sites in relation to their taxonomic composition. The NMDS ordination method is based on ranked BrayeCurtis dissimilarity distances and is not susceptible to problems associated with zero truncation. To avoid over domination of the analysis by the very abundant taxa and to allow those of lower abundances to contribute to the analysis, macroinvertebrate taxa were 4th-root transformed before calculation of similarities. In addition, ANOSIM was performed to test spatiotemporal differences in macroinvertebrate data between sites. Canonical ordination techniques were used to examine the relationship between the environmental variables and macroinvertebrate assemblages. To avoid multicollinearity between environmental variables, a Pearson Product Moment correlation analysis was performed and those variables highly associated with any other (r > 0.95, p 0.05) were removed from the analysis, as they would have no unique contribution to the regression equation. Prior to Canonical Correspondence Analysis (CCA), variation in macroinvertebrate data was examined by running a Detrended Correspondence Analysis (DCA) to ensure a unimodal rather than linear distribution. Gradient of variation is provided by the first DCA axis in which taxon compositional turnover is measured in standard deviation units (SD). Along each axis a full turnover in taxon composition between samples occurs after 4.0 SD. The unimodal assumption of DCA is accepted if the gradient length of the first axis is greater than 3.0 SD (ter Braak and Prentice, 1988; Leps and Smilauer, 2003). The first DCA axis (SD: 3.756) confirmed the unimodal assumption and thus the CCA application while the first four DCA axes accounted for 28.1% of the variation in macroinvertebrate data. Forward selection of environmental variables was used to ascertain the minimal set of variables that explain macroinvertebrate data. Significance of environmental variables was determined by means of a Monte Carlo permutation test. Canonical ordination techniques were carried out using the package CANOCO for Windows 4.5, correlation analysis with STATISTICA version 6 and NMDS, PCA, SIMPER and ANOSIM with PRIMER 6.
3.
Results
3.1.
Environmental conditions
Mean values of abiotic variables upstream and downstream the OMW outlet differed among periods and years (Table 1). Mean concentrations of COD, BOD5, total suspended solids (TSS) and chloride, as expected, were higher in the downstream sites while sewage bacteria flourished as a result of OMW residue on the stream substratum during the wastewater discharge period (Table 1). Dissolved oxygen concentration showed no marked variation among periods in the upstream sites in contrast to the downstream sites where oxygen concentration decreased during and after the wastewater discharge period, especially at the 2nd year of sampling
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Table 1 e Mean and standard deviation (SD) values for abiotic variables upstream and downstream the OMW outlet before, during and after OMW discharge period from the 2 year sampling campaign. 1st Year Before
Upstream pH Conductivity [mS/cm] Dissolved Oxygen [mg/L] COD [mgO2/L] BOD5 [mgO2/L] TSS [mg/L] T [C] Cl [mg/L] NO3 [mg/L] NO2 [mg/L] NH4 [mg/L] PO4 [mg/L] Total N [mg/L] Total P [mg/L] Total Hardness [mg/L] Total Phenols [mg/L] Average stream width [m] Mean depth [m] Discharge [L/s] Mean water velocity [cm/s] Sewage bacteria [%] Downstream pH Conductivity [mS/cm] Dissolved Oxygen [mg/L] COD [mgO2/L] BOD5 [mgO2/L] TSS [mg/L] T [C] Cl [mg/L] NO3 [mg/L] NO2 [mg/L] NH4 [mg/L] PO4 [mg/L] Total N [mg/L] Total P [mg/L] Total Hardness [mg/L] Total Phenols [mg/L] Average stream width [m] Mean depth [m] Discharge [L/s] Mean water velocity [cm/s] Sewage bacteria [%]
2 nd Year
During
After
Before
During
After
MEAN
SD
MEAN
SD
MEAN
SD
MEAN
SD
MEAN
SD
MEAN
SD
7.90 560 9.64 4.90 0.77 3.37 11.56 8.73 0.36 0.01 0.03 0.04 0.50 0.02 2.92 0 1.27 0.35 17.17 0.13 0
0.38 224 2.23 2.42 0.35 2.74 2.19 4.05 0.34 0 0.01 0 0 0.01 0.61 0 0.25 0.39 15.48 0.08 0
8.01 554 8.81 7.52 1.00 2.09 11.88 7.80 0.44 0.01 0.01 0.04 0.50 0.01 3.13 0 1.25 0.35 13.03 0.17 0
0.38 218 1.66 7.39 0.35 2.27 2.28 2.40 0.40 0 0.01 0 0 0.00 1.13 0 0.18 0.39 6.69 0.09 0
7.91 472 8.63 6.31 2.04 2.93 14.25 8.20 0.71 0.01 0.01 0.04 0.49 0.02 2.73 0 2.21 0.40 28.46 0.22 0
0.29 148 0.79 1.36 0.76 1.75 0.89 3.37 0.79 0 0.01 0 0.01 0.00 1.02 0 1.33 0.37 13.70 0.06 0
7.92 551 7.02 11.40 7.65 1.68 13.26 11.05 1.60 0.01 0.01 0.06 0.75 0.02 3.09 0 1.75 0.47 11.43 0.17 0
0.23 147 1.29 2.78 2.37 1.19 2.14 5.54 2.85 0.01 0.01 0.03 0.50 0.01 0.74 0 1.04 0.39 9.78 0.10 0
8.18 436 7.45 6.51 3.28 1.55 10.73 8.77 1.06 0.01 0.02 0.04 0.63 0.02 2.45 0 2.04 0.47 69.11 0.38 0
0.28 151 0.66 1.43 1.11 1.77 1.94 2.64 1.92 0 0.01 0 0.25 0.01 0.41 0 1.33 0.39 36.60 0.07 0
8.10 488 7.89 3.20 0.00 1.85 14.73 11.41 0.22 0.01 0.01 0.04 0.50 0.01 2.04 0 1.60 0.47 23.50 0.27 0
0.28 186 0.56 2.71 0.00 1.44 1.81 4.22 0.29 0 0 0 0 0 0.70 0 1.07 0.39 6.66 0.06 0
8.08 506 10.84 13.30 1.20 4.10 12.20 14.20 2.65 0.04 0.18 0.08 1.10 0.03 2.76 1.33 1.45 0.52 35.50 0.22 0.00
0.16 76.01 0.44 6.51 1.41 4.95 3.92 10.75 3.69 0.04 0.22 0.06 0.85 0.02 0.20 1.88 0.07 0.54 18.53 0.08 0.00
8.06 465 9.17 7.32 4.22 2.82 11.75 12.17 1.79 0.01 0.01 0.08 0.75 0.04 2.47 1.67 1.65 0.61 32.20 0.21 66.67
0.17 85.73 0.26 6.83 5.05 3.98 2.23 4.95 2.52 0.00 0.00 0.07 0.43 0.03 0.42 1.80 0.75 0.41 20.89 0.03 57.74
7.89 418 8.00 6.99 1.68 2.78 13.58 11.34 2.07 0.01 0.02 0.06 0.68 0.04 2.38 0.05 1.86 0.66 29.93 0.23 12.50
0.42 9.49 0.49 1.97 0.43 1.42 1.18 5.38 1.81 0.00 0.03 0.02 0.26 0.01 0.16 0.10 1.23 0.35 16.45 0.08 25.00
8.00 511 7.56 47.00 12.20 0.97 13.48 15.20 0.80 0.01 0.01 0.04 0.50 0.01 2.84 0.00 2.20 0.52 5.70 0.10 0.00
0.13 0.53 0.77 49.50 8.20 0.30 4.28 8.49 0.42 0.00 0.01 0.00 0.00 0.00 0.06 0.00 1.41 0.54 0.14 0.01 0.00
7.82 475 5.00 909.78 645.78 135.66 11.18 18.17 1.50 0.26 0.19 0.51 1.91 0.62 2.47 43.22 1.95 0.66 64.06 0.38 91.67
0.36 74.61 1.67 1282.48 867.83 126.02 2.01 7.35 1.92 0.48 0.20 0.69 2.09 0.76 0.37 41.83 1.05 0.35 40.85 0.17 16.67
7.89 445 5.01 31.55 23.75 2.66 15.00 15.02 0.16 0.01 0.01 0.07 0.50 0.03 1.86 2.16 1.33 0.66 20.98 0.16 65.00
0.64 61.22 3.30 59.64 47.50 2.06 1.64 5.40 0.23 0.00 0.01 0.06 0.00 0.03 0.12 2.63 0.54 0.35 22.32 0.13 25.17
(Table 1). Total phenols were detected only from downstream sites during the wastewater discharge period and were significantly higher in the 2nd sampling year compared to the 1st. PCA analysis explained c. 50% of variation in the first three principal components. The first component (PC1) revealed strong associations with BOD5, total suspended solids, chloride, total phenols, sewage bacteria and cropland (negative correlations) and reflected impacted sites, while forests, altitude, dissolved oxygen, slope, cobbles and gravel which reflected upstream sites showed strong positive correlations. The second component (PC2) showed strong associations with conductivity, water hardness, temperature, distance from
OMW outlet and open grassland/bushland (negative correlations), and with slope, water velocity, water depth and altitude (positive correlations). The third component (PC3) showed strong association with pH, SO4 and total hardness (positive correlations) and with algae and macchie (negative correlations).
3.2.
Macroinvertebrate assemblages
Overall, 170 taxa were identified that belong to 64 families. Of the 170 macroinvertebrate taxa, 14 comprised 81% of the total benthic community. Gammarus sp. (14.7%) and species of the Baetidae family (14.5%) were the most abundant taxa,
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followed by Ecdyonurus graecus (9%), Chironomidae (6.2%), Hydropsyche peristerica (5.9%), Leptophlebiidae (4.8%), Brachyptera spp. (4.4%), Perla marginata (4.1%), Simuliidae (4%) and Isoperla spp. (3%) that accounted for 71% of the total upstream fauna. Chironomidae (54.1%), followed by Simuliidae (11.1%), Baetidae (10.5%) and Culicoides sp. (6.9%) represented the downstream benthic community and accounted for c. 83% of the total downstream fauna. Taxa richness upstream the OMW outlets did not vary considerably between periods and years (Fig. 2a). Taxa richness downstream the OMW outlets was overall lower than upstream. During the wastewater discharge period the number and adundance of taxa was significantly decreased; the effects during the 2nd year being more pronounced (Fig. 2a and b). The downstream biotic fauna before the discharge period comprised of 12 and 11 taxa during the 1st and 2nd year, respectively and declined to 7 and 3 taxa during the 1st and 2nd sampling year, respectively (Fig. 2a and b). After the end of the discharge period, taxa richness increased to 13 and 7 taxa during the 1st and 2nd sampling year, respectively, while taxa abundance increased significantly especially during the 2nd year (Fig. 2b). The structure and composition of the upstream fauna were relatively stable and did not fluctuate among periods and years (Fig. 3). In contrast, the downstream fauna fluctuated
considerably among periods and was dominated by Diptera species in most periods. Ephemeroptera, Plecoptera and Trichoptera taxa which dominated the upstream communities were almost depleted during and after the OMW discharge period (Fig. 3). Overall, downstream assemblages were significantly distinct from the upstream as confirmed by ANOSIM (R ¼ 0.405). SIMPER analysis showed that in all periods the upstream fauna was dominated by species of the Gammaridae, Taeniopterygidae, Baetidae, Leuctridae, Heptageniidae, Chironomidae, Perlodidae and Hydropsychidae families that accounted for about 70% of the total community. The structure of the downstream community before the wastewater discharge period was dominated by Nemouridae (58%), Simuliidae (24%), Baetidae (5%), Chironomidae (5%) and Leptophlebiidae (4%) that accounted for 96% of the total benthic community. During the wastewater discharge period the fauna was dominated mainly by Chironomidae (64%), followed by Ceratopogonidae (15%) and Simuliidae (8%), which accounted for 87% of the total fauna. Species of the Chironomidae family represented the fauna and after the end of the wastewater discharge period (72%) followed by Baetidae (13%) Perlodidae (5%), Simullidae (3%), Ceratopogonidae (2%) and Taeniopterygiidae (2%). Similarly, taxa richness did not vary considerably between years and periods upstream the OMW outlets of mountainous sites (Fig. 4a) as it did in lowland sites (Fig. 4b). In the mountainous sites (permanent sites; may dry for small period of time only during extreme droughts) the number of taxa and their abundances declined significantly during the OMW wastewater discharge period where effects were more pronounced during the 2nd year (Figs. 4a and 5a). The effects of OMW on taxa richness and their abundances of lowland sites (intermittent sites) were relatively more intense than in mountainous sites due to their intermittent character and no fauna recovery was recorded during the 2nd year (Figs. 4b and 5b). Lowland sites downstream the OMW outlets did not retain water during the summer and first months of the winter (OctobereNovember) and recolonisation patterns were delayed and initiated at the same time as the operation of olive mills. SIMPER analysis showed that in all periods the upstream fauna of mountainous sites was dominated by species of the Gammaridae, Baetidae, Hydropsychidae, Perlidae and Leptophlebiidae families that accounted for about 45% of the total community. The downstream fauna of the mountainous sites was dominated by species of the Chironomidae, Simuliidae, Baetidae, Dytiscidae and Hydropsychidae families that accounted for 77% of the total community. The structure of the downstream community of the lowland sites comprised of species of the Baetidae, Heptageniidae, Simuliidae, Dytiscidae and Gammaridae families that accounted for 46% of the total community. The benthic fauna downstream the OMW outlets of the lowland sites was mainly composed of Chironomidae, followed by Ceratopogonidae, Simuliidae and Baetidae which accounted for 92% of the total fauna.
Fig. 2 e Mean (±SD) number of taxa (a) and their abundances (b) before, during and after the OMW disharge period for the 2 year sampling campaign. (UP-Upstream sites; DW-Downstream sites).
3.3.
Biological and ecological status
The biological status of the upstream sites was classified from good to high in all months (Fig. 6a and b). The biological status
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Fig. 3 e EPT (Ephemeroptera, Plecoptera and Trichoptera) taxa and Diptera richness upstream and downstream the olive mill outlet for the 2 year sampling period.
of the downstream sites varied from good and high before the wastewater discharge period, to moderate and bad during the discharge period (Fig. 6b). Fauna recovery was relatively rapid during the 1st period (November 2006 to July 2007) after the end of the olive mill season whereas at the 2nd period (November 2007 to May 2008) biological status varied from poor to moderate (Fig. 6b). Table 2 presents several key biotic metrics that show the effects of OMW on mountainous (permanent) and lowland (intermittent) sites upstream and downstream the OMW outlet. The mean biological and physicochemical status of all samples was considered in order to classify the ecological status of the sites for the years 2006e2008. Upstream sites that were used as control, presented good and high ecological status whereas the ecological status of the sites affected from OMW pollution ranged from moderate to bad (Table 3).
3.4. Relationship of macroinvertebrate assemblages and environmental factors Non metric multidimensional scaling (NMDS) ordination revealed that macroinvertebrate assemblages in biologically high (:) and good (;) status sites were consistent while assemblages of moderate (>), poor (X) and bad (Δ) status sites were scattered along the NMDS axes (Fig. 7), thus revealing distinct assemblages. An overlapping was observed among poor and bad status sites thus suggesting relatively similar taxa composition. The first DCA axis (SD: 3.756) confirmed the unimodal assumption and thus the CCA application. The first DCA axes accounted for 11.8% of the variation in macroinvertebrate data while it was very well correlated with the environmental data (r ¼ 0.915). The remaining three axes also showed significant correlations (r > 0.810) and accounted for 28.1% of
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Fig. 4 e Mean (±SD) number of taxa upstream and downstream the OMW outlet in mountainous (permanent) sites (a) and lowland (intermittent) sites (b).
the variation. Nine out of the 33 environmental variables were significant ( p 0.05) in explaining macroinvertebrate variation as derived from CCA. Total variance in macroinvertebrate abundance data was 2.82 and the sum of all canonical eigenvalues 1.58 (Table 4). The percentage of the total variation of taxa explained by the environmental variables accounted thus for 56% (1.58 100/2.82). The four specieseenvironment axes were strongly correlated (specieseenvironmental correlation coefficients ranged from 0.9 to 0.943). The relationship of macroinvertebrate taxa with the 9 environmental variables is presented in Fig. 8 (a) while Fig. 8b presents the association of environmental variables with samples (sites). The first ordination axis (horizontal axis) highlighted the influence of OMW pollution and reflected a gradient mostly related to sewage bacteria, distance from OMW outlet, BOD5, dissolved oxygen and slope (Fig. 8a). Distance from OMW outlet decreased from the left towards the right end of the axis. In contrast, the presence of sewage bacteria increased towards to the right end of the axis. The second axis (vertical axis) indicated that water depth and temperature had the next largest effect on taxa occurrence. Water depth decreased from the upper quadrants of the ordination diagram towards the bottom quadrants whereas temperature decreased from the bottom quadrants towards the upper quadrants (Fig. 8a). The ordination diagram clearly divided macroinvertebrate assemblages and samples affected by OMW pollution (right
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Fig. 5 e Mean (±SD) number of taxa abundance upstream and downstream the OMW outlet in mountainous (permanent) sites (a) and lowland (intermittent) sites (b).
quadrants) from unpolluted samples and taxa occurring in clean waters (Fig. 8a and b). Samples indicated with transparent boxes represented the downstream sites that were ordinated in the right quadrants while samples indicated with dark boxes represented the samples of upstream sites (Fig. 8b). Taxa richness and abundance decreased from the left quadrants towards the right ones, where during the flourish of sewage bacteria in the river bottom only populations of Chironomidae, Ceratopogonidae and Tabanidae were present. Populations of the Notonectidae, Gerridae, Mesovelidae and Ephemerellidae were highly associated with lower depths and warmer waters (bottom left quadrant). On the bottom right quadrant abundances of Polycentropodidae, Perlidae, Elmidae, Heptageniidae, Gammaridae, Ephemeridae, among others, increased as distance from OMW outlets increased. On the upper left quadrant, the EPT families of Taeniopterygidae, Nemouridae, Hydropsychidae, Leptophlebiidae, Philopotamidae and Rhyacophilidae were highly associated with increased levels of oxygen in streams with relatively high slope and altitude. Baetidae being near to the center of the ordinations appeared to be associated with most environmental variables of the ordination. Finally, taxa of the upper right quadrant were associated with depth and stream width.
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was significant but not as much as the second year where concentrations of polluting variables were extremely high. During the OMW discharge episodes, BOD5, COD and TSS were extremely high, causing significant decrease in dissolved oxygen concentrations and creating anoxic conditions in many cases. A significant increase in chloride and total phenols concentration was also observed in the downstream sites during the wastewater discharge period as well as a marked increase in nutrients. The presence of large quantities of nutrients, sugars and organic compounds of OMW provide a perfect medium for microorganisms to multiply and contaminate waters, and subsequently form a thick oily layer on the river bed, which have severe consequences to the local aquatic life. Aquatic organisms are trapped into this thick oily layer, known as sewage fungus or sewage bacteria, and their functional organs such as gills, oral parts, etc., are being disabled resulting in mortality. BOD5, COD, TSS, total phenols, sewage bacteria and chloride were the most important parameters in distinguishing impacted from unimpacted samples as shown by ordination procedures (PCA). However, water quality degradation is a short-term effect due to the constant renewal of water. Self-purification occurred relatively fast and the physicochemical status of the sites was restored after OMW discharge ceased.
4.2. Spatial and temporal effects on macroinvertebrate assemblages
Fig. 6 e Box plots illustrating the mean (±SD), minimum and maximum biological status (STAR_ICMi) of the upstream (a) and downstream (b) olive mill sites for the 2 year sampling period. OMW discharge months (underlined) include December 2006, January 2007, and December 2007, January, February and March 2008. Class boundaries of biological status are High > 0.946; Good 0.709; Moderate - 0.473; Poor - 0.236; Bad - 0.
4.
Discussion
4.1.
Hydrochemical conditions
The main polluting effects of OMW on receiving waters are related to their concentration, composition, and to their seasonal production. The most visible effect of OMW pollution is the discoloring of natural waters, which is attributed to the oxidation and subsequent polymerization of tannins that give dark colored polyphenols, which are difficult to remove from the effluent (Hamdi et al. 1992). Overall, the effects of OMW on water chemistry were more pronounced on the 2nd year of the sampling campaign due to the higher olive fruit production that yielded a greater quantity of wastewater. During the first year of the sampling campaign, where the volume and the intensity of OMW was lower compared to the second year, water quality degradation
The findings of this study showed that OMW had severe effects on the fauna of aquatic ecosystems. The vast majority of macroinvertebrate taxa diminished and only a few tolerant Diptera species survived with very limited abundances (1e4 individuals/1.25 m2). Overall, macroinvertebrate diversity was lower in downstream than upstream sites. Similarly, downstream assemblages were markedly distinct from upstream assemblages during OMW discharge. Differences would have been greater but because the pollution is episodic, sites are not degraded throughout the year, but for only some months and macroinvertebrate communities recover, depending on the intensity of the wastewater pollution. It was shown that impacts on benthic communities mainly depend on the intensity and duration of pollution (quantity and time of the waste present in the receiving waters), the distance from the outflow of the olive mill and the amount and duration of water in the receiving water. During the first sampling period (November 2006eJuly 2007), where OMW disposal lasted only two months (December to end of January), impacts on sites that maintain flow most of the year (i.e. sites 2 and 8) were significant during the period of OMW discharge, as the biota was almost eliminated. However, biota recovery was relatively successful after the end of the milling operation. During the second year (November 2007eMay 2008) in which oil production was much greater and lasted 4 months (December 2007eend of March 2008), effects were more pronounced even after the oil production period as biota had not recovered successfully. During the second sampling period, impacts on intermittent streams (4 and 5) were more pronounced in comparison to permanent flow streams. During OMW discharge, impacts
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Table 2 e Biotic metrics showing the effects of OMW on different stream ecosystems; Mountainous (permanent) sites versus lowland (intermittent) sites upstream and downstream the OMW outlet. 1st Year Metric
2 nd Year
1st Year
2 nd Year
BEFORE DURING AFTER BEFORE DURING AFTER BEFORE DURING AFTER BEFORE DURING AFTER
Abundance Number of Taxa BMWP ASPT IBMWP IASPT Diptera [%] EPT-Taxa [%] Diptera EPT-Taxa Sel_Ephemeroptera_M Sel_Plecoptera_M ALL/Diptera EPT/Diptera Trichoptera_taxa Plecoptera_taxa Portuges Gold-Index sel EPTD
110 19 98 6 103 6 13 52 4 7 18 19 11 2 2 3 1 1
137 22 114 7 121 7 7 68 4 12 22 13 10 3 2 5 1 1
Abundance Number of Taxa BMWP ASPT IBMWP IASPT Diptera [%] EPT-Taxa [%] Diptera EPT-Taxa Sel_Ephemeroptera_M Sel_Plecoptera_M ALL/Diptera EPT/Diptera Trichoptera_taxa Plecoptera_taxa Portuges Gold-Index sel EPTD
152 16 83 7 89 6 16 68 4 8 18 2 4 2 0 4 1 1
178 19 106 7 113 7 7 59 5 9 15 36 9 2 1 4 1 1
Mountainous 152 23 116 7 122 7 17 53 4 12 10 21 7 3 3 4 1 1
Upstream 155 234 21 23 112 119 7 7 121 129 7 7 7 13 56 66 4 5 10 13 4 7 19 33 10 10 3 3 3 5 4 4 1 1 1 2
Lowland Upstream 186 41 20 9 92 45 6 6 98 53 6 7 9 14 49 25 4 2 9 3 16 1 1 1 13 13 2 1 2 1 2 1 1 1 1 0
195 23 111 7 119 6 26 55 6 11 6 15 2 2 3 4 1 1
196 18 91 6 97 6 19 67 5 9 30 7 8 2 3 3 1 1
were of the same intensity in both stream types, however, no marked recovery occurred in intermittent streams after the end of wastewater discharge period and sites remained in bad biological status. The benthic community of site 4 for
218 22 90 6 108 6 15 53 8 8 28 4 4 1 1 2 1 2
122 12 49 5 60 4 29 40 4 5 6 35 3 1 1 2 1 1
Dry
Mountainous Downstream 106 204 229 30 8 18 12 5 24 82 49 18 5 6 5 5 27 89 53 20 4 6 5 4 66 33 70 84 28 58 26 13 4 5 5 3 3 8 3 2 13 13 0 0 0 14 48 0 1 5 1 0 1 2 1 1 1 2 2 1 0 2 1 0 0 1 0 0 0 1 1 0
604 12 50 6 50 6 84 14 4 5 0 0 0 1 1 2 0 0
Lowland Downstream 229 9 37 5 39 5 65 20 3 Dry 4 5 0 0 1 1 1 0 0
89 3 5 3 5 3 100 0 3 0 0 0 0 0 0 0 0 0
60 8 38 5 38 5 75 21 3 3 4 0 0 1 1 1 0 0
3 2 3 2 6 3 81 0 1 0 0 0 0 0 0 0 0 0
example, showed significant recovery potential just few weeks after the termination of OMW discharge during the first year, but it was interrupted due to early drought leading to species mortality. Resumption of flow the next rainy season
Table 3 e Ecological Status of the stream sites upstream and downstream the OMW outlet as classified by physicochemical (NCS), and biological (STAR_ICMi) status. (UP: Upstream; DW: Downstream). Sites 1 2 3 4 5 6 7 8
OMW OUTLET
NCS
Physicochemical Status
STARICMi
Biological Status
ECOLOGICAL STATUS
UP DW UP DW DW UP UP DW
4.433 3.600 3.820 2.588 2.783 4.375 4.325 3.459
HIGH GOOD GOOD MODERATE MODERATE HIGH HIGH GOOD
1.003 0.598 0.885 0.232 0.345 0.770 0.986 0.503
HIGH MODERATE GOOD BAD POOR GOOD HIGH MODERATE
HIGH MODERATE GOOD BAD POOR GOOD HIGH MODERATE
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Fig. 7 e Non metric multidimensional scaling (NMDS) ordination of fourth square root macroinvertebrate assemblages in all sites and all years. Assemblages were ordinated according to biological status. (1: High, 2: Good, 3: Moderate, 4: Poor, 5: Bad).
coincided with the opening of the mills at the same time as the first colonizers appear thus leaving no opportunity for biota recolonisation. Distance from OMW outflow plays an important role in the intensity of pollution as verified by multivariate analysis. At the time of discharge, the macroinvertebrate fauna of the sites located few meters downstream of the OMW outlet (5e200 m) were almost eliminated and only few very tolerant species survived (e.g. Chironomus thummi-Gr., Culicoides sp., Tipula sp.). Similar effects were recorded in sites located further downstream from the outflow (>400 m); however fauna depletion was gradual rather than abrupt. For example, in site 8 located 400 m downstream the mill outlet, the number of taxa and macroinvertebrate abundance decreased significantly during the first sampling period, but not dramatically, due to mild pollution loads as a result of small wastewater volume and limiting time of mill operation.
Table 4 e Results of the CCA analyses between environmental variables and macroinvertebrate fauna. Total inertia is the total variance in macroinvertebrate abundance data. Axes
1
2
3
4
Eigenvalues : 0.289 0.213 0.154 0.127 Specieseenvironment 0.928 0.943 0.9 0.94 correlations: Cumulative percentage variance of species data : 10.2 17.8 23.2 27.8 of specieseenvironment 18.3 31.7 41.5 49.6 relation: Sum of all eigenvalues Sum of all canonical eigenvalues
Total inertia 2.82
2.82 1.58
Similar results were recorded by Voreadou (1994) that examined the effects of OMW in small stream in Crete Island. The results showed a significant reduction of biodiversity of streams during OMW discharge, while the intensity of the effects were proportional to the amount and duration of water in streams, results that are consistent with those of this study. In streams with high water discharge and 7e8 months of flow duration, a decrease of species was detected up to 41% of the total stream length, while in streams with less water volume, species decline was recorded at 71% of the total stream length (Voreadou, 1994). The significance of the distance from the outflow of the wastewater is also reflected in similar studies, such as in the River Ray in England, where 50 m downstream from the diesel oil spill only 9% survival of individuals (excluding oligochaeta worms) and 56% survival of invertebrate families occurred (Smith et al., 2010). The percentage survival of macroinvertebrates increased progressing downstream from the spill, with no detectable impacts beyond approximately 4 km downstream, while the recovery of macroinvertebrates was nearly complete after 13.5 months with only minor effects on sections closest to the spill (Smith et al., 2010).
4.3.
Ecological status
This is the first study that assesses the effects of OMW to the ecological status of stream ecosystems. The ecological status of the upstream sites was classified from good to high in all months while minimal variations among and within sites were mainly attributed to seasonality. In contrast, downstream sites varied from good and high before the wastewater discharge period, to moderate and bad during the discharge period. Sites with relatively high slope, altitude and oxygen presented moderate ecological status due to the high selfpurification capacity whereas sites located in lowlands were classified from moderate to bad.
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Fig. 8 e CCA plot showing the relationship of macroinvertebrate families (a) and samples (b) with the significant environmental variables. First axis is horizontal and second axis vertical. Only those families contributing more than 10% of the total macroinvertebrate abundance are shown in the graph for clarity. Taxa centroids were labeled by the first four or five letters of the family name. The transparent boxes of the second figure (b) represent samples from downstream sites affected by OMW pollution whereas the dark boxes represent samples from upstream sites. (Alt: Altitude; DO: Dissolved oxygen; BOD5: Biological oxygen demand; Pipe: Distance from olive mill outlet; T: Temperature; SB: Sewage bacteria; ASW: Average stream width).
5.
Conclusions
The results of this study revealed the spatial and temporal structural deterioration of the aquatic community due to OMW pollution with consequent reduction of the river capacity for reducing the effects of polluting substances through internal mechanisms of self-purification. OMW, even highly diluted, has significant impacts on the aquatic fauna and to the ecological status of fluvial ecosystems. The organic load of the wastewater (BOD5, COD, TSS), substrate contamination (sewage bacteria) and distance from the mill outlet, were the most important factors affecting macroinvertebrate assemblages while the typology of the stream site (i.e. mountainouselowland) and the intensity and volume of the wastewater were the most important determinants of selfpurification processes. OMWs are usually discharged in small stream catchments (<10 km2) which are not considered in the Water Framework Directive 2000/60/EC. Therefore, there is a need for including small streams into monitoring and assessment schemes as small streams contribute to the pollution load of the river basin. Furthermore, guidelines to manage these wastes through technologies that minimise their environmental impact and lead to a sustainable use of resources are critical. Finally, while studying the effects of OMW on river ecosystems it has been brought to attention
that the effects of these organic wastewaters on coastal and estuarine ecosystems are largely unknown. Therefore, research on these ecosystems must also be initiated in order to fully assess the magnitude of the adverse effects of OMW.
Acknowledgments This work was funded by the European Union project LIFE e Environment “Environmental Friendly Technologies for Rural Development”, LIFE05ENV/GR/000245. The authors wish to thank the two anonymous reviewers for their valuable comments towards the improvement of this manuscript.
references
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AQEM Consortium, 2002 Manual for the Application of the AQEM Method. A Comprehensive Method to Assess European Streams Using Benthic Macroinvertebrates. Developed for the Purpose of the Water Framework Directive, Version 1.0. February 2002. Arienzo, M., Capasso, R., 2000. Analysis of metal cations and inorganic anions in olive mill wastewaters by atomic absorption spectroscopy and ion chromatography. Detection of metals bound to the organic polymeric fraction. Journal of Agricultural and Food Chemistry 48 (4), 1405e1410. Ben-Gal, A., Yermiyahu, U., Zipori, I., Presnov, E., Hanoch, E., Dag, A., 2010. The influence of bearing cycles on olive oil production response to irrigation. Irrigation Sciences. doi:10. 1007/s00271-010-0237-1. Buffagni, A., Erba, S., Furse, M.T., 2007. A simple procedure to harmonize class boundaries of assessment systems at the pan-European scale. Environmental Science Policy 10 (7e8), 709e724. Chapman, M.G., Underwood, A.J., 1999. Ecological patterns in multivariate assemblages: information and interpretation of negative values in Anosim tests. Marine Ecology - Progress Series 180, 257e265. Clarke, K.R., Warwick, R.M., 1994. Changes in Marine Communities. An Approach to Statistical Analysis and Interpretation, p. 144, Natural Environmental Research Council, U.K. D’Annibale, A., Casa, R., Pieruccetti, F., Ricci, M., Marabottini, R., 2004. Lentinula edodes removes phenols from olive-mill wastewater: impact on durum wheat (Triticum durum Desf.) germinability. Chemosphere 54 (7), 887e894. Davies, L.C., Vilhena, A.M., Novias, J.M., Martins-Dias, S., 2004. Olive mill wastewater characteristics: modeling and statistical analysis. Grasas Aceites 55, 233e241. De Marco, E., Savarese, M., Paduano, A., Sacchi, R., 2007. Characterization and fractionation of phenolic compounds extracted from olive oil mill wastewaters. Food Chemistry 104 (2), 858e867. DellaGreca, M., Monaco, P., Pinto, G., Pollio, A., Previtera, L., Temussi, F., 2001. Phytotoxicity of low-molecular-weight phenols from olive mill waste waters. Bulletin of Environmental Contamination and Toxicology 67 (3), 352e357. European Commission (EC), 2007. WFD Intercalibration Technical Report. MedGIG Intercalibration Technical Report e Part 1 Rivers, Section 1 Benthic Invertebrates, p. 17. 15 June 2007. European Commission, Brussels. Fiorentino, A., Gentili, A., Isidori, M., Monaco, P., Nardelli, A., Parrella, A., Temussi, F., 2003. Environmental effects caused by olive mill wastewaters: toxicity comparison of lowmolecular-weight phenol components. Journal of Agricultural and Food Chemistry 51 (4), 1005e1009. Hamdi, M., 1993. Future prospects and constraints of olive mill wastewaters use and treatment: a review. Bioprocess Engineering 8, 209e214. Hamdi, M., Garcia, J.L., Ellouz, R., 1992. Integrated biological process for olive mill wastewater treatment. Bioprocess Engineering 8, 79e84. Kapellakis, J.E., Tsagarakis, K.P., Avramaki, C., Angelakis, A.N., 2006. Olive mill wastewater management in river basins: a case study in Greece. Agricultural Water Management 82 (3), 354e370. Karaouzas, I., Cotou, E., Albanis, T.A., Kamarianos, A., Skoulikidis, N., Giannakou, U., 2010. Bioassays and biochemical biomarkers for assessing olive mill and citrus
processing wastewater toxicity. Environmental Toxicology. doi:10.1002/tox.20606. Kotsou, M., Mari, I., Lasaridi, K., Chatzipavlidis, I., Balis, C., Kyriacou, A., 2004. The effect of olive oil mill wastewater (OMW) on soil microbial communities and suppressiveness against Rhizoctonia solani. Applied Soil Ecology 26 (2), 113e121. Leps, J., Smilauer, P., 2003. Multivariate Analysis of Ecological Data Using CANOCO. Cambridge University Press, Cambridge. Mekki, A., Dhouib, A., Sayadi, S., 2006. Changes in microbial and soil properties following amendment with treated and untreated olive mill wastewater. Microbiological Research 161 (2), 93e101. Mekki, A., Dhouib, A., Feki, F., Sayadi, S., 2008. Assessment of toxicity of the untreated and treated olive mill wastewaters and soil irrigated by using microbiotests. Ecotoxicology and Environmental Safety 69 (3), 488e495. Paixa˜o, S.M., Mendon‚ca, E., Picado, A., Anselmo, A.M., 1999. Acute toxicity evaluation of olive mill wastewaters: a comparative study of three aquatic organisms. Environmental Toxicology 14 (2), 263e269. Paraskeva, P., Diamadopoulos, E., 2006. Technologies for olive mill wastewater (OMW) treatment: a review. Journal of Chemical Technology and Biotechnology 81 (9), 1475e1485. REFCOND Guidance, 2003. Guidance on Establishing Reference Conditions and Ecological Status Class Boundaries for Inland Surface Waters. Version 7.0. 5 March 2003efinal. CIS Working Group 2.3. Rinaldi, M., Rana, G., Introna, M., 2003. Olive-mill wastewater spreading in southern Italy: effects on a durum wheat crop. Field Crops Research 84 (3), 319e326. Rouvalis, A., Iliopoulou-Georgudaki, J., Lyberatos, G., 2004. Application of two microbiotests for acute toxicity evaluation of olive mill wastewaters. Fresenius Environmental Bulletin 13 (5), 458e464. Skoulikidis, N., 2008. Defining chemical status of a temporary Mediterranean River. Journal of Environmental Monitoring 10 (7), 842e852. Skoulikidis, N., Amaxidis, Y., Bertahas, I., Laschou, S., Gritzalis, K., 2006. Analysis of factors driving stream water composition and synthesis of management toolseA case study on small/ medium Greek catchments. Science of the Total Environment 362 (1e3), 205e241. Smith, P., Snook, D., Muscutt, A., Smith, A., 2010. Effects of a diesel spill on freshwater macroinvertebrates in two urban watercourses, Wiltshire, UK. Water and Environment Journal 24 (4), 249e260. ter Braak, C.J.F., Prentice, C., 1988. A theory of gradient analysis. Advance Ecological Research 18, 271e317. Venieri, D., Rouvalis, A., Iliopoulou-Georgudaki, J., 2010. Microbial and toxic evaluation of raw and treated olive oil mill wastewaters. Journal of Chemical Technology & Biotechnology 85 (10), 1380e1388. Vlyssides, A.G., Loizidou, M., Gimouhopoulos, K., Zorpas, A., 1998. Olive oil processing wastes production and their characteristics in relation to olive oil extraction methods. Fresenius Environmental Bulletin 7 (5e6), 308e313. Voreadou, K., 1994. Liquid Wastes from Olive Mills e Effects on the Natural Water Ecosystems of Crete e Present Management of the Wastes and Tendencies. Proceedings of the Conference of Management of Olive Wastes, Siteia, Crete. 9e14. Yesilada, O., Sam, M., 1998. Toxic effects of biodegraded and detoxified olive oil mill wastewater on the growth of Pseudomonas aeruginosa. Environmental Toxicology and Chemistry 65 (1), 87e94.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 4 7 e6 3 5 4
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Development of biomass in a drinking water granular active carbon (GAC) filter Silvana Velten a, Markus Boller a, Oliver Ko¨ster b, Jakob Helbing b, Hans-Ulrich Weilenmann a, Frederik Hammes a,* a b
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Duebendorf, Switzerland Stadt Zurich, Wasserversorgung, Hardhof 9, Postfach 1179, 8021 Zurich, Switzerland
article info
abstract
Article history:
Indigenous bacteria are essential for the performance of drinking water biofilters, yet this
Received 25 January 2011
biological component remains poorly characterized. In the present study we followed
Received in revised form
biofilm formation and development in a granular activated carbon (GAC) filter on pilot-
4 September 2011
scale during the first six months of operation. GAC particles were sampled from four
Accepted 6 September 2011
different depths (10, 45, 80 and 115 cm) and attached biomass was measured with aden-
Available online 16 September 2011
osine tri-phosphate (ATP) analysis. The attached biomass accumulated rapidly on the GAC particles throughout all levels in the filter during the first 90 days of operation and main-
Keywords:
tained a steady state afterward. Vertical gradients of biomass density and growth rates
Granular activated carbon (GAC)
were observed during start-up and also in steady state. During steady state, biomass
Biologically activated carbon (BAC)
concentrations ranged between 0.8e1.83 x 106 g ATP/g GAC in the filter, and 22% of the
Drinking water
influent dissolved organic carbon (DOC) was removed. Concomitant biomass production
Biofiltration
was about 1.8 1012 cells/m2h, which represents a yield of 1.26 106 cells/mg. The bacteria
Biodegradation
assimilated only about 3% of the removed carbon as biomass. At one point during the operational period, a natural 5-fold increase in the influent phytoplankton concentration occurred. As a result, influent assimilable organic carbon concentrations increased and suspended bacteria in the filter effluent increased 3-fold as the direct consequence of increased growth in the biofilter. This study shows that the combination of different analytical methods allows detailed quantification of the microbiological activity in drinking water biofilters. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Granular activated carbon (GAC) filters are commonly used during drinking water treatment for the removal of undesirable dissolved organic carbon (DOC) fractions including biodegradable organic matter, micropollutants, halogenated hydrocarbons and taste and odor compounds by adsorption (Servais et al., 1994; Urfer et al., 1997; Fonseca et al., 2001; Velten et al., 2007). Filters in which the GAC is not regularly replaced
or regenerated evolve naturally into biofilters, where most of the DOC removal is a result of biodegradation instead of adsorption (Lee et al., 1981; Servais et al., 1991; Moll et al., 1999; Velten et al., 2007; Hammes et al., 2008). In this case, indigenous microbial communities colonize the surfaces of the GAC particles, and such filters are also referred to in literature as biologically activated carbon (BAC) filters. This transition from a GAC to a BAC filter is a time-dependent process that alters the performance of the system considerably. BAC filters typically
* Corresponding author. Tel.: þ41 44 823 5350; fax: þ41 44 823 5028. E-mail address:
[email protected] (F. Hammes). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.017
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remove less total DOC than GAC filters, but targets specifically the biodegradable organic carbon fraction. GAC/BAC filters are usually placed in a treatment train after an oxidation step, such as ozonation. During ozonation DOC is oxidized to low molecular weight oxygen-containing organic carbon molecules, resulting in an increase of the biodegradable fraction of DOC (Volk and LeChevallier, 2002; von Gunten, 2003; Hammes et al., 2006). Subsequent removal of this biodegradable fraction is essential and contributes to the biological stability of the water, thereby reducing the likelihood of undesirable bacterial re-growth in the water distribution system (van der Kooij et al., 1989). Since the major part of DOC removal during biofiltration can be attributed to biological processes, it is important to be able to quantify and characterize the microbial biomass responsible for this process. Quantitative knowledge of these microbiological processes increases the understanding of the system and the ability to operate and maximize the potential of the biofilter. For example the time required for indigenous bacteria to completely colonize the GAC filter, thus the transition from GAC to BAC filters, as well as the differentiation between adsorbed and biodegraded organic carbon are important parameters for water utilities. Data can also be used as for modeling the performance of biological filters and allow optimization of design and operation (Rittmann and Stilwell, 2002; van der Aa et al., 2006). However, despite several decades of biofiltration research and extensive full-scale application of this technology, relatively little is known about the indigenous microbial communities that colonize biofilter opportunistically and thus contribute to the treatment of drinking water (Simpson, 2008). Some practical limitations in previous studies were the absence of data over extended time periods (Servais et al., 1994; Carlson and Amy, 1998; Urfer and Huck, 2001) or a lack of representative samples from the entire filter bed, with sampling often only feasible from the top (Wang et al., 1995; Magic-Knezev and van der Kooij, 2004; van der Aa et al., 2006; Velten et al., 2007). The goal of this study was to follow the initial colonization and development of biomass in a pilot-scale GAC drinking water filter over time and vertical filter depth in a long-term experiment subject to uncontrolled changes in raw water parameters. We used a direct method based on ATP-measurements to quantify the biofilm development on the GAC particles and flow cytometry for analyzing suspended bacteria. These microbial data were compared to the organic carbon removal in order to evaluate the filter performance.
2.
Materials and methods
2.1.
Pilot plant lay-out and operation
The experiments were conducted at a pilot plant that was set up at the Zurich Waterworks (WVZ Lengg, Switzerland) and operated at a capacity of 5.6 m3/h. The pilot plant consisted of pre-filtration (20 mm), ozonation, GAC filtration and ultrafiltration, treating water from lake Zurich, and has been described previously (Hammes et al., 2008). The GAC filter was operated in down-flow mode with ozonated surface water (Table 1). The design of the GAC filter allowed sampling of both water and GAC particles over the filter bed height, as well as
Table 1 e GAC filter and water quality parameters. Carbon type Packed bed density Reactor volume GAC depth Column diameter Filtration velocity Empty bed contact time Influent DOC Influent pH Temperature
() (kg/m3) (m3) (m) (m) (m/h) (min) (mg/L) () ( C)
Chemviron SGL 8 18 460 1.47 1.55 1.1 5.9 15.76 1.1 (0.04) 7.79 (0.14) 7.05 (0.7)
influent and effluent water samples (Fig. 1). GAC was sampled at four sampling points that were distributed over the filter with interspaces of 35 cm. Fig. 1 shows that sample points are labeled from the top of the GAC filter downwards; thus the GAC 1 sample was taken 10 cm below the top of the filter. Similarly, water sample 1 (WS 1) was taken at the same height. The data reported herein cover the first 6 months of operation. For the explicit purpose of studying distribution of biomass, no backwashing was applied to the filter during this period.
2.2.
Sampling
For GAC sampling, a metal tube (inner and outer diameter 0.9 and 1.1 cm, respectively) was inserted 0.8 m into the GAC filter from the side through purpose-build sampling ports and about
Fig. 1 e Schematic presentation of the investigated pilotscale granular activated carbon (GAC) filter. Ozonated lake water (Lake Zurich) was used as influent water. Water samples (WS 0e5) and GAC samples (GAC 1e4) were sampled from different depths of the filter.
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20 g GAC particles were collected from each sampling point. Water samples (250 mL) were taken from taps located at the side of the GAC filter, as well as before and after the filter. These samples were collected in sterile, carbon-free glassware prepared as described previously (Hammes and Egli, 2005). During the first 70 days, samples were taken twice a week and afterward sampling was reduced stepwise to once every two weeks. A higher sampling intensity was employed during the biofilm development phase to track the dynamic changes expected in this period. The sampling intensity was decreased with decreasing biofilm growth rates. GAC samples were analyzed for biomass concentrations (see below for details) while the water samples were divided and analyzed for microbial abundance with flow cytometry, assimilable organic carbon (AOC) and DOC analysis (see below for details). All samples were transported to the laboratory in cold storage, and analyzed within 3 h of sampling.
2.3.
Quantification of biomass on GAC particles
time interval between these points. The yield at steady state was calculated as follows (Eq. (2)): yield ¼ ðbiomass productionÞ=ðDOC removalÞ
The total cell concentration (TCC) in the water samples was measured with SYBR Green I staining and flow cytometry (FCM) as described previously in detail (Hammes et al., 2008). FCM was performed using a Partec CyFlow Space instrument (Partec GmbH, Mu¨nster, Germany). The CyFlow Space is equipped with volumetric counting hardware and has an experimentally determined quantification limit of 1000 cells/ mL (Hammes et al., 2008). Every one out of ten water samples was measured in triplicate, to control the standard instrumental error, which never exceeded 5%.
To obtain the case-specific ATP/cell value, a fraction of the bacteria was removed from the GAC surface by gentle manual shaking for 1 min (from the rinsed GAC sample, see above). The ATP concentration of these suspended bacteria was determined as described previously (Velten et al., 2007). The number of bacteria in the sample was measured with flow cytometry (see below). From the combination of the data, a case-specific ATP/cell value was derived.
2.5.
Calculation of growth rate and yield in GAC filter
2.8.
m ¼ ðln ðNt2 Þ ln ðNt1 ÞÞ=Dt
(1)
where Nt1 and Nt2 are the biofilm concentrations (cells/g GAC) measured at subsequent time points and Dt is the expired
DOC analysis
DOC was detected by an infrared (IR) detector after complete oxidation of DOC to CO2 in a Graentzel Thin-Film Reactor (DOC-Labor Dr. Huber, Germany). The detection limit was 10 mg/L (Huber and Frimmel, 1996).
Analysis of phytoplankton
Phytoplankton in the lake water was measured as described in a previous study (Mu¨ller et al., 2003). For the analysis, the samples were immediately fixed with Lugol’s solution. Phytoplankton genera species were differentiated and counted by means of an inverted microscope (Zeiss AXIOVERT 10, Germany). The biovolume was calculated by multiplying the counts of the different phytoplankton species by their respective biovolume. The biomass was following calculated from the biovolume of the phytoplankton (1 mg/L ¼ 106 mm3/mL).
3.
The biofilm specific growth rate (m) was calculated from the ATP-measurements as follows (Eq. (1)):
AOC analysis
AOC was measured with a method comprising the use of a sitespecific natural microbial community, fluorescent staining and flow cytometry for growth quantification (Hammes and Egli, 2005; Hammes et al., 2006). In short, a natural microbial community is inoculated into a bacteria-free water sample (0.22 mm filtered) and incubated at 30 C until stationary phase is reached. The bacteria concentration at stationary phase is converted to a concentration of AOC with a conversion factor of 1 mg AOC giving 1 107 cells (Hammes et al., 2006). All samples were measured in triplicate.
2.9. 2.4. Determining a case-specific bacterial ATP concentration
(2)
2.6. Total suspended bacterial concentrations measurements with flow cytometry
2.7.
GAC samples were treated as described previously (Velten et al., 2007). In short, the GAC particles were rinsed thrice in phosphate buffer. Thereafter, 200 mg (wet weight) was transferred to an Eppendorf tube together with 100 mL sterile phosphate buffer and 300 mL BacTiterGlo (Promega Corporation, Madison, WI, USA), and the resulting luminescence was measured as relative light units (RLU). Results were converted to ATP concentrations using a calibration curve and, where applicable, converted to a corresponding number of bacteria by using a case-specific bacterial ATP concentration (see below). All GAC samples were analyzed in triplicate. For the calculation of total filter biomass, the filter was partitioned into four segments, with the intersection half of the distance between adjacent sampling points. Each sampling point was assumed to give the average ATP concentration of the segment that it represents and was multiplied with the mass of GAC in that particular segment. The sum of ATP in all four segments gives the total ATP content for the entire GAC filter, which was calculated for every time point (Supplementary Information Fig. S1).
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Results and discussion
3.1. Start-up phase: biofilm development and filter transition The first 11 days prior to the actual filter initialization (day 0) were used for testing the hydraulics of the filter using nonchlorinated drinking water. During that time the virgin GAC
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was exposed to the microbial community present in the water, which contributed to a rapid initial colonization. Once the filter was initialized and the ozonation reactor was activated (t ¼ 0 days), suspended bacteria in the influent were completely damaged and inactivated by ozone (Hammes et al., 2008) and all subsequent biomass increase on the GAC is therefore regarded as growth and not as attachment. The biofilm biomass accumulated rapidly on the new GAC particles throughout all levels in the filter during the first three months of operation (t1: 11e91 days) (Fig. 2). During the initial development period, different biomass concentrations and growth rates were clearly observed in the different levels of the filter (Fig. 2A). The biomass accumulation ceased after about 90 operational days (¼8300 empty bed volumes (EBV)) in all sampling points, indicating the establishment of a steady
Fig. 2 e (A) Development of attached biofilm biomass (g ATP/g GAC) at different depths of the GAC filter (GAC 1 [ 10 cm; GAC 2 [ 45 cm; GAC 3 [ 80 cm; GAC 4 [ 115 cm) as a function of empty bed volume (EBV) and operational time. All data points are average values of triplicate measurements, with an average standard deviation always below 15% (see Fig. 4). (B) Evolution of influent and effluent DOC concentrations of the GAC filter as a function of EBV and operational time. The dotted line indicates the beginning of the steady state whereas the gray zone shows the period of increased phytoplankton concentrations in the raw water.
state (t2: 91e198 days). The steady state was defined as the period when the average biomass growth rate was zero throughout the filter. Importantly, this transition from a GAC to BAC filter requires a paradigm shift in the perception of filter performance: simultaneous to the development of biomass in the filter, organic carbon removal decreases as a result of the saturation of adsorption capacity on the GAC (Fig. 2B). Interestingly, the biological steady state was established during the same period as when the DOC effluent concentration stabilized (Fig. 2B). This was also observed in a previous study and suggests that straightforward DOC data could potentially be used as an easy indicator for the biological steady state in the filter (Velten et al., 2007). In period (t1), initial biofilm development (expressed as the growth rate) proceeded at the highest rate in the upper layer of the filter (GAC 1 ¼ 0.0041 h1) and 54% slower in the bottom layer (GAC 4 ¼ 0.0019 h1). The higher growth rate in the upper layer resulted in a rapid establishment of attached biomass, whereas the lower growth rates at the bottom of the filter are ascribed to a decreasing availability of organic nutrients downward through the filter. Very little DOC is present in the effluent of the filter in the initial stage of operation (Fig. 2B). Fig. 3 shows an example of the dynamic changes in the DOC profiles through the filter on two days for the first period (t1) and two days for the second period (t2), respectively. In the first period (t1) evidently, less than 20% of the initial DOC concentration reaches the bottom level of the filter, which is directly
Fig. 3 e Typical DOC filter profiles for four sampling days (day 14, 35, 91 and 196). At day 14 (1300 EBV) a high adsorption capacity for DOC existed still, whereas at day 196 (17906 EBV) the adsorption capacity was decreased and resulted in an increased DOC effluent concentration. The standard error is <10%.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 4 7 e6 3 5 4
ascribed to adsorption. At this time point the adsorption capacity of the fresh GAC for DOC is high and one can assume that the GAC filter functions primarily as adsorber. This adsorption process has a double advantage for a faster biofilm development in the upper layers of the filter: while bacteria in the bottom layers are deprived of easily available DOC, adsorption in the upper layers would create a nutrient-rich micro-environment on the surface of the GAC granules that is favorable to biological growth (Li and DiGiano, 1983; Urfer et al., 1997; Herzberg et al., 2003). In the second period the adsorption capacity of the carbon clearly became saturated (Fig. 2B). It is assumed that during this period the GAC filter functions primarily as a BAC filter, where the DOC was removed biologically. However, an abiotic control for this hypothesis was not feasible on such a large scale (5.6 m3/h) during such a long experimental period (200 days). The benefit of measuring and calculating biofilter growth rates is that it provides insight into biological development in the biofilter, and allows the modeling and estimation of reactor start-up times, which is essential for water utilities. However, the rate of biofilm development is likely to differ considerably in different situations, and can be influenced by amongst other things water temperature, organic carbon quality and quantity and microbial community composition. Throughout the initial biofilm development period, the growth rates obtained in this study (0.0001e0.0043 h1) were considerably lower than those reported by Servais et al. (1991) for a full-scale GAC filter. These authors calculated growth rates of 0.038e0.16 h1 for similar empty bed contact times (EBCT). The difference in growth rates can partly be ascribed to the water temperature difference, which was up to 15 C higher (9e22 C) than in the present study (7.05 (0.7) C). Moll et al. (1999) similarly noted lower biomass concentration and lower growth rates in GAC filters operated at cold (5 C) temperatures opposed to ambient temperatures. Additionally, the DOC concentrations of 1.7e2.95 mg/L reported by Servais et al. (1991) were much higher than in the investigated system (1.13 (0.05) mg/L), which would all be supportive of higher growth rates. Further, bacterial biomass was determined by a different method based on 14CO2 respiration of added 14C-Glucose that may also have contributed to different interpretation of growth rates.
3.2. Steady state period: biomass concentration and distribution in the GAC filter In the 2nd period (t2), differences from 1.5 up to 2.3 times in biomass concentrations occur in the separate levels. The average concentration (n ¼ 14) for each level during the steady state period was: GAC 1 GAC 2 GAC 3 GAC 4
(10 cm) ¼ 1.17 (0.2) (45 cm) ¼ 1.83 (0.4) (80 cm) ¼ 1.18 (0.2) (115 cm) ¼ 0.8 (0.2)
106 g 106 g 106 g 106 g
ATP/g GAC; ATP/g GAC; ATP/g GAC; ATP/g GAC.
These values are comparable with previous reported concentrations for GAC filters of 0.3e1.8 106 g ATP/g GAC (van der Aa et al., 2006; Velten et al., 2007).
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The highest biomass concentration (1.83 106 g ATP/g GAC) was established at the second sampling point (GAC 2; 45 cm from the top) and decreased thereafter by a factor of 2.3 to the bottom of the filter (Fig. 2A). Evidently a considerable amount of biomass has established over the vertical profile of the filter. This suggests that biological activity occurs throughout a biofilter and is not only limited to the top few centimeters as suggested previously (Urfer and Huck, 2001; van der Aa et al., 2006). Interestingly, Servais et al. (1994) showed a decreasing biomass concentrations over the filter bed only during operational temperatures of 20 C and not for low temperatures (e.g. 9 C), as in this study. Regarding the lower concentration at the first sampling point, a previous study explained similar observations by the presence of residual ozone in the influent of the filter (Urfer and Huck, 2001). However, this explanation would contradict the high growth rates observed at the top of the filter in the start-up phase (Fig. 2A). Rather, inhibitive effects as a result of accumulated phytoplankton biomass (discussed below), or the development of a unique microbial community (Boon et al., 2011), can explain the lower biomass concentration at the top of the filter. In both the present study and that previously reported (Urfer et al., 1997), the biomass concentration decreased likewise with the GAC filter depth. The additional benefit of measuring the filter at different levels/depths is that the total amount of filter biomass can be calculated more accurately. Based on the average data (above), the entire GAC filter contained in total approximately 0.8 g ATP, which equates to about 1.8 1015 cells/m3, when considering the specific ATPper-cell values measured for this particular system (on average 3 1.5 1016 g ATP/cell; n ¼ 105) (for additional information, see Fig. S1, Supplementary Information).
3.3. Filter performance - DOC removal and related biomass production While the influent DOC concentration remained constant over the investigation period, the DOC concentration in the effluent increased continuously, which is the direct result of a decreasing adsorption capacity of the GAC (Figs. 2B and 3). At the same time the primary function of the GAC filter e adsorption of DOC e is taken over by biological processes. For the purpose of this paper we considered the adsorption after 90 days to be negligible and the combination of bacterial respiration and biomass assimilation predominantly accountable for the DOC removal. Biomass production was measured as the concentration of detached bacteria suspended in the effluent of the reactor (measured with flow cytometry) as well as the increase of attached biofilm biomass on the GAC particles in the filter (Fig. 4A and Supplementary Information Fig. S1). The average concentration of suspended bacteria in the filter effluent was 2.53 0.6 105 cells/mL, corresponding to a production of 1.49 1012 cells/m2h (Fig. 4B). The attached biomass displayed only a slight increase at a rate of about 0.0001 h-1 (¼2.74 1011 cells/m2h) (Fig. 4A). This implies that detached bacteria represent the majority (84%) of the total bacteria production (1.78 1012 cells/m2h) in the GAC filter during steady state. The average DOC removal at steady state was 240 (24) mg/L (n ¼ 14), which equals 22% of the influent DOC concentration and which implies a removal per filter surface
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since by-product formation (in this case bacterial cells) is low in comparison with target-compound removal (in this case DOC).
3.4. Changes in influent quality impact the GAC filter performance The pilot plant used in the present study was fed with actual lake water (Lake Zu¨rich) and therefore subject to natural changes in the influent. Algae in the raw water increased from an average concentration of 300 mg/L up to 1530 mg/L between day 50e70 (DecembereJanuary) and the algal concentration remained relatively high until day 198 (Fig. 5A). At the same time the turbidity increased from about 0.2 to 0.5 NTU (Fig. 5B) and the total phosphate concentration from 7 to 15 mg/L (Fig. 5C). Both of the latter parameters were related to the increase in phytoplankton (R2 ¼ 0.77 and R2 ¼ 0.54). Such a dramatic algal increase has potential implications for GAC filtration. In the present system, an ozonation step preceded the GAC filtration. Ozonation severely damages algal cells, causing the release/ formation of AOC (Mu¨ller et al., 2003; Hammes et al., 2007). Fig. 6 shows average AOC profiles in the filter for the periods before Fig. 4 e Evolution of total filter biomass attached to GAC particles (A) and detached bacteria suspended in the effluent of the filter (B) as a function of EBV and operational time. Attached biomass was measured with ATP analysis while suspended bacteria in the effluent were determined with FCM. The gray zone indicates the period of the higher phytoplankton concentrations in the raw water.
area of 1.41 g/m2h (flow rate of 5.6 m3/h). This removal is comparable to previous studies, which suggest that in surface waters anything between 5 and 49% of the total DOC can be present as BDOC and be removed biologically (Servais et al., 1991; Volk and LeChevallier, 2000). In this study, the fraction of biologically removed DOC is lower compared to the amount of DOC that was removed by adsorption on fresh GAC (Fig. 2B). However, it is important to realize that BAC filters remove the crucial DOC fraction that is relevant for the biological stability of the water. This also means that GAC/BAC filters should be considered and designed from a biological perspective. The total biomass production of 1.78 1012 cells/m2h, combined with the total DOC removal (1.41 g/m2h) translates to a yield of 1.26 106 cells/mg. This value is about 10-times lower than yield values for suspended bacteria of natural microbial communities in drinking water growing under optimal conditions (van der Kooij, 2002; Vital et al., 2008). However, it should be considered that these bacteria grew in biofilms where severe nutrient limitation prevailed. Coupled with the low temperatures and a high dilution rate (Table 1) a lower yield can be expected (Vital et al., 2008). By using an average bacterial carbon content of 2 1014 g/cell (Servais et al., 1991; Batte´ et al., 2003), it was calculated that 0.035 g/ m2h carbon was assimilated as biomass. This fraction is only 3% of the total DOC amount removed in the filter (1.41 g/ m2h), which is lower than the findings of Servais et al. (1991) of 8%. The low yield in biofilters is beneficial for operation,
Fig. 5 e The profiles of phytoplankton (A), total phosphate (B) and turbidity (C) show changes in the influent water quality over the investigation period. The gray zone indicates the period of the higher phytoplankton concentration in the raw water.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 4 7 e6 3 5 4
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localized variations in AOC concentrations. Also, no correlation between AOC concentrations and biomass growth was obvious. From these observations the conclusion can be drawn that AOC analysis has limited value to study biodegradability and performance in GAC filters and comparable systems. Consequently, consideration should be given to BDOC analysis as an alternative or at least complimentary tool for this purpose (Volk and LeChevallier, 2000; Escobar and Randall, 2001; Prevost et al., 2005). Apart from AOC, the combination of the different analytical tools can be a valuable approach for other researchers who aim to characterize biological filters. Specifically, filter biomass analysis with the ATP method provided meaningful scientific information on biofilm development and distribution in the filters, which in turn can be combined with additional data on the filter performance. However, regular sampling of biomass from different levels of full-scale filters is often not feasible. In such a case, the data have shown that straightforward DOC analysis and FCM total bacterial counts of suspended cells provided meaningful data, which described both the general filter performance as well as specific changes/events in the water quality (e.g. FCM increase during algal intake). These methods are fast and easy to use, and therefore have considerable practical value for both researchers and end-users. Fig. 6 e AOC filter profiles for two periods, the first average data from 0 to 60 days and the second average data from 60 to 200 days showing the impact of high algae influx. Data points are mean values of 14 data points in steady state.
and during the increased algal influx. During the period of increased algal influx, the AOC concentration in the filter influent was higher (from 80 mg/L to 110 mg/L). Moreover, instead of decreasing, the AOC concentrations increased at level WS 1/ GAC 1 (average ¼ 145 mg/L). We propose that this can be ascribed to the absence of backwashing of the filter, resulting in the accumulation of dead algal cells on top of the filter bed, where ongoing chemical degradation (residual ozone is on average 0.22 mg/L) and enzymatic degradation contributed to increased AOC concentrations in the water. A direct correlation between AOC concentrations and biomass in the filter were not observed (R2 ¼ 0.03). However, as shown in Figs. 4 and 5, the increased algae concentration coincided with in an increase of suspended bacteria in the effluent of the filter, as well as an increase in the total biofilm biomass. This indicates that changes in the influent affected the GAC filtration process and these changes could be observed by monitoring the effluent of a biological filter.
3.5.
Practical value of applied methods
In this study various analytical tools have been applied to increase the understanding of biological filters. One shortcoming was the estimation of the biodegradable carbon fraction, for which we used the AOC assay. AOC analysis detected only about 35% of the DOC that was actually removed during steady state. Moreover, the data in Fig. 6 suggest that organic carbon turnover in a GAC filter can potentially lead to
4.
Conclusions
The combination of ATP and FCM total cell counts has been successfully applied to describe the system and can be a valuable tool for the characterization of biological filters. A steady state in biofilm concentration was reached after 90 days of operation; in the same period the DOC effluent concentration stabilized. The highest biomass concentration was established 45 cm from the filter top (1.83 106 g ATP/g GAC) and this decreased to the bottom of the filter (0.8 106 g ATP/g GAC) by a factor of 2.3. During steady state, 22% of the total DOC was removed but only 3% of the consumed DOC was assimilated as biomass; 84% of this biomass was measured as suspended cells in the filter effluent.
Acknowledgment The authors kindly acknowledge financial assistance through the Eawag Wave21 project, BAFU, the European Framework Project Techneau (018320), the Zurich Water Works (WVZ) and Wabag, as well as scientific assistance form Se´bastien Meylan, Jacqueline Traber, Iris Hu¨lshoff and Michael Berney.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.017.
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Batte´, M., Koudjonou, B., Laurent, P., Mathieu, L., Coallier, J., Pre´vost, M., 2003. Biofilm responses to ageing and to a high phosphate load in a bench-scale drinking water system. Water Res. 37 (6), 1351e1361. Boon, N., Pycke, B., Marzorati, M., Hammes, F., 2011. Nutrient gradients in a granulated activated carbon biofilter drives bacterial community organization and dynamics. Water Res 45 (19), 6355e6361. Carlson, K.H., Amy, G.L., 1998. BOM removal during biofiltration. J. AWWA 90 (12), 42e52. Escobar, I.C., Randall, A.A., 2001. Assimilable organic carbon (AOC) and biodegradable dissolved organic carbon (BDOC): complementary measurements. Water Res. 35 (18), 4444e4454. Fonseca, A.C., Summers, R.S., Hernandez, M.T., 2001. Comparative measurements of microbial activity in drinking water biofilters. Water Res. 35 (16), 3817e3824. Hammes, F.A., Egli, T., 2005. New method for assimilable organic carbon determination using flow-cytometric enumeration and a natural microbial consortium as inoculum. Environ. Sci. Technol. 39 (9), 3289e3294. Hammes, F., Salhi, E., Koster, O., Kaiser, H.-P., Egli, T., von Gunten, U., 2006. Mechanistic and kinetic evaluation of organic disinfection by-product and assimilable organic carbon (AOC) formation during the ozonation of drinking water. Water Res. 40 (12), 2275e2286. Hammes, F., Meylan, S., Salhi, E., Koester, O., Egli, T., von Gunten, U., 2007. Formation of assimilable organic carbon (AOC) and specific natural organic matter (NOM) fractions during ozonation of phytoplankton. Water Res. 41 (7), 1447e1454. Hammes, F., Berney, M., Wang, Y., Vital, M., Koster, O., Egli, T., 2008. Flow-cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 42 (1e2), 269e277. Herzberg, M., Dosoretz, C.G., Tarre, S., Green, M., 2003. Patchy biofilm coverage can explain the potential advantage of BGAC reactors. Environ. Sci. Technol. 37 (18), 4274e4280. Huber, S.A., Frimmel, F.H., 1996. Size-Exclusion chromatography with organic carbon detection (LC-OCD): a fast and reliable method for the characterization of hydrophilic organic matter in natural waters. Vom Wasser 86, 277e290. Li, A.Y.L., DiGiano, F.A., 1983. Availability of sorbed substrate for microbial-degradation on granular activated carbon. J. Water Pollut. Control. Fed. 55 (4), 392e399. Lee, M.C., Snoeyink, V.L., Crittenden, J.C., 1981. Activated carbon adsorption of humic substances. J. AWWA 73 (8), 440e446. Magic-Knezev, A., van der Kooij, D., 2004. Optimisation and significance of ATP analysis for measuring active biomass in granular activated carbon filters used in water treatment. Water Res. 38 (18), 3971e3979. Moll, D.M., Summers, R.S., Fonseca, A.C., Matheis, W., 1999. Impact of temperature on drinking water biofilter
performance and microbial community structure. Environ. Sci. Technol. 33 (14), 2377e2382. Mu¨ller, K.C., Forster, R., Gammeter, S., Hambsch, B., 2003. Influence of ozonated cyanobacteria on bacterial growth in rapid sand filters. J. Water SRT-Aqua 52 (5), 333e340. Prevost, M., Laurent, P., Servais, P., Joret, J.-C., 2005. Biodegradable Organic Matter in Drinking Water Treatment and Distribution. AWWA, Denver, CO. Rittmann, B.E., Stilwell, D., 2002. Modelling biological processes in water treatment: the integrated biofiltration model. J. Water SRT-Aqua 51 (1), 1e14. Servais, P., Billen, G., Ventresque, C., Bablon, G.P., 1991. Microbial activity in GAC filters at the Choisy-Le-Roi treatment-plant. J. AWWA 83 (2), 62e68. Servais, P., Billen, G., Bouillot, P., 1994. Biological colonization of granular activated carbon filters in drinking-water treatment. J. Environ. Eng. 120 (4), 888e899. Simpson, D.R., 2008. Biofilm processes in biological active carbon water purification. Water. Res. 42 (12), 2839e2848. Urfer, D., Huck, P.M., Booth, S.D.J., Coffey, B.M., 1997. Biological filtration for BOM and particle removal: a critical review. J. AWWA 89 (12), 83e98. Urfer, D., Huck, P.M., 2001. Measurement of biomass activity in drinking water biofilters using a respirometric method. Water Res. 35 (6), 1469e1477. van der Aa, L.T.J., Kolpa, R.J., Magic-Knezev, A., Rietveld, L.C., van Dijk, J.C., 2006. Biomass Development in Biological Activated Carbon Filters. Recent Progress in Slow Sand and Alternative Biofiltration Processes. In: Gimbel, R., Graham, N., Collins, R. (Eds.). IWA Publishing, Mulheim an der Ruhr, Germany, pp. 293e302. van der Kooij, D., Hijnen, W.A.M., Kruithof, J.C., 1989. The effects of ozonation, biological filtration and distribution on the concentration of easily assimilable organic-carbon (AOC) in drinking-water. Ozone-Sci. Eng. 11 (3), 297e311. van der Kooij, D., 2002. Assimilable organic carbon (AOC) in treated water: determination and significance. In: Bitton, G. (Ed.), Encyclopedia of Environmental Microbiology. John Wiley & Sons., pp. 312e327. Velten, S., Hammes, F., Boller, M., Egli, T., 2007. Rapid and direct estimation of active biomass on granular activated carbon through adenosine tri-phosphate (ATP) determination. Water Res. 41 (9), 1973e1983. Vital, M., Hammes, F., Egli, T., 2008. Escherichia coli O157 can grow in natural freshwater at low carbon concentrations. Environ. Microbiol. 10 (9), 2387e2396. Volk, C.J., LeChevallier, M.W., 2000. Assessing biodegradable organic matter. J. AWWA 92 (5), 64e76. Volk, C.J., LeChevallier, M.W., 2002. Effects of conventional treatment on AOC and BDOC levels. J. AWWA 94 (6), 112e123. von Gunten, U., 2003. Ozonation of drinking water: part I. Oxidation kinetics and product formation. Water Res. 37 (7), 1443e1467. Wang, J.Z., Summers, R.S., Miltner, R.J., 1995. Biofiltration performance 1. relationship to biomass. J. AWWA 87 (12), 55e63.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 5 5 e6 3 6 1
Available online at www.sciencedirect.com
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Nutrient gradients in a granular activated carbon biofilter drives bacterial community organization and dynamics Nico Boon a,*, Benny F.G. Pycke a, Massimo Marzorati a, Frederik Hammes b a
Laboratory of Microbial Ecology and Technology (LabMET), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000 Gent, Belgium b Eawag, Swiss Federal Institute of Aquatic Science and Technology, U¨berlandstr. 133, CH-8600 Du¨bendorf, Switzerland
article info
abstract
Article history:
The quality of drinking water is ensured by hygienic barriers and filtration steps, such as
Received 27 January 2011
ozonation and granular activated carbon (GAC) filtration. Apart from adsorption, GAC
Received in revised form
filtration involves microbial processes that remove biodegradable organic carbon from the
5 September 2011
ozonated ground or surface water and ensures biological stability of the treated water. In
Accepted 7 September 2011
this study, microbial community dynamics in were monitored during the start-up and
Available online 16 September 2011
maturation of an undisturbed pilot-scale GAC filter at 4 depths (10, 45, 80 and 115 cm) over a period of 6 months. New ecological tools, based on 16S rRNA gene-DGGE, were correlated
Keywords:
to filter performance and microbial activity and showed that the microbial gradients
Drinking water
developing in the filter was of importance. At 10 cm from the top, receiving the freshly
Microbial ecology
ozonated water with the highest concentration of nutrients, the microbial community
Microbial resource manangement
dynamics were minimal and the species richness remained low. However, the GAC samples at 80e115 cm showed a 2e3 times higher species richness than the 10e45 cm samples. The highest biomass densities were observed at 45e80 cm, which corresponded with maximum removal of dissolved and assimilable organic carbon. Furthermore, the start-up period was clearly distinguishable using the Lorenz analysis, as after 80 days, the microbial community shifted to an apparent steady-state condition with increased evenness. This study showed that GAC biofilter performance is not necessarily correlated to biomass concentration, but rather that an elevated functionality can be the result of increased microbial community richness, evenness and dynamics. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Organic matter in drinking water is the prime contributor to heterotrophic regrowth in the distribution system, regardless of the presence of elevated chlorine residuals (Liu et al., 2002). Therefore, ozonation in conjunction with granular activated carbon (GAC) is implemented in drinking water treatment to remove the dissolved organic carbon (DOC), including
micropollutants, foul taste and odor compounds through successive oxidation and adsorption. In treatment systems that are operated over longer time periods without the replacement or regeneration of the GAC, the occurrence of microbial growth on GAC particles is ubiquitous (Flemming, 2000). Ozonation transforms some recalcitrant DOC molecules into biodegradable compounds; thus, ozonation can increase microbial nutrient concentrations and stimulate biofilm formation/growth on the
* Corresponding author. Tel.: þ32 (0) 9 264 59 76; fax: þ32 (0) 264 62 48. E-mail address:
[email protected] (N. Boon). URL: http://www.labmet.Ugent.be 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.016
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GAC filter (Siddiqui et al., 1997; Hammes et al., 2008). This whole process can be of particular interest from a bioengineering perspective, since the microbiota enhances the functionality of the GAC filters through additional biological nutrient removal (Chien et al., 2008; Magic-Knezev et al., 2009), and several treatment plants are operated specifically with this process in mind (e.g., Hammes et al., 2010). Recently, it was proposed to explore the interphase between microbial ecology and environmental biotechnology by taking microbial community analyses to the next level, i.e. from "stamp collecting" toward an all-integrating process- and theory-driven approach (Rittmann et al., 2006; Verstraete et al., 2007). The goal of microbial ecologists and environmental engineers is to relate microbial community structure and dynamics with system performance, and to use this information to steer the system to the benefit of the process in question. In this respect, it was shown that higher community biodiversity could lead to increased ecosystem stability (McCann, 2000). Hence, an ecosystem can be protected through functional redundancy if many species co-exist (Yachi and Loreau, 1999). In addition, community evenness was shown to be an important parameter for conferring high functional resilience (Wittebolle et al., 2009). Finally, the dynamics of change of the community composition could be just as important, since it was proposed that microbial communities should be considered a cooperative community continuum rather than a stable entity (Curtis and Sloan, 2004; Verstraete et al., 2007). In this overall perspective, the scrutiny of community richness, dynamics, and functional organization (Marzorati et al., 2008) in drinking water biofilters could be a first step toward optimizing reactor functioning through a straightforward analysis of the natural microbial communities. In the present study, a pilot-scale GAC biofiltration reactor was examined as a model system because of its particular ecological interest and practical relevance. While bacteria have a significant and recognized function in GAC filters, little is known about microbial community distribution and dynamics in such filters, with a significant part of state-of-the-science knowledge still based on cultivation-dependant plating methods (MagicKnezev et al., 2009; Niemi et al., 2009). The goal of this work was to monitor the microbial community during the six-months start-up period of a GAC pilot reactor treating ozonated lake water. We have used a 16S rRNA gene-targeting DGGE approach to determine the microbial richness, community dynamics, and community organization based on Marzorati et al. (2008) at 4 different bed depths of the GAC reactor. The ecological interpretation of the microbial community was then related with reactor performance at the different bed depths, aiming to improve our understanding of the microbial resources of GAC biofilters.
2.
Results
2.1.
Reactor performance
The reactor was operated as an undisturbed pilot-scale GAC filter during 160 days, starting from plain granular activated carbon without any biomass. A specific biomass concentration gradient established in the reactor as a result of this plug-flow
operation that, in turn, resulted in a nutrient gradient along the reactor bed. We considered two periods: a start-up period (0e90 days) where a gradual increase in biomass was observed and a steady-state operation period (90e160 days), when the amount of biomass in all different sections remained constant. The average biomass concentrations in the second period were: 10 cm ¼ 1.2 106 g ATP g GAC1, 45 cm ¼ 1.8 106 g ATP g GAC1, 80 cm ¼ 1.2 106 g ATP g GAC1 and 115 cm ¼ 0.8 106 g ATP g GAC1 (Velten et al., 2011). After the start-up period (day 90), the removal efficiency of DOC, humic substances, building blocks, polysaccharides and low molecular weight organics from the ozonated drinking water was evaluated for the different sections (Fig. 1). The DOC removal efficiency increased along the four sections of the GAC bed (10 cm, 45 cm, 80 cm, and 115 cm), with the top 10 cm of the GAC filter bed hardly contributing to the DOC removal (Fig. 1). Despite similar biomass concentrations at the different sections (above), the normalized DOC removal efficiency clearly increased with increasing filter depth; at the top section (10 cm) it was below 1.2 mg L1 g ATP1, at 45 cm it was 3.5 mg L1 g ATP1, at 80 cm it was 5.1 mg L1 g ATP1, while the bottom section (115 cm) showed the highest removal efficiency in excess of of 6.6 mg L1 g ATP1 (Fig. 1). A similar trend was observed for humic substances and building blocks, as the biomass-normalized removal efficiencies increased along the GAC bed. Also for these substances, the removal efficiency of the top section was below 0.3 mg L1 g ATP1 or even negative, while the middle sections (45 and 80 cm) had removal efficiencies of 1.5 and 1.4 mg L1 g ATP1 for humic acids and 0.7 and 1.2 mg L1 g ATP1 for building blocks, respectively (Fig. 1). Again, the bottom section (115 cm) was the best performing, with removal efficiencies of 2.7 mg L1 g ATP1 and 1.4 mg L1 g ATP1 for humic substances and building blocks, respectively. Removal of the polysaccharide fraction was insignificant across the filter bed, and for the low molecular weight organics no trend was observed (although the highest removal efficiency (0.7 mg L1 g ATP1) was again observed in the bottom-most sampling point (115 cm; Fig. 1).
Fig. 1 e The removal efficiencies for dissolved organic carbon (DOC), polysaccharides humic substances, building blocks, low molecular weight (LMW) organics in the GAC biofilter at four bed depths (10, 45, 80 and 115 cm). The removal efficiencies were normalized for the active biomass (gATP) and reactor volume preceding each sampling point.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 5 5 e6 3 6 1
2.2.
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Bacterial community analysis
The bacterial community structure was determined at four different GAC bed depths (10, 45, 80 and 115 cm) and over a time frame of 160 days (with every two weeks sampling). Community profiles were generated using Denaturing Gradient Gel Electrophoresis (DGGE) analysis of the 16S rRNA genes during both the start-up period (days 0e90) and the steady-state period (90e160 days). As such, 12 community profiles were obtained for each bed depth over time. Each community fingerprint was used to estimate the microbial richness, dynamics, and organization using the indices we introduced previously (Marzorati et al., 2008).
2.2.1.
Range-weighted richness (Rr)
The range-weighted richness (Rr) increased for the three bottom sections (45, 80 and 115 cm) during the course of the operation, but not at the same rate (Fig. 2). Hence, at biomass steady-state (after 90 days), the Rr differed significantly between GAC sections and a clear stratification could be observed differentiating the top section (10 cm), the second section (45 cm), and the bottom two (80 and 115 cm), as community richness steadily increased along the GAC bed. The top section (10 cm) had a very low Rr (<10). For the three bottom sections (45 cm, 80 cm and 115 cm), the richness steadily increased until day 100, 124 and 140 of operation, respectively. Hence, the second section (45 cm) attained an Rrindex of 17 1 at biomass steady-state (days 84e156), while the bottom two sections (80 and 115 cm) became the most diverse after the start-up period with Rr-indices of 37 8 and 43 6 respectively. The top section at 10 cm remained very low, with an average Rr value of 5 2.
2.2.2.
Community dynamics
The community dynamics were determined by moving window analyses (MWA), comparing the community fingerprints at two successive time points at an interval of 14 days (Fig. 3A), and moving endpoint analyses (MEA), comparing the community profiles from different time points with the profile from the first sampling point as a reference fingerprint (Fig. 3B). Both approaches clearly showed that the community dynamics of the top and bottom sections along the GAC bed behaved significantly different (Fig. 3). The top section (10 cm)
Fig. 3 e Community dynamics at four bed depths (10, 45, 80 and 115 cm) of the GAC biofilter during the first 156 days of operation, (A) visualized with a moving window analysis (MWA); (B) community dynamics visualized through the moving endpoint analysis (MEA).
was the most stable one in terms of community composition, as the community fingerprints kept 93e99% of similarity during the six months of operation, with the exception of the first two weeks (Fig. 3A). The second bed section (45 cm) allowed a more dynamic community to develop as the community fingerprints kept around 95% of similarity every two weeks (Fig. 3A). Hence, the cumulative change after six months of operation resulted in a similarity of 67% with the community composition at the start of the reactor (Fig. 3B). For the third section (80 cm), the dynamics were very high, as the community fingerprint after merely 70 days had only 44% similarity with the community at reactor start-up. The community in the 80 cm bed section stabilized briefly between 70 and 120 days, but started shifting again with an endpoint similarity of merely 34% (Fig. 3B). Finally, the bottom section (115 cm) was the most dynamic in the GAC bed, as the community fingerprint changed 5e10% every 14 days. Because of this steady change in community composition during six months, the endpoint community had only 15% similarity with the community at GAC start-up (Fig. 3B).
2.2.3.
Fig. 2 e Range-weighted richness of the microbial community at the four bed depths (10, 45, 80 and 115 cm) of the GAC biofilter during the first 156 days of operation.
Community organization
The community organization was described by the shape of the Lorenz curves (Fig. 4), where low organization is characterized by a high degree of evenness (i.e., when all OTUs have similar relative abundances). Based on the Lorenz curves, it was clear that for the top sections, the organization hardly changes,
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Fig. 4 e The community organization of (A) the topmost section (10 cm) and (B) the bottom section (115 cm) of the GAC biofilter illustrated through the Lorentz graph. For the top section, no shift in functional organization was observed, whereas in the bottom section, the community initially became more functionally organized, after which the community became more stable and even from day 84 on. Stability in community organization was correlated with reaching the biomass concentration steady-state.
while for the bottom sections, a clear difference could be observed between the start-up period and the steady-state period. To mathematically describe the shape of the Lorenz curves, the Community organization coefficient (Co) was introduced. This Co is proportional to the surface below the Lorenz curve and is an indication for the species evenness: low Co values (0e40) are typical for a highly even community, while uneven communities have high Co values (60e100). The community evenness was very dissimilar for the different sections along GAC bed at reactor start-up (Fig. 5). Where the topmost section (10 cm) was moderately even at start-up (Co ¼ 60), the lower sections were relatively to very uneven, with a Co ¼ 85 for the 115 cm section, indicating a very specialized and skewed community in the bottom sections (Fig. 5). Nevertheless, during the first 90 days of operation, the communities in the three lower bed sections (45, 80 and 115 cm) gradually became more evenly distributed, and readily became more even than the top section. Hence, the difference in community between the top (10 cm) and the bottom section (115 cm) became apparent through the differential evolution of the community. Subsequently, the three bottom sections
Fig. 5 e Community organization Co of the microbial community at four bed depths (10, 45, 80 and 115 cm) of the GAC biofilter during the first 156 days of operation.
stabilized more or less after reaching biomass steady-state around day 90. The 115 cm section was particular because it was the only bed section that continuously became evermore even during our six months follow-up study of the biofilter. At the end of our testing period (day 156), the evenness in nearly all of the sections of the reactor were relatively similar with Co coefficients between 45 and 60, where initially the Co- coefficients had started between 60 and 85.
3.
Discussion
The current challenge remains for wastewater engineers and microbiologists to relate the analysis of microbial communities and their metabolic functions with overall system performance (Bramucci and Nagarajan, 2006; Verstraete et al., 2007). The latter is even more true for the field of drinking water treatment, where much work remains to be done on the intriguing functional role of microbial communities in the treatment process. Therefore, the present study aimed to determine the factors contributing to DOC removal efficiency in a GAC biofilter and identified the role of community composition and dynamics therein. By applying molecular (culture-independent) approaches to study microbial communities, our understanding of ecosystem robustness has improved significantly over the past years (Briones and Raskin, 2003). Despite the wealth of information on microbial communities, comparison between results of different studies has been hindered because of the high variety of molecular techniques and data processing approaches. Moreover, the use of high-throughput molecular tools and in depth sequencing (pyrosequencing) has led to the production of large and intricate datasets. This led to the need of new tools to interpret these data and to give them a more descriptive and even predictive power, which could be of practical value (Prosser et al., 2007). In this frame, the present study worked further on the microbial community parameters introduced by Marzorati et al. (2008), which allow an ecological interpretation
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of the community fingerprints. Besides the already existing range-weighted richness (Rr) and dynamics (Dy) parameters, the community organization (Co) was developed further. In a new and barely-colonized GAC filter, one would expect high carbon removal in sections with low biomass concentrations through adsorption (Cairo et al., 1979; Lee et al., 1981), while in extensively-colonized GAC bed removal would be expected to occur predominantly through biological degradation (Moll and Summers, 1999; Tian et al., 2009). In our ozonated-water treating GAC filter, the highest DOC removal efficiencies were found in the bottom section (115 cm). This bottom section had a similar biomass density as the topmost section (10 cm), but the latter section hardly contributed to the DOC removal. It could be that the biomass in top section of the GAC filter was negatively impacted by residual ozone, which resulted in a lower DOC removal efficiency. The selective environment in this top area together with higher nutrient concentrations could have narrowed the niche and resulted in the enrichment of particular microorganisms. This indicated that while the DOC removal in the GAC biofilter was mainly biologically-driven, process efficiency was independent of the biomass concentration or ATP content. Indeed, these observations suggested that the microbial communities themselves could be at the heart of the differences in removal efficiencies between the four sections. Hence, the differences in biodiversity of the different sections could represent differences in the functional gene pool, which would allow the community of the bottom section to be better adapted to adapt to changing environmental conditions (Naeem and Li, 1997; Loreau, 2000). The significance of this gene pool is reflected in the fact that the bottom section (115 cm) was colonized by a microbial community with high species biodiversity, high community dynamics and community evenness that became moderately even, while in the topmost section a stable microbial community was observed with low richness and relatively stable, moderate evenness. Thus, the most important parameters determining high functionality in the GAC biofilter were community structure parameters (richness, dynamics and evenness) and not biomass content. Through high richness and dynamics, an ecosystem can be protected against declines in its functionality (Yachi and Loreau, 1999), since they provide a greater guarantee that some will take over when others fail. It must be mentioned, however, that the niche in the top section was very narrow, due to the residual ozone that was suspected to enter the reactor at that position (Velten et al., 2011). Regardless of this stressor, a significant amount of viable biomass was able to settle at that section of the GAC bed (Velten et al., 2011). However, in contrast to what is general believed, our results showed that this community contributed less efficiently to the DOC removal in the reactor than the communities in deeper sections of the filter. This is most probably due to the fact that the biomass is continuously challenged and stressed (Albuquerque et al., 2008), and therefore too much of the cellular resources have to be invested into protective mechanisms (Whiteside and Hassan, 1987). The bottom section on the other hand did not come into contact with ozone, and received ample nutrients and metabolites from the higher located sections of the bed. Hence, it appeared to be the most efficient and diverse community in the GAC biofilter arose in the sections that were
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deemed to have a lower concentration of oxidative radicals and presumably possess higher growth substrate diversity. From the ecological perspective, our results showed that as the richness and dynamics increased in the three bottom sections (45, 80, and 115 cm), the community also became more even and attained a higher degree of functional organization. The evenness of the GAC community became or remained relatively moderate in the lower sections, hence, indicating that the communities in all sections are probably well guarded against environmental stresses (Wittebolle et al., 2009). The correlation between community richness and evenness is also an indication of a broad carrying capacity and health of the ecosystem, as there are more niches to fill than e.g. in the topmost section, which is more restricted due to the residual ozone. Another possibility explaining the striking differences in richness and dynamics might be the concentration of available nutrients. According to Monod kinetics, bacteria with similar maximal growth yield but different substrate affinity (Ks) would be less sensitive for substrate concentration fluctuations when higher nutrient concentrations were present. However the lower sections would receive a more oligotrophic type of water and small changes in Ks would lead large differences in growth rate. Therefore, we expect that an oligotrophic environment with fluctuating nutrient concentrations would lead to higher dynamics and richness. In summary, the present study showed that (i) the specific community composition inside the GAC reactor determined the DOC removal efficiencies along the bed and caused a nutrient stratification, (ii) the community richness and community dynamics increased along the GAC bed depth, and (iii) the community organization of the GAC became more stable and even with increasing reactor operation time at all of the GAC bed depths.This knowledge can help us to formulate a plan toward the management of our varied microbial communities in drinking water plants and to look at ways of harnessing their unique abilities for future practices. We need this acquired knowledge for a more sustainable solution to our on going global challenges such as our diminishing energy and water supply.
4.
Material and methods
4.1.
GAC bioreactor sampling
The full-scale GAC reactor is part of a drinking water treatment pilot plant situated at the Zurich Waterworks (WVZ Lengg, Switzerland). The plant is comprised of a pre-filtration unit (20 mm), an ozonation unit, a GAC biofilter, and an ultrafiltration installation (Hammes et al., 2008). The GAC filter was operated in a down-flow mode and treated pre-filtered lake water for 11 days, during which microbial biomass readily attached to the GAC particles prior to our study (Velten et al., 2011). The reactor was constructed such that both water samples and GAC particles could be sampled at four sampling points distributed over the filter with interspaces of 35 cm along the total GAC bed of 155 cm (10 cm, 45 cm, 80 cm, and 115 cm depth). For a schematic presentation of the investigated pilot-scale granular activated carbon, see Fig. 1 in Velten et al. (2011).
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4.2.
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Quantification of biomass on GAC particles
Triplicate GAC samples were treated as described by Velten et al. (2011). Briefly, the GAC particles were rinsed in phosphate buffer and 200 mg (wet weight) was transferred to an Eppendorf tube together with 100 mL sterile phosphate buffer and 300 mL BacTiterGloTM (Promega Corporation, Madison, WI, USA). The resulting luminescence was converted to ATP concentrations using a calibration curve.
4.3.
DOC fractionation and analysis
After 90 days of continuous operation, the GAC reached a steady-state phase where biomass concentration and effluent DOC concentrations remained constant (Velten et al., 2011). At that time, the DOC removal in the GAC reactor was assessed by analyzing 12 sampling points between day 91 and 210 (Supplementary Table 1) with Liquid Chromatography Organic Carbon Detection (LC-OCD) as described by Huber et al. (2010) using the following criteria: total DOC, polysaccharides, humic substances, building blocks, and LMW organics. The DOC was determined by an infrared detector after complete oxidation to carbon dioxide in a Graentzl Thin-Film Reactor (DOC-Labor Dr. Huber, Germany).
4.4.
DGGE analysis
DNA was extracted from the biomass attached to the GAC particles using the previously described DNA extraction protocol (Boon et al., 2000). Thereafter, 16S rRNA gene-based DGGE analyses were performed with bacterial primers (P338f with GC-clamp and P518r) (Øvrea˚s et al., 1997) that target bacteria, as previously described (Boon et al., 2002). DGGE gels (8% (w/v) polyacrylamide, and a denaturing gradient ranging from 45 to 60%) were run on a Bio-Rad DGene system (Hercules, CA, USA) as described previously (Boon et al., 2002).
4.5.
Analysis of the DGGE patterns
The obtained DGGE patterns were further processed using BioNumerics software version 2.0 (Applied Maths, SintMartens-Latem, Belgium).
4.5.1.
Range-weighted richness (Rr)
Range-weighted richness (Rr) values (Marzorati et al., 2008) were calculated based on the total number of bands (N), and the denaturing gradient comprised between the first and the last band of the pattern (Dg), according to Eq. (2). Rr ¼ N2 Dg
4.5.2.
(1)
Microbial community dynamics
The matrix of similarities for the densitometric curves of the band patterns was calculated based on the Pearson productemoment correlation coefficients and was used to perform cluster analysis, moving endpoint analysis (MEA) and moving window analysis (MWA) For MEA, the correlation between day X and day 1 was plotted. This way, each data point is compared with the initial microbial community. For the MWA, the correlation between day X and day X-14 was
plotted (Wittebolle et al., 2005). This way, each data point is e on itself e a comparison of two samples taken every two weeks. Eq. (2) recalculates these similarity percentage values to change percentage values. change% ¼ 100 similarity%
(2)
The rate of change or Dt(week) values (Marzorati et al., 2008) were calculated as the average and standard deviation of the respective change% values. The more differences between the DGGE profiles of day X and day X-14, the higher the Dt(week) value will be.
4.5.3.
Community organization
In order to graphically represent the evenness of the bacterial communities, Lorenz distribution curves (Lorenz, 1905) were set up, based on the DGGE profiles as previously described (Marzorati et al., 2008). For each DGGE lane, the respective bands were ranked from high to low based on their intensities. Consecutively, the cumulative normalized number of bands was used as x-axis, and their respective cumulative normalized band intensities represented the y-axis. Finally, the curves were evaluated by calculating the Gini coefficient (Marzorati et al., 2008). The Co coefficient (ranging from 0 to 100) is a single value that describes a specific degree of evenness, measuring the normalized area between a given Lorenz curve and the perfect evenness line. The higher the Co coefficient, the more uneven a community is.
Acknowledgments This work was supported by research grants from the EU Biotreat project (Contract number 266039, call FP7-KBBE2010.3.5.01), the Flemish Fund for Scientific Research (FWOVlaanderen, 3G070010), the Geconcerteerde Onderzoeksactie (GOA) of Ghent University (BOF09/GOA/005) and by a PhD grant (No. 43428) of the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWTVlaanderen). We thank the Zurich Waterworks (WVZ) for providing the samples, Jacqueline Traber and Sebastian Meylan for the LC-OCD data, Petra Van Damme for assistance during the molecular work, and Willy Verstraete for the fruitful discussions.
Appendix. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011.09. 016.
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Adsorption combined with ultrafiltration to remove organic matter from seawater Chatkaew Tansakul a,b,c, Ste´phanie Laborie a,b,c,*, Corinne Cabassud a,b,c a
Universite´ de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France INRA, UMR792 Inge´nierie des Syste`mes Biologiques et des Proce´de´s, F-31400 Toulouse, France c CNRS, UMR5504, F-31400 Toulouse, France b
article info
abstract
Article history:
Organic fouling and biofouling are the major severe types of fouling of reverse osmosis (RO)
Received 17 December 2010
membranes in seawater (SW) desalination. Low pressure membrane filtration such as
Received in revised form
ultrafiltration (UF) has been developed as a pre-treatment before reverse osmosis.
8 September 2011
However, UF alone may not be an effective enough pre-treatment because of the existence
Accepted 10 September 2011
of low-molecular weight dissolved organic matter in seawater. Therefore, the objective of
Available online 17 September 2011
the present work is to study a hybrid process, powdered activated carbon (PAC) adsorption/ UF, with real seawater and to evaluate its performance in terms of organic matter removal
Keywords:
and membrane fouling. The effect of different PAC types and concentrations is evaluated.
Hybrid processes
Stream-activated wood-based PAC addition increased marine organic matter removal by
Ultrafiltration
up to 70% in some conditions. Moreover, coupling PAC adsorption with UF decreased UF
Adsorption
membrane fouling and the fouling occurring during short-term UF was totally reversible. It
Powdered activated carbon
can be concluded that the hybrid PAC adsorption/UF process performed in crossflow
Organic matter
filtration mode is a relevant pre-treatment process before RO desalination, allowing
Seawater
organic matter removal of 75% and showing no flux decline for short-term experiments. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Seawater reverse osmosis (SWRO) desalination is an efficient and reliable process for drinking water supplies. Today, RO membranes are the leading technology for new installations and the area is still in constant development (Greenlee et al., 2009). Efficient desalination involving RO needs good pretreatment to improve its performance and to increase the life of the RO membrane, a membrane very sensitive to fouling. Scaling, colloidal fouling, organic fouling and biofouling are the major types of RO membrane fouling. Biofouling and organic compounds are responsible for more than 60% of RO fouling (Khedr, 2000). Many researchers report
that biofoulants cause severe fouling on RO membranes: microorganisms secrete polymers that attach themselves to the surface of the membrane (Chua et al., 2003; Ma et al., 2007). Besides, because it is a carbon source and promotes the growth of microorganisms, organic matter in seawater is a significant factor in the control of RO membrane fouling. Conventional pre-treatments e i.e. coagulation, sedimentation and filtration e were initially used as RO pre-treatment. But because of unpredictable variation and also of constant degradation of seawater quality, microfiltration (MF) and, to a larger extent ultrafiltration (UF), are increasingly used as pre-treatment systems. The main advantage of a membranebased process is that it produces a consistent quality of
* Corresponding author. Universite´ de Toulouse, INSA, UPS, INP, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France. Tel.: þ33 561559287; fax: þ33 561559760. E-mail addresses:
[email protected] (S. Laborie),
[email protected] (C. Cabassud). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.024
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permeate regardless of the condition of the feed water (Brehant et al., 2002; Pearce, 2007). However, about 75% of the organic carbon in seawater is low-molecular weight dissolved organic carbon (<1 nm) (Benner et al., 1997). In consequence, marine organic carbon cannot be totally removed by UF, which has pore sizes in range of 0.01e0.1 mm. Thus, Shon et al. (2008) reported that UF membrane removed only an insignificant quantity (20% removal) of marine organics of 1200, 950 and 90 Da contained in surface seawater. A solution to reduce dissolved organic concentration could be to introduce coagulants in UF systems at the same concentration as conventional pre-treatment but this has drawbacks of excessive sludge production. A large number of studies (Konieczny and Klomfas, 2002; Tomaszewska and Mozia, 2002; Fabris et al., 2007; Haberkamp et al., 2007; Kim et al., 2008) have been performed to enhance the ultrafiltration process performance by adding powdered activated carbon (PAC) to the solution to be treated. The influence of PAC addition on organic matter removal efficiency and on membrane fouling has been studied. Tomaszewska and Mozia (2002) filtered a mixture of humic acids and phenol. The application of a PAC/UF system was found to be very effective in the removal of organic substances having both low and high molecular weights. Humic acids were removed to about 90% and phenol was removed totally for the same PAC concentration of 100 mg L1. Haberkamp et al. (2007) suggested that activated carbon adsorbed organic compounds of a wide range of molecular weights, with differences in the removal efficiencies depending on the type of activated carbons used. Kim et al. (2008) studied the effects of different adsorbents, including PAC, on membrane fouling by natural organic matter (NOM). They showed that metal oxide particles and PAC all adsorbed some foulant and some non-foulant NOM molecules from lake water. Fabris et al. (2007) evaluated the efficiency of different combined pre-treatment methods for reducing NOM fouling of low pressure membranes (MF), including treatment with adsorbents such as PAC. They reported that the pre-treatments that reduced dissolved organic carbon (DOC) of all MW ranges, including colloidal material (very high MW), successfully prevented short-term fouling of MF (cake formation), whereas pre-treatments that removed DOC but did not remove colloidal particles were unable to prevent fouling. All the experiments in the literature mentioned above were done with fresh water and only very few works have been performed with seawater. Shon et al. (2008) studied the comparison between conventional and membrane pretreatments of seawater before desalination. They demonstrated that, among the various real seawater pre-treatments e MF, UF, FeCl3 flocculation and PAC adsorption e the PAC adsorption pre-treatment showed the highest removal of seawater organic matter (SWOM) and preferentially removed small SWOM. Earlier studies (Tansakul et al., 2009, 2010) compared different hybrid processes coupling ultrafiltration and coagulation or adsorption. The aim of this study is to couple ultrafiltration with powdered activated carbon adsorption in order to enhance organic matter removal and also to reduce the UF membrane fouling. We evaluated the effect of PAC concentration on interactions between organic matter and membrane.
Membrane surface characterization was also performed. The effect of crossflow velocity on membrane fouling and on retention rate was finally evaluated.
2.
Materials and methods
2.1.
Source water
Seawater from the Mediterranean Sea, with a salinity of 38 g L1 and an average pH of 8.12 was used. Three different samples of seawater were used with different dissolved organic carbon (DOC) concentration values: [DOC]Sample1 ¼ 1.77 mg L1 e [DOC]Sample2 ¼ 1.55 mg L1 e [DOC]Sample3 ¼ 1.62 mg L1.
2.2.
Powdered activated carbons (PAC)
Two powdered activated carbons, named PAC-1 and PAC-2, supplied by the PICA company (France) were used as the adsorbents. Table 1 shows the characteristics of the two types of PAC (data from manufacturer). It is important to note that both the PACs are wood-based but the activation modes are different. Three concentrations: 50, 100 and 200 mg L1 were studied for both PAC types. For all the experiments, the mixture of PAC and seawater was immediately introduced into the filtration cell, i.e. there was no contact time between PAC and seawater before UF.
2.3.
Membrane and filtration devices
A flat sheet regenerated cellulose membrane (YM30, Millipore, France) with a molecular weight cut-off (MWCO) of 30 kDa was used. This low MWCO was chosen to perform a relatively high retention of organics. Pure water permeability of the membrane at 20 C was between 220 and 230 Lh1m2 bar1 (Membrane resistance ¼ [1.5e1.6] 1012 m1). Contact angle measurements were performed with a goniometer (GBX, Digidrop): contact angle of the membrane was 15.9 . Two filtration devices were used. The one for dead-end filtration experiments is shown schematically in Fig. 1(a). The dead-end stirred cell (Amicon 8400, Millipore, France) had a capacity of 400 mL. The membrane was a disc of 7.1 cm diameter (surface area of 40 cm2). A 5 L stainless steel solution reservoir was connected to a circuit of compressed air allowing the transmembrane pressure (TMP) to be fixed in the range 0.4e2.4 bar. Permeate mass was measured by an electronic balance connected to a personal computer. The temperature for all filtration tests was 20 C.
Table 1 e Characteristics of powdered activated carbons. Property
PAC-1
PAC-2
Type BET surface area (m2g1) Particle diameter (mm)
Wood-based 1050 15e35
Microporous volume (cm3g1) Mesoporous volume (cm3g1)
0.4 0.15
Wood-based 1800 8e15 15e35 0.6 0.5
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solution was kept constant by a heat exchanger. Permeate was collected and measured by a balance connected with a computer. It was not recycled to the feed tank. The flat sheet UF module was a rectangle of 15.1 7.2 cm (filtering surface area of 109 cm2) and the UF membrane was the same as the one used in dead-end filtration. The crossflow filtration unit is shown schematically in Fig. 1(b).
2.4.
Filtration methods
Two operating modes of filtration were investigated. The first was a pressure step filtration performed in a dead-end filtration device. In this method, feed water was filtered at constant TMP until a fixed permeate volume (25 L m2) had been produced and then the TMP was increased to another value, with a step of 0.4 bar. The TMP was varied between 0.4 and 2.4 bar. According to the resistance-in-series model, for each constant TMP, a decrease of permeate flux is described as an increase of fouling resistance with time. Jð20 CÞ ¼ Lpð20 CÞ $TMP ¼
Fig. 1 e Schematic diagram of filtration device (a) for deadend filtration (b) for crossflow filtration.
Crossflow experiments were also performed with a benchscale system at a constant TMP of 2 bar. The feed solution was pumped from a 20 L feed tank by a centrifugal pump that maintained the feed flow rate to 150 L h1 (crossflow velocity of 0.15 m s1 in the module). The temperature of the feed
a 100
Analytical methods
2.5.1.
Organic concentration measurement
The dissolved organic carbon concentration was measured by a TOC-metre (TOC-V, Shimadzu, France). The non-purgeable organic carbon (NPOC) method was used. The detection limit was 0.1 mg L1 in presence of high salt concentration as in the case of seawater.
100
70.4 80
53.5
40
20
Retention rate (%)
Retention rate (%)
2.5.
80
60
56.9
60
48.6
40 26.0 20
8.6
4.7
0
0 UF
UF+PAC1 UF+PAC1 UF+PAC1 100 mg/L 200 mg/L 50 mg/L
(1)
where J(20 C) is the permeate flux at 20 C, Lp(20 C) is the membrane permeability at 20 C, TMP is the transmembrane pressure, m(20 C) is the viscosity of the permeate at 20 C, and Rm and Rf are the resistance of membrane and fouling respectively. Thus, an average fouling rate (dRf =dt) can be calculated for each constant TMP and plotted versus TMP. Uncertainty in fouling rate due to flux and pressure measurement error was estimated to be very low, so it was neglected. This method has been fully described in a previous paper (Tansakul et al., 2009). The second filtration mode was a constant pressure mode. The TMP was fixed at 2 bar. Permeate flux values were measured over time and plotted versus volume filtered per unit area. This protocol was used in both dead-end and crossflow filtration devices.
b 69.1
TMP mð20 CÞ Rm þ Rf
UF
UF+PAC2 UF+PAC2 UF+PAC2 50 mg/L 100 mg/L 200 mg/L
Fig. 2 e Effect of PAC dose on retention rate (a) PAC-1 (Sample 2), (b) PAC-2 (Sample 1) (Filtered volume [ 150 L mL2).
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equation using DOC concentration as the parameter of organic matter concentration: Rð%Þ ¼
Fig. 3 e HPLC-SEC chromatograms from seawater and from permeates (Filtered volume [ 400 L mL2).
2.5.2.
Microscopic observation of fouled membrane
Scanning electronic microscopy (SEM, JEOL 5410 LV Instrumentation) was used to autopsy samples of membrane surface. Before analysis, the membranes were dried over night in a desiccator. The SEM was combined with an Energy Dispersive Spectrometer (EDS analysis Quantax, Bruker AXS, Germany) in order to obtain an elementary chemical analysis of samples.
2.5.3.
HPLC-SEC analysis
Molecular weight (MW) distributions of different samples were determined by high-pressure size exclusion chromatography (HPLC-SEC, AKTA) with a fluorescence detector at 350e445 nm. The SEC column used in this study could measure MW in range of 10e100 kDa.
3.
Results and discussion
3.1.
Organic matter removal
3.1.1. Effect of PAC type and concentration on organic matter removal In this section, the influence of PAC type and concentration on organic matter removal is examined. The apparent retention rate of organic matter was calculated by the following
Cpermeate 100 1 Cretentate
(2)
where Cpermeate and Cretentate are the DOC concentration in the permeate and retentate respectively. For these tests, seawater was mixed and stirred (with a velocity gradient of 50 s1) with PAC and the mixture was immediately fed to the UF cell. The operating mode was the pressure step filtration performed in dead-end filtration device. In all cases, the retention rate remained constant during the experiment. Fig. 2 represents the apparent rate of organic matter retention for the two PAC types, for seawater ultrafiltration alone and for doses of PAC varying between 50 and 200 mg L1. The first point to be noted is that UF alone has a retention rate of between 4.7% and 8.6% as shown in Fig. 2. This discrepancy of retention rate values for UF alone may be due to DOC measurement uncertainty, especially in permeate that has a low organic concentration, close to the limit of DOC detection. The second point is the variation of seawater characteristics according to the season and also variation among the samples received from the Mediterranean Sea. The DOC removal increased from about 5% without PAC to around 26% when 50 mg L1 of PAC-2 was used (as shown in Fig. 2(b)) and it increased again to 49% and 57% when 100 mg L1 and 200 mg L1, respectively, of PAC-2 was added. This result was to be expected as an increase of applied PAC dose leads to more sites being available to adsorb organic matter. As PAC particles are totally retained by UF membrane, the apparent retention rate of organic matter is thus increased. As shown in Fig. 2(a), PAC-1 showed even better retention rates than PAC-2. The DOC removal increased from 9% to 54% when only 50 mg L1 PAC-1 was added to seawater and it rose to 70% for 100 and 200 mg L1 PAC-1. Although PAC1 has a lower BET surface area and lower volumes of micropores and mesopores, it provided better DOC removal. These results can probably be explained by the difference of PAC functional groups linked to different activation modes for the two PAC. PAC-1, which is a steam-activated wood-based carbon, has a higher capacity for organic matter adsorption and retention than PAC-2.
Fig. 4 e Effect of PAC dose on fouling rate (a) PAC-1 (Sample 2), (b) PAC-2 (Sample 1).
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Fig. 5 e Variation of the permeate flux versus filtrated volume for UF alone and for UF coupled with the two PAC ([PAC] [ 100 mg LL1, TMP [ 2 bar, Sample 3).
3.1.2.
HPLC-SEC analysis
The molecular weight distribution of organic matter in different samples is presented in Fig. 3: in seawater, in permeate of UF alone, and in permeates of the adsorption/UF combination (with PAC-1 and PAC-2). No obvious difference is observed between seawater and permeate of UF alone. In contrast, coupling UF with PAC adsorption drastically reduces the peak of organic matter around 10 000 Da MW. These results confirm that adsorption by the powdered activated carbon allowed an increase of the organic matter retention by UF membrane.
3.2.
UF fouling behaviour
3.2.1.
Effect of PAC dose on fouling rate
The effect of PAC dose on fouling rate was evaluated by using PAC concentrations of 50, 100 and 200 mg L1 for the two PAC types. Fig. 4 represents the average fouling rate (dRf =dt) for each constant TMP. For all operating conditions, as expected, the fouling rate increased with TMP. Nevertheless, this fouling rate increase was clearly reduced when PAC was added to seawater. Moreover, whatever the PAC type, the higher the PAC dose was, the smaller was the fouling rate increase, especially
Table 2 e Pure water permeability before and after filtration in different conditions (Filtered volume [ 400 L/m2). Pure water permeability (Lh1m2 bar1)
Difference (%)
Before filtration After filtration UF alone UF þ PAC-1 100 mg L1 UF þ PAC-2 100 mg L1
230 230
222 224
3.8 2.6
221
225
þ1.8
Fig. 6 e Filtration resistances (Rm, Rf, Rirreversible) for the different pre-treatments (Sample 3 e Filtered volume [ 400 L mL2).
when PAC-1 was added. These results prove that adsorption by PAC reduces membrane fouling by preventing organics from adsorbing onto the membrane surface. Comparing the two PAC types for the same concentration, experiments with PAC-1 showed a lower fouling rate than that with PAC-2 for all PAC concentrations. This result confirms previous results: PAC-1, which removes more organic matter than PAC-2, allows better fouling reduction.
3.2.2.
Longer term fouling
Experiments at constant pressure mode, with a TMP of 2 bar, were performed in the dead-end stirred cell for longer durations (about 3 h). The variation of normalized permeate flux (J/ J0) versus the filtered volume per surface area of the filter is plotted on Fig. 5, in the case of UF alone and for UF coupled with adsorption by both PAC, at a dose of 100 mg L1. Ultrafiltration alone of seawater undergoes a large flux decline. J/J0 is about 0.35 for a filtered volume of 400 L m2. The flux decline is reduced when UF is coupled with an adsorption step: for the same 400 L m2 filtered volume, the ratio J/J0 is about 0.5 for PAC-2 and 0.7 for PAC-1. These results confirm those observed on the fouling rate: PAC adsorption reduces membrane fouling.
3.2.3.
Fouling reversibility
Pure water permeability was measured before and after ultrafiltration to evaluate reversibility of the fouling that occurred during short-term experiments (Filtered volume ¼ 400 L m2) (Table 2). The pure water permeability was measured immediately before UF. After UF, the
Table 3 e Contact angle measurement of different membranes (Filtered volume [ 400 L/m2). Membrane fouled by:
Contact angle ( )
None (clean membrane) SW alone SW þ PAC-1 100 mg L1 SW þ PAC-2 100 mg L1
15.9 89.0 14.9 21.8
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Fig. 7 e SEM micrograph and EDS analysis of the membrane surface fouled by (a) seawater alone (without PAC), (b) PAC-1 and seawater and (c) PAC-2 and seawater (Filtered volume [ 400 L mL2).
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membranes were scrubbed gently by hand to remove fouling by cake formation on the membrane surface and then the pure water permeability was evaluated. The pure water permeability both before and after UF varied in range of 221e230 Lh1m2 bar1 for all tests. A very small difference (between 3.8% and þ1.8%) of water permeability before and after UF was observed for all experiments. Therefore, it can be noted that fouling occurring during short-term ultrafiltration is reversible and that PAC addition shows no effect on the reversibility of membrane fouling in our experimental conditions. Fig. 6 presents the different filtration resistances: Rm, Rf and Rirreversible (Filtered volume ¼ 400 L m2). Rirreversible is the irreversible fouling resistance i.e. the fouling resistance remaining after membrane cleaning. For UF alone, the total fouling resistance is twice the membrane resistance, which is about 1.5 1012 m1. The fouling resistance decreases for PAC-2 and is even lower for PAC-1, the fouling resistance being half of the membrane resistance (Rf ¼ 0.7 1012 m1) in that case. The irreversible fouling resistance is always very low for all cases: maximum Rirreversible is 0.13 1012 m1. All these results allow us to affirm that the main fouling mechanism is the formation of a cake deposit on the membrane surface and this fouling is totally reversible for short-term experiments.
3.3.
Membrane surface characterization
3.3.1.
Membrane hydrophobicity
these inorganic crystals cannot constitute the major foulants of the membrane as they are few and dispersed. Organic compounds are certainly present on the membrane but they are difficult to analyse by EDS because i) organic matter is in very low concentration in seawater, and ii) organic matter elements are the same as those constituting the membrane material (C and O). Thus EDS analysis of the membrane surface (point 2 Fig. 7(a)) does not allow adsorbed organic matter to be distinguished from membrane material. Fig. 7 also shows the membrane surface fouled by seawater with PAC-1 (Fig. 7(b)) and PAC-2 (Fig. 7(c)). In both cases, the membrane surface is no longer observable. The entire surface is densely covered with PAC particles. Some inorganic components are still present (Na, Cl, Si, Al) but the membrane is principally covered by a PAC cake. This cake is non-foulant: ultrafiltration of PAC alone was performed for a filtered volume of 500 L/m2 without a flux decline being observed (Fig. 5).
3.4. Effect of crossflow velocity on UF fouling and on organic matter removal 3.4.1.
Effect of crossflow velocity on UF fouling
Filtration experiments were performed in crossflow mode in order to further characterize the interactions between particles, organic matter and membrane. Fig. 8(a) presents the variation of normalized permeability (Lp/Lp0) according to filtered volume per filtrating surface area
The contact angles shown in Table 3 are the internal angle between droplet and membrane surface. The contact angle of a clean membrane is 15.9 , showing that the membrane used in this study is hydrophilic. The contact angle increases from 15.9 for clean membrane to 89.0 for membrane fouled by seawater alone (without PAC). This is due to the hydrophobic property of organic matter in seawater resulting in an increase of the membrane surface hydrophobicity. Moreover, the contact angle decreases from 89.0 to 21.8 and 14.9 when PAC-2 and PAC-1, respectively, are added into seawater. These contact angle values are very close to that of clean membrane. This indicates that PAC adsorption extensively reduces membrane surface modification. Comparing the two PAC types, the membrane hydrophobicity obtained with PAC-1 is the nearer to that of a clean membrane. Once again, this result confirms previous observations, i.e. the higher adsorption and retention of hydrophobic organic matter by PAC-1 leads to lower membrane surface modification compared to a nonfouled membrane.
3.3.2.
Analysis of SEM-EDS
SEM-EDS analysis was first performed on virgin membrane surface. The membrane material was mainly composed of carbon and oxygen, the principal components of regenerated cellulose membrane. Fig. 7(a) presents the analysis of membrane surface fouled by seawater alone (without PAC). Some crystals can be observed on the membrane surface. Elementary analysis by EDS showed the presence of inorganic elements: Na and Cl, which are the main components of seawater, and also Si, which indicates the formation of silicate salts. Nevertheless,
Fig. 8 e Effect of crossflow filtration on UF performance: (a) normalized permeability and (b) retention rate (Sample 3).
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for different operating conditions: ultrafiltration of seawater alone and ultrafiltration of seawater mixed with PAC-1, in dead-end and in crossflow modes. First of all, the previous results are confirmed: PAC addition reduces permeability decline, and thus membrane fouling, during the experiment whatever the filtration mode. Secondly, it can be observed that lower permeability decline appears in crossflow filtration compared with dead-end mode, whatever the solution filtered. This result was expected: creating a crossflow velocity at the membrane surface reduces UF membrane fouling in the case of seawater alone and also in the case of seawater mixed with PAC.
3.4.2.
Effect of crossflow velocity on organic matter removal
Fig. 8(b) presents the retention rates obtained for the same trials: ultrafiltration alone and ultrafiltration of seawater mixed with PAC-1, in dead-end and in crossflow modes. For UF alone, the retention rate obtained in crossflow filtration is higher than that obtained in batch mode: 7.4% and 15.1% respectively. This result can be explained by a decrease of the polarisation concentration due to an increase of the feed velocity near the membrane surface. The concentration of organic matter near the membrane is thus reduced and the apparent rentention rate is increased (Fig. 9(a)). In contrast, in presence of PAC, the effect of crossflow velocity on organic matter retention rate is the opposite, i.e., an increase in the feed velocity leads to a slight decrease in the retention rate: 60.5% in batch mode and 55.3% in crossflow mode. This result was confirmed by other experiments performed with increasing feed velocities. This quite surprising result can be explained by the contribution of adsorption to the organic matter retention. When the feed velocity is increased, the thickness of the PAC cake at the membrane surface decreases (Fig. 9(b) and (c)). Thus the contact time between the organic matter and the PAC decreases and so the concentration profile is modified: concentration of organic matter near the membrane is higher in the case of the crossflow mode and the apparent rentention rate is thus decreased. Nevertheless, the differences are very small (max. 5%). In conclusion, these tests prove the industrial feasibility of the hybrid PAC/UF process as a pre-treatment before RO desalination. For correctly chosen conditions, i.e. adequate dose of PAC and crossflow velocity, it appears that hardly any permeate flux decline is observed during the filtration and very high organic matter removal (more than 75%) can be achieved.
4.
Fig. 9 e Schematic diagram of TOC concentration profiles (a) in absence of PAC (b) in presence of PAC in dead-end mode (c) in presence of PAC in crossflow mode (TOCfeed: TOC concentration in the feed bulk, TOCm: TOC concentration at the membrane surface, TOCpermeate: TOC concentration in the permeate, ePAC: thickness of PAC cake).
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Conclusions
1. The application of the hybrid PAC/UF process is very effective in organic matter removal from seawater. DOC removal by UF increases from 7% without PAC to 70% when wood-based PAC is added. 2. UF performance depends on PAC concentration for both types of PAC studied and also on PAC functional groups linked to the mode of activation. 3. Fouling rate is also reduced thanks to PAC addition. Marine organic molecules are adsorbed onto PAC particles, which prevents their sorption onto the membrane surface, thus reducing organic fouling. 4. The fouling mechanism occurring for all experiments is reversible for short-term filtration. 5. Crossflow filtration shows very interesting performance: no permeate flux decline is observed during the filtration and very high organic matter removal (more than 75%) can be achieved. Experiments at pilot scale during few months on a UFSWRO system will be performed to test the benefits of better organic removal by PAC-UF on RO biofouling reduction.
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Acknowledgement The authors appreciatively acknowledge funding from MEDINA (http://medina.unical.it), a STREP research project supported by the European Commission under the Sixth Framework Programme (Project number: 036997).
Notations Cpermeate DOC concentration in the permeate, mg L1 Cretentate DOC concentration in the retentate, mg L1 J Permeate flux, L h1 m2 J0 Initial permeate flux, L h1 m2 LP Membrane permeability, L h1 m2 bar1 LP0 Initial membrane permeability, L h1 m2 bar1 R Apparent retention rate Membrane resistance, m1 Rm Rf Fouling resistance, m1 Rirreversible Irreversible fouling resistance, m1 dRf =dt Fouling rate, m1 min1 TMP Transmembrane pressure, bar Abbreviations EDS Energy Dispersive Spectrometer HPLC-SEC High-Pressure Liquid Chromatography - Size Exclusion Chromatography MW Molecular Weight SEM Scanning Electronic Microscopy SWRO Seawater Reverse Osmosis PAC Powdered Activated Carbon TMP Transmembrane pressure DOC Dissolved Organic Carbon UF Ultrafiltration
references
Benner, R., Biddanda, B., Black, B., McCarthy, M., 1997. Abundance, size distribution, and stable carbon and nitrogen isotopic compositions of marine organic matter isolated by tangential-flow ultrafiltration. Marine Chemistry 57, 243e263.
Brehant, A., Bonnelye, V., Perez, M., 2002. Comparison of MF/UF pretreatment with conventional filtration prior to RO membranes for surface seawater desalination. Desalination 144, 353e360. Chua, K.T., Hawlader, M.N.A., Malek, A., 2003. Pretreatment of seawater: results of pilot trials in Singapore. Desalination 159, 225e243. Fabris, R., Lee, E.K., Chow, C.W.K., Chen, V., Drikas, M., 2007. Pretreatments to reduce fouling of low pressure micro-filtration (MF) membranes. Journal of Membrane Science 289, 231e240. Greenlee, L.F., Lawler, D.F., Freeman, B.D., Marrot, B., Moulin, P., 2009. Reverse osmosis desalination: water sources, technology, and today’s challenges. Water Research 43, 2317e2348. Haberkamp, J., Ruhl, A.S., Ernst, M., Jekel, M., 2007. Impact of coagulation and adsorption on DOC fractions of secondary effluent and resulting fouling behaviour in ultrafiltration. Water Resarch 41, 3794e3802. Khedr, M.G., 2000. Membrane fouling problems in reverse osmosis desalination applications. Desalination and Water Reuse 10 (3), 8e17. Kim, J., Cai, Z., Benjamin, M.M., 2008. Effects of adsorbents on membrane fouling by natural organic matter. Journal of Membrane Science 310, 356e364. Konieczny, K., Klomfas, G., 2002. Using activated carbon to improve natural water treatment by porous membranes. Desalination 147, 109e116. Ma, W., Zhao, Y., Wang, L., 2007. The pretreatment with enhanced coagulation and a UF membrane for seawater desalination with reverse osmosis. Desalination 203, 256e259. Pearce, G.K., 2007. The case for UF/MF pretreatment to RO in seawater applications. Desalination 203, 286e295. Shon, H.K., Vigneswaran, S., Cho, J., 2008. Comparison of physicochemical pretreatment methods to seawater reverse osmosis: detailed analyses of molecular weight distribution of organic matter in initial stage. Journal of Membrane Science 320, 151e158. Tansakul, C., Laborie, S., Cabassud, C., 2009. Membrane hybrid processes for pretreatment before seawater reverse osmosis desalination. Desalination and Water Treatment 9, 279e286. Tansakul, C., Laborie, S., Cabassud, C., 2010. Study on performance of ultrafiltration membrane-based pretreatment for application to seawater reverse osmosis desalination. Water Science & Technology 62 (9), 1984e1990. Tomaszewska, M., Mozia, S., 2002. Removal of organic matter from water by PAC/UF system. Water Research 36, 4137e4143.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 7 1 e6 3 8 0
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Feasibility studies: UV/chlorine advanced oxidation treatment for the removal of emerging contaminants C. Sichel a,*, C. Garcia b, K. Andre a a b
Siemens AG, Industry Sector, Industry Solutions Division, Water Technologies, Auf der Weide 10, 89312 Gu¨nzburg, Germany Formerly Siemens Water Technologies, Wallace & Tiernan GmbH, Auf der Weide 10, 89312 Gu¨nzburg, Germany
article info
abstract
Article history:
UV/chlorine (UV/HOCl and UV/ClO2) Advanced Oxidation Processes (AOPs) were assessed
Received 26 July 2011
with varying process layout and compared to the state of the art UV/H2O2 AOP. The process
Received in revised form
comparison focused on the economical and energy saving potential of the UV/chlorine
1 September 2011
AOP. Therefore the experiments were performed at technical scale (250 L/h continuous
Accepted 10 September 2011
flow reactor) and at process energies, oxidant and model contaminant concentrations
Available online 22 September 2011
expected in full scale reference plants. As model compounds the emerging contaminants (ECs): desethylatrazine, sulfamethoxazole, carbamazepine, diclofenac, benzotriazole, tol-
Keywords:
yltriazole, iopamidole and 17a-ethinylestradiol (EE2) were degraded at initial compound
UV/chlorine
concentrations of 1 mg/L in tap water and matrixes with increased organic load (46 mg/L
AOP
DOC). UV/chlorine AOP organic by-product forming potential was assessed for trihalometh-
ECs EDCs
anes (THMs) and N-Nitrosodimethylamine (NDMA). A process design was evaluated which
Advanced water treatment
can considerably reduce process costs, energy consumption and by-product generation from UV/HOCl AOPs. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
AOPs take advantage of highly reactive radical species, based on hydroxyl radicals (.OH) for degradation of toxic or non- or less biode-gradable hazardous contaminants (Legrini et al., 1993; Linden et al., 2007; Cernigoj et al., 2007). A variety of AOP technologies have been reported for the generation of .OH radicals for water treatment, many of them using UV photolysis of conventional oxidants as H2O2/UV, Ozone/UV, and, UV/ S2O8 AOPs (Legrini et al., 1993; Anipsitakis and Dionysiou, 2003). Only recently the radical yielding UV photolysis of chlorine species (Oliver and Carey, 1977; Vogt and Schindler, 1991) was proposed as .OH radical generating AOP technology in water treatment (Watts and Linden, 2007; Jin et al., 2011). Until today the UV/chlorine AOP has only been studied in detail regarding
the radical forming potential, without indications of economical or technical feasibility for the removal of ECs. Apart from UV light, the generation of radical species can be obtained via chemical reactions e. g. the H2O2/O3 AOP or directly from the water matrix e. g. with boron doped diamante electrodes (BDDs) (Marselli et al., 2003). The resumed technologies show how different the methods for ∙OH radical generation can be hence energy consumption and capital costs can vary significantly. As one important parameter the electrical energy per order of compound removal (EEO) can be calculated via (pseudo) firstorder curve fitting or following Eq. (1) (Bolton et al., 1996). EEO ¼
* Corresponding author. Tel.: þ49 82 21 904 147; fax: þ49 82 21 904 121. E-mail address:
[email protected] (C. Sichel). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.025
P Flog c0 =cf
(1)
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where P(kW ) is the electrical power input into the reaction, c0 and cf are the initial and final contaminant concentrations, respectively, assuming first-order reaction kinetics and F (m3h1) is the flow rate (for continuous flow reactors). Another important parameter for the description of AOPs is the quantum yield ðfÞ, or integral quantum yield defined as the number of molecules participating in a given photochemical process or reaction divided by the number of photons absorbed (Eq. (2)) (IUPAC Definitions).
fl ¼
number of events number of photones absorbed
(2)
where f is the quantum yield and l the UV wavelength. UV based AOP state of the art solutions can be equipped with low pressure (LP) or medium pressure (MP) mercury lamps. While LP lamps show better energy conversion in the UV range of 30e38%, with their major irradiation peak at 254 nm, MP lamps have lower energy conversion of 10e20%, are characterized by several UV peaks from 200 nm to 400 nm and can be advantageous due to much lower space consumption of the systems (EPA, 2006). The interest in cost and energy efficient AOPs increases especially in the municipal water sector for the treatment of the so called emerging contaminants (ECs). ECs refer to residuals of persistent pharmaceuticals (PPs), hormones, pesticides, X-ray contrast media, corrosion inhibitors and other non-bio-degradable substances which only recently can be detected due to advancements in water analytics. These substances originate from daily human activities and can be found globally in many surface water sources (Kolpin et al., 2002; Kasprzyk-Hordern et al., 2008). Apart from AOPs, technologies for the removal of ECs can be ozonation, activated carbon, or membrane technologies (reverse osmosis) with high capital/consumable costs. State of the art waste water treatment plants (WWTPs) are not designed for the removal of ECs; therefore, their presence can be detected even at WWTP effluents at low concentrations of approximately 0.1e5 mg/L (Semard et al., 2008; Glassmeyer et al., 2005). At such low compound concentrations no acute effects have been reported for consumers. Nevertheless at higher concentrations many ECs impact endocrine systems and therefore these are referred to as endocrine disrupting compounds (EDCs). Since water resources have high value, the presence of ECs and the long term exposure of water consumers or the environment to even very low concentrations of such contaminants is a situation that big part of the water sector would like to change. Legal initiatives have been proposed on monitoring and limitations for ECs in surface waters and WWTP effluents on national (Germany and Switzerland (OGewV, 2011; Kase et al., 2011)) and international level (Grummt and Fuerhacker, 2011). The state of the art UV/AOP is the UV/H2O2 process which has already been installed in large scale drinking water applications (Kruithof et al., 2007). Main drawback of the technology is the relatively high consumption of electrical energy and therefore considerable treatment costs. Many studies regarding AOPs in water treatment were carried out for single compounds at contaminant concentrations much higher than expected in the environment, this
implies the need for extrapolation (most often first-order fitting), and uncertainties in upscale and cost considerations of treatment plants. Deionized water was often used, without any background contamination and experiments were performed at lab scale without paying attention to the energy consumption of the systems. Therefore, energetic and economic comparison of AOPs has been challenging. Only the combined treatment of compound mixtures in environmentally relevant concentrations, in real water matrixes at technical scale, and the comparison of energy consumptions can give certain insight into the economical feasibility and differentiation of future treatments. The present work shows a comparison of the UV/HOCl and UV/ClO2 AOPs and state of the art UV/H2O2 AOP in realistic treatment conditions to allow precise upscale and find the most energy and cost efficient solution. To show process feasibility in secondary waste water matrixes some experiments were performed with increased organic load as expected for WWTP effluents. Under these conditions generation of organic chlorinated by-products (THMs and NDMA) was monitored under the specifically developed design parameters of the AOP solution.
2.
Materials and methods
2.1.
Reagents
Technical grade oxidants, sodium hypochlorite at 12% active ¨ FA Chemikalien GmbH&Co KG, Germany) and chlorine (BU H2O2 at 35 wt.% (Kruse GmbH & Co KG) were used as received and diluted directly into the tank. Chlorine dioxide was produced by the reaction of HCl 9% (Kruse GmbH & Co KG, Germany) with NaClO2 (Siemens AG, Germany) and stored 1e3 experimental days. In the experiments with enriched DOC load, the tap water (Table 1) was spiked with 100 mg/L citric ¨ FA Chemikalien GmbH&Co KG, Germany) acid (anhydrous) (BU and 40 mg/L urea (Merck, Germany), to simulate organic loads with high THM formation potential (THMFP). To obtain realistic treatment situations, ECs of high relevance in surface water (Table 2 and Fig. 3) were monitored at environmentally relevant initial concentrations of 1 mg/L (0.1 mg/L for the estrogen). The EC standards were received predissolved (AcN/MeOH 1:1) and in concentrations of 200 mg/L (100 mg/L for the estrogen) from the LW laboratory and were diluted directly into the tank (200 L). The injected volume of 1 mL for the ECs leads to a dilution of 1:200,000 of the solvent and therefore the traces of AcN/MeOH add to the background
Table 1 e Ion concentration of local tap water. Tap water analysis Magnesium Calcium Sodium Potassium Chloride Sulfate Nitrate
17.5 mg/L 100 mg/L 8.2 mg/L 1.6 mg/L 18.7 mg/L 22.1 mg/L 3.7 mg/L
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Table 2 e Summary of model water contaminants. Desethylatrazine CAS-No. 6190-65-4 Sulfamethoxazole (SMX) CAS-No. 723-46-6 Carbamazepine (CBZ) CAS-No. 298-46-4 Diclofenac CAS-No. 15307-79-6 Benzotriazole CAS-No. 95-14-7 Tolyltriazole CAS-No.64665-57-2 Iopamidole CAS-No.60166-93-0 17a-ethinylestradiol CAS-No. 57-63-6
Degradation product of atrazine (herbizide) Sulfonamide bacteriostatic antibiotic Anticonvulsant and mood stabilizing drug Non-steroidal anti-inflammatory drug (NSAID) Corrosion inhibitor Corrosion inhibitor X-ray contrast media Estrogen
water matrix without expected influence on the degradation reactions.
2.2.
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Wallace & Tiernan P42 i-cal photometer and DPD reagents (Lovibond, Germany). This method is not able to distinguish between the HOCl and other chlorine species as the OCl anion (ratio at pH 7 approximately 1:1). Nevertheless a robust on-site measurement could be achieved for the free chlorine equivalents referred to as mg/L Cl2 or mg/L ClO2. H2O2 concentration was followed as well on-site spectrophotometrically (Hach DR/4000U spectrometer) at 410 nm with the help of the yellow complex formed with Titanium(IV) oxysulfate (Sigma Aldrich, USA) and H2O2 in glass cuvettes with 1 cm path length. Temperature (20e22 ) and pH (adjusted to 7) were monitored during the experiments. Citric acid and urea could not be monitored because the adequate analytical methods were not easily available. As the background matrix contamination simulates organic load of waste water, which can change based on the local situation and weather conditions, possible variations of citric acid and urea concentrations over the experimental time are not expected to have influence on the overall experimental results. The initial weight of both substances was controlled, before predissolving in tap water and dilution into the tank.
Analytical methods 2.3.
Analysis of the EC’s were performed by a collaborative specialized laboratory for drinking water Zweckverband Landeswasserversorgung, Betriebs- und Forschungslaboratorium, Langenau (LW), Germany with highly sensitive triple quadrupole LC/MS/MS analytics (Seitz et al., 2006; LAWA). Via different quantitative screening methods, the analytes were monitored without pre-concentration step, down to a quantification limit of 10 ng/L (Seitz et al., 2006) and of 1 ng/L for the estrogen after enrichment via solid phase extraction. As well organic byproduct analysis of THMs with quantification limit of 0.1 mg/L, NDMA with quantification limit of 1 ng/L were performed by LW laboratory. The concentration of HOCl and ClO2 as free chlorine equivalents were analyzed on-site via diethyl-p-phenylene diamine (DPD) spectrophotometric method with a Siemens
The experiments were performed on-site (Siemens AG, Gu¨nzburg, Germany) at technical scale with the experimental set-up shown in Fig. 1. The UV reactor was a LP chamber (Fig. 2), which could be equipped with LP lamps (lmax ¼ 253.7nm) of varying power 40 W, 80 W and 200 W (WTL 40, 80 and 200, Siemens AG, Germany), the irradiance was monitored by a 4e20 mA signal UV sensor, and the lamp was protected by a standard 1 mm quartz sleeve, with cut-off at l ¼ 200 nm (Siemens AG, Germany). Even when the lamps were exchanged, the quartz and therefore flow resistance and turbulence remained the same at all lamp powers. The reactor could be run in batch mode, recirculating the water, or in flow trough (continuous) mode. The flow rate was adjusted to 250 L/h. The water was local tap water (Table 1) and the pH was adjusted to 7 with HCl.
2.4.
Fig. 1 e Flow chart of the experimental AOP set-up. Legend: 1) sample valve 1, 2) sample valve 2, 3) selector valve (continous/batch mode), 4) adjustment valve (flow rate), 5) 200 L tank, 6) recirculation pump, 7) flow meter, 8) UV reactor, 9) temperature sensor, 10) pH sensor.
Reactor and set-up characteristics
Process description
Homogenization was achieved with the continuous recirculation of the centrifugal pump (Fig. 1 and 0.18 kW). After 10 min homogenization time of contaminants the first sample (c0) was taken from the sample valve 2 and the oxidant was added. The oxidant concentration was adjusted to the initial concentration exceeding the initial oxidant demand of the water. After another 10 min mixing, the mode was changed to continuous mode, and the UV lamp was switched on. After another 10 min the UV signal had stabilized and the degradation sample (cf) was taken from sample valve 1. Batch experiments without UV irradiance (dark experiments) were sampled directly from the tank after 15 min recirculation with oxidants (sample valve 2). To simulate a patent pending post treatment for residual oxidant quenching process design, all samples (including dark runs) were quenched with sodium thiosulfate (Na2S2O3 Sigma Aldrich, USA) right after the AOP (European Patent Application, EP 11167641.7). All experiments were performed three times. The presented experiments show the mean value and the error is the standard deviation.
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Fig. 2 e LP AOP reactor technical drawing. Legend: 1) UV sensor, 2) quartz sleeve with UV LP lamp inside, 3) water inlet, 4) water outlet, 5) power supply.
Fig. 3 e Molecular structures of the studied model compounds.
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3.
Results
3.1.
EC degradation by conventional oxidants
Fig. 4 shows the degradation potential of free chlorine (6 mg/L Cl2) and ClO2 (6 mg/L) in the dark after 15 min of recirculation. It can be observed that sulfamethoxazole could be readily degraded in all cases. Diclofenac was completely removed by ClO2, while HOCl only achieved 30% degradation. All other model compounds except desethylatrazine (20% degradation by HOCl) could not be significantly degraded by the conventional oxidant treatments without irradiation. Over 1 h recirculation in the dark without oxidants and in the presence of H2O2 at 5 mg/L no significant drop in EC concentration was observed (data not shown). EE2 dark degradation was not experimentally assessed as these values could be extracted from already existing literature (Lee and von Gunten 2010), see discussion in Section 4.1.
3.2. UV/H2O2 versus UV/chlorine at state of the art energy consumption The comparison of UV/H2O2, UV/HOCl and UV/ClO2 at H2O2 concentrations of 5 mg/L and chlorine concentrations of 1 mg/L Cl2 and 0.4 mg/L ClO2 with subsequent thiosulfate quenching and under UV irradiation without oxidants is shown in Fig. 5. The 80 W UV lamp was used, leading to an electrical power consumption of 0.32 kWh/m3. It can be observed that the estrogen EE2 was readily degraded by both chlorine AOPs and almost completely degraded by the UV/H2O2 AOP. Photolysis without oxidants did not yield significant degradation of the estrogen. Benzotriazole and tolyltriazole showed similar degradation patterns. Degradation was achieved by AOPs in following yield order UV/HOCl > UV/H2O2 > UV/ClO2. The UV-photolytic degradation without oxidants was only considerable for tolyltriazole with 30% removal. Desethylatrazine and carbamazepine were
Fig. 5 e Comparison AOPs: degradation of model compounds at state of the art energies. 1 mg/L Cl2, 0.4 mg/L ClO2, 5 mg/L H2O2, UV LP 80 W, 0.32 kWh/m3.
removed best with UV/H2O2 > UV/HOCl UV/ClO2, with only low photolytic degradation. Sulfamethoxazole and diclofenac were readily degraded by UV/H2O2 and the chlorine AOP processes. The photolytic degradation alone yielded over 50% compound degradation in both cases. Iopamidole was degraded with the following yield order UV/HOCl > UV/H2O2 > UV/ClO2. The photolysis yielded 30% removal. Table 3 shows the oxidant concentrations of c0 and cf for Fig. 5. Highest oxidant photolysis in mg/L was always observed for the ClO2 oxidant, followed by HOCl. Fig. 6 shows a degradation kinetic that fits the pseudofirst-order and percentage of degradation (inlay) of the UV/ H2O2 AOP obtained by performing experiments with different UV LP lamps, with 40 W, 80 W and 200 W. Table 4 shows the EEO of the UV/H2O2 process in the presented experimental set-up, based on the first-order curve fitting. The chlorine based AOPs did not obey to pseudo-first-order degradation kinetics. For the exact determination of prevalent reaction kinetics the data was not sufficient, and therefore the data was not fitted. Further studies with focus on reaction kinetics of UV/chlorine AOPs with higher oxidant concentrations will be needed to confirm exact degradation path-ways for the model compounds.
Table 3 e Oxidant concentrations for initial c0 and final cf AOP samples. Oxidant
Oxidant concentration (mg/L) Fig. 5 (LP 80 W)
Fig. 4 e Degradation of model compounds in the reactor in the presence of oxidants without UV irradiance. 6 mg/L Cl2, 6 mg/L ClO2, 15 min dark reaction.
H2O2 H2O2 HOCl (Cl2) HOCl (Cl2) ClO2 ClO2
4.9 0.1 4.2 0.1 1 0.04 0.4 0.04 0.4 0.1 0.2 0.1
Sample
Fig. 7 (LP 40 W) 5.2 5.1 6.1 5.4 6.2 5.1
0.5 0.2 0.1 0.2 0.3 0.5
c0 cf c0 cf c0 cf
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Fig. 6 e Model compound degradation of the UV/H2O2 AOP at various UV energies (kWh/m3), first-order fit. 5 mg/L H2O2, UV LP 40 W, 80 W, 200 W, 0.16 kWh/m3, 0.32 kWh/m3, 0.8 kWh/m3 Inlay: compound degradation of the UV/H2O2 AOP at different energy consumptions (kWh/m3) in percentage.
3.3.
Low energy UV/chlorine AOP
For energetic and economic optimization of the UV/chlorine AOP, experiments at higher oxidant concentrations of 6 mg/L free chlorine and 6 mg/L ClO2 followed by thiosulfate quenching (simulating patent pending process step) were compared against the state of the art UV/H2O2 AOP at 5 mg/L H2O2 (Fig. 7). EE2 degradation was not assessed anymore as complete removal was already achieved with low HOCl and ClO2 concentrations. The electrical energy consumption of the UV
Table 4 e EEO of the UV/H2O2 process for assessed model compounds and experimental set-up. Contaminant Benzotriazole Tolybenzotriazole Desethylatrazine Carbamazepine Sulfamethoxazole Diclofenac Iopamidole
EEO UV/H2O2 0.52 0.59 1.00 0.62 0.29 0.17 0.42
Fig. 7 e Comparison AOPs: UV/H2O2 versus UV/chlorine AOP at low energy consumption. 6 mg/L Cl2, 6 mg/L ClO2, 5 mg/L H2O2, UV LP 40 W, 0.16 kWh/m3.
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reactor was reduced from 0.32 kWh/m3 to 0.16 kWh/m3 by exchanging the 80 W for the 40 W lamp. It can be observed in Figs. 7 and 8 that the EEO of 0.16 kWh/m3 for carbamazepine (90% degradation) implies very important energy saving of approximately 75% for the UV/chlorine AOP compared to the UV/H2O2 AOP with an EEO of 0.62 kWh/m3 (Table 4). As well for benzotriazole, tolyltriazole, sulfamethoxazole, and iopamidole relevant yield improvements of over 30e50% were achieved for the UV/HOCl process compared to the state of the art technology at same energy consumptions. For sulfamethoxazole and diclofenac, the quantification limit was reached for the UV/ HOCl AOP so the complete improvement could not be appreciated. The only compound that showed limited yield improvement was desethylatrazine. Table 3 shows oxidant concentrations of c0 and cf for Fig. 7. Highest photolytic oxidant reduction was observed for ClO2 followed by HOCl. The low photolysis of H2O2 as well implies low .OH radical generation for the UV/H2O2 process with the 40 W lamp.
3.4. UV/chlorine AOP for waste water under simulated organic load Experiments with increased organic load, approximately 100 mg/L citric acid and 40 mg/L urea, total DOC ¼ 46 mg/L were performed to assess process robustness and by-product formation for high DOC load (WWTP effluent) and high THMFP water. It can be observed that the organic load did not significantly reduce the UV/chlorine AOP yield (Fig. 8). By-product formation of the treated water was monitored for all experiments with increased organic load. Thiosulfate quenching of the samples simulated again the post treatment. With this process design the THM concentration was cf ¼ 3.5 0.4 mg/L and NDMA concentration was below the quantification limit. The initial concentrations of free chlorine in tap water experiments were c0 ¼ 6.1 0.1 mg/L and final cf ¼ 5.4 0.2 mg/L (before quenching). For DOC spiked water the initial chlorine concentrations were c0 ¼ 6.1 0.1 mg/L and the final cf ¼ 5.2 0.3 mg/L (before quenching).
4.
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Discussion
4.1. Reactivity of model compounds with chlorine species in the dark The dark study represents the degradation potential of the model compounds without any oxidant activation by UV light. EE2 degradation by chlorine species was not anymore assessed within this experimental series because the fast reactivity of EE2 to HOCl and ClO2 in excess of these oxidants has been reported before to 3.5 105 M1 s1 and 4.6 108 M1 s1, respectively (Lee and von Gunten, 2010). For H2O2 no significant EE2 reduction was observed (Lee and von Gunten, 2010). The results reported here are insufficient to establish the mechanism of the degradation for sulfamethoxazole and diclofenac but it was shown before that the reaction of the chlorine species with target compounds depends on functional groups of high electron-density, so called electron rich moieties (ERMs) such as aromatic, phenolic and anilin-moities and neutral secondary and tertiary amines (Hoigne´ and Bader, 1994). Therefore it can be expected that ClO2 degraded sulfamethoxazole attacking the amine group of the aniline ring and diclofenac at the secondary amine structure, or due to steric effects on the aromatic rings. The pH dependency was not assessed in this experimental series but lower reaction rates for chlorine dioxide degradation of sulfamethoxazole were reported previously due to the protonation of the reactive functional groups at low pH (Huber et al., 2005). Nevertheless most municipal water sources are around neutral pH, as represented in this study. While sulfamethoxazole was completely degraded by HOCl, diclofenac was only partially degraded under the set conditions. Lee and co-workers reported higher affinity of HOCl to secondary amines (dimethylamine) but lower affinity to aromatic anilin than ClO2 at neutral pH and regarding molar based ratios (Lee and von Gunten 2010). The reason for higher affinity of HOCl to sulfamethoxazole than to diclofenac could be the HOCl attack on the aniline ring instead of the secondary amine group due to steric effects.
4.2. Comparison of AOPs with state of the art energy consumption
Fig. 8 e UV/chlorine AOP yield in tap water compared to DOC enriched water (46 mg/L DOC). 6 mg/L Cl2, 5 mg/L H2O2, UV LP 40 W, 0.16 kWh/m3.
The experimental series (Fig. 5) showed that depending on the characteristics of each model compound the UV/H2O2 AOP and the chlorine based AOPs had comparable degradation yields. The low chlorine concentrations were chosen to be able to simulate treatment conditions at low oxidant consumption. The H2O2 concentration of 5 mg/L was chosen to have similar conditions compared to state of the art technical solutions that work between 5 and 6 mg/L. The first full scale drinking water plant in Europe for example, PWN’s water treatment plant in Andijk, the Netherlands has installed a UV/H2O2 process applying a H2O2 concentration of 6 mg/L and UV MP lamps with approximately 0.5 kWh/m3 achieving 80% reduction of most organic contaminants (Kruithof et al., 2007). For the present work it was tried to adjust, the experimental conditions closely to the state of the art conditions to be able to compare other AOP processes under realistic
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assumptions. The EEO for the UV/H2O2 AOP in the used reactor set-up (Table 4) could be obtained via pseudo-first-order degradation fitting (Fig. 6). The EEO was between 0.4 kWh/m3 and 1 kWh/m3. Considering the lower energy conversion of MP to LP lamps, the EEO can be considered in the same range but a bit higher than for the PWN plant (Kruithof et al., 2007) probably due to less optimization of the technical scale UV chamber for the process. While the compound degradation by the UV/H2O2 AOP could be fitted into fluence-based pseudo-first-order degradation behavior (Fig. 6), the UV/chlorine AOP did not obey such degradation patterns. The bare photolysis of 1e3 mg/L Cl2 HOCl in the former studies was reported to obey pseudo-first-order fluence-based reaction kinetics based on the removal of the radical scavengers parachlorobenzoic acid (pCBA) and nitrobenzene (NB) (Watts and Linden, 2007). The deviation from pseudo-firstorder behavior in the presented degradation studies can be explained by the assumption that the model compounds were not only degraded by the radical path-way but by the synergistic attack of the existing free chlorine species and the generated radicals. Besides different reaction mechanisms and affinities of the generated .OH and ∙Cl radicals for the studied ECs can be expected than for pCBA and NB. It was shown that .OH radicals (low selectivity) show high reactivity with almost all organic moieties (k 108 M1 s1) while the selective oxidants like HOCl and ClO2 react with only some exceptions with ERMs like phenols, anilines, olefins, and amines. The degradation of ECs by selective oxidants often follows second-order-kinetics as resumed in the work of Lee and von Gunten and references (Lee and von Gunten, 2010). While the UV/H2O2 AOP benefits from non-selective .OH radical degradation UV/chlorine processes can benefit from both, the two different generated radicals and the selective compound degradation. Therefore the reaction kinetics are composed out of the separate kinetics and synergies or interactions. As this study did not focus on the determination of reaction kinetics but on energetic and economic potential of the UV/chlorine AOP, further studies will be needed, to determine the reaction kinetics of present experimental results.
4.3.
Low energy UV/chlorine AOP
For the overall low energy experiment it can be resumed that the energetic yield of the AOPs with the process design of Fig. 7 follows the order UV/HOCl > UV/H2O2 UV/ClO2. The reduction in energy consumption of the UV/HOCl AOP over the state of the art UV/H2O2 AOP yields 30e75%. The improved degradation yield of the chlorine AOP compared to the UV/H2O2 AOP can partly be attributed to the more efficient radical generation of the chlorine species compared to H2O2 due to different quantum yields at l ¼ 254 nm. Watts and Linden reported for LP irradiated solutions of HOCl higher quantum yields regarding .OH radical generation than values found in the literature for the photolysis of H2O2: UV/ HOCl f ¼ 1:4 0:18 Mol Es1; UV/H2O2 f ¼ 1 Mol Es1 (Watts and Linden, 2007; Baxendale and Wilson, 1957). The UV/chlorine AOP at relatively high HOCl concentrations as well benefits from lower scavenger rates than the UV/
H2O2 process (8.46 104 M1 s1 (HOCl) << 2.7 107 M1 s1 (H2O2) (Watts and Linden, 2007)). If the generated radicals are not scavenged by the oxidants themselves they can contribute more efficiently to the overall degradation process. Another consideration is that the impact of the water matrix on the degradation yield as background scavenger is stronger at relatively low oxidant concentrations than at higher, leading to improved degradation yield of the UV/chlorine AOP at increased oxidant concentrations, overcoming the competition of the contaminants and the water matrix for selective oxidants and radical species. For EE2 and sulfamethoxazole similar effects were observed in WWTP effluents, showing strong increase of degradation yield of HOCl and ClO2, after overcoming a lag phase due to matrix effects. The .OH radical degradation path-way did not show such behavior (Lee and von Gunten, 2010). The main drawback for process design with high chlorine concentrations is the residual chlorine. If the UV/HOCl process is applied in a treatment scenario for secondary waste water treatment, the residual chlorine has to be removed in most applications. Only after removal of excess chlorine, the water can be discharged into a receiving stream or water body. As the removal of residual chlorine species after the UV/ chlorine AOPs is considered crucial for treatment economies (see below) an oxidant quenching post treatment has been developed by Siemens Water Technologies R&D, which is simulated in the presented studies by simple addition of thiosulfate to the sample containers. During quenching the excess chlorine is transformed to innocuous salts and water.
4.4. UV/chlorine AOP and by-product formation for waste water applications with organic load The increased DOC did not significantly reduce the yield of the UV/chlorine AOP. This shows that the concentration of 6 mg/L free chlorine is high enough to overcome even matrix effects of 46 mg/L simulated DOC. The simulated DOC was approximated to the COD limits existing for municipal WWTP effluents in Germany. These reach from 75 mg/L COD to 150 mg/L depending on the WWTP size and load (ABWV). In the experimental conditions the stoichiometric relationship of COD/DOC of 2.67 was assumed therefore 46 mg/L DOC were compared to 122 mg/L COD and therefore the experiment focused on the upper range of the accepted DOC limits. In the work of Lee and von Gunten, a real WWTP effluent with 5 mg/L DOC showed a lag phase for the degradation of sulfamethoxazole by HOCl that was overcome at oxidant concentrations of 3e4 mg/L free chlorine (Lee and von Gunten, 2010). Second-order rate constants for the degradation of DOC by HOCl can be assumed under these conditions. Due to the variations in local water characteristics and water treatment practices, it is difficult to standardize the content and nature of DOC occurring in secondary WWTP effluents. Therefore it is recommended to verify optimized oxidant concentrations at each AOP treatment site during piloting. Not only UV/chlorine AOP yield-reduction in water matrixes of high organic load but also the generation of chlorinated by-products is one major concern for modern water treatment technologies based on chlorine. Since the 1970th, THMs are well known for their frequent presence in
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chlorinated water (Rock, 1977). THMs can be seen as indicator of organic chlorinated disinfection by-products and have negative health effects for the consumers and the environment. To asses the potential of chlorinated by-product formation, THMs were analyzed for the experiments with high organic load and high THMFP. The THM concentrations of 3.5 0.4 mg/L detected in water with high THMFP, spiked with citric acid and urea, are very low, much lower than the accepted value of 50 mg/L in the German drinking water norm. Other countries like the USA accept higher THM values of 80 mg/L for drinking water in their EPA National Primary Drinking Water Standards. These low concentrations observed can be achieved due to the removal of excess chlorine by quenching. In experiments without quenching much higher THM concentrations could be measured if the contact time with chlorine was increased, i. e. the chlorine was not removed (data not shown). Therefore, even in cases where residual chlorine concentrations are required, it is recommended to reduce the excess chlorine to the target chlorine concentration reducing possible by-products. Full scale automation is expected to lead to even lower chlorinated by-product generation because of the reduction of chlorine contact time with the organics in the water before the UV treatment. This contact time was relatively high over the experimental series (20 min) due to experimental limitations. NDMA was not detected down to 1 ng/L (quantification limit), therefore, it is not expected to occur during the assessed UV/chlorine AOP process. Other tests to exclude mutagenic effects of degradation byproducts can be performed with standard protocols like the “Ames test”, or the “Comet essay” (Eaton et al., 2005). For UV/ H2O2 AOPs such genotoxicity tests of degradation by-products or reaction intermediates were negative for both, the classic Ames test and the Comet essay (Martijn and Kruithof, 2011). Apart from the studied THM and NDMA by-products, other possible organic and inorganic by-products from the UV/ chlorine AOP are planned to be investigated in future studies to further improve the confidence into the applied UV/chlorine AOP followed by quenching post treatment.
4.5. Cost and efficiency considerations for the removal of trace organics Process costs of the UV/H2O2 state of the art process applying a H2O2 dose of 6 mg/L and UV MP lamps with approximately 0.5 kWh/m3 (Kruithof et al., 2007) are mainly due to the electrical energy consumption. Considering average energy costs of 0.11 V/kWh (Eurostat), and chemical costs of 1e3 V/kg HOCl/H2O2, depending on country, leverage and production conditions, 70e90% of the consumable operating costs for one m3 of treated water can be attributed to electrical energy consumption (including only process relevant factors). Even taking into account the variety of energy and chemical pricing at different places, the main saving for AOPs is the reduction of energy. The energy reduction of the UV/HOCl process compared to the UV/H2O2 process is very considerable yielding up to 75% for difficultly degradable contaminants as carbamazepine and for almost all other contaminants relevant yield improvement of over 30e70%, therefore price reduction of 30e50% for
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operational process costs and important reduction in capital costs due to smaller numbers of AOP reactor chambers are expected for full scale applications. Other solutions for EC removal with relatively low electrical energy consumption like activated carbon adsorption can remove a big part of ECs (Knappe et al., 1998), but the process for WWTP effluents includes high consumable costs. Membrane technologies, like ultra filtration (UF) can improve notably the water quality of WWTP effluents, but even reverse osmosis (RO) membranes can not remove completely ECs with small molecule structures (micro pollutants) like NDMA (Plumlee et al., 2008). Another negative side effect is that activated carbon and membrane technologies only transfer the pollutants from the treated water to solid phase or concentrated streams. As oxidation technology ozone is often applied and has shown good removal capacities for a big number of contaminants (Ternes et al., 2003; Kasprzyk-Hordern et al., 2009). Nevertheless membrane treatments and ozonation imply much higher investment costs (capital costs) than the presented UV/chlorine solution. Therefore the UV/chlorine AOP is expected to be one of the most promising economic solutions for future EC control in WWTP effluents, or similar applications.
5.
Conclusions
The UV/chlorine AOP followed by post treatment has been assessed at technical scale and at process energies, oxidant and model contaminant concentrations expected in full scale reference plants. Under these conditions degradation of the environmentally relevant ECs: desethylatrazine, sulfamethoxazole, carbamazepine, diclofenac, benzotriazole, tolyltriazole, iopamidole and 17a-ethinylestradiol (EE2) was achieved. Energy reductions of 30e75%, depending on the specific compound were observed compared to the state of the art UV/H2O2 process. As energy consumption generates the big part of the process costs up to 30e50% cost savings can be expected. Even high organic load, simulating the application of UV/chlorine AOPs in WWTP effluents did not notably reduce the degradation yield of the UV/chlorine process. The quenching post treatment simulated in the assessed samples led to very low organic by-product concentrations for THMs (3.5 0.4 mg/L) and NDMA (below quantification limit).
Acknowledgements The highly specialized analytics and the know-how regarding environmental pollution patterns of ECs from the Zweckverband Landeswasserversorgung, Betriebsund Forschungslaboratorium, Langenau (LW) Germany, was highly appreciated. The authors want to thank especially Dr. Weber, Dr. Schulz and Dr. Seitz for their expertise. Thanks to the detailed studies and publications of the PWN plant by Dr. Kruithof and Dr. Martijn a realistic comparison of new AOP technologies and the presented development could be performed in the presented way.
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references
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Knappe, D.U., Matsui, Y., Snoeyin, V.K., Roche, P., Bourbigo, M., 1998. Predicting the capacity of powdered activated carbon for trace organic compounds in natural waters. Environmental Science and Technology 32, 1694e1698. Kolpin, D.W., Furlong, E.T., Meyer, M.T., Thurman, E.M., Zaugg, S. D., Barber, L.B., Buxton, H., 2002. Pharmaceuticals, hormones, and other organic wastewater contaminants in U. S. streams, 1999e2000: a national reconnaissance. Environmental Science and Technology 36 (6), 1202e1211. Kruithof, J.C., Kamp, P.C., Martijn, B.J., 2007. UV/H2O2 treatment: a practical solution for organic contaminant control and primary disinfection ozone. Science and Engineering 29, 273e280. LAWA http://www.lw-online.de/. Lee, Y., von Gunten, U., 2010. Oxidative transformation of micropollutants during municipal wastewater treatment: comparison of kinetic aspects of selective (chlorine, chlorine dioxide, ferrateVI, and ozone) and non-selective oxidants (hydroxyl radical). Water Research 44 (2), 555e566. Legrini, O., Oliveros, E., Braun, A.M., 1993. Photochemical processes for water treatment. Chemical Reviews 93, 671e698. Linden, K.G., Rosenfeldt, E.J., Kullman, S.W., 2007. UV/H2O2 degradation of endocrine-disrupting chemicals in water evaluated via toxicity assays. Water Science and Technology 55 (12), 313e319. Marselli, B., Garcia-Gome, J., Michaud, P.A., Rodrigo, M.A., Comninellis, C., 2003. Electrogeneration of hydroxyl radicals on boron-doped diamond electrodes. Journal of The Electrochemical Society 150, 79e83. Martijn, B.J., Kruithof J.C., 2011. By-product formation and advanced genotoxicity testing to characterize the potential harmful side effects of chemical oxidation and disinfection. In: Proceedings of 2011 Joint World Congress & Exhibition, 20th IOA World Congress e 6th IUVA World Congress. ISBN 978-2-9528298-8-5. OGewV, 2011. Verordnung zum Schutz der Oberfla¨chengewa¨sser (Oberfla¨chengewa¨sserverordnung e OGewV). Bundesanzeiger Verlagsgesellschaft mbH. ISSN 0720e2946. Oliver, B.G., Carey, J.H., 1977. Photochemical production of chlorinated organics in aqueous solutions containing chlorine. Environmental Science and Technology 11 (9), 893e895. Plumlee, M.H., Lopez-Mesasa, M., Heidlberger, A., Ishida, K.P., Reinhard, M., 2008. N-nitrosodimethylamine (NDMA) removal by reverse osmosis and UV treatment and analysis via LCeMS/ MS. Water Research 42, 347e355. Rock, J.J., 1977. Chlorination of fulvic acids in natural waters. Environmental Science and Technology 11, 478e482. Seitz, W., Schulz, W., Weber, W.H., 2006. Novel applications of highly sensitive liquid chromatography/mass spectrometry/ mass spectrometry for the direct detection of ultra-trace levels of contaminants in water. Rapid Communication Mass Spectrometry 20, 2281e2285. Semard, G., Bruchet, A., Cardinae¨l, P., Bouillon, J.P., 2008. Use of comprehensive two-dimensional gas chromatography for the broad screening of hazardous contaminants in urban wastewaters. Water Science and Technology 57 (12), 1983e1989. Ternes, T.A., Stu¨ber, J., Herrmann, N., McDowell, D., Ried, A., Kampmann, M., Teiser, B., 2003. Ozonation: a tool for removal of pharmaceuticals, contrast media and musk fragrances from wastewater? Water Research 37, 1976e1982. Vogt, R., Schindler, R.N., 1991. Product channels in the photolysis of HOCl. Journal of Photochemistry and Photobiology A: Chemistry 66, 133e140. Watts, M.J., Linden, K.G., 2007. Chlorine photolysis and subsequent OH radical production during UV treatment of chlorinated water. Water Research 41, 2871e2878.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 8 1 e6 3 9 2
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Solving the problem at the source: Controlling Mn release at the sediment-water interface via hypolimnetic oxygenation Lee D. Bryant a,b,*, Heileen Hsu-Kim b, Paul A. Gantzer a,1, John C. Little a a b
Department of Civil and Environmental Engineering, 418 Durham Hall, Virginia Tech, Blacksburg, VA 24061, USA Department of Civil and Environmental Engineering, 121 Hudson Hall, Box 90287, Duke University, Durham, NC 27708, USA
article info
abstract
Article history:
One of the primary goals of hypolimnetic oxygenation systems (HOx) from a drinking water
Received 10 June 2011
perspective is to suppress sediment-water fluxes of reduced chemical species (e.g.,
Received in revised form
manganese and iron) by replenishing dissolved oxygen (O2) in the hypolimnion. Manga-
11 September 2011
nese (Mn) in particular is becoming a serious problem for water treatment on a global scale.
Accepted 13 September 2011
While it has been established that HOx can increase sediment O2 uptake rates and
Available online 19 September 2011
subsequently enhance the sediment oxic zone via elevated near-sediment O2 and mixing, the influence of HOx on sediment-water fluxes of chemical species with more complicated
Keywords: Manganese
redox kinetics like Mn has not been comprehensively evaluated. This study was based on Mn and O2 data collected primarily in-situ to characterize both
Biogeochemical cycling
the sediment and water column in a drinking-water-supply reservoir equipped with an
Sediment porewater
HOx. While diffusive Mn flux out of the sediment was enhanced by HOx operation due to
Hypolimnetic oxygenation
an increased concentration driving force across the sediment-water interface, oxygenation
Lake and reservoir management
maintained elevated near-sediment and porewater O2 levels that facilitated biogeochem-
in-situ
ical cycling and subsequent retention of released Mn within the benthic region. Results show that soluble Mn levels in the lower hypolimnion increased substantially when the HOx was turned off for as little as w48 h and the upper sediment became anoxic. Turning off the HOx for longer periods (i.e., several weeks) significantly impaired water quality due to sediment Mn release. Continual oxygenation maintained an oxic benthic region sufficient to prevent Mn release to the overlying source water. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
The condition of water supplies in many regions of the world is deteriorating and water may soon prove to be the most critical natural resource governing human and ecosystem health (Gleick, 2003; NRC, 2004). Alternative approaches for improving water quality are being explored to address the serious issue of global drinking-water supplies. Hypolimnetic
oxygenation systems (HOx) are being used by drinking-water and hydropower utilities, as well as other lake and reservoir managers, to improve water quality by replenishing dissolved oxygen (O2) and decreasing levels of reduced chemical species in the bulk hypolimnion (Beutel, 2003; Moore, 2003) while preserving stratification (Wu¨est et al., 1992; Singleton and Little, 2006; Gantzer et al., 2009a,b). Hypolimnetic water is desirable as a drinking-water source because it is cooler and
* Corresponding author. Department of Civil and Environmental Engineering, 121 Hudson Hall, Duke University, Durham, NC 27708, USA. Tel.: þ1 919 660 5034; fax: þ1 919 660 5219. E-mail addresses:
[email protected],
[email protected] (L.D. Bryant),
[email protected] (H. Hsu-Kim),
[email protected] (P.A. Gantzer),
[email protected] (J.C. Little). 1 Present address: Gantzer Water Resources LLC, 14816 119th Place NE, Kirkland, WA 98084, USA. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.030
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contains less organic matter compared to the epilimnion. However, seasonal hypoxia often causes the hypolimnion to have increased levels of reduced chemical species as they are released from anoxic sediment (Gafsi et al., 2009). Manganese (Mn) specifically is becoming a serious problem for water treatment on a global scale (Kohl and Medlar, 2006). Although Mn is regulated in drinking water for aesthetic reasons (odor, taste, color), neurological health risks associated with excess Mn in drinking water are being identified worldwide (Hafeman et al., 2007; Walker et al., 2007; Bouchard et al., 2011). Treatment of source water (i.e., water designated as a drinking-water source prior to treatment-plant intake) with elevated Mn concentrations can be difficult due to the complexity of Mn redox kinetics (Balzer, 1982; Nealson et al., 1988). The removal of metals from source water during water treatment is feasible; however, the process is not always effective and requires the use of chemical oxidants that can react with naturally occurring organic matter to form carcinogenic compounds, thus posing a health risk (Budd et al., 2007; Brandhuber and Clark, 2008). It has been established that HOx can decrease soluble Mn levels in the hypolimnion (Zaw and Chiswell, 1999; Gantzer et al., 2009b) by increasing hypolimnetic O2 which facilitates Mn oxidation and/or adsorption to other particles and subsequent precipitation to the sediment. However, O2 dynamics and biogeochemical transformation processes are highly variable across the sediment-water interface (SWI) due to large spatial gradients in chemical, physical, and microbial properties (Santschi et al., 1990). Thus, as increased levels of oxide precipitates reach the sediment, potentially significant changes in the sediment O2 uptake rate (JO2 ) and other chemical fluxes at the SWI may occur (Jørgensen and Revsbech, 1985; Zhang et al., 1999). As a result, the influence of HOx on sediment-water fluxes of dissolved species (e.g., O2, Mn) may not be as well understood as the influence on the water column. Recent work has indicated that JO2 is more strongly controlled by HOx-induced mixing rather than hypolimnetic O2 levels, highlighting the need for continual mixing to maintain an oxic sediment zone (Bryant et al., 2011a). Additionally, it has been shown that technical lake management procedures such as HOx can fail to effectively decrease reduced-species flux from the sediment and enhance water quality in some systems (Zaw and Chiswell, 1999; Matzinger et al., 2010). While previous work has established that HOx increase JO2 via elevated near-sediment O2 and turbulence levels (Moore et al., 1996; Beutel, 2003; Bryant et al., 2011a), the influence of HOx on sediment-water fluxes of reduced chemical species has not been comprehensively evaluated. Much of the work that has been done on using oxygenation to improve water quality has focused on the water column (Zaw and Chiswell, 1999; Matthews and Effler, 2006; Gantzer et al., 2009b). However, the complex nature of biogeochemical cycling at the SWI must be taken into account when assessing the overall influence of HOx. The few studies that have been done on HOx-induced variation in sediment-water fluxes have been largely theoretical or laboratory based (Moore et al., 1996; Beutel, 2003). The work presented here is based on sediment and water-column Mn and O2 data collected primarily in-situ in a drinking-water-supply reservoir equipped with an HOx to
specifically investigate how HOx operations affect sedimentwater Mn cycling and source-water quality under actual reservoir conditions. Mn and O2 profile data obtained via insitu porewater analyzers, multi-species voltammetric electrodes, and O2 microsensors are (1) compared and (2) evaluated to determine the influence of HOx operations and resultant sediment O2 conditions on the vertical distribution of Mn across the SWI. This study also characterizes (3) HOxinduced variation in diffusive flux of soluble Mn (JMn) out of the sediment and (4) biogeochemical cycling and subsequent retention of Mn in the benthic region. The multiple methods used to collect data were employed at different locations and time periods (defined in Sec. 2.1) to assess Mn cycling on temporal as well as local and reservoir-wide spatial scales. This work enables greater understanding of biogeochemical cycling across the SWI and the influence of HOx on sediment Mn retention and also illustrates how to successfully manage HOx operation for controlling sediment release of reduced chemical species to the hypolimnion.
2.
Materials and methods
2.1.
Study site and in-situ instrumentation
This study was based on work performed at Carvins Cove Reservoir (CCR), a eutrophic drinking-water-supply reservoir (maximum depth of 23 m, width of w600 m, and length of w8000 m) managed by the Western Virginia Water Authority in Virginia, USA (Fig. 1). A linear bubble-plume HOx (Mobley et al., 1997; Singleton et al., 2007) was installed in the deepest section of CCR near the treatment-plant outtake (Fig. 1) in 2005 and since that time ongoing field campaigns have been performed to monitor performance (Gantzer et al., 2009a,b; Bryant et al., 2011a,b). A network of in-situ and laboratory measurements was performed from 2005 to 2008 to evaluate sediment-water flux. Each year, focused field sampling typically lasted from the start of stratification in March through the end of fall turnover in November. Samples were obtained primarily from nearfield sites CCR-1 and CCR-2 in the immediate vicinity of the HOx, mid-reservoir site CCR-6, and in the back region at site CCR-7 to characterize the reservoir-wide influence of the HOx (Fig. 1). The HOx was installed in CCR to address source-waterquality issues with both Mn and iron (Fe); hypolimnetic Mn and Fe concentrations typically increased to w20 mmol L1 during summer stratification prior to oxygenation (Gantzer et al., 2009b). Although soluble Fe was quickly eliminated with oxygenation, as Fe oxidizes easily in the presence of O2, complex Mn redox kinetics contributed to more persistent CCR water-quality problems with Mn (Santschi et al., 1990; Nealson and Saffarini, 1994; Gantzer et al., 2009b). While Mn, Fe, and O2 were tracked concurrently throughout the campaign, Mn data are the primary focus of the current study to determine the influence of oxygenation on Mn sedimentwater cycling. The vertical distribution of O2 at the SWI was measured at mm-scale increments using an in-situ microprofiler. O2 concentration profile data were also obtained from undisturbed sediment cores extracted from the study sites and transported to the laboratory for analysis via O2
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 8 1 e6 3 9 2
microsensors and solid-state voltammetric electrodes (Brendel and Luther, 1995). Reduced Mn (Mn2þ) profiles were also measured using the voltammetric electrodes. Diffusive porewater samplers (“peepers”; Hesslein, 1976) were used to obtain in-situ soluble Mn concentration profiles across the SWI at cm-scale increments. Peeper data were used to temporally track how JMn responded to variations in HOx oxygen-gas flow rates while voltammetric-electrode data from sediment cores were used to assess spatial variation in sediment-porewater composition on a reservoir-wide scale and to verify peeper data. Bulk sediment samples were analyzed for total Mn and Fe. Water samples from different elevations in the water column and water overlying the sediment of core samples were used to track changes in total and soluble Mn and Fe. Water-column profiles were obtained using a SeaBird Electronics SBE19Plus with SBE43 DO sensor (CTD; Conductivity-Temperature-O2 as a function of Depth) to track water-column O2 levels and density stratification as a function of temperature. Further details on primary measurements are provided below.
2.1.1.
O2 microprofiles at the SWI
Data characterizing the vertical distribution of O2 at the SWI were used to track sediment oxic conditions (quantified by depth of the sediment oxic zone (zmax) and O2 concentration at the SWI). During 2005 and 2006, O2 microprofiles at the SWI were measured via microsensor profiling of sediment cores. Sediment cores were obtained using a ball corer (Uwitec) with a core-tube diameter of 90 mm and height of 120 cm. Following extraction, sediment cores were kept in the dark
Fig. 1 e Map of Carvins Cove Reservoir (CCR) showing locations of sampling sites (near-field locations CCR-1 (0 m; relative to start of hypolimnetic oxygenation system (HOx) lines) and CCR-2 (189 m); mid-reservoir locations CCR-3 thru CCR-6 (683, 1011, 1373, and 1814 m, respectively); back-reservoir location CCR-7 (3000 m)) and the linear bubble-plume HOx. From Bryant et al. (2011a).
6383
and on ice as they were transported to the laboratory and were typically profiled within w1 h of extraction. Sediment cores were cut to an appropriate height (w20 cm) for profiling using a Uwitec corecutter. Care was taken to ensure sediment cores remained undisturbed during the extraction and cutting process. Sediment cores were profiled using Clark-type O2 microsensors (OX-100; Unisense A/S) which have an internal reference and a guard cathode. OX-100 microsensors have an extremely small tip size and depth resolution (100 mm), rapid response time (90% response in <10 s), and negligible stirring sensitivity. The microsensors were manually controlled by a micromanipulator (M3301R; World Precision Instruments, Inc.) and were supported by a high-sensitivity picoammeter (PA2000; Unisense A/S). Mild mixing of the core water column was maintained during profiling. Cores were profiled at 1-mm depth resolution and three measurements were obtained at each depth. Additional single-point measurements of the SWI were also obtained in triplicate. A linear calibration of the microsensor was obtained for each core using O2 concentrations in the overlying water, as determined via Winkler titration, and in the anoxic sediment of the core. During 2007 and 2008, an autonomous microprofiler (MP4; Unisense A/S) equipped with microsensors (O2 (OX-100), temperature (TP-100), and pH (PH-100); Unisense A/S) was used to obtain SWI microprofiles in-situ. The MP4 OX-100 has the same specifications as described for the laboratory microsensor. The thermo-coupled TP-100 has a spatial resolution of w200 mm, resolution of 0.1 mV per C, and 90% response time of <3 s. The pH-100 is a miniaturized conventional pH electrode with a spatial resolution of w200 mm, detection limit of 0.1 pH unit, and 90% response time of <20 s. The pH microsensor was used solely to obtain single-point measurements and the longer response time was not an issue for profiling. In-situ profiles were collected in duplicate at each sample site and ten measurements were taken at each depth (excluding multi-day campaigns in 2008 where profiles were measured continuously and three measurements were obtained per depth due to data storage limitations; Bryant et al., 2011a). The profile sequence was as follows: 10-mm resolution from 10 cm to 1 cm above the SWI, 1-mm resolution from 1 cm to 0.5 cm above the SWI, 0.1-mm resolution from 0.5 cm above the SWI to 0.5 cm below the SWI. Following a pause at each depth for equilibration, data were collected at a rate of 1 Hz. A full profile was obtained every w50e70 min. A video camera was used periodically to ensure that the MP4 remained stable during extended in-situ campaigns. During sediment-core profiling, the SWI location was easily identified visually. For microprofiles obtained in-situ, the SWI location was determined via both visual interpretations of O2 profile data (based on identifying linear regions characteristic of the diffusive boundary layer (DBL) immediately above the sediment and kinks in the profile due to porosity differences between the sediment and the water column) and using standard deviations of O2 temporal fluctuations obtained at each sampling-depth increment (Bryant et al., 2010a). The depth where O2 dropped to <3 mmol L1 was designated as zmax.
2.1.2.
Voltammetric O2 and Mn2þ profiles at the SWI
During two sampling events in July 2007 and July 2008, sediment cores were collected along a longitudinal transect (Sites
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CCR-1 to CCR-6, Fig. 1) to characterize spatial variation in the vertical distribution of O2 and Mn2þ across the SWI. Dissolved Mn2þ and O2 concentration-depth profiles were simultaneously measured using a solid-state mercury-gold-amalgam voltammetric electrode positioned above the SWI of the cores. The voltammetric electrodes, fabricated according to previous work (Brendel and Luther, 1995; Luther et al., 1999), consisted of 100-mm-diameter gold (Au) wire encased in an epoxy-filled borosilicate glass tube. The glass tube was heated at the end and stretched to a diameter of w1 mm at the tip. After smoothing the tip of the electrode with diamond-paste polish, the gold wire surface was plated with a mercury (Hg) amalgam. The Hg-Au voltammetric electrode was used as the working electrode in conjunction with a silver/silver-chloride reference electrode and a platinum-wire counter electrode for a three-electrode voltammetric system controlled by a DLK-60 potentiostat (Analytical Information Systems, Inc.). Sediment cores for voltammetric-electrode profiling were obtained and profiled as described in Sec. 2.1.1. Additional details on electrode voltammetry and calibration are included in Sec. S.1 (Supplementary Information).
While chemical and biological processes may influence JMn, solute flux models of the SWI are typically based on diffusive transport (Jørgensen and Boudreau, 2001; Lavery et al., 2001). Diffusive fluxes were estimated using peeper (JMn), voltammetric electrode (JO2 , JMn2þ ), and O2 microsensor (JO2 ) profile data and were analyzed using Fick’s law (Rasmussen and Jørgensen, 1992):
2.1.3.
Ji ¼ 4Dsi
In-situ soluble Mn profiles at the SWI
Peepers, constructed following Hesslein (1976), Urban et al. (1997), and Lewandowski et al. (2002), were used to obtain insitu data tracking temporal and spatial variation in the vertical distribution of soluble Mn across the SWI and corresponding JMn. Each peeper had a single column of 40 sampling chambers with a vertical profile resolution of 1 cm. The chambers (each 20-mL in volume) were covered with Millipore 0.45-mm HV Durapore membrane. Peepers were deployed with aluminum frames that positioned the peeper vertically into the sediment so that half of the peeper chambers were exposed to the water overlying the sediment and half of the chambers were exposed to sediment porewater (Fig. S1; Supplementary Information). Additional details on peeper construction and deployment are included in Sec. S.2. Peepers were deployed in duplicate for 2e4 weeks at a time to allow the peepers to come to equilibrium with in-situ water at locations CCR-1 (2006e2007), CCR-2 (2007e2008), CCR6 (2007e2008), and CCR-7 (2008). Immediately upon retrieval, water samples were obtained via sterilized pipettes from each peeper chamber, transferred to acidified plastic tubes, and then analyzed for metals via inductively-coupled plasma (ICP) spectroscopy (Clesceri et al., 1998).
2.1.4.
Water sample measurements
Because peeper data could only be obtained on a bi-weekly basis, “near-sediment” water samples were used to increase the frequency of metals data collection. Near-sediment water samples were obtained using a syringe with attached tubing to withdraw water from w5 cm above the SWI in sediment cores. Additionally, water samples were collected from the hypolimnion at 3-m-increments from the surface using a Kemmerer bottle to characterize the water column. Water samples for total metal analysis were transferred directly to pre-acidified plastic bottles. Samples for soluble metal analysis were filtered through 0.45-mm Millipore filter paper before being transferred and acidified. Samples were then analyzed via ICP (Clesceri et al., 1998). For this study, the zone from w1 m above the sediment down to the upper sediment layer is designated
as the “benthic region.” Near-sediment water samples and bulk-hypolimnion samples obtained at w1 m above the sediment were both used to characterize this region.
2.1.5.
Bulk sediment measurements
The upper w2 cm of sediment from undisturbed sediment cores was transferred directly into sterile glass containers. Samples were analyzed following standard methods for total solids (SM2540B; Greenberg et al., 1992), total Fe and Mn (SW6010B and SW3050B; EPA, 1996), and total organic carbon (Lloyd Kahn; EPA, 1988).
2.2.
Flux analyses
vC Cbulk CSWI ¼ Di dDBL vz sed water
h
mmol m2 d
1
i
(1)
where Ji is diffusive flux for species i (soluble Mn, Mn2þ, or O2) out of the sediment, 4 is sediment porosity (m3 voids m3 total volume), Dsi and Di are the species-dependent diffusion coefficients in sediment and water, respectively (m2 s1), and dDBL is DBL thickness (m; Jørgensen and Revsbech, 1985). C indicates species concentration, vC/vz is the linear concentration gradient immediately below the SWI (mmol m4), Cbulk is the concentration in the bulk water (mmol L1), and CSWI is the concentration at the SWI (mmol L1). Depth z and flux Ji are defined negative into the sediment with the SWI at z ¼ 0. Diffusive transport in the sediment is given by the second term in Eq. (1) and in the water by the third term. Water-side profile data are frequently more difficult to evaluate due to rapid turbulence-induced variation in dDBL (Bryant et al., 2010a). Furthermore, dDBL, which is typically mm scale, could not be accurately measured using cm-scale peeper data. Thus, Ji was estimated by applying the second term to sediment-side data. For voltammetric electrode and microsensor data, vC/vz was calculated from the slope of a linear regression of concentration data spanning from the interface (z ¼ 0) to depths of z ¼ 1 to 3 mm. For peeper data (of lower vertical resolution), vC/vz was based on the linear regression slope of concentration data spanning from z ¼ 0 to depths of z ¼ 20 to 40 mm. Quasi-steady-state conditions were assumed for peeper data based on the extended deployment period. The temporal change in O2 concentration (vC/vt) was evaluated for in-situ O2 profile series as described by Bryant et al. (2010a) and was found to be on average <5% of JO2 , indicating that O2 profiles were also at quasi-steady state. Sediment cores from the primary sampling locations (Fig. 1) were evaluated for 4 as described by Bryant et al. (2010a) and 4 values of 0.95e0.97 were obtained. To correct for sediment tortuosity as a function of 4 (Glud, 2008; Bryant et al., 2010b), Dsi was defined as Dsi ¼ 4Di. Values for Di were based on DMn2þ ¼ 6.88 1010 m2 s1 at 25 C and DO2 ¼ 1.97 109 m2 s1 at 20 C and were corrected for temperature using the
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StokeseEinstein relationship (Li and Gregory, 1974; Arega and Lee, 2005). For peeper-based JMn calculations, it was assumed that soluble Mn data were completely in the dissolved, reduced form of Mn2þ. This assumption may have resulted in slightly overestimated JMn values due to transport effects of insoluble, colloidal particles small enough to pass through 0.45-mm filters; however, its validity is supported by Mn2þ profile data obtained via voltammetric electrode which confirm that a majority of near-surface soluble Mn was in the reduced Mn2þ form (Fig. 2 and Fig. S2).
3.
Results and discussion
3.1.
Comparison of profile methods
Peepers and multi-species voltammetric electrodes were used to obtain Mn profile data while microsensors and voltammetric electrodes were used to obtain O2 profile data across
Fig. 2 e Comparison of voltammetric electrode and in-situ porewater analyzer (“peeper”) profile data from sites CCR-1 and CCR-6 (July 2007; a) and CCR-2 and CCR-6 (July 2008; b). Duplicate peeper sets designated by A and B in (a); duplicates not obtained in July 2008 (b). Manganese (Mn) data from peepers are in the soluble Mn (colloidal and dissolved Mn2D) form and data from voltammetric electrodes are in the dissolved Mn2D form. Sediment-water interface (SWI) location indicated by dashed black line.
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the SWI. Both sets of methods have advantages and limitations. Peepers were deployed in-situ but measured only soluble metals at cm-scale vertical resolution; mm-scale measurements of O2 and dissolved Mn2þ were obtained via sediment-core profiling using the voltammetric electrodes and microsensors though these data may have been influenced by redox changes occurring during transport and measurement of the cores under ex-situ conditions. Nevertheless, analogous trends were obtained from both sets of methods for changes in concentration with depth of Mn and O2 (Fig. 2 and Fig. S2, respectively). While peeper and voltammetric-electrode data were obtained at different vertical resolutions (10-mm vs. 1-mm increments, respectively) and by different sampling methods (in-situ vs. ex-situ, respectively), changes in concentration with depth were relatively consistent between these methods (Fig. 2). For some profile sets (e.g., Fig. 2a), deviation between soluble Mn (peeper) profiles and dissolved Mn2þ (electrode) profiles was observed as Mn2þ decreased at deeper depths which may be attributed to the upward diffusion of Mn2þ, the presence of dissolved Mn3þ, increased levels of colloidal Mn3þ and/or Mn4þ, and/or Mn adsorption to Fe-oxides and other particulate matter. Similar microsensor and voltammetric-electrode measurements of depth-specific O2 concentrations were obtained (Fig. S2). Depth-specific Mn concentrations obtained via voltammetric electrode and peepers were comparable though they were not entirely consistent (Fig. 2). Nevertheless, while soluble (<0.45 mm) Mn measurements from peepers could include colloidal Mn, as mentioned in Sec. 2.2, Mn2þ concentration quantified by voltammetric-electrode profiles of sediment cores as compared to peeper data indicated that most Mn measured by peepers was Mn2þ. While voltammetric electrode and peeper measurements yielded similar concentration-depth Mn profiles, diffusional fluxes calculated from these data were not comparable (Fig. S3). Absolute values of electrode-based flux measurements, jJMn2þ j, were an order of magnitude higher than peeper-based jJMnj due to the difference in the thickness of the gradient layer used to quantify vC/vz (mm- vs. cm-scale). As shown by voltammetric-electrode data (Fig. 2), the steepest concentration gradient occurs within the top few mm of sediment. Peeper-based jJMnj values were quantified over the top few cm of sediment (Fig. 2) resulting in ‘smoothed’ profiles and smaller vC/vz values that would underestimate the actual diffusional flux. These results highlight the importance of obtaining profiles at the finest vertical resolution possible to fully characterize fluxes at the SWI. For this study, the voltammetric electrodes were beneficial for characterizing the vertical distribution of Mn2þ at a fine depth resolution and on a reservoir-wide scale; however, peepers were advantageous in that they allowed in-situ profiles to be obtained almost continuously for three years. Peeper-based JMn values over time were used as a relative comparison to characterize the effect of temporal changes in soluble Mn as a function of HOx operation while acknowledging that these JMn estimates may not fully characterize true diffusional Mn2þ flux due to colloidal Mn effects and the increased vertical resolution of peeper measurements relative to changes in the vertical distribution of Mn near the SWI.
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3.2.
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Influence of HOx on vertical Mn distribution at SWI
Contour plots were created based on a Kriging interpolation scheme using peeper data from the near-field region and were evaluated with respect to HOx flow during 2006e2008 (Fig. 3). Soluble Mn levels at the SWI varied significantly in response to changes in HOx operation. When the HOx was turned off for one month in 2006, soluble Mn levels both in the water overlying the sediment and in sediment porewater increased substantially at CCR-1 (Fig. 3a). Similar results were obtained at CCR-2 during 2007 in response to decreased HOx flow (Fig. 3b). Conversely, near-sediment Mn concentrations remained low while sediment porewater concentrations increased at CCR-2 during continuous HOx operation in 2008 (Fig. 3c). Analogous results were also obtained mid-reservoir at CCR-6 (Fig. S4). Soluble Mn levels in the sediment steadily increased over each season of HOx operation. As Mn
Fig. 3 e Contour plots based on peeper data obtained near the HOx at near-field sites CCR-1 in 2006 (a) and CCR-2 in 2007 (b) and 2008 (c). Corresponding variations in HOx flow are delineated by the solid black line. The dashed black line indicates the SWI location.
precipitated out of the oxygenated water column and became incorporated into the upper sediment, Mn-oxide particles were likely transformed back to soluble Mn as they reached the more reducing sediment environment (Jørgensen and Boudreau, 2001). While soluble Mn porewater concentrations were slightly higher in CCR relative to other freshwater studies, they are on the same order of magnitude (Urban et al., 1997; Schaller and Wehrli, 1997) and regional geology is known to have high levels of Mn (Stose et al., 1919; Mussman and Reid, 1986). The abiotic oxidation rate of Mn is dependent on pH and is relatively slow, with an oxidation-rate constant for Mn of 0.0006 mmol1 m3 h1 as compared to 0.004 mmol1 m3 h1 for Fe (Wang and Van Cappellen, 1996; Morgan, 2005; Brand et al., 2009), particularly at typical pH values (pH 7e9) of natural waters (Belzile et al., 1996; Crittenden et al., 2005). Microsensor pH data confirmed that pH remained between w8 and 9 in the sediment in CCR and a correlation between porewater Mn concentration and pH was not found. Mn oxidation and/or precipitation may have therefore been controlled by biological oxidation processes and/or adsorption onto particulate matter (Nealson et al., 1988; Santschi et al., 1990). As the upper sediment became more oxic, biologically mediated reduction of Mn may also have been hindered. The influence of sediment microbes on CCR Mn dynamics is being explored in a companion study (Bryant et al., 2011b). Core-water data obtained at 5 cm above the SWI provide additional verification of HOx-induced variation in nearsediment Mn and Fe (Fig. 4). Typically, a consistent proportion of soluble Mn relative to total Mn was maintained with Mn
Fig. 4 e Total and soluble Mn and iron (Fe) data from nearsediment water samples taken at 5 cm above the SWI. Samples were obtained near the HOx (site CCR-2; a) and mid-reservoir at sites CCR-6 (b) and CCR-5 (c) during 2006 HOx operations.
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almost completely in soluble form while a greater percentage of Fe remained in the insoluble, particulate form. This observation is consistent with previous work highlighting the ease with which Mn reduces in low O2 conditions compared to Fe (Balzer, 1982; Davison, 1985). Mn and Fe concentrations remained relatively low at all sampling locations during periods when the HOx was in operation. However, considerable increases in total and soluble metal concentrations at 5 cm above the sediment were observed when HOx flow was reduced or halted (Fig. 4a, b). While HOx-induced variation in Mn and Fe concentrations was observed throughout the reservoir, as indicated by data obtained directly alongside the HOx at CCR-2 (Fig. 4a) and mid-reservoir at CCR-6 (Fig. 4b), not all sampling locations were equally affected. As shown by Fig. 4c, near-sediment metal concentrations at CCR-5 remained fairly constant. While CCR-6 is located farther away from the HOx than CCR-5, CCR-6 was more directly influenced by HOx plume detrainment (McGinnis et al., 2004; Singleton et al., 2007) due to its higher elevation and typically had an enhanced sediment oxic zone during HOx operation (Fig. 1; Bryant et al., 2011a). These results illustrate the influence of plume dynamics which are affected by reservoir bathmetry, diffuser location, applied gas-flow rate, and watercolumn thermal structure (Gantzer et al., 2009a). Analogous results based on in-situ O2 data also emphasize the influence of HOx operations on SWI conditions (Fig. 5). Insitu MP4 profiles and near-sediment water-sample measurements at CCR-6 reveal a strong correlation between O2 at the SWI, zmax, near-sediment Mn and Fe, and HOx flow. When the HOx was turned off for w48 h in August 2008, SWI O2 levels dropped to zero as the sediment became anoxic for over a week until the vertical O2 distribution was finally re-
established (Bryant et al., 2011a). The delayed sediment response to turning off the HOx is likely due to the time required for O2 and mixing conditions to return to steadystate up-reservoir at CCR-6. A substantial increase in total and soluble Mn and Fe in the overlying water was observed during this anoxic period (Fig. 5) which clearly reveals how HOx operations can control sediment O2 availability and metal cycling near the SWI. Results show that HOx operations strongly influenced sediment O2 conditions and Mn retention throughout CCR (Figs. 3e5) although the response was not the same at all locations (Fig. 4). To determine if localized sediment composition and corresponding porewater concentrations were a factor in the observed response to HOx operations, porewater profiles obtained along the longitudinal transect of the reservoir from all seven CCR sampling sites during normal HOx operating conditions were assessed (Fig. 1). Based on July 2007 voltammetric-electrode profile data, zmax was 1e3 mm and porewater concentrations were w100 mmol L1 O2 and w100e300 mmol L1 Mn2þ in the upper mm of sediment at most sites (CCR-1 thru CCR-5; Fig. S2). At CCR-6, sediment conditions were more oxic with an increased zmax and no Mn2þ was measured in the upper sediment which again may be attributed to the influence of HOx plume detrainment in this region (Bryant et al., 2011a). In July 2008, however, O2 and Mn2þ profiles were more uniform and average porewater Mn2þ concentrations in the upper sediment decreased considerably. Nevertheless, while sediment oxic conditions were relatively similar in 2007 and 2008, no discernable correlation was found between HOx proximity and the vertical distribution of Mn2þ and corresponding JMn2þ at each sampling site (Figs. S2, S3). Porewater Mn2þ concentrations and JMn2þ values were consistently higher in 2007 (e.g., JMn2þ was 1.7e15.6 mmol mm2 day1 in 2007 and 1.4e5.4 mmol mm2 day1 in 2008). Operating the HOx at a higher flow rate in July 2007 than in July 2008 (Fig. 3) may have resulted in increased porewater Mn2þ due to enhanced precipitation and incorporation of Mn oxides (discussed in Sec. 3.4). At CCR-7, the site farthest upstream, core-water and peeper data show that near-sediment and porewater Mn concentrations were significantly lower than those measured throughout the rest of CCR (e.g., 130 mmol L1 versus 360 mmol L1 average soluble Mn in the upper 5 cm; Fig. S4). This may be attributed to decreased levels of total Mn in the bulk sediment (data not shown) and negligible sediment focusing in this region relative to deeper CCR regions.
3.3.
Fig. 5 e Variations in near-sediment total and soluble Fe and Mn concentrations (near-sediment water-sample data), dissolved oxygen (O2) concentration at the SWI (MP4 microprofile data), and depth of the sediment oxic zone (zmax; MP4 microprofile data) at CCR-6 in response to turning the HOx off for w48 h during the August 2008 campaign.
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HOx-induced variation in JMn
Diffusive flux at the SWI is driven by the concentration gradient (vC/vz) within the DBL, a mm-scale laminar layer immediately above the sediment (Jørgensen and Revsbech, 1985), which is in turn regulated by turbulence in the overlying water and subsequent dDBL. Natural variation in turbulence has been shown to have a significant effect on dDBL and sediment-water fluxes (Lorke et al., 2003; Brand et al., 2009; Bryant et al., 2010a). HOx-induced increases in turbulence and changes in near-sediment and porewater concentrations of O2 and Mn could decrease dDBL and increase vC/vz,
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respectively, thereby significantly affecting JO2 and JMn (per Eq. (1) and Bryant et al., 2011a). Concentrations of O2 and soluble Mn at the SWI typically increased in the near field as a result of HOx operation (Fig. 6), supporting mid-reservoir O2 data presented in Fig. 5. Soluble Mn concentrations at 1 m above the sediment generally paralleled Mn concentrations at the SWI, with increases in Mn observed as each oxygenation season progressed in 2006e2008. Soluble Mn levels in the bulk hypolimnion at 4 m above the sediment, however, remained negligible excluding the period when the HOx was turned off for one month in 2006 (Figs. 3a and 6). Average concentrations of soluble Mn in the bulk hypolimnion increased by a factor of 10 (from w1 mmol L1 to w10 mmol L1) during this period (Gantzer et al., 2009b). These results indicate that HOx operation did facilitate the precipitation of oxidized Mn and/or sorption of Mn to particles that settled to the benthic region, thereby decreasing Mn levels in the overlying bulk water. However, these Mn-containing particles likely became reduced upon reaching the less-oxic benthic region, resulting in increased soluble Mn concentrations in sediment porewater and in regions immediately above the sediment (e.g., at 1 m). Regardless, Mn concentrations at the SWI and at 1 m above were typically lower during oxygenation, particularly during periods of steady HOx flow, than when HOx flow was reduced or halted and O2 concentrations at the SWI dropped (also supported by near-sediment water data; Figs. 3e6). Hypolimnetic Mn concentrations dropped dramatically each year following fall turnover as O2 levels were replenished, after which HOx flow was decreased and sediment data were no longer collected until the following spring (Zhang et al., 1999; Gantzer et al., 2009b). Enhanced JMn is observed during periods of increased HOx flow rate. While focus should be placed on the temporal variation in JMn with respect to HOx flow rather than quantified flux values, since JMn estimates may differ from actual JMn2þ (discussed in Secs. 2.2, 3.1), estimates of CCR JMn are comparable to
values obtained from other freshwater lake systems (Johnson et al., 1991; Belzile et al., 1996). JMn is shown to increase when the HOx was in operation and decrease when HOx flow was significantly reduced or halted for an extended period (e.g., June 2006 and September 2007). Following constant oxygenation, JMn peaked at 0.67 mmol m2 d1 in August 2008. When the HOx was turned off for one month in July 2006, JMn reached a minimum value of 0.34 mmol m2 d1 as the sediment became anoxic and near-sediment Mn levels increased due to sediment Mn release. Direct correlation between soluble Mn levels in the benthic region, JMn, and HOx flow rate was observed both near the HOx (Fig. 6) and mid-reservoir (Fig. S5). It has been shown that JO2 into the sediment was increased by HOx operations due to elevated near-sediment O2 levels and a subsequently increased O2 vC/vz through the DBL (Beutel, 2003; Bryant et al., 2011a). Likewise, water-column levels of Mn can be significantly decreased by oxygenation (Gantzer et al., 2009b); as a result, sediment concentrations should increase as the metals are oxidized and precipitate to the sediment. As evidenced by peeper and water-sample data (Figs. 3e6), continuous HOx operations did maintain decreased Mn levels in the overlying water while Mn porewater concentrations increased. A comparison of watercolumn data obtained during June 2006 when the HOx was turned off for one month and during June 2008 following continuous oxygenation supports that HOx-induced oxidation and precipitation of Mn did occur, as mid-hypolimnetic concentrations of soluble Mn decreased from 0.6 to 0.2 mmol L1 while particulate Mn decreased from 3.5 to 1.1 mmol L1 and particulate Fe, which can enhance Mn adsorption and subsequent precipitation, decreased from 2.3 to 0.7 mmol L1 in response to oxygenation (data not shown). Hence, Mn vC/vz at the SWI and corresponding JMn were enhanced (Fig. 6), albeit in the opposing direction of JO2 . HOxinduced decreases in dDBL (Bryant et al., 2011a) would have also facilitated increased JMn out of the sediment during oxygenation. Conversely, when the HOx was turned off, observed decreases in JMn may be attributed to decreased Mn vC/vz at the SWI as dDBL increased in the absence of HOxinduced mixing and the sediment became anoxic, resulting in substantial increases in soluble metal concentrations in the overlying water (Figs. 3e6). An initial increase in JMn very likely occurred immediately after HOx operations were halted and Mn was released as the sediment became anoxic (Figs. 3a and 5). However, the temporal scale of peeper measurements, which were obtained over a two-week deployment period, did not facilitate capturing such rapid changes in flux. The release of Mn2þ as the sediment became anoxic would have contributed to observed decreases in O2 at the SWI (e.g., June 2006 in Fig. 6) as Mn2þ and other reduced species were subsequently re-oxidized (Glud et al., 2007) and near-sediment O2 was not re-supplied by the HOx.
3.4. Fig. 6 e Correlation between O2 and soluble Mn concentrations at the SWI, soluble Mn at 1 m (benthic region) and 4 m (lower hypolimnion) above the sediment, and diffusive flux of soluble Mn (JMn) in the near field as a function of HOx flow rate.
Biogeochemical cycling effects
While JMn results showing enhanced JMn during oxygenation (Fig. 5) may appear to be in conflict with the overall goal of using HOx to reduce Mn levels in the water column, it should be emphasized that JMn is a diffusive flux and therefore does not take into account other processes (e.g., adsorption/
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desorption, Mn-oxide precipitation, microbial oxidation) influencing Mn transport at the SWI. Results shown in Fig. 6 support that while HOx operations increased JMn out of the sediment, reduced Mn is re-oxidized in the oxic benthic region and prevented from reaching the bulk hypolimnion, thereby sustaining an HOx-supported Mn redox cycle. Simple mass balance calculations were performed on CCR-1 data from the 2006 sampling season to evaluate the influence of benthic biogeochemical cycling on hypolimnion water quality. Maximum hypolimnetic Mn concentrations were predicted based on JMn data shown in Fig. 6, sediment trap data for total Mn in the lower hypolimnion over the HOx (P. Gantzer, unpubl.), and average volumes of both the bottom meter and full depth of the hypolimnion, as follows: Mn ¼
JMn Ssed þ Rsed Vhyp days
X t¼198
mmol L1
(2)
where Ssed is surface area of hypolimnion sediment (m2), Vhyp is volume of hypolimnion evaluated (lower 1-m or full 14-m depth; m), and Rsed is the Mn sedimentation rate (mmol L1). An average Rsed value of 4.4 mmol m3 d1 was estimated for the 2006 oxygenation season. For simplicity, it was assumed that there was no Mn initially present in the hypolimnion and that no Mn was removed via oxidation and/or precipitation. Thus, all Mn diffusing out of the sediment and/or precipitating from the bulk water was considered retained in the volume assessed (benthic (1 m) region or full hypolimnion). Initially, measured Mn levels at 1 m above the sediment exceeded maximum Mn levels predicted with Eq. (2) (Fig. 7). It should be noted that this period includes the time during which the HOx was turned off for one month. By mid-season, however, actual Mn concentrations at 1 m were far lower than predicted Mn concentrations. Average hypolimnetic Mn data, based on water samples obtained mid-hypolimnion at 7 m above the sediment, remained low and fell far below predicted maximum Mn at 7 m with the deviation indicating a net gain of Mn to the sediment. These rough calculations characterizing the influence of biogeochemical cycling indicate that while JMn may be enhanced by HOx-induced increases in the vC/vz driving force at the SWI, Mn was suppressed from the bulk hypolimnion due to re-oxidation, precipitation, and/or sediment adsorption. This unclosed mass balance based on JMn and sedimentation rates (Fig. 7) highlights the influence of processes that could not be quantified in this study and that remain poorly understood (Cerrato et al., 2010). Retention of Mn in the benthic region via biogeochemical cycling (Figs. 6 and 7) is further supported by bulk-sediment data indicating that Mn oxidation, precipitation, and subsequent incorporation into the sediment increased near the HOx as a result of oxygenation (Fig. 8). Bulk-sediment Mn levels in the near-field region increased by an order of magnitude during the first three years of oxygenation. Following the start of HOx operations in July 2005, both total Mn and Fe levels increased substantially in the sediment at CCR-1. However, after the first year of HOx operation, bulk-sediment measurements remained relatively constant. CCR is largely isolated from allochthonous input and annual nutrient loading likely remains fairly consistent. Hence, these results indicate that after background levels of soluble Mn and Fe
Fig. 7 e The effect of biogeochemical cycling was evaluated at 1 m (benthic region) and 7 m (mid-hypolimnion) using a mass balance based on JMn, sedimentation rate (Rsed), and average hypolimnetic volume (based on 1-m and 14-m depths).
were oxidized in the first year of HOx operation, new steadystate conditions based on oxygenation were established. Combined results from bulk-sediment data (Fig. 8), peeper data (Fig. 3), and JMn estimates (Fig. 6) clearly show how
Fig. 8 e Bulk sediment concentrations of total Mn and Fe in the near-field region characterizing the period before CCR HOx operations began in July 2005 through 2008 (no data collected in 2007).
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continuous oxygenation facilitated Mn removal from the hypolimnion via Mn oxidation and precipitation in the oxic water column and subsequent incorporation into the sediment (Jørgensen and Boudreau, 2001; Gantzer et al., 2009b). Substantially lower levels of Fe and Mn were measured in the bulk sediment at the mid-reservoir CCR-6 and upstream CCR-7 sites than at near-field sites CCR-1 and CCR-2 (data not shown). Enhanced sediment Mn retention in the near field may be a result of oxidation and adsorption processes due to increased levels of oxide particles in the region closest to the HOx and/or sediment focusing effects in this deeper CCR region (Fig. 1; Balzer, 1982; Santschi et al., 1990). Sediment traps deployed throughout CCR in the lower hypolimnion showed elevated sedimentation rates in the near field during summer oxygenation in 2006 and 2007 (Gantzer et al., 2009b). Accelerated oxidation and precipitation of Mn and geochemical focusing in response to oxygenation have also been observed by Schaller and Wehrli (1997) and Zaw and Chiswell (1999). Based on peeper data from 2006 to 2008 (Fig. 3), average annual Mn porewater concentrations remained fairly constant in the upper sediment (Fig. 6). Paired with water-column data in Fig. 6 and bulk-sediment data in Fig. 8, these results indicate that HOx can decrease metal concentrations in the bulk hypolimnion by facilitating increased precipitation of metal oxides to the sediment and biogeochemical cycling within the benthic region. Although previous work has suggested that historical sediment O2 demand may be diminished by oxygenation (Beutel, 2003), the fact that annual porewater concentrations of soluble Mn did not decrease indicates that the amount of available electron acceptors in the sediment and the corresponding intrinsic sediment O2 demand (Bryant et al., 2010a) were not reduced. Deposition of Mn- and Fe-oxides to the sediment and subsequent reduction of these metals in the anoxic sediment contributes to this O2 demand (Yagi, 1996; Matzinger et al., 2010). Nevertheless, while a substantial source of reduced metals may remain in the sediment, using continuous oxygenation to maintain elevated near-sediment O2 levels and an oxic sediment zone (Fig. 5; Bryant et al., 2011a) can prevent the release of soluble species into the bulk hypolimnion, as shown by Jørgensen and Boudreau (2001), Beutel et al. (2008), and Gantzer et al. (2009b).
4.
Conclusions
Results from this study have established that continuous oxygenation promotes decreased Mn concentrations in nearsediment water (Figs. 3e6) and elevated Mn levels in sediment porewater (Figs. 2 and 3) as Mn is incorporated into the sediment (Fig. 8). While JMn out of the sediment (Fig. 6) was enhanced by an increased vC/vz driving force at the SWI, hypolimnetic water data (Figs. 6 and 7) and simple flux calculations (Fig. 7) show that near-sediment biogeochemical cycling maintained by HOx-increased O2 levels prevented Mn from reaching the bulk hypolimnion. The overall effect of the HOx on source water quality is revealed in data showing that annual average hypolimnetic Mn levels have decreased by >97% since the start of HOx operations in 2005 (Gantzer et al., 2009b).
This study elucidates the influence of HOx operation on sediment-water Mn cycling and supports the viability of using HOx to improve drinking water quality by decreasing sourcewater Mn levels prior to the treatment plant. Significant conclusions include: 1. Comparable measurements of the vertical distribution of soluble Mn and Mn2þ were obtained using independent tools (peepers and voltammetric electrodes) which suggests that a majority of soluble Mn in porewater was in the dissolved Mn2þ form rather than the colloidal form. The influence of the scale at which these measurements were obtained, cm-scale for peepers vs. mm-scale for the electrodes, on estimated flux is emphasized. 2. The vertical distribution of Mn at the SWI was strongly affected by HOx operations, with soluble Mn levels in the benthic region increasing substantially when the HOx was turned off for as little as w48 h and the sediment became anoxic. Leaving the HOx off for longer periods of time, e.g., several weeks, resulted in significantly impaired sourcewater quality due to elevated levels of Mn in the bulk hypolimnion. 3. Perhaps seemingly contradictory to the overall goal of decreasing hypolimnetic Mn levels, the driving force for diffusive flux JMn out of the sediment was shown to increase as Mn concentrations increased in the sediment and decreased in the overlying water in response to continual oxygenation. 4. However, while JMn was enhanced by oxygenation, an oxic SWI and near-sediment region facilitated biogeochemical cycling that prevented Mn release to the bulk hypolimnion. More disruptive aeration techniques (e.g., artificial destratification) may not maintain an oxic zone that facilitates such near-sediment biogeochemical cycling due to sediment resuspension. By evaluating JMn and corresponding Mn cycling under a range of O2 conditions using multiple measurement techniques, the complex network of abiotic and biotic controls on Mn redox kinetics is emphasized. 5. An oxic sediment zone and retention of soluble Mn within the benthic region were achieved throughout the reservoir by continuous oxygenation, thereby decreasing Mn levels in the source water. O2 required for the oxidation of elevated levels of reduced Mn (as well as other species such as Fe, nitrite, and methane) in the benthic region should be taken into account when designing HOx. This work and that of Bryant et al. (2011a,b), which take a sediment perspective, combined with the companion papers of Gantzer et al. (2009a,b), which take a water-column perspective, collectively show how oxygenation systems can be used to optimize source-water quality by using the watersupply reservoir as the initial stage in the treatment process. Results may be used to improve operation of HOx by providing a mechanistic understanding of water-quality effects in relation to system operation. In the face of problems like dwindling water supplies, climate change (which may enhance thermal stratification and increase deep-water anoxia), and the need to rely on unconventional water supplies, the importance of alternative water-treatment methods like HOx may become greater.
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Acknowledgments The authors thank Elizabeth Rumsey, Kevin Elam, Lindsay Olinde, and the staff at Western Virginia Water Authority who offered invaluable support in the field and with laboratory samples. Alfred Wu¨est, Daniel McGinnis, Peter Berg, and Peter Vikesland provided valuable discussion. Feedback from three anonymous reviewers greatly improved the manuscript. This publication is based upon work supported by the National Science Foundation (NSF) under grants EAR-PF 0848123 and CBET 1033514. Additional financial support was provided by the NSF IGERT Program and the Western Virginia Water Authority. The research described in this paper was also partially funded by the United States Environmental Protection Agency (EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program. EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.030.
references
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W a t e r R e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 9 3 e6 4 0 2
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Ecotoxicological assessment of a polyelectrolyte flocculant Andrew J. Harford*, Alicia C. Hogan, David R. Jones, Rick A. van Dam Environmental Research Institute of the Supervising Scientist (ERISS), Department of Sustainability, Environment, Water, Population and Communities, GPO Box 461, Darwin, Northern Territory 0801, Australia
article info
abstract
Article history:
Flocculant blocks are commonly used as a component of (passive) water treatment
Received 8 November 2010
systems to reduce suspended sediment loads in the water column. This study investigated
Received in revised form
the potential for aquatic biological impacts of a flocculant block formulation that contained
23 May 2011
an anionic polyacrylamide (PAM) active ingredient and a polyethylene glycol (PEG) based
Accepted 14 September 2011
carrier. The toxicity of the whole flocculant block was assessed and the individual
Available online 29 September 2011
components of the block were also tested separately. Five Northern Australian tropical freshwater species (i.e. Chlorella sp. Lemna aequinoctialis, Hydra viridissima, Moinodaphnia
Keywords:
macleayi and Mogurnda mogurnda) were exposed to a range of concentrations of the whole
Flocculants
flocculant block, and of the individual PAM and PEG components. The concentration of
Polyelectrolytes
Total Organic Carbon (TOC) in solution was used to provide a measure of the total amount
Toxicity
of PAM and PEG present. An extremely wide range of toxic responses were found, with the
Polyacrylamide
flocculant blocks being essentially non-toxic to the duckweed, fish and algae
Polyethylene glycol
(IC50 > 1880 mg l1 C TOC,
Protective concentrations
1
(IC50 ¼ 610e2180 mg l
IC10 > 460 mg l1 C TOC),
slightly
toxic
to
the
hydra
C TOC, IC10 ¼ 80e60 mg l1 C TOC) and significantly more toxic to
the cladoceran (IC50 ¼ 10 mg l1 C TOC, IC10 ¼ 4 mg l1 C TOC). More detailed investigation of the two components indicated that the PAM was the primary “toxicant” in the flocculant blocks. Derived Protective Concentrations (PCs) for the flocculant blocks, expressed as equivalent TOC concentrations, were found to be lower than typically measured natural environmental concentrations of TOC. It will thus be possible to use TOC as measure of the concentration of PAM only in those situations where lower levels of ecosystem protection (i.e. higher PCs) are applicable. Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved.
1.
Introduction
For many decades, high molecular weight water-soluble polyelectrolyte polymers have been used in water purification processes to coagulate and flocculate particles, aiding their removal from the water column (Bolto and Gregory, 2007). These polyelectrolytes have been classed by their ionic nature (i.e. cationic, anionic or non-ionic). Anionic polymers acts as true flocculants and bind suspended particles together to form larger particles that settle out of solution
more rapidly, while cationic polymers acts as coagulants through neutralising the surface charges of particles (Liber et al., 2005). Polyelectrolyte flocculants have been primarily used for the production of potable water and treatment of sewage treatment plant sludges, as well as the reduction of suspended sediment loads of mining effluents. The control of suspended sediments is important because elevated suspended sediments and turbidity can result in significant ecosystem degradation (Bilotta and Brazier, 2008). As such, these polymers have been viewed as a pollution control
* Corresponding author. Tel.: þ61 8 8920 1173; fax: þ61 8 8920 1195. E-mail address:
[email protected] (A.J. Harford). 0043-1354/$ e see front matter Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.032
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measure and have rarely been considered to be potential toxicants. Based on its intrinsic chemical properties the active ingredient in flocculant block products should readily bind to suspended solids in the waters to be treated, causing aggregation of fine suspended particles and settling to the base of settling ponds, where they would not be released to the environment. However, overdosing of flocculants has led in the past to the release of significant quantities of polyelectrolytes into the environment (Liber et al., 2005). Despite their very common and widespread use only a limited number of studies have investigated the toxicity of polyelectrolyte flocculants. Existing aquatic toxicity information indicates that the anionic class of polyelectrolyte flocculants has a relatively low toxicity to aquatic organisms, while the cationic class is at least 100 times more toxic (Hamilton et al., 1994). Consequently, cationic polymers have been studied to a greater degree and are reported to be toxic at free dissolved concentrations less than 1 mg l1 (Biesinger et al., 1976; Biesinger and Stokes, 1986; Cary et al., 1987; Goodrich et al., 1991; Liber et al., 2005). It has been shown that the cationic polyelectrolytes affect cell membrane integrity and that the effect is generally dependent on the charge density and hydrophobicity of the polymer (Narita et al., 2001). However, there are some cases where species-specific mechanisms of action have been elucidated. In particular, the toxicity of cationic polymers to phytoplankton appears to depend more on the molecular weight of the polymers rather than charge density (de Rosemond and Liber, 2004). The primary mechanism of action of the anionic polyelectrolytes is binding to membranes (membranotrophic), which results in the inhibition of the cross-membrane transport of nutrients and essential elements (Bolto and Gregory, 2007). Consequently, the mechanism of action of anionic polymers is dependant on the chain length, with longer chains being more toxic (Bolto and Gregory, 2007). Fish appear to be more tolerant of anionic polymer exposure with 100% survival commonly occurring at the highest concentrations tested, and with reported LC50s of >20 mg l1 to >1000 mg l1 (de Rosemond and Liber, 2004; Beim and Beim, 1994). Cladocerans are the most sensitive species that have been tested, and the reported acute toxicities (LC50) of anionic polymers to Ceriodaphnia dubia (48 h) and Daphnia magna (96 h) were 218 mg l1 and 14e17 mg l1, respectively (de Rosemond and Liber, 2004, Beim and Beim, 1994, Biesinger et al., 1976). Two recent studies have reported Lowest Observed Effect Concentrations (LOECs) for sublethal/chronic endpoints of 1.0 and 1.6 mg l1 (nominal PAM concentrations) for 96-h D. magna growth (Acharya et al., 2010) and 6e8ed C. dubia reproduction (Weston et al., 2009), respectively. Beim and Beim (1994) also reported LC50s of 2100 mg l1, >100 mg l1 and >1000 mg l1 PAM for an amphipod, flatworm and adult minnow, respectively. The flocculant formulation investigated in the present study consists of an anionic polyacrylamide (PAM, w60%) active ingredient and a polyethylene glycol (PEG, w40%) carrier. Ecotoxicological data provided in the Material Safety Data Sheets (MSDS) reported acute EC/LC50 values for PAM of 212 mg l1 for D. magna (96-h immobilisation); 357 mg l1 for Brachydanio rerio (96-h survival); 892 mg l1 for Pseudomonas putida (24-h respiration); and >1000 mg l1 for Chlorella vulgaris
(72-h growth rate). These toxicity data were produced by the Polyelectrolyte Producers Group to comply with several legislatory requirements including self-classification under European Union directive 67/548/EEC (Vehaar, 2002). They have since been re-quoted in a number of MSDSs and product notifications (e.g. NICNAS, 2005). The carrier agent, PEG, is known for its very low toxicity. A limited number of studies have reported no adverse responses in fish and phytoplankton following exposures up to 5 g l1 (Wildish, 1974; Bridie et al., 1979; Chan et al., 1981). Indeed, many studies have used PEG as an inert carrier agent or negative control (Wildish, 1974; Harford et al., 2007). In the case of the flocculant block formulation its function is to increase the solubility of the active agent (PAM). Studies concerning the entry of PAMs and PEGs into natural environments are very limited. Lentz et al. have reported concentrations of PAM ranging from 1 to 10 mg l1 in tailwater following agricultural application (Chen et al., 2009; Lentz et al., 1996, 2002; Weston et al., 2009). Whilst noting that PAM is unlikely (owing to its high affinity for surfaces) to enter natural waterways following this type of application they did not confirm this assertion by measuring the PAM concentrations downstream of the tail-ditches (Lentz et al., 2002). de Rosemond and Liber (2004) and Liber et al. (2005), identified synthetic polymers as the primary toxicant in a processed kimberlite effluent (PKE) following a Toxicity Identification Evaluation (TIE) and also estimated that very large volumes of polyelectrolytes were being discharged into the environment (Liber et al., 2005). They stated that “It was difficult to estimate the final concentrations of these polymers in PKE because.analytical procedures to detect polymers were not available” (Liber et al., 2005). Despite a number of published methods for identifying PAM, many appear to suffer from technical challenges when measuring low concentrations of these chemicals in natural waters (Bolto and Gregory, 2007). The aim of the present study was to assess the toxicity of a commercial flocculant block formulation and its two ingredients, PAM and PEG, to freshwater species in order to (i) determine which ingredient was the primary cause of toxicity, and (ii) derive Protective Concentrations (PCs) for the formulation and its constituents.
2.
Materials and methods
2.1.
General laboratory procedures
All equipment which test organisms or media came in contact with, or were exposed to, was made of chemically inert materials (e.g. Teflon, glass or polyethylene). All plastics and glassware were washed by soaking in 5% (v/v) HNO3 for 24 h before being washed with a non-phosphate detergent (Gallay Clean A powder, Gallay Scientific, Burwood, Australia) in a laboratory dishwasher operated with reverse osmosis/ deionised water (Elix, Millipore, Molshiem, France). All reagents used were analytical grade and stock solutions were made up in high purity water (18 MU, Milli-Q Element, Millipore, Molshiem, France).
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2.2.
Test compounds
MCW had a pH of 6.1e6.8 units, a conductivity of 10e22 mS cm1 and dissolved oxygen of >90%.
The flocculant formulation (Magnasol AN2, Ciba Specialty Chemicals, Wyong, NSW, Australia), is supplied in block form (w200 mm 145 mm 90 mm; herein referred to as the flocculant block). As noted above, it comprises 60% PAM (w15 to 20 106 Da) and 40% PEG (w6 to 8 103 Da). The PAM (w15 to 20 106 Da, MAGNAFLOC 1011, Ciba Specialty Chemicals) and PEG (w6 to 8 103 Da, DPW-1-1111, Ciba Specialty Chemicals) were both sourced in powder form from the manufacturer. The concentrations of the acrylamide monomer in PAM products are limited to <0.1%, while acrylamide is typically present at concentrations of w0.05% for mining grade flocculant blocks (National Sanitation Foundation, 2007; John Bellwood, Ciba Specialty Chemicals, pers comms).
2.3.
Test diluent
Natural Magela Creek water (MCW) was used for the control treatment and dissolution/dilution of the test compounds in all tests and was obtained from Bowerbird Billabong (latitude 12 460 1500 , longitude 133 020 2000 ). This natural water has been extensively characterised and has been used as a diluent in toxicity testing for over 20 years in our laboratory. The water was collected in 20 l acid-washed plastic containers and placed in storage at 4 1 C within 1 h of collection. The water was then transported to the laboratory in an air-conditioned vehicle. At the laboratory, it was stored at 4 1 C prior to filtration through Whatman #42 (2.5 mm pore size) filter paper within 4 days of collection. Throughout the testing period the
2.4.
Toxicity test species and methods
The toxicity of the flocculant block, PAM and PEG were assessed using five Australian tropical freshwater species: the unicellular green alga (Chlorella sp.); the duckweed (Lemna aequinoctialis); the green hydra (Hydra viridissima); the cladoceran (Moinodaphnia macleayi); and the Northern trout gudgeon (Mogurnda mogurnda). All the organisms were isolated from soft surface waters in Kakadu National Park and have been cultured continuously at the Environmental Research Institute of the Supervising Scientist over many years (10e25 years depending on the species). The test methods are described in detail by Riethmuller et al. (2003). Key details of each test are provided in Table 1. Tests were conducted in filtered (2.5 mm) MCW. For the L. aequinoctialis and Chlorella sp. tests, nutrients (nitrate e NO3 and phosphate e PO4) were added at the minimum concentrations that would sustain acceptable growth (see Table 1). The MCW used in the Chlorella sp. tests also had 1 mM HEPES buffer added to maintain a stable pH. Dry solids of the flocculant block, PAM and PEG were added directly to the MCW and stirred for 2 days at medium-high speed until the solutions appeared homogenous. The highest concentrations tested were 5 g l1, 2 g l1 and 12 g l1 for flocculant block, PAM and PEG, respectively and these stocks were serially diluted to produce the test solutions. Tests using H. viridissima were undertaken using several different feeding methods, as described below.
Table 1 e Details of toxicity tests for the five Australian tropical freshwater species used to assess the toxicity of flocculant block and its constituents. Full details of the methods are provided in Riethmuller et al. (2003). Species (common name)
Test duration Control response and acceptability endpoint criterion
72-h population 1.4 0.3 growth rate doublings day1; % CVa <20% 96-h growth Mean growth rate rate (k) 0.40; % CV <20% Hydra viridissima 72-h population Mean growth (green hydra) growth rate rate (k) 0.27; % CV <20% Moinodaphnia macleayi 3 brood Mean adult (cladoceran) (120e144 h) survival 80%; reproduction mean neonates per adult 30; % CV <20% Mogurnda mogurnda 96-h survival Mean larval (Northern trout survival 80%; gudgeon) % CV <20% Chlorella sp. (unicellular green alga) Lemna aequinoctialis (tropical duckweed)
Temperature, light intensity, photoperiod 29 1 C, 100e150 mmol m2 s1, 12:12 h 29 1 C, 100e150 mmol m2 s1, 12:12 h 27 1 C, 30e100 mmol m2 s1, 12:12 h 27 1 C, 30e100 mmol m2 s1, 12:12 h
Feeding/ nutrition
No. of Test Static/daily replicates volume renewals (individuals per (mL) replicate)
3 14.5 mg l1 NO3, 0.14 mg l1 PO4 (3 104 cells ml1)
50
Static
3 mg l1 NO3, 0.3 mg l1 PO4
3 (4)
100
Static
3e4 Artemia nauplii per dayb
3 (10)
30
Daily renewals
30
Daily renewals
30
Daily renewals
30 ml FFVc and 6 106 cells of Chlorella sp. per day
Nil 27 1 C, 30e100 mmol m2 s1, 12:12 h
10 (1)
3 (10)
a CV: percent coefficient of variation. b As part of the study, three different feeding methods were used for H. viridissima. See text in Section 2.5 for details. c FFV: fermented food with vitamins. Represents an organic and bacterial suspension prepared by method described in Riethmuller et al. (2003).
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2.5.
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Variation of H. viridissima feeding method
For H. viridissima, three different feeding methods were assessed, as follows, in order to try to elucidate reasons for the effects caused by the flocculant block and PAM: (i) standard feeding e each hydra was presented with 3e4 Artemia nauplii daily, using an equal amount of effort across all treatments (i.e. no additional effort was used to feed hydra having difficulty capturing the prey); (ii) facilitated feeding e as for standard feeding, however, additional effort was used where necessary to ensure all hydra received approximately equal amounts of food; and (iii) passive feeding e three to four Artemia nauplii per hydra were added to, and briefly mixed throughout, the test container, as one addition (i.e. not presented to hydra individually). All three feeding methods were assessed for the flocculant block testing, while feeding methods 1 and 3 were assessed for PAM testing. Only the standard feeding method was assessed for PEG testing, as it did not exhibit the same viscous properties as the flocculant block and PAM.
2.6.
Water quality parameters
Dissolved oxygen (DO), electrical conductivity (EC) and pH were measured daily on samples of fresh and 24 h old test waters from each of the daily renewal experiments (i.e. the M. macleayi, H. viridissima, A. cumingi and M. mogurnda tests), and on a sub-sample of the test waters at the start and end of the Chlorella sp. and L. aequinoctialis static tests.
2.7.
Water chemistry
Chemical analyses of test solutions were undertaken to ensure that (i) the dilutions undertaken for the tests were accurate; (ii) no chemical contaminants were introduced to the test solutions during preparation; and (iii) the nutrient concentrations used for the Chlorella sp. and L. aequinoctialis tests were within specification. Total Organic Carbon (TOC, Shimadzu TOC-V CSH) was measured and used as a quantitative indicator of the PAM and PEG concentrations in the flocculant block test solutions. Acidified (1% analytical grade HNO3, Merck, Darmstadt, Germany) sub-samples of the MCW controls, procedural blanks and Milli-Q water blanks were analysed for a suite of metals (i.e. Al, Cd, Co, Cr, Cu, Fe, Mn, Na, Ni, Pb, Se, U and Zn) using Inductively Coupled Plasma Mass Spectrometry (ICPMS; Agilent 7500CE series). Additionally, an analysis of 66 metals (ICP-MS) was conducted on the highest concentrations tested of all three test compounds. Nitric-acid digestion of the flocculant block and PAM solutions was necessary to breakdown the matrices prior to analyses.
2.8. Data analysis and derivation of protective concentrations for the flocculant block The derivation of PCs was based on accepted probabilistic risk assessment methods (see Posthuma et al., 2002). Briefly, chronic toxicity estimates from at least 5 species that represent 4 different trophic levels of the ecosystem were used to construct a Species Sensitivity Distribution (SSD). The SSD can be used to calculate the concentrations that may protect
a proportion of species, which can be compared to environmental chemistry data to conduct a risk assessment. For the chronic tests, linear interpolation was used to determine point estimates of Inhibitory Concentrations (ICs) that reduced endpoint responses by 10% and 50% (i.e. IC10 and IC50) relative to the control response. For the M. mogurnda acute tests, maximum likelihood logit analysis was used to determine the 5% and 50% lethal concentrations (i.e. LC05 and LC50). For the H. viridissima toxicity tests, the IC10 values from the standard feeding method (method 1; 120 mg l1) and the passive feeding method (method 3; 160 mg l1) were averaged, because both methods provided similar concentrationeresponse relationships, and there was no justification to prefer one set of data over the other. Using the geometric mean of multiple IC10 values from the same endpoint is also consistent with the guidance provided in ANZECC and ARMCANZ (2000). The IC10 (and LC05 for M. mogurnda) toxicity estimates were used to construct a SSD based on a loglogistic distribution, from which Protective Concentrations (PCs) and 95% confidence limits (CLs) were calculated that would be protective of 80, 90, 95 and 99% of species (Minitab 15.1.1.0, Minitab Inc., PA, USA). Where no IC10 could be reported, the highest concentration tested was used in the SSD. The PCs were calculated in terms of measured TOC (mg l1 C) for flocculant block and PEG and nominal TOC (mg l1 C) for PAM. Existing PAM toxicity data were sourced from the literature and combined with the results from this study to construct another combined PAM SSD to determine PCs using a wider range of species (i.e. 11 species in total; see Supplementary data). Where possible, IC10s and LC05s were recalculated from the existing data and used in the SSD. Where necessary, acute EC50 values for cladoceran species were converted to chronic values using appropriate, Acute to Chronic Ratios (ACRs) based on the M. macleayi flocculant block survival and reproduction data from the present study (i.e. ACRs of 24 and 6.7 for 48-h and 96-h experiments, respectively). For one species (Eulimnogammarus verrucosus), an ACR was not calculable and a default ACR of 10 was used. Toxicity estimates from the literature that reported no effect for a test (i.e. EC50 > X mg l1) were not used because some studies failed to expose organisms at high concentrations. All data from this study (including observations of no effect) were included because each organism was exposed to very high concentrations of PAM.
3.
Results
3.1.
Physico-chemical analyses
Strong linear relationships were observed between nominal flocculant block concentrations and measured TOC (measured TOC mg l1 ¼ 0.51 nominal mg l1; r2 ¼ 0.99, n ¼ 45, P < 0.001). Metal analyses of a nominal 5000 mg l1concentration of flocculant block detected Al, Cd, Cr, Cu and Zn at 168, 0.6, 3.6, 17 and 43 mg l1, respectively. All other analytes were below detection limits. Chemical analyses of the test solution dilutions using TOC as a measure of PAM indicated that there were problems with preparing accurate dilutions of the solutions for
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a Percentage of control response
analysis or with dilution of the stock concentrates (see Section 4 for details). Consequently, the results for PAM presented in this paper are based only on the nominal TOC concentrations, which was calculated from the carbon content of acrylamide (i.e. 50.69%, Merck Index (1996)). Metal analyses of 2000 mg l1 PAM detected Al, Cd, Cu and Zn at 216, 1.0, 16 and 56 mg l1, respectively. A strong linear relationship was observed between nominal PEG concentrations and measured TOC (measured TOC mg l1 ¼ 0.58 nominal mg l1; r2 ¼ 0.99, n ¼ 7, P < 0.001). Metal analyses of 12,000 mg l1 PEG detected Al, Cr, Cu and Zn at 121, 25, 2.8 and 4.5 mg l1, respectively.
160 140 120 100 80 60 40 20 0 1
10
100
1000
10000 -1
The flocculant block formulation exhibited an extremely wide range of toxicity, with the IC50 values ranging from 10 mg l1 C TOC for M. macleayi to [ 2590 mg l1 C TOC for L. aequinoctialis (measured concentrations) (Fig. 1a and Table 2). The order of sensitivity (from highest to lowest based on IC10 and IC50 values) of the five species assessed was (noting that the M. mogurnda response represents an acute effect): M. macleayi [ H. viridissima > Chlorella sp. > M. mogurnda > L. aequinoctialis. M. macleayi was the most sensitive species by approximately two orders of magnitude. In addition to the observed reproductive impairment, an inhibition of growth rate was evident. Furthermore, although 70% of the individuals exposed to 11 mg l1 C TOC flocculant block survived the full duration of the test (6 days), they only managed to produce one brood and their average reproductive output was only 0.8 neonates per adult compared to 36.9 neonates per adult in the controls. M. mogurnda, L. aequinoctialis and Chlorella sp. were all very tolerant to flocculant block exposure. However, some interesting observations were made during the tests. For example, M. mogurnda survived high concentrations of the dissolved flocculant block but locomotion was greatly inhibited and the larva’s operculum movement was increased (indicating a higher respiratory rate), although this was not quantified. L. aequinoctialis growth rate increased by 27% relative to the control response in the presence of 155 mg l1 C TOC flocculant block (Fig. 1a). In the Chlorella sp. test, the algal cells’ in all the flocculant block treatments appeared to be in a flocculated state (i.e. they were observed in aggregates). However, algal growth was not inhibited by the highest concentrations tested. For H. viridissima, feeding methods 1 and 3 resulted in similar concentrationeresponse relationships following exposure to flocculant block solutions compared to feeding method 2 (Fig. 2a and Table 2). When facilitated feeding (method 2) was used, the flocculant block had no effect on H. viridissima (see Section 3.5 for a description of how the toxicity estimates were incorporated into the PC).
3.3.
Toxicity of polyacrylamide (PAM)
PAM exhibited an extremely wide range of toxicity, with the IC50 values ranging from 3 mg l1 C nominal TOC for
b 120 Percentage of control response
Toxicity of flocculant block formulation
100 80 60 40 20 0 0.1
1
10
100
1000 -1
c Percentage of control response
3.2.
Flocculant Block - measured Total Organic Carbon (mg l C)
Polyacrylamide - nominal Total Organic Carbon (mg l C)
140 120 100 80 60 40 20 0 1000
10000 -1
Polyethylene gylcol - measured Total Organic Carbon (mg l C) Chlorella sp.
L. aequinoctialis
M. macleayi
H. viridissima
M. mogurnda
Fig. 1 e Effect of (a) flocculant block measured TOC concentrations; (b) PAM nominal TOC concentrations; and (c) PEG measured TOC concentrations on five freshwater species. Data points represent the mean ± standard error (n [ 3, except for M. macleayi where n [ 10). Control responses are reported in Table 2.
M. macleayi to >1020 mg l1 C nominal TOC for M. mogurnda (Fig. 1b and Table 2). The order of sensitivity (from highest to lowest based on IC10 and IC50 values) of the five species assessed was (noting that the M. mogurnda response represents an acute effect): M. macleayi [ H. viridissima z Chlorella sp. z L. aequinoctialis > M. mogurnda. M. macleayi was nearly two orders of magnitude more sensitive than the other species, and the observed reproductive, growth and survival responses were similar to those observed for the tests with the flocculant block formulation.
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Table 2 e Summary of toxicity of flocculant block and its constituents, polyacrylamide (PAM) and polyethylene glycol (PEG), to the five tropical freshwater species tested. Toxicity data are presented as measured TOC for flocculant block and PEG and nominal TOC for PAM. Species
Flocculant block Control responsea
Chlorella sp.
1.9 2
L. aequinoctialis
59 12
H. viridissimab 1.
0.33 3.1
2. 3.
0.33 12 0.26 0.9
M. macleayi
37 5
M. mogurnda
100 0
Toxicity (mg l IC10 (95% CL)
1
Polyacyrlamide C TOC)
IC50 (95% CL)
Control response
1.6 6
460 (60e810) >2590
1880 (1540e2280) >2590
57 10
60 (30e170) >620 80 (0e150) 4 (4e5)
2180
0.28 1.6
>620 610
n.t. c 0.29 1.6
10
38 21
350 (10e700)d
3440 (2310e13900)
93 7
Polyetheylene glycol
1
Toxicity (mg l
C TOC)
IC10 (95% CL)
IC50 (95% CL)
40 (10e100) 70 (0e200)
220 (140e290) 190 (30e290)
40 (0e50) n.t. 10 (0e20) 1 (1e2) >1020d
170 (150e200) n.t. >250
Control response
Toxicity (mg l1 C TOC) IC10 (95% CL)
IC50 (95% CL)
1.6 4
>7000
>7000
36 27
>7000
>7000
0.34 1.7
470 (310e1640) n.t. n.t.
>7000
470 (140e670) 1370 (780e1910)d
1170 (850e1350) 5670 (4160e9410)
n.t. n.t.
3 (3e3)
32 17
>1020
100 0
n.t. n.t.
a Control responses are expressed as the mean % coefficient of variation for the following endpoints: Chlorella sp. e growth rate (doubling/ day); L. aequinoctialis e growth rate (fronds/flask); H. viridissima e population growth rate (per day); M. macleayi e reproduction (neonates/adult); M. mogurnda e survival (%). b As part of the study, three different feeding methods were used for H. viridissima: feeding method 1 e three to four Artemia nauplii presented directly to each hydra using an equal amount of effort across all treatment groups; feeding method 2 e three to four Artemia nauplii presented directly to each hydra with additional effort provided where necessary to ensure all hydra captured the same amount of food; feeding method 3 e four Artemia nauplii per hydra per day added to, and mixed through the test container in one addition. c n.t.: Not tested. d Value reported for M. mogurnda represents the LC05 (i.e. concentration lethal to 5% of individuals relative to the controls).
H. viridissima exhibited an 82% reduction in population growth at the highest concentration tested of 250 mg l1 C nominal TOC, while the growth rates of Chlorella sp. and L. aequinoctialis were reduced by 88 and 96% following exposure to the highest concentration tested of 1020 mg l1 C nominal TOC PAM (Fig. 1b). However, there were notable differences in the sensitivity of some species to PAM concentrations compared to the dilutions of flocculant block corresponding to equivalent PAM concentrations, i.e. noting that the flocculant block is 60% PAM. For example, the growth rate of L. aequinoctialis was almost completely inhibited at 1020 mg l1 PAM but showed no response when exposed to 2550 mg l1 C nominal TOC for the flocculant block. Conversely, M. mogurnda was tolerant to PAM, displaying no significant effects following exposure to 1020 mg l1 C (nominal TOC). However, it is important to note that although the fish larvae survived the testing period, their behaviour and condition was similar to that of the surviving larvae at high flocculant block concentrations, i.e. that they could not swim in the medium and appeared stressed (e.g. rapid gill movement). The influence of feeding method on the effects of PAM on H. viridissima is shown in Fig. 2b. H. viridissima fed using the passive method (method 3) were initially more sensitive to PAM than those fed using the standard method (method 1), as reflected in the IC10 estimates (Table 2). However, beyond 60 mg l1 C nominal TOC the response of the passively fed hydra plateaued at approximately 40% growth inhibition (relative to control response) and, by the highest concentration (250 mg l1 C nominal TOC), the hydra fed using the standard
method were more sensitive, exhibiting a 82% reduction in population growth.
3.4.
Toxicity of polyethylene glycol
PEG was much less toxic compared to PAM and the flocculant block formulation (Fig. 1c and Table 2). At high concentrations of up to 7000 mg l1 C TOC the PEG produced a slightly foamy solution, which is indicative of its surfactant-like properties (Wildish, 1974). Both Chlorella sp. and L. aequinoctialis were extremely tolerant of PEG, with concentrations 7000 mg l1 C TOC having no effect on these species. Indeed, the L. aequinoctialis growth rate was slightly higher following exposure to PEG, as it was for the flocculant block. M. mogurnda and H. viridissima exhibited partial inhibitory responses (w60% and 25% relative to the control response, respectively) following exposure to 7000 mg l1 C TOC PEG. M. macleayi exhibited a strong reproductive impairment (33% relative to the control response) at 1750 mg l1 PEG, and a full (100%) impairment at 7000 mg l1 PEG. The order of sensitivity (from highest to lowest based on IC10 and IC50 values) of the five species assessed was (noting that the M. mogurnda response represents an acute effect): M. macleayi [ M. mogurnda > H. viridissima > Chlorella sp. z L. aequinoctialis.
3.5. Protective concentration values for the flocculant block formulation The derived PCs (and 95% confidence limits, CLs) corresponding to the 99, 95, 90 and 80% species protection levels
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Percentage of control response
a
Table 3 e Calculated protective concentrations for the flocculant block and its two constituents, polyacrylamide and polyethylene glycol.
120
100
Species Protective Concentration (mg l1 C and 95% CL) protection Flocculant Polyacrylamide Polyethyelene level block glycol This Combined (n ¼ 5) (n ¼ 5) study data (n ¼ 5) (n ¼ 11)
80
60
40
99% 95% 90% 80%
feeding method 1 feeding method 2 feeding method 3
20
1 (0e75) 5 (0e158) 12 (1e235) 35 (3e392)
0.1 (0e14) 1 (0e29) 2 (0e43) 7 (1e72)
0.03 (0e2) 0.4 (0e7) 1 (0.1e14) 4 (0.4e31)
48 (3e705) 172 (25e1203) 306 (59e1602) 570 (140e2325)
0 1
10
100
1000
10000 -1
Flocculant block - measured Total Organic Carbon (mg l C)
Percentage of control response
b
120
in PC values approximately half those derived from just the tropical species dataset.
100
4.
Discussion
80
60
40
20 feeding method 1 feeding method 3 0 1
10
100
1000 -1
Polyacrylamide - nominal Total Organic Carbon (mg l C)
Fig. 2 e Effect of (a) flocculant block and (b) PAM on Hydra viridissima growth rates using different feeding methods. Data points represent the mean ± standard error (n [ 3). Control responses are reported in Table 2. Feeding method 1 e three to four Artemia nauplii presented directly to each hydra using an equal amount of effort across all treatment groups. Feeding method 2 e three to four Artemia nauplii presented directly to each hydra with additional effort provided where necessary to ensure all hydra captured the same amount of food. Feeding method 3 e four Artemia nauplii per hydra per day added to, and mixed through the test container in one addition.
for the flocculant block, PAM, PEG and combined PAM (i.e. data from this study and literature sourced toxicity estimates) data are shown in Table 3. The logelogistic distribution represented a better fitting model of the toxicity estimates for the flocculant block and PAM than it did for the PEG. The AndersoneDarling goodness of fit statistic was w0.25 for flocculant block and PAM and 0.5 for the PEG data (data not shown). The derived PCs displayed wide 95% confidence limits (CLs), although much less so for the PCs derived from the combined PAM dataset. The inclusion in the SSD of the PAM toxicity data from other studies resulted
While the term ‘toxicity’ has been used to describe the effects of the flocculant block formulation and its constituents, it should be noted that a significant contribution to the observed effects could be due to physical factors resulting from the viscosity of the dissolved flocculant block, rather than chemical toxicity per se. The present study found that the toxicity of a polyelectrolyte flocculant to freshwater biota varied considerably between species. It is also clear from the results that the active ingredient, PAM, is the primary toxicant to most species. Furthermore, PAM alone appeared to elicit stronger responses when present in the flocculant block formulation (Fig. 1 and Table 2). Adding weight to the assertion that PAM was the primary toxicant was the fact that the carrier, PEG, was relatively innocuous even at extremely high concentrations of up to grams per litre. However, it is interesting to note that the LC05 of 1370 mg l1 C TOC for M. mogurnda, was similar to the LC05 for PAM (>1000 mg l1 C TOC), which indicates that PEG may be the primary fish toxicant in the flocculant block, albeit of very low toxicity. Another noteworthy observation from the PEG tests was that exposure of M. macleayi resulted in a high, concentration-dependent mortality of offspring neonates, i.e. 37, 64, 76, 95 and 100% of neonates survived following exposure to 3445, 1750, 890, 450 and 0 mg l1 C TOC PEG, respectively. Neonate mortality was not observed following exposure to the flocculant block or PAM. Significant offspring mortality is rarely observed during chronic toxicity tests for M. macleayi, which indicates that PEG may have directly affected the embryos in the brood sac or the neonates post-hatching. The low toxicity of PEG found in this study concurs with a limited number of studies that have used concentrations up to 5 g l1 and rarely reported an adverse response in aquatic organisms (Wildish, 1974; Bridie et al., 1979; Chan et al., 1981). M. macleayi was by far the most sensitive organism tested in the present study (IC10 ¼ 4 mg l1 C TOC flocculant block). Its sensitivity was comparable to that of other cladocerans, for which Lowest Observed Effect Concentrations (LOECs) of 1.0 mg l1 and 1.6 mg l1 (nominal PAM concentrations) have
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been reported for 96-h D. magna growth (Acharya et al., 2010) and 6e8 days C. dubia reproduction (Weston et al., 2009), respectively. The result was also comparable to acute data for D. magna, for which 96-h LC50s of 14 and 17 mg l1 (nominal PAM concentrations; Beim and Beim 1994; Biesinger et al., 1976, respectively) and a 48-h LC50 of 218 mg l1 have been reported (nominal PAM concentrations; de Rosemond and Liber, 2004). Applying the ACRs derived from the M. macleayi data in the present study (ACRs of 24 and 6.7 for 48 h and 96 h, respectively), the converted chronic toxicity estimates equate to 2.1, 2.5 and 4.5 mg l1 C TOC for the Beim and Beim (1994), Biesinger et al. (1976) and de Rosemond and Liber (2004) studies, respectively. Thus, the available data (albeit based on a limited number of species) indicate that cladocerans are more sensitive to anionic polyelectrolytes than other species, and much more toxic than had previously been thought based on the historical focus on acute toxicity data. It is important to note that mechanisms for the observed effects on M. macleayi may be several-fold. Flocculation by the PAM of the cladocerans’ food source may have made it more difficult for them to access food, while the higher viscosity of the test solutions would have increased the energy demands for locomotion and filter feeding, reducing the energy available for reproduction and growth. Such a physical effect was also hypothesised by Weston et al. (2009). At low concentrations of flocculant block and PAM, a specific chemical effect (cf. the above physical effects) may have contributed to the toxic response. The highest tested concentrations of flocculant block, PAM and PEG solutions contained numerous metals (Al, Cr, Cu and Zn) at concentrations that might be toxic to the organisms tested if in a dissolved free metal form. However, the extent to which these metals may have been bioavailable in the PAM solutions is questionable, since numerous di- and trivalent metal cations have been reported to form stable complexes with PAM matrices (Henderson and Wheatley, 1987). Although PEG may not have formed complexes with such metals in an aquatic medium, very low toxicity was observed for this chemical. It should also be noted that the toxicity of the PAM and flocculant block was unlikely to have been due to residual unpolymerised acrylamide. Specifically, there are limits for polyacrylamide products, which require acrylamide concentrations to be <0.1% in order to be certified as non-hazardous by the National Sanitation Foundation (2007). Consequently, concentrations of acrylamide in the current toxicity tests would have been <2 mg l1 in the highest treatments. These concentrations are 30 times lower than reported NoObserved-Effect Concentrations of 60 mg l1 for D. magna (Krautter et al., 1986) and, hence, were unlikely to cause adverse effects. The influence of viscosity on the test performance and toxicity estimates was also investigated with the H. viridissima test. The observed growth inhibition of H. viridissima following flocculant block exposure using feeding method 1 (i.e. the standard method) was due to either to an inability of the hydra to capture Artemia, or difficulties experienced by the test operator in presenting the Artemia to the hydra due to the viscous nature of the flocculant block medium (Fig. 2a). Feeding method 2 demonstrated that the hydra in the flocculant block medium could capture Artemia if
sufficient direct contact occurred between Artemia and the hydras’ tentacles. The ‘facilitated feeding’ method adopted in feeding method 2 resulted in the flocculant block media having no adverse effect on H. viridissima population growth up to the highest test concentration of approximately 610 mg l1 C TOC. Notwithstanding this, it was evident that the viscosity of the higher flocculant block concentrations (i) made it difficult for the hydras’ tentacles to make contact with Artemia, and (ii) reduced the swimming ability of Artemia. Thus, feeding method 3, a ‘passive’ feeding method, was used to assess the extent to which H. viridissima could encounter and capture its food source without any assistance. The results were similar to those of feeding method 1, and confirmed that the effects of flocculant block on H. viridissima were due to its viscosity, which reduced the likelihood and ability of H. viridissima to encounter, and therefore capture, sufficient food. Quantifying the extent of a viscosity-response in the toxicity tests was attempted but hampered due to the nonNewtonian (pseudo-plastic) rheology of the PAM solutions (Bjorneberg, 1998) and the lack of sensitivity of the viscosity measurements at the low concentrations of interest. Other issues associated with the chemical and physical analyses of the PAM were the result of the properties of the PAM solutions at the concentrations tested in this study. In the absence of the PEG carrier, the PAM solutions were in the form of a heterogeneous gel-like solution. The PAM appeared to settle-out with the gel sitting on the bottom of the test containers, and consequently was also extremely difficult to pipette and decant. Obtaining a representative sub-sample of the test solution was difficult. The handling difficulties encountered in this work are also of relevance to all previous studies, which have used PAM at high concentrations without a carrier, reported nominal concentrations and assumed a homogenous test solution. Consequently, PAM toxicity estimates in the absence of a carrier, for this study and previous studies, should be viewed with caution because, depending on how the test species behaves in the medium, they may have underestimated or overestimated the dose to the organism. Notwithstanding the PAM quantitation issues, the toxicity test results clearly indicate that PAM as the primary toxicant in the flocculant block formulation. There are some notable differences between the results of the current study and the PAM toxicity data provided by the manufacturer, most notably for C. vulgaris and B. rerio. The present study found an EC50 of 440 mg l1 (nominal concentration) PAM for Chlorella sp., while the EC50 for C. vulgaris was >1000 mg l1 (nominal concentration). The only significant difference in these toxicity tests is that the present study was conducted at a slightly higher temperature (i.e. 27 C compared to 25 C for the other reported toxicity tests), although it is unclear whether this could account for the differences in the toxicity. The difference is unlikely to be species-related, as these two species are very similar phylogenetically. The PAM was markedly less toxic to the Australian tropical fish, M. morgurnda (LC50 >2000 mg l1, nominal concentration), compared to the northern hemisphere tropical fish, B. rerio (LC50 357 mg l1, nominal concen-
W a t e r R e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 9 3 e6 4 0 2
tration). This difference might be due to inter-species variability and/or the fact that the small and relatively immobile M. mogurnda larvae used in the present study, may have been less likely to become stressed than the significantly larger (i.e. w3 cm) B. rerio. This is the first time PCs have been derived for anionic PAM incorporating data from chronic toxicity tests. The low 95% PCs derived in this study highlight that anionic PAMs affect cladocerans (and potentially other microcrustaceans) at much lower concentrations than for other species tested and can be much more toxic than had previously been estimated based on the historical focus on acute toxicity data. Indeed, toxicity testing using chronic and sublethal endpoints have shown cladocerans are adversely effected by PAM at concentrations w1 to 2 mg l1 (nominal PAM; Acharya et al., 2010; Weston et al., 2009). The results of these two studies, and the current study, have shown the need for more thorough effects assessments using data from chronic toxicity tests. The resultant PCs for the flocculant block, PAM and PEG represent useful indicative concentrations of these compounds above which adverse effects on aquatic organisms may result. Unfortunately, to date, no studies have reported PAM concentrations in natural waters receiving effluents that may contain these products. Such information would be required to conduct a proper risk assessment of flocculant block products but it is worthy to note that Lentz et al. (1996) measured up to 10 mg l1 PAM in irrigation canals following agricultural application. This concentration is 10 times higher than the 90% PC of 1 mg l1 for the combined dataset and indicates that PAM may enter natural waters at toxicologically significant concentrations. However, the application of the PCs in an environmental monitoring/regulatory sense may be limited due to the fact that the quantitative indicator measurement of the flocculant block (and its constituents) used for the present study, TOC, is a non-specific parameter. In aquatic systems there are many contributors to the total pool of TOC including many natural and/or anthropogenic organic compounds. Moreover, the low concentrations required to be sufficiently protective of higher value aquatic ecosystems (e.g. PC95 values) will often be below the background level of TOC in many catchments. Although more specific methods of analysis are available to measure concentrations of PAM in water, e.g. flocculationbased methods (Lentz et al., 1996) or size exclusion chromatography coupled with UV absorption or fluorescent detection (Lu et al., 2003; Becker et al., 2004), these techniques are more technically challenging, not standardised or rapid, and some still suffer interference from DOC. Thus, their usefulness in routine environmental monitoring is also limited. Becker et al. (2004) used a low-toxicity fluorescent tracer (fluorescein isothiocyanate) placed in a polyelectrolyte formulation and were able to measure concentrations as low as 10e40 mg l1. However, the method required the removal of dissolved organic carbon and has not been tested on environmental samples. Consequently, future research and development efforts for polyelectrolytes could be directed towards developing and applying products that can be monitored effectively in receiving environments.
5.
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Conclusions
Until recently, flocculant compounds, and specifically anionic polyacrylamide ingredients, have been considered relatively non-toxic. The present study has demonstrated that the sensitivity of freshwater species to anionic polyacrylamide can vary considerably. More importantly, the value of measuring chronic, sublethal responses to properly quantify the effects of these substances has been demonstrated by a sensitive reproductive effect on a cladoceran species. A key challenge with respect to the application of water quality guideline values for flocculant compounds is the ability of a standard water quality monitoring program to measure and detect concentrations of dissolved flocculant components in receiving waters where such products have been used.
Acknowledgments We would like to thank John Bellwood (Ciba Specialty Chemicals Australia) for, supplying the flocculant blocks and its individual constituents and for the additional information regarding these products. We would also like to thank Dennis Marroni (Polyelectrolytes Producers Group) for making available the Verhaar (2002) report and the raw data for the SEPC studies. Thanks also to Kim Cheng, Claire Costello and Melanie Houston for their technical expertise in the laboratory. Approval for the ethical use of M. mogurnda was granted through the Charles Darwin University’s Animal Ethics Committee.
Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version at doi:10.1016/j.watres.2011.09.032.
references
Acharya, K., Schulman, C., Young, M., 2010. Physiological response of Daphnia magna to linear anionic polyacrylamide: ecological implications for receiving waters. Water, Air & Soil Pollution 212 (1), 309e317. ANZECC and ARMCANZ, 2000. Australian and New Zealand Guidelines for Fresh and Marine Waters. Australian and New Zealand Environment and Conservation Council and Agriculture and Resource Management Council of Australia and New Zealand, Canberra. Becker, N.S.C., Bennett, D.M., Bolto, B.A., Dixon, D.R., Eldridge, R.J., Le, N.P., Rye, C.S., 2004. Detection of polyelectrolytes at trace levels in water by fluorescent tagging. Reactive and Functional Polymers 60, 183e193. Beim, A.A., Beim, A.M., 1994. Comparative ecologicaletoxicological data on determination of maximum permissible concentrations (MPC) for several flocculants. Environmental Technology 15, 195e198. Biesinger, K.E., Lemke, A.E., Smith, W.E., Tyo, R.M., 1976. Comparative toxicity of polyelectrolytes to selected aquatic
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animals. Journal of the Water Pollution Control Federation 48 (1), 183e195. Biesinger, K.E., Stokes, G.N., 1986. Effects of synthetic polyelectrolytes on selected aquatic organisms. Journal of the Water Pollution Control Federation 58 (3), 207e213. Bilotta, G.S., Brazier, R.E., 2008. Understanding the influence of suspended solids on water quality and aquatic biota. Water Research 42 (12), 2849e2861. Bjorneberg, D.L., 1998. Temperature, concentration, and pumping effects on PAM viscosity. Transactions of the ASAE-American Society of Agricultural Engineers 41 (6), 1651e1656. Bolto, B., Gregory, J., 2007. Organic polyelectrolytes in water treatment. Water Research 41 (11), 2301e2324. Bridie, A.L., Wolff, C.J.M., Winter, M., 1979. The acute toxicity of some petrochemicals to goldfish. Water Research 13. Cary, G.A., McMahon, J.A., Kuc, W.J., 1987. The effect of suspended solids and naturally occurring dissolved organics in reducing the acute toxicities of cationic polyelectrolytes to aquatic organisms. Environmental Toxicology and Chemistry 6 (6), 469e474. Chan, K.-y., Wong, K.H., Ng, S.L., 1981. Effects of polyethylene glycol on growth and cadmium accumulation of CU-1. Chemosphere 10 (8), 985e991. Chen, L., Zhu, J., Young, M.H., Susfalk, R.B., 2009. An integrated approach for modeling solute transport in streams and canals with applications. Journal of Hydrology 378 (1e2), 128e136. de Rosemond, S.J.C., Liber, K., 2004. Wastewater treatment polymers identified as the toxic component of a diamond mine effluent. Environmental Toxicology and Chemistry 23 (9), 2234e2242. Goodrich, M.S., Dulak, L.H., Friedman, M.A., Lech, J.J., 1991. Acute and long-term toxicity of water-soluble cationic polymers to rainbow trout (Oncorhynchus mykiss) and the modification of toxicity by humic acid. Environmental Toxicology and Chemistry 10 (4), 509e515. Hamilton, M., Reinert, K., Freeman, M.B., 1994. Aquatic risk assessment of polymers. Environmental Science and Technology 28 (4), 187Ae192A. Harford, A.J., O’Halloran, K., Wright, P.F.A., 2007. Effect of in vitro and in vivo organotin exposures on the immune functions of Murray cod (Maccullochella peelii peelii. Environmental Toxicology and Chemistry 26 (8), 1649e1656. Henderson, J.M., Wheatley, A.D., 1987. Factors effecting a loss of flocculation activity of polyacrylamide solutions: shear degradation, cation complexation, and solution aging. Journal of Applied Polymer Science 33 (2), 669e684. Krautter, G.R., Mast, R.W., Alexander, H.C., Wolf, C.H., Friedman, M.A., Koschier, F.J., Thompson, C.M., 1986. Acute
aquatic toxicity tests with acrylamide monomer and macroinvertebrates and fish. Environmental Toxicology and Chemistry 5 (4), 373e377. Lentz, R.D., Sojka, R.E., Foerster, J.A., 1996. Estimating polyacrylamide concentration in irrigation water. Journal of Environment Quality 25, 1015e1024. Lentz, R.D., Sojka, R.E., Mackey, B.E., 2002. Fate and efficacy of polyacrylamide applied in furrow irrigation: full-advance and continuous treatments. Journal of Environmental Quality 31 (2), 661e670. Liber, K., Weber, L., Levesque, C., 2005. Sublethal toxicity of two wastewater treatment polymers to lake trout fry (Salvelinus namaycush). Chemosphere 61 (8), 1123e1133. Lu, J., Wu, L., Gan, J., 2003. Determination of polyacrylamide in soil waters by size exclusion chromatography. Journal of Environmental Quality 32, 1922e1926. Narita, T., Ohtakeyama, R., Matsukata, M., Gong, J.P., Osada, Y., 2001. Kinetic study of cell disruption by ionic polymers with varied charge density. Colloid and Polymer Science 279 (2), 178e183. NICNAS, 2005. Full Public Report: Polymer in Ultimer 00LT053, File No PLC/458, National Industrial Chemicals Notification and Assessment Scheme, Canberra, ACT, Australia. National Sanitation Foundation, 2007. Drinking Water Treatment Chemicals e Health Effects. Standard 60-2007. National Sanitation Foundation, Washington, USA. Posthuma, L., Suter, G.S., Traas, T.P., 2002. Species Sensitivity Distributions in Ecotoxicology. CRC Press, Boca Raton, Florida, USA. Riethmuller, N., Camilleri, C., Franklin, N., Hogan, A.C., King, A., Koch, A., Markich, S.J., Turley, C., van Dam, R.A., 2003. Ecotoxicological Testing Protocols for Australian Tropical Freshwater Ecosystems. Supervising Scientist Report 173. Supervising Scientist Division, Darwin, Northern Territory, Australia. Vehaar, H.J.M., 2002. Anionic polyelectrolytes: classification and labelling within the framework of EU directive 67/548/EEC, supporting documentation, OpdenKamp registration & notification, The Hague, The Netherlands. Unpublished report. Weston, D.P., Lentz, R.D., Cahn, M.D., Ogle, R.S., Rothert, A.K., Lydy, M.J., 2009. Toxicity of anionic polyacrylamide formulations when used for erosion control in agriculture. Journal of Environmental Quality 38 (1), 238e247. Wildish, D.J., 1974. Lethal response by Atlantic salmon Parr to some polyoxyethylated cationic and nonionic surfactants. Water Research 8 (7), 433e437.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 0 3 e6 4 1 6
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Relationship between types of surface shear stress profiles and membrane fouling C.C.V. Chan a,*, P.R. Be´rube´ b, E.R. Hall b a b
Department of Civil Engineering, British Columbia Institute of Technology, 3700 Willingdon Ave, Burnaby, British Columbia, Canada Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, British Columbia, Canada
article info
abstract
Article history:
Shear stress has been recognized as an important parameter in controlling particle back-
Received 12 May 2011
transport from membrane surfaces. However, little is known of the relationship between
Received in revised form
transient shear conditions induced by air sparging and fouling control near membrane
12 September 2011
surfaces. In this paper, the different types of surface shear stress profiles that had bene-
Accepted 14 September 2011
ficial effects on minimizing reversible surface fouling were examined. The relationship
Available online 22 September 2011
between different statistical shear parameters (e.g. time-averaged shear, standard deviation of shear and amplitude of shear) and fouling control that have been used by others
Keywords:
were examined as well. It was found that the fouling rate for membranes subjected to
Filtration
transient shear conditions was lower than for membranes subjected to constant shear
Membrane fouling
conditions. The magnitude, duration and frequency of the shear conditions were found to
Surface shear stress
have an impact on the fouling rate of membranes. It was also found that although some
Hydrodynamics
statistical shear parameters could generally be used to relate shear and fouling, they were
Gas sparging
inadequate to relate surface shear stress to fouling, for all transient shear conditions examined. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Membrane filtration processes in the treatment of water and wastewater is a common and popular technology. However, the problem of membrane fouling increases both operational as well as capital costs associated with the process. Membrane fouling occurs when material accumulates on a membrane and forms a cake layer on the surface, or when material is completely or partially blocking the pores of the membrane (Field et al., 1995). These mechanisms increase the resistance to permeate flow. Material accumulation and the formation of a cake layer are largely dependent on the suspension composition, membrane properties and operating conditions (Gaucher et al., 2002). Operating conditions, such as the hydrodynamic conditions near the membrane play a very
important role in the erosion of the cake layer, thus improving overall membrane filterability. Shear stress has been recognized as an important parameter in eroding the cake layer as it influences particle backtransport from membrane surfaces (Cui et al., 2003; LeBerre and Daufin, 1996). Several mechanistic models have been developed to describe particle back-transport from membrane surfaces, e.g. the shear-induced diffusion, the inertial lift and the surface transport models (Belfort et al., 1994). However, these models assume constant laminar flow conditions, such as those observed during single-phase crossflow inside confined membrane systems (e.g. tubular membranes) (Belfort et al., 1994). On the other hand, the hydrodynamic conditions inside submerged gas-sparged hollow fiber membrane systems are characterized by highly variable and
* Corresponding author. Tel.: þ1 604 412 7406. E-mail address:
[email protected] (C.C.V. Chan). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.031
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turbulent flow conditions which generate non-uniform and transient shear conditions at membrane surfaces (Berube et al., 2006; Chan et al., 2007a). Ochoa et al. (2007) found that non-uniform shear stress imposed on a biofilm is much more effective in detaching the biofilm from the membrane surface than a constant shear stress. These observations suggest that non-uniform and transient shear stress plays an important role in the removal of accumulated material on membrane surfaces. There have been several attempts to experimentally establish a relationship between the transient shear stress and fouling control in oscillating flow conditions. Several statistical shear stress parameters such as the mean shear stress, amplitude of shear stress, standard deviation of shear stress, and the frequency of oscillation were considered to attempt to establish relationships (Al Akoum et al., 2002; Beier and Jonsson, 2007; Cabassud et al., 2001; Ducom et al., 2002; Jaffrin et al., 2004). Although these relationships highlight the importance of shear stress in controlling fouling and improving overall filterability, the proposed relationships between the shear stress parameters and fouling control do not provide insights into the mechanisms of fouling control. The motivation for the present study began with the observations that different types of surface shear stress profiles are induced by gas sparging on the fiber surface in a submerged hollow fiber membrane system, due to differences in bubble dynamics that occur in the water matrix (Chan et al., 2007a). Bubble dynamics can be affected by membrane configuration, including packing density, gas sparger design, degree of fiber looseness, and flow properties (i.e. gas and liquid flow rates, matrix viscosity) (Chan et al., 2007a). Three different bubble dynamics scenarios, with respect to the distance between the bubbles and the fiber surface, can occur (Chan et al., 2007a): (1) bubbles rising in contact with the fiber, (2) bubbles rising in relative close vicinity to, but not in contact with the fiber, and (3) bubbles rising relatively far away from the fiber. A bubble rising and in periodic contact with a fiber may result in very high transient shear stresses of short duration at the fiber surface, due to the scouring of the membrane surface by the bubble in contact with the membrane. A bubble rising in close vicinity to, but not in contact with the fiber, induces shear stresses of lower magnitude and short duration compared to bubbles rising in contact with the fiber. These shear stresses of lower magnitude are generated by the wake at the tail end of the rising bubble. Bubbles rising far away from the membrane surface induce relatively low but constant and continuous shear stress. These shear stresses of low but constant magnitude are generated by liquid that is entrained by the rising bubble (i.e. single-phase flow condition). For bubbles rising near a fiber but not in contact with the fiber, different types of surface shear stress profiles can be observed (Chan et al., 2007a). Fig. 1 illustrates two typical surface shear stress profiles observed during the passage of one bubble in a bench-scale gas-sparged submerged hollow fiber module. One of the profiles exhibits a shear peak with a relatively long duration, while the other exhibits a shear peak that is similar in magnitude, but with a shorter duration. The profiles are repeated during the passage of several gas bubbles. Depending on the magnitude as well as the
frequency of these repeating surface shear stress profiles, one may obtain similar statistical shear parameters (e.g. timeaveraged values and amplitudes (maximum and minimum) of shear stress) for both the profiles presented in Fig. 1a and b, even though these profiles are different. To date, it is not known whether the different surface shear stress profiles generated by these different scenarios result in different particle back-transport mechanisms and/or degrees of fouling control during membrane filtration. The overall objective of the present study was to qualitatively identify the types of surface shear stress profiles that produce the greatest beneficial effect on minimizing reversible surface fouling. The relationship between the different statistical shear stress parameters that have been used by others to establish a relationship between shear stress and fouling was examined as well. A number of surface shear stress profiles of different magnitudes, durations and frequencies were chosen to simulate the three different bubble dynamic scenarios described above. Filtration experiments were performed under these simulated shear stress scenarios.
2.
Materials and methods
2.1.
Description of the bench-scale filtration apparatus
The experimental apparatus consisted of a shear apparatus, a hollow fiber membrane, a permeate flux pump, and a pressure monitoring system.
2.1.1.
Shear apparatus
The shear apparatus was used to induce different surface shear stress profiles near the hollow fiber membrane surface during filtration. The shear apparatus consisted of a cylindrical tank, an impeller system, a rig which secured the impeller system, and a test-fiber, as shown in Fig. 2. The cylindrical tank was made of plexiglass with an internal diameter of 19 cm and a height of 30 cm. The impeller system consisted of a motor and different types of impellers which were capable of generating surface shear stress profiles of interest. The test-fiber, on which a shear probe was embedded flush to its surface, was used to measure the surface shear stress profiles generated during the passage of the impellers. The test-fiber was a piece of Teflon tubing with the same flexibility and geometry (o.d. 1.8 mm) as that of a hollow fiber membrane. Measurement of shear using the shear probe is based on an electrochemical technique. Details of the shear probe and the electrochemical technique, and the data acquisition system can be found in Berube et al. (2006) and Chan et al. (2007a). To ensure that the test-fiber was placed securely against the interior wall of the cylindrical tank, the test-fiber was attached onto a piece of flexible rubber backing (length width ¼ 21.6 cm 3.8 cm) using epoxy. The test-fiber with rubber backing was placed snug against the interior wall of the cylindrical tank. Different surface shear stress profiles with controllable magnitudes, duration and frequencies were generated by changing the impeller blade geometry, the distance between the impeller blade and the hollow fiber membrane (i.e.
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b 1.4
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a
0.8
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0.0 440
460
480
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520
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600
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700
800
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Fig. 1 e Two typical shear profiles observed in a gas-sparged submerged hollow fiber module. The bubble is not in contact with the fiber (Chan et al., 2007a) (Magnitudes of peak shear for both cases are lower than the peak shear when bubble is in contact with the fiber). (a) High shear with long duration, (b) high shear with relatively shorter duration.
impeller diameter), and the impeller rotation speeds. The magnitudes of the surface shear stress profiles considered in the present study are similar to those observed in a pilot-scale gas-sparged submerged hollow fiber membrane module (Fulton et al., 2011). Moreover, these different surface shear stress profiles considered have comparable time-averaged shear stress, maximum and minimum shear stresses to those observed at pilot scale.
The four different types of surface shear stress profiles considered were: 1. 2. 3. 4.
Continuous surface shear stress profile. Sustained peak surface shear stress profile. Low peak surface shear stress profile. High peak surface shear stress profile.
Different impeller blades were used to generate the four surface shear stress profiles; the relative position of the different impeller blades to the test-fiber is illustrated in Fig. 3. For the low peak surface shear stress profile, the impeller was raised approximately 20 mm above the test-fiber. For the high peak surface shear stress profile, there was no visually observable gap between the impeller blade and the test-fiber. As such it is possible that there was contact between the impeller blade and the test-fiber. For the sustained peak and the continuous surface shear stress profile, the distance between the impeller blade and the test-fiber was approximately 0.5 mm. The rotational speeds of the impellers were adjusted to achieve comparable statistical shear stress parameters between each experiment (i.e. comparable time-averaged shear stress, and baseline shear stresses). These statistical shear stress parameters are presented in the Results and Discussions section (Table 5). Additionally, careful leveling of the apparatus was conducted prior to the start of the filtration experiment to ensure that the surface shear stress profiles along the entire length of the test-fiber were the same.
2.1.2.
Fig. 2 e Schematic of the shear apparatus. (a) Side view, (b) top view.
Surface shear stress profiles
The continuous surface shear stress profile, as illustrated in Fig. 4a, is characterized by a sustained shear stress with limited variation in shear stresses (i.e. non-transient surface shear stress profile), which is similar to the surface shear stress profiles generated during single-phase liquid flow with no gas sparging. The sustained peak surface shear stress profile, as illustrated in Fig. 4b, is characterized as transient sustained high shear stresses of long duration, followed by sustained shear stresses similar to that of the surface shear stress profile generated when a bubble is rising in relatively close vicinity to, but not in contact with fiber (Fig. 1a). Maximum shear stress is of longer duration than the high peak and low peak surface shear stress profiles. The low
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a
b
c
d
Fig. 3 e The side view of tank that shows the relative position of the different types of impellers to the test-fiber. (a) Continuous, (b) sustained peak, (c) low peak, (d) high peak.
peak surface shear stress profile, as illustrated in Fig. 4c, is characterized as transient high shear stresses of short duration, followed by a period of low shear stresses similar in magnitude to that of the surface shear stress profile generated when a bubble is rising in relatively close vicinity to, but not in contact with fiber (Fig. 1b). The high peak surface shear stress profile, as illustrated in Fig. 4d, is characterized as transient shear stresses of short duration, followed by a period of high shear stresses similar in magnitude to the surface shear stress profile generated when bubble is rising and in contact with a fiber. The high peak surface shear stress profiles are similar to those of the low peak surface shear stress profiles, except that the maximum values are much higher than those seen for the low peak surface shear stress profiles. All profiles presented in Fig. 4 were generated with impellers consisting of 2 blades. Surface shear stress profiles
generated with impellers consisting of different number of blades result in different frequencies of shear events with comparable baseline, maximum and duration of peak shear stress. Profiles generated with impellers consisting of 1 blade, 3 and 4 blades are presented in Appendix A. The comparisons of the baseline and maximum shear stresses, and the duration of the peak between the different surface shear stress profiles are presented in Table 1.
2.1.3. Hollow fiber membrane, permeate flux pump, and pressure monitoring system The membrane fiber used for the filtration experiments was a ZW500 type outside-in PVDF hollow fiber membrane, which was provided by GE-Zenon (Oakville, Ontario). The surface properties were non-ionic and hydrophilic, with an outside
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Fig. 4 e Shear stress profiles generated using 2 blades on each impeller. (a) Continuous shear stress profile, (b) sustained peak shear stress profiles, (c) low peak shear stress profiles, (d) high peak shear stress profiles.
diameter of 1.8 mm and nominal pore diameter of 0.04 mm. The total length of the hollow fiber membrane used in the experiment was 20 cm. As for the test-fiber, the hollow fiber membrane was attached onto a piece of flexible rubber backing (length width ¼ 21.6 cm 3.8 cm) using epoxy. Through visual observations, it was estimated that approximated 40% of the total surface area of the hollow fiber membrane was covered with the epoxy coating. This fraction of the surface area of the hollow fiber membrane did not contribute to filtration. The total surface area available for membrane filtration was approximately 600 mm2. The permeate pump was connected to the hollow fiber membrane and permeate was drawn by suction. The volumetric flow rate of 0.5 mL/min was set to generate a permeate flux of 50 L/m2/h. A slow flow peristaltic pump (Lachat Instruments Model 2200) was used to generate a constant flow during filtration. The flow was monitored to confirm that it was constant during filtration. The fouling rate during filtration was assessed by monitoring the increase in suction pressure over time when the hollow fiber membrane was subjected to the different types of surface shear stress profiles (i.e. continuous, sustained peak, low peak and high peak surface shear stress profiles) when filtering solution containing bentonite particles. The pressure monitoring system
consisted of a pressure gauge (Cole Palmer GPI 9675), and a pressure transducer (Omega PX240) connected to a data logger (National Instrument USB-6009). The data logger collected pressure measurements at a rate of 1 Hz. A custom Labview application (Labview Version 7.0) recorded the collected pressure measurements. All of the filtration transmembrane pressure (TMP) curves presented are the average pressures calculated from the replicated experiments. The confidence intervals presented in select pressure curves correspond to a confidence interval equivalent to one standard deviation of the measured pressure at a given time for the replicated experiments. All statistical comparisons of the pressure curves between the different experiments are based on this confidence interval. For the purpose of presentation, the confidence levels are only shown in the figures after 30 min of data for the 0.2 g/L experiment, and after 60 min for the 0.5 g/L. Below these times the confidence intervals of the pressure curves overlapped. The only type of fouling to be considered in the filtration experiments was surface fouling (i.e. cake formation). Therefore, the water matrix used for the experiments could not result in internal fouling, such as pore clogging, or adsorption onto the membrane. A solution containing reverse osmosis (R.O.) filtered tap water and sodium bentonite particles was chosen as the water matrix for the filtration experiments. The average mean
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Table 1 e Comparison of the baseline and maximum shear stresses, and the duration of the peak between the different surface shear stress profiles. Type of shear stress profile
Baseline shear stress (Pa)
Maximum shear stress (Pa)
Duration of peak shear stress (s)
Constant at 0.5 0.05e0.15 0.1e0.25 0.1e0.25
e 0.45e0.6 0.5e0.95 7e10
e 3e5 <1 <1
Single phase Sustained peak Low peak High peak
size of the bentonite particles of repeated measurements was 3 mm; while the smallest particle size was 0.3 mm. The particle size distributions of the bentonite solution were analyzed using a laser particle size analyzer (Mastersizer Hydro 2000S, Malvern). Two bentonite solution concentrations were considered during the experiment: 0.2 and 0.5 g/L. It should be noted that soluble material, such as organic matter, can also affect surface fouling. However, the effect of soluble material on surface fouling was not considered in the present study. All experiments were conducted at room temperature. Prior to the start of the filtration experiments, the bentonite solution was allowed to equilibriate with room temperature (20 C 2 C). The duration of filtration experiments with 0.2 g/L bentonite was 120 min, while the duration of filtration experiments with 0.5 g/L bentonite was 70 min. This ensured that TMP did not exceed 68.95 kPa (10 psi), which is the manufacturer-recommended maximum operating pressure for the membrane fibers. During filtration, permeate was returned to the system tank to maintain a constant bentonite concentration during filtration. After each filtration experiment, to ensure that all cake foulants formed on the hollow fiber membrane surface during filtration were removed, the hollow fiber membrane was cleaned thoroughly via intense surface scouring by air bubbles. The hollow fiber membrane was also subjected to integrity testing at the beginning and the end of each filtration experiment (before cleaning) to ensure that the membrane was not breached during filtration. Integrity testing was conducted using a pressure hold test, wherein the hollow fiber membrane was submerged in water and pressurized to 68.95 kPa (10 psi), isolated and the pressure was monitored over a period of 60 s. If a pressure decrease greater than 15% was recorded, then the hollow fiber membrane was considered breached. Additionally, if bubbles were observed leaking from the hollow fiber membrane during the pressure hold test the membrane was also considered breached.
2.2.
bentonite, the TMP increase in the first 5 min was rapid, after which the TMP increase slowed over the duration of the experiment. The rapid change during the first 5 min was due to the pressure building up to a level equivalent to that of the clean water filtration pressure. As expected, due to differences in the rates of convective mass transfer, the rate of fouling for the experiment performed with the solution containing 0.5 g/L of bentonite was greater than that for the experiment performed with a solution containing a lower bentonite concentration (0.2 g/L). The time to reach 68.95 kPa (10 psi) was approximately 65 min when filtering the solution containing a higher concentration of bentonite, while the time to reach the same pressure was approximately 110 min for the solution containing the lower concentration of bentonite. Typical TMP curves for the sustained peak surface shear stress profile experiments are presented in Fig. 6. Similar to the observations from continuous surface shear stress profile experiments, the fouling rate for the solution containing 0.5 g/L of bentonite was greater than that for the solution containing 0.2 g/L of bentonite (i.e. approximately double). For the 0.2 g/L experiment, when comparing the effect of different frequencies of shear events (i.e. number of blades), the experiment with the more frequent shear events (i.e. 4 blades) resulted in the
Table 2 e Experimental program. Shear stress parameters for the different types of shear stress profiles are presented in Table 5. Bentonite concentration (g/L)
Type of shear stress profile
Number of impeller blades
0.2
Sustained peak
1 2 3 4 1 2 4 1 2 4 e
Low peak
Experimental program High peak
Table 2 summarizes the experiments conducted in this study. Each experiment was either repeated two times or three times. All experiments, including the replicate experiments were performed in random order.
3.
Results and discussions
3.1.
Typical filtration pressure curves
Continuous 0.5
Sustained peak
Low peak
High peak
Typical TMP curves for the continuous surface shear stress profile experiment are presented in Fig. 5. For both experiments performed with a solution containing 0.2 g/L and 0.5 g/L of
Continuous
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Pressure (kPa)
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lowest fouling rate compared to experiments performed with less frequent shear events (1, 2 and 3 blades), even though these shear events have comparable baseline, maximum and duration of peak shear stress (see Fig. A1). However, the opposite trend was consistently observed when filtering the solution containing 0.50 g/L of bentonite, wherein more frequent shear events (4 blades) resulted in a faster fouling rate than the experiments performed with less frequent shear events. Further research is required to identify the mechanisms responsible for this apparent discrepancy. Note also that the fouling rates were relatively similar for all experiments performed with less frequent shear events (i.e. 1 blade, 2 blade and 3 blades). These results suggest that there may be a minimum amount of energy required for fouling control to take place, below which the control of fouling is not effective. The critical energy may be a function of the concentration of particles near the membrane surface. This is also consistent with results observed by LeBerre and Daufin (1996) who observed that there was a critical shear stress that was required before cake erosion occurred on the membrane surface when filtering a solution of milk using crossflow tubular membrane systems. Further research is required to confirm this hypothesis. Typical TMP curves for the low peak surface shear stress profile experiments are shown in Fig. 7. Similar to the observations for the continuous and sustained peak surface shear stress profile experiments, the fouling rates of the experiment performed with the solution containing a higher bentonite concentration (0.5 g/L) were higher than those for the solution containing 0.2 g/L of bentonite (approximately double). For both experiments containing 0.5 g/L and 0.2 g/L of bentonite, the fouling rates for the experiments performed with different frequencies of shear events (1, 2 and 4 blade) were relatively similar (i.e. their confidence intervals overlapped), indicating that the frequency of shear events did not affect fouling control resulting from the low peak surface shear stress profile.
Fig. 6 e Fouling for the sustained peak shear profiles with confidence interval (Upper and lower limit corresponds to a confidence interval equivalent to one standard deviation of the measured pressure at a given time for the replicated experiments.). (a) Bentonite concentration 0.2 g/L, (b) bentonite concentration 0.5 g/L.
Typical TMP curves for the high peak shear experiments are shown in Fig. 8. Similar to the observations for the continuous, sustained peak and low peak surface shear stress profile experiments, the fouling rates of the experiment performed with the solution containing a higher bentonite concentration (0.5 g/L) were higher than those for the solution containing 0.2 g/L of bentonite (approximately double). For both experiments containing 0.5 g/L and 0.2 g/L of bentonite, the fouling rates for the experiments performed with different frequencies of shear events (1, 2 and 4 blade) were also relatively similar (i.e. their confidence intervals overlapped). Again, these results indicate that the frequency of shear events did not affect fouling control resulting from the high peak surface shear stress profile.
3.2. Comparison of filtration pressure curves for different types of surface shear stress profiles considered In this section, the TMP curves of all surface shear stress profiles are compared. A comparison of the pressure curves
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Fig. 7 e Fouling for the low peak shear profile with confidence interval (Upper and lower limit corresponds to a confidence interval equivalent to one standard deviation of the measured pressure at a given time for the replicated experiments.). (a) Bentonite concentration 0.2 g/L, (b) bentonite concentration 0.5 g/L.
for the experiment performed with the solution containing a low concentration of bentonite (0.2 g/L) is presented in Fig. 9, while a comparison for the experiment performed with the solution containing a high concentration of bentonite (0.5 g/L) is presented in Fig. 10. For clarity, the pressure curves are presented with and without confidence intervals. For the experiment performed with the solution containing 0.2 g/L of bentonite, the fouling rates for the high peak and the sustained peak surface shear stress profile experiments were significantly less than that for the continuous surface shear stress profile experiment. However, for all experiments, the fouling rate for the low peak surface shear stress profile was not significantly different than that of the continuous surface shear stress profile. Similarly, for the experiment performed with the solution containing 0.5 g/L of bentonite, the fouling rate of the high peak and the sustained peak surface shear stress profiles were less than that of the continuous surface shear stress profile. Figs. 9 and 10 illustrate that, with the exception of the experiments performed with the low peak surface shear stress profiles, all experiments performed with transient surface
Fig. 8 e Fouling for the high peak shear profile with confidence intervals (Upper and lower limit corresponds to a confidence interval equivalent to one standard deviation of the measured pressure at a given time for the replicated experiments.). (a) Bentonite concentration 0.2 g/L, (b) Bentonite concentration 0.5 g/L.
shear stress profiles resulted in significantly better fouling control (slower fouling rate) during filtration, compared to the continuous surface shear stress profile. This was expected, and is consistent with the observations of others when investigating fouling under single-phase and double-phase flow conditions (Cui and Wright, 1996; Berube and Lei, 2006; Laborie et al., 1997; Cheng et al., 1998; Ghosh and Cui, 1999; Mercier et al., 1997). It is however interesting to note that among the different transient surface shear stress profiles, different levels of fouling control could be achieved. Among the experiments performed with the transient surface shear stress profiles, the experiments performed with high peak surface shear stress profiles resulted in the best fouling control (slowest fouling rate) compared to the experiments performed with the sustained peak and the low peak surface shear stress profiles. The experiment performed with the low peak surface shear stress profile resulted in the worst fouling control (fastest fouling rate), compared to the other two experiments performed with the transient surface shear stress profiles, and was similar to that of the experiment performed with the continuous surface shear stress profile. These results suggest that
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Fig. 9 e The comparison of the fouling for different shear profiles with bentonite concentration of 0.2 g/L (a, b) 4 Blade, (c, d) 2 blade, (e, f) 1 blade, (a and c) time-averaged shear only, (d and f) time-averaged shear with confidence interval.
there may be a minimum (or critical) energy required before particle transport away from membrane can occur. Therefore, the energy supplied by the low peak and the continuous surface shear stress profile conditions may not have been sufficient in inducing the particle transport, compared to the conditions induced by the sustained peak and high peak surface shear stress profiles. It should be noted that there was no visible gap between the impeller blade and the fiber membrane for the experiment performed with the high peak surface shear stress profile, which may have resulted in physical contact between the membrane fiber and the impeller blade. However, this is unlikely as no breach in the integrity was ever observed during an experiment.
When comparing the experiments performed with the low peak surface shear stress profiles to those performed with the sustained peak surface shear stress profile experiments, the experiment performed with the sustained peak surface shear stress profile resulted in better fouling control. The greater fouling observed for the experiment performed with the low peak surface shear stress profile is possibly due to the disruption of particle back-diffusion which can occur due to excessive oscillatory flow (induced by variable shear conditions) at the membrane surface. Levesley and Bellhouse (1993) reported that although oscillatory flow enhanced particle backtransport, a critical oscillatory flow frequency was observed above which particle back-transport was inhibited.
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Fig. 10 e The comparison of the fouling for different shear profiles with bentonite concentration of 0.5 g/L. (a, b) 4 Blade, (c, d) 2 blade, (e, f) 1 blade, (a, c and e) time-averaged shear only, (b, d and f) time-averaged shear with confidence interval.
3.3. Relationship between the fouling rate and shear stress parameters Several researchers have proposed that the permeate flux (and subsequent fouling rate) can be linked to shear stress parameters, such as those listed in Table 3. For the present study, in addition to the shear stress parameters listed in Table 3, other parameters such as number of shear events (Ns) and the duration of the peak shear (Tmax), and several combinations of these parameters were also evaluated as potential relationships that could be use to link fouling rate to shear stress measurements. A summary of the additional parameters considered in the present study is listed in Table 4.
The calculated values of these shear parameters for the different types of surface shear stress profiles considered are summarized in Table 5. The fouling rate was calculated based on the change of pressure vs. time near the end of the experiment, at which point the pressure increase was approximately linear for all experiments. For the experiments performed with the solution containing 0.2 g/L experiment, the fouling rate was calculated between 80 and 120 min of the experiment, while for the experiments performed with the solution containing 0.5 g/L, the fouling rate was calculated between 50 and 70 min of the experiment. Based on Pearson’s correlation analysis (Table 6), for both experiments containing 0.2 and 0.5 g/L of bentonite, the
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Table 3 e Shear stress parameters suggested by others to be linked to fouling. Shear stress parameter
Symbol
References which suggest relationship to fouling
Time-averaged shear stress profile
s
Standard deviation of shear stress profile Amplitude of shear stress profile Peak of shear stress profile Oscillation frequency Ratio of two-phase time-averaged shear stress to continuous wall shear stress
sstd samp smax f ðstwophase =ssinglephase Þ
(Al Akoum et al., 2002; Beier and Jonsson, 2007; Ducom et al., 2002) (Yeo et al., 2007) (Ducom et al., 2002) (Jaffrin et al., 2004; Al Akoum et al., 2002) (Chan et al., 2007a and b) (Ducom et al., 2002)
frequency of oscillation ( f ) and the number of shear events (Ns), two parameters, which were speculated to be linked to fouling rate, could not be used to provide a satisfactory relationship between shear stress measurement and fouling rate, as initially suggested by Chan et al. (2007a and 2007b). Similarly, the additional parameters considered in this study (Ns s, Ns smax, and Ns smax Tmax) also could not provide a satisfactory relationship between shear stress measurements and fouling rate. The shear stress parameters that were found to be significantly correlated to fouling rate for both experiments containing 0.2 and 0.5 g/L of bentonite were time-averaged shear stress ðsÞ, standard deviation of shear stress (sstd) and the ratio of averaged shear induced by two-phase flow conditions to the averaged shear stress induced by single-phase flow ðstwophase =ssinglephase Þ. The significant correlation observed between time-average of shear stress ðsÞ and fouling rate is consistent with observations by others (Beier and Jonsson, 2007; Ducom et al., 2002). However, based on the results of the present study, the time-averaged shear stress ðsÞ is likely not the only factor affecting fouling rate. This is evident when comparing the results obtained for the experiments performed with the high peak-4 surface shear stress profile with those for the experiment performed with the sustained peak-1 and sustained peak-2 surface shear stress profiles, two of which had higher time-averaged shear stress values than those of the high peak-4 surface shear stress profile. However, the fouling rates of all of the experiments performed with the sustained peak surface shear stress profiles were significantly higher than those of the experiments performed with the high peak-4 surface shear stress profiles, as presented in Figs. 9 and 10. Therefore, time-averaged shear stress ðsÞ alone cannot
Table 4 e Additional statistical shear parameters considered in the present study as potentially linked to fouling control. Shear parameter Number of shear events Duration of peak shear (in seconds) Product of number of shear events and average shear Product of number of shear event and peak shear Product of number of shear event, peak shear and duration of peak shear
Symbol Ns Tmax Ns s Ns smax Ns smax Tmax
fully describe the relationship between shear stress measurements and fouling rate. This observation is consistent with those by Yeo et al. (2007) who found that timeaveraged shear stress ðsÞ cannot be used as the sole parameter in defining fouling control during filtration under twophase flow conditions. The same argument can be applied for the ratio of averaged shear induced by two-phase flow conditions to the averaged shear stress induced by singlephase flow ðstwophase =ssinglephase Þ. The relationship between standard deviation of shear stress (sstd) and fouling rate is consistent with the observations by Yeo et al. (2007) who demonstrated a good correlation between standard deviation and the rate of TMP increase during filtration. These results suggest the importance of high variability of shear stress in controlling fouling. However, the variability of shear stress is likely not the only factor in affecting fouling. This is evident when comparing the results obtained for the experiments performed with the sustained peak-1 surface shear stress profile with those for the experiment performed with the low peak-4, low peak-2 and low peak-1 surface shear stress profiles, all of which had relatively similar variability of shear stresses (i.e. sstd). However, the fouling rates of all of the experiments performed with the low peak surface shear stress profiles were significantly higher than those of the experiments performed with the sustained peak4 surface shear stress profiles, as shown in Figs. 9 and 10. Therefore, similar to time-averaged shear stress ðsÞ, the variability of shear stresses (i.e. sstd) alone cannot fully describe the relationship between shear stress and fouling. Note that although a high variability in shear stress (i.e. sstd) typically result in better fouling control, however, as previously discussed, excessive variability in shear stress caused by oscillatory flow can create conditions near membrane surfaces that can be detrimental to fouling control (Levesley and Bellhouse, 1993). The relationships between maximum shear stress (smax) and fouling rate, and the amplitude of shear stress (samp) and fouling rate are different for the experiments performed with solutions containing different concentrations of bentonite. For the experiments containing 0.2 g/L of bentonite, no relationship was observed between maximum shear stress (smax) and fouling rate, and the amplitude of shear stress (samp) and fouling rate. For the experiments performed with a solution containing 0.5 g/L of bentonite, a correlation is observed between maximum shear stress (smax) and fouling rate, as well as the amplitude of shear stress (samp) and fouling rate. This observed correlation
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Table 5 e Shear stress parameter values to different experimental conditions. s
sstd
samp
smax
f
ðstwophase =ssinglephase Þ
Ns
Tmax
Ns s
0.18 0.24 0.33 0.40
0.17 0.17 0.17 0.11
0.55 0.54 0.52 0.46
0.58 0.58 0.58 0.59
0.28 0.22 0.15 0.08
0.34 0.46 0.64 0.78
1020 780 540 300
2.5 2.5 2.5 2.5
181 187 178 121
595 453 311 176
1487 1132 778 441
High peak-4 High peak-2 High peak-1
0.27 0.44 0.60
0.73 0.83 1.00
10.13 9.02 7.48
10.16 9.10 7.60
0.57 0.28 0.15
0.53 0.85 1.16
2040 1020 540
1.0 1.0 1.0
555 450 323
20,732 9281 4104
20,732 9281 4104
Low peak-4 Low peak-2 Low peak-1
0.23 0.27 0.29
0.14 0.12 0.09
1.26 0.67 0.55
1.33 0.75 0.66
0.57 0.28 0.15
0.45 0.52 0.56
2040 1020 540
1.5 1.5 1.5
477 273 157
2708 764 357
4061 1147 536
Continuous
0.52
0.06
e
e
e
e
e
e
e
Shear profile-number of blades Sustained Sustained Sustained Sustained
peak-4 peak-3 peak-2 peak-1
Ns smax
e
Ns smax Tmax
e
Table 6 e Pearson correlation coefficient for different shear parameters and pressure increase rate. Only coefficient (absolute value) greater than 0.60 is considered significant (based on confidence level of 95%). Bentonite concentration 0.2 g/L 0.5 g/L
s
sstd
samp
smax
f
ðstwophase =ssinglephase Þ
Ns
Ns s
Ns smax
Ns smax Tmax
0.67 0.65
0.67 0.78
0.47 0.71
0.47 0.72
0.25 0.22
0.66 0.65
0.25 0.22
0.08 0.18
0.10 0.16
0.09 0.50
between maximum shear stress (smax) and fouling rate is consistent with observations by Jaffrin et al. (2004). However, similar to the above discussion of time-averaged shear stress ðsÞ and variability of shear stress (sstd), maximum shear stress (smax) alone cannot fully explain why the experiments performed with the low peak surface shear stress profiles yielded a higher fouling rate compared to the experiments performed with the sustained peak surface shear stress profiles, even though the maximum shear stress (smax) of the low peak surface shear stress profile were higher than that of the sustained peak surface shear stress profile. Similarly for the amplitude of shear stress (samp), it was observed that the amplitudes of shear stress for the low peak surface shear stress profiles were higher than those for the sustained surface shear stress profile, however, the experiment performed with the low peak surface shear stress profile yielded a higher fouling rate compared to the experiment performed with the sustained peak surface shear stress profile, indicating that the amplitude, and the maximum shear stress of the transient shear conditions alone cannot be used to fully describe the relationship between shear stress and fouling rate. The results from the present study suggest that inducing different types of surface shear stress profiles may have different physical effects on fouling control at the membrane surface. For example, the mechanism of fouling control for the high peak shear experiment may have been primarily due to scouring of the cake layer, while fouling control for low peak and sustained shear experiments may have resulted primarily from particle back-transport via inertial lift or shear-induced diffusion. These physical effects cannot be properly described by the simple shear stress parameters listed in Tables 3 and 4. Further research is required to investigate the mechanisms of fouling control induced by the different types of surface shear stress profiles, and to develop a better
description of the relationship between fouling control and the different transient shear conditions.
4.
Conclusions
Different types of surface shear stress profiles can be observed in a gas-sparged submerged hollow fiber membrane module. The overall objective of the present study was to qualitatively identify the types of surface shear stress profiles that produce the greatest beneficial effect on minimizing reversible surface fouling. The relationship between the different statistical shear parameters that have been used by others to establish a relationship and fouling control (e.g. time-averaged shear, standard deviation of shear and amplitude of shear) were examined as well. A number of surface shear stress profiles of different magnitudes, durations and frequencies were chosen to simulate the type of shear stresses induced by different types of bubbles in a air-sparged membrane system. Filtration experiments were performed under these simulated shear scenarios. The present study is the first of its kind to investigating the relationship between types of shear conditions and fouling control. Shear events of different magnitudes, durations and frequencies were imposed onto a submerged hollow fiber membrane, and the resulting increases in transmembrane pressure were monitored and analyzed. The following are the main conclusions from this study: Filtration experiments in which membranes were subjected to transient shear conditions (i.e. sustained peak surface shear stress profile, low peak surface shear stress profile, and high peak surface shear stress profile) resulted in lower fouling rates, compared to constant shear conditions (i.e. continuous surface shear stress profile).
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The magnitude, duration and frequency of the shear conditions all have an impact on the fouling rate. Experiments performed with the high peak surface shear stress profile resulted in the best fouling control compared to those performed with other surface shear stress profiles. For a given maximum peak shear value, experiments performed with surface shear stress profiles with peak values of relatively long duration (sustained peak surface shear stress profile) were more effective at controlling surface fouling than frequent short shear events (low peak surface shear stress profile). No significant correlations were observed between the fouling rate and Ns, Ns s, Ns smax, f, and Ns smax Tmax. A possible relationship between time-averaged shear stress
ðsÞ, the ratio of averaged shear induced by two-phase flow conditions to the averaged shear stress induced by singlephase flow ðstwophase =ssinglephase Þ, standard deviation of shear stress (sstd) and fouling rate was observed. However, these parameters alone could not be used as the sole parameters in defining the relationship between shear stress measurements and fouling rate. The relationship between maximum shear stress (smax) the amplitude of shear stress (samp) and fouling rate is different for the experiments performed with solutions containing different concentrations of bentonite.
Appendix A.
1.0
Shear Stress (Pa)
0.8
0.6
0.4
0.2
0.0 0
10
20
30
40
50
1.0
1.0
0.8
0.8
Shear Stress (Pa)
Shear Stress (Pa)
Time (s)
0.6
0.4
0.2
0.6
0.4
0.2
0.0
0.0 0
10
20
30
40
50
0
10
20
30
40
50
Time (s)
Time (s)
1.0
1.0
0.8
0.8
Shear Stress (Pa)
Shear Stress (Pa)
Fig. A1 e Sustained peak shear profiles: different frequencies created using different number of blades on each impeller (a) 1 blade, (b) 3 blade, (c) 4 blade.
0.6
0.4
0.6
0.4
0.2
0.2
0.0
0.0 0
10
20
30
Time (s)
40
50
0
10
20
30
40
50
Time (s)
Fig. A2 e Low peak shear profiles: different frequencies created using different number of blades on each impeller (a) 1 blade, (b) 4 blade.
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10
10
8
8
Shear Stress (Pa)
Shear Stress (Pa)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 0 3 e6 4 1 6
6
4
2
6
4
2
0
0 0
10
20
30
40
50
Time (s)
0
10
20
30
40
50
Time (s)
Fig. A3 e High peak shear profiles: different frequencies created using different number of blades on each impeller (a) 1 blade, (b) 4 blade.
references
Al Akoum, O., Jaffrin, M.Y., Ding, L.H., Paullier, P., Vanhoutte, C., 2002. A hydrodynamic investigation of microfiltration and ultrafiltration in a vibrating membrane module. Journal of Membrane Science 197 (1e2), 37e52. Beier, S.P., Jonsson, G., 2007. Separation of enzymes and yeast cells with a vibrating hollow fiber membrane module. Separation and Purification Technology 53 (1), 111e118. Belfort, G., Davis, R.H., Zydney, A.L., 1994. The behavior of suspensions and macromolecular solutions in cross-flow microfiltration. Journal of Membrane Science 96 (1e2), 1e58. Berube, P.R., Lei, E., 2006. The effect of hydrodynamic conditions and system configurations on the permeate flux in a submerged hollow fiber membrane system. Journal of Membrane Science 271 (1e2), 29e37. Berube, P.R., Afonso, G., Taghipour, F., Chan, C.C.V., 2006. Quantifying the shear at the surface of submerged hollow fiber membranes. Journal of Membrane Science 279 (1e2), 495e505. Cabassud, C., Laborie, S., Durand-Bourlier, L., Laine, J.M., 2001. Air sparging in ultrafiltration hollow fibers: relationship between flux enhancement, cake characteristics and hydrodynamic parameters. Journal of Membrane Science 181 (1), 57e69. Chan, C.C.V., Berube, P.R., Hall, E.R., 2007a. Shear profiles inside gas sparged submerged hollow fiber membrane modules. Journal of Membrane Science 297 (1e2), 104e120. Chan, C.C.V., Berube, P.R., Hall, E.R., 2007b. Shear stress at radial positions of submerged hollow fiber under gas sparging, and the effects of physical contact between fibers on shear profiles. In: Proceeding AWWA Membrane Technology Conference, Florida. Cheng, T.W., Yeh, H.M., Gau, C.T., 1998. Enhancement of permeate flux by gas slugs for crossflow ultrafiltration in tubular membrane module. Separation Science and Technology 33 (15), 2295e2309. Cui, Z.F., Wright, K.I.T., 1996. Flux enhancements with gas sparging in downwards crossflow ultrafiltration: performance and mechanism. Journal of Membrane Science 117 (1e2), 109e116. Cui, Z.F., Chang, S., Fane, A.G., 2003. The use of gas bubbling to enhance membrane processes. Journal of Membrane Science 221 (1e2), 1e35.
Ducom, G., Puech, F.P., Cabassud, C., 2002. Air sparging with flat sheet nanofiltration: a link between wall shear stresses and flux enhancement. Desalination 145 (1e3), 97e102. Field, R.W., Wu, D., Howell, J.A., Gupta, B.B., 1995. Critical flux concept for microfiltration fouling. Journal of Membrane Science 100 (3), 259e272. Fulton, B.G., Redwood, J., Tourais, M., Berube, P.R., 2011. Distribution of surface shear forces and bubble characteristics in full-scale gas sparged submerged hollow fiber membrane modules. Desalination. doi:10.1016/j.desal.2011.07.050. Gaucher, C., Jaouen, P., Comiti, J., Legentilhomme, P., 2002. Determination of cake thickness and porosity during crossflow ultrafiltration on a plane ceramic membrane surface using an electrochemical method. Journal of Membrane Science 210 (2), 245e258. Ghosh, R., Cui, Z.F., 1999. Mass transfer in gas-sparged ultrafiltration: upward slug flow in tubular membranes. Journal of Membrane Science 162 (1e2), 91e102. Jaffrin, M.Y., Ding, L.-H., Akoum, O., Brou, A., 2004. A hydrodynamic comparison between rotating disk and vibratory dynamic filtration systems. Journal of Membrane Science 242 (1e2), 155e167. Laborie, S., Cabassud, C., Durand-Bourlier, L., Laine, L.M., 1997. Flux enhancement by a continuous tangential gas flow in ultrafiltration hollow fibres for drinking water production: effects of slug flow on cake structure. Filtration and Separation, 887e891. LeBerre, O., Daufin, G., 1996. Skimmilk crossflow microfiltration performance versus permeation flux to wall shear stress ratio. Journal of Membrane Science 117 (1e2), 261e270. Levesley, J.A., Bellhouse, B.J., 1993. Particulate separation using inertial lift forces. Chemical Engineering Science 48 (21), 3657e3669. Mercier, M., Fonade, C., Lafforgue-Delorme, C., 1997. How slug flow can enhance the ultrafiltration flux in mineral tubular membranes. Journal of Membrane Science 128, 103e113. Ochoa, J., Coufort, C., Escudie, R., Line, A., Paul, E., 2007. Influence of non-uniform distribution of shear stress on aerobic biofilms. Chemical Engineering Science 62, 3672e3684. Yeo, A.P.S., Law, A.W.K., Fane, A.G., 2007. The relationship between performance of submerged hollow fibers and bubbleinduced phenomena examined by particle image velocimetry. Journal of Membrane Science 304 (1e2), 125e137.
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In situ feeding assay with Gammarus fossarum (Crustacea): Modelling the influence of confounding factors to improve water quality biomonitoring Romain Coulaud a,b, Olivier Geffard a,*, Benoıˆt Xuereb a,1, Emilie Lacaze a, Herve´ Que´au a, Jeanne Garric a, Sandrine Charles b, Arnaud Chaumot a,* a
Cemagref, UR MALY, 3 bis quai Chauveau-CP 220, F-69336 Lyon, France Universite´ de Lyon, F-69000, Lyon, Universite´ Lyon 1, CNRS, UMR5558, Laboratoire de Biome´trie et Biologie Evolutive, F-69622 Villeurbanne, France b
article info
abstract
Article history:
In situ feeding assays implemented with transplanted crustacean gammarids have been
Received 11 March 2011
claimed as promising tools for the diagnostic assessment of water quality. Nevertheless
Received in revised form
the implementation of such methodologies in biomonitoring programs is still limited. This
31 August 2011
is explained by the necessity to improve the reliability of these bioassays. The present
Accepted 15 September 2011
study illustrates how modelling the influence of confounding factors could allow to
Available online 22 September 2011
improve the interpretation of in situ feeding assay with Gammarus fossarum. We proceeded in four steps: (i) we quantified the influence of body size, temperature and conductivity on
Keywords:
feeding rate in laboratory conditions; (ii) based on these laboratory findings, we computed
In situ assay
a feeding inhibition index, which proved to be robust to environmental conditions and
Feeding rate
allowed us to define a reference statistical distribution of feeding activity values through
Gammarus
the data compilation of 24 in situ assays among diverse reference stations at different
Temperature
seasons; (iii) we tested the sensitivity of the feeding assay using this statistical framework
Biomonitoring
by performing 41 in situ deployments in contaminated stations presenting a large range of
Modelling
contaminant profiles; and (iv) we illustrated in two site-specific studies how the proposed methodology improved the diagnosis of water quality by preventing false-positive and false-negative cases mainly induced by temperature confounding influence. Interestingly, the implementation of the developed protocol could permit to assess water quality without following an upstream/downstream procedure and to compare assays performed at different seasons as part of large-scale biomonitoring programs. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
In aquatic ecosystems, organisms are constantly exposed to different levels of physical and chemical stressors. To
estimate and predict their biological effects, the need for relevant tools has increased considerably in the last decades, which is of broad importance in the regulatory framework for the diagnosis of ecological impacts of chemicals (e.g. EU Water
Abbreviations: FR, feeding rate; FI, feeding inhibition index. * Corresponding authors. Tel.: þ33 4 72208788; fax: þ33 4 78477875. E-mail addresses:
[email protected] (O. Geffard),
[email protected] (A. Chaumot). 1 Present address: Laboratoire d’Ecotoxicologie - Milieux Aquatiques (LEMA: EA 3222), Universite´ du Havre, 76058 Le Havre Cedex, France. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.035
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Framework Directive, 2000/60/EC). Up to now, water quality has been monitored using both chemical and biological measures. Concerning biological measures, several biotic indices have been developed. Because these methods referred to changes in community structure, the established diagnosis of ecosystem quality reflects integrative effects from diverse sources of degradation. That is why the identification of pressure/impacts relationships, is often difficult. To disentangle the role of chemical contaminations in the degradation of environmental quality, a complementary approach consists of methods based on lower levels of biological organization for assessing biological impacts (Chapman, 2007; Dagnino et al., 2008; Dama´sio et al., 2008), e.g. measuring sublethal responses of single species (Maltby et al., 2002). These methods are expected to be more specific and sensitive to the toxic effects of contaminants, and thus to supply early warning indicators of pollution impacts. Nevertheless, the use of individual responses still remains limited because their interpretation under non-controlled environmental conditions often lacks the definition of relevant reference values (Maltby et al., 2002; Hagger et al., 2008). Individual responses can supply ecologically relevant endpoints because some of them constitute or can at least be related to fitness traits (survival, reproduction, growth). In the diagnostic context, they are rarely used because the measurement of such physiological or demographic rates necessitates the adaptation of laboratory bioassay protocols to field exposure. Hence, protocols for post-exposure measurements with either indigenous or transplanted organisms (Soares et al., 2005; Galloway et al., 2006; Barata et al., 2007; Krell et al., 2011), and protocols for in situ measurements with caged organisms (Maltby and Crane, 1994; Dedourge-Geffard et al., 2009) are developed for physiological rate and life-history trait measurements. Among the individual responses which can be monitored, feeding inhibition is of great interest for multi-scale assessment of water quality. On one hand, it is an ecological concern because it can be related to alteration in life-history traits (Maltby, 1999; Baird et al., 2007; Barata et al., 2007) and because it can be correlated with ecosystem processes (Forrow and Maltby, 2000; Maltby et al., 2002). On the other hand, its interpretation can be linked with the modulation of molecular biomarkers of specific modes of action (Barata et al., 2007; Xuereb et al., 2009b). In aquatic invertebrates, feeding inhibition is in most cases one of the first observed responses to environmental pollution (Gerhardt, 1995; Macedo-Sousa et al., 2007; Alonso et al., 2009; Mouneyrac et al., 2010). Since the 1990s, several laboratory studies have shown that the feeding rate (FR) of amphipods (in particular freshwater gammarids) can be inhibited by a large range of chemical stressors (metals, insecticides, fungicides, herbicides, drugs, organic compounds. see Suppl. Table 1A). Gammarus pulex (Linnaeus) and Gammarus fossarum (Koch) are highly relevant as sentinel species to study feeding inhibition in streams. They are widespread in European ecosystems, where they play a key role in nutrient cycles as decomposers of coarse organic matter. By performing a short review of the literature since 1990, we noted that several studies showed in situ feeding inhibitions in gammarids in various contamination profiles (industrial wastes, acid mine drainage, agricultural
catchments. see Suppl. Table 1B). Consequently, FR assessment that can be easily measured in situ with caged gammarids (mainly by leaf-mass feeding assays), has been proposed as an ecologically relevant in situ indicator of water quality (Maltby et al., 2002). The main limitation for the use of individual responses in monitoring programs is the difficulty to define baseline values due to spatial and seasonal variability related to the effects of biotic and abiotic factors (Hagger et al., 2008; Hanson et al., 2010). Such biotic and non-toxic environmental influences could lead to the misinterpretation of individual markers in water chemical quality assessment during in situ or postexposure assays with caged organisms (Maltby et al., 2002; Moreira et al., 2006; Kater et al., 2001; Krell et al., 2011). Indeed, the inflated variability of responses in controls results in a decreased statistical power explaining a low sensitivity of bioassays (i.e. high rate of false negatives). In addition, confounding effects could give rise to false-positive cases, when deviation from controls is caused by a difference in the level of a non-toxic influential factor (i.e. low specificity). FR measurement in gammarids can be affected by many biotic and abiotic factors. Biotic factors include source population (Maltby and Crane, 1994; Veerasingham and Crane, 1992; Crane et al., 1995), parasite load (McCahon et al., 1988; Pascoe et al., 1995; Fielding et al., 2003; Lettini and Sukhdeo, 2010), or body size (Nilsson, 1974). With the aim to reduce the variability related to these biotic factors, the use of transplanted standard organisms is proposed for water quality assessment (Liber et al., 2007) because it allows to play down the impact of biotic factors (one population source, same physiological parameters such as size, sex, reproductive and energetic status). The confounding effect of abiotic factors, which can not be controlled during in situ exposure, has limited the application of bioassays with transplanted organisms to paired comparisons between stations upstream/downstream from identified point-source pollutions. In this specific context, the assessment of chemical water quality strongly relies on a questionable experimental design which assumes that physicochemical conditions are similar between stations, excepted for levels of bioavailable toxic compounds (Liber et al., 2007). As an alternative, modelling the influence of confounding factors can make measurements comparable in space and time (Maltby et al., 2002; Moreira et al., 2006; Krell et al., 2011). This could allow to benefit from robust reference conditions defined at larger scales of space and time. For instance, through an empirical analysis of the influence of environmental conditions (temperature, alkalinity,.) on FRs in Gammarus, Maltby et al. (1990b, 2002) underlined that taking into consideration the most influential environmental conditions in order to define reference values of biological activities could improve the in situ approach for site-specific studies. Furthermore, such a methodological advance could permit the application of FR in situ bioassays to large scale and longterm biomonitoring programs. The present study illustrates how modelling the influence of confounding factors allows to improve the interpretation of in situ feeding assays with the widespread keystone species G. fossarum as an indicator of water quality. We proceeded in four steps: (i) we quantified the influence of important
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
confounding factors in laboratory conditions; (ii) based on these laboratory results, we computed a feeding inhibition index (FI ), which proved to be robust to environmental conditions and allowed us to define a reference statistical distribution of feeding activity values through the data compilation of 24 in situ assays among diverse reference stations at different seasons; (iii) we tested the sensitivity of the feeding assay using this statistical framework by performing 41 in situ deployments in contaminated stations presenting a large range of contaminant profiles; and (iv) we illustrated how the proposed methodology improved water quality diagnosis in two site-specific studies of impacted watersheds previously reported in the literature, which were focused on the development of biomarkers (Dedourge-Geffard et al., 2009; Lacaze et al., 2011).
2.
Material and methods
2.1. Sampling and maintenance of transplanted G. fossarum Organisms were collected by kick sampling at La Tour du Pin, upstream of the Bourbre River (France). This station displayed good water quality according to RNB data records (French Watershed Biomonitoring Network), and a high density of gammarids was found. The organisms were kept during a 15 days acclimatisation period in 30 L tanks under constant aeration. They were continuously supplied with groundwater mixed with osmosed water at constant conductivity, 200 or 600 mS cm1, depending on the conductivity level of the subsequent experimental environment (water hardness: 88.2 or 223.0 mg L1 of CaCO3, respectively). A 10/14 h light/dark photoperiod was maintained and the temperature was kept at 12 (1) C. Organisms were fed ad libitum with alder leaves (Alnus glutinosa), previously conditioned for at least 6 (1) d in groundwater. Twice a week, freeze-dried Tubifex sp. worms were added as a dietary supplement.
2.2.
FR assays
Analyses of water physicochemical parameters were performed for each experiment by a French accredited chemical analysis laboratory (Laboratoire d’analyses physicochimiques des milieux aquatiques, Cemagref, UR Milieux Aquatiques, Ecologie et Pollutions). Temperature was continuously measured using Tinytag temperature logger Aquatic 2.
2.2.1.
Laboratory exposure
Because we chose to perform in situ FR assays through the transplantation of standard organisms, we tried to improve this methodology by analyzing, first, the influence of size of selected organisms on the FR, and second, the influence of main potential confounding environmental factors reported in the literature (temperature and conductivity). The body size of transplanted organisms was thereafter fixed for the in situ bioassay protocol used for field experiments (Section 2.2.2). During experiments 1 and 2, conductivity, temperature, pH, and dissolved oxygen were monitored daily.
6419
Experiment 1: Influence of body size on FR. Three size classes of gammarids - 7.3 (0.5), 10.6 (0.7) and 12.8 (0.9) mm - were considered for the experiment (water temperature: 12.1 (0.01) C; conductivity: 600 mS cm1; water hardness: 88.2 mg L1 of CaCO3). Here the body size corresponded to the dorsal length between the start of the prosoma (at the base of the antenna) and the end of the metasoma (thus excluding urosoma and telson). For the first class, we selected juvenile gammarids, and for the two others, adult male gammarids were selected in order to exclude impacts of sex on the FR. Four replicates of 20 gammarids were studied for each condition. We used a flow-through system which consisted of 0.5 L glass beakers filled with continuously renewed water (four renewals per day), a continuous pumping system, and a 10/14 h light/dark photoperiod. 20 alder leaf discs (20 mm in diameter, without major veins) were supplied in each beaker. Two beakers, containing only leaf material, were deployed to control leaf weight gain or loss unrelated to gammarid feeding activity. After 7 days of exposure, gammarids were counted (for survival rate assessment), alder leaf discs were collected, and a new batch of leaf discs was placed in each beaker for the second period of 7 days. At the end of the experiment, gammarids were counted again. The methodology for FR computation is described in Section 2.3. Experiment 2: Influence of water temperature and conductivity on FR. The influence of temperature and conductivity on FR was studied by exposing adult male gammarids (10.6 0.7 mm) to three temperatures: 6.9 (0.05), 12.1 (0.01) and 16.4 (0.4) C and two conductivity levels: 200 and 600 mS cm1 in a fully factorial design. These levels corresponded to the range of physicochemical characteristics usually encountered in the streams in Rhoˆne-Alpes region. 24 h before initiating the experiment, gammarids were acclimatised to each water temperature and conductivity treatment. We used the same experimental design as for experiment 1 to conduct the exposure under the six treatments.
2.2.2.
In situ deployments
In situ feeding assays were adapted according to the method described by Maltby et al. (1990a,b). We deployed four replicates of 20 adult male gammarids with homogenous body size (10e11 mm) in stations presented in Tables 1AeD. As in experiment 2, we used a size class, which corresponds in mean to the first class of adults in experiment 1. This is because it is the more numerous in collected samples from the source population and thus it makes easier to constitute homogenous replicates. Organisms were placed in polypropylene cylinders (diameter 5 cm, length 10 cm) capped at their ends with pieces of net (mesh size: 1 mm). 20 alder leaf discs (20 mm in diameter, without major veins) were supplied in each container. Two containers with only leaf material, were deployed at each station as a control. After 7 days of exposure, the gammarids were counted (for survival rate assessment), and the alder leaf discs were collected. The methodology for FR computation is described in Section 2.3. Experiment 3: In situ characterisation of FR variability among reference stations. 24 deployments in reference stations (detailed in Table 1A) were implemented during two
6420
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
Table 1A e Detailed information on in situ deployments for the different feeding assays considered in experiment 3: 24 deployments in reference stations in Rhoˆne-Alpes region. Station
GPS coordinates
River, location, (coding letter on Suppl. Fig. 1) Doux, Labatie d’Andaure (a) Cance, Saint Julien Vocance (b) Gier, La Valla en Gier (c) Ain, Saint Maurice de Gourdans (d) Albarine, Chaley (e) Mandorne, Oncieux (f) Vareze, Cours et Buis (g) Galavayson, Saint Clair sur Galaure (h) Drevenne, Rovon (i) Guiers Mort, Saint Laurent du Pont (j) Boussuivre, Saint Marcel l’Eclaire´ (k) Ardie`res, Les Ardillats (l) Ergues, Poule les Echarmeaux (m)
04 290 45 010 04 300 45 100 04 300 45 260 05 110 45 480 05 320 45 570 05 280 45 580 04 580 45 260 05 070 45 150 05 270 45 120 05 450 45 210 04 230 45 510 04 310 46 110 04 260 46 080
41.5" 23.6" 11.9" 39.5" 36.4" 36.3" 20.0" 27.5" 31.8" 22.8" 23.7" 36.1" 52.0" 15.3" 50.3" 26.5" 55.5" 11.6" 17.4" 42.2" 39.3" 52.7" 15.9" 11.8" 45.5" 21.2"
E N E N E N E N E N E N E N E N E N E N E N E N E N
National grid reference
6105568
Deployment date (no.)
Water temperature ( C)
Conductivity (mS cm1)
Hardness (mg L1 of CaCO3)
6101905
10/2009 (R1) 06/2010 (R13) 10/2009 (R2)
14.8 13.4 13.4
64 55 78
20 <14 21
6820138
10/2009 (R3)
13.1
78
21
6092000
10/2009 (R4)
15.0
394
266
6300001
10/2009 (R5)
11.9
440
316
6069650
10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009
(R6) (R14, R20) (R7) (R15, R21) (R8) (R16, R22) (R9)
11.9 10.9, 11.9 13.9 14.0, 15.0 13.5 13.8, 15.1 13.9
380 415 411 325 292 130 323
245 238 288 168 260 55 242
6580673
10/2009 (R10) 06/2010 (R17, R23) 06/2010 (R18, R24)
10.7 9.2, 10.1 12.9, 13.6
300 290 245
266 156 72
6051375
10/2009 (R11)
12.8
295
60
6053830
10/2009 (R12) 06/2010 (R19)
12.6 14.2
150 110
75 28
6820073 6104900 6147220 6078200
campaigns in October 2009 (R1 to R12) and June 2010 (R13 to R24). These stations were chosen among the national reference network (WFD implementation) in collaboration with the regional public water agency, which built this network by expert judgement using data on land use, chemical monitoring (including micropollutants), and ecological diagnosis (http://sierm.eaurmc.fr/eaux-superficielles). For our study, stations were selected on rivers in Rhoˆne-Alpes region, seeking to cover a large range of physicochemical characteristics and geographical locations (w20000 km2) (Suppl. Fig. 1A). Mean weekly water temperature ranged from 9.2 C to 15.1 C, and conductivity from 110 mS cm1 to 420 mS cm1 between the 24 deployments. Experiment 4 : In situ exposure at contaminated stations. 41 deployments (detailed in Table 1B) were performed during the same two campaigns in the same region (Suppl. Fig. 1A) as experiment 3 in October 2009 (P1 to P15) and June 2010 (P16 to P41). These stations were chosen among the national control network (WFD implementation) in collaboration with the regional water agency. They displayed depreciated water chemical quality and faunistic indices and typology of micropollutant contamination (Table 1B) was previously established by the water agency (http://sierm.eaurmc.fr/eauxsuperficielles). For our study, stations were selected in order to supply diverse contamination profiles (pesticides, metals, urban). Mean weekly water temperature varied between 11.8 C and 20.7 C, and conductivity between 80 mS cm1 and 868 mS cm1.
Experiment 5: The Lot watershed. Four campaigns of in situ caging were performed from November 2009 to June 2010 on the Lot watershed in the Decazeville area. This river system has been intensively studied during different scientific programs (e.g. ANR 08-CES-014 RESYST) because of its polymetalic contamination due to former open-cast coal mining and zinc ore treatment. Experiments were performed in autumn, winter, spring and summer. Four stations were used (Suppl. Fig. 1B), for which chemical characterisation, survival and biomarker responses in caged G. fossarum were provided in a previous report (Lacaze et al., 2011): i) one station upstream of the Lot-Riou Mort confluence (Upstream Lot), considered as a reference site for the studied water system because of low metal concentrations in the water column; ii) one station downstream of the Lot-Riou Mort confluence (Downstream Lot); iii) one station on the Riou Mort river upstream of the polymetallic contamination (Decazeville) which was another metal-free site but located in an urban area, iv) the fourth station (Riou Viou) is on the Riou Viou river, a tributary of the Riou Mort river which presented significant metal concentrations in the water column. Mean water temperature varied between these 20 deployments from 6.2 to 17.4 C, and mean conductivity from 135 to 1552 mS cm1 (detailed in Table 1C). Experiment 6: The Amous watershed. In situ caging was performed in March 2008 in four stations on the Amous watershed (Suppl. Fig. 1C), a French river known to be highly contaminated by heavy metals originating mainly from acid
Table 1B e Detailed information on in situ deployments for the different feeding assays considered in experiment 4: 41 deployments in contaminated stations in RhoˆneAlpes region. Typology of contamination (D) established by the regional water agency for national control network (WFD implementation). Station River, location, (coding letter on Suppl. Fig. 1) Doux, Saint Jean de Muzols (n) Cance, Sarras (o)
Veyle, Lent (q) Veyle, Servas (r) Ange, Brion (s) Drac, Fontaine (t) Turdine, Arbresle (u) Azergues, Legny (v) Azergues, Lucenay (w) Gier, Givors (x) Rhoˆne, Givors (y) Bourbre, Pont de Cheruy (z) Saoˆne, Ile Barbe (aa) Ardie`res, Saint Jean (ab)
04 490 45 040 04 470 45 110 05 260 45 560 05 110 46 060 05 100 46 070 05 330 46 100 05 420 45 110 04 360 45 500 04 340 45 540 04 430 45 540 04 450 45 350 04 470 45 350 05 100 45 040 04 490 45 470 04 440 46 070
39.5" 40.2" 47.6" 30.9" 01.8" 32.1" 48.4" 58.7" 31.3" 37.9" 05.3" 12.3" 04.3" 36.6" 09.1" 15.5" 21.4" 24.6" 33.1" 41.5" 42.3" 15.4" 03.4" 36.4" 29.9" 00.3" 57.3" 49.4" 00.9" 18.4"
E N E N E N E N E N E N E N E N E N E N E N E N E N E N E N
National grid reference
6106030 6103500 6300001 6048570 6049550 6086100 6146500 6057200 6800009 6057700 6097000 na na 6059500 6051550
Deployment date (no.)
10/2009 06/2010 10/2009 06/2010 10/2009
(P1) (P16, P29) (P2) (P17, P30) (P3)
10/2009 06/2010 10/2009 06/2010 10/2009
(P4) (P18, P31) (P5) (P19, P32) (P6)
10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009 06/2010 10/2009 06/2010
(P7) (P20, P33) (P8) (P21, P34) (P9) (P22, P35) (P10) (P23, P36) (P11) (P24, P37) (P12) (P25, P38) (P13) (P26, P39) (P14) (P27, P40) (P15) (P28, P41)
Metals
Pesticides
Water temperature ( C)
Conductivity (mS cm1)
Hardness (mg L1 of CaCO3)
+
17.1 18.0, 18.7 14.8 16.9, 17.3 12.7
123 80 613 160 386
47 20 173 36 243
+++
12.6 15.6, 16.0 14.0 17.3, 17.9 12.7
473 345 447 330 602
364 167 360 154 289
15.1 11.8, 13.6 17.2, 13.2 15.3, 15.8 18.2, 16.1 17.8, 19.1 17.4, 15.2 16.6, 18.8 20.7, 14.3 17.0,
317 240 868 465 307 265 664 430 377 240 434 390 720 650 702 490 270 160
191 118 242 116 187 99 364 151 275 75 280 180 348 331 156 218 153 53
Other contaminants
+
+
+
+
+
+
++ +++ ++ +++
+++
+
+++ +
+++
+
+++
+++
+
++
+
++
++
+
++
+
+
+
++
+++
+
+++
+++
+
12.1 17.2 15.3 17.6 17.0 17.9 16.7 20.3 16.6
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
Albarine, Saint Rambert (p)
GPS coordinates
6421
6422
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
Table 1C e Detailed information on in situ deployments for the different feeding assays considered in experiment 5: Lot watershed (2009/2010). Season Autumn
Station
GPS coordinates 02 140 44 350 02 110 44 340 02 140 44 330 02 120 44 330
Upstream Lot Downstream Lot Decazeville Riou Viou
Winter
Spring
Summer
30.1" 53.5" 51.5" 49.7" 14.9" 38.1" 44.6" 10.1"
Conductivity (mS cm1)
Hardness (mg L1 of CaCO3)
10.7
145
68
10.6
158
77
11.1
1180
485
9.2
270
120
6.4 6.2 8.2 7.7 12.1 12.2 15.8 12.1 14.9 15.2 17.4 15.6
162 180 717 215 135 177 1072 255 135 155 1530 207.5
75 76 256 88 38 67 556 79 55 67 894 85
E N E N E N E N
Upstream Lot Downstream Lot Decazeville Riou Viou Upstream Lot Downstream Lot Decazeville Riou Viou Upstream Lot Downstream Lot Decazeville Riou Viou
mine drainage from the former lead and zinc mine at Carnoule`s (Dedourge-Geffard et al., 2009). Four stations were studied: three stations along the Amous river: Upstream 1500 m, Downstream þ1200 m and Downstream þ3500 m, with different levels of metallic contamination (with a maximum in Downstream þ1200 m); and a fourth reference station on a tributary from the same river system (Tributary) that is not impacted by metal-loaded mine leachates (see Dedourge-Geffard et al., 2009 for chemical characterisation data). Mean water temperature varied between stations from 9.5 to 11.8 C and mean water conductivity from 520 to 600 mS cm1 (detailed in Table 1D).
2.3.
Water temperature ( C)
FR computation
Leaf discs were numerically scanned using an Epson perfection 3490 PHOTO scanner after 7 d of exposure. The surfaces of the discs were measured using SigmaScan Pro v5.0 imaging software (Systat Software). FR, expressed as
a consumed surface per day per gammarid (mm2 .d1. organism1), was calculated for each replicate as follows:
FRi ¼
ðScontrol Si Þ li;0 þ li;t 2 t
(1)
where FRi is the feeding rate of replicate i; Scontrol the total surface of leaf discs present at the end of experiment in the control without gammarids; Si the total surface of leaf discs present at the end of the experiment in replicate i; t is the duration in days of the experiment (here t ¼ 7 days for all assays); li,0 and li,t are the number of living gammarids at the start and at the end of experiment (here li,0 ¼ 20). We reported on Suppl. Fig. 2 the relationship between surface and mass of leaf discs after different levels of consumption to allow the comparison with FR from the literature expressed as mg consumed per day and per gammarid. In order to propose a sufficiently reliable measure of FR for all the experiments (in the laboratory or in situ), we decided to only consider situations when gammarid survival remained higher than 75%.
Table 1D e Detailed information on in situ deployments for the different feeding assays considered in experiment 6: Amous watershed (March 2008). Station Tributary Upstream-1500 m Downstream þ1200 m Downstream þ3500 m
GPS.coordinates 03 590 44 060 03 590 44 060 03 590 44 050 03 580 44 040
51.9" E 13.1" N 17.2" E 35.5" N 43.2" E 57.7" N 57.8" E 30.6" N
Water temperature ( C)
Conductivity (mS cm1)
Hardness (mg L1 of CaCO3)
9.45
520
308
11.80
525
311
10.25
590
347
11.65
600
351
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
2.4.
Statistical analyses
Statistical procedures were carried out with the R software (R Development Core Team, 2008). Normality and homoscedasticity were checked using ShapiroeWilk test and Bartlett test, respectively. The influence of body size and exposure week on FRs in experiment 1, and the influence of temperature, conductivity and exposure week on FRs in experiment 2 were tested using linear modelling (ANCOVA procedure) including the interaction terms in full models. Among explanatory variables, body size and temperature were included as continuous, conductivity and exposure week as categorical. In experiment 3, the between-deployment variability was also tested by linear modelling (ANOVA test). To compare FR measurement between deployments during experiments 4, 5 and 6, non-parametric testing was used because of heteroscedasticity in FR distributions observed between deployments in contaminated contexts. Furthermore, as in biomarker studies reported in Xuereb et al. (2009a, 2011), we built a reference distribution of in situ feeding levels based on the measurements in the reference stations (experiment 3). This was possible after removing the between-deployment variability in FR measurements in reference stations, by means of the computation of a feeding inhibition index (FI ) (see Section 3.2). A normal distribution was fitted to FI values from reference stations thanks to the fitdistr function in the MASS R-package. Such a distribution allowed us: (i) to compute a 5% confidence interval of FI values expected in reference conditions; and (ii) to calculate the likelihood that the replication of FI values measured in a given in situ deployment could be observed under reference conditions. A p-value associated to this likelihood was calculated using an empirical null distribution built by computing the likelihoods of replications of FI values in 105 theoretical deployments simulated from the reference distribution.
3.
6423
Fig. 1 e Effects of body size, temperature and conductivity on feeding rates (FR) in laboratory conditions: (A) FRs for three body size classes of gammarids (7.3 (±0.5), 10.6 (±0.7) and 12.8 (±0.9) mm); (B) FRs for three water temperatures (6.9 (±0.05), 12.1 (±0.01) and 16.4 (±0.4) C) and two levels of conductivity (200 mS cmL1 in black and 600 mS cmL1 in grey). Circle and triangle symbols correspond to first and second weeks of experiment, respectively.
Results
3.1. Influence of body size, temperature and conductivity on FRs in the laboratory In experiment 1 (Fig. 1A), we did not observe any significant differences in FR between the two successive weeks of experiments (ANCOVA test: interaction term p ¼ 0.53, week effect p ¼ 0.68). We noted that the feeding activity of gammarids increases with body size (ANOVA test, p < 1014). This influence was strong considering for instance that a deviation from 10 to 11 mm in the mean body size of the 20 selected organisms would give rise to a relative increase of 20% in FR level. In experiment 2 (Fig. 1B), no effect of conductivity and exposure week on FR was detected (ANCOVA test: interaction terms p > 0.10, week effect p ¼ 0.99, conductivity effect p ¼ 0.80). We noted that the FR increases with temperature (ANOVA test, p < 1015). Therefore, for male gammarids measuring 10.6 0.7 mm, we related FR, expressed as a consumed surface per gammarid per day (mm2.day1.organism1), to temperature T ( C) with a linear regression model (r2 ¼ 0.79, n ¼ 48):
FR ¼ 1:85 T þ 3:14
(2)
From this equation, it appeared for instance that an increase in mean temperature from 12 C to 13 C would result in an augmentation of 7.3% in FR.
3.2. Modelling the in situ variability of FR measurements among reference stations Important physicochemical differences were observed between the 24 deployments, concerning mean water temperature (from 9.2 to 15.1 C) or conductivity (from 110 to 420 mS cm1) (details in Table 1A). We observed significant differences for FR measurements between deployments (ANOVA test, p ¼ 0.04) (Fig. 2A). Considering the findings from laboratory experiment 2, we assumed that temperature was the main determinant of this variability. This was also confirmed a posteriori by the analysis of FR variance among the 24 deployments fitting conductivity and temperature as
6424
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
A
B
Fig. 2 e In situ feeding assays in reference and contaminated stations in October 2009 (deployments R1-R12 and P1-P15) and June 2010 (deployments R13-R24 and P16-P41): (A) Raw measurement of feeding rates (FR) (black points: mean for each deployment; grey points: four replicates per deployment; segments: min-max range); (B) Temperature correction via the computation of the feeding inhibition index (FI ) (see Eq. (3) in the text). The solid and dotted lines represent the mean and the 95% confidence interval of the distribution of FI values assessed in deployments within reference stations. The deployments with a significant deviation of FI values from this reference distribution are marked with stars (see Section 2.4 for the calculation of likelihood and p-value).
explaining factors (ANOVA test: conductivity effect p ¼ 0.49; temperature effect p < 107). In order to take into account the influence of temperature on FR, we calculated a feeding inhibition index (FI) which was calculated as: FI ¼
FRpred FRobs 100 FRpred
(3)
where FRobs is the feeding rate measured during the in situ experiment, and FRpred is the feeding rate predicted with Eq. (2) at the mean temperature during the in situ exposure. With this index, we no longer detected significant differences in feeding activity between the different deployments in reference stations (ANOVA test, p ¼ 0.12) (Fig. 2B). Fitting a normal distribution to the calculated FI values led to a mean value of FI, which is not significantly different from zero (Student test, p ¼ 0.52). This demonstrates congruence between the in situ measurements and the predicted values from the laboratory experiments. This finding validated the assumption that temperature was the main determinant of FR variability between deployments. In addition, the robustness of FR measurement was reinforced since we observed that not only the mean value of FR was unchanged between laboratory and in situ measurements, but also that the variability of FR was not affected (see Suppl. Fig. 3). Using the normal distribution of FI in the reference stations, we confirmed the good replicability between the stations and the seasons. Indeed data
invariably fitted the reference distribution when the likelihood of observed replicated FI values for each deployment ( p > 0.5 for all deployments, Fig. 2B) was tested. From these findings, FI can be interpreted as a feeding inhibition index, because it contrasts a given observed FR in one assay with the expected value of feeding activity in a non-contaminated context for the same temperature.
3.3. Sensitivity of the FR assay during in situ exposure at contaminated stations Important physicochemical differences were observed between the 41 deployments : mean temperature ranged from 11.8 to 20.7 C, conductivity from 80 to 868 mS cm1 (details in Table 1B). We observed significant differences between the FR (Fig. 2A, Kruskal & Wallis rank sum test, p < 1010) and greater variability compared to reference stations (experiment 3). We assumed that this increase in the range of of observed FRs was due on one hand to the possible influence of higher temperatures, and on the other hand, to possible inhibitions. In order to remove temperature-induced variability in feeding activity, notably for the comparison of contaminated and reference stations (experiment 3), we calculated FI for all deployments (Fig. 2B). This revealed significant feeding inhibitions ( p < 0.05) for 14 deployments (P7, P20, P21, P24, P25, P28, P29, P30, P32, P33, P34, P37 and P41). We observed feeding
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
inhibition in 7% of the deployments in October 2009, and 54% in June 2010. In addition, FI values did not exceed the upper limit of the reference confidence interval, implying that the higher values of the FR were mainly explained by higher exposure temperatures.
3.4.
Case studies
The Lot watershed. We first performed a paired comparison (within each season) between FR measurements in contaminated stations and the reference station Upstream Lot
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(Fig. 3A). Significantly lower FR values were observed from the Decazeville station in winter, spring and summer, from the Riou Viou station in autumn and from the Downstream lot station in spring (unilateral Wilcoxon rank sum tests, p < 0.05). The Riou Viou station also presented higher FR values than the reference station in winter (unilateral Wilcoxon rank sum tests, p < 0.05). The computation of FI values (Fig. 3B) confirmed the significant feeding inhibition at the Decazeville station in winter, spring and summer, and at the Downstream lot station in spring when compared to the reference distribution from experiment 3 ( p < 0.05). In
A
B
Fig. 3 e Site-specific surveys of Lot (four seasons) and Amous (spring) watersheds (reference stations are in bold): (A) Raw measurement of feeding rates (FR) (same conventions as Fig. 2). Crosses represent stations with significant differences in FRs in comparison with the reference station; (B) Temperature correction via the computation of the feeding inhibition index (FI ) (see Eq. (3) in the text). The solid and dotted lines represent the mean and the 95% confidence interval of the distribution of FI values assessed in deployments within reference stations from Fig. 2. The deployments with significant deviation of FI values from this reference distribution are marked with stars (see Section 2.4 for the calculation of likelihood and p-value).
6426
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 1 7 e6 4 2 9
addition, FI values revealed significant feeding inhibitions at the Decazeville station in autumn, as well as at the Downstream Lot station in winter. This constituted two falsenegative cases, i.e. inhibitions not detected with the FR. These false-negatives would not have been uncovered if we had simply compared FI to values from the only reference station (Upstream Lot) for the same season (unilateral Wilcoxon rank sum tests, p > 0.05). The computation of FI values also revealed false-positive cases: the decrease of FR values from the Riou Viou station in autumn and the increase in winter were masked once temperature heterogeneity influence was taken into account with FIs. These false-positives were eliminated either by the comparison with the reference station or by the comparison with the reference distribution from experiment 3. In addition, we observed that the FI values from the reference Upstream Lot station corresponded well with the reference distribution, and that the significant seasonal variability in FR values for this station (Kruskal Wallis rank sum test, p < 0.05) was negated once the FIs which integrated temperature influence were considered (Kruskal Wallis rank sum test, p ¼ 0.68). The Amous watershed. Considering FR values (Fig. 3A), we observed significantly lower feeding activities from the two stations Upstream 1500 m and Downstream þ1200 m in comparison to FR measurements from the reference station (Tributary) (unilateral Wilcoxon rank sum test, p < 0.05). Using FI values (i.e. temperature corrected) (Fig. 3B), we still detected significant feeding inhibition but only at the Downstream þ1200 m station (unilateral Wilcoxon rank sum test, p < 0.05). This pattern was supported when comparing the FIs with either the measurements from the reference station (Tributary) or with the reference distribution of FI values (experiment 3).
4.
Discussion
4.1. Identification of influential factors on FR in caged G. fossarum Despite their recognized importance, the influence of biotic (including body size, source population or parasite load) and abiotic (including dissolved oxygen concentration, alkalinity, temperature or pH) factors on feeding activity of gammarids has rarely been scrutinized. The influence of parasitism is one of the most described biotic factor (McCahon et al., 1988; Pascoe et al., 1995; Fielding et al., 2003; Lettini and Sukhdeo, 2010). For other factors, quantitative studies are scarcer. Here, we showed a significant linear positive relationship between FR values and organism size (Fig. 1A), agreeing with the study of Nilsson (1974) on G. pulex. Blockwell et al. (1998), on the contrary, did not find any significant differences in feeding activity of G. pulex between juveniles of 5.1 and 7.0 mm. However, they measured feeding activity through the consumption of Artemia salina eggs, which makes the comparison with our results difficult, and their methodology may have been less sensitive than leaf consumption methods. The linear relationship reported on Fig. 1A contradicts the theoretical prediction, which states that FR should be proportional to length squared due to allometric constrains
(Kooijman, 2000). Such a parabolic relationship may be concealed because we tested a too narrow range of body sizes. Nevertheless, considering that the weight of organisms is proportional to the length cubed, the positive relationship between FR and length is consistent with negative correlations reported for amphipods between weight-specific consumption and body weight (Nilsson, 1974; Sutcliffe et al., 1981; Lozano et al., 2003). Furthermore, in Diporeia, the coefficient of this exponential decrease has been quantified as 0.84 0.08 (Lozano et al., 2003) which is closer to our finding (2/3 is expected) than to a parabolic pattern (1/3 is expected). The possible interaction between the effect of body size on FR and abiotic factors such as temperature is reported in some studies (Nilsson, 1974) but not in others (Lozano et al., 2003). In our study, we did not test such an interaction because we chose to control the variability induced by biotic factors thanks to the transplantation of standard organisms, using male gammarids from a unique population, with no visible parasites, with homogenous body size, and acclimatized in the laboratory before in situ transplantation. Regarding the influence of environmental conditions (abiotic factors) with caged gammarids, Maltby et al. (2002) showed that temperature heterogeneity explained 76% of the between-deployment FR variation in reference stations. Taking into account additional physicochemical variability did not strongly increase the amount of explained variance (less than 8%). Our results confirmed these findings. We described a significant linear influence of temperature on feeding activity (Fig. 1B), with a 50% reduction of the FR at 7 C compared to 16 C. This was consistent with results on G. pulex (Nilsson, 1974) where the FR was dropped by 90% at 2 C compared to 15 C under laboratory conditions. According to the theoretical model of Arrhenius, we could expect an exponential relationship between temperature and FR, because it is a physiological rate (Kooijman, 2000). This pattern could have been described more precisely if we had extended the range and the number of tested temperatures. Nevertheless, considering the residual variability of FR values (Fig. 1B), the minor difference which could be detected between linear and exponential model should not improve our prediction of temperature effect within the range of the temperatures considered in our study, which already constitutes a large range of conditions for in situ biotests with G. fossarum. We did not detect any influence of conductivity on FRs in the laboratory. This pattern of a major influence of temperature on the FR was supported by in situ results in different rivers, and during different seasons, with highly contrasting physicochemical characteristics and geographical locations. Indeed, removing temperature effects, erased spatial and seasonal differences in measurements under reference conditions (during experiment 3 and for the two case studies, Figs. 2 and 3) and we observed an important decrease in the variability of feeding activity in contaminated stations (Fig. 2).
4.2. Definition of reference values and accuracy of in situ FR assay In order to limit false positives induced by confounding environmental factors, one approach - implemented for some
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biomarkers (Xuereb et al., 2009a; Hanson et al., 2010) - would consist in defining a range of reference values by including the whole annual and spatial variability observed in reference stations (Hagger et al., 2008). This appears problematic for individual responses such as FR, because of their high natural variability (induced by temperature) which would lead to a lack of statistical power and thus to difficulties in discriminating feeding inhibitions related to contaminations (Fig. 2A). A current practical solution consists in using only local/ seasonal controls. As exemplified in our two case-studies (Fig. 3), this is questionable since it does not fully prevent confounding effects of temperature occurring even at small spatial scales. In the Lot case-study, the deviation of the FR between the Riou Viou and the reference station was entirely explained by temperature heterogeneity within the watershed. In the Amous case-study, the same pattern was observed between the Upstream 1500 m and Tributary stations. In addition, it appears that the interpretation of bioassays according to local controls does not solve the problematic lack of statistical power, not because of significant variability in controls but due to the reduced number of available control measurements (Hanson et al., 2010). For example, in the Lot case-study, FI values measured from Decazeville station in autumn and from the Downstream Lot station in winter showed inhibition of feeding activity (as during other seasons), but this was not revealed by the simple comparison to the reference Upstream Lot station (Fig. 3). As an alternative, modelling the influence of confounding factors permits to correct observed FR and supplies comparable feeding activities even in variable environmental conditions (Maltby et al., 2002; Moreira et al., 2006; Krell et al., 2011). We show here that this allows to take advantage of robust reference conditions defined at large scale of space and time. This permits a gain in the statistical power for the detection of impacted feeding activities because of the reduced variability of FI in controls, and because of the large set of reference measurements which could be compiled to define the distribution of reference values (Hanson et al., 2010). Following the same approach as Moreira et al. (2006) and Krell et al. (2011), we chose to model the influence of temperature and conductivity in preliminary experiments under controlled laboratory conditions. Maltby et al. (2002) modelled the influence of temperature and other environmental factors on gammarids FR through the statistical analysis of results from a set of in situ bioassays in reference conditions. In our case, we observed similar results with the two approaches (Suppl. Fig. 3). Nevertheless, when the measurements of individual responses are comparable between laboratory and in situ conditions, the first approach would be preferred because it should be more predictive (between regional contexts for instance). Indeed, the latter data-driven approach may confound the influences of environmental factors which could be correlated only in the specific reference dataset. For the assessment of the impact of an identified pointsource pollution, the definition of baseline values robust to environmental variability also permits to qualify the value of a local reference. For instance, in the Lot study, the Decazeville station was first considered as a reference location to evaluate the impact of the past mining activity because of its metal-free water chemistry. But, it appears that urban
6427
effluents highly impact FR in this station, consistent with the alteration of water chemical quality revealed by the induction of biomarkers of genotoxicity in this station (Lacaze et al., 2011). Thus, as already proposed with laboratory controls for post-exposure FR bioassays in Daphnia (Barata et al., 2007), in such comparative approaches (e.g. upstream/downstream), the use of independent reference benchmarks for feeding activity could help to accurately assess any alteration of chemical water quality.
4.3. Relevance of the in situ FR bioassay with G. fossarum With the aim to integrate the complexity of field exposure to contaminants in water quality assessment, bioassays with gammarids appear as promising tools because leaf-mass consumption methodology permits in situ measurements of FR, while the majority of protocols with invertebrates are only achievable by post-exposure measurement in laboratory conditions: bivalves (Hagger et al., 2008), gastropods (Krell et al., 2011), daphnids (Barata et al., 2007; Dama´sio et al., 2008), decapods (Moreira et al., 2006), chironomids and annelids (Soares et al., 2005). In addition, these assays are not less influenced by environmental conditions during exposure than in situ FR measurements. The feeding assay with Gammarus appeared sensitive to contaminants in multiple contexts (agricultural, industrial, mining,.): in experiment 4, 37% of the contaminated stations showed significant feeding inhibitions. These results supported findings from previous laboratory or field studies (Suppl. Table 1) that demonstrated feeding inhibition with gammarids in response to a large variety of environmental contaminants. The FI index allowed us to compare impacts of contamination at different times. We observed important seasonal variations of in situ impacts on feeding activity. For instance in experiment 4, only 7% of the stations showed significant feeding inhibitions in autumn, whereas 54% of the stations were impacted in summer. Such a seasonal variation is also reported by Maltby et al. (2002), who explained this pattern by variation in waterflow. Complementary hypotheses are variation in run-off and more specifically seasonal treatments (pesticides) in agricultural contexts. Because we used standard organisms, such variation in individual responses could not be understood by variation in susceptibility due to changes in the biological status of tested organisms, as in the proposed interpretation of bioassays performed with indigenous organisms (Hagger et al., 2008). Thus, our approach, which makes short-term tests comparable in time would facilitate the inclusion of the seasonality of in situ toxicity for water quality assessment. The transplantation of standard organisms only supplies partial information to understand long-term chronic impacts of contamination on higher integration levels of biological organization (Liber et al., 2007). Nevertheless, feeding inhibition is of great interest for such multi-scale assessment. On one hand FR is recognized as an ecologically relevant endpoint because it can be related to alteration in life-history traits, and mechanistic modelling is proposed to fill the gap between feeding inhibition and drop in population dynamics in particular with Gammarus (Baird et al., 2007). Because they are keystone species in freshwater ecosystems, alteration in
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population dynamics of Gammarus sp could be interpreted in terms of alteration of ecosystem functioning (e.g. leaf decomposition). On the other hand, feeding inhibition has been correlated with the impact of contaminants with diverse modes of actions traced by the modulation of specific molecular biomarkers (Barata et al., 2007; Xuereb et al., 2009b). This position of FR between biomarkers and fitness traits offers the opportunity to describe adverse outcome pathways in multiscale assessment schemes (Kramer et al., 2011), which could reinforce weight of evidence approaches for the diagnostic of contaminant impacts on ecosystems (Dama´sio et al., 2008). It is illusory to think that all sources of uncertainty (standard vs indigenous organisms, within-population, between-populations, or between-species variability, intra and interspecies interactions, food availability, .) could be taken into account mechanistically in such a scheme which is based on information obtained with standard organisms in specific experimental conditions. Yet, it has already been shown that reduction of feeding activities of transplanted Gammarus can be correlated to reduction in leaf decomposition efficiency in streams, regardless of the presence of Gammarus in indigenous communities (Forrow and Maltby, 2000). This underlines the strong potential of FR in situ bioassay with Gammarus as an ecologically relevant indicator of water quality.
5.
Conclusions
We proposed an innovative protocol for an in situ feeding assay based on the standardisation of FR measurements through the combination of experimental and computational methodologies (caging and statistical modelling). As it corrected the confounding influence of temperature, which appeared as the main environmental influence on in situ FR values, our protocol permitted a more accurate assessment of the alteration of feeding activity when between-station comparisons in space and time were performed. A reliable interpretation of our bioassay results was made feasible via the comparison to a distribution of reference values. Such a methodology increased the specificity and the sensitivity of the assay in multiple contaminant, geographical, and seasonal contexts. It also enhanced the relevance of toxicity assessment in site-specific studies by validating reference station measurements. Lastly it could offer the possibility to assess water quality in isolated stations as part of large scale surveys, notably in non-point-source pollution contexts. Further research will focus on the influence of source population for FR measurements (within and between-species variability), which could limit the development of such bioassays for large scale (national, continental) biomonitoring programs. Modelling approaches will also be developed with G. fossarum to extrapolate such assessments of feeding inhibitions to the potential impacts on population dynamics.
Acknowledgements RC received financial grants from the Cluster Environnement Re´gion Rhoˆne-Alpes. The present work was partially funded
by the programs ANR 08-CES-014 RESYST, ANR ECCOECODYN convention # 06CV050, and the French national agency for water and aquatic ecosystems (ONEMA). We are grateful to T. Pelte (Regional water agency Rhoˆne-Me´diterrane´e-Corse) and A. Tilghman (Cemagref) for providing information from national river reference/control networks for the selection of deployment stations. The authors thank A. Tilghman for her critical reading of the English of the MS.
Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011.09.035.
references
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Relation between EPS adherence, viscoelastic properties, and MBR operation: Biofouling study with QCM-D Amer Sweity a, Wang Ying a, Mohammed S. Ali-Shtayeh b, Fei Yang a, Amos Bick c, Gideon Oron a, Moshe Herzberg a,* a
Ben Gurion University of the Negev, Zuckerberg Institute for Water Research, Sede Boqer Campus, Midreshet Ben Gurion, 84990, Israel Biodiversity & Environmental Research Center (BERC), Til Village, P.O.BOX 696, Nablus, West Bank, Palestinian Authority c Department of Industrial Engineering and Management, Jerusalem College of Technology, Jerusalem, Israel b
article info
abstract
Article history:
Membrane fouling is one of the main constraints of the wide use of membrane bioreactor
Received 23 June 2011
(MBR) technology. The biomass in MBR systems includes extracellular polymeric
Received in revised form
substances (EPS), metabolic products of active microbial secretion that adversely affect the
16 September 2011
membrane performance. Solids retention time (SRT) in the MBR is one of the most
Accepted 19 September 2011
important parameters affecting membrane fouling in MBR systems, where fouling is
Available online 29 September 2011
minimized at optimal SRT. Among the operating parameters in MBR systems, SRT is known to strongly influence the ratio of proteins to polysaccharides in the EPS matrix. In
Keywords:
this study, we have direct evidence for changes in EPS adherence and viscoelastic prop-
Biofouling
erties due to changes in the sludge removal rate that strongly correlate with the membrane
MBR
fouling rate and EPS composition. EPS were extracted from a UF membrane in a hybrid
QCM-D
growth MBR operated at sludge removal rates of 59, 35.4, 17.7, and 5.9 L day-1 (corre-
EPS
sponding SRT of 3, 5, 10, and 30 days, respectively). The EPS adherence and adsorption
Ultrafiltration
kinetics were carried out in a quartz crystal microbalance with dissipation monitoring
Wastewater
(QCM-D) technology in several adsorption measurements to a gold sensor coated with Polyvinylidene Fluoride (PVDF). EPS adsorption to the sensor surface is characterized by a decrease of the oscillation frequency and an increase in the dissipation energy of the sensor during parallel flow of aqueous media, supplemented with EPS, above the sensor surface. The results from these experiments were further modeled using the Voigt based model, in which the thickness, shear modulus, and shear viscosity values of the adsorbed EPS layers on the PVDF crystal were calculated. The observations in the QCM-D suggested that the elevated fouling of the UF membrane is due to higher adherence of the EPS as well as reduction in viscosity and elasticity of the EPS adsorbed layer and elevation of the EPS fluidity. These results corroborate with confocal laser scanning microscopy (CLSM) image analysis showing thicker EPS in close proximity to the membrane surface operated at reactor conditions which induced more fouling at elevated sludge removal rates. ª 2011 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ972 8 6563520; fax: þ972 8 6563503. E-mail address:
[email protected] (M. Herzberg). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.038
W a t e r R e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 3 0 e6 4 4 0
1.
Introduction
Membrane fouling is one of the main constraints of the wide use of membrane bioreactor (MBR) technology (Judd, 2006) causing an increase in the trans-membrane pressure (TMP) or a decrease in the permeate flux. During the biofouling process, membrane permeability decreases and energy consumption increases (Yang et al., 2006). Membrane fouling in MBR processes almost always consists of a combination of colloidal, organic, and microbial deposits (biofouling) as well as inorganic precipitates (scaling). These fouling factors increase the membrane hydraulic resistance over time and the permeate flux is consequently reduced. In most cases, deposition of the foulants are found both on the external membrane surface with some degree of foulant deposition inside the microfiltration (MF) and ultrafiltration (UF) membrane pores (Chang et al., 2002). Membrane biofouling is strongly related to membrane properties, operational conditions and biomass characteristics that include extracellular polymeric substances (EPS) properties. Hybrid growth MBR (HG-MBR) system can be defined as the combination of a membrane separation process and a hybrid growth processes, in which both suspended and attached-growth microorganisms are part of the MBR (Sombatsompop et al., 2006; Yang et al., 2006). HG-MBR allows for upgrading the treatment capacities of existing MBR treatment plants by increasing biomass level. Since both attached- and suspended-growth are involved, the HG-MBR can be operated at lower mixed liquor suspended solids (MLSS) concentrations. Membrane fouling is minimized without loss of the treatment efficiency due to biological activity of the microorganisms that are attached to the support carriers. EPS, metabolic products of active bacterial secretion (Comte et al., 2006; Nuengjamnong et al., 2005), can be found either in a soluble form (also termed as soluble microbial products e SMP) or bound to cells or flocs in the reactor forming the cohesive matrix of the biofilms. Bound EPS consist of proteins, polysaccharides, nucleic acids and lipids accumulating on the bacterial cell surface (Morgan et al., 1990). The EPS strongly affect the microbial microenvironment heterogeneity including changes in porosity, density, water content, sorption properties, charge, hydrophobicity, and mechanical stability (Flemming and Wingender, 2001). One of the most effective MBR operating parameters with an impact on fouling propensity is solids retention time (SRT) or sludge age. SRT affects various sludge properties such as floc size, bound and soluble EPS content, and settling characteristics (Le-Clech et al., 2006). Contradictory reports regarding a relationship between SRT and membrane biofouling show that even though higher SRT leads inevitably to increase of MLSS concentration, this in itself may not necessary lead to greater fouling. In general, optimal SRT, reported in plethora of studies between 20 and 50 days, is required to achieve a minimal fouling tendency (Meng et al., 2009; Drews, 2010; Kraume and Drews, 2010). Improved membrane permeability was observed at longer SRT of 10 and 20 days in comparison to SRT of 3 and 5 days. The results were attributed to elevated concentrations of SMP and EPS concentrations that were observed to induce membrane
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fouling rate when SRT was decreased (Ng et al., 2006). Cho et al. (2005a) showed that as SRT decreased, the amount of bound EPS in the sludge flocs increased (Cho et al., 2005b). Han et al. (2005) has reported that membrane fouling rate increased with increasing SRT of 30, 50, 70, and 100 days due to a large amount of foulants and high sludge viscosity (Han et al., 2005). In contrast, Lee et al. (2003) tested three labscale submerged MBRs at SRT of 20, 40, and 60 days with a constant permeate flux and no major change in EPS concentration was observed as SRT increased (Lee et al., 2003). In another study, at elevated MLSS concentrations from 7 to 18 g/l corresponding to an increase in SRT from 30 to 100 days, fouling rate was twice for the extended SRT (Al-Amoudi and Farooque, 2005). This increase was probably due to the raised viscosity at the high MLSS concentration that attenuates the effect of bubbling and scouring of the membrane surface. Not surprisingly, fouling rate increased nearly 10 times when SRT was lowered from 10 to 2 days, probably due to the increased levels of EPS production (Trussell et al., 2006).Chang and Lee (1998) found that when the SRT was increased from 3 to 8 and to 33 days, a significant increase in sustainable flux was observed (Chang and Lee, 1998). The reduced fouling rates associated with a decrease in sludge production rates at longer sludge ages, is usually attributed to lower EPS concentrations in the reactor. In addition, increasing SRT could enhance the development of slow growing microorganisms that are able to consume polysaccharides and proteins as substrates and produce less biopolymers (Masse et al., 2006). Overall, it is likely that there is an optimal SRT, between the high fouling tendency at very low SRT and the high viscosity of mixed liquor at very long SRT. EPS play a major role in the cohesion of the sludge flocs in the MBR as well as the cohesion of the biofilm layers located on carriers in the HG-MBR systems. EPS are also in charge of biofilms viscoelastic properties which in turn, can strongly affect the microbial flocs and biofouling layer resistance to shear. Eventually, EPS are recognized as the most direct and significant factor affecting biofouling in MBRs (Laspidou and Rittmann, 2002; Le-Clech et al., 2006). Soluble EPS in the MLSS was reported as an important factor influencing membrane fouling. A high concentration of soluble EPS was shown to boost membrane fouling tendency (Kimura et al., 2005). Ouyang and Liu (2009) showed that soluble EPS concentration increased at shorter SRT, in which total protein concentrations was higher than polysaccharides in the MLSS supernatant, whereas the total polysaccharide content was higher than the protein in the flocs attached to the membrane surface causing a significant fouling. By increasing the SRT, soluble EPS content was decreased on the membrane surface and membrane filtration resistance was reduced (Ouyang and Liu, 2009). EPS production and accumulation on the UF membranes in MBR systems is a complex process influenced by several factors like the substrate composition, mechanical stress, organic loading rate, MLSS concentration, presence of soluble EPS compounds and membrane properties (Chang and Lee, 1998; Rojas et al. 2005; Rosenberger and Kraume, 2003). Since it would be hard to point out how a combination of so many parameters may influence the properties of the
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accumulated on the membrane, direct membrane autopsy and analysis of the accumulated EPS can help to relate between EPS properties and membrane fouling. In this study, we hypothesized that membrane filterability is strongly influenced by EPS cohesion and viscoelastic properties, important properties of the EPS produced at different sludge removal rates in the MBR (Ng et al., 2006; Ying et al., 2009). Different EPS originated from the membrane at different sludge removal rates showed different adherence and viscoelastic properties that were correlated to the EPS composition and to the fouling rate of the membrane. It was intriguing to see how EPS adherence and viscoelasticity change in correlation to different conditions that promote biofouling to different degree. We also suggest a novel parameter in fouling phenomena of membranes in general, first to be applied to UF membranes, in this study e the fluidity of the adsorbed EPS layer. This parameter is frequently used to describe biopolymer layers in order to estimate their viscoelasticy (deKerchove and Elimelech, 2006; Feiler et al., 2007). The working objective of this study focused on defining if the EPS accumulated on the UF membrane is more fluidic or more rigid under conditions that promote biofouling. To study the adherence and viscoelastic properties of the EPS, we utilized a quartz crystal microbalance with dissipation monitoring (QCM-D) technology. QCM-D provides realtime, label free measurements of molecular adsorption and/ or interactions taking place on various surfaces (Eydelnant and Tufenkji, 2008; Wang et al., 2007). In addition to assessing adsorbed mass (ng/cm2 sensitivity), measured as changes in oscillating frequency (F) of the quartz crystal, the energy dissipation (D), which is the reduced energy per oscillation cycle provides novel insights regarding structural properties of adsorbed layers (Nguyen and Elimelech, 2007; Voinova et al., 1999). EPS originated from the UF membrane at different sludge removal rates during the MBR operation was extracted and analyzed. Furthermore, confocal laser scanning microscopy (CLSM) of the biofilm on the UF membrane and EPS composition results were correlated to fouling rate of the UF membrane and to the EPS cohesion and viscoelastic properties.
2.
Materials and methods
2.1.
HG-MBR system and operating conditions
The HG-MBR was equipped with an immersed UF membrane module of ZeeWeed (ZW-10) (Zenon Environmental Inc, Canada). The membrane module was made of hollow fibers of polyvinylidene fluoride (PVDF) with a mean pore size of 0.04 mm and a total effective filtering surface area of 0.93 m2 allowing the removal of pathogens and organic matter. The volume of the bioreactor process tank was 190 L and included activated sludge, AqWise carriers (AqWise, Israel), and the membrane module. AqWise carriers were filled as biofilm support with a filling ratio (carrier volume/reactor volume) of 50% (13.64 kg). The carriers are made from high-density (0.96 g/cm3) polyethylene with diameter and height of 13 mm and a specific surface area of 600 m2/m3. The carriers’ circulation was driven by an air diffuser. The membrane
module was surrounded by an 8 mm mesh for avoiding damage from the moving carriers. The system operated under constant-flux mode with a mode of 5 min filtration and 15 s backwash. A feed domestic sewage mixed with chickens’ manure was injected into the bioreactor that was operated under desert ambient conditions. Membrane cleaning was maintained by soaking the membrane module in 750 mg/L sodium hypochlorite supplemented with 250 mg/L sodium dodecyl sulfate (SDS) solution for 16 h, repeatedly for 4 times after each experiment, until the membrane permeability was recovered. Aeration was done through an air diffuser installed directly beneath the membrane module for supplementing oxygen to microorganisms, mixing the liquor and cleaning the membrane with aeration rate of 2.3 m3/h. Airflow rate was controlled by a rotameter, filtration flux of permeate was monitored volumetrically and TMP was monitored by a digital pressure indicator. The mixed liquor temperature was monitored by a temperature indicator located in the reactor MLSS. The dissolved oxygen (DO) concentration is the mean of the upper, middle and bottom locations in the bioreactor vessel (Model 550, YSI, USA). The bioreactor was employed with a water level sensor was used to keep a constant liquid level in the bioreactor. The HG-MBR was operated over a period of two months at sludge removal rate values of 2, 5, 10, and 30 days. The hydraulic retention times (HRT) of this HG-MBR was 5.5 h for all the experiments. Operating conditions of the HG-MBR at different sludge removal rates are listed in Table 1. The influent and effluent characteristics of the HG-MBR operated at different sludge removal rates are listed in Table 2. As expected, at different sludge removal rates, biomass concentration varies. The suspended and attached biomass concentrations versus time at different sludge removal rates are presented in the supporting information section (Figure S1). By reducing the suspended sludge age, an increased washout of the suspended biomass was observed: The MLSS concentration was lower at shorter sludge removal rates. The mean MLSS concentrations were 4055, 2686, 1678 and 1392 mg/L for the sludge removal rates of 30, 10, 5 and 3 days, respectively (Table 1). Interestingly, the attached biofilm concentration was also reduced. In a similar trend of the decline in MLSS concentration, the decline of the attached biofilm concentration was also observed (Figure S1). The sludge removal rate calculation is taking into account that there was no biomass lost in the effluent of the MBR during its entire operational period. Therefore, the biomass concentration in both the reactor and in the removed sludge stream is the same. SRT, was calculated following Li et al. (1984), SRT ¼ V$XV =VWS $DXV The reactor volume was 190 L multiplied by the volumetric fraction occupied by the biofilm’s carriers (50%). V is the reactor volume occupied by MLSS (L), VWSis the flow rate of the removed sludge per day (L day1), XV is the MLSS concentration and DXV is the MLSS concentration in the removed sludge per day. Since in this work, XV ¼ DXV, the suspended sludge retention time is the reactor volume (L) divided by sludge removal rate (L day1), V/VWS.
2.2.
EPS extraction and analysis
EPS extraction was performed from a single hollow fiber that was cut from the ZW-10 module at the end of every
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Table 1 e Operating conditions of the HG-MBR at different removal rates (L dayL1) of MLSS. Parameter Estimated SRT (days) Temperature Initial membrane permeability (L/(m2.hr.bar)) at 20 C Initial membrane resistance (m1) Filtrate flux (L/(m2 h)) Aeration rate (m3/hr) Hydraulic retention time (hours) pH in the reactor Dissolved oxygen, mg O2/L MLSS range (mg/L) AqWise carriers in the reactor Operating time (days)
5.9 L day1
17.7 L day1
35.5 L day1
59 L day1
30 15.5 w 29.4 C (mean 22.3 C) 631.1
10 22.2 w 28.9 C (mean 26.3 C) 465.5
5 20.0 w 30.6 C (mean 28.5 C) 449.8
3 25.5 w 29.4 C (mean 28.0 C) 527.7
0.56 1012 0.76 1012 0.79 1012 44.5 to 38.9 44.8 to 40.6 45.2 to 38.58 2.3 2.3 2.3 5.1 w 5.8 5.0 w 5.5 5.0 w 5.9 6.6 w 7.8 6.2 w 6.9 6.6 w 6.9 0.31 w 6.5 0.7 w 5.6 0.25 w 3.66 (mean 2.6) (mean 2.8) (mean 0.89) 2440 to 4580 1000 to 3520 1225 to 2110 (mean 4055) (mean 2686) (mean 1678) 13.64 kg carriers in the reactor with a bulk filling ratio of 50% 34 26 16
experiment. The EPS extraction step was carried out according to Liu and Fang (Liu and Fang, 2002). A 10 cm piece of the fiber was cut and the ends of the fibers attached to the module were sealed. Briefly, the fiber was suspended into 10 mL of 0.1 M NaCl solution in a 50 mL polypropylene tube, and vortexed for 45 min to make sure that the biofilm is totally suspended. Then, 60 mL of 35% formaldehyde (SigmaeAldrich, Israel) were added to the solution and incubated 1 h in a Vortex Genie 2 (Scientific Industries, USA) at a minimum mixing setting and 4 C, followed by the addition of 4 mL 1 M sodium hydroxide at 4 C for 3 h incubation period in order to facilitate dissociation of the acidic groups from the EPS to the solution. Thereafter, the suspension was centrifuged (35,000 rpm, 30 min, 4 C), the supernatant was filtered through a 0.2 mm hydrophilic nylon filter (Millipore Co.), and dialyzed through a dialysis membrane of 3500 Da (Spectra/Por) for a few days until salts were completely removed. Then the extracted EPS was a lyophilized (FreeZone 2.5 plus) at 80 C and 0.01 mbar for 48 h. The frozen and dried EPS samples were re-dissolved in 10 mL of double distilled water (DDW) for the determination of dissolved organic carbon (DOC), proteins, and polysaccharides concentrations. Extracellular protein of the extracted EPS was analyzed using the colorimetric quantitative protein determination with the Bio-Radª Protein Assay according to Bradford (Bradford, 1977). Polysaccharides contents were determined according to Dubois et al. (DuBois et al., 1956), using glucose and alginic acid as standards. EPS extracted was expressed as DOC concentration measured by using an Apollo 9000 TOC Analyzer (Teledyne Tekmar, United States).
2.3. Adherence and viscoelastic properties analysis with QCM-D EPS was extracted from the UF membrane surface after operating the HG-MBR under different conditions, i.e., at different sludge removal rates of 59, 35.4, 17.7, and 5.9 L day1 (correspond to calculated SRT of 3, 5, 10, and 30 days). The adherence and adsorption kinetics of the EPS was carried out in a QCM-D (Q-Sense AB, Gothenburg, SWEDEN). The QCM-D
0.67 1012 45.2 to 37.7 2.3 5.0 w 6.0 6.7 w 7.1 2.4 w 6.2 (mean 4.3) 1170 to 1575 (mean 1392) 9
measurements were performed with AT-cut quartz crystals mounted in an E1 system (Q-sense AB, Gothenburg, SWEDEN). The gold coated crystals with a fundamental resonant frequency of around 5 MHz were coated with Polyvinylidene Fluoride (PVDF) batch number (QSX999, Q-sense). Before each measurement, the crystals were soaked in a 5 mM ethylenediaminetetraacetic acid (EDTA) solution for 30 min, rinsed thoroughly with DDW and dried with pure N2 gas. The EPS was used in several adsorption measurements to the QCM-D PVDF coated gold sensor. EPS adsorption to the sensor surface is characterized by the change of the oscillation frequency of the PVDF coated gold sensor during parallel flow of aqueous media with flow rate of 150 ml/min above the sensor surface. The variations of frequency, f (Hz) and dissipation factor, D were measured for the three overtones (n ¼ , 5, 7, and 9). The working stages for applying aqueous media to the QCM-D flow cell include 5 stages of 20 min each at constant temperature (22 C). The stages include the following fluids being injected to the QCM-D flow cell: DDW, 10 mM NaCl aqueous solution, 20 mg/L of EPS as DOC (from membrane after MBR operation at different sludge removal rates) dissolved in 10 mM NaCl, 10 mM NaCl aqueous solution, and DDW. The QCM-D results from these experiments were further modeled in which the thickness, shear modulus, and shear viscosity values of the adsorbed EPS layers on the PVDF crystal were calculated. The viscoelastic properties of the EPS layers were calculated based on the Voigt model according to Voinova et al. (Voinova et al., 1999). The density and viscosity of the solution used in this model were 1 g/cm3 and 103 Pa s, respectively. The density of the adsorbed layer was fixed at 1.030 g/cm3, following the recommendations of Gurdak et al. (2005). The best fitting values of the shear viscosity (h), shear modulus (m), and thickness of the adsorbed layer were obtained by modeling the experimental data of f and D for three overtones using the program Q-Tools provided by Q-Sense AB.
2.4.
CLSM analysis
At the end of every experiment in which different sludge removal rates were applied in the HG-MBR operation, membrane autopsies were carefully cut to pieces of around
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1 cm length from the fiber that was cut for the EPS extraction. The membrane pieces were double stained with concanavalin A (ConA) conjugated to Alexa fluor 633, and SYTO9 for probing EPS or microorganisms, respectively. Microscopic observation and image acquisition were performed using Zeiss-Meta 510, a CLSM equipped with Zeiss dry objective LCI Plan-NeoFluar (25 magnification and numerical aperture of 0.8). The CLSM was equipped with detectors and filter sets for monitoring SYTO9 stained cells and Alexa fluor 633 dye (excitation wavelengths of 488 and 633 nm, respectively). CLSM images were generated using the Zeiss LSM Image Browser. Gray scale images were analyzed, and the specific biovolume (mm3/mm2) in the biofouling layer was determined by COMSTAT imageprocessing software (Heydorn et al., 2000b). For every sample between 4 and 6 positions on the membrane were chosen and microscopically observed, acquired, and analyzed. The ConA, conjugated to Alexa fluor 633 (Invitrogen Co.), was used as a probe to determine the presence of EPS.Briefly, frozen (20 C) 100 mL aliquots of 1 mg/mL labeled ConA stock solution were thawed and diluted in 10 mM phosphate buffer (pH 7.5) to 100 mg/mL prior to use in 10 mM phosphate buffer (pH 7.5). An excess electrolyte solution was carefully drawn off from the fouled membrane by gently touching the edge of the specimens with an adsorbing paper (Kimwipes). Then, 100 mL of ConA staining solution were added to cover the samples, which were then incubated in the dark at room temperature for 20 min. Unbound ConA was drawn off the specimens using a three-step wash of 10 mM phosphate buffer. The unbound ConA solution and the washing solutions were carefully removed by gently touching the edge of the specimen with an adsorbing paper. CYTO9 was used for probing the microorganisms in the fouling layer. Excess electrolyte solution was carefully drawn off from a piece of a fouled membrane in the same manner used for ConA staining. Then, 5 mM SYTO9 solution (prepared in 10 mM phosphate buffer, pH 7.5) was added to cover the samples, which were then incubated in the dark at room temperature for 20 min. Excess SYTO9 solution was carefully drawn off with an adsorbing paper. The excess SYTO9 nucleic acid stain that did not bind to the samples was then removed by rinsing three times with a 10 mM phosphate buffer at pH 7.5.
Transmembrane Pressure, Bar
89.8 99.5 97.7 17.4 12.9 99.6 100.0 e e 18 0.3 1.1 4.8 11.3 1.2 0.1 0.1 0.1 51 1.5 0.9 34.6 16.2 1.0 0.3 1.1 7.4 50 67 4.6 15 11.4 105 186 0.1 0.2 495 317 38 41.9 18.6 269 330 1.3 7.0 91.3 99.5 99.1 33.0 29.9 99.5 99.8 e e 25 0.4 0.2 3.3 2.2 1.1 0.3 0.1 0.3 42 1.2 0.3 31 8.4 0.8 0.3 1.1 7.2 140 63 5.7 6.3 3.6 70 85 0.2 0.2 483 229 38 46 12 176 201 1.3 7.5 COD, mg/L BOD, mg/L NHþ 4 eN, mg/L TN, mg/L PO3 4 eP, mg/L TSS, mg/L Turbidity, NTU EC, mS/cm pH
418 171 30 35 10 171 231 1.3 7.5
123 45 6.8 8.1 4.2 63 79 0.2 0.3
36 1.3 1.4 24 7.6 0.2 0.2 1.2 7.5
13 0.4 2.8 7.4 3.3 0.5 0.1 0.2 0.4
529 231 38 49 14 176 245 1.2 7.3
205 60 16 12 6.8 97 186 0.1 0.2
37 1.1 0.1 37 12 0.8 0.3 1.1 6.7
18 0.5 0.2 6.3 4.4 1.0 0.1 0.1 0.4 91.4 99.2 95.4 31.2 25.0 99.9 99.9 e e
93.1 99.5 99.8 24.4 15.2 99.5 99.9 e e
Percent removal Effluent Influent Percent Removal Effluent Influent Percent Removal Effluent Influent Percent removal Effluent Influent
59 L day1 (estimated SRT ¼ 3 days) 17.7 L day1 (estimated SRT ¼ 10 days) 5.9 L day1 (estimated SRT ¼ 30 days) Parameter
Table 2 e The influent and effluent characteristics of the HG-MBR operated at different removal rates (L dayL1) of MLSS.
35.5 L day1 (estimated SRT ¼ 5 days)
W a t e r R e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 3 0 e6 4 4 0
0.8
-1
TMP, 5.9 L·day -1 TMP, 17.7 L·day -1 TMP, 35.4 L·day -1 TMP, 59 L·day
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
5
10
15
20
25
30
35
40
Time, Days Fig. 1 e The effect of different sludge removal rates (L dayL1) on the UF membrane TMP (Bar).
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3.
Results and discussion
3.1. The effect of sludge removal rate on the filtration performance of the MBR Fig. 1 shows the variations of TMP over time at the various sludge removal rates. At sludge removal rate of 5.9 and 17.7 L day1 (estimated SRT of 10 and 30 days), the TMP increased slowly, displaying a linear tendency with the increasing TMP rate of 0.0055 and 0.0064 bar per day, respectively. It seems that at sludge removal rate of 17.7 L day1 (estimated SRT of 10 days), the system has the lowest fouling rate. When sludge removal rate was changed to 59 L$day1 (estimated SRT of 3 days), a very sharp increase of TMP from 0.08 to 0.55 bar was observed after 13 days, with an increase rate of 0.027 bar/day, while at sludge removal rate of 35.5 L day1 (estimated SRT of 5 days), an increase rate of 0.016 bar/day was observed. In other words, the fouling rate at sludge removal rate of 59 L day1 (estimated SRT of 3 days) is nearly 5 times higher than that of sludge removal rate of 17.7 L day1 (estimated SRT of 10 days). The extent of fouling is likely to vary according to the MLSS composition including EPS and SMP in the bioreactor that interact with the membrane surface and pores (Chang et al., 2002; Drews, 2010). Therefore, we decided to analyze and compare the adherence and viscoelastic properties of EPS deposited on the membrane. EPS extracted from the membrane operated in the MBR at different SRTs was used for adsorption experiments to a PVDF coated sensors in the QCM-D as well as fouling experiments of single fiber UF membrane unit.
3.2. The effect of sludge removal rate on EPS adherence and viscoelastic properties In this part of the study, EPS adherence and viscoelastic properties were analyzed by conducting EPS adsorption experiments to PVDF coated sensors in a QCM-D flow cell
A
DDW
10 mM NaCl EPS
10 mM NaCl
(Kwon et al., 2006; Li and Wang, 2006; Voinova et al., 1999). As a proof of concept, we used PVDF coated crystals as a model that mimics membrane surface as a substratum for EPS to delineate their adherence and viscoelastic characteristics. EPS were extracted from the membrane surface at the end of each of the fouling experiments (estimated SRT of 3, 5, 10 and 30 days). The final EPS solution was set to 20 mg DOC per liter. Fig. 2(AeB) describes the decrease in frequency and increase in dissipation energy of the PVDF crystal due to adsorption of EPS originated from the membrane taken from the MBR operated at different sludge removal rates. It should be mentioned that EPS measurements with QCM-D are from EPS that was reconstituted on the QCM-D sensor and due to the methodology, physical characteristics of the EPS might be different compare to the EPS on the membrane. Interestingly, the results were very consistent with the effect of sludge removal rate on membrane performance (Fig. 1). The highest EPS adsorption rate expressed as a decrease in the crystal frequency was observed for the EPS extracted from the membrane fiber surface at sludge removal rate of 59 L day1 (estimated SRT of 3 days) while the lowest EPS adsorption rate was observed for the EPS originated from MBR operation at sludge removal rate of 17.7 L day1 (estimated SRT of 10 days). The simultaneous measurements of the change in frequency Df are associated with changes in adsorbed mass per area according to the Sauerbrey relation: Dm ¼ C/nDf, where Dm is the mass adsorbed to the sensor, n is the overtone mumber (n ¼ 1, 3,.), and C is the mass sensitivity constant of the crystal (C ¼ 17.7 ng Hz1 cm2 for a 5 MHz quartz crystal). This relation holds for sufficiently thin, rigid, and non-dissipative film with very limited viscoelastic behavior. Biofilm in general, and EPS layers in particular, are not rigid and they undergo deformation under shear oscillatory motion. In this case, the fluidity of the film can be inferred from the dissipation of the crystal oscillation. The dissipation factor, D, is defined as the ratio of the dissipated and stored energies according to the following: D ¼ Edissipated/2p Estored.
B
DDW
10 mM NaCl EPS 59 L·day
0
1.5
D is s ip a tio n F a c to r
Frequency Shift, Hz
DDW
-2 -4 -1 59 L·day -1 35.4 L·day -1 17.7 L·day -1 5.9 L·day
-6 -8 0
20
40
-1
35.4 L·day 17.7 L·day
1.0
10 mM NaCl DDW
5.9 L·day
-1 -1
-1
0.5
0.0 60
Time, Minutes
80
100
0
20
40
60
80
100
Time, Minutes
Fig. 2 e EPS adherence properties, extracted from the UF membrane, after runs operated at different sludge removal rates (L dayL1): Frequency shifts (A) and dissipation factors (B) during EPS adsorption to PVDF coated QCM-D sensors. A background solution of 10 mM NaCl and ambient pH of 6.2 supplemented with EPS at 20 mg DOC/L was injected to an E1 QCM-D parallel flow cell (Q-Sense, SWEDEN) at a flow rate of 150 mL/min.
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Disspation Factor
1.6
59 L·day
1.4
-1
35.4 L·day
S=0.23±0.011
-1
S=0.15±0.0088 -1 17.7 L·day S=0.11±0.0095 -1 5.9 L·day S=0.088±0.013
1.2 1.0 0.8 0.6 0.4 0.2 -1
-2
-3
-4
-5
-6
-7
-8
-9
Frequency Shift, Hz Fig. 3 e Comparison of the fluidity of different EPS, extracted from the UF membrane, after runs operated at different sludge removal rates (L dayL1): Dissipation factors versus frequency shifts during adsorption. Slope of the linear regression for the different plots is presented as S, indicating the relative fluidity/rigidity of the EPS layer at each condition (smaller slope relates to higher rigidity of the layer). An ionic strength of 10 mM was adjusted with NaCl at an ambient pH of 6.2 ± 0.1.
The shifts in dissipation (D) associated with the decreased frequency (F ) during EPS adsorption to the PVDF coated sensor are presented in Fig. 2B. Fig. 2B shows representative dissipation shifts obtained during adsorption of EPS extracted from the membrane originated from the MBR operated at different sludge removal rate conditions. As previously reported, the slope of DD over DF gives the magnitude of the variations in the adsorbed layer fluidity, the main factor in charge of damping the quartz vibration (Notley et al., 2005; Schofield et al., 2007). This DD over DF shows the induced energy dissipation per coupled unit mass, eliminating time as an explicit parameter, and making it possible to analyze the effects of EPS adsorption on the damping of the crystals’ resonance frequency. The fluidic properties of the EPS layer on the crystal are determined by studying this relationship, between the shifts in dissipation (D) and the shifts in frequency (F ) obtained by the QCM-D (Fig. 2). Harmonic 7 (35 MHz) was used for this relation. For each of the sludge removal rate conditions, a linear relationship was observed between D and F during the EPS adsorption onto the crystal surface. Each linear
correlation corresponds to the EPS adsorption stage after acquiring a baseline with the background solution of 10 mM NaCl. The slopes of the linear relationship between D and F for each of the HG-MBR operational conditions are shown in Fig. 3. The trends observed for the change in slopes show an interesting behavior in which at higher sludge removal rate of 35.5 and 59 L day1 (estimated SRT of 5 and 3 days, respectively), the extracted EPS layers are more fluid compared to the EPS layers extracted from the membrane exposed to slower sludge removal rates of 5.9 and 17.7 L day1 (estimated SRT of 30 and 10 days, respectively). It seems that in addition to a higher EPS adherence (Fig. 2A), fluidity of the fouling layer is likely playing an important role in its accessibility to the membrane pores that eventually are being accumulated more rapidly by the EPS extracted at a faster sludge removal rate (estimated SRT of 5 and 3 days). Recently, using similar UF membrane, we have shown that EPS fluidity and swelling induced at high pH, have major contribution to pore clogging (Sweity et al., 2011). The fitted values of the elastic shear modulus and shear viscosity of the adsorbed EPS layers were calculated using the Voigt model (Voinova et al., 1999, Q-Tools software of the QCM-D). The fitted values further confirmed the results showing higher fluidity of EPS extracted from the faster fouled membrane. The variations in these two parameters are calculated for each EPS obtained from the membrane under different conditions of sludge removal rate and are shown in Table 3. It is shown in Table 3 that for the slower sludge removal rate of 5.9 and 17.7 L day1 (estimated SRT 30 and 10 days), the EPS is much more viscoelastic. An ambitious study would be to find the operational conditions that induce such characters of the EPS that eventually deposits on the UF membranes. As already mentioned, the way soluble EPS is produced and deposited on the UF membrane is a complex process affected by many parameters. Possible reasons for the differences in the adherence and viscoelasticity of the EPS originated from the membrane can be differences in the biomass concentration in the HG-MBR (Supporting information e Figure S1), feed to biomass (F/M) ratio (Supporting information e Figure S2) as well as different levels of proteins and polysaccharides in EPS at different locations in the HGMBR (Supporting information e Figure S3). In conclusion, EPS extracted from the membrane operated at lower sludge removal rate (longer estimated SRT) was more viscoelastic with more rigid conformation analyzed in the QCM-D, while in contrast, a higher fluidity was detected for EPS extracted from the membrane operated at faster sludge removal rates (shorter estimated SRT) (Table 1 and Fig. 3).
Table 3 e Thickness, shear modulus, and viscosity of the deposited EPS layers extracted from the HG-MBR at different removal rates (L dayL1) of MLSS (presented in duplicate). 35.5 L day1 17.7 L day1 5.9 L day1 59 L day1 (estimated SRT ¼ 3 days) (estimated SRT ¼ 5 days) (estimated SRT ¼ 10 days) (estimated SRT ¼ 30 days) Thickness, nm Viscosity, kg m1 s1 Shear modulus, Pa
2.8 0.0010 2.6∙104
2.4 0.0012 3.2∙104
3.5 0.0011 6.4∙104
2.6 0.0011 4.5∙104
2.2 0.0015 1.5∙105
2.8 0.0018 1.5∙105
3.3 0.0018 2.1∙105
2.8 0.0018 4.5∙105
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3.3. The relation between EPS composition, adherence, and membrane fouling rate To further study EPS adherence and accumulation on the membrane, filtration of the extracted EPS from the membrane of the MBR was performed through a single UF fiber under representative ionic strength of 10 mM with and without 0.5 mM of calcium cations (Sweity et al., 2011). Hence, faster decline in membrane permeability was observed for EPS originated at higher sludge removal rate of 59 L day1 (estimated SRT of 2 days) under both conditions (Fig. 4A and B). The slowest decline in membrane permeability was observed for sludge removal rates of 17.7 and 5.9 L day1 (estimated SRT of 10 and 30 days), with and without calcium (Fig. 4A and B). EPS composition and amount per membrane surface area was quantified and related to the membrane fouling rate in the HG-MBR and in the single fiber filtration unit as well as to the QCM-D analysis. For EPS extraction and analysis, one fiber was cut from the membrane module at the end of each experiment operated at a constant sludge removal rate. Fig. 5 presents the EPS (as DOC content), proteins, and polysaccharides accumulation on the membrane
Normalized Permeability
A 1.0 0.9 0.8 0.7 0.6
5.9 L·day
0.5
17.7 L·day
-1
-1 35.4 L·day -1 59 L·day
0.4 0.3 0
100
200
300
400
500
400
500
Time, Minutes
B Normalized Permeability
-1
1.0 0.9
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surface (mg/cm2) of the HG-MBR at the different sludge removal rates. The results show that the highest EPS accumulation on the membrane of the HG-MBR occurred at a sludge removal rate of 59 L day1 (estimated SRT of 3 days), followed, in turn, during MBR operation at sludge removal rates of 35.5, 5.9 and 17.7 L day1 (estimated SRT of 5, 30 and 10 days, respectively). The protein accumulation at the estimated SRT of 10, 5 and 3 days was very similar and at a relatively low level (Fig. 5). However, the accumulation of polysaccharides on the membrane surface exhibited a different behavior in contrast to the proteins, in which an extremely high level of polysaccharide accumulation was observed for the highest sludge removal rate of 59 L day1 (estimated SRT of 3 days), while at sludge removal rate of 17.7 L day1 (estimated SRT of 10 days), the lowest accumulation was obtained (Fig. 5). Combining the results so far, at the highest removal rate of sludge, polysaccharides content in EPS is elevated on the membrane (Fig. 5) and in general, per biomass unit (VSS), also in other locations in the HG-MBR (Figure S4). The increased polysaccharides content on the membrane is proposed to be a result of stronger adherence properties of the EPS. This stronger adhesion of EPS, eventually reduce membrane permeability observed in Fig. 4 and most likely increase the rate of TMP elevation (bar/day) in the MBR (Fig. 1). It is generally accepted that polysaccharides can mediate cohesion of cells, and play an important part in maintaining the structural integrity of biofilms (Christensen, 1989; Liu and Tay, 2001; Ross, 1984). Polysaccharides can mediate cell-to-cell interaction in two ways: firstly, polysaccharides bridge cells to form a three-dimensional structure, which may then interact with more bacterial cells and particulate matter (Ross, 1984); secondly, dispersed bacteria are negatively charged at usual pH values, and electrostatic repulsion exists between cells. It had been proposed that extracellular polymers could change the surface negative charge of bacteria, and thereby bridge two neighboring bacterial cells physically to each other as well as other inert particulate matter (Schmidt and Ahring, 1994; Shen et al., 1993). In this study, the higher polysaccharides content in EPS extracted at the fastest sludge removal rate (estimated SRT of 3 days) showed the strongest adherence as
0.8 0.7 0.6 -1 5.9 L·day -1 17.7 L·day -1 35.4 L·day -1 59 L·day
0.5 0.4 0.3 0
100
200
300
Time, Minutes Fig. 4 e Normalized permeability during fouling of a single fiber UF membrane with EPS extracted from the HG-MBRUF membrane at the end of runs operated at different sludge removal rates (L dayL1). Fouling experiments were carried out at ionic strength of 10 mM (adjusted with NaCl) with (A) and without (B) 0.5 mM calcium cations. The pressure was set at all experiments between 0.14 and 0.18 bar. Initial permeability of the UF PVDF fibers (Zenon, GE) was 0.15 ± 0.02 cm∙minL1∙barL1.
Fig. 5 e The concentrations of accumulated components of EPS presented as TOC, proteins, and polysaccharides on the membrane surface. Inserted figure shows protein/ polysaccharide ratio of EPS components on the membrane surface.
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Fig. 6 e CLSM analysis of the biofouling layer on the membrane surface at the end of runs operated at different sludge removal rates (L dayL1). Sludge removal rates are (A) 59 L dayL1; (B) 35.4 L dayL1; (C) 17.7 L dayL1; and (D) 5.9 L dayL1. Blue and green spots represent extracellular polysaccharides and microorganisms, respectively. Total biomass of EPS and cells is expressed as specific biovolume (mm3/mm2) as analyzed with COMSTAT biofilm software: (A) 95.8 ± 25.2; (B) 45.4 ± 10.1; (C); 31.1 ± 13.2 and (D) 57.2 ± 22.2 for EPS and (A) 28.5 ± 5.1; (B) 24.3 ± 3.6; (C) 6 ± 3.5; and (D) 13.1 ± 2.71 for viable cells. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
analyzed in the QCM-D and led to a greater loss of filterability in the single UF fiber unit (Figs. 2 and 4). Other studies also correlated between polysaccharides concentration and membrane fouling rate in MBR systems (Rosenberger et al., 2006; Fan et al., 2006). Interestingly, a higher fluidity of the EPS adsorbed layer was observed for the EPS extracted from the membrane at the faster sludge removal rate of 59 and 35.5 L day1 (estimated SRT of 3 and 5 days). The higher fluidity of EPS is likely a part of increased accessibility of the EPS to the membrane pores that eventually increase its concentration within the membrane (Fig. 5). EPS adherence properties are primarily affected by polysaccharides (Herzberg et al., 2009a,b) and corroborated with PN/PS ratios analyzed in the EPS extracted from the membrane at different sludge removal rates (Fig. 5, inserted graph): Lower PN/PS ratio correlates well with the higher adherence of the EPS observed by the QCM-D (Fig. 2). With regard to the fluidity of the EPS layer, previous results in our lab also showed a decrease in elasticity (lower shear modulus) of EPS due to over-expression of alginate (Results not shown) as well as a relation between increased EPS swelling, fluidity, and reduced UF membrane permeability (Sweity et al., 2011).
3.4. Variations in biofilm formation on the membrane at different SRTs A correlation between a decrease in membrane performance at different sludge removal rates and an increase in the EPS content on the membrane is observed in Figs. 1 and 6. Variations in biofilm formation (amount of EPS and viable cells) were observed on the membrane surface using CLSM imaging (Fig. 6AeD) and analyzed using COMSTAT biofilm software (Heydorn et al., 2002, 2000a). On the membrane surface, at the highest sludge removal rate of 59 L day1 (estimated SRT of 3 days), the highest polysaccharides content was analyzed
using the labeled lectin, concanavalin A (Figs. 5 and 6A). The lowest polysaccharides content was observed at sludge removal rate of 17.7 L day1 (estimated SRT of 10 days) (Figs. 5 and 6C). CLSM results also corroborate with the measured fouling rate, in which at a sludge removal rate of 17.7 L day1 (estimated SRT of 10 days), the increase in TMP was the lowest and at sludge removal rate of 59 L day1 (estimated SRT of 3 days), the increase in TMP was the highest.
4.
Concluding remarks
In this work, a novel approach of EPS analysis was taken for studying membrane biofouling in HG-MBR system. EPS, originating from a fouled UF membrane was extracted and its adherence and viscoelasticity were determined using QCM-D. EPS was collected from the membrane under different fouling conditions affected by the sludge removal rate from the HGMBR. The different fouling conditions of the UF membrane were correlated well to EPS adherence, where stronger adhesion of the EPS was observed for EPS extracted from the fouling experiments conducted under conditions of higher sludge removal rate, in which the TMP elevation rate was higher. EPS layer fluidity, a new parameter to be used in membrane fouling phenomena, as well EPS viscoelastic properties can also explain the stronger fouling propensity of EPS extracted from membranes with lower permeability. We propose that the more fluidic the EPS layers are, their accessibility to the membrane pores is higher, where they can penetrate and block the pores. Shear modulus of elasticity and shear viscosity are critical parameters influencing biofilm and EPS cohesion (Ahimou et al., 2007; deKerchove and Elimelech, 2006). These parameters correlate to an improved membrane performance: As the EPS in the membrane is more elastic and viscous, reduced fouling is observed and the ratio of proteins to polysaccharides is higher.
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Acknowledgement This study was supported by USAID Middle East Regional Cooperation (MERC) Program, project number: M29-048.
Appendix. Supplementary material Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2011.09.038.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 4 1 e6 4 5 2
Available online at www.sciencedirect.com
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Stability and maturity of thickened wastewater sludge treated in pilot-scale sludge treatment wetlands Alexandros I. Stefanakis a, Dimitrios P. Komilis b,*, Vassilios A. Tsihrintzis a a
Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, School of Engineering, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi, Greece b Laboratory of Solid and Hazardous Waste Management, Department of Environmental Engineering, School of Engineering, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi, Greece
article info
abstract
Article history:
Thickened wastewater activated sludge was treated in 13 pilot-scale sludge treatment
Received 1 June 2011
wetlands of various configurations that operated continuously for three years in North
Received in revised form
Greece. Sludge was loaded for approximately 2.5 years, and the beds were left to rest for the
11 September 2011
remaining period. Three different sludge loading rates were used that represented three
Accepted 18 September 2011
different population equivalents. Residual sludge stability and maturity were monitored for
Available online 12 October 2011
the last year. Sludge was regularly sampled and microbial respiration activity indices were measured via a static respiration assay. The phytotoxicity of sludge was quantified via
Keywords:
a seed germination bioassay. Measurements of total solids, organic matter, total coliforms,
Activated sludge
pH and electrical conductivity were also made. According to microbial respiration activity
Maturity
measurements, the sludge end-product was classified as stable. The germination index of
Phragmites australis
the final product exceeded 100% in most wetland units, while final pH values were
Reed beds
approximately 6.5. The presence of plants positively affected the stability and maturity of
Stability
the residual sludge end-product. Passive aeration did not significantly affect the quality of
Sludge treatment wetlands
the residual sludge, while the addition of chromium at high concentrations hindered the sludge decomposition process. Conclusively, sludge treatment wetlands can be successfully used, not only to dewater, but also to stabilize and mature wastewater sludge after approximately a four-month resting phase. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Sewage sludge management is a critical environmental issue and one of the most expensive processes in municipal wastewater treatment. Increasing sludge production has required the development of alternative treatment technologies, with the goal to reduce sludge volume, organic matter, microbiological and heavy metal content, as well as the content of several emerging toxic contaminants. Although vertical flow constructed wetlands have been employed in wastewater treatment for several years, systems
for sludge dewatering are far less numerous. The use of Sludge Treatment Wetlands (STWs) or Sludge Drying Reed Beds (SDRBs) has received attention from the scientific community over the last 10e15 years. The dewatering efficiency of these systems is based on evapotranspiration and draining and is comparable to mechanical methods, such as the commonly used filter presses (Nielsen, 2003; Uggetti et al., 2010a). The main advantages of STWs over other techniques include, among others, lower initial investment cost, lower operation and maintenance costs and relatively low power consumption. However, they usually require larger areas compared to
* Corresponding author. Tel./fax: þ30 25410 79391. E-mail addresses:
[email protected] (A.I. Stefanakis),
[email protected] (D.P. Komilis),
[email protected] (V.A. Tsihrintzis). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.036
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mechanical methods (de Maeseneer, 1997; Edwards et al., 2001; Stefanakis et al., 2009; Stefanakis and Tsihrintzis, 2009; Uggetti et al., 2010a, b; Bianchi et al., 2011). STWs not only dewater sludge but also have the potential to stabilize it (Uggetti et al., 2010a). Therefore, provided that adequate stabilization occurs in an STW unit, the sludge endproduct could be used as a beneficial organic fertilizer (Nielsen and Willoughby, 2005; Uggetti et al., 2010a), making STWs an alternative, environmentally friendly technology to manage wastewater sludge from small and medium-size communities. The potential utilization of sludge as a fertilizer for agricultural use requires that the end-product is both stable and mature, i.e., non-phytotoxic (Iannotti et al., 1993; Wu et al., 2000). Current discussion in published literature clearly distinguishes the terms “stability” and “maturity” (Iannotti et al., 1993; Barrena et al., 2006; Komilis and Tziouvaras, 2009; Ceustermans et al., 2010). Stability refers to the decomposition of organic matter by the microbial activity, which is usually measured via the oxygen consumption or the CO2 generation (Iannotti et al., 1993). On the other hand, maturity is directly related to the effect of an organic material on plant growth or on seed germination (Wu et al., 2000). Several compost stability indices have been suggested by researches (Barrena et al., 2006). Despite the extensive research on this topic, however, there is no widely accepted index to measure stability of organic wastes. Variability exists not only on the units of the indices adopted, but also on the materials and methods used to quantify the indices. Dewatering and mineralization processes have been investigated during the operation of STW systems (Nielsen, 2003; Panuvatvanich et al., 2009; Stefanakis and Tsihrintzis, 2009, 2011; Uggetti et al., 2010b). However, information on the stability (microbial respiration) and the maturity
(phytotoxicity) of sludge treated in such systems is very limited (Uggetti et al., 2010c; Bianchi et al., 2011; Wang et al., in press). In this study, 13 pilot-scale STW units were constructed, operated and monitored continuously for three years. The microbial respiration activity and the maturity of the residual sludge in each unit were measured during the last (third) year of operation, which included six months of sludge loadings followed by a prolonged final resting phase. Results of the final year of operation are presented in this work. Based on the above, the objective of this work was to assess the stability and maturity of sludge treated in STWs, and to investigate the effect of variable design and operation system parameters on the quality of the residual sludge. The overall objective of this work was to produce a mature and stabilized sludge that could be safely applied to the land.
2.
Materials and methods
2.1.
Pilot-scale unit description and experimental design
Thirteen (13) pilot-scale STW units (S1eS13) were constructed and operated in an open-air laboratory in Xanthi, North Greece (41 080 4700 N, 24 550 0900 E). Units S1eS11 were constructed and planted in June 2007, while sludge loading started 4 months later to let the plants adequately grow (Stefanakis and Tsihrintzis, 2011). Units S12 and S13 had been constructed and started their operation one-year earlier, i.e., in October 2006 (Stefanakis et al., 2009). Table 1 presents the operational and construction characteristics of the 13 units. The parameters that varied were: (i) origin of the porous media; (ii) size of the porous media; (iii) presence of plants; (iv) presence of aeration tubes; (v) sludge loading rate; and (vi) sludge chromium concentration.
Table 1 e Experimental design and operating characteristics of the 13 pilot-scale STW units. Unit
Porous media a
S1 S2 S3 S4g S5 S6 S7 S8 S9 S10 S11 S12 S13 a b c d e f g
Origin
Size
R Q R R R R R R R R R R R
Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Coarse Fine Fine
Plant species
Aeration tubes
Cr spike
SLRc (kg TS/m2/yr)
Total sludge application over study period (kg TS/m2)
Sludge loading (PE/m2)d
Reed Reed Cattailb No Reed Reed Reed Reed Reed Reedb Reed Reed Reed
Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes
No No No No No No Yes Yes Yes No No No No
75 75 75 75 30 60 75 30 60 75 75 30 75
160 160 160 160 67 129 160 67 129 160 160 106e 136f
4.7 4.7 4.7 4.7 1.9 3.8 4.7 1.9 3.8 4.7 4.7 1.9 4.7
R ¼ river bed obtained igneous rock, Q ¼ quarry obtained carbonate rock. Plants dried out during the first summer of operation. Sludge loading rate. Based on a typical sludge loading rate of 16 kg TS/PE/yr (Uggetti et al., 2010a). Operated for 3.5 years. Operated for 1.5 years. Control unit.
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Each pilot-scale wetland unit consisted of a plastic cylindrical tank (0.82 m diameter and 1.5 m height, i.e., a total surface of 0.53 m2). All units contained a 10 cm thick drainage layer made of cobbles (D50 ¼ 90 mm), placed at the bottom of each unit. Aeration tubes were placed within the drainage layer in all units except of unit S10. Other porous media layers were placed on top of the drainage layer. Most of the pilotscale units included, from bottom to top, a 15 cm thick medium gravel layer (D50 ¼ 24.4 mm) and a 15 cm-thick fine gravel layer (D50 ¼ 6 mm). One unit (S11) had an extended 25 cm thick cobbles layer below the medium gravel layer. In one unit (S2) the porous media was obtained from a quarry (carbonate rock) and in the other units from a river bed (igneous rock). Two plant species were used: common reeds (Phragmites australis) and cattails (Typha latifolia). Unit (S4) was unplanted. The pilot-scale units received three different Sludge Loading Rates (SLRs), respectively 30, 60 and 75 kg dry matter/m2/yr, as shown in Table 1. The three different SLRs represented three different population equivalents (PE), respectively, according to Table 1. Chromium (in the form of CrCl3 and K2Cr2O7 at a 1:1 ratio) was added to the sludge in units S7, S8 and S9 during the loading phase. The Cr spike achieved concentrations equal to 5 1.5 g total Cr/kg dry matter (dm). The addition of chromium was done to simulate a heavy metal latent industrial sludge. Note that the cattails in unit S3 and reeds in unit S10 (no aeration tubes) dried out during the first summer period (2008); both units continued their operation, however, as unplanted units during the whole time of this study, under similar conditions with the initial unplanted unit S4. Fig. 1 presents a vertical section and a picture of the pilot-scale units. The thickened surplus wastewater activated sludge (TSAS) was acquired from the nearby publicly owned treatment wastewater plant (POTW) of the city of Komotini in North Greece. The POTW uses an extended aeration scheme with a 20-day retention time. Feeding of the units with TSAS was carried out manually, using a device (a vertical perforated plastic pipe), which allowed for uniform distribution by flooding the entire bed surface. TSAS was introduced to the units in loading cycles as follows: a loading period of 7 days with daily equal portions was followed by a resting phase of 1e3 weeks. Shorter resting phases (1e2 weeks) were used during summer. Raw thickened sludge was added four times to the STWs during the loading period. The loading period lasted approximately 5 months (from the end of October 2009 to April 2010) and the resting phase lasted till early November 2010 (i.e., the resting phase lasted 7 months), when the final sludge samples were collected. Units S1eS11 operated for 2.5 years continuously (from October 2007 to April 2010), while units S12 and S13 operated for one additional year (October 2006eApril 2010). Loading of unit S13 stopped in June 2008 and the bed was left unloaded for more than two years (June 2008eearly November) to monitor sludge characteristics under an extended resting phase. There were 3 and 5 sampling events during the loading and resting periods, respectively. This study focuses on the last year of operation of the STW units (i.e., from October 2009 to November 2010) since the goal was to monitor the stability and maturity properties of the residual sludge.
2.2.
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Sampling and solids analyses
Sludge samples were collected using a core sampler, in order to sample the entire depth of the residual sludge layer. Samples taken were split in a top and a bottom part to measure potential differences of the top and bottom sludge layers. All samples were taken from at least two random locations of each sludge bed surface and separately from the top and bottom layers. Grab samples from each layer were then mixed to obtain one composite sample per bed layer and unit. Sludge samples were always received right prior the loading of the new sludge during the loading phase. All samples were analyzed immediately in the laboratory to determine total solids (TS), volatile solids (VS) (i.e., organic matter) and total coliforms (TC), according to APHA and AWWA (1998). The pH and the electrical conductivity (EC) of
Fig. 1 e (a) Schematic vertical section of the pilot-scale sludge treatment wetland units; and (b) view of some pilotscale units.
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the sludge were measured in the water filtrates during the seed germination bioassay (described in chapter 2.4) using common pH and EC electrodes.
2.3.
Total rootlength of germinated seeds ðsampleÞ Number of germinated seeds ðsampleÞ 100 Total rootlength of germinated seeds ðcontrolÞ Number of germinated seeds ðcontrolÞ
Microbial respiration activity indices
Two microbial respiration indices were calculated in this work based on a static respirometric assay developed by Komilis and Tziouvaras (2009). The assay calculates the cumulative carbon dioxide (CeCO2) generated and the maximum oxygen consumption rate over a 12-h period (SRI12) during a 7-day incubation at 35 C (Komilis et al., 2011). In this work, the first index will be referred to as CeCO2 and the second index as Static Respiration Index (SRI12). Both indices have to be jointly used to assess the biological activity and stability of an organic substrate (Komilis et al., 2011). Forty (40) g of wet composite samples obtained from each bed layer and unit were placed in 1 L manometric respirometers. Depending on the initial moisture content of the sludge sample, water was accordingly added to adjust the initial moisture content to between 80 and 100% of the water holding capacity of the material. This corresponded to a moisture content of approximately 50e70% on a wet weight basis (% ww), which is considered optimum for biodegradation experiments. If the initial moisture content of the sludge was above 70% ww, no additional water was added. The oxygen consumption was calculated using the principles of the ideal gas law and the pressure drops that were recorded and logged at regular time intervals in each respirometer (Komilis et al., 2011). The total CeCO2 produced during the 7-day period was calculated after measuring the total and phenolphthalein alkalinities of a 50 mL alkaline solution placed in each respirometer to absorb the generated CO2. SRI12 and CeCO2 were expressed in mg O2/dry kg/h (SRI12) and in g CeCO2/dry kg, respectively.
2.4.
Number of germinated seeds ðsampleÞ Total number of seeds in Petridish ðsampleÞ GI ¼ Number of germinated seeds ðcontrolÞ Total number of seeds in Petridish ðcontrolÞ
Seed germination bioassay
A seed germination bioassay was used according to Komilis and Tziouvaras (2009). The test is briefly described below: approximately 20 wet g of each sample was mixed with the appropriate amount of deionized water to achieve a mixing ratio of 10:1 (water volume: dry mass of sludge). Mixing was performed for 1 h on a vibration table at 120 rpm. The mixture was, then, let stand for 30 min and 5 mL was withdrawn from the supernatant and added to a 110 mm diameter Petri dish that contained two layers of filtrate paper and 35, equally spaced, tomato seeds (Lycopersicon esculentum). Three (3) replicates were performed per sample. The control was deionized (DI) water (5 replicates were used for the control at each sampling event). The seeds were, then, incubated at 22 0.5 C in the dark for 7 days. At the end of the 7 days, germinated seeds were counted and total root length was precisely measured with a ruler. Seeds with root lengths less than 2 mm were not considered grown. Germination rates in the control ranged from 75% to 94% throughout the experiment, with an average of 85% (n ¼ 6). Results were expressed as a percentage relative to the control using Equation (1) (Komilis and Tziouvaras, 2009):
(1)
Based on Equation (1), GIs less than 100% indicate a potentially phytotoxic material, while values above 100% indicate phytoenhancing effects.
2.5.
Time series statistical analysis
A paired t-test (Berthouex and Brown, 2002) was employed here to compare the time series of measurements: (i) between top and bottom layers within each unit; and (ii) between a pair of units with a different design characteristic. The paired ttest calculates the confidence interval of the mean of the differences of the parameters measured during the same sampling time during a time period. Therefore, this test can be properly applied here, since it is not affected by the variability of a measurement vs time (Berthouex and Brown, 2002). In that sense, an independent t-test would have been false to use in this situation. Based on the above, Equations (2) and (3) were used: Di
j; t
¼ Pi t Pj
(2)
t
where: i and j designate the unit i (or top layer of bed) and unit j (or bottom layer of a bed); Pi_t: a parameter (P) measured at unit i at sampling time t; Pj_t: a parameter (P) measured at unit j at sampling time t; Di_j,t: the difference of the parameters measured at the same sampling time. The mean difference of the two time series of measurements was calculated according to Equation (3): Pn Mean Di
j; t
¼
Dij; t n
t¼1
(3)
where: n is the number of sampling events; other parameters were defined earlier.
Table 2 e Average properties of the thickened surplus activated sludge used in the experiments. Parameter TS (% wet weight) VS (% TS) CeCO2 (g C/dry kg)b SRI12 (mg O2/dry kg/h)c GI (% of the control)d pH EC (mS/cm) Total Cr (g Cr/kg TS)
Valuea 3.1 73.7 79.0 1512 63.5 8.7 1.97 0.28
0.73 3.2 23.0 709 52.3 0.26 0.08 0.072
a Mean values standard deviation. b Total CeCO2 generated after a 7-day incubation period at 35 C. c Static respiration index, which is the maximum oxygen uptake rate over a 12-h period during a 7-day incubation at 35 C using static manometric respirometers. d Germination index based on a seed germination bioassay.
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The confidence interval of the mean Di_j,t was calculated at a 95% confidence level for (a) the whole one-year period (October 2009eNovember 2010), and (b) the resting phase (April 2010eNovember 2010), separately. Based on the above, if 0 is contained within a confidence interval of the mean, no statistically significant differences would exist between the units. The parameters used in the statistical analyses were: CeCO2, SRI12, GI, TS and VS. Comparisons were performed over the whole one-year period (sample size n ¼ 6e8), whilst a separate pairwise comparison was performed for the resting phase only (sample size n ¼ 3e5). Statistical analyses were performed with MINITAB Release 14.0.
values shown are averages of the sludge properties from three loading events.
3.2.
Fig. 2 depicts the time evolution of CeCO2, SRI12, GI and total coliforms (TC) during the one-year study, while Fig. 3 depicts the time variations of Total Solids (TS), Volatile Solids (VS), pH and Electrical Conductivity (EC) for all 13 STW units. Values included in the figures are averages from the measurements of the top and bottom layers of each unit.
4. 3.
Results
3.1.
Initial characterization of sludge and loading rate
Time profile of sludge properties
Discussion
4.1. Statistical differences between the top and bottom residual sludge layers According to the paired t-test, there were no statistically significant differences (SSD) of the stability indices (CeCO2 and SRI12) and the TS content between the bottom and top
Table 2 includes the average properties of the influent sludge used throughout the experimental period of this study. The
a 25
Last sludge loading
Resting phase
S1 S2 S3
g C-CO2/dry kg
20
S4 S5 S6
15
S7 S8 S9
10
S10 S11 S12
5
S13
Least stable Most stable
0 Oct-09 0
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
200
Jun-10 240
Jul-10 Aug-10 280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation
b
770
Last sludge loading
Resting phase
700 630
mg O2/dry kg/h
560 490 420 350 280 210 140
Least stable
70 Most stable
0 Oct-09 0
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
200
Jun-10 240
Jul-10 Aug-10 280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation
Fig. 2 e Evolution of: (a) CeCO2; (b) SRI12; (c) GI; (d) total coliforms for the 13 STW units during the one-year monitoring period. Dotted horizontal lines in (a), (b), (c) indicate stability and maturity limits according to Komilis et al. (2011).
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c
350
Last sludge loading
Resting phase
GI (% of the control)
300 250 200 150 Ad-hoc phytotoxicity limit
100 50 0 Oct-09 0
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
200
Jun-10
Jul-10 Aug-10
240
280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation
d 1000000 11000
Last sludge loading
Resting phase
10000 9000
CFU/g
8000 7000 6000 5000 4000 3000 2000 1000
EU limit (EC, 2000)
0 Oct-09 0
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
200
Jun-10
Jul-10 Aug-10
240
280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation
Fig. 2 e (continued).
layers for any of the units (at p < 0.05). This implies that stability and dewatering processes proceeded simultaneously in the whole sludge bed. On the other hand, SSD were obtained for the germination indices between the two layers in unit S2, in which the bottom layer was always more mature (i.e., had a higher GI) compared to the top layer throughout the one-year study period. Possible explanation lies on the fact that unit S2 was the only unit with a carbonate porous media, which contains calcium that is a valuable nutrient for plants. The likely release of water soluble nutrients and phyto-enhancing elements may have positively affected the maturity of the bottom residual sludge layer, which is in direct contact with the substrate material and contains sludge from older applications. Moreover, since the plant root system is denser in the bottom sludge layer compared to the top, the water absorption and likely nutrient storage in roots is expected to assist plant activity in that layer leading to an increased maturity of the bottom sludge layer. The bottom layer in unit S11 was also significantly more mature compared to the top layer during the whole period. Since no such SSD was calculated during the resting phase, it seems that the differences existed during the loading phase only. In the case of unit S11, it is speculated that the difference could be attributed to the fact that this unit contained a coarse-
grained porous media. The porous media may have facilitated the vertical water drainage along the whole bed depth, resulting in higher drained water volume. Thus, the downward passage of water soluble elements from the top of the bed to the bottom layers was favored and may have led to the concentration of certain nutrients in the bottom residual sludge layer. This positively affected the maturity of sludge in that layer. SSD between the volatile solids content of the two sludge layers were found in units S6, S7, S9 and S11. These differences were present throughout the whole period and the resting phase (except in the case of S11). In these units, the top sludge layer had always a higher organic matter content compared to the bottom layer, as also reported elsewhere (Kim and Smith, 1997; Melidis et al., 2010). It appears, therefore, that the bottom layer contains sludge from older applications, which has been oxidized to a greater degree compared to the top layer, which is the immediate receptor of new sludge loadings. On the other hand, no similar statistical differences of the stability indices were observed for units S6, S7, S9 and S11.
4.2.
Statistical differences of the STWs
The same statistical analysis was employed to compare pairs of units with different design characteristics (Table 3). For
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example, units S1 and S5 differed only on the SLR, with all other design characteristics being similar (see Table 1). Average values from the top and bottom layer were used in the statistical analyses of the different units. The comparison included CeCO2, SRI12, GI, TS, and VS.
operated at the medium and high SLRs, respectively. On the other hand, the stability of the residual sludge was not significantly affected by the level of SLR, as indicated by the lack of SSD for CeCO2 and SRI12.
4.2.2. 4.2.1.
Effect of different SLRs
The comparison of the units S5, S6 and S1 that had different SLRs (30, 60 and 75 kg dm/m2/yr, respectively) revealed that there were SSD of GI and VS between units S1 and S5 (Table 3). Since no SSD were found during the resting phase, it appears that the differences occurred during the loading phase only. This is explained by the fact that at lower loading rates, and respective smaller amounts of sludge to be treated, maturity was reached faster and organic matter decomposed at a higher rate compared to higher SLRs. This is also illustrated in Fig. 2c, in which the GI values of S5 were consistently higher than the GI values of S1. Fig. 3b also shows that the VS content of S5 was consistently lower than that of S1. It is noted that GI values for both units (S1 and S5) were above the ad-hoc phytotoxicity limit of 100% (Fig. 2c). The differences became statistically insignificant for units S6 and S1, which were
a
Effect of plants
The presence of vegetation clearly affected the performance of the units. As indicated in Table 3, the unplanted unit S4 produced sludge with a higher respiration activity (CeCO2 and SRI12) and a lower GI, compared to the planted unit S1 with the same SLR (75 kg dm/m2/yr). In addition, the planted unit S1 contained a drier sludge (i.e., higher TS content) compared to that of unit S4. The same significant differences were also obtained from the comparison of unit S1 and the other unplanted unit S3. Finally, the sludge of vegetated unit S1 had always a lower organic matter content, throughout the whole one-year period, compared to both unplanted units S3 and S4. This is explained by the fact that reeds not only absorb water (de Maeseneer, 1997) and nutrients for their growth, but they can also transfer oxygen to the bed through their extended root system. Thus, they can create aerobic micro-sites and improve organic matter oxidation (Uggetti et al., 2010a).
Last sludge loading
Resting phase
100
S1 S2 S3
80
S4
TS (% ww)
S5 S6
60
S7 S8 S9
40
S10 S11 S12
20
S13
0 Oct-09 0
b
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
200
Jun-10 240
Jul-10 Aug-10 280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation 80 Last sludge loading
Resting phase
VS (%TS)
70
60
50
40
30 Oct-09 0
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
200
Jun-10 240
Jul-10 Aug-10 280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation
Fig. 3 e Evolution of: (a) total solids (% ww); (b) volatile solids (% TS); (c) pH; (d) EC (mS/cm), in the 13 STWs during the oneyear monitoring period.
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c
9
Last sludge loading
Resting phase
pH
8
7
6
5 Oct-09 0
d
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
Jun-10
200
240
Jul-10 Aug-10 280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation 7
Resting phase
Last sludge loading
6
EC (mS/cm)
5 4 3 2 1 0 Oct-09 0
Dec-09 Jan-10 Feb-10 40
80
120
Mar-10 Apr-10 160
Jun-10
200
240
Jul-10 Aug-10 280
Sep-10 320
Nov-10 360
400
Days after the first sludge loading of the third year of operation Fig. 3 e (continued).
Therefore, it can be stated that the presence of plants in STW units is a necessity to obtain a stable, mature and dry residual sludge end-product.
4.2.3.
Effect of porous media
As Table 3 shows, the origin (S1 vs S2) and size of porous media (S1 vs S11 and S2 vs S11) did not significantly affect the stability and maturity of the sludge layer. The main role of the substrate material is to adsorb elements (e.g., phosphorus) from the drainage fluid as it moves downwards. Since the contact time between the material and the fluid is relatively low (drainage by gravity), the material origin does not play a significant role in sludge properties. In addition, the presence of a coarse-grained material (as in unit S11) did not appear to affect the stability and maturity of the residual sludge.
4.2.4.
Effect of passive aeration
The presence of aeration tubes aims to enhance substrate aeration, and thus, to assist plant growth (Edwards et al.,
2001). As mentioned earlier, the absence of aeration tubes in unit S10 led to plant death during the first summer of operation (2008). Despite that, the absence of aeration tubes in unit S10 did not affect system performance, as was shown by the comparison with the other two unplanted units S10 and S3.
4.2.5.
Effect of chromium
The spike of chromium to the sludge affected CeCO2 generation. In particular, the sludge of unit S7, which had been spiked with Cr, had a higher microbial respiration activity during the loading phase, compared to sludge of unit S1 that had not been spiked with Cr. It seems, therefore, that under high loading rates and high influent Cr concentration, the degradation proceeded at a slower rate compared to when no Cr was added, probably due to the negative effect of Cr to the microbial population (Epstein, 1997). Although the plants in unit S7 did not show any obvious toxicity signs (e.g., yellowing, total coliform density decrease), additional time may have
Table 3 e Statistical differences for various STW pairs (at p < 0.05). Effect of
Vegetation Porous media origin Porous media size Aeration tubes Chromium spike
Identical units Loading of sludge (S1) vs no sludge loading (S13)
S1 vs S5 S1 vs S6 S5 vs S6 S1 vs S4 S1 vs S3 S1 vs S2 S1 vs S11 S2 vs S11 S3 vs S10 S1 vs S7 S5 vs S8 S6 vs S9 S3 vs S4 S5 vs S12 S1 vs S13
CeCO2
SRI12
Germination index (GI)
Total solids (TS)
Volatile solids (VS)
One-year period
Resting phase
One-year period
Resting phase
One-year period
Resting phase
One-year period
Resting phase
One-year period
Resting phase
ns (8) ns (8) ns (8) S1 < S4a (8) ns (7) ns (7) ns (8) ns (8) ns (7) S1 < S7a (8) ns (8) ns (8) ns (8) ns (5) S1 > S13a (7)
ns (5) ns (5) ns (5) ns (5) S1 < S3a (4) ns (5) ns (5) ns (5) ns (4) ns (5) ns (5) ns (5) ns (4) ns (2) ns (4)
ns (7) ns (7) ns (7) S1 < S4a (7) S1 < S3a (7) ns (8) ns (6) ns (6) ns (5) ns (6) ns (7) ns (6) ns (4) ns (5) ns (5)
ns (4) ns (4) ns (4) S1 < S4a (4) ns (4) ns (5) ns (4) ns (4) ns (4) ns (4) ns (5) ns (5) ns (3) ns (2) ns (2)
S1 < S5a (8) ns (8) ns (8) S1 > S4a (8) S1 > S3a (8) ns (8) ns (8) ns (8) ns (5) ns (8) ns (8) ns (8) ns (8) ns (5) S1 < S13a (8)
ns (5) ns (5) ns (5) S1 > S4a (5) S1 > S3a (5) ns (5) ns (5) ns (5) ns (4) ns (5) ns (5) ns (5) ns (5) ns (2) S1 < S13a (5)
ns (8) ns (8) ns (8) S1 > S4a (8) S1 > S3a (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) S1 < S13a (8)
ns (5) ns (5) ns (5) S1 > S4a (5) S1 > S3a (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) S1 < S13a (5)
S1 > S5a (8) ns (8) ns (8) S1 < S4a (8) S1 < S3a (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) ns (8) S1 > S13a (8)
ns (5) ns (5) ns (5) S1 < S4a (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5) ns (5)
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Sludge loading rate
Unit comparison
See Table 1 for identification of units; values in parentheses are the number of samples (n) used in the paired t-test; ns: non-significant difference; statistically significant differences are indicated with “<” or “>”. a Indicates a statistically significant difference at p < 0.05.
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been required to reach sludge stability. Also, the amount of total coliforms in unit S7 (Cr added) was more than double compared to unit S1, while TS content was always lower in unit S7 (Fig. 3a). Lower TS contents (4e6% ww) were also observed in units S8 and S9 (both receiving high Cr concentration) compared to units S5 and S6, indicating that dewatering is likely decelerated. No statistically significant differences were found between units S5 and S8 (low SLR) and units S6 and S9 (medium SLR), which implies that under lower Cr loadings, sludge stabilization is not practically affected by Cr concentration.
The comparison of units S1 and S13 was included to show the effect of an extended resting phase (S13) on sludge quality indices. As shown in Table 3, lower CeCO2 production and higher germination indices were observed for the sludge of unit S13 compared to S1. The comparison between units S1 (2.5 year loading, 6 months resting) and S13 (more than two years resting phase) confirmed that, during an extended resting phase, sludge can reach even higher levels of stability and maturity (see also Fig. 2aec). The importance of the final resting phase in sludge treatment wetlands has also been discussed by Uggetti et al. (2010c).
concentration of certain elements. Thus, a corresponding EC increase was observed. Moreover, taking into account the limited available water for plant growth needs (due to absence of new sludge loadings and the decrease in precipitation), the rooted plants extracted all available water from sludge that led to the simultaneous release of salts (Kadlec and Wallace, 2009). This phenomenon also contributed to the increase of EC. This is further supported by the fact that the unplanted units S3, S4 and S10 had the lowest EC values among all units at that specific sampling event (Fig. 3d). The transitory total coliform increase at the same time could be explained by the gradual plant drying and decay, since sludge loading had been already ceased for more than two months. This plant decay apparently added new readily degradable organic matter to the system that may have led to the temporary TC and SRI12 increase. On the other hand, the CeCO2 index did not increase at the same time. This is because the CeCO2 stability index is a 7-day cumulative value, while the SRI12 index represents a short-term O2 consumption rate (over a 12 h period). An organic waste may exhibit a relatively high short-term respiration activity that may not necessarily coincide with a high long-term respiration activity. This was probably the case for the residual sludge during that specific sampling event of early June.
4.2.7.
4.3.2.
4.2.6.
Effect of the resting phase
Performance of identical units
The comparison of identical units S3 and S4 (both unplanted) did not show any SSD for any of the parameters examined (Table 3). Additionally, no statistical differences were found between units S5 and S12, which had similar design characteristics (note that S12 had started operation one-year earlier than unit S5). This indicates that STW systems have the potential to provide stable and mature final sludges after extended periods of application.
4.3. Time profile of sludge properties and quality of the end-product 4.3.1.
Stability indices
Fig. 2a clearly shows that there is a gradual decrease of the CO2 generation (CeCO2 index) during the resting phase. The values of 1.0 and 3.0 g CeCO2/dry kg and of 30 and 130 mg O2/dry kg/h are limits for most stable and least stable composts, respectively, according to the work of Komilis et al. (2011). These limits are shown in Fig. 2a and b as horizontal dotted lines. As depicted in Fig. 2a and b, final residual sludge from all units is always below the limit for least stable composts, indicating a stabilized end-product. This stability is reached approximately 4 months after the final sludge loading, since this is the time required for CeCO2 generation to fall below approximately 3.0 g C/dry kg. A similar trend was also found for SRI12, which decreased to values less than 130 mg O2 dry/dry kg/h for all units at the end of the monitoring period. On the other hand, a rather sharp increase of SRI12 was observed at the 2nd sampling event during the resting phase. It is interesting to note that this increase coincided with a sharp increase of the EC (Fig. 3d) and the total coliform counts (Fig. 2d). This increase was observed in early June (i.e., beginning of summer) and can be likely explained by the fact that the temperature increase led to water removal and the
Phytotoxicity index
The maturity index (GI; Fig. 2c) showed a decrease, during the loading phase, to values less than 100% in almost all units. This decrease is attributed to the decomposition of sludge during that phase and the likely release of intermediate metabolites (e.g., acids) that can hinder the germination of seeds (Epstein, 1997). The GI of the unloaded unit S13 reached the highest values among all units (around 300%) during the resting phase. This value indicates the adequate maturity of this product, which is a likely result of the extended resting phase (more than two years). Final GI values, after approximately 5 months of resting, ranged from 74% to 176%. It is also worthy to note that the unplanted units (S3, S4 and S10) produced a residual sludge with lower GI (mean 78.9%) values compared to the planted units (mean 122.8%). This confirms the importance of the presence of reeds to obtain a nonphytotoxic (mature) product.
4.3.3.
Total coliforms (TC)
The total coliform counts also showed a gradual decrease during the study period (Fig. 2d). Although there are still no regulated concentration limits for pathogens (EU, 1986), the European Commission has proposed a limit of 5 102 E. coli CFU/g, as a faecal bacteria indicator (EC, 2000). This limit has been recently increased to 5 105 E. coli CFU/g, based on a recent European document on Sludge and Bio-waste (EC, 2010). Final TC counts in this work ranged from 15 to 102 CFU/g for all planted units, far below the above two suggested limits. Similar results have been reported by Nielsen (2007) and Uggetti et al. (2010c). The unplanted units (S3, S4 and S10), on the other hand, had a mean TC concentration of 640 CFU/g. Unloaded unit S13 had a steady TC concentration from 5 to 10 CFU/g throughout the whole study period, indicating the adequate sanitation of sludge in STW systems after extensive resting phases.
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4.3.4.
Total solids and organic matter
The TS content (Fig. 3a) remained below 35% for all units (except for unit S13) during the loading phase. This is explained by the fact that the loading phase coincided with the winter and early spring seasons with increased precipitation events and limited plant activity (due to lower temperatures). After the last sludge loading (early April 2010), the TS content gradually increased and reached a mean final value of 69.2% for units with high SLR (S1, S2, S7 and S11), 82.0% for unit with medium SLR (S6 and S9) and 90.1% for units with low SLR (S5 and S8). These values are indicative of adequately dewatered end products. The unplanted units had a final TS content of only 38.8%. In the last sampling event (November 2010), a sharp TS reduction was observed, due to the initiation of the rain season. The VS content observed a gradual reduction during the whole period (Fig. 3b). VS started from around 60%e68% TS for all units during the loading phase, except of the unloaded unit S13 (53% TS). The VS contents reduced to less than 55% TS in all units by the end of August. As indicated in Fig. 2c, a small gradual reduction of the organic matter content was observed during the resting phase. The above agree with the findings of Uggetti et al. (2010a). They can be explained by the fact that most of sludge degradation occurs at the initial stages, right after sludge loading, due to the presence of readily degradable organics in the sludge, which degrade first at relatively high rates. The remaining refractory organics decompose at lower rates (Haug, 1993).
4.3.5.
pH profile
pH remained above 7.5 during the whole loading phase for almost all units. Upon entering the resting phase, pH followed a gradual reduction to values below 6.5. As mentioned above, the lack of water during the resting phase may have forced plants to satisfy their transpiration needs from the pore water in the system with a simultaneous release of various salts (Kadlec and Wallace, 2009). This fact, combined with the gradual decomposition of the remaining organic matter during the resting phase and the consequent acid production, resulted in a decrease of pH. On the other hand, pH in the residual sludge of the unplanted units S3, S4 and S10 increased during the resting phase. The relatively high pH values for these three units may be attributed to the limited sludge biodegradation during the resting phase. It is worth noting that the unloaded unit S13 had a constant pH from 6.0 to 6.4 throughout the whole study period. This implies that the pH of the residual sludge is expected to lie within that range upon stability. This proved to be true, since the pH of the residual sludge of all the planted units steadily decreased to values below 6.5. This phenomenon contradicts the common belief that the pH of stable organic wastes is always in the alkaline region (Epstein, 1997). The initial alkaline pH values of sludge may be attributed to the presence of alkaline additives.
5.
Conclusions
The present study focused on the stability and maturity of the residual sludge treated in 13 pilot-scale STW units with
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variable design and operational settings. After one-year of continuous monitoring, the following conclusions can be drawn: STWs are capable of producing a stable, mature and dry sludge end-product, at loading rates up to 75 kg dm/m2/yr, after a 4 month resting phase. Final total coliform counts of the residual sludge were far below the legislation limits for total coliforms for all the planted units. There were no significant differences of the stability and maturity measurements between the top and bottom sludge layers within a bed. The presence of plants was crucial and led to the production of a more stable, more mature and drier residual sludge compared to the unplanted units. The loading with sludge containing high chromium concentration at high SLRs hindered the microbial respiration activity during the loading phase. The presence of aeration tubes and the type and size of porous media did not affect the quality of the end sludge product.
references
APHA and AWWA, 1998. Standard Methods for the Examination of Water and Wastewater, twentieth ed. American Public Health Association, Washington, D.C. Barrena, R., Va´zquez, F., Sa´nchez, A., 2006. The use of respiration indices in the composting process: a review. Waste Management and Research 24, 37e47. Berthouex, P.M., Brown, L.C., 2002. Statistics for Environmental Engineers, second ed. Lewis Publishers, NY, USA, pp. 150e158. Bianchi, V., Peruzzi, E., Masciandaro, G., Ceccanti, B., Mora Ravelo, S., Iannelli, R., 2011. Efficiency assessment of a reed bed pilot plant (Phragmites australis) for sludge stabilisation in Tuscany (Italy). Ecological Engineering 37 (5), 779e785. Ceustermans, A., Coosemans, J., Ryckeboer, J., 2010. Compost microbial activity related to compost stability. In: Insam, H., Franke-Whittle, I., Goberna, M. (Eds.), Microbes at Work. Springer Verlag, Heidelberg, pp. 115e134. de Maeseneer, L.J., 1997. Constructed wetlands for sludge dewatering. Water Science & Technology 35, 279e285. Edwards, K.J., Gray, R.K., Cooper, J.D., Biddlestone, J.A., Willoughby, N., 2001. Reed bed dewatering of agricultural sludges and slurries. Water Science & Technology 44, 551e558. Epstein, E., 1997. The Science of Composting. Technomic Pub. Co., Lancaster, PA, USA. European Commission-DG Environment, 2000. Working Document on Sludge, 3rd Draft. Brussels. Available from: http://ec.europa.eu/environment/waste/sludge/pdf/sludge_ en.pdf (accessed September 2011). European Commission-DG Environment, 2010. Working Document, Sludge and Biowaste. Brussels. Available from: http://www.compostnetwork.info/wordpress/wp-content/ uploads/2010/12/101021_ECN_bio-sludge-working-doc_ comments.pdf (accessed September 2011). European Union, 1986. Council directive on the protection of the environment, and in particular of the soil, when sewage sludge is used in agriculture, 86/278/EEC. Official Journal of the EC No L181/6-12, 4/7/1986.
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Haug, R., 1993. The Practical Handbook of Compost Engineering. Lewis Publishers, Boca Raton, FL, USA. Iannotti, D.A., Pang, T., Toth, B.L., Elwell, D.L., Keener, H.M., Hoitink, H.A.J., 1993. A quantitative respirometric method for monitoring compost stability. Compost Science and Utilization 1, 52e65. Kadlec, R.H., Wallace, S.D., 2009. Treatment Wetlands, second ed. CRC Press, Boca Raton, USA. Kim, J.B., Smith, D.E., 1997. Evaluation of sludge dewatering reed beds: a niche for small systems. Water Science & Technology 35 (6), 21e28. Komilis, D.P., Tziouvaras, I.S., 2009. A statistical analysis to assess the maturity and stability of six composts. Waste Management 29, 1504e1513. Komilis, D., Kontou, I., Ntougias, S., 2011. A modified static respiration assay and its relationship with an enzymatic test to assess compost stability and maturity. Bioresource Technology 102 (10), 5863e5872. Melidis, P., Gikas, G.D., Akratos, C.S., Tsihrintzis, V.A., 2010. Dewatering of primary settled urban sludge in a vertical flow wetland. Desalination 250 (1), 395e398. Nielsen, S., 2003. Sludge drying reed beds. Water Science & Technology 48 (5), 101e109. Nielsen, S., 2007. Helsinge sludge reed bed system: reduction of pathogenic microorganisms. Water Science & Technology 56, 175e182. Nielsen, S., Willoughby, N., 2005. Sludge treatment and drying reed bed systems in Denmark. Journal of Water and Environmental Management 19 (4), 296e305. Panuvatvanich, A., Koottatep, T., Kone, D., 2009. Influence of sand layer depth and percolate impounding regime on nitrogen transformation in vertical-flow constructed wetlands treating faecal sludge. Water Research 43, 2623e2630.
Stefanakis, A.I., Tsihrintzis, V.A., 2009. An experimental study of activated sludge treatment in sludge drying reed beds. In: Proceedings of the 3rd International Conference on Advances in Resources and Hazardous Waste Management towards Sustainable Development. AMIREG09, 7e9 September, Athens, Greece, pp. 400e405. Stefanakis, A.I., Tsihrintzis, V.A., 2011. Dewatering mechanisms in pilot-scale sludge drying reed beds: effect of design and operational parameters. Chemical Engineering Journal 172, 430e443. Stefanakis, A.I., Akratos, C.S., Melidis, P., Tsihrintzis, V.A., 2009. Surplus activated sludge dewatering in pilot-scale sludge drying reed beds. Journal of Hazardous Materials 172, 1122e1130. Uggetti, E., Ferrer, I., Llorens, E., Garcı´a, J., 2010a. Sludge treatment wetlands: a review on the state of the art. Bioresource Technology 101, 2905e2912. Uggetti, E., Ferrer, I., Molist, J., Garcı´a, J., 2010b. Technical, economic and environmental assessment of sludge treatment wetlands. Water Research 45 (2), 573e582. Uggetti, E., Ferrer, I., Llorens, E., Gu¨ell, D., Garcı´a, J., 2010c. Properties of biosolids from sludge treatment wetlands for land application. In: Vymazal, J. (Ed.), Water and Nutrient Management in Natural and Constructed Wetlands. Springer, pp. 9e21. Wang, R., Baldy, V., Pe´rissol, C., Korboulewsky, N., in press. Influence of plants on microbial activity in a vertical-downflow wetland system treating waste activated sludge with high organic matter concentrations, Journal of Environmental Management, doi:10.1016/j.jenvman.2011.03.021. Wu, L., Ma, L.Q., Martinez, G.A., 2000. Comparison of methods for evaluating stability and maturity of biosolids compost. Journal of Environmental Quality 29, 424e429.
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Sludge quality aspects of full-scale reed bed drainage Dominik Dominiak, Morten Lykkegaard Christensen, Kristian Keiding, Per Halkjær Nielsen* Department of Biotechnology, Chemistry and Environmental Engineering, Aalborg University, Sohngaardsholmsvej 49, 9000 Aalborg, Denmark
article info
abstract
Article history:
Sludge-drying reed beds can be a cost-effective and sustainable solution to surplus acti-
Received 4 July 2011
vated sludge dewatering and mineralization, especially for small wastewater treatment
Received in revised form
plants. However, the simplicity as well as low energy and monitoring requirements of this
19 September 2011
technology are often counterbalanced by frequent operational problems consisting of slow
Accepted 20 September 2011
and insufficient dewatering, poor vegetation growth, odor, and overall poor mineralization
Available online 29 September 2011
of the sludge residues. The main reason is that the general rules for facility design and operation are based on empirical experience rather than on the actual and current sludge
Keywords:
parameters. In this study a new method for the assessment of activated sludge drainage
Dewatering
properties has been applied to determine the reasons behind operational problems faced
Drainage
by the operators of reed bed facility accepting surplus activated sludge from two waste-
Sludge
water treatment plants in Esbjerg, Denmark. The importance of sludge quality monitoring
Wastewater
as well as the damaging effect of shear forces, oxygen depletion, and long-distance sludge transportation were demonstrated. Finally, more general guidelines for reed bed facility design and operation are given, based on experimental data from seven full-scale plants. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Sludge-drying reed beds can be a cost-effective and sustainable solution to surplus activated sludge dewatering and mineralization (De Maeseneer, 1997). They have been widely applied in Denmark since 1988, where approx. 95 systems existed in 2002 (Nielsen and Willoughby, 2007). They have also become widespread in most of Europe (Haberl et al., 1995). Sludge volume reduction takes place due to both water drainage and plant-driven evapotranspiration, and mineralization of organic matter (Aagot et al., 2000). Simultaneous degradation of hazardous organic compounds and pathogen reduction occurs, which allows for the application of sludge residues in agriculture (Nielsen, 2005a). However, the simplicity as well as low energy and monitoring requirements of this technology are often
counterbalanced by frequent operational problems, consisting of slow and insufficient dewatering, poor vegetation growth, odor, and overall poor mineralization of the sludge residues (Nielsen, 2005b). Insufficient dewatering is most often due to the quality of the sludge (Nielsen, 2011), and studies by Nielsen (Nielsen, 2002, 2003, 2005b) have led to the definition of certain guidelines for facility design and operation, all based on the capillary suction time (CST) as a measure of sludge dewatering characteristics at low pressures normally found on reed beds. However, the operation of reed beds is generally only based on empirical experience and prescribed guidelines such as the dry matter loading limit of 60 kg dry matter/m2/year, rather than on any sludge characteristics. For this reason, operational failures in reed bed facilities occur quite often and account for this technology’s reputation of being unpredictable (De Maeseneer, 1997). Although Nielsen’s choice of the CST
* Corresponding author. Tel.: þ45 9940 8503; fax: þ45 9814 1808. E-mail address:
[email protected] (P.H. Nielsen). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.045
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technique was correct from the viewpoint of low-pressure compressibility of sludge, this method does not directly take into account the solids content of sludge, which has been shown to be one of the critical factors that decide the drainage rate and final cake water content (Dominiak et al., 2011). Furthermore, the measurement of cake compressibility with CST is impossible, so the actual hydraulic resistance cannot be calculated, and the drainage process cannot be correctly assessed or modeled. In a recent study, we presented a novel technique for the determination of drainage properties of activated sludge during gravity dewatering (Dominiak et al., 2011), which we named the Specific Resistance to Drainage (SRD) method. This technique considers the settling velocity of sludge particles, the SRD value and compressibility at low pressure. It was found that the volumetric loading of sludge was most critical to the drainage rate as cake compressibility caused SRD to increase proportionally to increasing load (i.e. increasing pressure). It was also found that the compressibility depends on sludge properties and conditions and that treatments promoting sludge deflocculation, such as anaerobic storage and shear, worsen the drainage properties by increasing SRD under constant load. It is well known that anaerobic conditions, changes in microbial aerobic metabolism, and shear stresses all can cause deflocculation of activated sludge floc (Morgan-Sagastume and Allen, 2003; Wilen et al., 2000; Bruus et al., 1993) and eventually lead to changes in the normal high pressure dewatering (Bruus et al., 1992). These phenomena are expected to be even more pronounced in drainage of activated sludge, where both floc-settling velocity and drainability may be affected. Since the most common reason for poor dewatering in vertical flow reed beds is substrate clogging, believed to originate from the accumulation of suspended solids and their compaction (Platzer and Mauch, 1997; Langergraber et al., 2003), sludge handling prior to its application to reed beds appears to be critical to fast, efficient, and reliable operation of these facilities. Understanding the mechanisms behind reed bed operating problems and application of the SRD methodology presented in this paper should lead to improvements in the operation of sludge-drying reed bed facilities and, eventually, increase the reliability and competitiveness of this simple, sustainable, and cost-efficient technology. In this study, a case story of two Danish wastewater treatment plants sharing a reed bed facility is presented. One treatment plant is located next to the reed beds, whereas the other is required to pump surplus sludge to the facility through a long pipeline. The initial observation that sludge from the distant treatment plant caused frequent dewatering problems in the reed beds, while sludge from the nearby plant did not, inspired the investigation of the reasons behind the dewatering problems and the factors of importance to effective and reliable operation of reed bed facilities. The aims of this study were to investigate the reasons behind the operational problems faced by Esbjerg reed bed facility, and to test the hypothesis that the sludge gravity drainage characteristics depend on floc properties, and that deflocculation of activated sludge is responsible for the deterioration of these properties in both lab scale and full scale. Furthermore, by studying sludge quality variations in
a number of full-scale activated sludge treatment plants, we wanted to find more general guidelines for reed bed facility design and operation.
2.
Materials and methods
2.1.
Site presentation
Two wastewater treatment plants located in Esbjerg in southwest Denmark by the North Sea were studied, Esbjerg East (design persons equivalents (PE) of 125,000) and Esbjerg West (PE of 290,000). Both plants perform nitrification and denitrification, as well as both biological and chemical phosphorus removal. The fraction of industrial wastewater in the influents of both plants is approx. 66% (by COD). The reed bed facility, used for handling surplus sludge from the two treatment plants, is composed of 24 basins, each with an approximate area of 2200 m2 (Fig. 1). All basins are covered by vegetation of common reeds. Activated sludge from the aeration tank of each treatment plant (SS of 3e5 g/L) is pumped into a separate storage tank equipped with an aeration system and allowing calcium carbonate dosing. The distance between plant Esbjerg East and the reed bed facility is approx. 1400 m, whereas plant Esbjerg West is located approx. 6300 m away with a transportation time of about 6.5 h. Table 1 presents the loading schemes for the reed bed basins. The lower loadings of sludge from Esbjerg West were implied by frequent operational failures consisting of sludge overflows and odor problems, but even with smaller portions, drainage takes 30e36 h whereas it takes 20e25 h for sludge from Esbjerg East. Examination of reed bed residues in basins treating sludge from Esbjerg West revealed the presence of dark, dense, and sticky residue layers.
2.2.
Samples of activated sludge
Samples of activated sludge for SRD determination were taken from aeration tanks from both Esbjerg wastewater treatment
Fig. 1 e Satellite view of both wastewater treatment plants and the reed bed facility in Esbjerg, Denmark. Esbjerg West is pasted into the image of Esbjerg East and the reed beds.
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Table 1 e Loading schemes for reed bed basins treating sludge from both wastewater treatment plants in Esbjerg.
Design loading [kg DM/m2/year] Actual loading [kg DM/m2/year] Number of portion added Portion volume [m3] Drainage time between batches [h] Rest [weeks]
Plant Esbjerg East basins
Plant Esbjerg West basins
55 38e42 5 400e500 20e25
55 27e32 5 375e400 30e36
5
5
plants, the pipeline transporting sludge from Esbjerg West, and from the storage tank. Samples of mixed-liquor-activated sludge for SRD determination were also taken from aeration tanks of five other Danish wastewater treatment plants. The suspended solids (SS) and dry matter contents (DM) of activated sludge and filtration cakes were determined according to standard methods (APHA et al., 2005) by overnight weight loss at 104 C. Microscopic analysis of floc morphology and filament index (0e5 scale) was carried out with a light microscope and Eikelboom’s manual for microscopic investigation of activated sludge (Eikelboom, 2000). SVI measurements were made by 30 min sludge settling in a 1 L graded cylinder.
2.3.
Gravity drainage experiments
Measurements of SRD and settling velocity were performed as previously described (Dominiak et al., 2011). A sample of activated sludge was introduced into a vertical transparent tube with a paper filter as a plug. The drainage process was recorded by a camera at a specified frame rate. The images were analyzed to determine the height of the clear liquid phase (h) that developed above the suspension during drainage. Initially, h increases due to particle settling and the settling velocity was determined from this increase i.e. vsed ¼ dh/dt. After the cake was fully developed at time t*, h started to decline again. The clear liquid phase is filtered through the newly formed cake and the filtration velocity can be calculated as
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was determined by measuring weight loss after drying at 104 C overnight. The standard deviation was determined to be 5% of the measured value of SRD.
2.4.
Shear experiments
Shear experiments under aerobic, anoxic, and anaerobic conditions were performed on samples of activated sludge from Esbjerg East and West in order to simulate the effect of pumping and oxygen availability on the drainage properties of sludges. In each case, a 1-l baffled reactor containing 600 ml of activated sludge was used, the shear rate was set to 300 rpm, and the experiment lasted for 6 h (Klausen et al., 2004). Anaerobic and anoxic conditions during shear experiments were assured by seal-closed reactors and nitrogen gas bubbling, with addition of sodium nitrate (final concentration 15e20 mg N/L), and regular nitrate and nitrite monitoring with paper tests in case of anoxic trials.
3.
Results and discussion
3.1.
Esbjerg case study
3.1.1.
Determination of SRD in Esbjerg treatment plants
The problems with sludge draining and mineralization were only noticed in reed bed basins handling sludge from Esbjerg West, but were not reported in basins handling sludge from Esbjerg East. In order to unveil the reason behind these differences, sludge quality in terms of drainage was measured by SRD on activated sludge from the aeration tanks of both treatment plants. The SRD of sludge from Esbjerg West was 1.4 1010 m/kg, and was lower than in Esbjerg East with a value of 2.5 1010 m/kg (Fig. 2). Lower SRD implies faster drainage, so the measured values for both plants contradicted the reports on operational failures.
dh rgðh þ hc Þ ¼ dt ch0 m$SRD where r is the liquid density, g the gravity coefficient, c the dry matter content of the feed, h0 the initial height of the suspension, hc is the height of the cake, and m is the viscosity. The cake height is constant during the filtration of the clear liquid phase; hence the following equation can be derived (Christensen et al., 2010) ðtt Þrg
h ¼ h ech0 m$SRD The equation was fitted to experimental data to calculate SRD. Time of drainage was determined as the point where 90% of the sample was drained. All the experiments were performed on site, immediately after sampling, because it had been determined earlier that sludge transportation affects SRD (data not shown) negatively. In each case, 200 ml of sludge were drained, and the dry matter content of filtration cakes
Fig. 2 e SRD values of sludge samples taken from both Esbjerg treatment plants, the end of the transportation pipeline from plant West, the storage vessel for sludge from Esbjerg West, and the samples of sludge from both plants subjected to a combination of shear and aerobic/ anoxic/anaerobic conditions.
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SRD was also measured at the end of the transportation pipeline connecting Esbjerg West and the reed bed facility and in the storage tank with sludge from Esbjerg West after calcium carbonate addition and prior to basin application (Fig. 2, black bars). Pumping of sludge over 6.3 km and lasting approx. 6.5 h more than tripled the initial SRD value found in Esbjerg West. Addition of calcium carbonate (a common flocculant) to the sludge restored its drainability significantly, leaving the SRD value at approx. double the initial value found in the plant. The improvement caused by calcium carbonate indicated that the loss of drainability could originate from sludge deflocculation caused by shear due to pumping and extended anaerobic conditions. This assumption led to the hypothesis that sludge gravity drainage characteristics depend on floc properties in a similar way as does pressure dewaterability (Bruus et al., 1992), and that deflocculation of activated sludge during transportation was responsible for the deterioration of the drainage properties.
Addition of approx. 15 mg N/L ensured that nitrate was still present after 6e7 h pumping (5e7 mg N/L), and this combined treatment (calcium carbonate to pH 8 and nitrate addition) significantly improved the drainage situation by reducing the operational failures on reed beds, as indicated by the empirical experience of facility operators. These findings confirm the experimental evidence that shear and anoxic conditions cause less damage than the same shear imposed on sludge under anaerobic regime. A laboratory trial of anaerobic sludge deflocculation and subsequent aerobic reflocculation was performed on sludge samples from both Esbjerg plants (data not shown). The positive effect of extended aeration (6 h) was only noted in connection with sludge from Esbjerg East, whereas the same treatment caused further drainability loss in sludge from Esbjerg West.
3.1.2.
A number of SRD measurements on activated sludge from Aalborg East, Esbjerg East, and Esbjerg West wastewater treatment plants during a period of approx. two years showed a fairly constant level over time for each treatment plant (data not shown). In order to find out more about the variation in activated sludge drainage properties among different treatment plants, we performed a survey in seven Danish wastewater treatment plants representing different design types. The SRD value of samples in the aeration tanks analyzed on site turned out to be very different and ranged from 0.5 1010 to 4.2 1010 m/kg (Fig. 3). These differences are significant and clearly show that the drainage properties e thus the potential for using reed beds for dewatering e vary greatly among different wastewater treatment plants. So far it is unknown why the drainage properties were so different among the 7 sludges investigated. Microscopic observation of each sludge sample revealed some potential factors (Table 2). The treatment plants presented in Table 2 are arranged according to increasing values of SRD, from left to right, i.e. sludge quality in terms of drainability decreases from left to right. It is easy to see that the SVI values, traditionally used to describe the quality of sludge in terms of its
Simulation of pumping and oxygen depletion
In order to test the hypothesis that the combination of shear and anaerobic conditions was responsible for sludge deflocculation and the resulting increase of SRD, drainage experiments were performed on sludge sample subjected to simulated pumping. In order to estimate the contribution of oxygen depletion to the overall loss of drainability, shear experiments under aerobic and anoxic conditions were performed at the same shear rate (Fig. 2, gray bars). In each case, shear caused a significant deterioration of sludge draining properties. Although the initial values of SRD of sludges from both plants were almost identical, sludge from Esbjerg East appeared to be much more susceptible to quality loss. In both cases, shear combined with anaerobic conditions caused the most damage to sludge drainability, whereas anoxic and aerobic conditions, respectively, limited the severity of SRD loss due to shear. It is interesting to note that the simulated pumping, which consisted of shearing under anaerobic conditions, raised the SRD value to almost that found at the end of the transportation pipeline (10% difference). This suggests that the set of conditions chosen for simulation of pumping reflected the actual situation quite accurately and that, most probably, shear and anaerobic conditions were responsible for sludge quality loss during its transportation in Esbjerg. Shear and anaerobic conditions had earlier been shown to worsen activated sludge quality, presumably through the lack of aerobic microbial activity, or by anaerobic respiration and reduction of trivalent iron (Bruus et al., 1992; Wilen et al., 2000). These experiments clearly show that such deflocculation has a substantial worsening effect on lowpressure drainage of activated sludge.
3.1.3.
3.2. Survey of sludge drainage properties in Danish wastewater treatment plants
Remedies for sludge quality loss due to pumping
Several strategies for overcoming the difficulties with sludge dewatering on reed beds have been proposed to Esbjerg facility operators. The positive effect of calcium carbonate addition was verified, and this strategy is continuously applied. Furthermore, as in this study nitrate was shown to minimize the negative effects of anaerobic conditions, addition of nitrate to the pumped stream of surplus sludge from Esbjerg West was tested over a period of several months.
Fig. 3 e SRD values of sludge samples from seven Danish wastewater treatment plants.
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Table 2 e SRD values, other parameters, and summary of microscopic observations of samples from seven Danish wastewater treatment plants examined. Wastewater treatment plant
Bramming South
Esbjerg West
Treatment plant information
PE 6000 C, N, DN, CP
SRD[m/kg] Settling velocity [m/s] Dry matter content of cake [%] SVI [ml/g] SS [g/l] VS [g/l] Microscopic floc observations
0.5 1010 90 105
PE 290000 C, N, DN, CP, BP 1.3 1010 1.2 105
Filament index (0e5)
4.3 31 3.5 2.6 Large, compact, round, dark flocs
1
4.3 167 5.3 3.9 Large, regular, compact flocs
2
Hjorring
Esbjerg East
Aalborg East
PE 160000 C, N, DN, CP, BP 2.1 1010 1.7 105
PE 125000 C, N, DN, CP, BP 2.4 1010 1.3 105
PE 125000 C, N, CP, BP
PE 6000 C, N, DN, CP
3.2 1010 2.4 105
4.1 1010 0.54 105
4.4
4.7
93 4.4 3.7 Medium-sized flocs, both round, regular and open, irregular
99 5.9 3.9 Open, irregular, medium-sized flocs, significant amount of inorganics 2.5
1
5.2 111 4.7 2.9 Medium-sized flocs, both compact and open
2
Bramming North
Aalborg West PE 330000 C, N, DN, CP, BP 4.2 1010 1.5 105
4.1 121 6.3 4.9 Very small, irregular, disintegrated flocs, many branched filamentous bacteria 3.5
4.1 211 3.9 3.3 Small, irregular flocs of open structure
2
C e carbon removal; N e nitrification; DN e denitrification; CP e chemical phosphorus removal; BP e biological phosphorus removal.
settleability, also increase from left to right (with one exception in the case of Esbjerg West plant), following the SRD. High SVI values typically indicate many filamentous bacteria or deflocculated sludge with irregular floc structure and many small particles, which would naturally render filterability more difficult (Karr and Keinath, 1978; Barber and Veenstra, 1986; Mikkelsen et al., 1996). This is largely what was recorded during the microscopic investigation of sludge samples. The trend of increasing SRD of sludge is followed by a transition from large, compact, and regular flocs through mediumsized, slightly irregular ones, to small, irregular flocs of open structure, which resembles a decrease of floc strength and progression of deflocculation. Interestingly, sludges of good quality in terms of drainage tend to have a filament index of 1e2 (few to moderate filamentous bacteria), whereas those harder to drain exhibit values of 2e3.5. Filamentous bacteria could be part of the explanation, if one imagines that small particles could be entrapped by filaments protruding from flocs in the filtration cake, which would eventually lead to more resistance to water flow. Usually a general correlation between number of filamentous bacteria and settling velocity exists in activated sludge (Eikelboom, 2000). Such connection also seemed to be present in the plants investigated. Flocs with the lowest filament index and with the most compact structure (Bramming South) settled most quickly, whereas those with the highest FI and with the most irregular structure (Bramming North) settled most slowly. The hydraulic drag posed by filaments and irregular floc structure as the floc settles may be responsible for higher flow resistance inside the cake, and thus for higher SRD of the entire cake. Not all floc properties of importance to gravity drainage have been revealed by this study, but it is clear that the morphology, size, and amount of filaments are important. Other factors known to be of importance for pressure
dewatering may also be of interest, e.g. the amount and composition of extracellular polymers (and thus microbial composition producing these), cations, and the inorganic fraction (Frølund et al., 1996; Park and Novak, 2007). Future studies should investigate these factors better.
3.3. Recommendations for sludge handling and application to reed beds The general guidelines for reed bed operation proposed by Nielsen (Nielsen, 2002, 2003, 2005b) distinguish between the maximum loading rates of 60 kg dry matter/m2/year for surplus activated sludge and 50 kg dry matter/m2/year for surplus sludge mixed with anaerobically digested sludge, but do not include the actual sludge quality monitoring. The survey of seven Danish wastewater treatment plants revealed that the inherent quality of activated sludge in terms of gravity drainage can be very different for different treatment plants. Findings of this, and our previous study, indicate that regular sludge quality monitoring by means of SRD measurements is necessary and would make sludge treatment on reed beds much more predictable and efficient. The methodology is very simple and requires no sophisticated equipment. In the simplest approach, only a cylinder, a filter, scales, and a timer are needed. Plant performance optimization by ‘trial and error’ approach is not recommended, since the sludge residues, once formed, remain in the basin and determine its further hydraulic performance for the entire basin life cycle. In our previous study which described gravity drainage of activated sludge, we presented some relationships between drainage and sludge loading (Dominiak et al., 2011), which can be used together with the results obtained in this study to establish some improved guidelines for sludge handling on reed beds. Fig. 4 presents the relationships between SRD and
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Fig. 4 e Relationships between SRD of activated sludge and time of drainage for different SS concentrations of sludge, and measured points for seven Danish wastewater treatment plants.
the time of drainage at different concentrations of suspended solids. The actual points, representing the values measured in the survey presented in this study, are mapped into the graph. The slope of SRD versus time of drainage increases with increasing SS concentration, which can be translated into longer drainage of the same sludge at constant load if it gets thicker. Fig. 5 illustrates the effect of sludge permeability, which depends on its condition and previous treatments. The harsher the treatment of sludge prior to its drainage, the higher the SRD at a given load, which translates to longer drainage time according to Fig. 4. A practical way of using these two graphs is to set a limit of time for drainage, which should not be too long if anaerobic conditions are to be avoided in the sludge layer on reed bed. Having decided about the time of drainage (e.g. 60 min), and knowing the SS content of sludge (e.g. 4 g/L), the desired SRD can be determined according to Fig. 4. It is only necessary to take one
Fig. 5 e Relationships between the volumetric loading of activated sludge in gravity drainage and the resulting SRD for samples of the same sludge subjected to different treatments.
measurement of SRD of a certain activated sludge sample at one load value (one sample volume, e.g. 200 ml), and the slope of the relationship depicted in Fig. 5 can easily be determined, since it always transects point (0, 0). This reveals the permeability of sludge, which allows choosing a proper load in order to attain a desired SRD (read from Fig. 4) and, eventually, a desired time of drainage. In the alternative case, when loading rate adjustments are impossible, the SRD should be measured for a given load, the relationship as shown in Fig. 5 should thus be determined, and the SRD corresponding to the present loading of the reed bed can be established. Fig. 4 would then help to select the proper SS concentration (adjustable through dilution of sludge in the storage tank with effluent or thickening it by settling) in order to achieve sufficiently fast drainage. Generally, it might always be better to apply smaller portions of sludge more frequently than to overload the basins with large portions of sludge. Due to the compressible nature of activated sludge, loading is the most critical factor when deciding on the drainage rate (Dominiak et al., 2011). If a large volume of sludge is applied to a bed, drainage will proceed very slowly or even stop in extreme cases, which can lead to the development of anaerobic layers in the sludge sediment. Since anaerobic conditions lead to reduced floc strength and deflocculation (Mikkelsen and Keiding, 1999; Wilen et al., 2000), such anaerobic layers can turn into compacted, impenetrable skins creating a barrier to downward water flow. This can in the long run lead to a temporary or permanent loss of bed permeability. It is especially important not to overload the bed in the initial phase of its exploitation because a layer of high resistance present at the bottom of the bed would remain there for a long time and, in the worst case, the entire period of bed exploitation, which could be up to ten years. An alternative solution is to dilute the sludge with effluent, which would accelerate the drainage, but then the pressure would also be increased due to higher liquid levels, the risk of higher cake compaction also having to be taken into account. It is also necessary to evaluate all the possibilities of sludge quality improvement by flocculation through aeration or calcium carbonate addition so that the final effect is significant, but also economically acceptable. Some general guidelines for the design of reed bed facilities can be formulated, based on the findings of this study. The most important operational parameter for a reed bed is the yearly average solids loading, hence the design process should start with the estimation of this value. Esbjerg East operates its basins at approx. 40 kg DM/m2/year (Table 1) and reports consistently predictable operation with no significant problems. Whether this could be slightly increased is presently unknown. According to Fig. 3, the SRD of sludge in this plant is approx. 2.4 1010 m/kg, which is an average value among the plants tested in this study. If a reed bed facility is to be designed, the first thing to do is to check the sludge-SRD as a measure of sludge quality in terms of drainability, taking into account the possible sludge transportation. If this value turns out to be high in the range presented in Fig. 3, it is worth running a series of tests, similar to those described in Section 3.1.2, in order to check whether nitrate dosing, flocculation with calcium, or aeration can improve the drainage properties, and if so, to what extent. Having established the
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 5 3 e6 4 6 0
attainable value of SRD for a given sludge, it needs to be compared to that of Esbjerg East so that the annual average solids loading can be selected through comparison with the benchmark value of 40 kg DM/m2/year reported by that plant. The exact deviation from the benchmark value cannot, however, be given at the moment and requires more full-scale trials. Once the design yearly average solids load is known, the number of basins can be calculated, based on the average sludge production for a given plant. Finally, Figs. 4 and 5 can be used to select the proper operational parameters for the facility (loading, sludge SS concentration). After the commencement of the facility operation, the sludge quality needs to be regularly monitored by means of the SRD technique so that the overall performance of the reed beds is consistent and high. Experiments presented in this study and in our previous reports show the effect of sludge handling on its subsequent drainage properties and how these can be handled. The reed bed operators from Esbjerg introduced changes according to the recommendations presented in this paper. The SS concentration in the aeration tanks of both plants was lowered from 4e6 to 3.5e4 g/L. Nitrate was continuously dosed to the sludge transportation pipeline, and calcium carbonate was continuously used to flocculate sludge prior to its application to the basins. Finally, the sludge application program was changed for all basins, and sludge is now applied in smaller portions, but with higher frequency. It is now 2000 m3/ basin every 6 weeks, and this volume is divided into 5 batches on each basin. Each batch is pumped out during 1 h with 25 h to drain before the next batch is added. Thus, the problems with the operation of basins handling sludge from Esbjerg West were eliminated, and the overall performance of the reed bed facilities was significantly improved after 1 year.
4.
Conclusions
The method for measuring the sludge specific resistance of drainage (SRD) allows quick assessment of sludge quality prior to its application to reed beds, and the guidelines given in this report help to select the proper load and concentration of sludge so that efficient and predictable operation of reed beds is assured. Drainage properties in two Esbjerg plants were followed over two years, showing significant differences in sludge drainability, even though the two plants are very similar in terms of design and inflowing wastewater composition. The long-distance transportation of sludge was revealed to be responsible for the poor performance of reed beds. It is of the utmost importance to keep the sludge aerobic and flocculated so that drainage proceeds fast and risk of flooding the beds is minimized. It is especially important to avoid operational failures in the initial phase of reed bed operation, since every failure leaves behind a compacted layer of sludge residue of high resistance, which negatively affects the bed performance for a long time, and in the worst case e throughout its entire operational period. Seven full-scale wastewater treatment plants showed very significant differences in sludge-SRD values, which highlights the importance of direct and regular sludge quality
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measurements if a sustainable and high performance of reed beds is to be achieved. The new approach to assess sludge quality opens the possibility of formulating new guidelines for reed bed designers and operators, based on direct measurements. This could lead to increased competitiveness of reed bed sludge handling by making this technique more efficient and reliable.
references
Aagot, S., Hansen, G., Nielsen, S., Jensen, J., 2000. Investigation and Monitoring Program for Decomposition of Organic Matters Injurious to the Environment in Constructed Wetlands e Reed Beds Plant for Sludge Drying and Treatment and in Sludge Deposit. Danish Environmental Protection Agency. Working report no. 22 (summary in English). APHA, AWWA, WEF, 2005. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, American Water Works Association, Water Environment Federation, Washington D.C. Barber, J.B., Veenstra, J.N., 1986. Evaluation of biological sludge properties influencing volume reduction. Journal of the Water Pollution Control Federation 58, 149e156. Bruus, J.H., Nielsen, P.H., Keiding, K., 1992. On the stability of activated sludge flocs with implications to dewatering. Water Research 26, 1597e1604. Bruus, J.H., Christensen, J.R., Rasmussen, H., 1993. Anaerobic storage of activated sludge: effects on conditioning and dewatering performance. Water Science and Technology 28, 109e116. Christensen, M.L., Dominiak, D.M., Nielsen, P.H., Keiding, K., 2010. Gravitational drainage of compressible organic materials. AIChE Journal 56, 3099e3108. De Maeseneer, J.L., 1997. Constructed wetlands in Europe. Water Science and Technology 35, 279e285. Dominiak, D., Christensen, M., Keiding, K., Nielsen, P.H., 2011. Gravity drainage of activated sludge: new experimental method and considerations of settling velocity, specific cake resistance and cake compressibility. Water Research 45, 1941e1950. Eikelboom, D.H., 2000. Process Control of Activated Sludge Plants by Microscopic Investigation. IWA Publishing, London. Frølund, B., Palmgren, R., Keiding, K., Nielsen, P.H., 1996. Extraction of extracellular polymers from activated sludge using a cation exchange resin. Water Research 30, 1749e1758. Haberl, R., Perfler, R., Mayer, H., 1995. Constructed wetlands in Europe. Water Science and Technology 32, 305e315. Karr, P.R., Keinath, T.M., 1978. Influence of particle size on sludge dewaterability. Journal of the Water Pollution Control Federation 50, 1911e1928. Klausen, M.M., Thomsen, T.R., Nielsen, J.L., Mikkelsen, L.H., Nielsen, P.H., 2004. Variations in microcolony strength of probe-defined bacteria in activated sludge flocs. FEMS Microbiology Ecology 50, 123e132. Langergraber, G., Haberl, R., Laber, J., Pressi, A., 2003. Evaluation of substrate clogging processes in vertical flow constructed wetlands. Water Science and Technology 48, 25e34. Mikkelsen, L.H., Gotfredsen, A.K., Agerbæk, M.L., Nielsen, P.H., Keiding, K., 1996. Effects of colloidal stability on clarification and dewatering of activated sludge. Water Science and Technology 34, 449e457. Mikkelsen, L.H., Keiding, K., 1999. Equilibrium aspects of the effect of shear and solids content on aggregate
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deflocculation. Advances in Colloid and Interface Science 80, 151e182. Morgan-Sagastume, F., Allen, D.G., 2003. Effects of temperature transient conditions on aerobic biological treatment of wastewater. Water Research 37, 3590e3601. Nielsen, S., 2002. Sludge drying reed beds. In: Proceedings of the International Conference on the Use of Constructed Wetlands in Water Pollution Control, Arusha, Tanzania, September 2002. Nielsen, S., 2003. Sludge drying reed beds. Water Science and Technology 48, 101e109. Nielsen, S., 2005a. Mineralization of hazardous organic compounds in a sludge reed bed and sludge storage. Water Science and Technology 51, 109e117. Nielsen, S., 2005b. Sludge reed bed facilities: operation and problems. Water Science and Technology 9, 99e107.
Nielsen, S., Willoughby, N., 2007. Sludge treatment and drying reed bed systems in Denmark. Water and Environment Journal 19, 296e305. Nielsen, S., 2011. Sludge treatment reed bed facilities e organic load and operation problems. Water Science & Technology 63, 941e947. Park, C., Novak, J.T., 2007. Characterization of activated sludge exocellular polymers using several cation-associated extraction methods. Water Research 41, 1679e1688. Platzer, C., Mauch, K., 1997. Soil clogging in vertical flow reed beds e mechanisms, parameters, consequences and...solutions? Water Science and Technology 35, 175e181. Wilen, B.M., Nielsen, J.L., Keiding, K., Nielsen, P.H., 2000. Influence of microbial activity on the stability of activated sludge flocs. Colloids and Surfaces B: Biointerfaces 18, 145e156.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 6 1 e6 4 7 0
Available online at www.sciencedirect.com
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N2O emission from a partial nitrificationeanammox process and identification of a key biological process of N2O emission from anammox granules Satoshi Okabe a,b,*, Mamoru Oshiki a,b, Yoshitaka Takahashi a, Hisashi Satoh a a
Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan b Japan Science and Technology Agency, CREST, Japan
article info
abstract
Article history:
Emission of nitrous oxide (N2O) during biological wastewater treatment is of growing concern.
Received 11 April 2011
The emission of N2O from a lab-scale two-reactor partial nitrification (PN)eanammox reactor
Received in revised form
was therefore determined in this study. The average emission of N2O from the PN and
25 June 2011
anammox process was 4.0 1.5% (9.6 3.2% of the removed nitrogen) and 0.1 0.07%
Accepted 20 September 2011
(0.14 0.09% of the removed nitrogen) of the incoming nitrogen load, respectively. Thus,
Available online 29 September 2011
a larger part (97.5%) of N2O was emitted from the PN reactor. The total amount of N2O emission from the PN reactor was correlated to nitrite (NO 2 ) concentration in the PN effluent rather
Keywords:
than DO concentration. In addition, further studies were performed to indentify a key bio-
Partial nitrification
logical process that is responsible for N2O emission from the anammox process (i.e., granules).
Anammox
In order to characterize N2O emission from the anammox granules, the in situ N2O production
Nitrous oxide emission
rate was determined by using microelectrodes for the first time, which was related to the
Granules
spatial organization of microbial community of the granule as determined by fluorescence in
Microelectrodes
situ hybridization (FISH). Microelectrode measurement revealed that the active N2O production zone was located in the inner part of the anammox granule, whereas the active ammonium consumption zone was located above the N2O production zone. Anammox bacteria were present throughout the granule, whereas ammonium-oxidizing bacteria (AOB) were restricted to only the granule surface. In addition, addition of penicillin G that inhibits most of the heterotrophic denitrifiers and AOB completely inhibited N2O production in batch experiments. Based on these results obtained, denitrification by putative heterotrophic denitrifiers present in the inner part of the granule was considered the most probable cause of N2O emission from the anammox reactor (i.e., granules). ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Nitrous oxide (N2O) has a more than 300-fold greater potential for global warming effects than carbon dioxide, even though N2O only accounts for approximately 0.03% of total
greenhouse gas emissions (Bates et al., 2008). Thus, the actual impact of N2O on global warming has been estimated up to 10% of total greenhouse gas emissions. It also takes part in stratospheric ozone depletion and is toxic to humans. Wastewater treatment systems, especially, biological nitrogen
* Corresponding author. Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan. Tel./fax: þ81 (0)11 706 6266. E-mail address:
[email protected] (S. Okabe). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.040
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removal processes, have been known to be a potential N2O emission source. It is, therefore, in urgent need of reducing the emission and of identifying the factors that control the emission of N2O from wastewater treatment plants (WWTPs). Several measurements at lab-scale and full-scale WWTPs have indicated that N2O can be produced in substantial amounts from biological nitrogen removal processes (Foley et al., 2010; Osada et al., 1995; Tallec et al., 2006; Kampschreur et al., 2008, 2009b). Both nitrification and denitrification processes can lead to emission of N2O. However, N2O emissions are extremely variable and depend on many operational parameters such as dissolved oxygen (DO) and nitrite (NO 2 ) concentrations in both nitrification and denitrification stage (Beline et al., 2001; Gejlsbjerg et al., 1998; Itokawa et al., 2001; Kampschreur et al., 2008; Park et al., 2000) and carbon availability (low chemical oxygen demand (COD)/N ratio) in the denitrification stage (Itokawa et al., 2001; Park et al., 2000). A recent review by Kampschreur et al. (2009a) showed that there are large variations in the N2O emissions from full-scale WWTPs (0e14.6% of the nitrogen load) and lab-scale WWTPs (0e95% of the nitrogen load). Recently, sustainable wastewater treatment systems that can minimize energy consumption, emission of greenhouse gases, and sludge production have been attracting the attention. A nitrogen removal process via anaerobic ammonium oxidation (anammox) has been recognized as a promising costeffective and low energy alternative to the conventional nitrificationedenitrification processes due to a significant reduction of aeration and external carbon source (van Dongen et al., 2001; Kartal et al., 2010). In nitrogen removal via anammox process, ammonium in wastewater is partly pre-oxidized to nitrite (i.e., partial nitrification) by ammonium-oxidizing bacteria (AOB) before feeding into the anammox process. The produced nitrite together with remaining ammonium is then converted to dinitrogen gas (N2) in the anammox process. In the two-reactor partial nitrificationeanammox process, significant N2O production could be expected during the partial nitrification due to accumulation of high NO 2 and DO-limited conditions. In addition, N2O emission can also be expected from the anammox process since the anammox processes have been generally operated at high volumetric nitrogen removal load as described by Tsushima et al. (2007) and Tang et al. (2011) and at low COD/N ratio, even though anammox bacteria have not been shown to produce N2O under physiological conditions. Emission of N2O from a full-scale tworeactor partial nitrificationeanammox process treating reject water was determined to be 2.3% of the total nitrogen load (1.7% in the partial nitrification process and 0.6% in the anammox process) (Kampschreur et al., 2008). Emission of N2O from a full-scale single-stage partial nitrificationeanammox reactor treating wastewater from a potato processing factory and reject water of a municipal sludge dewatering plant was 1.2% of the total nitrogen load (Kampschreur et al., 2009b), which is higher than the emission from a lab-scale single reactor partial nitrificationeanammox system on artificial wastewater (less than 0.1% of the nitrogen load) (Sliekers et al., 2002). The magnitude and source of N2O emission in the combined partial nitrification and anammox process are, however, relatively unknown, especially, the potential and
mechanism of N2O emission from anammox reactors or granules (or biofilms) is also unknown. Emission of N2O from an energy-saving and cost-effective partial nitrificationeanammox process would hamper the practical application and should therefore be avoided. In this study, a lab-scale partial nitrificationeanammox process was developed in two separate reactors to investigate N2O emission from both processes. In addition, further studies were performed to indentify a key biological process that is responsible for N2O emission from the anammox process (i.e., granules). In order to characterize N2O emission from the anammox granules, microelectrodes were used to determine in situ N2O production rate, which was related to spatial organization of microbial community of the granule analyzed by fluorescence in situ hybridization (FISH).
2.
Materials and methods
2.1.
Lab-scale partial nitrification reactor
An up-flow biofilm partial nitrification (PN) reactor with a working volume of 800 cm3 and nonwoven fabric sheets (4.0 4.0 0.8 cm 18 sheets; Japan Vilene Co., Ltd., Tokyo, Japan) as support material for biofilms was used. The PN reactor was established and operated for 680 days as described previously (Cho et al., 2011; Okabe et al., 2011). Synthetic nutrient medium (Okabe et al., 2011) and air was supplied continuously from the bottom of the reactor. Although the dissolved oxygen concentration (DO) was not controlled during the experiment, the air-flow rate was adjusted in the range 100e650 mL min1. The incubation temperature was maintained at 35 C. The influent pH was adjusted to 7.8 0.1. The hydraulic retention time (HRT) of the reactor was fixed at 4 h.
2.2.
Anammox reactor
An up-flow granular-sludge anammox reactor with a working volume of 150 cm3 has been stably operated at 35 C for more than 2 years (Cho et al., 2010). This reactor was originally inoculated with anammox biomass taken from an anammox reactor (a maximum nitrogen removal rate of 34.2 kgN m3 d1) developed previously in our laboratory (Kindaichi et al., 2007; Tsushima et al., 2007). Only the reactor performance after about 2 years is presented.
2.3.
Partial nitrificationeanammox process
After achieving stable partial nitrification (after 680 days) and anammox reaction (after approximately 2 years), the PN reactor was combined with the anammox reactor. The half amount of ammonium in the influent was oxidized to nitrite in the PN reactor, resulting in the ammonium and nitrite ratio of about 1:1 in the effluent. The effluent of the PN reactor was introduced into the anammox reactor via a flow equalizing tank (500 mL), in which dissolved oxygen (DO) carried over from the partial nitrification reactor are removed, and pH was adjusted to around 7.2. Flow rate into the anammox reactor was set to obtain a HRT 0.3e0.8 h. After stable performance of the combined PN and anammox reactor was achieved, N2O
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 6 1 e6 4 7 0
was measured in the off-gas and liquid phase of the PN and anammox reactor. The off-gas stream from the anammox reactor was generated by the gas production.
2.4.
Analytical procedure
To monitor the performance of partial nitrificationeanammox reactor, one grab influent and effluent sample was collected at regular time interval during the operation. Ammonium (NHþ 4 N), nitrite (NO 2 -N), and nitrate (NO3 -N) in the influent and effluent were measured three times by using ion-exchange chromatography (DX-100, DIONEX, CA., USA) with an IonPac CS3 cation column and IonPac AS9 anion column after filtration with 0.2-mm pore size membranes (ADVANTEC, Tokyo, Japan). Analytical errors were within 5% for each chemical during the experiment. Dissolved oxygen (DO) concentration in the effluent was measured by using a DO meter (DO-5Z, KRK, Japan).
2.5.
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anammox granules, penicillin G (500 mg L1) was added directly to the medium to inhibit the activity of the peptidoglycan-containing bacteria, but not anammox bacteria (van de Graaf, et al., 1996). The bottles were then incubated at 35 C. Gas and medium samples were taken for chemical analyses at appropriate time intervals.
2.7.
Fixation and cryosectioning of biofilm samples
Granule samples obtained from the anammox reactor were fixed in a 4% paraformaldehyde solution for 24 h at 4 C, washed three times with phosphate-buffer saline (PBS) (10 mM sodium phosphate buffer, 130 mM sodium chloride; pH 7.2), and embedded in Tissue-Tek OCT compound (Sakura Finetek, Torrance, CA) overnight to infiltrate the OCT compound into the biofilm, as described previously (Okabe et al., 1999a). After rapid freezing at 21 C, 10- to 20-mm-thick vertical thin sections were prepared with a cryostat (Reichert-Jung Cryocut 1800, Leica, Bensheim, Germany) (Okabe et al., 1999b)
N2O measurement 2.8.
The off-gas grab samples were collected from the PN and anammox reactor with a gas-tight syringe. The N2O concentration in the off-gas was measured with a GC-12A gas chromatograph (Shimadzu, Japan) equipped with an electron capture detector (ECD) and using nitrogen gas as carrier gas. Temperatures of the injector, column, and detector were 330, 60, and 330 C, respectively. The dissolved N2O gas concentration in the liquid phase was measured by using the headspace method. Briefly, a sample was transferred to a 70-mL glass vial. The vial was sealed by a butyl-rubber stopper and aluminum cap. After the glass vial was shaken for a few minutes, N2O in the gas phase was measured by the gas chromatograph as described above. The N2O dissolved in the liquid phase was calculated by the solubility formula of Weiss and Price (1980). For calculation of N2O emission rate from the process, the N2O emission rate (mg-N m3 d1) was calculated relative to the nitrogen load into the partial nitrification reactor and the nitrogen conversion rate of each reactor, respectively.
2.6. Batch experiments for estimating N2O emission characteristics
The 16S rRNA-targeted oligonucleotide probes used in this study were follows; EUB mix probe (EUB338, EUB338II, and EUB338III) for all bacteria (Daims et al., 1999), which were used in an equimolar, Amx820 for Candidatus Brocadia anammoxidans and Candidatus Kuenenia stuttgartiensis (Schmid et al., 2001), Nse1472 for Nitrosomonas europaea, Nitrosomonas halophila, and Nitrosomonas eutropha, Nsv443 for Nitrosospira spp. (Mobarry et al., 1996) and Nso190 for ammonia-oxidizing bproteobacteria (Mobarry et al., 1996). The probes were labeled with fluorescein isothiocyanate (FITC) or tetramethylrhodamine 5-isothiocyanate (TRITC) at the 50 end. In situ hybridization was performed according to the procedure described by Okabe et al. (1999b). A model LSM510 confocal laser-scanning microscope (CLSM, Carl Zeiss, Oberkochen, Germany), equipped with an Ar ion laser (488 nm) and HeNe laser (543 nm), was used. The average surface area fraction of probe-hybridized cells was determined from at least 10 representative LSM projection images of each cross-section of the biofilm samples using image analysis software provided by Zeiss (Okabe et al., 2004).
2.9. For batch experiments, anammox biomass taken from the anammox reactor was disrupted by intensive magnetic stirring to reduce mass transfer limitation at low substrate concentrations. For each batch experiment, 800 mL of disrupted anammox biomass (the aggregate diameter <100 mm) was mixed with 20 mL of the anammox nutrient medium in 34 mL serum bottles (ca. 0.2 g-VSS L1). The serum bottles were sealed with butyl-rubber stoppers and purged with N2 gas (99.99%) to remove oxygen. The anammox nutrient medium consisted of 120 mg-N L1(NH4)2SO4, 100 mg-N L1 NaNO2, 500 mg L1 KHCO3, 27 mg L1 KH2PO4, 300 mg L1 MgSO4$2H2O, 180 mg L1 CaCl2$2H2O, and 1 mL of trace element solution I and II (van de Graaf et al., 1996). The same medium was used for all batch experiments. pH of the medium was adjusted at 7.2. To identify the key biological process that is responsible for N2O production in the
Fluorescence in situ hybridization (FISH)
Microelectrode measurements
N2O and NHþ 4 concentration profiles in anammox granules were measured using Clark-type microelectrodes and LIXtype microelectrodes, respectively. N2O microelectrode was purchased from Unisense (Arhus, Denmark) and calibrated according to the instruction provided by Unisense. The LIX microelectrodes were prepared, calibrated, and operated as described by Okabe et al. (1999a) and Satoh et al. (2003). For microelectrode measurements, the granules with diameters of 2e3 mm were selected and positioned, using five insect needles, in the flow chamber (4.0 L) that was filled with the synthetic medium at 35 C (Satoh et al., 2007). The anammox granules used for microelectrode measurements were bigger than the mode of the granule size distribution (1.5e2.0 mm). The medium used for microelectrode measurement was the same as the synthetic nutrient medium fed to the PN reactor
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except for NHþ 4 and NO2 concentrations, which were both 4.5 mM. The medium in the flow chamber was kept anaerobic by continuous bubbling with N2 gas (99.9%), which also provided sufficient mixing of the medium. The granules were acclimated in the medium at least 3 h to ensure that steady-state profiles were obtained. At least three profiles were measured for each chemical. A concentration profile was measured only once in a granule due to deterioration of anammox activity during the measurement. The net specific consumption or production rates of NHþ 4 and N2O were calculated from the mean concentration profiles by using Fick’s second law of diffusion as described by Kindaichi et al. (2007). Molecular diffusion coefficients of 5 cm2 s1 for N2O in 1.38 105 cm2 s1 for NHþ 4 and 2.10 10 water at 35 C were used for the calculation (Laverman et al., 2007).
3.
ALR was decreased to about 2.5 kg-N m3 d1 by reducing the influent flow rate (corresponding to HRT of 3.4 h) in order to þ achieve the stable effluent NO 2 -N/NH4 -N ratio of 1:1. The airflow rate was also reduced accordingly. Although the operational parameters were changed, the effluent concentrations of NHþ 4 , NO2 , and NO3 were relatively stable during N2O measurement (Fig. 1A). The effluent NHþ 4 and NO2 concentra1 tions were in the range of 100e222 mg-N L and 80e180 mgN L1, respectively. Approximately 40% of influent ammonium was oxidized to nitrite, resulting in an average nitrite production rate (NIPR) of 1.15 0.35 kg-N m3 d1 (Fig. 1B). The NIPR of the PN reactor was limited by air-flow rate in this study (Fig. S1). The nitrate production rate (NAPR) was negligible (0.02 0.01 kg-N m3 d1) during this period. Dissolved oxygen (DO) concentration fluctuated but was below 2.0 mg O2 L1 after the air-flow rate was reduced to around 200 mL h1 (day 33) (Fig. 1C). During the measurement period, 100% of the effluent of the PN reactor was continuously fed into the anammox reactor. The performance of the anammox reactor is shown in Fig. 2. The anammox reactor has been operated more than 2 years in advance and only the reactor performance was shown during N2O measurement. The anammox reactor was operated at high nitrogen loading rate (NLR) of 21.5 2.0 kg-N m3 d1 for initial 40 days, and the NLR was decreased to 11.6 1.2 kgN m3 d1 by reducing the influent flow rate to the half (Fig. 2C). The nitrogen removal rate (NRR) fluctuated (7.5e15.0 kgN m3 d1) during the initial 40 days, but thereafter it became stable (6.9 1.2 kg-N m3 d1).
Results and discussion
3.1. Performance of the partial nitrification (PN) and anammox reactor The partial nitrification (PN) reactor has been operated more than 680 days in advance and only the PN reactor performance during N2O measurement (after 680 days) is shown in Fig. 1. The PN reactor was operated at a high ammonium loading rate (ALR) of 3.5e4.1 kg-N m3 d1 (a constant influent ammonium concentration of 300 mg-N L1). Thereafter (after 33 days), the
A
B
C
D
D L Fig. 1 e The performance of the partial nitrification reactor: (A) concentrations of NHD 4 -N in influent, and NH4 -N, NO2 -N, and L NO3 -N in effluent, (B) ammonium loading rate (ALR), nitrite production rate (NIPR), nitrate production rate (NAPR), and N2O emission rate (kg mL3 dayL1) (C) influent flow rate, air-flow rate, and dissolved oxygen (DO) concentration, and (D) N2O concentrations in the off-gas and in the effluent of the partial nitrification reactor.
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Concentrations (mg-N L-1)
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L L Fig. 2 e The performance of the granular-sludge anammox reactor: (A) concentrations of NHD 4 -N, NO2 -N, and NO3 -N in D L L influent, (B) concentrations of NH4 -N, NO2 -N, and NO3 -N in effluent, (C) influent flow rate, nitrogen loading rate (NLR), and nitrogen removal rate (NRR), and (D) N2O concentrations in the off-gas and in the effluent of the anammox reactor.
3.2. N2O emission from the partial nitrificationeanammox process N2O concentrations in the off-gas and effluent of both reactors were measured, respectively (Figs. 1D and 2D). The N2O concentration in the off-gas of the PN reactor varied widely (79e645 ppm) probably due to the fluctuation of the air-flow rate, while the N2O concentration in the liquid phase was around 1.0 mM during the measurement (Fig. 1D). This result indicates that most of the produced N2O was stripped out from liquid phase by aeration. Thus, total amount of N2O emission from the PN reactor was determined by multiplying N2O concentration in the off-gas by air-flow rate. Since the ammonium loading rate and air-flow rate of the PN reactor were changed at 33 days, only the N2O emission data obtained after 33 days were used for the calculation. The total amount of N2O emission from the PN reactor seemed to respond to the 2 NO 2 concentration (r ¼ 0.61 (P < 0.05)) (Fig. S2A) in the effluent of the PN reactor, which corresponds to the previous findings by Kampschreur et al. (2008, 2009a). However, it should be noted that since only grab samples were taken and measured at given time intervals in this study, further studies will be needed to obtain clear correlations. On the other hand, there was no significant correlation between the N2O levels in the off-gas and DO concentration (r2 ¼ 0.18 (P > 0.5)) (Fig. S2B). Since the PN reactor was made up of relatively thick biofilms, anoxic zones could be developed in the biofilms regardless of the fluctuation of DO concentrations, leading to N2O production in the anoxic zones. Thus, it is thought that there was no
clear relation between the total amount of N2O emission and DO concentrations in this study. In the anammox reactor, the N2O concentration in the offgas fluctuated widely (93e1358 ppm) probably due to the changes in influent flow rate and NLR (Fig. 2D). The N2O levels in liquid and gas phases after 33 days were taken into account for nitrogen balance of the overall PNeanammox system (Fig. 3). In the PN reactor, on average 4.0 1.5% of the incoming nitrogen load (or 9.6 3.2% of the removed nitrogen in the PN reactor) was converted to N2O. On the other hand, the average emission of N2O from the anammox reactor was 0.1 0.07% of the incoming nitrogen load (0.14 0.09% of the removed nitrogen in the anammox reactor). Thus, the larger part (97.5%) of N2O was emitted from the PN reactor (Fig. 3). Based on a nitrogen mass balance, about 75% of the nitrogen load was removed from the water phase as N2 gas in this system (N2 gas was not measured in this study); the remaining 25% was present in the effluent as NHþ 4 (10%), NO2 (6%), and NO3 (9%). The N2O emission level from the lab-scale PN process (4.0 1.5% of the incoming total nitrogen load) and anammox process (0.1 0.07% of the incoming total nitrogen load) in this study seem to be the same range as those reported in the literature. In a lab-scale partial nitrification system, 5.4% of converted nitrogen was emitted as N2O at a DO level of 1.0 mgO2 L1 (Zheng et al., 1994). In a lab-scale nitrifying airlift reactor operated at DO concentration below 0.032 mg-O2 L1, 5.5% of the consumed ammonium was emitted as N2O (Sliekers et al., 2005). The emissions from lab-scale anammox
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Fig. 3 e Average nitrogen mass balance and N2O emission from the partial nitrification and anammox reactor during the measurement period (only the data after 33 days were used for the calculation). Numbers are average nitrogen loads (in mgN dL1) of influent and effluent of the PN and anammox reactor. N2O concentrations were measured in the liquid and off-gas. The off-gas stream of anammox reactor was created by the gas production. Percentages are relative to the nitrogen load of the partial nitrification reactor.
enrichment reactors were 0.03e0.06% (Strous et al., 1998), <0.1% (van de Graaf et al., 1997; Wyffels et al., 2004), and <0.01% (Kampschreur et al., 2008). Emission of N2O from a partial nitrification reactor treating the anaerobically treated concentrated black water at 25 C was 0.6e2.6% (average 1.9%) of the total nitrogen load (de Graaff et al., 2010). Furthermore, emission of N2O from a full-scale two-reactor partial nitrificationeanammox process treating reject water was determined to be 2.3% of the total nitrogen load (1.7% in the partial nitrification process and 0.6% in the anammox process) (Kampschreur et al., 2008). Emission of N2O from a full-scale single-stage partial nitrificationeanammox reactor treating wastewater from a potato processing factory and reject water of a municipal sludge dewatering plant was 1.2% of the total nitrogen load to the reactor (Kampschreur et al., 2009b). However, emission of N2O from a lab-scale single reactor partial nitrificationeanammox system on artificial wastewater was less than 0.1% of the nitrogen load (Sliekers et al., 2002). Moreover, Ahn et al., (2010) have surveyed several fullscale conventional nitrogen removal processes and found that 0.01e3.3% of influent total nitrogen was converted to N2O. Thus, the N2O emission level observed in this study is similar to other reported cases. In the PN reactor, since both ammonium and nitrite were in excess of oxygen (i.e., oxygen-limited condition), it is suspected that ammonium-oxidizing bacteria (AOB) outcompeted nitrite-oxidizing bacteria (NOB) (Okabe et al., 1996; Kindaichi et al., 2006) and produced N2O during denitrification of nitrite with ammonium as electron donor (Colliver and Stephenson, 2000). Oxygen is the most influential
factor affecting the production of N2O; a decrease in oxygen can result in activation of nitrite reductase and a severalfold increase in N2O production (Colliver and Stephenson, 2000).
3.3. NHþ 4 and N2O concentration profiles in anammox granules Even though N2O emission from the anammox rector was low, the steady-state concentration profiles of N2O and NHþ 4 in the anammox granules were determined with microelectrodes to study the mechanism of N2O emission. The concentration profiles of N2O and NHþ 4 were measured at least three times, and the average profiles are presented in Fig. 4A. The spatial distributions of net volumetric NHþ 4 consumption and N2O production rates were calculated based on the NHþ 4 and N2O concentration profiles (Fig. 4B and C). The Oxygen concentration was under the detection limit (ca. 0.3 mM) throughout the granules at all points. The concentration profiles indicated that NHþ 4 consumption by anammox reaction was restricted to the upper 1200 mm of the anammox granule with a peak around the upper 300 mm (Fig. 4B), while N2O production was found within a depth of 600e1300 mm with a peak around 800 mm (Fig. 4C). These results indicate that the anammox activity and N2O production are spatially separated, which probably suggests that the ammonium oxidation by anammox bacteria could not be the major biological process that is responsible for N2O production. This result does not completely negate the contribution of anammox bacteria to N2O emission, and thus further study is essential.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 6 1 e6 4 7 0
40
were also detected around the anammox bacteria, and its relative abundance increased to 15e30% of the total bacteria hybridized with EUB mixed probe (EUB338, EUB338II and EUB338III) with the granule depth (Fig. 5E). The concentration profile of N2O in the anammox granules has neither been determined with microelectrode nor related to microbial community structure so far. Since active N2O production was detected in the deeper part of granule where aerobic AOB was not detected, the contribution of AOB (i.e., nitrifier denitrification) to the N2O production was negligible. Furthermore, there are no indications of N2O production by anammox bacteria so far, it seems that anammox bacteria are not responsible for N2O production. Taken together, the bacteria present in the depth of granules are most likely putative heterotrophic denitrifying bacteria and responsible for N2O emission in anammox granules.
30
3.4.
20
In order to examine the effect of pH on N2O emission in the anammox granules, the concentration profiles of N2O in the anammox granules were determined with microelectrodes at pH 7.0 and 8.0 (Fig. 6). The N2O production was higher at pH 7.0 than at pH 8.0. Batch culture experiments at different pH also reveal that the N2O production at pH 6.5 was about 11 times higher than pH 8.0 (Fig. 7). This pH dependent N2O production is probably attributed to inhibition of N2O reductase at low pH (Knowles, 1982). Actually, N2O reductase of heterotrophic denitrifiers is strongly inhibited by free nitrous acid (HNO2). A HNO2 concentration of 0.004 mg HNO2-N L1 completely inhibited N2O reduction during denitrification (Zhou et al., 2008). The medium used for microelectrode measurement contained 4.5 mM (63 mg-N L1) of NO 2 , corresponding to about 0.015 mg HNO2-N L1 at pH 7 and 0.0013 mg HNO2-N L1 at pH 8, respectively. Thus, it could be expected complete inhibition of N2O reductase at pH 7 or 6.5. Similar result has been reported by Hanaki et al. (1990), where N2O emission during heterotrophic denitrification increased when the pH decreased 8.5e6.5. Thoern and Soerensson (1996) have also reported that significant N2O formation was observed only at pH 6.8 in a denitrification basin. If N2O were produced via nitrifier denitrification, N2O emission could increase when pH increases (Kampschreur et al., 2009a,b). The pH dependent N2O emission in this study supports our conclusion that heterotrophic denitrification is most likely the main biological mechanism of N2O emission in anammox granules. In order to identify the source of N2O production in the anammox granule, we further determined N2O emission from anammox granules cultured in the medium supplemented þ with; (1) NHþ 4 and NO2 , (2) NO2 only, and (3) NH4 , NO2 , and penicillin G (Fig. 8). In the anoxic batch culture supplemented with NHþ 4 and NO2 , AOB, anammox bacteria, and heterotrophic denitrifiers are supposed to be active. In the anoxic batch cultures supplemented with NO 2 , only heterotrophic denitrifiers are supposed to be active. Furthermore, in the anoxic batch cultures supplemented with NHþ 4 , NO2 , and penicillin G, only anammox bacteria are supposed to be active because penicillin G inhibits most of the heterotrophic denitrifiers and AOB (van de Graaf et al., 1996; Gu¨ven et al., 2005). N2O production in the batch culture supplemented with NO 2 only
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Fig. 4 e (A) The steady-state concentration profiles of NHD 4 and N2O in the anammox granule. The surface of the granule is at a depth of 0 mm. The points are measured means ± standard deviations (n [ 3), and the solid lines are the best fits from the model to calculate the volumetric consumption rate of NHD 4 and production rate of N2O. Spatial distributions of the estimated volumetric consumption rate of NHD 4 (B) and production rate of N2O (C).
The microbial community structure in the anammox granule was analyzed by FISH (Fig. 5A and B). The FISH images of cross-section of the granules showed that the anammox bacteria hybridized with the probe Amx820 were present throughout the granules (Fig. 5C). The relative abundance of the anammox bacteria was more than 90% of the total bacteria hybridized with EUB mixed probe (EUB338, EUB338II and EUB338III). Aerobic AOB hybridized with the probe Nse1472 were detected only in the granule surface and around the clusters of anammox bacteria (Fig. 5D). The bacteria that were not hybridized with the probes Amx820, Nso190 and Nsv443
Effect of pH on N2O emission in anammox granules
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Fig. 5 e (A) An enlarged photo of granular-sludge anammox reactor, (B) A anammox granule with a diameter of approximately 3 mm, (C) Confocal laser-scanning microscope image of thin cross-section of the anammox granule showing in situ spatial organization of anammox bacteria (yellow) and coexisting other bacteria (green) after fluorescence in situ hybridization with FITC-labeled EUB338mix probe and TRITC-labeled probe Amx820, (D), Coexistence of FITC-labeled Nse1472 probe-hybridized Nitrosomonas eutropha/europaea-like AOB (green) and TRITC-labeled Amx820 probe-hybridized anammox bacteria (red) in the surface of granule, and (E) Coexistence of FITC-labeled EUB338mix probe hybridized other bacteria (green) and TRITC-labeled Amx820 probe-hybridized anammox bacteria (yellow) in the inner part of granule. All bars on the images indicate 20 mm.
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autotrophic reactor, leading to N2O emission due to incomplete denitrification. Another possibility is endogenous denitrification (i.e., utilization of internal storage compound such as poly-b-hydroxybutyrate (PHB)) (Kampschreur et al., 2009a). However, the carbon source for heterotrophic denitrification is presently unknown. The result of this study is not consistent with the literature showing that denitrification by AOB was considered the most probable cause of N2O production (0.6% of the nitrogen load) in a full-scale anammox reactor treating sludge reject water (Kampschreur et al., 2008). Thus,
(g-N2O emitted / g-N-consumed)
(263 39.2 ppm, n ¼ 3) was higher than that in the culture supplemented with NHþ 4 and NO2 (148 10.0 ppm, n ¼ 3) (Fig. 8). No detectable N2O production was observed in the culture supplemented with NHþ 4 , NO2 , and penicillin G. Based on the results obtained in this study, it is conceivable that heterotrophic denitrification could be a main process of N2O emission in the anammox granule. In this case, N2O is produced as an intermediate of incomplete heterotrophic denitrification due to low COD/N ratio. The putative heterotrophic denitrifying bacteria could use organic matter liberated by anaerobic degradation of biomass inside granules. However, biodegradable organic matter is limited in such
0.012
* 0.01 0.008 0.006 0.004 0.002 0
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Fig. 6 e The concentration profiles of N2O at pH [ 7.0 and 8.0 in the anammox granule. The surface of the granule is at a depth of 0 mm. The points are measured means ± standard deviations (n [ 3).
*
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Fig. 7 e The pH effect on N2O production in anammox granules cultured at pH of 6.5, 7.0 and 8.0, respectively. The N2O emission was expressed as g-N2O emitted per g-N consumed. The error bars indicate standard deviations of triplicate measurements (n [ 3). NC: negative control (the autoclaved biomass was incubated in the same medium at pH 8). P values were determined using the Student’s t-test. *, P < 0.05. N.D, not detected.
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Core Research of Evolutional Science & Technology (CREST) for “Innovative Technology and System for Sustainable Water Use” from Japan Science and Technology Agency (JST).
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Appendix. Supplementary material
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Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.040.
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Fig. 8 e The production of N2O in the anammox granules cultured in the medium supplemented with; (1) NHD 4 and L D L NOL 2 , (2) NO2 only, and (3) NH4 , NO2 , and penicillin G. The error bars indicate standard deviations of triplicate measurements (n [ 3). P values were determined using the Student’s t-test. *, P < 0.05. N.D, not detected.
further research using a dual-isotope (18Oe15N) labeling technique (Wrage et al., 2005) is required to distinguish between nitrous oxide (N2O) from nitrification, nitrifier denitrification and denitrification in anammox granules.
4.
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Conclusions
A lab-scale two-reactor partial nitrificationeanammox process was developed to investigate N2O emission from both processes. The average emission of N2O from the lab-scale partial nitrification and anammox process was 4.0 1.5% (9.6 3.2% of the removed nitrogen) and 0.1 0.07% (0.14 0.09% of the removed nitrogen) of the incoming nitrogen load, respectively. The total amount of N2O emission from the PN reactor was correlated to nitrite (NO 2 ) concentration in the PN effluent rather than DO concentration. The active N2O production zone was located in the inner part of the anammox granule, whereas the active ammonium consumption zone was located above the N2O production zone. Based on all experimental results (including microelectrode, FISH, and batch experiments with an inhibitor) obtained, the N2O emission in the anammox reactor (i.e., granules) is most likely originated from heterotrophic denitrification.
Acknowledgments This research was financially supported by Grant-in-Aid for the “Development of High-efficiency Biological Wastewater Treatment Technology Using Artificially Designed Microbial Communities” Project from the New Energy and Industrial Technology Development Organization (NEDO), Japan and by
references
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Kampschreur, M.J., Temminl, H., Kleerebezem, R., Jetten, M.S.M., van Loosdrecht, M.C.M., 2009a. Nitrous oxide emission during wastewater treatment. Water Res. 43 (17), 4093e4103. Kampschreur, M.J., Poldermans, R., Kleerebezem, R., van der Star, W.R.L., Haarhuis, R., Abma, W.R., Jetten, M.S.M., van Loosdrecht, M.C.M., 2009b. Emission of nitrous oxide and nitric oxide from a full-scale single-stage nitritationanammox reactor. Water Sci. Technol. 60 (12), 3211e3217. Kartal, B., Kuenen, J.G., van Loosdrecht, M.C.M., 2010. Sewage treatment with anammox. Science 328, 702e703. Kindaichi, T., Kawano, Y., Ito, T., Satoh, H., Okabe, S., 2006. Population dynamics and in situ kinetics of nitrifying bacteria in autotrophic nitrifying biofilms as determined by real-time quantitative PCR. Biotechnol. Bioeng. 94 (6), 1111e1121. Kindaichi, T., Tsushima, I., Ogasawara, Y., Shimokawa, M., Ozaki, N., Satoh, H., Okabe, S., 2007. In situ activity and spatial organization of anaerobic ammonium-oxidizing (anammox) bacteria in biofilms. Appl. Environ. Microbiol. 73 (15), 4931e4939. Knowles, R., 1982. Denitrification. Microbiol. Rev. 46, 43e70. Laverman, A.M., Meile, C., van Cappellen, P., Wieringa, E.B.A., 2007. Vertical distribution of denitrification in an estuarine sediment: integrating sediment flow through reactor experiments and microprofiling via reactive transport modeling. Appl. Environ. Microbiol. 73 (1), 40e47. Mobarry, B.K., Wagner, M., Urbain, V., Ritmann, B.E., Stahl, D.A., 1996. Phylogenetic probes for analyzing abundance and spatial organization of nitrifying bacteria. Appl. Environ. Microbiol. 62 (6), 2156e2162. Okabe, S., Itoh, T., Satoh, H., Watanabe, Y., 1999a. Analyses of spatial distributions of sulfate-reducing bacteria and their activity in aerobic wastewater biofilms. Appl. Environ. Microbiol. 65 (11), 5107e5116. Okabe, S., Oozawa, Y., Hirata, K., Watanabe, Y., 1996. Relationship between population dynamics of nitrifiers in biofilms and reactor performance at various C:N ratios. Water Res. 30 (7), 1563e1572. Okabe, S., Oshiki, M., Takahashi, Y., Satoh, H., 2011. Development of long-term stable partial nitrification and subsequent anammox process. Bioresour. Technol.. doi:10.1016/j.biortech. 2011.04.011. Okabe, S., Satoh, H., Watanabe, Y., 1999b. In situ analyses of nitrifying biofilms as determined by in situ hybridization and the use of microsensors. Appl. Environ. Microbiol. 65 (7), 3182e3191. Okabe, S., Satoh, H., Watanabe, Y., 2004. Analysis of size distribution and areal cell density of ammonia-oxidizing bacterial microcolonies in relation to substrate microprofiles in biofilms. Biotechnol. Bioeng. 85 (1), 86e95. Osada, T., Kuroda, K., Yonaga, M., 1995. Reducing nitrous oxide gas emissions from fill-and-draw type activated sludge process. Water Res. 29 (6), 1607e1608. Park, K.Y., Inamori, Y., Mizuochi, M., Ahn, K.H., 2000. Emission and control of nitrous oxide from a biological wastewater treatment plant bioreactors. J. Biosci. Bioeng. 90 (3), 247e252. Satoh, H., Okabe, S., Yamaguchi, Y., Watanabe, Y., 2003. Evaluation of the impact of bioaugmentation and biostimulation by in situ hybridization and microelectrode. Water Res. 37 (9), 2206e2216. Satoh, H., Miura, Y., Tsushima, I., Okabe, S., 2007. Layered structure of bacterial and archaeal communities and their in
situ activities in anaerobic granules. Appl. Environ. Microbiol. 73 (22), 7300e7307. Schmid, M., Schmitz-Esser, S., Jetten, M., Wagner, M., 2001. 16Se23S rDNA intergenic spacer and 23S rDNA of anaerobic ammonium-oxidizing bacteria: implications for phylogeny and in situ detection. Environ. Microbiol. 3, 450e459. Sliekers, A.O., Derwort, N., Campos Gomez, J.L., Strous, M., Kuenen, J.G., Jetten, M.S.M., 2002. Completely autotrophic nitrogen removal over nitrite in one single reactor. Water Res. 36 (10), 2475e2482. Sliekers, A.O., Haaijer, S.C.M., Stafsnes, M.H., Kuenen, J.G., Jetten, M.S.M., 2005. Competition and coexistence of aerobic ammonium- and nitrite-oxidizing bacteria at low oxygen concentrations. Appl. Microbiol. Biotechnol. 68 (6), 808e817. Strous, M., Heijnen, J.J., Kuenen, J.G., Jetten, M.S.M., 1998. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Appl. Microbiol. Biotechnol. 50 (5), 589e596. Tang, C.-J., Zheng, P., Wang, C.-H., Mahmood, Q., Zhang, J.-Q., Chen, X.-G., Zhang, L., Chen, J.-W., 2011. Performance of highloaded ANAMMOX UASB reactors containing granular sludge. Water Res. 45 (1), 135e144. Tallec, G., Garnier, J., Billen, G., Gousailles, M., 2006. Nitrous oxide emissions from secondary activated sludge in nitrifying conditions of urban wastewater treatment plants: effect of oxygenation level. Water Res. 40 (15), 2972e2980. Thoern, M., Soerensson, F., 1996. Variation of nitrous oxide formation in the denitrification basin in a wastewater treatment plant with nitrogen removal. Water Res. 30 (6), 1543e1547. Tsushima, I., Ogasawara, Y., Kindaichi, T., Okabe, S., 2007. Development of high-rate anaerobic ammonium-oxidizing (anammox) biofilm reactors. Water Res. 41 (8), 1623e1634. van Dongen, U., Jetten, M.S.M., van Loosdreght, M.C.M., 2001. The SHARON-anammox process for treatment of ammonium rich wastewater. Water Sci. Technol. 44 (1), 153e160. van de Graaf, A.A., de Bruijn, P., Robertson, L.A., Jetten, M.S.M., Kuenen, J.G., 1996. Autotrophic growth of anaerobic ammonium-oxidizing micro-organisms in a fluidized bed reactor. Microbiology (Reading UK) 142 (8), 2187e2196. van de Graaf, A.A., de Bruijn, P., Robertson, L.A., Jetten, M.S.M., Kuenen, J.G., 1997. Metabolic pathway of anaerobic ammonium oxidation on the basis of 15N studies in a fluidized bed reactor. Microbiology (Reading UK) 143 (7), 2415e2421. Wrage, N., van Groenigen, J.W., Oenema, O., Baggs, E.M., 2005. A novel dual-isotope labelling method for distinguishing between soil sources of N2O. Rapid Commun. Mass Spectrom. 19, 3298e3306. Weiss, R.F., Price, B.A., 1980. Nitrous oxide solubility in water and seawater. Mar. Chem. 8, 347e359. Wyffels, S., Boeckx, P., Pynaert, K., Zhang, D., van Cleemput, O., Chen, G., Verstraete, W., 2004. Nitrogen removal from sludge reject water by a two-stage oxygen-limited autotrophic nitrification denitrification process. Water Sci. Technol. 49 (5e6), 57e64. Zheng, H., Hanaki, K., Matsuo, T., 1994. Production of nitrous oxide gas during nitrification of wastewater. Water Sci. Technol. 30 (6), 133e141. Zhou, Y., Pijuan, M., Zeng, R.J., Yuan, Z., 2008. Free nitrous acid inhibition on nitrous oxide reduction by a denitrifyingenhanced biological phosphorous removal sludge. Environ. Sci. Technol. 42 (22), 8260e8265.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 7 1 e6 4 7 8
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Precoagulation-microfiltration for wastewater reuse J.W. Hatt a, E. Germain b, S.J. Judd a,* a b
Cranfield Water Science Institute, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK Thames Water R&D, Island Road, Reading, RG2 0RP, UK
article info
abstract
Article history:
A range of coagulant chemicals and doses, up to 2 mg/L, were trialled on a microfiltration-
Received 28 January 2011
based indirect potable reuse (IPR) pilot plant to evaluate their impact on membrane
Received in revised form
reversible and irreversible fouling. Jar tests revealed these doses to have negligible impact
25 June 2011
on organic matter removal, whilst scoping pilot trials showed them to have a positive
Accepted 20 September 2011
impact on fouling rates. Initial trials carried out over a 6-h period suggested that ferric
Available online 24 September 2011
sulphate was the most promising of the coagulants tested with regards to irreversible fouling. Extended five-day trials using ferric sulphate at 0.5 mg/L were conducted at fluxes
Keywords:
of 40e50 l/(m2h) (LMH). Operation at 50 LMH without coagulant resulted in rapid fouling
Coagulation
and a subsequent shortening of the chemical cleaning interval. The addition of the ferric
MF
coagulant resulted in a reduction in both reversible and irreversible fouling to those levels
Membrane
experienced at 40 LMH, enabling sustainable operation. The use of low levels of coagulant
Fouling reduction
thus enables the pilot plant to operate at a 25% increased flux, equating to a 20% reduction
Reversible fouling
in membrane area and overall savings of >0.1 p per m3 for a seven year membrane life. ª 2011 Elsevier Ltd. All rights reserved.
Irreversible fouling Ferric sulphate Aluminium sulphate Polyaluminium chloride
1.
Introduction
A key problem encountered in the application of membrane filtration technology is fouling, which results in the loss of hydraulic performance and may reduce membrane life. Fouling can be characterised in terms of the method by which it is removed (reversible and irreversible for physical and chemical removal respectively), its chemical nature or origin (e.g. organic, inorganic, biological, etc), or its physical form (dissolved, colloidal, particulate, etc). Particulate fouling is considered to be reversible, since it is largely removed by physical cleaning. Other types of fouling may be irreversible, requiring chemical cleaning for their removal. Much work has been aimed at elucidating fouling mechanisms to expedite its control and/or removal. Wiesner et al.
(1989) concluded that particles greater than 3 mm should not contribute significantly to membrane fouling at normal operating fluxes, but that for many membrane configurations particles between 0.1 and 1 mm are more likely to. The proposed use of coagulants to aggregate foulants that would otherwise plug the membrane pores dates back many years (Mietton Peuchot and Ben Aim, 1992). Studies have subsequently been undertaken to further identify the size and nature of foulants, and the coagulant types and coagulation conditions most effective in fouling amelioration (Howe and Clark, 2006; Howe et al., 2006; Lee et al., 2007). Favourable results appear to be contingent on feed water quality, membrane characteristics (such as pore size), and membrane configuration. Jar tests have been used to determine coagulant dose and type based on organic matter removal. Work initially by
* Corresponding author. Tel.: þ44 1234 754842. E-mail address:
[email protected] (S.J. Judd). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.039
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Edzwald and Benschoten. (1990) on surface waters revealed organic matter removal rates to be dependent on the its hydrophobicity as represented by the specific ultraviolet absorbance (SUVA), the ratio of the UV light absorbance at 254 nm to the dissolved organic content. Table 1 demonstrates how organic matter removal rates vary with SUVA and alkalinity. Where coagulant is used on those waters with a high SUVA value and low alkalinity (<30 mg/l as CaCO3), high removal rates in the range 60e80% can be achieved. However, those waters with high alkalinity and low SUVA, as are likely to be encountered in the trials for this paper result in particularly low removal rates of 10e15% despite the use of coagulant (Fan et al., 2008; Pernitsky and Edzwald, 2006). Fouling suppression, however, appears not to be contingent upon organic matter removal: work by Choi et al. (2004) and Konieczny et al., (2009) showed fouling to be suppressed at coagulant doses not significantly influencing organic matter removal. However, fouling is also affected by the plant operating and maintenance (O&M) regime, and in particular the flux or transmembrane pressure (TMP), backflush flux and frequency, and the chemical cleaning protocol. There is therefore obvious synergy between the coagulant dosing regime and the plant O&M, though few studies have focused on this synergy since many have been conducted on the bench-scale using non-backflushable flat sheet (FS) membranes (Lee et al., 2000; Scha¨fer et al., 2001; Shon et al., 2005). Moreover, results obtained from bench-scale studies cannot be considered representative of full-scale operation due to differences in membrane module geometry and configuration, which inherently yields differences in flux distribution and fouling rate (Carroll and Booker, 2000; Fane et al., 2002; Howe et al., 2007; Kim and DiGiano, 2006; Yeo et al., 2006). Table 2 summarises studies of coagulation impacts on reversible and irreversible fouling of microfiltration/ultrafiltration (MF/UF) membranes at laboratory and pilot plant scale. Whereas reversible fouling pertains to fouling between backwashing, irreversible fouling relates to the rise in the TMP post-backwash. Results show the use of coagulant to enhance backwash efficiency, providing greater flux recovery or TMP reduction and so a reduction in irreversible fouling rate. Alum is the most widely used coagulant and can reduce the
irreversible fouling rate of hollow fibre (HF) membranes by 75e100%. The dose used varies from 0.2 to 0.5 mg/l as Al when dosed via an aerated mixing tank to 1.3e2.5 mg/l when added inline, with no obvious correlation between dose and water quality or membrane pore size. Laboratory scale results showed much smaller reductions in the irreversible fouling rate which could be attributable to higher feedwater turbidities, differing coagulants and/or differing hydrodynamics between FS and HF membranes. Ferric chloride has given mixed results with regards to the reduction of the irreversible fouling rate. Citulski et al. (2009, 2008) found that it gave rapid and irreversible fouling rate at low doses (10e40 mg/l as Ferric Chloride), an observation corroborated by Judd and Hillis (2001) who found that at low doses the floc growth rate was insufficient to avoid pore plugging. However, at doses of 3.1 mg/l as Fe3þ the same authors, found that the irreversible fouling rate to be reduced by 30%, corroborating previous reports elsewhere (Fan et al., 2008). Pilot scale results indicated low doses of alum (0.5 mg/l as Al) to increase reversible fouling rate, whereas doses of 1.3e2.5 mg/l reduce reversible fouling. However, such papers have not considered the effect of coagulant on the relationship between turbidity and reversible fouling (Raffin et al., 2011 In progress). Citulski et al., (2008) investigated the statistical significance of turbidity on TMP stability and, contrary to that for total suspended solids, found it to be insignificant. However, little detail was provided, other than average and standard deviation turbidity values recorded during the trials (4.37 and 3.69 respectively). It is unclear from the report whether turbidity measurements used in the statistical analysis were daily spot samples or averages, such that the effect of turbidity spikes on fouling rates e known to be significant from operational practice e would have been overlooked. This paper reports on the evaluation of a range of coagulants on permeability decline (manifested as the TMP increase at constant flux) on a pilot-scale MF plant treating secondary wastewater. The study concentrates on the use of coagulant at doses similar to those previously reported (Table 1, 0.5e2 mg/l) whereby coagulated organic matter removal is through charge neutralisation rather than sweep flocculation. Charge neutralisation has been shown to provide enhanced removal rates
Table 1 e Summary of papers on organic matter removal values. Lead Author, Pub. Year
Pernitsky and Edzwald, (2006) Pernitsky and Edzwald, (2006) Fan et al., (2008) Best et al., (2001) Pernitsky and Edzwald, (2006) Bagga et al., (2008) Walsh et al., (2009)
Water Source
Surface Surface 2ndary effluent Surface Surface Surface Surface
Feedwater Quality
Coagulant
TOC UV Alkalinity pH SUVA (mg/l) (cm1) (mg CaCO3/l) l/(mg. m)
% TOC Dose (mg Me3þ/l) Removal
3.3 2.8 9.83a
0.05 0.04 0.26
120 <30 190
7.9 6.7 7.1
1.6 2.2 2.6
Al2(SO4)3 & PACl Al2(SO4)3 & PACl Fe2(SO4)3, Al2(SO4)3
1.5 1.2 2&5
10e15 35e65 10e15a
2.6 3.1 5.3a 1.72a
0.07 0.09 0.18 0.08
11 <30 57 3.6
6.8 7.2 7.5 5.8
2.8b 3.0 3.4 4.5
Al2(SO4)3 Al2(SO4)3 & PACl FeCl3 Al2(SO4)3
1 1.6 15 0.4e0.6
45 60e80 18e32a 65e77a
a Value is DOC not TOC. b SUVA value is an estimate based on TOC and DOC.
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Table 2 e Summary of papers investigating impact of coagulation on reversible and irreversible fouling of MF membranes. Research Laboratory trials LahoussineTurcaud et al. (1990)
Raw Water
Membrane
Coagulant
Details of cleaning
River water (2e60 NTU)
Lyonnaise des Eaux PS HF (10 fibres) 1 nm pore size
Polyaluminium chloride @ 103e104 M Al
No backflush
Dong et al. (2007)
River water (5e38 NTU)
Nitto Denko Corp. PVDF flat sheet 150 kDa MWCO
Alum @ 4 & 10 mg/l as Al 1 min @ 100 rpm & 29 @30 rpm
Backflushed every 60 min
Fan et al. (2008)
Secondary wastewater effluent (pH 7.1, 7.4 NTU, DOC:9.83 mg/l, CaCO3 :190 mg/l)
2 different flat sheet: PVDF 0.22 mm pore size & PES 100 kDa
2 & 5 mg/l Alum as Al & Ferric Chloride as Fe
No backflush
Upland reservoir water (pH 7.44, 0.89 NTU, TOC:2.4 mg/l, CaCO3 :12 mg/l
USF Acumen Optimem PS HF (in-out) 0.1e0.2 mm pore size
Ferric chloride dosed inline, mixed using a static mixer, pH adjusted to 5.4. 0e4 mg/l as Fe
Qin et al. (2004)
Secondary wastewater effluent (pH 7, TSS: 14.5 mg/l, COD:115 mg/l, CaCO3:100 mg/l)
Norit XIGA PS HF (in-out) 0.05 mm pore size
0e4.8 mg/l Alum as Al dosed inline, using MF feed pump as mixer
70 m3/d; const. flux (110 LMH), backflushed every 10 min, chemically cleaned after each 24 h trial 5 m3/h; const. flux, bw 2e4 times/h, Chemically enhanced backwash every 1e3 d
Farahbakhsh and Smith (2002)
Reservoir water (pH 6.3, 0.5 NTU, TOC 2.43 mg/l, low alkalinity)
2 different HF: Zenon 0.35 mm pore size; Memcor 0.2 mm (both in-out);
Zenon: 4e10 mg/l Alum via aerated mixing tank. Memcor: 2e8 mg/l polyaluminium chloride via inline static mixer
Const. flux. Zenon bw every 15e30 min þ intermittent at continuous aeration. Memcor bw every 22 min
Citulski et al. (2009, 2008)
Secondary wastewater effluent (pH 7.78, 4.37 NTU, COD:29 mg/l, TOC:14 mg/l)
Zenon Zeeweed 1000 (out-in). 0.02e0.1 mm pore size
Alum dosed at 10e70 mg/l and ferric chloride at 10e40 mg/l using gravity flocculation.
Const. flux, bw every 20e22 min, 15 min soak in NaOH every 24 h and a full chemical clean when TMP ¼ 80 kPa
Pilot plant trials Judd and Hillis (2001)
Conclusion Initially slowed reversible fouling (first 5 h), and 10% increase in flux recovery post surface wash with demineralised water; ie reduced rate of irreversible fouling Coagulation reduced reversible fouling with respect to flux decline by 30% & reduced irreversible fouling by 100% ie enabled full flux recovery with backwashing Coagulant reduced irreversible fouling (flux recovery increased by 30% on MF and 13e21% on UF Optimum dose of 3.1 mg/l of Fe3þ reduced the irreversible fouling rate by 30% Min. dose 2.5 mg/l as Al3þ required to stabilise the reversible fouling rate at 1.6 bar/min, full TMP recovery from backwash ie irreversible fouling rate negligible over 1 month test period Coagulant increased reversible fouling rate by a factor of 2 on Zenon membrane (no info for Memcor). Backwash efficiency improved in both cases, reducing the irreversible fouling rate by approx. 75% Min. dose of 30 mg/l alum (1.3 mg/l as Al3þ) stabilised reversible fouling rate; negligible irreversible fouling. Ferric chloride caused rapid & irreversible fouling
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and suppressed fouling when compared with sweep flocculation (Lee et al., 2007; Pernitsky and Edzwald, 2006; Lee et al., 2000), notwithstanding the lower dose demanded (e.g. 0.5e0.7 mg/l as as Al3þ), and this has been attributed to the formation of a less compressible but highly porous cake under charge neutralisation conditions (Lee et al., 2000). The current study aims to establish the impact of fluctuations in feedwater turbidity on coagulant performance, as manifested in the reversible and irreversible fouling rates and residual permeate dissolved organic matter concentration.
2.
Materials and methods
2.1.
Jar tests
Preliminary jar tests were carried out to assess the effect of the different coagulants at varying doses with reference to removal of turbidity, colour (UV400nm absorption) and dissolved organic matter (DOC and UV254nm). Tests were conducted using a Phipps-Bird jar test apparatus with six flat blade paddles and 2 L mixing vessels, following standard protocols (ASTM, 2003). Tests were triplicated with different water samples with no pH correction: previous reports (Tables 1 & 2) have demonstrated measureable fouling amelioration within the pH range measured for this raw water (pH 6.7e7.2). The mixing conditions used were 10 s rapid mixing at G ¼ 300 s1 followed by slow mixing for 120 s at G ¼ 25 s1. Samples were analysed after filtering through a 0.45 mm filter paper. These mixing conditions were chosen to replicate the hydraulic retention times and mixing regimes within the pilot plant at the point of coagulant addition.
2.2.
Pilot plant
The 600 m3/d pilot plant has been described elsewhere (Raffin et al., 2011), and comprised 16 immersed microfiltration (MF) membrane modules (Siemens Memcor CMF-S 0.04 mm). The plant received secondary effluent (see Table 3) from a conventional activated sludge municipal wastewater treatment works in north London, UK. The plant comprised a 500 mm-rated automatically backflushed filter (Bollfilter model 6.18) upstream of the MF skid, the permeate then being fed to a reverse osmosis (RO) unit (Hydranautics ESPA-2) and a hydrogen peroxide-UV advanced oxidation process (AOP). Coagulants trialled were polyaluminium chloride (PACl), including a standard version (PAX-10) and a high basicity
Table 3 e Feed water quality parameters based on continuous online monitoring. Parameter Turbidity (NTU) TOC (mg/l) pH Temperature ( C) Alkalinity (mg/l CaCO3)a TSS (mg/l)a
Average
Standard Deviation
19.7 7.2 7.0 19.1 202.8 14.4
87.9 6.5 0.2 2.6 12.2 25.8
a Results based on periodic spot sampling.
version (PAX-XL9), aluminium sulphate and ferric sulphate, all provided by Kemira Chemicals (Goole, UK). Coagulant doses refer to units of mg/l as Al or Fe. Coagulants were introduced using a peristaltic pump (Watson Marlow 520S) at a rate commensurate with their target concentration in the treated water, and dosed downstream of the MF feed pump and immediately prior to an inline static mixer (Chemineer 2KMS-6) and the membrane tank. The mixer and tank residence times were 10 s and 120 s respectively. For the preliminary trials coagulant was added at doses of 0.5, 1 and 2 mg/l daily for three consecutive days for a 6-h period each day. A different coagulant was trialled each week and the MF was chemically cleaned prior to the addition of each new coagulant without pH correction. MF backflushing was every 30 min for 5 min with air and water at a flux of 45 l m2 h1 (LMH). Membrane chemical cleaning was with. 540 mg/l sodium hypochlorite followed by sulphuric acid at pH3, both reagents being heated to 30 C. For the extended, week-long trials the coagulant was added at a fixed dose (0.5 mg/L) at different fluxes of 40, 45 and 50 LMH. Optimisation trials previously carried out (Raffin et al., 2011) revealed that it was only possible to operate the MF at 50 LMH for 3e5 days between chemical cleans whereas at 40 LMH the MF could operate at a range of influent conditions with chemical cleaning at 21 day intervals. A key research objective was thus to establish whether MF operation could be sustained at 50 LMH using coagulant.
2.3.
Monitoring analyses
Data were recorded on a supervisory control and data acquisition (SCADA) system. Online instrumentation for the MF included Siemens Magflow 6000 flowmeters on the feed and permeate lines, Hach Lange turbidity meters on feed and permeate, and ABB pH and temperature monitoring on the discharge. Samples taken before (pre and post coagulant addition) and after the MF were either analysed on site for TOC and UV254 nm or sent to the Thames Water Laboratory for total suspended solids (TSS), and residual aluminium/iron concentration measurement according to standard methods (Eaton, 2005). Samples were filtered using a 0.45 mm filter to provide the DOC through size exclusion comparable to that of the membrane filtration. Autopsies were carried out on membrane fibre samples taken before and after each coagulant condition and treated following the method described by Porcelli and Judd (2009). The resulting eluates were also analysed by Thames Water Laboratories for a range of metals according to standard methods.
3.
Results and discussion
3.1.
Jar tests
Results from the jar tests (Table 4) revealed increasing doses of coagulant to produce only a small improvement in organic matter removal rate. At the highest dose employed of 10 mg/L the measured DOC and UV254 removals were w13% and w25% respectively, the higher UV254 removal reflecting the preferential removal of the more hydrophobic aromatic compounds
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% DOC Alum removed PAX-10 PAX-XL9 Fe2(SO4)3 % UV254 Alum Removed PAX-10 PAX-XL9 Fe2(SO4)3
2.7 2.2 2.7 2.2 2.7 2.2 2.7 2.2
0.5
1
2
5
10
1.8 0.3 1.5 3.7 7.0 5.6 8.1 6.4
5.8 3.3 4.5 4.3 11.1 8.5 12.1 7.9
9.1 5.6 3.4 5.0 20.0 13.5 14.4 8.3
9.5 9.6 5.5 6.9 20.1 14.5 18.4 9.1
17.1 15.0 12.8 9.5 24.2 29.1 25.2 10.2
(Bagga et al., 2008; Lahoussine-Turcaud et al., 1990; Porcelli et al., 2009; Scha¨fer et al., 2001). These low rates of organic removal were assumed due to the low level of hydrophobicity and humic compounds, as indicated by the SUVA value which ranged from 2.2 to 2.7 l/(mg.m) during the trial. Further analysis of the raw water by resin fractionation confirmed the hydrophobic content to be around 30%. Results for organic matter removal are comparable to those previously reported (Fan et al., 2008) for waters with a similar SUVA value and high alkalinity (Table 1, #1 & #3 and Table 3). The coagulant dose applied appears to relate to the alkalinity, with the required dose increasing with alkalinity. Hence, the water in this trial required the highest dose (10 mg/l as Me3þ), whereas results reported for the water having the lowest alkalinity of 120 mg/l as CaCO3 (Pernitsky and Edzwald, 2006) required the commensurately lowest dose (1.5 mg/l as Al). Thus comparison of coagulant efficacy across studies is made challenging by the differing buffering capacities of the waters treated, given that pH adjustment to the optimum pH of w5 is rarely carried out in wastewater coagulation.
3.2.
Pilot test results
3.2.1.
Preliminary trials
Turbidity levels in the feedwater followed were found to follow a consistent diurnal cycle, such that experiments with different coagulants were carried out on consecutive days over the same time period could be assumed to be subject to a reproducible turbidity concentration transient. Statistical analysis using the student t-test on the turbidity data for each trial generally showed no significant difference between those trials with and without coagulant ( p < 0.05), the exception being the trial using 2 mg/l of PAX-XL9 where p ¼ 0.10 and somewhat lower turbidity levels were experienced. Trials conducted at doses of 0.5e2 mg/L coagulant as Me3þ, 40 LMH and backflushing every 30 min, revealed a linear relationship between reversible fouling rate and turbidity (Fig. 1), and irreversible fouling with time for periods of steady-state feedwater turbidity levels (Fig. 2). However, in both cases rapid changes in feedwater turbidity produced anomalously high reversible and irreversible fouling rates. Moreover, the permeability does not recover immediately, suggesting the single backflush sequence to be sometimes insufficient to remove high levels of contaminant loading.
8 y = 0.70x + 0.40 R = 0.87
7 6 5 4 3 2 1 0 0
1
2
3
4
5
6
7
8
9
10
Turbidity (NTU)
Fig. 1 e Reversible fouling rate vs. turbidity, 40 LMH, ferric sulphate dosed @ 0.5 mg/l, backwash every 30 min for 5 min, TMP corrected to 20 C.
Generally the use of coagulant approximately doubled the reversible fouling factor (Table 5), this being the reversible fouling rate per unit turbidity (Fig. 1). This result is comparable with that of Farahbakhsh et al., (2002) who used alum doses at 4 and 10 mg/l. Despite this significant increase in the reversible fouling rate, the current operational regime of backwashing at 30 min intervals for turbidities up to 25 NTU sustained operation without exceeding the 720 mbar maximum pressure limit of the system. However, alum addition at feedwater turbidities in excess of 10 NTU resulted in a TMP rise between backwashes reaching the maximum system limit and triggering a backwash before the 30 min interval had elapsed, increasing downtime and so reducing the net flux. Ferric sulphate and PAX-XL9 had the least influence on the reversible fouling factor over the three doses applied, with ferric sulphate at 2 mg/l reducing the reversible rate to a negligible level. The irreversible fouling rate determines the time interval between chemical cleans. For zero coagulant addition the irreversible fouling rate results in a cleaning interval of approximately 3 weeks. Ferric sulphate addition resulted in the largest decrease in irreversible fouling rate, with the smallest dose resulting in the lowest recorded irreversible
250
16 y = 5.3x + 141.3 R = 0.6
14
200 12 10
150
8
After-backflush TMP Turbidity
100
6
Turbidity (NTU)
SUVA Concentration of coagulant l/(mg. m) added (mg Me3þ/l)
After-backflush TMP (mbar)
Coagulant
Reversible Fouling Rate (mbar/min)
9
Table 4 e Jar test data showing effect of coagulant chemical and dose on organic matter removal.
4 50 2 0
0 0
1
2
3
4
5
6
No. of days
Fig. 2 e The effect of turbidity on the irreversible fouling rate of the MF membrane (40 LMH, ferric sulphate dosed @ 0.5 mg/l as Fe3D, backwash every 30 min for 5 min, TMP corrected to 20 C).
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Table 5 e Effect of coagulant chemical and dose on reversible and irreversible fouling rates. Coagulant
Reversible fouling factor (mbar/min. NTU)
Irreversible fouling rate (mbar/d)
Turbidity load (NTU/m2), based on a seven day period
Concentration of coagulant added (mg Me3þ/l)
PAX-XL9 Fe2(SO4)3 Alum
0
0.5
1
2
0
0.5
1
2
0
0.5
1
2
0.2
0.4 0.7 1.0
0.4 0.4 2.1
0.6 0.0 1.5
34
60 2 61
8 11 35
54 21 131
0.094
0.096 0.073 0.070
0.109 0.061 0.067
0.156 0.100 0.079
fouling rate of 2 mbar/d. Under these conditions, the time between chemical cleans increases from 21 days to approximately 10 months. PAX-XL9 at 1.0 mg/l also yielded a low irreversible fouling rate of 8 mbar/d, extending the time between chemical cleans to over 2 months. Conversely, alum dosing provided high levels of irreversible fouling, reducing the time between chemical cleans, despite the low and relatively stable feedwater turbidity (<5 NTU) recorded during this trial. As expected from the jar test data, the water quality measurements revealed a negligible increase in the removal of organic matter, as DOC, with coagulant addition at low doses (Table 6). No enhanced turbidity removal from coagulation arose, since the MF membranes provide excellent turbidity removal with the MF permeate turbidity below 0.02 NTU without precoagulation. Sampling of the MF permeate performed during the coagulant trials revealed the aluminium residuals to vary from 16 to 22 mg/l and the iron levels from 35 to 50 mg/l. These levels are within the normal range of permeate concentration in the absence of precoagulation (7e42 and 23e85 mg/L for Al and Fe respectively), indicating that coagulant dosing has no impact on the coagulant residual. This is significant, since levels above w50 mg/l as Al would be expected to cause fouling of the downstream RO membranes through the formation of aluminium silicate; the impact of residual levels of coagulant have been found to be exacerbated at pH levels of 7.5e8.5 when alum is used as the coagulant, but not PACl, with lower pH levels of 6.7 found to reduce residual Al concentrations (Gabelich et al., 2006; Moon et al., 2009). The pH of the feed water for the current trials was in the range 6.2e7.2. During each three-day trial, a total coagulant load of 365 g as Me3þ was applied to the MF membrane modules, equating to 38 mg per metre length of membrane fibre. Autopsies performed on the membrane fibre samples taken before and after
Table 6 e % Dissolved organic carbon removal during the preliminary trials. Coagulant
PAX-10 PAX-XL9 Ferric Sulphate Aluminium Sulphate
Concentration of coagulant added (mg Me3þ/l) 0
0.5
1
2
16.4 14.2 13.1 11.3
16.5 15.6 10.5 10.4
12.8 15.4 12.7 7.5
15.5 15.1 12.7 11.7
each three-day trial showed a negligible increase in metal residual post coagulant dosing (<0.01 mg per m of fibre). Autopsies performed on membrane fibre samples taken after the chemical clean, (performed after each three-day trial) showed that any increase in metal residual post coagulant dosing was fully removed by the chemical clean.
3.2.2.
Extended trials
Further trials were performed, each over a one-week period, to assess fouling amelioration by precoagulation under naturally dynamic conditions of feedwater turbidity. Ferric sulphate was selected, being the coagulant displaying the most consistent suppression of reversible and irreversible fouling, according to the scoping trials, as well as being 45% lower in cost than the next best performing reagent (PAX-XL9). Each trial included the same number of diurnal cycles, and statistical analysis of turbidity data using the student t-test for each trial showed no significant difference between those trials with and without coagulant ( p < 0.05). Results (Table 7) show fouling rate for each one-week trial to increase with increasing flux, but to decrease with coagulant addition In the absence of coagulant, fouling rates were greatly affected by the applied flux, with a doubling of reversible fouling factor and a six-fold increase in the irreversible fouling rate on increasing the flux from 40 to 50 LMH. However, on dosing with coagulant the reversible fouling factor decreased by 50% and the irreversible fouling increased by only 50% when compared with the corresponding rates recorded at 40 LMH without coagulant e despite a 50% higher turbidity load during the coagulant trial. Results demonstrate a beneficial impact on reversible and irreversible fouling from the use of coagulant at doses as low as 0.5 mg/l as Fe3þ, significantly below doses employed in reference installations (Table 2) and corroborating previous findings (Choi and Dempsey, 2004) where coagulant doses insufficient to affect organic matter removal were nonetheless found to reduce fouling. The irreversible fouling rates quoted in Table 7 demonstrate that, for operation between 0.2 and 0.8 bar transmembrane pressure (TMP), an interval of 21 days can be easily maintained in all cases except 50 LMH without coagulant. At 50 LMH without coagulant, a chemical cleaning interval of only 14 days is implied. However trials have shown that this can only be achieved if the turbidity is maintained below 5 NTU, which is not practical. Even a small increase in turbidity, for example an increase to 8 NTU over 12 h, was found to be sufficient to cause a rapid increase in the irreversible fouling rate which, combined with the increased fouling rate at this flux, caused the plant to shut down for chemical cleaning. In contrast, at
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Table 7 e Effect of coagulant on fouling rate at different fluxes over a 7 day period. Flux (LMH) 40 45 50
Ferric Sulphate Dose (mg Fe/l)
Reversible Fouling Factor (mbar/min. NTU)
Irreversible Fouling Rate (mbar/d)
Total Turbidity Load (NTU/m2)
Overall Permeability Decline (l(m2hbar)1)
0.0 0.5 0.5 0.0 0.5
0.6 0.7 0.3 1.2 0.4
6 5 12 37 9
1.60 1.99 2.89 2.04 3.10
72.8 55.8 115.5 134.5 120.7
50 LMH with coagulant the pilot plant could handle turbidity spikes up to 22 NTU over 12 h without permanent changes in fouling rate. The use of coagulant thus enables the pilot plant to operate sustainably at higher fluxes with fouling rates associated with operation at lower fluxes.
This is to be distinguished from the much higher doses employed by previous workers (Tables 1 and 2) of between 2 (Choi and Dempsey, 2004; Fan et al., 2008) and 15 mg/l (Bagga et al., 2008) : for a 5 mg/l ferric dose and a cost penalty 0f 0.26 p/ m3 would result based on the same assumptions.
3.3.
4.
Cost analysis
Results suggest that the flux rate can be increased by 25%, allowing a 20% reduction in membrane area and a commensurate reduction in capital expenditure (CAPEX) through membrane and tankage costs and reduced footprint (Fig. 3). Coagulant dosing, however, impacts negatively on CAPEX through installation of a coagulant dosing pump, chemical storage and a control system. If these two CAPEX elements are assumed roughly equal then the cost benefit provided by coagulant dosing and higher-flux operation is approximated by the reduced cost of membrane replacement vs. the cost of adding coagulant over the life of the membrane. An outline cost analysis thus proceeds through a consideration of the projected coagulant cost per kg of coagulant (Lc) and the cost per m2 of the membrane (Lm). The OPEX in £/m3 permeate associated with these two components are respectively given by Lm(1/J1 1/J2)/t and c Lm, where t is the membrane life and c the dose in coagulant mass per m3. Even with a conservative assumptions of a coagulant cost of £500/ tonne as Fe projected to increase at 8% p.a. coupled with a projected constant membrane replacement cost of £17/m2, based on current costs, precoagulation at a dose of 0.5 mg/l Fe provides a cost benefit for a membrane life up to 14 years provided the 25% increase in flux is sustained. Based on a realistic membrane life estimate of seven years, the cost benefit of coagulant dosing is around 0.10 p/m3 treated water.
Conclusions
The technical and cost benefit of using coagulant at the low doses associated with charge neutralisation, rather than higher doses for sweep flocculation, have been demonstrated at pilot scale. Pilot scale tests of ferric chloride, whose efficacy had been identified from bench-scale jar testing and scoping pilot trials, revealed it to provide sufficiently robust fouling amelioration during turbidity spikes. In the absence of coagulant addition an increased flux from 40 LMH to 50 LMH produced a disproportionate increase in fouling rate, exacerbated by small increases in turbidity and leads to unsustainable operating conditions. Employing coagulant dosing of 0.5 mg/l as Fe at 50 LMH reduced both the reversible and irreversible fouling to similar levels to those observed for optimised operating conditions without coagulant at 40 LMH. The coagulant dose required to influence fouling rate was only a fraction of that required to obtain significantly enhanced organic matter removal. Fouling amelioration appraisal based on organic matter removal measured from jar testing thus appears to greatly over-estimate the coagulant dose required for fouling reduction, corroborating previous reports. The cost benefit offered by dosing at this concentration to sustain a 25% higher flux exceeds 0.1 p/m3 based on a membrane life of seven years. Moreover, in practice coagulant dosing would be required only during periods of high turbidity loads, reducing the overall coagulant consumption and further increasing the cost benefit.
references
Fig. 3 e Cost benefit of precoagulation at 0.5 mg/l coagulant dose.
ASTM, 2003. Standard Practice for Coagulation e Flocculation Jar Test of Water. Bagga, A., Chellam, S., Clifford, D.A., 2008. Evaluation of iron chemical coagulation and electrocoagulation pretreatment for surface water microfiltration. Journal of Membrane Science 309 (1e2), 82e93. Best, G., Singh, M., Mourato, D., Chang, Y.J., 2001. Application of Immersed Ultrafiltration Membranes for Organic Removal and Disinfection by-Product Reduction 221e231.
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Carroll, T., Booker, N.A., 2000. Axial features in the fouling of hollow-fibre membranes. Journal of Membrane Science 168 (1e2), 203e212. Choi, K.Y.J., Dempsey, B.A., 2004. In-line coagulation with lowpressure membrane filtration. Water Research 38 (19), 4271e4281. Citulski, J., Farahbakhsh, K., Kent, F., 2009. Optimization of phosphorus removal in secondary effluent using immersed ultrafiltration membranes with in-line coagulant pretreatment e implications for advanced water treatment and reuse applications. Canadian Journal of Civil Engineering 36 (7), 1272e1283. Citulski, J., Farahbakhsh, K., Kent, F., Zhou, H., 2008. The impact of in-line coagulant addition on fouling potential of secondary effluent at a pilot-scale immersed ultrafiltration plant. Journal of Membrane Science 325 (1), 311e318. Dong, B.-z., Chen, Y., Gao, N.-y., Fan, J.-c., 2007. Effect of coagulation pretreatment on the fouling of ultrafiltration membrane. Journal of Environmental Sciences 19 (3), 278e283. Eaton, A.D. (Ed.), 2005. Standard Methods for the Examination of Water and Wastewater. American Public Health Association. Edzwald, J.K., Van Benschoten, J.E., 1990. In: Klute, R., Hahn, H.H. (Eds.), Aluminium Coagulation of Natural Organic Matter. Springer-Verlag, NY, Madrid, Spain, pp. 341e359. Fan, L., Nguyen, T., Roddick, F.A., Harris, J.L., 2008. Low-pressure membrane filtration of secondary effluent in water reuse: pretreatment for fouling reduction. Journal of Membrane Science 320 (1e2), 135e142. Fane, A.G., Chang, S., Chardon, E., 2002. Submerged hollow fibre membrane module e Design options and operational considerations. Desalination 146 (1e3), 231e236. Farahbakhsh, K., Smith, D.W., 2002. Performance comparison and pretreatment evaluation of three water treatment membrane pilot plants treating low turbidity water. Journal of Environmental Engineering and Science 1 (2), 113e122. Gabelich, C.J., Ishida, K.P., Gerringer, F.W., Evangelista, R., Kalyan, M., Suffet, I.H.M., 2006. Control of residual aluminum from conventional treatment to improve reverse osmosis performance. Desalination 190 (1e3), 147e160. Howe, K.J., Marwah, A., Chiu, K.P., Adham, S.S., 2007. Effect of membrane configuration on bench-scale MF and UF fouling experiments. Water Research 41 (17), 3842e3849. Howe, K.J., Clark, M.M., 2006. Effect of coagulation pretreatment on membrane filtration performance. Journal of American Water Works Association 98 (4). Howe, K.J., Marwah, A., Chiu, K.P., Adham, S.S., 2006. Effect of coagulation on the size of MF and UF membrane foulants. Environmental Science and Technology 40 (24), 7908e7913. Judd, S.J., Hillis, P., 2001. Optimisation of combined coagulation and microfiltration for water treatment. Water Research 35 (12), 2895e2904. Kim, J., DiGiano, F.A., 2006. Defining critical flux in submerged membranes: influence of length-distributed flux. Journal of Membrane Science 280 (1e2), 752e761.
ska, A., 2009. Water Konieczny, K., Rajca, M., Bodzek, M., Kwiecin treatment using hybrid method of coagulation and lowpressure membrane filtration. Environment Protection Engineering 35 (1), 5e22. Lahoussine-Turcaud, V., Wiesner, M.R., Bottero, J.Y., Mallevialle, J. , 1990. Coagulation pretreatment for ultrafiltration of a surface water. American Water Works Association Journal 82 (12), 76e81. Lee, C.W., Bae, S.D., Han, S.W., Kang, L.S., 2007. Application of ultrafiltration hybrid membrane processes for reuse of secondary effluent. Desalination 202 (1e3), 239e246. Lee, J.D., Lee, S.H., Jo, M.H., Park, P.K., Lee, C.H., Kwak, J.W., 2000. Effect of coagulation conditions on membrane filtration characteristics in coagulation e Microfiltration process for water treatment. Environmental Science and Technology 34 (17), 3780e3788. Mietton Peuchot, M., Ben Aim, R., 1992. Improvement of crossflow microfiltration performances with flocculation. Journal of Membrane Science 68 (3), 241e248. Moon, J., Kang, M.S., Lim, J.L., Kim, C.H., Park, H.D., 2009. Evaluation of a low-pressure membrane filtration for drinking water treatment: pretreatment by coagulation/sedimentation for the MF membrane. Desalination 247 (1e3), 271e284. Pernitsky, D.J., Edzwald, J.K., 2006. Selection of alum and polyaluminum coagulants: principles and applications. Journal of Water Supply 55, 121e142. Porcelli, N., Hillis, P., Judd, S., 2009. Microfiltration membrane plant start up: a case study with autopsy and permeability recovery analysis. Environmental Technology 30 (6), 629e639. Qin, J.J., Maung, H.O., Lee, H., Kolkman, R., 2004. Dead-end ultrafiltration for pretreatment of RO in reclamation of municipal wastewater effluent. Journal of Membrane Science 243 (1e2), 107e113. Raffin, M., Germain, E., Judd, S., 2011. Optimising operation of an integrated membrane system (IMS) e A Box-Behnken approach. Desalination 273, 136e141. Scha¨fer, A.I., Fane, A.G., Waite, T.D., 2001. Cost factors and chemical pretreatment effects in the membrane filtration of waters containing natural organic matter. Water Research 35 (6), 1509e1517. Shon, H.K., Vigneswaran, S., Ngo, H.H., Ben Aim, R., 2005. Is semi-flocculation effective as pretreatment to ultrafiltration in wastewater treatment? Water Research 39 (1), 147e153. Walsh, M.E., Zhao, N., Gora, S.L., Gagnon, G.A., 2009. Effect of coagulation and flocculation conditions on water quality in an immersed ultrafiltration process. Environmental Technology 30 (9), 927e938. Wiesner, M.R., Clark, M.M., Mallevialle, J., 1989. Membrane filtration of coagulated suspensions. Journal of Environmental Engineering 115 (1), 20e40. Yeo, A.P.S., Law, A.W.K., Fane, A.G., 2006. Factors affecting the performance of a submerged hollow fiber bundle. Journal of Membrane Science 280 (1e2), 969e982.
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
The management of undesirable cyanobacteria blooms in channel catfish ponds using a constructed wetland: Contribution to the control of off-flavor occurrences Fei Zhong a,b, Yunni Gao a, Tao Yu a, Yongyuan Zhang a, Dong Xu a, Enrong Xiao a, Feng He a, Qiaohong Zhou a, Zhenbin Wu a,* a
State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, PR China b Marine Fisheries Research Institute of Jiangsu Province, Nantong 226007, PR China
article info
abstract
Article history:
An exploratory study on the management of undesirable cyanobacteria blooms with
Received 18 January 2011
respect to off-flavor problems using an integrated vertical-flow constructed wetland (CW)
Received in revised form
was performed at a small commercial-scale channel catfish farm from 2004 to 2007. The
20 September 2011
results of the three-year experiment indicated that water treatment by the CW could
Accepted 21 September 2011
reduce the possibility of dominance by undesirable cyanobacteria species that often cause
Available online 29 September 2011
off-flavor problems. A detailed investigation in 2007, showed that the concentrations of
Keywords:
(4.3 ng L1, U.D. (undetected) and 0.2 ng L1, respectively) treated by the CW were signifi-
Constructed wetland
cantly lower than those in the control pond (152.6 ng L1, 63.3 ng L1 and 254.8 ng L1,
Aquaculture
respectively). In addition, the relationships among the cyanobacteria species, the off-flavor
Cyanobacteria
compounds and ten environmental variables were explored by canonical correspondence
Off-flavor problems
analysis (CCA). The results showed that Oscillatoria sp., Oscillatoria kawamurae and Micro-
Canonical correspondence analysis
cystis aeruginosa were the main sources of off-flavor compounds in the catfish ponds. The
geosmin, MIB (2-methylisoborneol), and b-cyclocitral in the water of the recirculating pond
successful manipulation of undesirable cyanobacteria species potentially resulted in lower concentrations of odorous compounds in the water of the recirculating pond. An investigation of the concentrations of geosmin and MIB in catfish fillets showed that the levels of odorous compounds were below the OTC (odor threshold concentration) values in the recirculating pond but were above the OTC values from July to October in the control pond. Water recycling by the CW could potentially be one of the best management practices to control off-flavor occurrences in aquaculture. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Static-style pond culture is the dominant practice of the aquaculture industry in China and has a centuries-old history. The urgent call for increasing yield in past decades has been
driving this traditional culture toward an intensive practice without optimization of the structure of the pond system or integration of water treatment facilities, thereby hindering sustainable development for the aquaculture industry in China.
* Corresponding author. Tel.: þ86 027 68780675. E-mail address:
[email protected] (Z. Wu). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.044
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High fish stocking densities (>10,000 fish ha1) and feeding rates (exceeding 70 kg ha1 d1) resulted in high waste loading rates that often caused excessive eutrophication in fish ponds, leading to the dominance of phytoplankton communities by cyanobacteria (Zimba and Grimm, 2003). Certain species of cyanobacteria are known to be responsible for the occurrence of off-flavor compounds (2-methylisoborneol (MIB), geosmin and b-cyclocitral) in water bodies, including species from the genera Oscillatoria (Matsumoto and Tsuchiya, 1988), Anabaena (Rosen et al., 1992), Microcystis (Ju¨ttner et al., 2010), Phormidium (Izaguirre, 1992), Lyngbya (Schrader and Blevins, 1993), and Pseudanabaena (Izaguirre and Taylor, 1998), imparting an undesirable earthy, musty, or muddy flavor to the fish. Off-flavors represent one of the most significant economic problems in aquaculture. Off-flavor problems can cause inconsistent product quality and lead to a major reduction in the consumption of the products or make them unsuitable for sale, decreasing profits for producers and processors. Offflavor problems can cost catfish producers as much as $60 million annually because of additional feeding, interruption of cash flow, forfeiture of income from foregone sales, and the possible loss of market-sized catfish during the depuration process (Engle et al., 1995; Tucker, 2000). How to produce fish free of off-flavor has gradually become a major challenge for today’s aquaculture industry. Because the off-flavors most frequently encountered in aquaculture have been linked to the presence of odor-producing cyanobacteria (Matsumoto and Tsuchiya, 1988; Ju¨ttner, 1995; Tucker, 2000), such preventive measures as the application of algaecides (Schrader et al., 2005), bio-manipulation using filterfeeding fish (Tucker, 2006) and other preventive measures have been taken to control or eliminate such groups of organisms. Among algaecides, a low dose of copper sulfate applied weekly was found to be beneficial in mitigating off-flavor problems in commercial-sized catfish ponds (Schrader et al., 2005). However, only a small acceptable range exists for the appropriate doses for copper treatments (i.e., the range of doses that are safe for the fish but toxic to the algae). Moreover, the toxicity of copper in aquatic organisms is strongly influenced by complex interactions with environmental variables such as pH, water temperature, and the concentrations of calcium and dissolved organic matter (Schrader et al., 2005; Hyne et al., 2005). These complex interactions are mostly not understood, making consistency in safe and effective copper treatments for algae control almost impossible. In addition, the prolonged use of copper sulfate may be related to the development and growth of copper-tolerant cyanobacteria (Tucker, 2000). Bio-manipulation by filter-feeding organisms, such as silver carp, can effectively filter phytoplankton larger than 10 mm, especially colony-forming Microcystis (Ma et al., 2010; Xie and Liu, 2001). However, some limitations have emerged on the application of bio-manipulation to aquaculture systems. For example, hypertrophic conditions in fish ponds may overwhelm the effect of silver carp grazing at lower densities, and odor-producing cyanobacteria cannot be eliminated (Tucker, 2006). At higher densities, even if silver carp could reduce the prevalence of off-flavor problems, a large quantity of them might greatly decrease profits because of the low value of the silver carp.
Off-flavors in fish have also been reported to correlate with odorous compounds in water through absorption via the gills (From and Hurlyck, 1984). The adsorption process was found to be rapid (Johnsen et al., 1996; Robertson et al., 2005), whereas the depuration of off-flavors in fish was much slower (Tucker, 2000). Although fish can be purged of off-flavors if exposed to taint-free water, the timely provision of taint-free water is a great challenge in the current pond system (Robertson et al., 2005). Many studies reported the successful removal of off-flavor compounds from water by oxidation (Kutschera et al., 2009), filtration (Kim and Bae, 2007), isolated bacteria (Lauderdale et al., 2004) and other processes such as ultrasonic degradation (Song and O’Shea, 2007) or membrane separation (Dixon et al., 2011). However, few of these methods have found practical application in aquaculture systems. Constructed wetlands have been gaining international interest because of their low maintenance and operational costs. The application of constructed wetlands to aquaculture systems confirmed that they have great potential for water treatment in fish ponds (Sindilariu et al., 2009; Konnerup et al., 2011). Constructed wetlands are thought to be able to manage cyanobacteria bloom in three ways: they are efficient in nutrient removal (Sindilariu et al., 2009), which might prevent excessive nutrient accumulation in ponds; they could remove phytoplankton directly by filtration (Kuang et al., 2000), which could be helpful for cyanobacteria colony removal; and they might cause water mixing by the discharge of treated water, which would possibly reduce the growth and surface bloom formation of cyanobacteria species (Visser et al., 1996). In this study, a constructed wetland was utilized in an attempt to manipulate the taxonomic composition of the phytoplankton assemblages to control off-flavor problems at a channel catfish farm.
2.
Material and methods
2.1.
Constructed wetland and ponds
An integrated vertical-flow constructed wetland (CW) (with an area of 160 m2) was built beside four channel catfish ponds located on the side of the East Lake, Wuhan, China (Fig. 1). The CW was divided into two equal chambers: a down-flow chamber and an up-flow chamber. The influent and the effluent pipes were situated on the surface of the down-flow and the up-flow chambers, respectively. At the bottom of the two chambers, pipes were distributed to collect water from the down-flow chamber and send water to the up-flow chamber. In particular, small holes (5-mm diameter) were designed on the underside of the influent and the effluent pipes for an even distribution and collection of water. In the down-flow and up-flow chambers, canna (Canna indica) and cumbungi (Typha orientalis) were planted, respectively. The CW was operated under intermittent flow with a hydraulic loading rate (HLR) of 0.2e0.4 m d1 to treat water from the recirculating pond. The catfish ponds with a historic earthy taint problem had a water surface of 200 m2 and average depths of 1.5 m. Because the application of CWs to an aquaculture system is
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 7 9 e6 4 8 8
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Fig. 1 e Schematic diagram of the recirculating aquaculture system and the constructed wetland. The water recycling process for the catfish farm is indicated. The basic structure and water flow inside the constructed wetland are indicated.
still under evaluation, it is impractical to construct and operate several commercial-scale experimental systems simultaneously. Time replication was, therefore, used instead of number replication. Two ponds were selected randomly for the experiments in each year (2004, 2006 and 2007). The water in one pond (10e20%) was recycled by the CW, whereas the water in the other pond was not treated to simulate the traditional culture. No water discharge or importing of water occurred during the entire period of the experiment, except for the supply of water because of water lost through evaporation (0.57e0.70 m3 d1 per pond). Fish were stocked in the recirculating pond and the control pond at the same density each year. The fish were fed to satiation with a commercial floating feed in the same manner in both ponds. Tube diffusers were installed in both ponds to supply oxygen when the oxygen concentration was below 3.0 mg L1. The fish were harvested at the end of each year, and the sediment in each pond was removed. No algicide was used in either of the ponds.
2.2.
Physicochemical and biological analysis
Water samples from the recirculating pond and the control pond, as well as the influent and the effluent of the CW, were collected periodically (monthly in 2004 and biweekly in 2006 and 2007) between 9:00 a.m. and 10:00 a.m. In the ponds, water samples were obtained by mixing the equal volume of water taken 50 cm below the water surface from each corner of the pond for physicochemical and phytoplankton
composition analysis. In the influent and effluent of the CW, water samples were obtained by mixing the equal volume of water sampled three times every ten minutes for physicochemical analysis. Water samples were analyzed for chemical oxygen demand (CODCr), 5-day biochemical oxygen demand (BOD5), total nitrogen (TN), total phosphorus (TP), soluble reactive phosphorus (SRP), and total suspended solids (TSS) within 24 h in the laboratory according to standard methods (State Environmental Protection Administration of China, 2002). Light intensity was measured with a ZDS-10 digital luxmeter (Shanghai Jiading Xuelian Meter Factory). Dissolved oxygen (DO), pH, conductivity (Cond) and water temperature (T ) were measured with the Thermo Orion 5 Star portable meter. With regard to phytoplankton composition analysis, water samples (1 liter) from both ponds were preserved immediately with 1% Lugol’s preservative solution for quantitative study of the phytoplankton. A sedimentation method was used to concentrate samples to 30 mL. Sub-samples were placed in a count-frame (40 mm 40 mm), and counted via an optical microscope (at 640 magnification). After the collection of water samples, the phytoplankton samples for qualitative identification were collected at the water surface in both ponds with a 25# phytoplankton net (pore size 64 mm) from each corner of the pond and preserved immediately with 1% Lugol’s solution. Phytoplankton was identified to the genus/species level according to the references (Zhang and Huang, 1991; Hu and Wei, 2006).
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2.3.
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Off-flavor compound analysis
3.
Unfiltered water samples from the two ponds and the influent and the effluent of the CW were collected in airtight glass bottles (capacity, 0.5 L) without headspace (biweekly in the autumn of 2006, and weekly in 2007). The odorous compounds (geosmin, MIB and b-cyclocitral) in the water samples were extracted by solid phase micro-extraction (SPME), and were analyzed by gas chromatography/mass spectrometry (GC/MS) (Hewlett-Packard Model 5973 and Hewlett-Packard 6890 plus) in SIM (Selected Ion Monitoring) mode according to Sung et al. (2005) and Li et al. (2005). In 2007, three catfish in each of the two ponds were sampled monthly at random for the analysis of geosmin and MIB. The catfish were slaughtered, filleted, and stored at 20 C for analysis. The stored catfish fillets were pretreated by microwave distillation followed by SPME, and the distilled samples were then analyzed for geosmin and MIB using GC/ MS (Robertson et al., 2005).
2.4.
Statistical analysis
All data were presented as means SD (standard deviation) and n refers to the number of samples. The treatment efficiency of the CW (represented as the difference between the in- and outflow values) was evaluated by paired t-test. The between-pond differences for each parameter were evaluated by non-parametric tests. The between-pond and the monthly differences in the concentrations of the three odorous compounds in the pond water were explored by a two-way analysis of variance (ANOVA). The relationship between the concentration of the two odorous compounds in the pond water and in the catfish fillets was explored by linear correlation analysis. The statistical analysis was performed with SPSS 13.0 software package for Windows, and the statistically significant level was set as p < 0.05. The relationships among the cyanobacteria species, odorous compounds and water quality were investigated using canonical correspondence analysis (CCA) (ter Braak, 1988). In particular, forward selection and Monte Carlo permutations were used to determine whether environmental variables exerted a significant effect upon the distribution pattern of the cyanobacteria species.
Results and discussion
3.1. Treatment performance of CW and pond water quality Paired t-tests detected significant differences between the influent and the effluent of the CW in terms of the CODCr, BOD5, TSS and TN values ( p < 0.05). The removal rates of BOD5, TSS and TN by the CW were 53.5e70.5%, 52.1e81.9%, and 48.1e58.9%, respectively, in the three-year experiment with an HLR of 0.2e0.4 m d1 (Table 1). The results were similar to the results reported by Lin et al. (2003), who used CWs operated at an HLR of 0.3 m d1. However, compared with the data reported in 2004 by Li et al. (2007) based on the same aquaculture system, the present study showed a large decrease in the TP removal rate in 2006 and 2007. Phosphorus removal in CWs is closely associated with the physicalchemical and hydrological properties of the filter material (Vohla et al., 2011); the lower TP removal efficiency in the present study might result from the decreased P-retention capacity of the filter material in the CW. The mean values of the physicochemical parameters in the recirculating pond and the control pond are shown in Table 2. The recirculating pond had significantly lower CODCr, BOD5, TSS, TN and TP concentrations than did the control pond ( p < 0.05), indicating that the trophic status of the recirculating pond was lower than that of the control. Considering that the fish were stocked at the same density and fed by the same method in both ponds, the differences in water quality might result from the effective purification by the CW. The threeyear experiment showed that CW could be beneficial to maintain the pond water at a relatively lower trophic status.
3.2. Variation of phytoplankton assemblages in the pond water The taxonomic composition of the phytoplankton assemblages evolved differently in the recirculating pond and the control pond (Fig. 2). In the recirculating pond, Cryptophyta (average density of 4.5 106 ind. L1 in 2004), Bacillariophyta (average density of 3.4 106 ind. L1 in 2006) and Chlorophyta (average density of 8.0 106 ind. L1 in 2007) were the
Table 1 e Water quality in the influent and effluent of the CW (means ± SD).
c
2004 (n ¼ 17)
2006 (n ¼ 13)
2007 (n ¼ 14)
1
Influent (mg L ) Effluent (mg L1) Mean removal rates (%) Influent (mg L1) Effluent (mg L1) Mean removal rates (%) Influent (mg L1) Effluent (mg L1) Mean removal rates (%)
CODCr
BOD5
TSS
TN
TP
e e e 36.5 18.3a 26.9 14.0b 26.4 23.6 8.8a 12.6 6.7b 46.8
5.8 2.3a 1.7 1.2b 70.5 4.2 2.0a 1.9 1.2b 53.5 3.1 1.4a 1.3 0.7b 59.9
21.5 17.0a 3.9 2.2b 81.9 12.4 7.7a 5.3 2.6b 57.1 12.7 8.4a 6.1 5.8b 52.1
2.84 1.20a 1.29 0.67b 54.6 1.21 0.41a 0.63 0.27b 48.1 1.23 0.52a 0.50 0.40b 58.9
0.35 0.21a 0.07 0.10b 80.1 0.09 0.03a 0.08 0.05a 16.6 0.09 0.05a 0.08 0.04a 14.9
a, b indicated there were significant differences between two groups ( p < 0.05). c Li et al., 2007 reported based on the same aquaculture system.
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Table 2 e Water quality in the recirculating pond and the control pond (means ± SD). 2004 (n ¼ 17)c
T ( C) pH DO (mg L1) CODCr (mg L1) BOD5 (mg L1) TSS (mg L1) TN (mg L1) TP (mg L1)
2006 (n ¼ 13)
2007 (n ¼ 14)
Recirculating pond
Control
Recirculating pond
Control
Recirculating pond
Control
24.0 5.3a 7.2 0.3a 4.6 1.5a e 6.2 2.0a 17.6 8.9a 2.07 0.82a 0.18 0.12a
23.6 4.9a 7.1 0.3a 3.6 1.9a e 12.0 2.2b 34.9 23.1b 3.22 1.28b 0.32 0.27b
23.6 6.8a 7.4 0.3a 4.8 3.4a 25.6 13.8a 3.5 1.6a 10.6 7.2a 0.83 0.44a 0.06 0.02a
23.4 6.9a 8.0 0.4a 7.5 4.6a 62.0 29.6b 11.6 6.6b 40.8 23.7b 3.12 1.44b 0.31 0.16b
25.8 4.2a 7.5 0.2a 3.4 2.2a 24.6 10.1a 4.5 3.2a 8.4 6.6a 1.32 0.56a 0.10 0.04a
25.5 4.3a 7.5 0.1a 4.9 2.1a 52.1 14.9b 7.4 2.9b 31.6 7.9b 1.67 0.50a 0.18 0.06b
a, b indicated there were significant differences between two groups ( p < 0.05). c Li et al., 2007 reported based on the same aquaculture system.
dominant phyla. In contrast, Cyanophyta (average density of 4.8 107 ind. L1 in 2004, 5.9 107 ind. L1 in 2006, and 7.1 107 ind. L1 in 2007) was the dominant phylum throughout all three years in the control pond. Furthermore, the densities of Microcystis and Oscillatoria species were significantly higher in the control pond than in the recirculating pond ( p < 0.05). The differences in the taxonomic composition of the phytoplankton assemblages and cyanobacteria species density in the two ponds were probably the result of two factors related to the water treatment by the CW: nutrient concentration reduction and pond water mixing. The nutrient supply and its ratios have a decisive effect on the species composition of the phytoplankton because different algal species have different nutrient requirements (Hodgkiss and Lu, 2004). Differences in the phytoplankton biomass and composition have been found in lakes with different trophic status (Watson et al., 1997). The difference in trophic status between the two ponds might contribute to the difference in taxonomic composition to some extent. Turbulent flow caused by the pond water mixing could prevent vertical migration of Microcystis and reduce sedimentation losses of non-buoyant phytoplankton (Paerl et al., 2011; Visser et al., 1996). Visser et al. (1996) reported that artificial mixing with compressed air bubble plumes in a hypertrophic lake was successful in preventing blooms of the cyanobacterium Microcystis. However, they indicated that intermittent mixing will give Microcystis the opportunity to attain a higher level of biomass because of the high floating speed of the colonies (as opposed to continuous mixing), especially in summer. In this system, the CW was operated intermittently. The successful management of cyanobacteria blooms in the recirculating pond throughout the three years might result from the combined effect of lower trophic status and water mixing caused by the CW.
3.3. water
Concentrations of odorous compounds in the pond
In the autumn of 2006, the concentrations of MIB, geosmin and b-cyclocitral (4.0 4.5 ng L1, 12.2 10.5 ng L1, and U.D. (undetected), respectively, n ¼ 6) in the recirculating pond
were lower than the concentrations in the control pond (8.0 19.6 ng L1, 16.9 8.2 ng L1, and 1941.6 1261.9 ng L1, respectively, n ¼ 6). The detailed variations of MIB, geosmin, and b-cyclocitral concentrations in the recirculating pond and the control pond were monitored in 2007 to explore the temporal variation of odorous compound concentrations in the two ponds (Fig. 3). From May to December, only a low concentration of geosmin and b-cyclocitral was detected in the recirculating pond. In contrast, relatively high concentrations and monthly fluctuations of the concentrations of all three odorous compounds were observed in the control pond ( p < 0.05). Previous research reported that the odor threshold concentrations (OTCs) for geosmin, MIB, and b-cyclocitral to produce off-flavor in drinking water are in the ranges of 4e10, 9e42, and 500e1000 ng L1, respectively (Cotsaris et al., 1995; Young et al., 1999; Watson et al., 2000). The geosmin concentration in the control pond was above the OTC throughout the entire experimental period, reaching a peak concentration of 921.4 ng L1, whereas the MIB and b-cyclocitral concentrations were above the OTC from August to October with the highest values of 294.9 ng L1 and 1289.6 ng L1, respectively, reached at the end of August. The off-flavor problems in the control pond lasted from May to October and were the most serious in August and September. In the recirculating pond, in contrast, the concentration of geosmin exceeded the OTC only sporadically.
3.4. Removal rates of the odorous compounds in the pond water by the CW The analysis of the odorous compounds in the influent and effluent of the CW showed that the average removal rate for geosmin was 72.2 5.5% (n ¼ 6) in the autumn of 2006. Similarly, the average removal rate was 88.1 12.6% (n ¼ 26) in 2007, and its concentration in the effluent was generally below 20 ng L1 although the geosmin concentration in the influent of the CW fluctuated (Fig. 3). In the two-year experiment, the other two odorous compounds were not detected in the influent and effluent of the CW. These results indicate that water treatment by the CW could effectively remove odorous
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Fig. 2 e Seasonal evolution of the algal density and taxonomic composition of the phytoplankton assemblages in the recirculating pond and the control pond in 2004, 2006 and 2007. Each column represents the sum of the densities of Euglenophyta, Cryptophyta, Bacillariophyta, Cyanophyta, Pyrrophyta, Chlorophyta and Chrysophyta. The highest peaks of phytoplankton density in the recirculating pond and the control pond are also indicated for each year.
compounds from the pond water given the set of conditions in this experiment.
3.5. Relationships among the odorous compounds, cyanobacteria species and water quality The relationships among the occurrence of off-flavor problems, the presence of cyanobacteria species, and the water quality were explored by CCA based on the data for the concentrations of three odorous compounds, the density of the cyanobacterial species, and ten physicochemical variables in 2007. The two CCA axes explained a substantial proportion
of the variation (44.6%) in the cyanobacteria-environment relationship (Fig. 4). In particular, Oscillatoria sp., Oscillatoria kawamurae and Microcystis aeruginosa were found to correlate with the occurrence of MIB, geosmin and b-cyclocitral in the pond water. The correlations were consistent with previous studies (Matsumoto and Tsuchiya, 1988; Ju¨ttner, 1995; Tucker, 2000) that attributed the prevalence of MIB, geosmin and bcyclocitral to the occurrence of certain Oscillatoria and Microcystis species. The average density of Microcystis and Oscillatoria species accounted for more than 70% of the total algal density, and M. aeruginosa was the dominant species in the control pond. The period (August and September in 2007)
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Fig. 3 e Concentrations of geosmin, 2-methylisoborneol, and b-cyclocitral (ng LL1) in the recirculating pond, the control pond, and the influent and effluent of the CW in 2007. Water samples were taken weekly from May 5th, 2007 to November 9th, 2007.
during which the highest concentrations of the three odorous compounds were detected overlapped with the period during which the highest algal density occurred when Cyanophyta was the dominant phylum. Because the cyanobacteria species
Fig. 4 e Two-dimensional ordination diagram of phytoplankton-environmental factors. The spatial ordination resulting from canonical correspondence analysis of cyanobacteria species with respect to physicochemical variables and odorous compounds is presented.
that were detected might be the main cause of the prevalence of off-flavor compounds, and water recycling by the CW could successfully control their presence, the CW was helpful in reducing the concentration of these odorous compounds in pond water. Via forward selection and the Monte Carlo test, CCA identified a subset of environmental variables including SRP, pH, light intensity, TSS, DO, and Cond as potential significant factors that influence the cyanobacteria community. Similarly, factors reported to be correlated with the dominance of cyanobacteria include: high nutrient loadings (especially low N/P ratio) (Smith, 1983), high pH and low carbon dioxide concentrations (King, 1970; Shapiro, 1984), low light availability (Smith, 1986), water column stability (Paerl and Tucker, 1995), and high water temperature (Mcqueen and Lean, 1987). In addition, the CCA analysis showed that SRP, BOD5 and TSS were positively associated with the three off-flavor compounds, whereas N/P, Cond and pH were negatively associated with the three off-flavor compounds (Fig. 4). The prevalence of off-flavor compounds in the control pond might be attributed to the eutrophic condition of the water. A principal component analysis by Parinet et al. (2010) also found that the eutrophication of water bodies was a major factor in the occurrence of off-flavor compounds. The ability of the CW to maintain the pond water at a lower trophic status could also be beneficial for the successful control of odorous compounds in the pond water.
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Fig. 5 e Concentrations of geosmin and 2-methylisoborneol in catfish fillets (mg kgL1) in the recirculating pond and the control pond in 2007. Each column represents the mean ± SD (n [ 3).
p < 0.05; r ¼ 0.98, p < 0.05, respectively). These results indicate that reducing the concentration of geosmin and MIB in pond water is essential to control off-flavor problems. Because the CW could control the concentrations of MIB and geosmin in the pond water by managing undesirable cyanobacteria blooms and maintaining the pond water at a relatively lower trophic status, the CW might be a promising technique to solve off-flavor problems encountered in aquaculture.
3.6. Concentrations of MIB and geosmin in catfish samples In 2007, the geosmin concentration in the catfish fillets from the control pond was consistently high with a peak of 1.13 mg kg1 in September, and MIB was detected four out of six times with the highest level (0.63 mg kg1) at the beginning of October (Fig. 5). In most cases, the term “off-flavor” is linked to an earthy/musty odor and taste that is caused by high concentrations of geosmin or MIB in the fish meat (Tucker, 2000). Grimm et al. (2004) reported that the OTC values of geosmin and MIB in catfish fillets were 0.25e0.5 mg kg1 and 0.1e0.2 mg kg1, respectively. Geosmin levels in catfish fillets in the control pond were above the lowest OTC values from July to October, and the MIB levels were above the lowest OTC values in September and October. In contrast, the concentrations of geosmin and MIB were below the lowest OTC values in the recirculating pond. The off-flavor problems encountered in aquaculture are often ascribed to the absorption of odorous compounds from the water (From and Hurlyck, 1984; Robertson et al., 2005). In this study, positive correlations were found between geosmin and MIB concentrations in the pond water and the concentration of those compounds in the catfish fillets (r ¼ 0.94,
3.7. Management of the off-flavor problems in the pond water at high stocking densities Other factors such as high feeding and stocking rates have been associated with off-flavor problems (Brown and Boyd, 1982). In general, lower stocking densities and feeding regimes would decrease profits. To avoid the occurrence of off-flavor problems at high stocking densities, reliable and reasonable water treatment processes are preferred. In the three years of the present study, the fish were stocked at high densities (10,000e15,000 fish ha1) in the recirculating and the control pond, and the fish were fed to satiation in the same way in both ponds with feeding rates ranging from 21.6 kg ha1 d1 to 50.0 kg ha1 d1 (Table 3). However, lower concentrations of the three odorous compounds were
Table 3 e Stocking density, feeding rate, survival rate, and yield of pond-raised fish in the recirculating pond and the control pond. 2004 Operating period Culture species
Apr.5eNov.6 Ictalurus punctatus
Stocking density (fish ha1) Initial body weight (g) Feeding rate (kg ha1 d1) Survival rate (%)
5300 167 30.0 (n ¼ 30) 50.0 97.2 90.6
Yield (kg ha1)
Recirculating pond Control pond Recirculating pond Control pond
Megalobrama amblycephala 8000 201 54.8 (n ¼ 30) 98.8 71.9 6914 5875
2006
2007
Apr.24eDec.3 Ictalurus punctatus
Mar.28eNov.3 Ictalurus punctatus
10,000 54.6 15.8 (n ¼ 50) 21.6 98.8 61.1 4830 1760
15,000 206.3 72.2 (n ¼ 50) 40.3 98.7 88.7 7235 5615
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identified in the recirculating pond in 2006 and 2007 compared with the control pond. The concentrations of MIB and geosmin in the catfish in 2007 in the recirculating pond were also lower than the concentrations in the catfish in the control pond. Furthermore, higher survival rates and yields of fish were found in the recirculating pond throughout the entire three years (Table 3), indicating that water treatment by the CW could potentially be one of the best management practices to control off-flavor problems at high stocking densities.
4.
Conclusions
Severe off-flavor problems were present in the control pond and were ascribed to the higher trophic status and the dominance of Oscillatoria sp., O. kawamurae and M. aeruginosa. Water treatment by the CW in the recirculating pond could control the pond water at a lower trophic status and adjust the taxonomic composition of the phytoplankton assemblages. Furthermore, water treatment by the CW could remove geosmin from the pond water efficiently. The CW might represent a promising approach to solving off-flavor problems encountered in aquaculture. Additional research is suggested to study the mechanisms of odorous compounds removal and phytoplankton assemblage manipulation by the CW. Further studies should also focus on the effective and economical operation of the CW.
Acknowledgments We are grateful to the Key Special Program on the S&T for the Pollution Control and Treatment of Water Bodies (2008ZX07316-004, 2009ZX07106-002) and the National Natural Science Foundation of China (50808172, 20877093, 51108335, and 30870221). We are also thankful to L.P. Zhang, Pro. B.Y. Liu, Dr. G. Li, Dr. S.Y. Zhang and other members of the research group for their advice and assistance during the work.
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size distribution in surface water: a field study in water works. Journal of Environmental Sciences 22 (2), 161e167. McQueen, D.J., Lean, D.R.S., 1987. Influence of water temperature and nitrogen to phosphorus ratios on the dominance of bluegreen algae in Lake St George, Ontario. Canadian Journal of Fisheries and Aquatic Sciences 44 (3), 598e604. Parinet, J., Rodriguez, M.J., Se´rodes, J., 2010. Influence of water quality on the presence of off-flavour compounds (geosmin and 2-methylisoborneol). Water Research 44 (20), 5847e5856. Paerl, H.W., Tucker, C.S., 1995. Ecology of blue-green algae in aquaculture ponds. Journal of the World Aquaculture Society 26 (2), 109e131. Paerl, H.W., Hall, N.S., Calandrino, E.S., 2011. Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Science of the Total Envrionment 409 (10), 1739e1745. Robertson, R.F., Jauncey, K., Beveridge, M.C.M., Lawton, L.A., 2005. Depuration rates and the sensory threshold concentration of geosmin responsible for earthy-musty taint in rainbow trout, Onchorhynchus mykiss. Aquaculture 245 (1e4), 89e99. Rosen, B.H., MacLeod, B.W., Simpson, M.R., 1992. Accumulation and release of geosmin during the growth phases of Anabaena circinalis (Ku¨tz Rabenhorst. Water Science and Technology 25 (2), 185e190. Schrader, K.K., Blevins, W.T., 1993. Geosmin-producing species of Streptomyces and Lyngbya from aquaculture ponds. Canadian Journal of Microbiology 39 (9), 834e840. Schrader, K.K., Tucker, C.S., Hanson, T.R., Gerard, P.D., Kingsbury, S.K., Rimando, A.M., 2005. Management of musty off-flavor in channel catfish from commercial ponds with weekly applications of copper sulfate. North American Journal of Aquaculture 67 (2), 138e147. Shapiro, J., 1984. Blue-green dominance in lakes: the role and management significance of pH and CO2. Internationale Revue der gesamten Hydrobiologie und Hydrographie 69 (6), 765e780. Sindilariu, P.D., Brinker, A., Reiter, R., 2009. Factors influencing the efficiency of constructed wetlands used for the treatment of intensive trout farm effluent. Ecological Engineering 35 (5), 711e722. Smith, V.H., 1983. Low nitrogen to phosphorus ratios favor dominance by blue-green algae in lake phytoplankton. Science 221 (4661), 669e671. Smith, V.H., 1986. Predicting the proportion of blue-green algae in lake phytoplankton. Canadian Journal of Fisheries and Aquatic Sciences 43, 148e153.
Song, W., O’Shea, K.E., 2007. Ultrasonically induced degradation of 2-methylisoborneol and geosmin. Water Research 41 (12), 2672e2678. Sung, Y.H., Li, T.Y., Huang, S.D., 2005. Analysis of earthy and musty odors in water samples by solid-phase microextraction coupled with gas chromatography/ion trap mass spectrometry. Talanta 65 (2), 518e524. State Environmental Protection Administration of China, 2002. Methods for Chemical Analysis of Water and Waste. China Environmental Science Press, Beijing. 200e284. Tucker, C.S., 2000. Off-flavor problems in aquaculture. Reviews in Fisheries Science 8 (1), 45e88. Tucker, C.S., 2006. Low-density silver carp Hypophthalmichthys molitrix (Valenciennes) Polyculture does not prevent cyanobacterial off-flavours in channel catfish Ictalurus punctatus (Rafinesque). Aquaculture Research 37 (3), 209e214. Visser, P.M., Ibelings, B.W., Van der Veer, B., Koedood, J., Mur, L.R., 1996. Artificial mixing prevents nuisance blooms of the cyanobacterium Microcystis in Lake Nieuwe Meer, the Netherlands. Freshwater Biology 36 (2), 435e450. ¨ , 2011. Vohla, C., Ko˜iv, M., Bavor, J.H., Chazarenc, F., Mander, U Filter materials for phosphorus removal from wastewater in treatment wetlands-A review. Ecological Engineering 37 (1), 70e89. Watson, S.B., McCauley, E., Downing, J.A., 1997. Patterns in phytoplankton taxonomic composition across temperate lakes of differing nutrient status. Limnology and Oceanography 42 (3), 487e495. Watson, S.B., Brownlee, B., Stachwill, T., 2000. Quantitative analysis of trace levels of geosmin and MIB in source and drinking water using headspace SPME. Water Research 34 (10), 2818e2828. Xie, P., Liu, J.K., 2001. Practical success of biomanipulation using filter-feeding fish to control cyanobacteria blooms: a synthesis of decades of research and application in a subtropical hypereutrophic lake. The Scientific World Journal 1, 337e356. Young, C.C., Suffet, I.H., Crozes, G., Bruchet, A., 1999. Identification of a woody-hay odor-causing compound in a drinking water supply. Water Science and Technology 40 (6), 273e278. Zhang, Z.S., Huang, X.F., 1991. Research Methods of Freshwater Plankton. Science Press, Beijing. Zimba, P.V., Grimm, C.C., 2003. A synoptic survey of musty/ muddy odor metabolites and microcystin toxin occurrence and concentration in southeastern USA channel catfish (Ictalurus punctatus Ralfinesque) production ponds. Aquaculture 218 (1e4), 81e87.
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The role of nitrobenzene on the yield of trihalomethane formation potential in aqueous solutions with Microcystis aeruginosa Zhiquan Liu, Fuyi Cui*, Hua Ma, Zhenqiang Fan, Zhiwei Zhao State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (SKLUWRE, HIT), PO Box 2650, Harbin 150090, China
article info
abstract
Article history:
Algae are one of the most important disinfection by-product (DBP) precursors in aquatic
Received 14 July 2011
environments. The contents of DBP precursors in algae are influenced by not only envi-
Received in revised form
ronmental factors but also some xenobiotics. Trihalomethane formation potential (THMFP)
12 September 2011
in both the separate and interactive pollution of Microcystis aeruginosa and Nitrobenzene
Accepted 21 September 2011
(NB) was investigated in batch experiment to discover the effects of xenobiotics on the
Available online 1 October 2011
yield of DBP precursors in the algal solution. The results show that in the separate NB solution, NB did not react with Cl2 to form trihalomethane (THM), whereas in the algae
Keywords:
solution, THMFP had a significant positive linear correlation with M. aeruginosa density in
Microcystis aeruginosa
both solution and extracellular organic matter (EOM). The correlation coefficients were
THMFP
0.9845 ( p ¼ 3.567 104) and 0.9854 ( p ¼ 1.406 104), respectively. According to regression
Nitrobenzene
results, about 77.9% of the total THMFP came from the algal cells, while the rest came from EOM. When the interactive pollution of M. aeruginosa and NB occurred, the growth of algae was inhibited by NB. The density of M. aeruginosa in a high concentration NB solution (280 mg/L) was only 71.1% of that in the solution without NB after 5 days of incubation. However, THMFP in the mixture (algae and NB) and the EOM did not change significantly, and the productivity of THMFP by the algae (THMFP/108cells) increased with the increase in NB concentration. There was a significant linear correlation between THMFP/108cell and NB concentration (r ¼ 0.9117, p < 0.01), which shows the contribution of the algae to THM formation was enhanced by NB. This result might be caused by the increased protein productivity and the biodegradation of NB by M. aeruginosa. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Chlorination of drinking waters can produce trihalomethanes (THMs), haloacetic acids (HAAs) and other types of disinfection by-products (DBPs) (Oliver, 1983). Previous research has indicated that natural organic matter (NOM) in aquatic environment (Li et al., 2000; Gang et al., 2003), such as humic and
fulvic acid, and algae (Graham et al., 1998; Plummer and Edzwald, 1998, 2001) are precursors of these by-products. The production rate of DBPs by algae varies depending on the algal biomass, algae species and algal growth phase. Moreover, it has been confirmed that both algal cells and extracellular organic matter (EOM) can be chloridised to form DBPs, and that the yield of the DBPs from the cells is greater
* Corresponding author. Tel.: þ86 451 86282098. E-mail address:
[email protected] (F. Cui). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.043
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than that from the EOM (Wachter and Andelman, 1984; Oliver and Shindler, 1980; Huang et al., 2009). Oliver and Shindler (1980) showed that the majority of the precursors came from algal cells or cell fragments. Industrial and agricultural growth has caused a large number of synthetic organic compounds to be discharged into the aquatic environment. Xenobiotics transferred into algal cells by bioaccumulation and bio-absorption might inhibit the reproduction of algae (Thies et al., 1996; Warshawsky et al., 1990; Sijm et al., 1998). Both xenobiotics and algae can cause direct or indirect problems in drinking water, and these problems may be more serious when interactive pollution occurs. The production of DBPs during chlorination is one of these problems. However, while the relationship between DBPs and algae has been thoroughly studied (Wachter and Andelman, 1984; Oliver and Shindler, 1980; Huang et al., 2009; Karimi and Singer, 1991; Hoehn et al., 1984), there is very little research on the effect of xenobiotics on the yield of DBPs in algal solutions. Nitrobenzene (NB) is used extensively in industrial manufacturing synthetic resins, pesticides, dyes, drugs, pharmaceuticals, and so forth. Large areas of soil and ground water have been contaminated by these xenobiotics (Patil and Shlnde, 1988; Kulkarni and Chaudhari, 2007), which may affect the growth of algae and cause DBP problems. Prior studies showed that it was difficult to form THMs in the presence of NB (Guo and Lin, 2009), which implies that NB should not affect the THM productivity of algae if there is no interaction between them. For this reason, NB was selected in this paper to study the effect of xenobiotics on the yield of DBPs by Microcystis aeruginosa, a species of cyanophytes that can cause algal blooms. The trihalomethane formation potential (THMFP) in both separate and interactive pollution scenarios was investigated.
2.
Materials and methods
2.1.
Materials
keep the algae in its exponential growth phase. The observed M. aeruginosa were individual spherical cells instead of colonies, which is quite different from M. aeruginosa that are observed in natural waters. The algae in the exponential growth phase were centrifuged and the algal pellets were resuspended with distilled water for further experimentation. The resuspended algal pellets were incubated in a 500-mL conical beaker with one-tenth of the full strength BG11 medium and with or without NB. The total volume of the algal solution was 250 mL. All the samples and algal stock solutions were incubated in an illuminating incubator with a temperature of 25 1 C. Cool white fluorescent-light was provided with 3300lx of illumination in a 14 h light/10 h dark cycle. Algal density was monitored by cell number counting with a microscope (Olympus BX41) according to the method adopted in Ma’s paper (Ma et al., 2009). Duplicate measurements were taken and the arithmetical means (SD) were obtained and used as the final density.
2.3. Extracellular and intracellular organic matter separation The algal solution was centrifuged for 20 min at a speed of 8000r/min. The supernatant contained EOM and was collected to assess the contribution of the algae to THM formation and the concentration of proteins and carbohydrates. The concentration of the EOM was correlated to the algal density in original algal solution instead of the dissolved organic carbon (DOC) which could be easily compared with the THMFP of the algal cells (approximately 4.5e6.5 108 cells/L). The pellets were resuspended in the same volume of distilled water and were subjected to sonication (Sonics 130 W/50 Hz, USA) in an ice bath with the amplitude of 100% for six 30-s periods separated by 15-s intervals. The suspension was centrifuged for 20 min at a speed of 8000r/min and the supernatant contained intracellular organic matter (IOM) that was used for the protein and carbohydrate analyses.
2.4. The stock culture of M. aeruginosa was purchased from the Institute of Hydrobiology at the Chinese Academy of Sciences. Nitrobenzene (CAS: 98-95-3), methyl tert-butyl ether (MTBE, CAS: 1634-04-4) and inorganic reagents were purchased from Tianjin Kermel Chemical Reagent Co. Ltd. All the chemical reagents were of analytical grade except MTBE (GR). The needed standard materials, CHCl3, CHBr2Cl, CHBrCl2 and CHBr3, were purchased from J&K Scientific Ltd. A Pierce BCA Protein Assay Kit was purchased from Thermo Fisher Scientific, Inc.
2.2.
Algal culturing
The purchased stock culture of M. aeruginosa was treated by a method adopted from Semple and Cain (1996) to obtain axenic M. aeruginosa. Axenic stock culture of M. aeruginosa was incubated in a 1000-mL flat bottomed flask with a cotton plug containing approximately 500 mL of BG11 medium (Stanier et al., 1971) to ensure sufficient CO2 exchange. Approximately half of each algal suspension was discarded and the same volume of BG11 medium was added every 15 days to
Chlorination and THMs analysis
THMs were detected by gas chromatography with electron capture detection by a modified EPA method (EPA 551.1), as described by Wang et al. (2009). The pH of all the samples (including the NB solution, algal solution, the mixture of NB, algae and EOM) was adjusted to 7 by HCl before chlorination. A phosphate buffer was added to maintain the pH. The samples were incubated in the dark at 25 1 C. The doses of chlorine and the reaction time were determined by experimentation to obtain the maximum production of THMs, which is THMFP. High density algal solutions were diluted with distilled water and the THMFP was calculated by the following formula: C ¼ ðC0 Cw Þ n þ Cw
(1)
where C is the THMFP concentration in original samples (mmol/L), C0 is the THMFP concentration in diluted samples (mmol/L), Cw is the THMFP concentration in distilled water (mmol/L) and n is the dilution factor. THMFP in the algal solution and in the EOM were measured, and the difference
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2.5.
Protein and carbohydrate analyses
Proteins were detected by the bicinchoninic acid (BCA) method (Pierce BCA Protein Assay Kit, Thermo Scientific, USA). Carbohydrates were detected by the phenolesulphuric acid method (DuBois et al., 1956), and the concentration was calculated in mg of glucose per 108 cells. Both proteins and carbohydrates in the EOM and the IOM were monitored.
25 Algal solution EOM 20 Total THMs(µmol/L)
between them was assumed to be the THMFP of the cells. The lack of bromine caused chloroform to be detected in all the samples. All the measurements were taken in triplicate and the arithmetical means (SD) were obtained and used as the final values.
15
10
5
0 0
3.
Results and discussions
3.1.
Chlorination conditions
2
4 6 Time (day)
8
10
Fig. 2 e Total THM in the algal solution and the EOM for different reaction times (1.1 3 108 cells/L, 1600 mgCl2/L, 25 ± 1 C, pH 7, in the dark).
In prior research (Graham et al., 1998; Plummer and Edzwald, 2001; Oliver and Shindler, 1980; Nguyen et al., 2005; Huang et al., 2009), chlorine doses were usually chosen to ensure a substantial residual amount of chlorine (>3 mg/L or more) in accordance with standard methods. However, the THMs in the samples always increased with the reaction time and the chlorine dose, which indicates that organic matter in algal solutions were not thoroughly chlorinated. In this paper, the doses of chlorine and the reaction time were determined by experimentation to ensure that the reaction was thoroughly completed. Experiments were conducted with a cell density of 1.1 108 cells/L under standardized pH (pH ¼ 7) and temperature (25 1 C) in the dark with different chlorine doses and reaction times. THMs were detected in the algal solutions and in the EOM. The results showed that with the same reaction time (3 days), the amount of THMs increased with the chlorine dose for low doses (less than 200 mg/L) and that dose of 200 mg/L is sufficient for chlorination (Fig. 1). With the same chlorine dose (1600 mg/L), the maximum amount of THMs was obtained after 7 days of reaction time (Fig. 2). According to the results, the actual dose is
confirmed to be 1600 mg/L, which ensures that thorough chlorination is completed in 7 days. When the density of algae is higher than 1.1 108 cells/L, the sample is diluted with distilled water until the density is lower than 1.1 108 cells/L, and then, the THMFP is calculated by formula (1). The chlorination experiments were performed to study the potential for by-products formation by algae in the separate and interactive pollution scenarios of M. aeruginosa and NB. High doses of Cl2 were used to ensure that all of the THMFP had been chloridised. Thus, the chlorination conditions were quite different from those applied to actual drinking water and do not reflect the real production of THMs under the actual conditions.
3.2.
THMFP in separate pollution samples
3.2.1.
THMFP in NB solution
NB solutions were chloridised under the pre-set conditions described in 2.4 and 3.1. The total THMFP in NB solutions
1.0 25 Algal solution EOM
0.8 Total THMs (µmol/L)
Total THMs(µmol/L)
20 15 10 5
0.6 0.4 0.2 0.0
0 0
300
600
900 1200 [Cl2](mg/L)
1500
1800
Fig. 1 e Total THM in the algal solution and the EOM for different chlorine doses (1.1 3 108 cells/L, 3 days, 25 ± 1 C, pH 7, in the dark).
0
50
100
150
200
250
300
350
concentration of NB(µg/L) Fig. 3 e Total THM in the NB solution (Chlorination conditions: 1600 mgCl2/L, 7 days, 25 ± 1 C, pH 7, in the dark).
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350
Total THMs EOM
3.2.2.
Linear fit of Total THMs Linear fit of EOM
Total THMs (µmol/L)
300
y=12.42x 2 R =0.959
250 200 150
y=2.74x 2 R =0.955
100 50 0 0
5
10 15 20 8 Algal density (10 cells/L)
25
30
Fig. 4 e Total THMFP in the algal solution and the EOM (no NB; THMFP is obtained by the formula 0 C [ (C L Cw) 3 n D Cw; Chlorination conditions: 1600 mgCl2/L, 7 days, 25 ± 1 C, pH 7, in the dark).
ranged from 0.58 to 0.71 mmol/L (Fig. 3), which was approximately equal to that of distilled water (0.65 0.043 mmol/L). No linear correlation was found between the THMFP and the NB concentration (r ¼ 0.2258, p ¼ 0.6264). This result indicated that NB could not react with chlorine to form THMs, and that the total THMFP of NB solution came from the solvent, rather than the NB. It also implied that the dosage of NB in the algal solution would not affect the yield of THMFP produced by the algae, if there was no interaction between the algae and the NB. Because of the high concentration of THMFP in distilled water, the formula in section 2.4 was used to eliminate the interference due to the distilled water.
8
60
6 40 4 20
2
0 0
50
100
150
200
250
8
Total THMs(mmol/L)
10 Algal density (×10 cells/L)
Algal density In the mixture of algae and NB in EOM of the mixture
80
0 300
THMFP in the M. aeruginosa solution
The concentration gradient of M. aeruginosa was obtained by incubating a low density of algae in conical beakers for different periods (10e20 days) to avoid the influence of dilution. The initial algal densities were lower than 108 cells/L. Chlorination and calculations were carried out as described in sections 2.4 and 3.1. The results are shown in Fig. 4. THMFP yields increased with increasing algal density, and there was a significant positive linear correlation between THMFP and algal density for solutions that included both algal solution and EOM; the related coefficients were 0.9845 ( p ¼ 3.567 104) and 0.9854 ( p ¼ 1.406 104), respectively. This result indicates that M. aeruginosa is an important precursor of disinfection by-products in the aquatic environment, as also found in previous research (Graham et al., 1998; Plummer and Edzwald, 2001; Oliver and Shindler, 1980; Nguyen et al., 2005). According to the results of regression, every 108 algal cells could yield 12.42 mmol THM (include the cells and the EOM), while the corresponding amount of EOM could only yield 2.74 mmol THM, which is approximately 22.1% of the total THMFP. The results are in agreement with previously published results (Plummer and Edzwald, 2001; Huang et al., 2009). The fact that the cells have higher THM formation than the EOM implies that it is necessary to remove algal cells physically before oxidation to reduce THM production. In some previous studies (Huang et al., 2009; Kanokkantapong et al., 2006), researchers have found that the composition and production of DBPs is influenced by the interaction of the algal cells with the EOM, and they have provided several explanations. However, the chlorine dosage was not mentioned in these explanations. Chlorination was usually performed that the residual of chlorine was no less than 0.5e3 mg/L and the reaction time was no more than 7 days, in accordance with practical situations. In those cases, the reactions were terminated without complete oxidation (Plummer and Edzwald, 1998, 2001; Wachter and Andelman, 1984; Oliver and Shindler, 1980) and the products may have been affected by the consumption of chlorine. This could explain why Kanokkantapong et al. (2006) found that the majority of HAAs formation changed from dichloroacetic acid in individual organic fractions to monochloroacetic acid in the mixture (Kanokkantapong et al., 2006). Huang et al. (2009) found that the yields of THMs in mixtures of algal cells and EOM were less than that in just the cells, even less than that in only EOM solutions (residual of chlorine>0.5 mg/L). This founding can also be explained by the lack of chlorine, which can be further supported by the phenomenon that the difference between the yields of THMs in the mixture, in the cells and in the EOM after a 1-day chlorination period is less obvious than the difference in the yield after a 7-day period, as shown in Huang’s paper. For this reason, excess chlorine was used in our experiment to reduce the amount of interference. The residual chlorine was usually more than 100 mg/L.
Concentration of NB (µg/L) Fig. 5 e Effect of NB on the yield of THMs and the reproduction of Microcystis aeruginosa (5 days incubation; 0 THMFP is obtained by the formula C [ (C L Cw) 3 n D Cw; Chlorination conditions: 1600 mgCl2/L, 7 days, 25 ± 1 C, pH 7, in the dark).
3.3. THMFP in interactive pollution of M. aeruginosa and NB The solutions with NB and algae were prepared by mixing the stock culture of M. aeruginosa and NB working solution. The
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Table 1 e Productivity of THMs in the mixture, the EOM and the cells (Chlorination conditions: 1600 mgCl2/L, 7 days, 25 ± 1 C, pH 7, in the dark). THMs productivity (mean SD, mmol/108 cells)
Initial NB concentration (mg/L)
In the mixture
0 40 80 160 200 240 280
10.31 11.50 11.22 11.50 11.83 12.68 13.46
In the EOM
2.10 1.81 1.10 2.10 1.11 1.97 1.94
% in EOM In the cells
2.36 0.51 2.68 0.44 2.43 0.40 3.11 0.39 2.45 0.21 2.98 0.90 3.86 0.30
7.95 8.82 8.78 8.38 9.38 9.70 9.60
1.61 1.42 0.86 1.84 0.93 1.13 1.64
22.92% 23.31% 21.71% 27.07% 20.73% 23.52% 28.66%
THMs/cell in the cells was obtained as the difference of that in the mixture and in the EOM.
6
IOM EOM
2
y=0.0037x+1.96 2 R =0.228 0 0
50
100 150 200 Concentration of NB (µg/L)
Fig. 6 e Effect of NB on carbohydrate productivity by M. aeruginosa (after 5 days incubation).
IOM EOM
10
Linear Fit of IOM Linear Fit of EOM
y=-0.00014x+3.53 R2=0.025
4
approximately 24% of the total THMFP, which was a little higher than that in the algal solution without NB, and no linear correlation was found between the percentage and the NB concentration. The results of the correlation analysis imply that some organics were produced by the algae that could be chloridised to form THMs. These organics come from two possible pathways. The first pathway is that the organics may come from algal secretion. The results of prior studies have shown that antioxidant enzyme activities in algal cells could be enhanced by exposure to xenobiotics (Yang et al., 2002; Pugmacher et al., 1999). It could be inferred that some other types of organic matter that might be in the THMs’ precursor pool may be secreted when the algae is exposed to xenobiotics. The concentrations of proteins and carbohydrates in the EOM and in the IOM were investigated and the results are shown in Figs. 6 and 7. The presence of NB did not significantly affect the algae’s carbohydrate productivity (r ¼ 0.4778, p ¼ 0.3378 in IOM; r ¼ 0.07377, p ¼ 0.7634 in EOM), but it did increase protein productivity significantly (r ¼ 0.7700, p ¼ 0.0733 in IOM; r ¼ 0.9301, p ¼ 0.00716 in EOM). Hong et al. (2009) and Philippe et al. (2010) proved that amino acids are precursors of trihalomethane. Proteins, as combined amino acids, can also be in THMs precursor pool (Scully et al., 1988). In this case, the
Proteins productivity by M.aeruginosa 8 (mg/10 cells)
Carbohydrates productivity by M.aeruginosa 8 (mg glucose/10 cells)
initial density of algae was 3.81 108 cells/L while the initial concentration of NB ranged from 0 m/L to 280 m/L. After 5 days of incubation, THMFP in both solutions and the EOM of the mixture was detected. The reproduction of M. aeruginosa was obviously inhibited by NB (Fig. 5). After 5 days of incubation, the density of the algae decreased with increasing NB concentration, and the density of M. aeruginosa in high concentrations of the NB solution (280 mg/L) was only 71.1% of that in the solution without NB. However, THMFP in the mixture of algae and NB did not change significantly with the algal density. The result was also observed for THMFP in the EOM, as shown in Fig. 5. The specific productivity of THMFP, expressed as mmol/108 cells, was used to show the capacity of THMFP produced by algae, which increased with increasing NB concentrations in both the mixture and the EOM (Table 1). The results indicate that the yield of THMFP by algae was enhanced by the presence of NB although NB could not be chloridised to form THMs directly. There was a significant linear correlation between the THMFP/cells in the mixture and the NB concentration (r ¼ 0.9117, p < 0.01). However, the correlations between THMFP/cell in the EOM or in cells and the NB concentration were not as significant as with the NB concentration in the mixture (r ¼ 0.7305, p ¼ 0.0622; r ¼ 0.8358, p ¼ 0.0192, respectively). The THMFP coming from the EOM accounted for
250
8
Linear Fit of IOM Linear Fit of EOM
y=0.01038x+5.095 2 R =0.592
6
y=0.01001x+3.747 R2=0.865
4 2 0 0
50
100 150 200 Concentration of NB (µg/L)
Fig. 7 e Effect of NB on protein productivity by M. aeruginosa (after 5 days incubation).
250
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increased proteins productivity can explain why the algal yield of THMs increased in interactive pollution of M. aeruginosa and NB. The second pathway is that the organics may come from the intermediate biodegradation of NB with M. aeruginosa. It has been proven that some species of algae are capable of heterotrophic growth on xenobiotics (Semple et al., 1999; Semple and Cain, 1996) and the biodegradation of NB with M. aeruginosa was also found in our research (data not shown). During the aerobic biodegradation of nitro-aromatic compounds, numerous phenolic hydroxyl groups are generated (Kulkarni and Chaudhari, 2007), which can be chloridised to form THMs (Galapate et al., 2001; Norwood et al., 1980). These intermediate compounds contained phenolic hydroxyl groups that may make a contribution to the increase of THMFP productivity in the interactive pollution of M. aeruginosa and NB. The fact that NB increases THMFP productivity by algae may not be a serious problem, but it is an important fact that cannot be overlooked by researchers who are interested in algal problems. Usually, researchers only pay attention to separate algae pollution, without considering the interference of xenobiotics. However, in the real-world situations, there is no separate algae pollution, only interactive pollution, and the algae may affected by some xenobiotics. Our study has shown the interaction between algae and xenobiotics really exists. Thus, it is necessary to understand the characteristics of interactive pollution of algae and xenobiotics and the effect of that interaction on water treatment.
4.
Conclusions
In this paper, the THMFP in both separate and interactive pollution of M. aeruginosa and NB were studied. The key findings of this research were listed below: Nitrobenzene barely reacts with chlorine to form THMs. M. aeruginosa is an important trihalomethane formation potential, and the algal cells can provide more THMFP than the EOM. Only approximately 22.4% of the total THMFP came from the EOM, according to the to the regression results. The presence of NB can increase THMFP productivity by M. aeruginosa. These increased THMFP may come from both the increased proteins productivity and the intermediate biodegradation of NB by M. aeruginosa.
Acknowledgements This work was supported by a grant from the National Creative Research Groups (Grant No. 50821002) and the National Natural Science Foundation of China (Number: 50778048).
Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.043.
references
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Philippe, K.K., Hans, C., MacAdam, J., Jefferson, B., Hart, J., Parsons, S.A., 2010. Photocatalytic oxidation of natural organic matter surrogates and the impact on trihalomethane formation potential. Chemosphere 81, 1509e1516. doi:10.1016/ j.chemosphere.2010.08.035. Plummer, J.D., Edzwald, J.K., 1998. Effect of ozone on disinfection by-product formation of algae. Water Science and Technology 37 (2), 49e55. Plummer, J.D., Edzwald, J.K., 2001. Effect of ozone on algae as precursors for trihalomethane and haloacetic acid production. Environmental Science & Technology 35 (18), 3661e3668. Pugmacher, S., Wiencke, C., Sandermann, H., 1999. Activity of phase I and phase II detoxication enzymes in Antarctic and Arctic macroalgae. Marine Environmental Research 48 (1), 23e36. Scully Jr., F.E., Howell, G.D., Kravitz, R., Jewell, J.T., Hahn, V., Speed, M., 1988. Proteins in natural waters and their relation to the formation of chlorinated organics during water disinfection. Environmental Science & Technology 22 (5), 537e542. Semple, K.T., Cain, R.B., 1996. Biodegradation of phenols by the alga Ochromonas danica. Applied and Environmental Microbiology 62 (4), 1265e1273. Semple, K.T., Cain, R.B., Schmidt, S., 1999. Biodegradation of aromatic compounds by microalgae. FEMS Microbiology Letters 170 (2), 291e300.
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Adsorption and heterogeneous oxidation of As(III) on ferrihydrite Zhixi Zhao a,b, Yongfeng Jia a,*, Liying Xu a, Shanlin Zhao c,* a
Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, No 72, Wenhua Road, Shenyang 110016, China b College of Chemistry and Chemical Engineering, Xinjiang Normal University, Urumqi 830054, China c Liaoning Shihua University, Fushun, Liaoning 113001, China
article info
abstract
Article history:
Redox transformation of arsenic strongly influences its fate and transport in the envi-
Received 11 March 2011
ronment. It is of interest to investigate heterogeneous oxidation of As(III) on the surface of
Received in revised form
major metal oxide in sediments. Whether As(III) can be oxidized on ferrihydrite and the
24 August 2011
role ferrihydrite plays as catalyst or oxidant are inconsistent in previous researches. In this
Accepted 25 September 2011
work, oxidation of As(III) on ferrihydrite was studied by analysis of dissolved and adsorbed
Available online 5 October 2011
As(III) and As(V) quantitatively and qualitatively. X-ray absorption near edge spectroscopy (XANES) and pHpznpc (point of zero net proton charge) of ferrihydrite with adsorbed As(III)
Keywords:
showed clear evidence for partial oxidation on ferrihydrite. Oxidation of As(III) occurred
As(III)
when it was brought to contact with ferrihydrite at high Fe/As molar ratio (i.e. 50, 200). The
Ferrihydrite
concentration of As(V) in solid phase increased gradually while adsorbed As(III) concen-
Adsorption
tration dropped. Fe(II) was not detectable during the oxidation of As(III). These results
Oxidation
showed that ferrihydrite had the catalytic effect on oxidation of As(III). Only a fraction of
Catalysis
As(III) was oxidized even when the system was exposed to air. The effects of ferrihydrite aging, media pH, coexistence of ions on As(III) oxidation were also investigated. The results suggest that catalytic oxidation of As(III) on ferrihydrite may play a role in geochemical cycling of arsenic in environment. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Arsenic is one of the most toxic and non-degradable contaminants in aquatic systems. It attracts increasing attentions in recent years due to widely occurring of arsenic contaminated drinking water either from natural sources or anthropogenic activities (Bundschuh et al., 2004; Nickson et al., 1998). It has been well recognized that the speciation of arsenic in natural environment strongly affects its mobility and bioavailability at wateremineral interface (Pedersen et al., 2006; Smedley and Kinniburgh, 2002). In aquatic environment
arsenic exists as inorganic As(III) and As(V) species predominantly, depending on the redox conditions. In reducing environment the predominant form of arsenic is As(III) while As(V) is more prevalent in oxidizing environment (Smedley and Kinniburgh, 2002). Arsenic is often strongly associated with naturally occurring minerals such as iron oxides like ferrihydrite, goethite, magnetite and so on (Jay et al., 2005; Karim, 2000). It has been found that As(III) is more mobile than As(V) due to its weaker affinity to soils and sediments (Smedley and Kinniburgh, 2002; Tufano and Fendorf, 2008). Hence the factors influencing the transformation between
* Corresponding authors. Tel./fax: þ86 24 8397 0503. E-mail address:
[email protected] (Y. Jia). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.051
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As(III) and As(V) will inevitably affect the fate and mobilization of arsenic in the natural environment. As(III) can be oxidized by dissolved O2 in the absence of other materials, but the oxidation rate is usually slow (Tallman and Shaikh, 1980). One of the most important abiotic pathways for the oxidation of As(III) in natural environment is by some minerals such as iron and manganese minerals (Amirbahman et al., 2006; De Vitre et al., 1991). Ferrihydrite is a poorly ordered hydrous iron oxide commonly present in low-temperature geochemical processes. It is one of the most important minerals in the sequestration of arsenic through adsorption and coprecipitation (Jang et al., 2006; Jia and Demopoulos, 2005; Michel et al., 2007). For example, reduction of adsorbed As(V) on ferrihydrite by microorganisms and subsequent desorption of less strongly bound As(III) was one of the major reasons for high concentration of arsenic in groundwater (Weber et al., 2010). There have been extensive studies on arsenic reduction and oxidation by bacteria in aquatic environment, while the works on the abiotic oxidation of arsenic in the presence of ferrihydrite is lacking (Bhandari et al., 2011; Greenleaf et al., 2003; Masue-Slowey et al., 2011). The views regarding whether As(III) can be oxidized on ferrihydrite and the role of ferrihydrite (catalyst or oxidant) are conflicting. Some researchers observed As(III) oxidation on goethite and amorphous iron oxides, while others found that As(III) adsorbed on iron oxides was stable and no oxidation occurred (Greenleaf et al., 2003; Jang and Dempsey, 2008; Oscarson et al., 1981; Sun and Doner, 1998). According to ATR-FTIR spectroscopy analysis, no As(III) oxidation on ferrihydrite was observed prior to H2O2 addition and subsequent decomposition of H2O2 on the ferrihydrite surface caused the catalyzed As(III) oxidation (Voegelin and Hug, 2003). In recent report, adsorption of As(III) on ferrihydrite was stable during 24 h, but converted to As(V) immediately after exposing to visible light (Bhandari et al., 2011). The XANES spectrum of As(III)-sorbed ferrihydrite sample prepared under oxic conditions showed about 15% As(V), indicating that As(III) partly oxidized due to Fenton reactions during the sorption experiment (Ona-Nguema et al., 2005). In their subsequent study, As(III) oxidized upon sorption onto ferrihydrite only after addition of Fe(II) and this confirmed that Fe(II) was able to catalyze As(III) oxidation in the presence of dissolved O2 (OnaNguema et al., 2010). Different conclusions were drawn in other studies that As(III) was oxidized upon adsorption on ferrihydrite. However, there is no agreement on the role of ferrihydrite in the oxidation reaction. In one scenario, oxidation of As(III) is observed with the reduction of ferrihydrite, which acting as the oxidant. For example, in a ferrihydrite column experiment, nearly all the As(III) in the aqueous phase was converted to As(V) (Greenleaf et al., 2003). Although ferrihydrite was thought as the oxidant in this reaction, no Fe(II) was detected. In another scenario, oxidation of As(III) on ferrihydrite was observed during coadsorption with As(V) and ferrihydrite was suggested acting as the catalyst and dissolved O2 as the electron accepter (Jang and Dempsey, 2008). It is necessary to further study the oxidation of As(III) on ferrihydrite especially at low arsenic concentration. The objectives of the present work are: (1) to study the heterogeneous oxidation of As(III) in contacting with ferrihydrite in oxic environment;
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(2) to assess the effects of Fe/As ratio, ferrihydrite aging, pH, coexisting inorganic ions and carboxylates on oxidation of As(III).
2.
Materials and methods
2.1.
Materials
All the chemicals used in this study were of analytical reagent grade. As(III) and As(V) stock solutions were prepared by dissolving Na3AsO3 and Na3AsO4 respectively in distilled water. All volumetric flasks and vessels were cleaned by soaking in 10% HNO3 for at least 24 h and rinsed several times with distilled water.
2.2.
As(III) adsorption and oxidation on ferrihydrite
Ferrihydrite used in adsorption experiments was synthesized according to the method with slight modification (Schwertmann and Cornell, 2000). Briefly, FeCl3 solution was mixed with NaOH solution under vigorous stirring until the pH was stabilized at 7.5. Stirring for another 2 h, the resulting suspensions were dosed volumetrically to achieve a known concentration of iron. As(III) oxidation experiments were carried out by contacting As(III) solution with ferrihydrite suspensions in batch reactors at 20 C, with the background electrolyte of 0.02 mol/L NaCl. Briefly, 0.1 mL of 1 g/L As(III) stock solution was added to 50 mL ferrihydrite suspension and the initial concentration of 2 mg/L As(III) was obtained. The vessels were covered with aluminum foil to avoid possible photochemical effects on the oxidation of As(III). The pH of the suspension was adjusted to target value and maintained constant using 0.01 mol/L HCl and NaOH solutions. At the time of 0, 0.2, 1, 5, 24, 48, 96, 192 h after adsorption test started, aliquot of each suspension was collected and centrifuged at 4000 rpm for 10 min. The supernatant was filtered through 0.22 mm membrane for the analysis of As(III) and As(T) in aqueous phase ([As(III)]aq and [As(T)]aq). At the same time, another aliquot of each suspension was dissolved in 0.6 mol/L HCl solution to determine the concentration of total As(III) and As(T) ([As(III)]tot and [As(T)]tot) in the whole ferrihydrite suspensions. Then, the concentrations of adsorbed arsenic ([As(III)]ad and [As(V)]ad) on ferrihydrite and aqueous As(V) ([As(V)]aq) were calculated by difference according to following equations: ½AsðIIIÞad ¼ ½AsðIIIÞtot ½AsðIIIÞaq
(1)
½AsðVÞaq ¼ ½AsðTÞaq ½AsðIIIÞaq
(2)
½AsðVÞad ¼ ½AsðTÞtot ½AsðTÞaq ½AsðIIIÞad
(3)
One set of solid As(V) concentration data (Fe/As ¼ 40 As(III)ferrihydrite reaction system) were also determined directly using modified molybdenum-blue method (Johnson, 1971; Jang and Dempsey, 2008) and the data agreed reasonably well with indirect method by Equation (3).
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2.3.
Acid-base potentiometric titrations
Shifts of pHpzc or pHpznpc of minerals with increasing ion concentration is evidence for strong specific ion adsorption and inner-sphere surface complex formation (Stumm and Morgan, 1996). The pHpznpc of the synthesized ferrihydrite was determined by acid-base potentiometric titrations, according to the method described previously (Shi et al., 2008). Acid-base potentiometric titrations were performed using a pH electrode (EUTECH, America) that had been calibrated with three buffers (pH 4.01, 6.86 and 9.18). 5 mmol/L HCl and NaOH solutions were used as titrants. Titrations were carried out in 50 mL 5.4 mmol/L ferrihydrite suspensions under N2 flow protection at 20 C, with the background electrolyte of 0.02 mol/L NaCl. The effect of As(III) or As(V) adsorption on pHpznpc of ferrihydrite was measured by titrating the preloaded ferrihydrite. The pre-loaded ferrihydrite was prepared by equilibrating ferrihydrite in 2.7 mmol/L As(III) solution for 1 h and 192 h and in 2.7 mmol/L As(V) for 1 h. For each titration point, when the pH drift was less than 0.01 per minute, the reaction was considered to reach equilibrium.
2.4.
Spectroscopic characterization
After contacting ferrihydrite with As(III) solution (Fe/As ¼ 200) for 1 h, 48 h and 192 h, the solid sample was separated by filtration, freeze dried and preserved in a nitrogen atmosphere. Arsenic K-edge XANES spectra were collected on beamline U7C (XAFS station) at the National Synchrotron Radiation Laboratory (NSRL) of China using Si(111) doublecrystal monochromator. The electron storage ring was operated with the energy of 0.8 GeV and a current of 90e230 mA. Data was acquired in transmission mode. XANES spectra for an As(III)-sorbed ferrihydrite sample were repeatedly collected over a duration of 30 min, and no evidence of As(III) oxidation during beam exposure was observed. Linear leastsquares fitting of XANES data was used to quantitatively determine the speciation of arsenic. The method is based on the assumption that the experimental spectrum can be modeled as the sum of one or two arctangent functions and a number of Gaussian functions (Jensen and Bond, 2002). The relative concentration of each arsenic species was determined
from the area under the respective Gaussian peak relative to the total area under the several Gaussian peaks. The error for the estimation of the percentage of each arsenic group is 5%.
3.
Results and discussion
3.1.
As(III) oxidation on ferrihydrite
XANES spectrum is very sensitive to the oxidation state of adsorbing atom. Arsenic K-edge XANES analysis can give clear evidence if oxidation of As(III) occurred on surface of ferrihydrite (Ona-Nguema et al., 2010; Sun and Doner, 1998). The XANES spectra of ferrihydrite-As(III) samples at different contacting time are compared with those of reference compounds in Fig. 1. The absorption edge of sodium arsenite (i.e. As(III)) and sodium arsenate (i.e. As(V)) was located at 11874.0 eV and 11877.9 eV respectively, with the difference of w4 eV. The absorption edge of ferrihydrite adsorbed arsenate (HFO-As(V)) and arsenite under the protection of N2 (HFOAs(III)eN2) at low Fe/As ratio (Fe/As ¼ 5) was at the same position to that of sodium arsenate and sodium arsenite respectively, indicating that no redox reaction occurred. The spectra of As(III)-ferrihydrite (Fe/As ¼ 200) at different contacting time (i.e. 1 h, 48 h and 192 h) showed the evidence of gradual As(III) oxidation on ferrihydrite (Fig. 1). The XANES spectrum of 1 h HFO-As(III) shows a well resolved absorption peak at similar position to that of sodium arsenite and adsorbed As(III) on ferrihydrite under N2 protection, suggesting that As(III) was the principal arsenic species on ferrihydrite. After raw data fitting peaks step 69%
192 h
Normalized absorption
The influences of Fe/As ratio, aging of ferrihydrite, pH, coexisting inorganic ions and organic carboxylates were studied respectively. Experiments were performed in the presence and absence of oxygen. The latter set of experiment was conducted inside an anaerobic chamber (Coy Laboratory Products) in a N2 atmosphere with 4% H2. The concentrations of As(III) and As(T) were measured using hydride-generation atomic fluorescence spectrophotometer (AFS-2202E, Haiguang Corp., Beijing) with the detection limit of 0.01 mg/L and duplication analysis agreed within 5%. The concentration of Fe(T) in ferrihydrite suspensions was determined using a flame atomic absorption spectrophotometer (AA240, Varian) with detection limit of 0.05 mg/L and duplication analysis agreed within 7%. The concentration of Fe(II) was determined by light absorbance measurement at 510 nm after complexing with 1,10-phenanthroline using a diode array spectrophotometer (Shimadzu UV-2550).
31%
77% 48 h 23% 95%
1h
5%
HFO-As(III)N Na AsO HFO-As(V) Na AsO 11860
11870
11880
11890
Energy (eV)
Fig. 1 e XANES spectra of ferrihydrite-As(III) samples after different contacting time (pH [ 7.0, Fe/As [ 200, [As(III)]ini [ 2 mg/L) together with reference compounds: sodium arsenate, sodium arsenite, arsenate adsorbed ferrihydrite (HFO-As(V)), arsenite adsorbed ferrihydrite under N2 protection (HFO-As(III)eN2).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 9 6 e6 5 0 4
contacting ferrihydrite with As(III) solution for 48 h, a shoulder emerges at the position similar to that of sodium arsenate and adsorbed As(V) on ferrihydrite. This indicates that a part of As(III) has been oxidized to As(V) on ferrihydrite. Peak deconvolution and curve fitting show that the fraction of As(III) and As(V) in the solid is 77% and 23% respectively. After 192 h of retention, the fraction of As(III) in solid decreased to 69%, while that of As(V) increased to 31%, agreeing reasonably well with wet chemistry analysis (i.e. 36% of As(V) in solid). pHpznpc of solid adsorbent may shift toward higher pH by specifically adsorbing anions (Stumm and Morgan, 1996). Oxidation of As(III) to As(V) on ferrihydrite leads to variation of surface charge of ferrihydrite and pHpznpc of ferrihydrite due to generation of anionic species. pHpznpc of ferrihydrite was determined by acid-base potentiometric titration. Fig. 2 shows the acid-base titration curves of ferrihydrite before and after adsorption of As(III) and As(V). A small shift of pHpznpc was observed for ferrihydrite after contacting with As(III) solution for 1 h at Fe/As ¼ 200. After 192 h of contact, the shift of pHpznpc increased to similar position to that of As(V) adsorbed ferrihydrite. It was reported that the surface charge and pHpznpc of ferrihydrite changed little after adsorption of As(III) via outer and inner complexion (Goldberg and Johnston, 2001). The significant shift of pHpznpc after contacting As(III) with ferrihydrite indicated that adsorbed As(III) was oxidized to As(V) which was still associated with the ferrihydrite surface. Similar shift of pHpznpc was also observed after As(III) oxidation on Mn-substituted iron oxyhydroxides surface (Lakshmipathiraj et al., 2006).
3.2.
Effect of Fe/As ratio
The concentration of As(III) and As(V) in aqueous and solid phase as a function of time at three initial Fe/As ratios is showed in Fig. 3. For the Fe/As ¼ 200 system, As(III) was adsorbed quickly on ferrihydrite. The concentration of As(III) remaining in aqueous phase decreased from 2 mg/L to less than 0.01 mg/L within 15 min, indicating almost complete removal of As(III) from solution. At this time, the dominant arsenic species in solid phase existed as As(III). Thereafter, the concentration of As(III) in solid phase decreased gradually
pH
11
0.0004
0.0002
0.0000
-0.0002
-0.0004
11
10
10
9
9
8
8
7
HFO HFO-As(III)-1h HFO-As(III)-192h HFO-As(V)
6 5
0.0004
0.0002
0.0000
-0.0002
while the concentration of As(V) increased simultaneously. The concentration of As(V) reached maximum of 0.7 mg/L in w96 h. The concentration of As(V) in aqueous phase was nearly undetectable in this process. Fe(II) was not detectable in aqueous or solid phase. The results of Fe/As ¼ 200 reaction system suggested that almost all As(III) was adsorbed on ferrihydrite in a quite short period and then converted to As(V) gradually on the surface of ferrihydrite with a moderate reaction rate. The produced As(V) was only detectable in solid phase and did not released to the aqueous phase. Apparently the absence of aqueous As(V) cannot not be indicative of no occurrence of As(III) oxidation on ferrihydrite, since the generated As(V) can be completely retained in the solid phase. Fe(II) was not generated during oxidation of As(III) on ferrihydrite. This indicates that dissolved O2 was the oxidant in As(III) / As(V) reaction. This was confirmed by the test under strictly anoxic condition. In anaerobic chamber filled with N2 and H2, As(III) was stable after contacting with ferrihydrite. As(V) was not detectable in both solid and aqueous phase in this process (data not shown). The results suggest that ferrihydrite has played a role of catalyst rather than oxidant in As(III) / As(V) oxidation reaction. At Fe/As ¼ 40 or 8, the sequestration of As(III) was less than that at Fe/As ¼ 200 and the final concentration of As(III) in aqueous phase was 0.04 or 0.65 mg/L. It was apparent that the scavenged As(III) on ferrihydrite increased with the applied Fe/ As ratio. As(V) was also detected in solid phase of lower Fe/As ratio systems, but the concentration decreased in comparison with the Fe/As ¼ 200 system (0.7, 0.45, 0.17 mg/L for Fe/As ¼ 200, 40, 8 respectively). As(V) in aqueous phase and Fe(II) in both aqueous and solid phases were not detectable. The results suggest that the oxidation degree of adsorbed As(III) dropped significantly with decreasing Fe/As ratio, probably due to reduced number of catalytic surface sites at lower Fe/As ratios. The above results suggest that As(III) oxidation on ferrihydrite may not occur at short contacting time or low Fe/As ratios. In this study, apparent oxidation of As(III) on ferrihydrite was observed at relatively high Fe/As molar ratio and reasonable reaction time. No oxidation of As(III) was observed on ferrihydrite as determined by using XANES or ATR-FTIR analysis in some previous studies (Bhandari et al., 2011; Voegelin and Hug, 2003). This may be attributed to the short retention time (45 min w24 h). As can be seen from Fig. 1 of this work, As(III) was adsorbed first by ferrihydrite and clearly visible As(III) oxidation occurred after 24 h. In other studies, low Fe/As molar ratio (i.e. 8.3 and 10) was probably the reason for the negligible amount of As(III) oxidation on ferrihydrite (Oscarson et al., 1981; Jang and Dempsey, 2008).
7
3.3.
Effect of ferrihydrite aging
6 5 4
4
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-0.0004
Cacid- Cbase Fig. 2 e pHpznpc shift of HFO with adsorbed As(III) and As(V) at Fe/As [ 200 ([As(III)]ini [ 2 mg/L).
Fig. 4 shows the effect of ferrihydrite aging on As(III) adsorption and oxidation in ferrihydrite suspensions. The influence of ferrihydrite aging was negligible on As(III) adsorption, but significant on As(III) oxidation. The concentration of aqueous As(III) was similar for ferrihydrite aged 3 d, 15 d or 30 d (w0.003 mg/L). In solid phase, the concentration of As(III) after adsorption for 192 h decreased from 1.52 mg/L on the ferrihydrite aged for 3 d to 1.24 mg/L on that aged for 30 d. At the
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 9 6 e6 5 0 4
[As(III)]aq
As (mg/L)
2.0
[As(III)]ad 2.0
Fe/As 200
1.5
1.5
1.0
1.0
0.5
0.5
[As(V)]aq
[As(V)]ad
Fe/As 40
[As(V)]ad Fe/As 8
2.0
1.6
1.2
0.8 0.04
0.04
0.02
0.02
0.00
0.00 0.1 1 10
100
200
0.4
0.0 0.1 1 10
100
200
0.1 1 10
100
200
Time (h) Fig. 3 e As(III) adsorption and oxidation on HFO at Fe/As [ 8, 40 and 200 ([pH [ 7.0, As(III)]ini [ 2 mg/L, open pentagons with cross represent direct measurement of As(V) concentrations using molybdenum-blue method).
ferrihydrite crystallites (Michel et al., 2007). Aging of freshly precipitated amorphous ferrihydrite will promote crystallization process and increase the size of ferrihydrite crystallites. This leads to the increase of active surface sites which are responsible for the enhanced As(III) oxidation.
same time, the final As(V) concentration at 192 h increased from 0.47 mg/L to 0.75 mg/L. The oxidation degree of As(III) increased with the aging time of ferrihydrite. As(V) in aqueous phase and Fe(II) in both aqueous and solid phases were not detectable in all three systems. It was also found in previous work that aging treatment enhanced As(III) oxidation on clays (Lin and Puls, 2000). The authors attributed the result to the variation of catalytic capabilities of the impurities like iron oxide in clays during its aging process. In the experiment of As(III) adsorption on montmorillonites intercalated by iron(III) nanoparticles, higher population of adjacent apexes of edge-sharing [FeO6] units and accordingly unsaturated sites were proposed to be the primary reasons for the remarkable As(III) oxidation (Izumi et al., 2005). [FeO4] units were found to decrease while [FeO6] units increased with increasing particle size of
[As(III)]aq
As (mg/L)
2.0
aging 3 day
2.0
3.4.
Effect of pH
The effect of pH on As(III) adsorption and oxidation was considered at pH 4.0, 7.0 and 10.0 (Fig. 5). The concentration of As(III) in aqueous phase decreased from 2 mg/L to 0.01 mg/L (at pH 4.0) or w0.003 mg/L (at pH 7.0 and 10.0) indicating almost all As(III) was removed from solution under three pHs. The media pH showed a significant influence on As(III) oxidation. As(V) was undetectable in aqueous phase at pH 4.0 and 7.0, but was present at pH 10.0 (w0.005 mg/L). In solid phase, there was also
[As(III)]ad
[As(V)]ad
aging 15 day
2.0
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.04
0.04
0.04
0.02
0.02
0.02
0.00
0.00 0.1 1 10
100
200
0.1 1 10
100
aging 30 day
0.00 200 0.1 1 10
100
200
Time (h) Fig. 4 e As(III) adsorption and oxidation on HFO aging for 3, 15 and 30 day (pH [ 7.0, Fe/As [ 200, [As(III)]ini [ 2 mg/L).
6501
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 9 6 e6 5 0 4
[As(III)]aq
[As(III)]ad
pH 4.0
2.0
[As(V)]aq pH 7.0
2.0
[As(V)]ad 2.0
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.01
0.01
0.01
As (mg/L)
1.5
0.00
0.00 0.1 1 10
100
200
pH 10.0
0.00 0.1 1 10
100
200
0.1 1 10
100
200
Time (h) Fig. 5 e As(III) adsorption and oxidation on HFO at pH 4.0, 7.0, and 10.0 respectively (Fe/As [ 200, [As(III)]ini [ 2 mg/L).
a notable difference in the distribution of As(III) and As(V) at lower and higher pH. The concentration of As(V) in solid phase was much higher at pH 4.0 and 7.0 (0.75 mg/L) than that at pH 10.0 (0.42 mg/L). Apparently As(III) oxidation in solid phase was promoted at lower pH. It was reported that As(III) was readily oxidized in aerobic solutions at pH > 9.23 (the pK1a of As(III)) (Pettine et al., 1999). The observed As(V) in aqueous phase at pH 10.0 was an indicative of homogenous oxidation of As(III) at basic pH. At acidic and neutral pH the oxidation degree of As(III) increased due to the heterogeneous oxidation on ferrihydrite. It was also observed that more As(III) was oxidized on goethite at acidic pH (Sun and Doner, 1998). However, in a column study As(III) was oxidized by ferrihydrite to a larger degree at slightly basic pH according to the concentration of aqueous As(III) and As(V) in the effluent (Greenleaf et al., 2003). In this experiment the stationary phase was porous polymeric particles within which submicron hydrated Fe(III) oxide particles had been irreversibly dispersed. The variation of As(III) and As(V) concentration in effluent is probably due to the difference of their adsorption capacity on ferrihydrite rather than the As(III) oxidation degree at different pH. More As(V) is adsorbed than As(III) at acidic pH, whereas at slightly alkaline pH As(III) is adsorbed to a larger degree than As(V) (Dixit and Hering, 2003). Hence the generated As(V) in column was less easily released to the aqueous phase at acidic pH, and the elution with more As(V) was investigated at basic pH.
3.5.
Effect of coexisting ions
The effect of coexisting inorganic ions on oxidation of As(III) was studied in the presence of 0.02 mol/L NaCl, CaCl2 and Na2SO4 solutions, respectively (Fig. 6). Ionic strength and the type of coexisting ions apparently influenced the adsorption and oxidation of As(III) on ferrihydrite. The concentration of residual As(III) in aqueous phase for different additives followed the order: Na2SO4 > CaCl2 > NaCl, i.e. 0.05, 0.04 and 0.003 mol/L respectively. In solid phase, the concentration of As(V) followed the order: NaCl w CaCl2 > Na2SO4, i.e. 0.75, 0.67
and 0.45 mol/L respectively. The ionic strength of NaCl system was much lower than that of Na2SO4 and CaCl2 systems. The higher ionic strength may be responsible for the lower degree of As(III) adsorption in the Na2SO4 and CaCl2 systems compared to NaCl system. It was reported that As(III) formed both of inner-sphere and outer-sphere complexes on ferrihydrite (Goldberg and Johnston, 2001). High ionic strength usually shows negative impact on outer-sphere complexation (Stumm and Morgan, 1996). In higher ionic strength system, i.e. Na2SO4 and CaCl2 systems, outer-sphere complexation of As(III) was suppressed in a large degree. Hence, the overall adsorption of As(III) was decreased in Na2SO4 and CaCl2 systems compared to NaCl media. Adsorption of As(III) was decreased in Na2SO4 system more than that in CaCl2 system, because sulfate suppressed adsorption of As(III) on iron oxide more greatly than chloride (Ciardelli et al., 2008). Ionic strength showed little effect on the As(III) oxidation, which can be seen from Fig. 6 (NaCl vs CaCl2). Chloride formed outersphere complex and sulfate formed both of outer-sphere and inner-sphere complex on iron oxyhydroxide (Persson and Lo¨vgren, 1996). As(III) oxidation was appreciably suppressed by sulfate (Fig. 6). This suggests that inner-sphere complex of sulfate affected formation of inner-sphere complex of As(III) on ferrihydrite. This indicated that inner-sphere complexation of As(III) play an important role in As(III) oxidation on ferrihydrite. Carboxylates were introduced to analyze the effect of surface complexes of As(III) on its oxidation on ferrihydrite. Ferrihydrite was pre-equilibrated with low concentration (0.2 mmol/L) of monocarboxylate ðC2 H3 O 2 Þ and dicarboxylate ðC2 O 4 Þ respectively for 2 h before contacting with As(III). The sorption of As(III) was not influenced by low concentration of coexisting Na2C2O4 and NaC2H3O2. In aqueous phase, concentration of As(III) was similar in systems with and without carboxylates. In solid phase, the concentration of As(V) was the lowest in Na2C2O4 system (0.43 mg/L). In NaC2H3O2 and NaCl system, the concentration of As(V) was similar (w0.75 mg/L) and much higher than that in Na2C2O4 system. This indicates that dicarboxylate significantly
6502
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 4 9 6 e6 5 0 4
[As(III)]aq 2.0
[As(III)]ad
2.0
NaCl
[As(V)]ad 2.0
CaCl2
Na2SO4
1.5 1.5
1.5
1.0
1.0
0.5
0.5
As (mg/L)
1.0 0.5
0.01
0.00
0.0 0.1 1 10
100
200
0.0 0.1 1 10
100
200
0.1 1 10
100
200
Time (h) Fig. 6 e As(III) adsorption and oxidation on HFO in presence of NaCl, CaCl2 and Na2SO4 (pH [ 7.0, Fe/As [ 200, [As(III)]ini [ 2 mg/L).
decreased oxidation of As(III) on ferrihydrite while monocarboxylate showed negligible effect. Fe(II) was not detectable in both aqueous and solid phases of all three systems, indicating that Fe(III) in ferrihydrite was not reduced by low concentration of mono- and dicarboxilate. Low molecular weight polyhydroxycarboxylates impacted the structure of precipitating ferrihydrite in the presence of citrate. As a consequence, the surface stability of ferrihydrite might be modified by sorbed carboxylates to such a degree that its intrinsic redox, acid-base, or adsorption properties are different from those of pure ferrihydrite (Mikutta et al., 2010). On the basis of model calculations, it indicated that the intrinsic affinity constants of organic acids to iron oxides increased with the number of carboxylic groups (Filius et al., 1997). The stronger affinity of dicarboxylate to ferrihydrite than monocarboxylates was responsible for the lower As(III) oxidization in Na2C2O4 system.
3.6.
As(III) oxidation mechanism
Ferrihydrite played a role of catalyst for As(III) / As(V) reaction and the dissolved oxygen functioned as the oxidant. It was also reported that iron (hydro)oxides acted as catalyst for the oxidation of organic compounds and inorganic ions (Anotai et al., 2009; Park and Dempsey, 2005; Pham et al., 2009; Zhang et al., 2009). The catalytic oxidation reaction of As(III) by ferrihydrite involved an initial step of As(III) adsorption on ferrihydrite followed by an As(III) / As(V) conversion step. Partial conversion of the adsorbed As(III) to As(V) indicates that not all of the complexation sites are active to catalyze As(III) / As(V) reaction, e.g. all of the added As(III) was adsorbed at Fe/As ¼ 200 but only 36% of As(III) was converted to As(V). Although it was proposed that the formation of surface complexation is necessary for As(III) oxidation (Jang and Dempsey, 2008), it is unclear about what types of surface site and what kinds of As(III)-ferrihydrite complexation play important roles. It was reported that the active sites for As(III)
oxidation were located only in a six-membered iron octahedral ring on ferrihydrite surface (Auffan et al., 2008). Arsenite is adsorbed by ferrihydrite via both inner-sphere and outersphere complexation (Goldberg and Johnston, 2001). More inner-sphere complexes form at acidic neutral pH compared to basic pH (Sverjensky and Fukushi, 2006). Inner-sphere complexation can be partly suppressed by competing anions, while outer-sphere complexation is negatively affected by ionic strength. The observation of higher oxidation degree at pH 4 and 7 compared to pH 10 (Fig. 5) may suggest that inner-sphere complexation is responsible for As(III) / As(V) conversion. This hypothesis is supported by the observation that specific adsorbing anions rather than ionic strength suppressed As(III) oxidation (Figs. 6 and 7). Inner-sphere complexes of As(III) on ferrihydrite are usually formed via bidentate binuclear interaction mode (Manning et al., 1998). After reaction with dissolved oxygen, As(III) surface complexes are converted to As(V) surface complexes which interact with ferrihydrite via bidentate binuclear mode as well. The oxidation reaction can be described schematically by the following equation: hFeðOÞ2 AsðOHÞ þO2 þ H2 O /hFeðOÞ2 AsðOÞðOHÞ þ2OH
(4)
Where hFe represents the surface of ferrihydrite.
4.
Implications
The oxidation state of arsenic controls its fate and biogeochemical cycling in natural environment. This study observed that As(III) converted to As(V) through the catalytic oxidation by ferrihydrite in oxic environment at low surface coverage. In previous studies, the abiotic oxidation of As(III) in aquifer sediments was largely attributed to manganese oxide (Amirbahman et al., 2006). This study observed another pathway for abiotic oxidation of As(III) in aquifers under oxic conditions. Although oxidation of As(III) on ferrihydrite was
6503
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[As(III)]aq NaCl
As (mg/L)
2.0
2.0
[As(III)]ad
[As(V)]ad
NaC2H3O2
2.0
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.01
0.01
0.01
0.00
0.00 0.1 1 10
100
200
0.1 1 10
100
Na2C2O4
0.00 200 0.1 1 10
100
200
Time (h) Fig. 7 e The effect of coexisting carboxylates on As(III) adsorption and oxidation on HFO (pH [ 7.0, Fe/As [ 200, [As(III)]ini [ 2 mg/L).
slower than that on manganese oxide, iron oxyhydroxides are abundant minerals and occur in a wide range of redox potentials than that for manganese oxide (Nickson et al., 1998; Pedersen et al., 2006; Smedley and Kinniburgh, 2002). Therefore, catalytic oxidation of As(III) on ferrihydrite may play an important role in As(III) transformation in aquatic systems.
5.
Conclusions
In this work, adsorption and oxidation of As(III) on ferrihydrite was studied by analysis of dissolved and adsorbed As(III) and As(V) quantitatively and qualitatively. The partial catalytic oxidation of adsorbed As(III) on ferrihydrite was observed under oxic condition. The major findings are: (1) As(III) was oxidized to As(V) in contacting with ferrihydrite in the presence of oxygen, in which ferrihydrite acted as the catalyst. The variation of the oxidation states of As(III) on ferrihydrite was confirmed by XANES spectra and shift of pHpznpc for As(III)-adsorbed samples. (2) The applied Fe/As ratio, aging of ferrihydrite, pHs, coexisting ions had significant influence on As(III) catalytic oxidation by ferrihydrite. Due to these factors determined the distribution of As(III) surface complexes on ferrihydrite, this indicated that surface complexion of As(III) have a dominant role in its catalytic oxidation reaction.
Acknowledgment The authors thank Ministry of Science and Technology of China through the National Basic Research Program (2009CB426301), the National Natural Science Foundation of China (40925011, 40803032) and Chinese Academy of Sciences (KZCX2-YW-446) for their support to this work.
references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5 e6 5 1 4
Available online at www.sciencedirect.com
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Water reclamation redesign for reducing Cryptosporidium risks at a recreational spray park using stochastic models Mark H. Weir a,*, Maria Tereza Pepe Razzolini b, Joan B. Rose a, Yoshifumi Masago c a
Department of Fisheries and Wildlife, Michigan State University, 303 Manly Miles Building, 1405 S. Harrison Rd., East Lansing, MI 48824, USA b School of Public Health, University of Sao Paul, Sao Paulo, Brazil c Department of Civil and Environmental Engineering, Tohoku University, Japan
article info
abstract
Article history:
Recreational outbreaks associated with sprayparks are well recognized, and may be partly
Received 30 November 2010
due to the engineering designs used for their water reclamation systems are problematic to
Received in revised form
control. This work is based on an outbreak of cryptosporidiosis linked to a spraypark in
22 September 2011
New York State, where it was determined, specifically that the spraypad (the main
Accepted 24 September 2011
attraction) was the primary exposure point. We first determined the likely dose the
Available online 14 October 2011
spraypad users were exposed to, then modeled the efficacy of the treatment system and used this to inform a Monte Carlo method to estimate the probability of infection and
Keywords:
illness for the users of the spraypad. The current treatment system which consists of; two
Risk assessment
holding tanks, a dual media filter and chlorine injection as well as two design change
Monte Carlo
recommendations were modeled using three independent Markov chain models. Within
Markov chains
the current treatment system design the receiving tank for the treatment train is also
Water treatment
connected with a second pipe to the spraypad used to deliver the return (treated) water,
Cryptosporidium
this return pipe is acting potentially as a bypass for the treatment train. Based on the risk assessments performed it is recommended that the bypass pipe be removed from the treatment system since in doing so the probability of infection and illness were reduced appreciably. Secondarily including an ozone contactor was shown to slightly reduce the risk further and provide a multiple barrier. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
1.1.
General overview
In recent years, an increase of waterborne outbreaks associated with treated recreational waters has been observed. In the United States from 2005 to 2006, treated water venues were associated with 58 recreational water outbreaks, with 24 outbreaks reported in 2005 and 34 in 2006, resulting in a total of 4167 cases of gastroenteritis (CDC, 2008). According to the
CDC, 33 (56.9%) of the 58 outbreaks of gastroenteritis were caused by protozoan parasites and of these 33 outbreaks 31 (93.9%) were identified as being caused by Cryptosporidium (CDC, 2008). Cryptosporidium has been recognized as the most frequent cause of recreational water-associated outbreaks of gastroenteritis, including treated and disinfected venues (CDC, 2007). Causer et al. (2006) reported an outbreak of Cryptosporidium infection at an Illinois recreational waterpark in 2001. From 13 August to 30 September a total of 358 cases were identified
* Corresponding author. Tel.: þ1 570 460 8459; fax: þ1 517 353 9807. E-mail address:
[email protected] (M.H. Weir). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.047
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(281 clinical cases and 77 laboratory-confirmed), 77.9% of them were children (those less than 18 years old). Laboratory analysis revealed the presence of Cryptosporidium oocysts in stool specimens from 77 patients, 22 of these specimens underwent genotypic testing, with ten (45.5%) positive for Cryptosporidium hominis. Water samples from the toddler/ wading pool and backwash filter system were positive for Cryptosporidium using USEPA Method 1622 but all of them were PCR negative. According to epidemiological study and environmental analysis, the evidence associated attending the waterpark with the outbreak. Later, Wheeler et al. (2007) reported an outbreak of cryptosporidiosis at a California waterpark in 2004, where more than 250 persons were ill due to a common exposure to the waterpark. Cryptosporidium oocysts were found in stool specimens from 52 persons as well as detected in water samples from the sand and backwash from the filter. No Cryptosporidium was detected from either the lake or three wells which supplied the park. According to the authors, both the epidemiological and environmental investigation supported the hypothesis that the outbreak was associated with using the waterslides in the waterpark. The work presented in this paper is based on a cryptosporidiosis outbreak which occurred at a New York State recreational spraypark which affected 746 people who reported gastrointestinal illness after attending the spraypark facility. In 2005 a formal request submitted by Joan B. Rose via the freedom of information acts, garnered information and data on the epidemiology, environmental and engineering investigation and the engineering designs of the spraypark. After this information was obtained a quantitative microbial risk assessment (QMRA) was developed in order to evaluate the probability of infection due to exposure to contaminated water with Cryptosporidium oocysts, as well as evaluate the efficacy of potential combined treatment retrofits aimed at reducing recreational risks. The goal of this work was first to evaluate this outbreak of cryptosporidiosis so as to inform the level of contamination and exposure for the recreating population at this spraypark. Second, stochastic models of the water reclamation treatment system (and design recommendations) were used as inputs to the Monte Carlo model, which was used as the means of modeling the risk of infection and illness. This risk estimate was then used to examine the effectiveness of engineering design change(s) for the recreational spray park to reduce the risk of cryptosporidiosis. Two main design changes were analyzed, first the removal of the pipe, (which was likely acting as a bypass) from the treatment system, and the combination of removing this pipe and adding an ozone contactor. These redesign options were chosen as likely responses which would ensure use of the current treatment system (as the entire system could not be bypassed) and a potential addition to the treatment train which is specifically able to inactivate oocysts (ozone contactor). The addition of chlorine was continued, however for this analysis chlorination is considered negligibly effective for Cryptosporidium oocysts.
1.2.
Cryptosporidium as an environmental Hazard
Cryptosporidium oocysts are environmentally robust, persistent in water and are resistant to common disinfectants
(Smith and Rose, 1998; WHO, 2002, 2006; Carey et al., 2004). Cryptosporidium transmission occurs by ingestion of food or water contaminated by oocysts which are relatively potent even at low doses compared to other pathogens (Smith and Rose, 1998; WHO, 2006; Haas et al., 1999). The oocysts are excreted in the feces of the infected host with levels as high as 107 oocysts per gram of feces and can be shed for as long as 50 days after the cessation of diarrhea (Chappell et al., 1996; CDC, 2007). The cryptosporidiosis symptoms are persistent diarrhea, fever, abdominal cramps, nausea, appetite loss and vomiting. Cryptosporidium oocysts in recreational water represents a public health concern, considering, its high inherent risks and persistence in the water environment (Rose et al., 2002). In recent years QMRAs have been undertaken for recreational waters (Ashbolt et al., 2010; Roser et al., 2006), however for drinking water, the probability of infection is estimated in addition the probability of illness so as to develop a conservative public health approach, by using probability of infection, thereby addressing the initial state of the disease process capturing all possible outcomes (Regli et al., 1991). Modeling the risk of infection is also beneficial when considering the more severe outcomes groups with greater susceptibility such as children can incur. Typically a conditional dose response model that estimates the probability of illness given the probability of infection, however this is not possible for Cryptosporidium (Teunis et al., 1999). Despite the inability to apply a conditional probability of illness, a probability of illness given infection can be developed using the morbidity ratio (illness given infection) recommended by the United States Environmental Protection Agency (EPA) (US EPA, 2006a). Therefore this work examines both the probability of infection and probability of illness to recreational users of the spraypark using both infection and illness for evaluation of risk reduction potential.
1.3.
Outbreak description
As discussed earlier this work is based on an outbreak at a recreational spraypark. The main attraction at the spraypark as with most recreational sites of this type is the spraypad. The spraypad is a large concrete surface, in this case in the shape of an oval. The spraypad has numerous water spray attractions that children play with and around. This location was determined the primary site of exposure by the New York State Department of Health (NYSDOH). In order to characterize the outbreak better a case control study and a cohort study were performed by the NYSDOH. The case control study was used to describe the overall outbreak; and the data from the cohort study were used to determine the attack rates to the spraypark users. In August 2005, the NYSDOH was notified of an outbreak of gastrointestinal illness associated with attending this spraypark facility. The attendance in August 2005, the time frame of the outbreak, was approximately 30,000 people. A total of 746 people reported gastrointestinal (GI) symptoms, of these; five were hospitalized, with no deaths reported. The reported symptoms were as follows: diarrhea, nausea, abdominal cramps, vomiting, fever and appetite loss. The types of cases were defined as follows:
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i. Primary e an individual with GI symptoms within 14 days of visiting the spraypark. ii. Secondary e an individual with GI symptoms and a history of visiting the spraypark. iii. Confirmed e a primary or secondary case with a positive laboratory result from a fecal sample for Cryptosporidium or other enteric pathogen. iv. Suspected e a primary or secondary case that did not have a positive lab result for Cryptosporidium or other enteric pathogen (generally due to no fecal specimen being submitted). For the case control study, case and control subjects were recruited from the Electronic Clinical Laboratory Reporting System (ECLRS). Case subjects were people who became ill within 14 days after visiting the spraypark and had confirmed cryptosporidiosis, while control subjects were those who visited the facility, within the same period, but did not develop cryptosporidiosis. A total of 79 records were entered with 34 being case subjects (43%) where all of them were exposed to the spraypad and another 45 control subjects (57%) that were exposed to the spraypad 87% of the time at the spray park (self-reported as entering the spraypad area to play or play with their families). The investigation found that the mean incubation period was 4.1days (0e12 d) and the mean duration of symptoms was 11.4 days (4e21 d). The frequency of symptoms were as follows:
Diarrhea e 100% Bloody diarrhea e 3% Nausea e 77% Vomiting e 68% Abdominal cramps e 82% Fever e 77% Appetite loss e 88%
The mean age among case subjects was 6.5 years old (range of 1e46 y/o) and 28.2 years old for control subjects (range of 0e62 y/o). Male gender frequency was 48% of case subjects and 24% among control subjects. According to this study the date where the frequency of cases peaked was on August 10th (29.4% of the cases). The attack rate was determined using the cohort study, for which recruitment was done from park attendance lists from the period of 27 July to 15 August. A total of 159 subjects, cases and controls, were interviewed by phone using a questionnaire. Within the recruited persons, 54 were considered primary cases, eight secondary cases and 97 as control cases, 18 subjects were excluded due to visiting the park more than once in the outbreak period. Table 1 summarizes the distribution of recruited subjects, cases and controls for the case control study. From the cohort study an epidemic curve and the attack rate were determined Fig. 1 and Table 2 respectively.
Table 1 e Distribution of recruited subjects for the cohort study. Subjects
Total
Mean age (range) years old
54 8 97 159
20.1 (1e75) 29.3 (2e55) 38.0 (2e81) 31.5 (1e81)
Primary cases Secondary cases Controls Total/mean
Female gender % (n) 61 50 61 60
(33) (4) (59) (96)
SG exposure % (n) 87 50 37 55
(47) (4) (36) (87)
SG e spray ground.
depiction of this can be seen in supplementary information Fig. S1). Fig. 2a is a flow chart of the Markov chain model for the current unaltered system which consists of two holding tanks. Tank-1 is meant to retain water from the spraypad (water requiring treatment) but tank-1 is also being used to hold and return treated water to the spraypad which has the unintended effect of bypassing the treatment system. Tank-2, is meant to store water after treatment, but only a fraction of the water delivered for treatment is treated by filtration and is stored in tank-2. Chlorine is added to the system immediately after filtration. The volume of water in the spraypad was estimated based on the area of the elliptical spraypad, and assumed a consistent minimum water depth of 1 inch on the surface of the spraypad. The spraypad has an overall slope from the concrete walk of 2% for sufficient flow to the two drains. The spraypark offices use a septic tank approximately 140 feet from the spraypad which serves the restrooms, however no cross contamination was found and the topography between the septic tank and the pad allows for a natural depression which would minimize the chances of an overflow reaching the pad. Also the spraypad is concrete therefore percolation from this potential source was considered negligible. Since the concrete spraypad is surrounded by a concrete walk with a six inch riser on the outer edge, the runoff from the surrounding area is also considered negligible. The source of Cryptosporidium oocysts is considered to be associated with infected individuals excreting oocysts and contaminating the water during recreation. Fecal samples from cases tested as C. hominis (a human genotype) and thus the impact on the spraypad water was deemed to be from some user(s) prior to the outbreak.
1.4. Site description and current water reclamation treatment system The treatment system receives water via 10 inch diameter PVC pipes from two drains in the center of the spraypad (basic
Fig. 1 e Epidemic curve developed from cohort study.
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Fig. 2 e Markov chain model flow charts of system configurations, a) configuration-1 which is the current unchanged system, b) configuration-2 which is the first suggested change with the removal of the bypass pipe, c) configuration-3 which is the last recommended change where the bypass pipe is removed and an O3 contactor is included as well.
2.
Modeling methods
2.1. Modeling of current and future changes to water reclamation system In order to develop a more dynamic understanding of the risks posed from a Cryptosporidium release onto the spraypad, a Markov model which describes the current treatment system was developed in the statistical programming package R. This model was then also adapted to take into account the removal of the pipe from tank-1 to the spraypad which acted as a bypass from the reclamation treatment system. A second
adaptation included an ozone contactor in addition to removing the bypass pipe. These Markov models allow for a rapid assessment of the current treatment system as well as the proposed adaptations to the treatment system. Information on the spraypark was determined using the design specifications and schematics, obtained from the freedom of information act request. The Markov model simulates treatment system operation after a fecal release to the spraypad of 2 (107) oocysts (approximately 2 g of feces). The loss rates used in the Markov model are shown in Fig. 2 aec. Parameters for current and recommended system configurations are available in supplementary information, (i.e. O3 contactor and bypass removal is shown as the third system configuration in Table S3).
2.1.1. Table 2 e Attack rate among exposed and unexposed subjects of the survey. Attack rate (%)
General 0e4 years old 5e24 years old 25e54 years old 55þ years old Unknown age
Exposed
Unexposed
51.35 53.33 65.52 45.00 Negligible 66.67
8.96 Negligible Negligible 9.68 12.50 Negligible
Markov model states
The Markov model was first built for the current system configuration (config-1). Since config-2 is the removal of the bypass pipe, therefore no additional states are needed, there are 8 states in which the oocysts could reside, during a specific time step (Dt) for both config-1 and config-2. Each of these states are shown in Table 3, where the oocysts are transported in the water through the different treatment system tanks, entrapped by the filter media (detritus), flowed back into the spraypad, lost to runoff from the spraypad or succumbed to the decay rate inherent to the oocysts’ lifecycle. It is assumed that the filter is operated continuously and properly during
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Table 3 e State numbers for treatment system all three configurations (current system configuration and removal of bypass and removal of bypass with O3 contactor included). Configurations 1 and 2 State number State 1 State 2 State 3 State 4 State 5 State 6 State 7 State 8
System location Tank 1 Filtration system Detritus from filter Tank 2 Spraypad surface Water lost to runoff Water lost to evaporation Oocyst death
Configuration 3 State number
System location
State 1 State 2 State 3 State 4 State 5 State 6 State 7
Tank 1 Filtration system Detritus from filter O3 contactor Cysts inactivated by O3 Tank 2 Spraypad surface
State 8 State 9
Water lost to runoff Water lost to evaporation Oocyst death
State 10
2.1.2.
Transition probabilities and loss rates
The term pij is the probability that oocysts in state i will move to state j during time step Dt. The overall rate at which oocysts are transported from state i is determined with the sum of the loss rates for removal from state i, denoted with li. Thus the probability that oocysts will remain in state i ( pii) within the time step (Dt), is determined with an exponential survival probability as seen in equation (1) (Nicas and Sun, 2006; Ross, 2007). pii ¼ expðli $DtÞ
(1)
In turn the probability of oocysts moving from state i to state j (equation (2)) is defined as the loss rate from state i to state j (lij) divided by the sum of the loss rates from state i (li). This allows for the determination of the unconditional probability that the oocysts in state i moved to state j during Dt, which is the complement of the probability of the oocysts in state i multiplied by the conditional probability (Nicas and Sun, 2006; Ross, 2007). pij ¼
lij $ 1 pii li
The three system configurations used are displayed in Fig. 2aec for config-1, 2 and 3 respectively. As described above the (l’s) in the figures are the loss rates of oocysts associated with moving from one state to another. The oocysts are delivered through the treatment system with a volumetric flow rate (Q), and the associated losses are experienced in the volumes (V) of the compartments, represented by states in the Markov chain. The volumetric flow rate from one state to another (i.e. tank-1 to the filtration tank) is divided by the volume of the current state which determines the loss of oocysts from the first state (tank-1) during transport to the next state (current state is symbolized by x) as shown in equation (3). Therefore in the example of moving from tank-1 to the filtration tank, the current state volume (Vx) would be that of tank-1. lx/y ¼ Qx/y= Vx
the season with a sufficient schmutzdecke developed for removal of oocysts (Schuler et al., 1991), schmutzdecke being the complex biological layer formed at the top of a filter, essentially the main treatment layer of the filter. In config-3 an O3 contactor is included in the original treatment train, along with the removal of the bypass pipe as was done in config-2. Therefore there are 10 states for config-3 (Table 3). Inactivation of the oocysts by O3 was accounted for as an additional loss mechanism removing oocysts. For all three configurations inactivation due to chlorine is considered negligible due to the resistance of Cryptosporidium to chlorine.
(2)
Therefore the loss rates are first determined in order to complete the transition probabilities. Since the decay rate of oocysts are in units of time1, all loss rates are first order as well.
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(3)
The oocysts are also lost during the treatment step(s) (filtration and ozonation), which is driven by the treatment efficiency (Elmeko, 2003; Hijnen et al., 2004; Mazounie et al., 2000), again assuming a schmutzdecke presence for filtration (Schuler et al., 1991). Equation (4) shows a generalization of oocyst losses due to treatment. This general form is used for filtration using a treatment efficiency (h), for filtration obtained from Schuler et al. (1991) and the O3 contactor inactivation efficiency for a 2 mg/L dose and contact time of 1 min (Corona-Vaszuez et al., 2002). As can be seen in equation (4), the physical transport through the treatment step was considered as well as reduction of oocysts in the water from treatment, this gave an overall reduction as the water was transported through the treatment options, represented as states in the Markov chain. In this general form a represents the current treatment system state that the oocysts reside in, they then in turn are transported and treated in state b. la/b ¼ Qa/b=V Qa/b=V $h a a b
(4)
There are three losses considered constant through the Markov models, being the decay rate as well as runoff and evaporation from the spraypad. It is estimated by the spraypark managers that there is typically a 5% loss of water per day from runoff as well as evaporation (first order loss rate of; 0.0000026 h1). Decay of Cryptosporidium parvum in neutral water was considered as well, typical value of 0.000363 h1 from Robertson et al. (1992) and considered constant for the simulation. The other loss rate functional forms can be seen in the supplemental information.
2.1.3.
Markov matrix
The loss rates described above allow for the determination of transition probabilities as described in equations (1) and (2). These transition probabilities are used to build the Markov matrix, which is the operational component of the Markov chain model. For config-1 and config-2 an 8 8 matrix was is for the eight states, and config-3 is a 10 10 matrix represents the ten states for this configuration. As discussed earlier pij is the probability that an oocyst in state-i transitions to state-j,
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therefore, p12 is the probability of an oocyst in state-1 transitioning to state-2, and so forth for each row. Therefore each of the rows represents each of the states and movement along the columns of the matrix represents transitioning from that state to another.
2.1.4.
Code and model verification for Markov models
Since this is a stochastic system, in this case a discrete time Markov chain is utilized to model the efficacy of the current and recommended treatment options. Two verification steps which included general code verification, which is performed in two steps, first by using an additional researcher unconnected to the project to visually inspect the code, checking for errors and a line-by-line debugging of the code. Line-by-line debugging is accomplished by evaluating each of the lines (or in the case of loops, individual loops) independently, to confirm that the intended and required result is gained from executing that line of code (or that loop). The second type of verification which is possible for this type of stochastic model is a check of conservation of mass. This was performed, where 500 oocysts are entered into each of the compartments of the model individually and the outputs from the compartments (states) are accounted for, the sum total of oocysts lost from the states equals the original amount going in. Therefore using the filter state as an example the oocysts can be lost due to entrapment as detritus, lost to decay or flow through the filter and these three loss mechanisms are the only means of oocysts being lost from the filter state. This verification test ensures that oocysts are not being generated in the states. Then a final conservation of mass verification is performed, where the entire system is executed (all states, the entire model executed) and conservation of mass is monitored, again where 500 oocyst were introduced to the systems and each of the pathways account for the loss of all 500 oocysts, therefore, mass is not created and only those loss mechanisms for the states are removing the oocysts from the individual states. Both of these verification steps were performed and passed. The external check and debugging of the code passed and was verified by the researcher external to this project. The checks for conservation of mass passed as well, where for each of the removal mechanisms of the model, the principle of conservation of mass held for individual states as well as the entire model.
2.2.
Risk assessment
QMRA is a means of determining the probability of infection due to exposure to water contaminated with Cryptosporidium oocysts. The exposure volume was defined using data from Dufour et al. (2006) as an assumed constant value of 0.108 L/ exposure. The dose response relationship for probability of infection (Pi) due to ingestion of Cryptosporidium oocysts was used as described by the exponential dose response model shown in equation (5) (Haas et al., 1996). The k parameter used in this analysis was optimized from human volunteer studies as recommended in US EPA (2006b), with an optimal value of k being 0.0907 and bounded on a 95% confidence interval of 0.0074 and 0.3044.
Pi ¼ 1 expðk$dÞ
(5)
If it is desired to estimate the dose from an observed probability of infection, equation (5) can be rewritten to solve for dose, since the k parameter is based on human feeding trials where the observed probability of infection was recorded. However, since attack rate (AR) was an estimate of the probability of illness equation (5) will not be as accurate as a dose response model specific for illness. This could be accomplished through a dose response model of probability of illness conditional on probability of infection. However for Cryptosporidium Teunis et al. (1999) in their work developing this type of conditional dose response model, it was determined that due to the decreasing trend with dose of the proportion of infected hosts who became ill such a conditional dose response model could not be developed. So as to develop a probability of illness (Pill) via the dose response model which models probability of infection, the recommended morbidity ratio (MR) of 0.50 from the US EPA (US EPA, 2006a) is multiplied by the dose response model in equation (5) (equation (6)). Then to obtain a dose estimate for the outbreak in question Pill is substituted with AR after equation (6) is rewritten to solve for dose (equation (7)). Pill ¼ ½1 expðk$doseÞ$MR
(6)
Pill 1 . k d ¼ ln MR
(7)
The Markov model was executed to simulate 3 h of treatment system operation after a bolus fecal release of 2(107) oocysts (approximately 2 g of feces) (Chappell et al., 1996; CDC, 2007). Thus the scenario being considered is 3 h of spraypad use after the fecal release. It is assumed that for a trip to the spraypark which lasts typically five to 6 h that this would give a good depiction of how much the spraypad is used. The Monte Carlo method models the risks to the users of the spraypad after a fecal release, using equation (5) to determine the risk of infection (Pi) from oocyst exposure and equation (6) to estimate the risk of illness (Pill). The Monte Carlo method is a means of developing a distribution of model results after repeated random sampling from probability distributions which describe uncertain variables. The numerical output of oocyst concentrations from the Markov models are used to fit probability distributions in MATLAB for the Monte Carlo model to address the user’s exposure to oocysts for the QMRA after simulating 3 h of spraypad use which includes an assumed bolus fecal release of 2 (107) Cryptosporidium oocysts. The KolmogoroveSmirnov test statistic was used to determine the goodness of fit for the distributions. Table 4 shows the probability distributions used in the Monte Carlo model, which is executed for 10,000 iterations in MATLAB and summarizes the probability distributions used in this study to estimate the probability of Cryptosporidium infection (Pi) and illness (Pill) given the morbidity ratio (US EPA, 2006a). Fitting the probability distributions around the numerical results from the Markov chain model addresses the first uncertain variable (the concentration of oocysts for each of the configurations), the second uncertain variable, the dose response parameter is described with a triangular distribution, based on the optimal value,
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Table 4 e Probability distributions and parameters for the Monte Carlo model. Uncertain parameter r unitless Cryptosporidium concentrationa Cryptosporidium concentrationa Cryptosporidium concentrationa
Scenario
Distribution
Distribution parameters
KolmogrozeSmirnoz test p-value
NA Filtration w/o bypass (current system) Filtration w/o bypass
Triangular Gamma
NA, assumed distribution 0.0061
Filtration þ O3
Gamma
0.00467 (CI95%b: 0.00195; 0.0097) Scale: 7392 Shape: 0.432 Scale: 6212 Shape: 0.442 Scale: 6110 Shape: 0.465
Gamma
0.0075 0.0055
a Data for fitting these distributions from the Markov chain models. b 95% confidence interval.
lower and upper 95th confidence intervals of k from the exponential model fit to human feeding studies (US EPA, 2006a) and the third uncertain variable is MR using a triangular distribution using the likeliest, maximum and minimum levels (US EPA, 2006b). The dose for equation (5) is determined by multiplying the concentration of oocysts sampled from the probability distributions by the volume of water ingested by spraypad users.
3.
Results
Using equation (7), the likeliest morbidity ratio of 0.5 (US EPA, 2006a) and the attack rates in Table 2, this analysis estimates that the population was exposed to an average dose of 12 oocysts (95th confidence interval of 4e149 oocysts). Rather than using the estimated risk levels as a comparison to current standards, considering they are associated with highly credible gastrointestinal illness (HCGI) the overall risk reductions were highlighted. It can be surmised however, that since attractions like these are targeted toward children a very low risk of infection or illness, given their greater susceptibility, would be considered acceptable by parents and park owners, however no known studies have highlighted what acceptable risks might be for such waterparks. Fig. 3a and d shows the Monte Carlo simulation results for Pi and Pill respectively to the population exposed to water contaminated with Cryptosporidium oocysts with the current treatment system (config-1). An overall risk to the exposed population is bimodal and skewed slightly to the higher risk levels with a low probability of experiencing an infection risk of less than 0.10 (approximately 18% chance). Infection risks equal to or greater than 0.9 did occur with a 60% probability of occurrence, for Cryptosporidium. There is also a 15% probability of incurring a probability of illness of 0e0.025, and a 60% probability of incurring a probability of illness of 0.5. This demonstrates the untenable original configuration with respect to Cryptosporidium risks with respect to the original configuration. This is attributed to the percentage of water that received no treatment via the bypass of the filter. These risk estimates do not include risks from other pathogens since risk from Cryptosporidium is being highlighted here.
When the bypass was eliminated from the water reclamation system and all the water was filtered, the overall risk levels were reduced, Fig. 3b and e for showing Pi and Pill respectively for config-2. As can be seen there is a 30% probability of experiencing an infection risk within the range of 0.00e0.01. The high risk level (>w0.9 for infection and >w0.35 for illness) was then reduced to a 44% probability of occurring. When removing the bypass and including an O3 contactor (Fig. 3c and f for Pi and Pill respectively for config-3), there was approximately a 37% probability of the risk being at or less than 0.01 for infection and 0.025 for illness and a 32% probability of experiencing a high risk of greater than 0.9 or 0.5 for Pi and Pill respectively. A sensitivity analysis is performed to determine which uncertain variable is driving the risk. In all conditions assessed, the greatest contributor to the risk for the exposed population is the concentration of oocysts (Fig. 4a and b for illness and infection respectively). The sensitivity analyses for the other configurations were essentially the same, showing the concentration of oocysts being the primary contributor to the estimated risk and associated uncertainty. Thus it can be recommended that at minimum the removal of the bypass pipe would be necessary to reduce the risks associated with waterborne disease. While inclusion of the ozone contactor reduced the risks further and to an even greater degree from the unaltered configuration, this was not as a sufficiently appreciable risk reduction from the simpler retrofit (configuration 2) and may not be justified with the higher costs from both installation and operation associated with the third configuration.
4.
Discussion
This study addressed an important public health problem, recreational waterborne disease. Swimming venues that use chlorine are now known to be at risk from Cryptosporidium and filtration or other types of disinfection including UV or ozone are needed to control the parasite. According to the CDC, recreational illnesses are on the rise. Between 2005 and 2006, 78 outbreaks were reported in 31 states, which is the largest number of outbreaks ever in a two-year period with 4500 people affected (CDC, 2008). There are over 1000 waterpark facilities in North America (city pools with waterpark features, independently-owned outdoor and
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Fig. 3 e Monte Carlo risk model results for: risk of infection for a) configuration-1, b) configuration-2 and c) configuration-3 respectively, and risk of illness for d) configuration-2, e) configuration-2 and f) configuration-3 respectively. Where the xaxis is the risk level experienced during the simulations, the primary y-axis is the frequency of observing that risk level from the 10,000 iterations performed and the secondary y-axis is the probability that each risk level was encountered during the simulation.
indoor waterpark resorts/hotels) and the attendance was reported at 78 million for 2006 (http://www.waterparks.com/ funfacts.asp; http://www.waterparks.org/otherarticles/ generalfacts.pdf). CDC recently reported on violations during inspections of 111,487 swimming venues. Immediate closure was warranted for 12% due to a lack of appropriate disinfection. Violations for the circulation and filtration systems were found to range from 24 to 38% and 35% of the waterparks specifically were found to have violations in the filtration system (CDC, 2010). These water venues and water/sprayparks in particular use large volumes of water and thus as pool codes are reexamined throughout the US, appropriate engineering
controls for pathogenic organisms which enter the system via infected swimmers will need to be implemented. It was found that use of epidemiological, environmental and engineering investigations of this particular NY spraypark outbreak could be used to develop an appropriate exposure and risk model. For this incident, this study estimated that the population was likely exposed to a small dose of Cryptosporidium oocysts resulting in risk of infection and disease. The current configuration (config-1) was compared to the potential remediation options, of removing the bypass pipe from tank-1 to the spraypad and removing the bypass pipe as well as including an O3 contactor (config-2 and config-3 respectively). Risk reductions were observed in conjunction with further improvements to the engineering design and
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of oocysts. Excretion of pathogens and release rates during swimming are not well known. Excretion estimates vary widely and it is clear that even asymptomatic infections prior to and after noticeable symptoms signify that enteric pathogens could end up in recreational waters.
5.
Fig. 4 e Sensitivity chart demonstrating the concentration of oocysts being the largest contributor to the risks of infection, and driver of uncertainty. This sensitivity chart is for one of the Monte Carlo models however, the other two showed nearly identical results.
disinfection process. For the unaltered configuration the high risk range of infection and risk of illness of 0.9e1.0 and 0.475e0.5 respectively occur with a 61% probability of occurrence, and an 18% probability of incurring a risk of infection and illness ranging from 0 to 0.10 and 0e0.05 respectively (Fig. 3a and d). The first of the recommended configurations reduced the risk of infection and illness for the same risk ranges to a probability of 45% for the high range and 30% probability of occurrence for the low range. This risk reduction was enhanced further by including the additional disinfection step for config-3, where for the same risk range, the probabilities of occurrence drop again to 32% for the high risk range and the low risk range just overcame the high with a probability of occurrence at 37%. The main treatment steps have an overall retention time that is designed to be between 1 and 2 min, a combination of increasing the ozone dose and contact time for that treatment step will increase the probability of incurring each person’s acceptable risk level (i.e. parents do not want any children to be taken ill). Should these recommendations have been implemented for this outbreak, the overall impact from the outbreak could have been reduced. This demonstrates the need to understand the risks associated with a fecal release of a number of pathogens and not just indicators like E. coli for recreational treatment systems. In the case of the spraypark the treatment system was designed to combat E. coli from a fecal release. The sensitivity analysis showed that the greatest uncertainty in estimating the risk was linked to the concentration
Conclusions
Using the QMRA framework, the level of contamination from a Cryptosporidium recreational outbreak was determined. This dose estimation was also bounded by confidence intervals to account for the uncertainty of exposure after a fecal release at the spray park. >A stochastic model was constructed to simulate the treatment system. This model was used to simulate the effectiveness of the current and recommended adaptations to the current system. The recommendations were presented in the context of a risk reduction potential by coupling the stochastic model with a Monte Carlo risk model. An overview of the treatment system layout, such as recognizing an inadvertent bypass pipe, is a simple and lower cost means of risk mitigation. And including an additional step of examining the risks to the recreating population can be enacted and serve as a quantitative overview of the treatment system. Given the high relative risks still related to this spray park and the exposure scenario, a risk communication strategy may be a simple and very cost effective means of mitigating risk to users. Something as simple as a pamphlets available for users to be warned of potential pathogen exposure, or a staff member who can be consulted with questions users may have based on the potential risks from spray park recreation. Water treatment for recreational venues such as sprayparks demands more attention to protect human health.
Acknowledgments Funding for this work is partially supported by the Center for Advancing Microbial Risk Assessment (CAMRA) under STAR grant #R83236201. The financial support of CNPq e Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (200007/2009-2) is also appreciated.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.047.
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quantitative microbial risk assessment (QMRA). Water Research 44, 4692e4703. Carey, C.M., Lee, H., Trevors, J.T., 2004. Biology, persistence and detection of Cryptosporidium parvum and Cryptosporidium hominis oocyst. Water Research 38, 818e862. Causer, L.M., Handzel, T., Welch, P., Carr, M., Culp, D., Lucht, R., Mudahar, K., Robinson, D., Neavar, E., Fenton, S., Rose, C., Craig, L. , Arrowood, M., Wahlquist, S., Xiao, L., Lee, Y.-M., Mirel, L., Levy, D. , Beach, M.J., Poquete, G., Dworkin, M.S., 2006. An outbreak of Cryptosporidium hominis infection at an Illinois recreational waterpark. Epidemiology and Infection 134, 147e156. Chappell, C.L., Okhuysen, P.C., Sterling, C.R., DuPont, H.L., 1996. Cryptosporidium parvum: intensity of infection and oocyst excretion patterns in healthy volunteers. Journal of Infectious Diseases 173, 232e236. CDC-Centers for Disease Control and Prevention, 2007. 2003e2005. Morbidity and Mortality Weekly Report Surveillance Summaries 56 (SS-07), 1e10. CDC-Centers for Disease Control and Prevention, 2008. Surveillance for waterborne disease and outbreaks associated with recreational water use and other aquatic facilityassociated health events e United States, 2005e2006. Morbidity and Mortality Weekly Report 57 (SS-09), 1e70. CDC-Centers for Disease Control and Prevention, May 21, 2010. Violations identified from routine swimming pool inspections d selected states and counties, United States, 2008. Morbidity and Mortality Weekly Report 59 (No. 19), 582e587. Corona-Vaszuez, B., Samuelson, A., Rennecker, J.L., Marinas, B.J., 2002. Inactivation of Cryptosporidium parvum oocysts with ozone and free chlorine. Water Research 36, 4053e4063. Dufour, A.P., Evans, O., Behymer, T.D., Cantu´, R., 2006. Water ingestion during swimming activities in a pool: a pilot study. Journal of Water and Health 04 (4), 425e430. Elmeko, M.B., 2003. Removal of viable and inactivated Cryptosporidium by dual- and tri-media filtration. Water Research 37, 2998e3008. Haas, C.N., Crockett, C.S., Rose, J.B., Gerba, C.P., Fazil, A.M., 1996. Assessing the risk posed by oocysts in drinking water. Journal of American Water Works Association 88 (9), 131e136. Haas, C.N., Rose, J.B., Gerba, C.P., 1999. Quantitative Microbial Risk Assessment. John Wiley and Sons, New York, NY. Hijnen, W.A.M., Schijven, J.F., Bonne´, P., Visser, A., Medema, G.J., 2004. Elimination of viruses, bacteria and protozoan oocysts by slow sand filtration. Water Science and Technology 50 (1), 147e154. Mazounie, P., Bernozeau, F., Alla, P., 2000. Removal of cryptosporidiosis by high rate contact filtration: the
performance of the prospect water filtration plant during the Sydney water crisis. Water Science and Technology 41 (7), 93e101. Nicas, M., Sun, G., 2006. An integrated model of infection risk in a health-care environment. Risk Analysis 26 (4), 1085e1096. Regli, S., Rose, J.B., Haas, C.N., Ferba, C.P., 1991. Modeling the risk from Giardia and viruses in drinking water. Journal of American Water Works Association 83 (11), 76e84. Robertson, L.J., Campbell, A.T., Smith, H.V., 1992. Survival of Cryptosporidium parvum oocysts under various environmental pressures. Applied Environmental Microbiology 58 (11), 3494e3500. Rose, J.B., Huffman, D.E., Gennacaro, A., 2002. Risk a control of waterborne cryptosporidiosis. FEMS Microbiology Review 26, 113e123. Roser, D.J., Davies, C.M., Ashbolt, N.J., Morison, P., 2006. Microbial exposure assessment of an urban recreational lake: a case study of the application of new risk-based guidelines. Water Science and Technology 54 (3), 245e252. Ross, S.M., 2007. Introduction to Probability Models. Academic Press, Burlington, MA. Schuler, P.F., Ghosh, M.M., Gopalan, P., 1991. Slow sand diatomaceous earth filtration of cysts and other particulates. Water Research 25 (8), 995e1005. Smith, H.V., Rose, J.B., 1998. Waterborne cryptosporidiosis: current status. Parasitology Today 14 (1), 14e22. Teunis, P.F., Nagelkerke, N.J., Haas, C.N., 1999. Dose-response models for infectious gastroenteritis. Risk Analysis 19, 1251e1260. US EPA Office of Water, 2006a. Economic Analysis for the Final Long Term 2 Enhanced Surface Water Treatment Rule EPA 815-R-06-001. US EPA Office of Water, 2006b. EPA 815-R-06-001. Appendices to the Economic Analysis for the Final Long Term 2 Enhanced Surface Water Treatment Rule, vol. II (H e U). Wheeler, C., Vugia, D.J., Thomas, G., Beach, M.J., Carnes, S., Maier, T., Gorman, J., Xiao, L., Arrowood, M.J., Gilliss, D., Werner, S.B., 2007. Outbreak of cryptosporidiosis at a California waterpark: employee and patron roles and the long road towards prevention. Epidemiology and Infection 135, 302e310. WHO e World Health Organization, 2002. Guidelines for drinkingwater quality. In: Addendum Microbiological Agents in Drinking-water, second ed. WHO, Geneva. WHO e World Health Organization, 2006. Guidelines for Drinkingwater Quality. First Addendum to 3rd ed. Vol. 1 e Recommendations. WHO, Geneva.
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Zebrafish larvae as a model for the evaluation of inorganic arsenic and tributyltin bioconcentration A. Lo´pez-Serrano Oliver a, J. Sanz-Landaluze a,*, R. Mun˜oz-Olivas a, J. Guinea b, C. Ca´mara a a b
Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Ciudad Universitaria, 28040 Madrid, Spain Zf BioLabs. Ronda de Valdecarrizo 41 B. 28760. Tres Cantos, Madrid, Spain
article info
abstract
Article history:
The European REACH legislation establishes the need to study the toxicity, persistence and
Received 9 March 2011
bioaccumulation of those chemicals with an exceeding production of 100 tons and/or
Received in revised form
chemicals considered PBTs substances (Persistence, Bioaccumulation and Toxicity).
23 September 2011
Currently, the OECD technical guideline 305 is the most used protocol to determine bio-
Accepted 25 September 2011
concentration factors of contaminants in aquatic environments. However, this procedure
Available online 2 October 2011
implies high cost and amount of adult fishes. Zebrafish (Danio Rerio) has been selected since this animal model has several advantageous features over other vertebrates, mainly fast
Keywords:
embryonic development and easy growth. The analytical methodology here developed has
Test OECD 305
been applied to calculate the bioconcentration factors (BCFs) of two contaminants: inor-
Bioconcentration
ganic arsenic and tributyltin (measured as arsenic and tin). The method is based on the use
Zebrafish larvae
of an ultrasonic probe assisted extraction for accelerating the sample treatment followed
Inorganic arsenic
by detection using graphite furnace atomic absorption spectrometry with Zeeman
Tributyltin
correction (ZGFAAS). Results obtained for the BCFs values are in good agreement with
Ultrasound-assisted extraction
previously reported data on freshwater aquatic organisms. In the case of arsenic, after exposing larvae to concentrations of 5 and 50 mg L1, very low BCFs were observed (between 2.2 and 9.5); while for tributyltin, the BCFs observed were within the range 840e1280 after exposure to concentrations of 0.2 and 2.0 mg L1, respectively. This study shows the use of zebrafish larvae together with the proposed analytical approach as a promising alternative to the OECD 305 test to evaluate the BCFs of classical and emergent contaminants. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Over the last couple of decades, environmental pollution has become an increasing relevant issue to our society. Evaluation of pollution comprises two different approaches: first, the determination of the damage in the environment caused by already polluted areas and the potential remediation of the harmful effects; and second, prevention of the contamination by previous evaluation of the potential impact of chemicals in
the environment. This chemical impact has been traditionally evaluated by studying the toxicity of the species. However, studying the toxicity alone is not sufficient to provide a complete environmental impact analysis. Other parameters such as ecotoxicity, mobility, persistence, bioaccumulation, and degradation have to be considered. Actually, the recently approved European regulation REACH (Registration, Evaluation and Authorisation of Chemicals) (European Commission, 2006) requires the evaluation of such parameters for those
* Corresponding author. Tel.: þ34 091 3944368; fax: þ34 091 3944329. E-mail address:
[email protected] (J. Sanz-Landaluze). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.052
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chemicals with a production exceeding 100 tons and/or those substances that are considered PBTs (Persistence, Bioaccumulation and Toxicity). Although there are different established methods to evaluate the bioaccumulation factor (ASTM E1022-94 from the American Society for Testing and Materials and OPPTS 850.1730 from US EPA), the OECD bioconcentration Test 305 (OECD, 1996) is the most commonly used. It is summarized on the REACH’s Test Methods Regulation (European Commission, 2008) as the standard method to calculate bioconcentration. Briefly, this test evaluates the accumulation in adult fish of a dissolved chemical by measuring its final concentration in both, the fish and the surrounding media after an equilibration time. This complex method is expensive and requires a large amount of adult fish (Weisbrod et al., 2007). Thus, the development of an alternative method for establishing the bioconcentration factor (BCF) of a given chemical but reducing the cost of the analysis and the amount of adult animals required would be of great interest. Taking into account that the European legislation calls for the use of non-animal alternative approaches to replace animal testing wherever possible, several frameworks have been developed in the last years. As a first approach to estimate theoretical BCFs of chemicals, computational methods as Quantitative Structure Activity Relationship (QSAR) (Meylan et al., 1999) or Baseline Models (POPs) (Dimitrov et al., 2005) can be used. Other approaches use in vitro methods such as cell-based assays using liver slices, hepatocytes, cell lines, S9 fractions, microsomes, recombinat enzymes and nuclear receptors (Weisbrod et al., 2009). Recently, a protocol using fish eggs under short-term exposure has become a substitute for the acute fish assay in the toxicity analysis of wastewater in Germany (DIN, 2001). In Europe (2010/63/EU Directive), fish embryos and larvae are legally considered to be in vitro systems until they become free-feeding larvae. As a result, they are increasingly being used as alternatives to acute fish toxicity tests (Scholz et al., 2008) and other applications (Petersen and Kristensen, 1998). Zebrafish, a small tropical fish native to the rivers of India and South Asia, is an animal of great scientific interest due to the advantageous features over other vertebrate model systems (Teraoka et al., 2003). The small size of larvae and adult zebrafish results in lower test cost. Transparent embryos allow the detection of morphologic and embryonic changes and also help to easily distinguish between dead and living embryos. A high production and fast embryonic development facilitates fast bioaccumulation kinetics (with a maximum bioaccumulation achieved in less than 72 h). The zebrafish also has a high genomic homology with humans (over 80%), which enables a significant correlation of the data obtained between the two species and, in addition, it is one of the model animals recommended by OECD Bioconcentration Test 305 (OECD, 1996). An alternative strategy for the determination of the BCFs of chemicals might therefore imply the use of fish (especially zebrafish) embryos or larvae as in vitro model (Teraoka et al., 2003; Schreiber et al., 2009). The key criteria identified for judging the reliability of alternative study were established by a workshop of experts from governments, industry, and academia (Parkerton et al., 2008): (1) clear specification of test
substances and fish species investigated, (2) analysis of test substances in both fish tissue and exposure medium, (3) no significant adverse effects on exposed test fish, and (4) a reported BCF test reflecting steady state conditions with unambiguous units. To measure the internal concentration of chemicals in fish embryos or larvae for BCF determination, highly sensitive analytical methods are required due to the extremely small sample size. Heavy metals, such as mercury, cadmium, lead, arsenic and tin are well-known pollutants that can cause evolutionary changes due to their harmful effects on living organisms. Among these, arsenic and tin are relatively toxic to the environment, mainly to aquatic organisms (Chagot et al., 1990; Prieto Garcı´a et al., 2006; Liao et al., 2008), observed high accumulation factors for both elements in several freshwater species exposed to arsenic or tin at the mg L1 (Bushong et al., 1998). Determination of those analytes in biological samples requires a previous extraction step. The most common methods applied for this purpose are: ultrasound- and microwave-assisted solvent extraction (Bermejo et al., 2004), acid solubilization and SPE/SPME (Pan and Pawliszyn, 1997; Go´mez-Ariza et al., 2000). Derivatization steps such as ethylation prior to separation/quantification are also needed for TBT analysis in many cases (Morabito et al., 2000). Finally, determination of these analytes is performed with highly sensitive detectors (ICP/MS, GFAAS, FAAS, GC/MS). One of the problems related to the use of such small samples as zebrafish larvae (wet weight of 0.4 mg) implies that the extraction techniques employed should be capable to use small quantities of extractant. In addition, clean-up and sample preparation procedures should be as simple as possible to avoid analyte loose. Quantification techniques should reach very low limits of detection since small sample volumes at very low analyte concentrations are used. The present study has been focused on developing analytical methods to evaluate the bioaccumulation factor of inorganic arsenic and tributyltin by zebrafish larvae considering that both species are stable in natural water (Go´mez-Ariza et al., 1999; Hall et al., 1999). The instrumental technique selected for determination of the total concentration of both analytes in such samples have been Zeeman corrected graphite furnace atomic absorption spectrometry (ZGFAAS) because of its high sensitivity, low sample consumption, compatibility with organic solvents, and ability to directly analyzed solid samples (Bryszewska et al., 2009).
2.
Experimental section
2.1.
Instrumentation
A PerkineElmer 4100 ZL atomic absorption spectrometer with a longitudinal Zeeman background correction, equipped with a transversely heated graphite tube atomizer (THGA) with L’vov platforms was used. The analyte concentration was calculated from the integrated absorbance of the atomic absorption signal. A volume of 20 mL was injected manually. The furnace operation was controlled using the PerkineElmer AA Winlab software, Version 4.1 SSP1. A PerkineElmer arsenic
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 1 5 e6 5 2 4
electrodeless discharge lamp (EDL) with wavelength 197.3 nm and instrument slit width 0.7 nm was used. A PerkineElmer EDL System was used to stabilize the lamp current between 349 and 351 mA. For tin, A PerkineElmer hollow cathode lamp (HCL) with wavelength 286.3 nm and instrument slit width 0.7 nm was used. A Vibra cell VCx130 ultrasonic processor (Connecticut, USA) equipped with a titanium 2-mm diameter microtip and fitted with a high-frequency generator of 130 W at 20 kHz was used for the leaching of the analytes from larvae in deionized water. Centrifugation was carried out in a centrifuge model type: FVL-2400N, Combi-Spin, Boeco (Germany).
2.2.
Reagents and standards
Analytical grade chemicals were used for all studies. Tributyltin chloride, CAS: 1461-22-9, (>97%) and Triton X-100, used as surfactant, were obtained from SigmaeAldrich (Madrid, Spain) and As2O3$H2O (99.5%), CAS: 1327-53-3, from J.T. Baker (Deventer, Holland). Glacial acetic acid was purchased from Panreac Quı´mica S.A. (Madrid, Spain); methanol was supplied by Scharlab S.L. (Barcelona, Spain), toluene was provided by Carlo Erba Reactifs-SDS (Cedex, France) and tropolone (98% purity) from Avocado (Lancashire, UK). Nitric acid was purchased from Merck (Damstadt, Germany) and purified by distillation. All solutions and samples were prepared using high-purity water with a resistivity of 18.0 MU cm obtained from a Millipore (Bedford, MA, USA) ZMFQ 23004 Milli-Q water system. The organotin chloride and arsenic oxide stock solutions containing 1000 mg L1 of tin and arsenic were prepared in pure methanol and deionized water, respectively, and stored at 4 C in the dark. Working solutions were prepared daily in deionized water with 2% nitric acid. The Pd(NO3)2 matrix modifier solution was made from a dilution of 10.00 0.03 g L1 Pd solution (Merck, Darmstadt, Germany) with water to the desired final concentration.
2.3.
Procedure for larvae exposure
Zebrafish larvae were supplied from ZF Biolabs (Madrid, Spain). The exposure solution was prepared according to the composition of fresh river water. Briefly, 16 mL of concentrated solution (containing 2.9 g of CaCl2, 17.2 g of NaCl, 0.76 g of KCl and 4.9 g of MgSO4 per litre) were diluted to 1 L with distilled water. According to OECD guidelines, conditions of this exposure solution were: 26 2 C, dissolved oxygen 60%, pH 6e8.5 (before and after renewal). To obtain the zebrafish larvae, it was necessary to develop embryos to 72 h post fecundation (hpf), the moment when the embryos hatched. Zebrafish larvae remain classified as such until another 48 h later (120 hpf) when they are regarded as proper fish, but can be consider non-feeding other 24 h (Westerfield, 2007). An appropriate larvae amount was placed into three tanks for each analyte: one for control (without the addition of the analyte) and two with different concentrations of the target analyte. The test consisted of two phases: absorption, (48 h in a contaminated exposure solution) and depuration (24 h in a clean exposure solution). About 15e25 larvae were removed from the tanks at different time (0, 2, 4, 6,
6517
21, 24, 45, 48, 50, 54 and 72 h), to determine the concentration of the analyte absorbed and accumulated. According to OECD 305 test, the loading rate of larvae at the beginning of the experiments ranged between 0.7 and 0.8 g L1 (wet weight) and the mortality of larvae was lower than 20% at the end of the test. The two nominal concentrations used to incubate the larvae for each analyte selected, are also dictated by Test OECD 305, which sets they must differ by a factor of ten, being the highest concentration at 1% of LC50 value (when detection limits allow the analytical determinations). Literature data indicates 96-h LC50 values of around 15e50 mg L1 for arsenic (Qadir-Shah et al., 2009) and 3e40 mg L1 for tributyltin (Dimitriou et al., 2003; Meador, 1997) using adult fishes and 30e50 mg L1 for tributyltin for Zebrafish larvae (Dong et al., 2006). Also, 48-h LC50 values for the zebrafish larvae were calculated using several morphological and functional endpoints and using the inverse cumulative distribution (probit) function, values of 3 mg L1 for arsenic and <60 mg L1 for tributyltin were obtained. Thus, nominal concentrations chosen to carry out these bioconcentration experiments were 50 and 5 mg L1 for arsenic and 2 and 0.2 mg L1 for tributyltin. Control (blanks) experiments were carried out in parallel to all contamination studies. Special care was taken to check that mortality rate of larvae was less than 20% (ranging between 5 and 10%), very similar to the results obtained in the control experiments. The exposure solution was changed every 24 h to fulfil the OECD 305 requirement, that nominal concentration of the chemical substance cannot fluctuate more than 20% throughout the whole experiment.
2.4.
Analytical procedure
The concentration of the target analytes was determined in two sample types as previously described: exposure solution and larvae. For each sample type and each analyte, different analytical methods were developed. Arsenic determination in the exposure solutions was carried out by adding nitric acid up to a concentration of 5% to eliminate the matrix effects. For TBT, a 25-fold preconcentration step was performed by liquideliquid extraction adding 200 mL of toluene followed by addition of 25 mL 0.5% tropolone in aqueous acetic glacial. The organic phase was then separated and finally analyzed. For the analysis of larvae, a leaching process to extract the analytes of interest was required. This step was carry out using an ultrasonic probe (USP) after addition of 15 mL of nitric acid (2.5e5%) followed by 12 mL of triton X-100 (1%) for sample homogenization or 15 mL of acetic acid (5% final concentration) for arsenic and TBT, respectively. After leaching with USP during 90 s and 55% of amplitude, methanol was added to precipitate the lipidic content of the samples whereas the analyte remained in the supernatant.
2.5.
Quality assurance
Quality assurance steps included controls, replicate analyses, surrogate recoveries (as no reference material with similar matrix was found) and calibrations. The limits of detection obtained for the whole method (MDLs) were 2.5 ng g1 in larvae and 0.15 ng mL1 in the exposure solution for TBT, and
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1.0 ng g1 in larvae and 1.5 ng mL1 in the exposure solution for arsenic. Concentrations of all target compounds in the control samples were below the MDLs. Linearity drift check and spike recovery analyses were carried out using commercial caviar eggs and proper zebrafish larvae. Analyses were carried out in triplicate showing good reproducibility (6e8% for exposure solutions and 13e20% for larvae). Recoveries averaged 95%, and all exceeded 90%. Calibrations showed good linearity (R ¼ 0.99).
2.6.
(3)
so Eq. (4) can be expressed as Cf ¼ Cf;0 ek2 t
(4)
This model has been widely employed to calculate BCFs (Gabric et al., 1990; Mortimer and Cornnell, 1993) but sometimes first-order kinetics are not suitable to fit experimental data and more complex models have to be employed (Spacie and Hamelink, 1982; Banerjee et al., 1984).
Toxicokinetics: bioconcentration factors (BCFs)
The bioconcentration factor is the most employed parameter to evaluate the accumulation capability of a contaminant by living organisms (Tsuda et al., 1998). Bioaccumulation factors accordingly to the OECD guideline 305 are calculated as the ratio between the concentration of the analyte in the larvae and the exposure solution at steady state, BCFss. Sometimes the steady state is not reached and BCF can be calculated from the ratio of k1 to k2 (BCFk), where k1 and k2 are conditional rate constants which mainly depend on the fish species used for the experiments and also on experimental factors such as temperature and pH. They can be obtained from bioaccumulation models. These models describe uptake and depuration process as a first-order kinetic (Eq. (1)) (Gobas and Zhang, 1992) dCf ¼ k1 Cw k2 Cf dt
(1)
where Cf is the concentration in fish (in ng g1), t is the exposure time (h), k1 is the first-order uptake constant (litre per kilogram dry weight per hour), Cw is the concentration of substance in water (ng/mL), and k2 is the first-order elimination rate constant (per hour). So, assuming that the initial concentration in fish is zero at t ¼ 0 and the concentration of chemical in water is constant, Eq. (2) is obtained: Cf ¼
dCf ¼ k2 Cf dt
k1 Cw 1 ek2 t k2
(2)
k1 and k2 values can be obtained if the experimental concentrations values obtained in the bioconcentration test fit to this equation. On the other hand, for the depuration phase, where Cw is assumed to be zero, the equation of first-orders kinetics may be reduced to Eq. (3):
3.
Results and discussion
3.1.
Optimization of ZGFAAS determination
The thermal furnace program applied, and the type and amount of chemical modifier were the three main parameters to be optimized in ZGFAAS for each analyte. Several common chemical modifiers, such as Pd(NO3)2, Mg(NO3)2 and NH4H2PO4, were tested individually or in combination (Daminelli et al., 1998). Injection of 5 mL of 2 g L1 Pd(NO3)2 was the best option for both analytes. Optimized thermal graphite conditions for both analytes are summarized in Table 1. Graphite tube thermal stabilization with only iridium was applied for the quantification of arsenic in the exposure solution (Ruella de Oliveira and Anchieta Gomes Neto, 2007). Additional chemical modifier was not needed in this case. Determination of arsenic required two different programmes: one for the exposure solution and one for the larvae. The latter required two additional steps to avoid the irreproducibility caused by bubble formation because of the surfactant TritonX100. TBT, however, could be detected in both types of samples with the same programme. An argon flow rate of 250 mL min1 was used in the furnace cavity in all steps except during atomization.
3.2.
Optimization of sample treatment
3.2.1.
Exposure solution
Significant matrix effects were observed for both analytes. This effect, in the exposure solution was minimized in the case of As determination using different concentrations of nitric acid, previously described as a matrix modifier
Table 1 e Thermal furnace programs optimized for arsenic and tin determination by GFAAS. As (ES): conditions for arsenic in the exposure solution; As(L): conditions for arsenic in larvae. T ( C)
Step
Dry 1 Dry (L) Dry 2 Pyrolysis Pyrolysis (L) Atomization Cleaning
Ramp(s)
Hold(s)
As(ES)
As(L)
TBT
As(ES)
As(L)
TBT
As(ES)
As(L)
TBT
110 e 130 1200 e 2300 2400
90 110 300 1100 1200 2100 2300
110 130 600 e 2400 2450
1 e 5 10 e 0 1
5 3 20 30 1 0 1
10 e 3 10 e 0 1
15 e 15 10 e 5 3
10 20 10 20 2 4 4
15 e 15 20 e 3 4
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(Daminelli et al., 1998). The optimal signal preserving the lifetime of the graphite tube was obtained with 5%. HNO3. The matrix effects found for tin were significantly more pronounced than for arsenic even in the presence of nitric acid at different concentrations. Glacial acetic acid is currently employed to increase the extraction efficiency of tin in the presence of tropolone as a chelating agent in an organic solvent; thus, its effect on interference removal was tested (Leroy et al., 1998; Bermejo-Barrera et al., 1998; Steve, 1997). Optimal conditions were: 1.25 and 5 mL of exposure solution (for the concentrations of 2 and 0.2 mg L1, respectively) treated with 25 mL of 0.5% tropolone in aqueous glacial acetic (2.5%) for tin leaching. Preconcentration was done using 200 mL of toluene as extractant. 20 mL of the organic extract was manually injected after mixing with 5 mL of Pd-based modifier. It is important to point out the high preconcentration factor achieved (up to 25) with the procedure.
3.2.2.
Larvae
A focused ultrasonic probe (USP) was employed to carry out leaching of the analytes from these samples. Time of ultrasonication was 90 s and the power of the probe used was 55%. In a first approach, each sample containing 15e20 larvae was sonicated with 150e200 mL of 2.5e5% nitric acid in deionized water to accelerate arsenic extraction. Matrix effects were very high due to the high viscosity of the resulting extract. Therefore, triton X-100 (0.04 %) was added to decrease such viscosity (Herna´ndez-Caraballo et al., 2002; Miller-Ihli, 1990; Byrd and Butchert, 1993; Kim et al., 2003). The surfactant combined with a complex or counter ion pair in the slurry could be separated as an organic layer. The high reproducibility showed homogeneity of the resulting suspension. Thus, recovery in the injected sample was complete. Combination of several acids and bases (NH4OH, HCl, HNO3) with Triton-X100 were tested. A combination of 5% HNO3 with 0.04% Triton X-100 was selected as the best option. The same USP system was employed to leach TBT from larvae. Following the same criteria as for exposure solution, 5% glacial acetic acid was added to promote the process of leaching. This process was carried out in 150e200 mL of deionized water, depending on the amount of larvae. The high lipidic content of these samples resulted in significant matrix interference. Thus, 70 mL of methanol was added and the
6519
resulting mixture was vortexed vigorously for one minute and then centrifuged at 4000 rpm for 15 min. The lipid fraction remained precipitated in the bottom of the tube and could be separated from the solution containing the analyte to avoid the interferences mentioned before (Steve, 1997). This procedure was applied twice to obtain quantitative recovery.
3.3.
Bioconcentration experiment
3.3.1.
Concentration in the exposure solution
Taking into consideration the possible inorganic arsenic and TBT instability in the exposure solution and how important is to know their real concentration all throughout the bioconcentration experiment, the concentration of both analytes was determined at the different sampling times. The evolution of arsenic concentration for the two concentration chosen (5 and 50 mg L1) has been represented in Fig. 1. Both concentrations had similar behaviour and remained practically constant during the absorption phase, and were negligible during depuration. Also concentration in the control exposure solution without adding any arsenic was negligible during the tested time. Results obtained for TBT are showed in Fig. 2. The nominal concentration of 0.2 mg L1 remained practically constant, like in the case of inorganic arsenic; however, a significant decrease is observed for the nominal value of 2 mg L1 (from 2 to 1.25 mg L1). Although this appreciable shut, it is important to signal that it occurred during the first 3 h and then, remained constant for the rest of the bioconcentration assay. As mentioned before, the application of the OECD 305 test requires that the concentration must remain constant during the whole experiment. The concentration measured in the depuration phase and in the control exposure solution without addition of the analytes, remained negligible, as expected.
3.3.2.
Concentration in the larvae
Results obtained for arsenic accumulation in larvae at both concentrations showed that larvae do not significantly bioaccumulate this analyte, since most of them provided a signal below the method detection limit (maximum concentration of 94 ng g1 for the exposure solution at 50 mg L1 and 40 ng g1 for the one at 5 mg L1 were obtained at 48 h). On the other hand, bioconcentration of tributyltin, was very significant at
Fig. 1 e Arsenic concentration (mg LL1) in the exposure solution. (a) nominal content of 5 mg LL1; (b) nominal content of 50 mg LL1.
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Fig. 2 e Tributyltin concentration (mg LL1) in the exposure solution. (a) Nominal content of 2 mg LL1; (b) nominal content of 0.2 mg LL1.
the two concentration levels tested (Table 2). Measured concentration of TBT in larvae increased with exposure time reaching a maximum value between 48 and 52 h after exposure. After this time, when the larvae were exposed to clean solutions (depuration step), the TBT concentration in the larvae decreased, showing their capability to depurate the analyte as Fent evidenced two decades ago (Fent, 1991). These results have been interpreted in terms of bioconcentration factors.
3.4.
concentration tested and 91.7% for the lowest one. BCFk values obtained from these parameters were 840 for 0.2 mg L1 and 1280 for the exposure to 2 mg L1, while the results for the BCFexp were 723 for the 0.2 mg L1 test and 970 for the high concentration test. The dispersion of the BCF data found in the literature varies enormously, ranging from 0.26 for Oryzias latines (American fish) (Nagase et al., 1991) to 21,100 l kg1 for Platichtys stallatus (flatfish) (Meador, 1997), but most of them are enclosed within the 500e5000 range. Some investigations carried out under the OECD 305 test (METI-NITE, 2006) established BCFs and the partition coefficient values for the species Crucian carp for different organic tin compounds. The value of the BCF found for TBT was not highly bioaccumulative (which means log BCF 3) for the chloride salt and in the range of 2500e9210 for 0.5 mg L1 and 1830e7510 for 0.05 mg L1 for the TBT hydroxide. Although it exits a high dispersion in the values, it can be confirmed that values obtained in this work are within the same range of the values previously reported. Several previous studies have illustrated a direct relationship between the octanol-to-water partition coefficient (Kow) of a substance and its BCF value (Meador, 1997; Petersen and Kristensen, 1998; Parkerton et al., 2008; Arnot and Gobas,
Calculation of bioconcentration factors (BCFs)
Bioconcentration factors can be calculated as the ratio of concentration of the analyte found in the larvae to the concentration of the analyte in the exposure solution at the steady state (BCFss) or by fitting these data to a first-order kinetic equation to obtain k1 and k2, which allows for BCFk calculation. As for TBT, k1 and k2 values obtained as described before (Fig. 3) have been summarized in Table 3. Adjustments were carried out using the NONLIN software (Sherrod, 1995) and the proportion of variance explained was 96.9% for the highest
Table 2 e Concentration mean values of three replicates for the tributyltin bioconcentration experiment. Larvae were incubated in (a) 2 mg LL1 TBT; (b) 0.2 mg LL1 TBT. Concentrations are expressed as ng TBT$gL1 larvae (wet weight). Time (hours)
2 4 6 21 24 45 48 52 54 69 72
Larvae incubated in 2 mg L1
Larvae incubated in 0.2 mg L1
[ng g1] Mean
Std. dev.
[ng g1] Mean
Std. dev.
108.8 170.4 229.3 691.0 775.3 1114.1 1137 1101.9 1097.4 965.1 869.3
66.0 72.3 47.3 81.8 100.1 113.2 28.7 28.7 174.1 84.4 230.4
27.7 44.2 70.3 54.2 64.9 138.2 117.9 113.2 110.9 53.2 93.8
16.2 5.9 13.2 29.3 32.1 2.8 30.6 43.9 42.2 32.4 75.3
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Conc. in larvae (ng/g)
Conc. in larvae (ng/g)
1400
160 1200
140
1000
120
800
100 80
600
60 400
Depuration time
40
200
Depuration time
20 0
0 0
10
20
30
40
50
60
70
80
Bioaccumulation time (hours)
0
10
20
30
40
50
60
70
80
Bioaccumulation time (hours)
Fig. 3 e TBT bioconcentration factors representation following a first-order toxicokinetics equation for the two concentration studied (50 and 5 microg L-1).
2006; Halfon, 1985). This BCF to Kow relationship results from the link between Kow and cell membrane permeability (Simkiss and Taylor, 1989). Based on that, different regression equations between BCF and Kow have been elaborated (Table 4). Having into account the value of log Kow ¼ 3.74 (Laughlin et al., 1986), and using all equations found in Table 4, a range of values of BCFs between 2.01 and 2.96 were obtained, which is quite similar to the values obtained in the present study (log BCF ¼ 2.92 for 0.2 mg L1 and 3.10 for 2 mg L1). Some authors (Petersen and Kristensen, 1998) have pointed that higher BCF values are expected for larvae compared to BCF values for juvenile/adult fish, because the relatively higher lipid content in larvae. Lipid content of zebrafish larvae at the end of the yolk sac stage has been reported to be near 20% of dry weight, whereas the mean lipid content of juvenile zebrafish has been reported to be 11.0% of dry weight. Also it is underlined that metabolism of the larvae can be lower than adult and juvenile fish, overestimating BCF values (Meador, 1997). Values obtained for the elimination rate constant k2 from the fitting of the data derived from the depuration period, showed that no significant changes were found respect to the same k2 (the first-order elimination rate constant) obtained from the fitting derived from the absorption period, thus indicating that there is not a big rate of TBT metabolized. But global BCFs values obtained here show no overestimation, having into account the data in the literature. Even so, future research of tin speciation will be carried out with the purpose of understanding tributyltin metabolism by zebrafish larvae. As mentioned before, low and dispersive values were obtained for arsenic concentration in larvae, so it was not
Table 3 e Toxicokinetics parameters (k1 and k2) values from a concentration-time profile of a tributyltin solution fit a first-order kinetics for both experiments. Larvae incubated in 2 mg L1
k1 k2 Cw (ng/mL)
Larvae incubated in 0.2 mg L1
Uptake
Depuration
Uptake
Depuration
37.85 0.029 1.178
0.025
35.12 0.042 0.195
0.028
possible to fit the experimental data to a first-order bioaccumulation model and so, calculate values of absorption and depuration constants and consequently BCFk. BCFss values calculated with maximum larvae concentration are 8.6 for 5 mg L1 and 2.2 for 50 mg L1. Data published for this compound from Japan METI-NITE, established BCFss for the species Crucian carp of <38 for 5 mg L1 and <4.0 for 50 mg L1. Other authors presented BCFs ranging from 15e17 (Santos et al., 2007) for Corbicula fluminea or maximum values of 35 for ten freshwater fish species in the Lake Manchar in Pakistan (Qadir-Shah et al., 2009). As no experimental data can be found for octanol-water partition coefficient of arsenic on the literature, the estimation software KOWWIN powered by EPI Suite of U.S. Environmental Protection Agency (Meylan and Howard, 1995) has been used, providing a value of Kow ¼ 0.74. Using this data with linear relation between Kow and BCFs, which can be found in Table 4 for Kow < 1, a value of BCF of 1.15 was obtained, close to the one experimentally calculated in this work. Summarizing, BCFs calculated in the present work are in good agreement with others published previously using adult fishes and also with BCFs values obtained from the octanolto-water partition coefficients linear relation. A number of recent studies have questioned whether the use of the BCF model is appropriate for describing the relationship between bioaccumulation and the potential effects for naturally occurring inorganic substances such as metals (McGeer et al., 2003, DeForest et al., 2007). Those authors have found an inverse relationship between BCF and exposure concentration for eight metals studied including arsenic but not tin. They indicate that BCF model was designed, developed, and adapted to describe neutral and lipid-soluble organic substances of anthropogenic origin, and its application to metals for the purposes of hazard identification is not supported by the scientific data. Bioaccumulation of metals follows a different paradigm relative to neutral organics. For example, metal uptake occurs via specific mechanisms that can often be modified as a result of exposure. So, for metals and metalloids, unlike organic substances, no one BCF can be used to express bioaccumulation and/or trophic transfer without consideration of the exposure concentration. Data presented here and calculated experimentally found that BCF values are proportional to the exposure concentration, but also that are in agreement to those values calculated with the
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Table 4 e Regression values for estimating BCF from log Kow using a linear relation log BCF [ a D b log Kow. a 0.46 0.46 0.23 0.05 0.06 0.11a 1.336
b
n
r2
Life stage
Reference
0.86 0.09 0.60 0.01 0.0006 0.05a 1
11 2393 84 71
0.91 0.52 0.00 0.95
Larvae Adult fishes Adult fishes Adult fishes
Petersen and Kristensen (1998) Arnot and Gobas (2006) Arnot and Gobas (2006) Halfon (1985)
a Only when Kow < 1
linear relation between octanolewater partition coefficient and BCFs.
4.
Conclusions
A new analytical methodology based on the use of Zeeman graphite furnace atomic absorption, to determine both inorganic arsenic and TBT in the exposure solution and in larvae exposed to a 0.1e1% of their LC50 has been developed. The treatment of such small and complex samples presented here represents a relevant analytical advance in terms of rapidity and effectiveness for analyte leaching, low solvent consumption and low hazardous residues production, detection limits achieved, etc. It has been demonstrated that the larvae exposure procedure is adequate to evaluate the BCF factors for both elements. BCFs values obtained for the two analytes tested are in good agreement with those previously obtained using the OECD 305 guideline. Thus, this should be the beginning of a series of different test to evaluate the proposed model using zebrafish larvae as an alternative to the Bioconcentration OECD 305 test, which requires many adult fish, implies high cost, as well as complex, time-consuming experiments. Actually new experiments are being performed to study other compounds with different accumulation properties (high BCF values) to be zebrafish larvae evaluated as an alternative test for a wide range of compounds. Also, new data is necessary to continue with discussion about the suitability of application of this bioaccumulation model to metals and metalloids and how these compounds behave in natural environments to proper evaluation of hazards of all the chemicals used by humans that contents metals.
Acknowledgments Authors would like to thanks to Jose Luis Luque for revising manuscript. This work was supported by Projects 046/PC08/214.4 from Spanish Environmental Department (GS1) and CTQ2008-01031/BQU from Spanish Science and Innovation Department (GS2).
references
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Effects of flow and water chemistry on lead release rates from pipe scales Yanjiao Xie*, Daniel E. Giammar Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Campus Box 1180, 1 Brookings Drive, St. Louis, MO 63130, USA
article info
abstract
Article history:
Lead release from pipe scales was investigated under different water compositions, stag-
Received 12 June 2011
nation times, and flow regimes. Pipe scales containing PbO2 and hydrocerussite
Received in revised form
(Pb3(OH)2(CO3)2) were developed on lead pipes by conditioning the pipes with water con-
12 August 2011
taining free chlorine for eight months. Water chemistry and the composition of the pipe
Accepted 25 September 2011
scales are two key factors affecting lead release from pipe scales. The water rarely reached
Available online 5 October 2011
equilibrium with pipe scales within one day, which makes solid-water contact time and corrosion product dissolution rates the controlling factors of lead concentrations for the
Keywords:
conditions tested. Among five water compositions studied, a solution with orthophosphate
Lead release rates
had the lowest dissolved lead release rate and highest particulate lead release rate. Free
Lead pipe scale
chlorine also decreased the dissolved lead release rate at stagnant conditions. Water flow
Lead(IV) oxide
increased rates of release of both dissolved and particulate lead by accelerating the mass
Water chemistry
transfer of lead out of the porous pipe scales and by physically destabilizing pipe scales.
Water distribution
Dissolved lead comprised the majority of the lead released at both stagnant and laminar
Corrosion
flow conditions. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Lead concentrations in tap water are regulated by the Lead and Copper Rule (LCR), which set an action level of 15 mg/L for lead (U.S.EPA, 1991). A recent study found that 50e75% of total lead in tap water can be attributed to lead release from lead service lines (Sandvig and Kirmeyer, 2008). Lead corrosion products that form scales on the interior pipe walls are in direct contact with water and control lead leaching to water (Schock et al., 2008). Lead release from lead pipes is influenced by stagnation time, flow velocity, and water chemistry. The stagnation time was found to substantially affect lead release from pipes, with most of the release occurring within the first 24 h (Lytle and Schock, 2000). Schock (1989) pointed out that lead
concentrations rarely reach equilibrium in distribution systems. A previous study of lead(IV) oxide dissolution determined that the dissolution rates were sufficiently slow that lead concentrations in distributions systems with lead(IV) oxide would be controlled more by stagnation time and dissolution rates than by equilibrium solubility (Xie et al., 2010b). The flow velocity can influence lead release in several ways. Flow velocity can influence erosion mechanisms of corrosion products and can affect the development of pipe scales (Schock, 1999). Lead-rich particles could also detach from pipe scales and contribute significantly to the total lead concentration (Kim et al., 2011; Triantafyllidou et al., 2007). The flow can also influence rates of mass transfer of lead from the pipe scales to the bulk water. At stagnant conditions,
* Corresponding author. Current address: Nalco Company, 1601 W. Diehl Road, Naperville, IL 60563, USA. Tel.: þ1 314 255 4790; fax: þ1 314 935 7211. E-mail addresses:
[email protected],
[email protected] (Y. Xie). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.050
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immobile water inside or adjacent to porous scales contains high concentrations of solutes and is not well mixed with bulk water, and the slow exchange with the bulk water may limit lead release rates (Nawrocki et al., 2010). With water flow lead release can be accelerated because of enhanced mass transfer of lead from the immobile water in the porous pipe scales to the mobile bulk water in the pipes. A separate study observed elevated lead release rates at turbulent flow conditions and attributed these rates to a reduced boundary layer thickness and increased radial mixing (Cartier et al., 2011a). With respect to water chemistry, disinfectants, dissolved inorganic carbon (DIC), pH, and orthophosphate are important parameters in controlling lead concentrations in tap water. Free chlorine and chloramines are commonly used residual disinfectants. Free chlorine can oxidize Pb(II) to Pb(IV) oxides (Liu et al., 2008) but chloramines cannot. The oxidation reduction potential provided by free chlorine but not by chloramines is high enough to maintain lead in the þIV oxidation state (Switzer et al., 2006). Therefore, notable amounts of the lead(IV) oxides scrutinyite (a-PbO2) and plattnerite (b-PbO2) have been found only on lead pipes of distribution systems with a history of elevated free chlorine usage (Schock and Giani, 2004). A switch of disinfectants, as may be done to limit disinfection byproduct formation, can influence lead concentrations in tap water (Edwards and Dudi, 2004; Vasquez et al., 2006). The dissolution of the lead(IV) oxide plattnerite was much faster in the presence of chloramines than of free chlorine (Lin and Valentine, 2008, 2009). The incident of high lead concentrations in Washington D.C. from 2001 to 2004 is an example of increased lead release from accelerated PbO2 dissolution following a switch from free chlorine to chloramines (Boyd et al., 2008; Edwards and Dudi, 2004). The effect of DIC on lead release depends on the specific lead corrosion products present in the pipe scales. The pH and DIC control the carbonate ion concentration; for most waters alkalinity is predominantly from carbonate species, and the carbonate ion concentration can be determined from the pH and alkalinity. Lead carbonate solids, such as cerussite (PbCO3) and hydrocerussite (Pb3(CO3)2(OH)2), have been found in lead pipe scales (Kim and Herrera, 2010; Schock, 1989). Initial increases in DIC can lower the solubility of these lead carbonate solids, although further increases in DIC can actually enhance their solubility (Noel and Giammar, 2007; Schock, 1980). If PbO2 or hydroxylpyromorphite (Pb5(PO4)3(OH)) is the dominant lead corrosion product, then carbonate from DIC can accelerate lead release from pipes scales by forming soluble lead carbonate complexes (Giammar et al., 2008; Xie et al., 2010b). Increasing pH has been recommended and widely applied to control lead release (Schock, 1989; U.S.EPA, 2003). Increasing pH from near neutral generally lowers the lead release rates from hydrocerussite and PbO2 (Noel and Giammar, 2008; Xie et al., 2010b). Kim et al. (2011) has shown that increasing pH generally reduced lead release from pipe scales in dissolution and pipe loop studies. Many water utilities have used orthophosphate as a lead corrosion inhibitor to maintain lead concentrations below the action level (Dodrill and Edwards, 1995). Orthophosphate has been demonstrated to inhibit lead release from lead pipes and
actual distribution systems (The Cadmus Group, 2007), and concentrations higher than 0.4 mg/L have been effective (Edwards and McNeill, 2002). The effect of orthophosphate is believed to result from the precipitation of lead phosphate solids, such as hydroxylpyromorphite, and hydroxylpyromorphite has been identified in some pipe scales (Schock et al., 2006). The precipitation of hydroxylpyromorphite dramatically decreased the net lead release rate during hydrocerussite dissolution in solutions with orthophosphate (Noel and Giammar, 2008). The LCR guidance manual recommended optimal pH range of 7.2e7.8 for orthophosphate treatment (U.S.EPA, 2003). As long as the same pH is maintained, the effects of zinc orthophosphate, sodium orthophosphate, and phosphoric acid were similar (Schneider et al., 2007). The objectives of this study were to a) investigate the roles of water chemistry and flow on lead release from the pipe scales and b) elucidate mechanisms of dissolved and particulate lead release for solutions with varied pH, DIC, disinfectant, and orthophosphate concentration. In this study, lead corrosion products that developed as scales on lead pipe in the presence of free chlorine were identified and characterized. Lead release experiments were conducted at different water compositions and flow regimes. The particulate and dissolved lead release rates obtained from this study can potentially be used to predict particulate and dissolved lead concentrations at similar water chemistry, pipe scale, and flow conditions. The proposed mechanisms of lead release would contribute to the understanding and further development of lead corrosion control strategies.
2.
Material and methods
2.1.
Development of pipe scales
Three 24-inch long new lead pipes with 3/4 inch inner diammeter (Vulcan Lead, Inc) were reacted with solutions designed to promote the formation of corrosion products on the inner pipe walls. Because lead release from actual lead service lines is primarily controlled by reactions with corrosion products and not with the elemental lead in the pipes, it was important to work with conditioned pipes and not with new lead pipes (Kim and Herrera, 2010; Sharp et al., 2009). Scales of aged lead pipes from actual distribution systems can incorporate nonlead site-specific corrosion products, such as iron, aluminum, or silica (Kim et al., 2011; Schock et al., 2008), which can make the composition of actual scales more complex than the pure lead corrosion scale developed by conditioning new lead pipes. Conditioning of new lead pipes in the laboratory allowed the development of relevant corrosion products at controlled conditions and provided pipes with reproducible corrosion product compositions. The lead pipes were fixed at an inclined angle of 20 from the horizontal plane in a holding rack for all experiments. The pipes were filled with an aqueous solution made from Milli-Q water at pH 10 with 10 mg C/L DIC and 3.5 mg/L free chlorine, kept stagnant for a day, and then emptied. The filling and emptying procedure was repeated daily (five days per week) for eight months of conditioning. Effluent and influent
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samples were collected for analysis once every week with the exception of the first month, during which samples were collected daily (five days per week). For these samples the pH and concentrations of residual chlorine and dissolved lead were measured. At the conclusion of the conditioning period, one pipe was used for characterization of the pipe scale. The pipe crosssection and interior wall were prepared for imaging. A 2inch section of the pipe was filled with epoxy to retain the pipe scale before a cross-section was cut and polished. The pipe cross-section was imaged using scanning electron microscopy (SEM) to see the layers and thickness of the pipe scale. A 12-inch section of the pipe was cut lengthwise to visually observe the scale. The scale materials were then gently scraped off with a metal spatula. The crystalline phases in the pipe scales were identified with X-ray diffraction (XRD), and the size and morphology of the scale particles were determined with SEM.
2.2.
For evaluation of the effect of stagnation time, the water in the pipe was sampled after the prescribed periods in Table 1 through valves at the bottom of the pipes. The pH of the solution was measured, and the water collected was split into filtered (0.02 mm polyethersulfone membrane) and unfiltered samples. This filter size was selected to measure the truly dissolved fraction of lead and would avoid measuring colloidal particles larger than 0.02 mm as dissolved. Filtered samples were analyzed for dissolved lead, orthophosphate, and free chlorine or monochloramine concentrations. Unfiltered samples were analyzed for total lead and orthophosphate concentrations. Before analysis of dissolved and total lead and phosphorus, samples were acidified to 2% nitric acid. For evaluation of the effect of water flow, fresh solution of the desired chemistry was recirculated through the pipe sections at a flow velocity of 0.1 m/s, which was selected to examine conditions in the laminar flow regime (Reynolds number of 953). In actual distribution systems, both laminar and turbulent flow regimes are possible, and greater particulate lead release may be occur for higher and more turbulent flows (Cartier et al., 2011b). The pipe reactor system consisted of a 1.15 L reservoir that had the target water chemistry, a lead pipe with a volume of 250 mL, and a peristaltic pump that provided flow through the system. Samples of the recirculating water were collected after 1 and 2 h for analysis of pH and concentrations of dissolved and total lead, free chlorine, monochloramine, and orthophosphate. The recirculation experiments were conducted to assess the impact of flow on lead release rates; however, it should be noted that the configuration of water recirculation through a pipe section is not representative of the flow path through an actual lead service line. Two hours of recirculation allows much more extensive lead release than is likely to occur during the much shorter contact time of water flowing through a pipe section without recirculation.
Lead release experiments from pipe scales
Duplicate lead release experiments were conducted using the two pipes that were not used for characterization. Experiments examined the effects of water chemistry, stagnation time, and flow on lead release from the pipes (Table 1). These experiments examined the release of lead from pipe scales in response to changes in the water chemistry relative to the condition with which the scales had acclimated during conditioning. Before conducting lead-release experiments, the pipes were reconditioned for 2 weeks with the original conditioning solution. This reconditioning was performed because the pipes had been stored in humidified air between the initial 8month conditioning period and the time of the release experiments. Reconditioning was verified by observation of similar chlorine consumption, pH, and lead concentrations after the 2-week reconditioning period as after the original 8month conditioning period. The tests with different experimental conditions were sequenced to avoid altering the pipe scales. Experiments with flow were conducted using the same pipes after stagnation time experiments to avoid potential problems of physically disturbing the scales. To recondition the pipes between experiments and provide a uniform starting point, they were filled and contacted for 1 day with an aqueous solution with the same composition as the original conditioning solution.
2.3.
Analytical methods
Dissolved concentrations of lead and phosphorus were determined by inductively coupled plasma mass spectrometry (ICP-MS) with an Agilent 7500ce instrument with a detection limit of 9 ppt (ng/L) and a method detection limit of 50 ppt (ng/L) for lead. Because all of the phosphorus was added as orthophosphate, measurements of phosphorus by ICP-MS were used to determine the orthophosphate concentration.
Table 1 e Factors evaluated in experiments with pipe reactors. Factor
Water chemistry
Stagnation time Flow velocity
Conditions evaluated Solution name
pH
DIC (mg C/L)
Cl2 (mg/L)
NH2Cl (mg/L as Cl2)
PO3 4 (mg/L as P)
Cl2 NH2Cl High pH High DIC High P 0, 1, 2, 4, 8, 24 h 0, 0.1 m/s
10 10 10 8.5 7.5
10 10 10 50 10
2 0 0 0 0
0 2 0 0 0
0 0 0 0 1
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3.1.
Pipe 1
Water chemistry during development of pipe scales
b
c
The interior wall of an eight-month conditioned pipe had changed from the original gray metallic color to white with red islands. XRD analysis of the scale materials (Fig. 2) showed that the major component of the scale was hydrocerussite and that the red islands contained the lead(IV) oxides scrutinyite and plattnerite. The reddish color is characteristic of lead(IV) oxides (Lytle and Schock, 2005), and hydrocerussite is a white solid. Very little lead(IV) oxide was identified in other areas. The pattern of heterogeneous patches of lead(IV) oxides on a layer of hydrocerussite was also found on pipes from actual water distribution systems (Schock et al., 2005). The free chlorine used during conditioning can oxidize the elemental lead to Pb(II) solids and those solids to Pb(IV) oxides. Hydrocerussite is predicted to be the most stable Pb(II) phase at pH 10 and 10 mg/L DIC. Production of Pb(IV) oxides can occur by oxidation of hydrocerussite (Liu et al., 2008; Wang et al., 1996), and the production of lead(IV) oxides on the pipes may be limited by the once daily resupply of free chlorine. The continuing consumption of free chlorine during the conditioning period, even after 8 months of conditioning, indicates that lead oxidation is still ongoing. Previous research has observed the coexistence of lead(II) and lead(IV) phases in pipe scales, and layers of different corrosion products can develop in pipe scales (Kim and Herrera, 2010; Schock et al., 2005). XRD peaks corresponding to elemental lead were found in most
Pipe 2
500
Pipe 3
400 300 200 100 0
50
100
150
200
250
0
50
100
150
200
250
0
50
100
150
200
250
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
11.4 11.2 11.0 10.8
pH Characterization of lead pipe scales
600
0
Results and discussion
During the first 10 days of the conditioning period, the dissolved lead concentration of the water in the pipes after 24-h stagnation periods initially increased to more than 500 mg/L (Fig. 1a). The lead concentration dropped significantly after this initial period, and for the remaining 230 days it fluctuated around 50 mg/L. The residual chlorine concentration after one day of contact was always in the range of 0e1 mg/L as Cl2, which indicates consumption of free chlorine from reaction with the lead pipe (Fig. 1b). The residual chlorine concentration was close to 0 during the first 10 days. After 10 days, it increased from 0 to 0.5 mg/L as Cl2 and then fluctuated around 0.5 mg/L as Cl2. The pH increased from the initial value of 10.0 to about 11, which can be explained by the chemistry of lead oxidation to Pb(II) and the formation of hydrocerussite (Pb3(CO3)2(OH)2(s)), and after the first two weeks the pH dropped back to a range from 9.9 to 10.6 (Fig. 1c).
3.2.
700
Residual Chlorine (mg/L)
3.
a Dissolved Lead (ug/L)
The pH of solutions was measured with a glass pH electrode and Accumet pH meter. Free chlorine and monochloramine concentrations were measured using the standard DPD colorimetric method (Clesceri et al., 1999). A PerkineElmer Lambda 2S spectrophotometer was used for analysis. XRD was performed on a Rigaku diffractometer that uses Cu-Ka radiation and has a vertical goniometer and a scintillation counter. A JEOL 7001LVF field emission scanning electron microscope was used to view the size and morphology of the solids.
10.6 10.4 10.2 10.0 9.8 9.6
Time (day) Fig. 1 e Evolution of (a) lead concentration, (b) residual free chlorine concentration, and (c) pH in three lead pipes during eight months of conditioning. The initial pH and free chlorine concentration of the filling solution are indicated by the dashed lines.
patterns because portions of unaltered pipe were scraped off while collecting materials of the pipe scale. The pipe scale contains large platy particles and aggregates of smaller roughly spherical particles (Fig. 3a). The large particles are hydrocerussite, which usually forms with a platy shape (Korshin et al., 2005). The smaller particles are probably plattnerite and scrutinyite. The larger particles are more abundant than the aggregated smaller particles, which is consistent with the predominance of hydrocerussite in the XRD patterns. The pipe scale was about 24 mm thick and had gaps and pores that would allow water infiltration (Fig. 3b).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 2 5 e6 5 3 4
Fig. 2 e X-ray diffraction patterns of pipe scale formed after conditioning for eight months. The reference patterns of scrutinyite (S), hydrocerussite (H), plattnerite (P), and elemental lead (L) are included for comparison. The peaks in the sample patterns that correspond to different phases are noted.
3.3. Effects of water chemistry on dissolved lead concentrations in pipes When the conditioned pipes were contacted with five different solutions in both stagnation and flow experiments, the solution with orthophosphate yielded the lowest dissolved lead concentration (Fig. 4a and b). Orthophosphate effectively controlled the dissolved lead concentration below the 15 mg/L action level for the first 8 h of the stagnation experiments, but the dissolved lead concentration after 24 h exceeded the action level. Orthophosphate can decrease the net lead release from lead(II) carbonates by forming lead phosphate precipitates (Noel and Giammar, 2008), and orthophosphate has been demonstrated to effectively decrease lead release from aged pipes in actual distribution systems (Edwards and McNeill, 2002; The Cadmus Group, 2007). The experiment with the high DIC solution had the highest dissolved lead concentration. Increasing DIC from 10 to 50 mg/ L can enhance dissolution of hydrocerussite and PbO2 by
6529
forming soluble Pb(II) carbonate complexes, and decreasing the pH from 10 to 8.5 could also enhance dissolution of hydrocerussite and PbO2 (Noel and Giammar, 2007; Xie et al., 2010b). Switching from 10 mg/L DIC at pH 10e50 mg/L DIC at pH 8.5 increased the dissolution rates of hydrocerussite and PbO2 and resulted in the highest lead release observed in this study. The impact of the residual disinfectant can be assessed by comparing lead release in three solutions that are all at pH 10 with 10 mg/L DIC. These are the solutions with high pH, monochloramine, and free chlorine. The dissolved lead concentrations decreased from monochloramine to no disinfectant to free chlorine in stagnation experiments (Fig. 4a). Free chlorine can raise the redox potential and inhibit PbO2 dissolution (Xie et al., 2010a), and it can oxidize Pb(II) released from hydrocerussite to PbO2 (Liu et al., 2008). The effect of free chlorine stabilizing PbO2 and oxidizing Pb(II) resulted in the solution with free chlorine having the lowest dissolved lead concentrations of all of the pH 10 and 10 mg/L DIC solutions. The dissolved lead concentrations stabilized from 4 to 8 h when free chlorine was present. Monochloramine increased lead concentrations relative to the solution without disinfectant; the effect of monochloramine was probably caused by the reduction of PbO2 by an intermediate species produced during monochloramine decay (Lin and Valentine, 2008). The pH was stable and did not vary more than 0.5 pH units except after 24 h of stagnation in the experiments with orthophosphate solution (Fig. 5). Therefore, the observed effects of water chemistry on the lead release cannot be attributed to changes in pH. During the first 8 h of reaction, the pH was between 7.3 and 7.8, which was in the optimum pH range (7.2e7.8) for orthophosphate addition (U.S.EPA, 2003). After 24 h of reaction in the orthophosphate solution, the dissolution of lead corrosion products and the precipitation of lead phosphate solids had increased the pH from 7.5 to 8.7. The majority of lead released was in the dissolved phase, but some particulate lead was also measured (Fig. 6). Leadcontaining suspended particles could come from (a) remobilization of lead corrosion products in pipe scales and (b) precipitation of new lead-containing solids in solution. The solution with orthophosphate generated the highest particulate lead concentration in both flowing water and stagnation experiments. The precipitation of lead phosphate solids may contribute to the particulate lead in the presence of
Fig. 3 e Electron micrographs of a) particles in the pipe scale and b) cross section of pipe surface with development of corrosion products. In the cross section, unaltered lead pipe is visible on the left and the epoxy used to fill the pipe prior to cutting and polishing is on the right.
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a
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 2 5 e6 5 3 4
experiments, V is the volume of water in the pipe (250 mL). For experiments with recirculating flow, V is the total volume of water in the overall pipe, reservoir, and tubing system (1.4 L).
120 Cl2 NH2Cl High pH High DIC High P
Dissolved Lead (µg/L)
100 80
Rmeasured ¼
60
40
20
0 0
5
10
15
20
25
30
Time (h)
b
40 Cl2
Dissolved Lead (µg/L)
35
NH2Cl High pH
30
High DIC High P
25 20 15 10 5 0 0
1
2
3
Time (h) Fig. 4 e Effects of solid-water contact time and water chemistry on the dissolved lead released from pipe scales in (a) stagnation and (b) flowing water experiments. Error bars represent one standard deviation of duplicate experiments. Although the points in panel b are slightly staggered to avoid overlap in the plot, the solid-water contact times shown for all five water compositions are for 1.0 h and 2.0 h.
orthophosphate, and this possibility is discussed more in a later section.
3.4.
Dissolved lead release rates
The dissolved lead concentrations consistently increased with time in all of the experiments, which indicates that the water in the pipes had not reached equilibrium with the pipe scales (Fig. 4). The dissolved lead concentrations were controlled by the dissolution rates and not by the equilibrium solubility of the corrosion products at the tested conditions. The average lead release rate Rmeasured (mol s1 or mg h1) over a prescribed period can be determined using Equation (1), in which Cs is the dissolved or particulate lead concentration, V is the volume of water in the pipe system, and trxn is the time for reaction between the water and pipe scale. For stagnation
CS $V trxn
(1)
The dissolved lead release rates were one order of magnitude higher with flowing water than during stagnation based on data collected at 2 h of exposure (Fig. 7a). Flow probably enhanced dissolved lead release rates by accelerating mass transfer of dissolved lead out of porous scales during the initial 2 h of contact. A recent experimental study showed that immobile water inside porous scales on cast iron pipe is not well mixed with bulk water. The immobile water contains high solute concentrations released from scales, and it exchanges more rapidly with the bulk water at flowing water conditions (Nawrocki et al., 2010). The calculated surface area normalized dissolved lead release rate in phosphate solutions in the present study was about 20 mg/h/m2 based on data collected after 8 h of exposure, which is similar to the average dissolved lead release rate of 8 mg/h/m2 from lead service lines in similar phosphate solution in a recent study by McFadden et al. (2011). Actual lead concentrations that could be observed in taps downstream of lead service lines will be affected by both the flowing water condition and also the stagnation time, and dissolved lead concentrations will be much higher following long periods of stagnation. Although the flowing water condition yielded higher lead release rates than the stagnant condition, the recirculation configuration has a much longer solid-water contact time than the once-through configuration of an actual distribution system. If the flow were in a oncethrough configuration, then the contact time would only be 6 s for a 24 inch pipe section. For this contact time and using the lead release rates determined in the present study, lead concentrations of only 0.08e0.3 mg/L could be reached, which is far less than the dissolved lead concentration after stagnation for 1 h (8e25 mg/L).
3.5.
Lead release profile
In stagnation experiments, most of the lead released was dissolved except in the experiment with orthophosphate (Fig. 6a) based on data collected at 2 h of exposure. Particulate lead constituted less than 10% of the total lead in solutions without phosphate. For solutions with phosphate, particulate lead accounted for 49% of the total. After 2 h, the particulate lead fraction increased up to 58e78% in phosphate solutions. The particulate lead fractions observed in the present stagnation experiments were consistent with those of a previous study that did not include orthophosphate (Kim et al., 2011) in which 10% to over 60% of the lead released from pipe scales containing hydrocerussite and cerussite was particulate following 24 h of stagnation. The large range of percentages of lead as particulate in the study of Kim et al. (2011) was attributed to the range of water chemistries, pH in particular, that were studied. A high particulate lead fraction for solutions with phosphate was also observed in a previous study (McFadden et al., 2011).
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Stagnation pH
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 2 5 e6 5 3 4
Pipe 2
11.0 10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 0
pH
Flow
Pipe 3 11.0 10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0
5
10
15
20
25
11.0 10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0
Cl2 NH2Cl High pH High DIC High P 0
5
0
5
10
15
20
25
10
15
20
25
11.0 10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 0
5
10
15
20
25
Time (h)
Time (h)
Fig. 5 e The pH profile during stagnation and flowing water experiments in Pipe 2 and Pipe 3.
In flowing water experiments, the majority of the released lead was also dissolved except in the experiment with orthophosphate (Fig. 6b) based on data collected at 2 h of exposure. Particulate lead made up less than 5% of the total lead in high DIC, free chlorine, and monochloramine solutions. For
Lead Concentration (µg/L)
a
Lead Concentration (µg/L)
b
40 35
Particulate Lead Dissolved Lead
30 25 20 15 10 5 0 Cl2
NH2Cl
High pH
High DIC
High P
40 35 30
3.6.
Particulate Lead Dissolved Lead
25 20 15 10 5 0 Cl2
NH2Cl
solutions with phosphate, particulate lead was 46% of the total lead. In the high pH solution without disinfectant, 13% of the total lead was present as particulate. For the same water chemistry, the particulate lead release rates at flowing water conditions were about one order of magnitude higher than at stagnation conditions (Fig. 7b). Flow could mechanically destabilize the pipe scales and mobilize particles in the scales. The surface area normalized particulate lead release rate in phosphate solution calculated in the present study were about 40 mg/h/m2 based on data collected after 8 h of exposure, which is in the range of particulate lead release rates (1e48 mg/ h/m2) from lead service lines in similar phosphate solution in the recent study by McFadden et al. (2011). The present experiments were done at laminar flow conditions, and particulate lead release could be much higher with turbulent flow. In a previous study with turbulent flow, particulate lead fractions from 75% to 98% were observed in flow-through experiments (Kim et al., 2011).
High pH
High DIC
High P
Fig. 6 e Lead profile of 2-hour samples in (a) stagnation and (b) flow experiments.
Orthophosphate case
The trends of orthophosphate and dissolved lead provide insights into the mechanism through which orthophosphate mitigates lead release. In stagnation experiments, decreasing phosphate concentrations correlated with increasing concentrations of dissolved lead (Fig. 8). Before the phosphate concentration dropped below 0.3 mg/L, the dissolved lead concentration was close to the calculated solubility of hydroxylpyromorphite (11 mg/L) in the presence of 0.3 mg/L orthophosphate as P at the pH and DIC of the experiment. The similarity of the measured lead concentration and the predicted solubility of hydroxylpyromorphite suggests that hydroxylpyromorphite precipitated and controlled the
6532
a
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 2 5 e6 5 3 4
4
dissolved lead concentration until the orthophosphate was depleted below a critical level. Prior research observed the formation of hydroxylpyromorphite on lead pipes harvested from distribution systems (Schock et al., 2006). The precipitation of hydroxylpyromorphite probably contributed to the particulate lead release rates, which was highest in the presence of orthophosphate (Fig. 7b). The particulate lead release in the presence of orthophosphate could cause potential water safety issues, although concerns may be somewhat mitigated by lead phosphate solids being less bioavailable than other lead-containing solids and dissolved lead (Ryan et al., 2004; Sonmez and Pierzynski, 2005).The high particulate lead released might just be a transient effect associated with the short duration of the present experiments, and particulate lead could reattach to the pipe surface during longer stagnation times (Edwards and McNeill, 2002). The orthophosphate was very effective in controlling dissolved lead concentration even for just 1 day of orthophosphate exposure. The effect of orthophosphate is expected to be greater for continued use in actual distribution systems as more of the scale is converted to lead phosphates. Long-term investigation of lead release from pipe scales in contact with orthophosphate-treated water and related transformation of corrosion products in scales is recommended to further validate the mechanisms of orthophosphate effect and lead release.
3
3.7.
35
Dissolved Lead Release Rate (µg/h)
Stagnation Flow
30 25 20 15 10 5 0
Cl2
Particulate Lead Release Rate (µg/h)
b
NH2Cl
High pH
High DIC
High P
9 Stagnation
8
Flow
7 6 5
2 1 0 Cl2
NH2Cl
High pH
High DIC
High P
Fig. 7 e Effects of flow on (a) dissolved and (b) particulate lead release rates from scales over the first 2 h of experiments. Error bars represent one standard deviation of the duplicates.
900
60
Free chlorine case
In stagnation experiments, the decreasing trend of free chlorine correlated with the increasing trend of dissolved lead (Fig. 9). The release of dissolved lead was suppressed and concentrations stabilized in the first 8 h, which was probably due to the oxidation of Pb(II) to PbO2 by free chlorine. The free chlorine concentration dropped over time because free chlorine was consumed by its oxidation of Pb(0) to Pb(II) and Pb(II) to Pb(IV). Free chlorine concentrations higher than 1 mg/L as Cl2 were able to maintain low lead levels. After 24 h of stagnation the free chlorine concentration had dropped to near zero and the dissolved lead concentration markedly increased. The present findings that free chlorine was able to suppress lead release were consistent with previous studies
800 50
Lead
500
Phosphate
30
Phosphate
400
20
300 200
10
70
Lead Cl2
60
3
Cl2
50 2
40 30
Cl2 (mg/L)
600
Lead
Lead
Dissolved lead (µg/L)
40
4
80
Phosphate (µg P/L)
Dissolved lead (µg/L)
700
1
20
100
10 0
0 0
5
10
15
20
25
30
Time (h)
Fig. 8 e Dissolved lead and orthophosphate concentrations in high orthophosphate solution during stagnation experiments. Data are shown for duplicate experiments.
0
0 0
5
10
15
Time (h)
20
25
30
Fig. 9 e Dissolved lead and free chlorine concentrations during stagnation experiments with free chlorine. Results are shown for both duplicate experiments.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 2 5 e6 5 3 4
(Edwards and Dudi, 2004; Lin and Valentine, 2008). The stagnation time affects the disinfectant levels in distribution systems and consequently the lead concentrations. Free chlorine levels will also vary with water age in a distribution system, and the most distant connections may be most vulnerable to lead release because of lower residual free chlorine concentrations.
4.
Conclusions
Lead release from pipe scales is related to the dissolution and precipitation of lead corrosion products in the present study, which facilitates the understanding and development of corrosion control strategies. Knowledge of the corrosion products in the pipe scales is key to understanding the response of lead release to water chemistry. Orthophosphate can be used as a lead corrosion inhibitor for pipes with scales comprised of hydrocerussite and PbO2. Orthophosphate decreased the dissolved lead release rate, although orthophosphate did increase the particulate lead release rate, an effect that may be transient and not persist with continued dosing of orthophosphate. For pipe scales consisting of hydrocerussite (major) and PbO2 (minor), a solution with 1 mg/ L free chlorine decreased lead release rates relative to solutions with monochloramine and without disinfectant in stagnation experiments. In addition, the solution at pH 8.5 with DIC 50 mg/L caused the highest dissolved lead release rate among five water chemistries. Equilibrium was not reached within 24 h of stagnation or 2 h of recirculating flow, which indicates that dissolution rates control dissolved lead concentrations for times of at least 24 h at stagnant conditions and 2 h at recirculating conditions. The dissolution rates calculated from the present study allow prediction of undersaturated lead concentrations released from similar pipe scales at similar water chemistry and flow conditions. Laminar flow accelerated release of both dissolved and particulate lead from pipe scales. Stagnation time influenced the dissolved lead concentration by allowing greater time for lead release and by affecting the concentrations of residual disinfectant or orthophosphate. The total lead concentration was primarily dissolved lead under both stagnant and laminar flow conditions.
Acknowledgments This work was supported by the Water Research Foundation (Project No. 4064). Washington University gratefully acknowledges that the Water Research Foundation is the joint owner of the technical information upon which this paper is based. Views and findings expressed here do not necessarily reflect those of the Water Research Foundation. Washington University thanks the Water Research Foundation for its financial, technical, and administrative assistance in funding the project. We are grateful for the advice of Michael Shock, Leland Harms, and Windsor Sung and the laboratory assistance of Yin Wang.
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references
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Noel, J.D., Giammar, D.E., 2007. The Influence of Water Chemistry on Dissolution Rate of Lead Corrosion Products. Water Quality Technology Conference, Charlotte, NC. Noel, J.D., Giammar, D.E., 2008. The Influence of Water Chemistry on Dissolution Rates of Lead(II) Carbonate Solids Found in Water Distribution Systems. Water Quality Technology Conference, Cincinnati, OH. Ryan, J.A., Scheckel, K.G., Berti, W.R., Brown, S.L., Casteel, S.W., Chaney, R.L., Hallfrisch, J., Doolan, M., Grevatt, P., Maddaloni, M., Mosby, D., 2004. Reducing children’s risk from lead in soil. Environmental Science & Technology 38 (1), 18Ae24A. Sandvig, A., Kirmeyer, G., 2008. Contribution of Lead Sources to Lead Levels at the Tap. Water Quality Technology Conference, Cincinnati, OH. Schneider, O.D., Lechevallier, M.N., Reed, H.F., Corson, M.J., 2007. A comparison of zinc and nonzinc orthophosphate-based corrosion control. Journal American Water Works Association 99 (11), 103e113. Schock, M., 1980. Response of lead solubility to dissolved carbonate in drinking water. Journal American Water Works Association 72 (12), 695e703. Schock, M., DeSantis, M.K., Lubbers, H., Gerke, T., 2006. The Occurrence and Significance of Tetravalent Lead in New England. Proc. NEWWAAC, Danvers, MA. Schock, M., Giani, R., 2004. Oxidant/Disinfectant Chemistry and Impacts on Lead Corrosion. Water Quality Technology Conference, San Antonio, TX. Schock, M.R., 1989. Understanding corrosion control strategies for lead. Journal American Water Works Association 81 (7), 88e100. Schock, M.R., 1999. In: Letterman, R.D. (Ed.), Water Quality and Treatment. McGraw-Hill, Inc., New York, New York. Schock, M.R., Hyland, R.N., Welch, M.M., 2008. Occurrence of contaminant accumulation in lead pipe scales from domestic drinking-water distribution systems. Environmental Science & Technology 42 (12), 4285e4291. Schock, M.R., Scheckel, K., Desantis, M., Gerke, T., 2005. Mode of Occurrence, Treatment, and Monitoring Significance of
Tetravalent Lead. Water Quality Technology Conference, Quebec City, Candada. Sharp, R., Rosenfeldt, B., Glaser, C., Freud, S., Laun, S., Becker, W., 2009. Impact of Disinfection Type, Stagnation Time, and Scale Chemistry on Lead Leaching from Lead Service Lines. Water Quality Technology Conference, Seattle, WA. Sonmez, O., Pierzynski, G.M., 2005. Phosphorus and manganese oxides effects on soil lead bioaccessibility: PBET and TCLP. Water Air and Soil Pollution 166 (1-4), 3e16. Switzer, J.A., Rajasekharan, V.V., Boonsalee, S., Kulp, E.A., Bohannan, E.W., 2006. Evidence that monochloramine disinfectant could lead to elevated Pb levels in drinking water. Environmental Science & Technology 40 (10), 3384e3387. The Cadmus Group, I, 2007. In: U.S.E.P.A.R (Ed.), Review of the Interim Optimal Corrosion Control Treatment for Washington, D.C, vol. III. Triantafyllidou, S., Parks, J., Edwards, M., 2007. Lead particles in potable water. Journal American Water Works Association 99 (6), 107e117. U.S.EPA, 1991. Maximum contaminant level Goals and national primary drinking water regulations for lead and copper. Final Rule. Federal Register 56, 26460. U.S.EPA, 2003. Revised Guidance Manual for Selecting Lead and Copper Control Strategies. Vasquez, F.A., Heaviside, R., Tang, Z.J., Taylor, J.S., 2006. Effect of free chlorine and chloramines on lead release in a distribution system. Journal American Water Works Association 98 (2), 144e154. Wang, Y., Xie, Y., Li, W., Wang, Z., Giammar, D.E., 1996. Formation of lead(IV) oxides from lead(II) compounds. Environmental Science & Technology 44 (23), 8950e8956. Xie, Y., Wang, Y., Giammar, D.E., 2010a. Impact of chlorine disinfectants on dissolution rates of the lead corrosion product PbO2. Environmental Science & Technology 44 (18), 7082e7088. Xie, Y., Wang, Y., Singhal, V., Giammar, D.E., 2010b. Effects of pH and carbonate concentration on dissolution rates of the lead corrosion product PbO2. Environmental Science & Technology 44 (3), 1093e1099.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 3 5 e6 5 4 4
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Sorbic acid as a quantitative probe for the formation, scavenging and steady-state concentrations of the tripletexcited state of organic compounds Janel E. Grebel a, Joseph J. Pignatello a,b, William A. Mitch a,* a b
Department of Chemical and Environmental Engineering, Yale University, Mason Lab 313b, 9 Hillhouse Ave., New Haven, CT 06520, USA Department of Environmental Sciences, Connecticut Agricultural Experiment Station, 123 Huntington St., New Haven, CT 06504, USA
article info
abstract
Article history:
Sorbic acid (trans,trans-hexadienoic acid) was developed as a probe for the quantification of
Received 20 July 2011
the formation rate, overall solution scavenging rate and steady-state concentrations of
Received in revised form
triplet-excited states of organic compounds. The method was validated against literature
19 September 2011
data for the quenching rate constant of triplet benzophenone by tyrosine obtained by laser
Accepted 25 September 2011
flash photolysis and by SterneVolmer plots of phosphorescence quenching. In contrast to
Available online 12 October 2011
these methods, the probe method does not require knowledge of the optical properties of triplets to monitor their quenching. Moreover, the probe method permits simultaneous
Keywords:
quantification of triplet formation, quenching and steady-state concentrations during
Triplet
illumination of complex chromophore mixtures, such as natural organic matter (NOM),
Sorbic acid
with polychromatic light >315 nm. Application of the method to de-aerated Suwannee
Probe
River NOM illuminated with polychromatic light (315e430 nm) resulted in a triplet
Photochemistry
quantum yield of 0.062. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
In organic chromophores, photon absorption by the singlet ground state (S0) leads to an excited singlet state (S1) that can relax to the ground state or undergo intersystem crossing (isc) to an excited triplet state (T1) in which the excited electron has undergone a spin flip (Carroll, 1998). Triplet state chromophores can revert to the ground state (S0) via a second isc involving an electron spin flip. Considered “spin-forbidden”, isc is a low probability event and characteristically slower than other intersystem processes. Triplet lifetimes (ms-s) are significantly longer than singlet lifetimes (ns-ms) (Turro et al., 2009), and triplets are more likely to engage in photochemical reactions. In the case of direct photolysis, such reactions may lead to chromophore destruction. In the case of indirect
photolysis, excited triplet chromophores, particularly chromophoric groups in natural organic matter (NOM), may transform other solution constituents by direct interaction, or by the formation of secondary photo-oxidants (e.g., hydroxyl radical or singlet oxygen). Previous research has indicated that certain solution components, including dissolved oxygen and halide ions, may affect triplet concentrations by altering intersystem crossing rates (S1 / T1 or T1 / S0), or reacting with triplets (Koziar and Cowan, 1978; Kuzmin and Chibisov, 1971; Treinin et al., 1983; Loeff et al., 1984). We had previously demonstrated that halides enhance the photobleaching of dissolved organic matter, but the mechanism was unclear (Grebel et al., 2009), except that it was not ionic strength-related. Because of the importance of triplets to photochemical processes, a method
* Corresponding author. Tel.: þ1 203 432 4386; fax: þ1 203 432 4387. E-mail address:
[email protected] (W.A. Mitch). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.048
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that can quantify the effects of solution components on triplet formation and scavenging, as well as quantify their steadystate concentrations, would be a valuable tool toward understanding photochemical reactions in the environment. Because organic triplets are present at very low concentrations and have short lifetimes, their measurement requires special analytical techniques. Several studies have examined triplet quenching rates using SterneVolmer plots of the reduction in phosphorescence upon adding a quenching agent (e.g., Turro and Engel, 1969; Zepp et al., 1985). Unfortunately, this method does not provide information on triplet formation rates or steady-state concentrations. Laser flash photolysis techniques (Canonica et al., 2000), which enable direct detection of triplets by their UV absorbance, have several limitations, especially for the study of natural waters which contain complex mixtures of chromophores (e.g., NOM). Expensive and delicate equipment is required. The decay of only relatively long-lived triplets can be monitored, and their formation rates often cannot be resolved by laser systems. The high photon intensities increase the likelihood of bi-photonic processes, which are not generally important in natural systems. Lasers, although able to scan over a wavelength range, are limited to monochromatic irradiation at any one time. Lastly, it is likely that NOM generates an array of triplets with different absorbance spectra, rendering their quantification by UV absorbance difficult. In contrast to direct measurement techniques, probe methods employ a chemical that reacts in a characteristic fashion with the species of interest (e.g., excited triplet organics). The probe concentration is low enough to avoid significant system perturbation, yet high enough that the reaction products can be monitored using standard laboratory equipment. The formation of reaction products can be monitored to quantify the rates of formation and removal, as well as the steady-state concentrations of the triplets. The objective of this study was to develop and evaluate sorbic acid (trans,trans-hexadienoic acid, t,t-HDA) as a probe of excited triplet chemistry, including solution constituent effects on triplet formation, scavenging and steady-state concentrations. Sorbic acid is an excellent triplet probe for several reasons. First, t,t-HDA and other simple dienes have been utilized previously to quench triplet reaction pathways (Zepp et al., 1985; Velosa et al., 2007), but have not been developed as quantitative probes. As a carboxylic acid, sorbic acid is more water-soluble than other diene quenching agents, such as 1,3-pentadiene. Quenching reactions occur
when excited state organics transfer energy to the diene. As a result, the excited state organic (sensitizer) reverts to the ground state (S0), while the diene is promoted to an excited state. Quenching reactions are possible only if the excited state energy of the diene probe is less than the relevant excited state of the sensitizer. With high singlet energies (ES w400 kJ/mol) and moderately low triplet energies (ET w200e250 kJ/mol), dienes will only interact with the most energetic of singlet sensitizers, but will quench a wide range of triplets (Montalti et al., 2006). Therefore, the technique is selective for quantification of triplet, rather than singlet, excited chromophores. In the case of NOM, previous research indicated that, of the NOM triplets with sufficient energy (94 kJ/mol) to activate dissolved oxygen to singlet oxygen, w35% had triplet energies of w250 kJ/mol that could be quenched by dienes (Zepp et al., 1985); these higher energy NOM triplets may be critical for NOM photochemistry. Second, the interaction of t,t-HDA with organic triplets results in HDA isomerization, producing a mixture of transetrans, transecis, cisetrans, and cisecis species (Scheme 1). Other oxidants, such as singlet oxygen and hydroxyl radicals, that potentially react with HDA, will not produce isomer products. Thus, quantification of isomer products, as opposed to t,t-HDA loss, ensures the only reaction pathway quantified is that with the desired triplet state organic species. This ability to monitor isomer reaction products is an advantage over other triplet probes. For example, loss of trimethylphenol has been used to probe triplets (Halladja et al., 2007), but because the reaction products are uncharacterized, and trimethylphenol also reacts with other reactive species (e.g., singlet oxygen), it is difficult to link trimethylphenol loss to triplet interactions alone. Third, triplet spectra of particular NOM components or organic compounds, necessary for direct detection in laser-based studies, are not required. Fourth, the method requires relatively simple apparatus. Samples can be analyzed by high performance liquid chromatography with ultraviolet detection (HPLCeUV). Fifth, low wattage or natural light sources can be used for irradiation. Users are able to experiment with true polychromatic irradiation, resulting in more realistic excitation conditions. For NOM, Zepp et al. (1985) found that NOM triplet energies varied with irradiation wavelength. Variations in energy of the photons absorbed led to excitation to different excited states, underscoring the importance of using the most realistic polychromatic irradiation conditions feasible. Lastly, because t,t-HDA absorbs only weakly in the near ultraviolet, and not at
kt,t t,t-HDA
*
kt,c t,c-HDA
+ 3SENS kc,t
c,t-HDA
kc,c c,c-HDA
Scheme 1 e Products of interaction between triplet-excited organic sensitizer and sorbic acid (trans-trans-HDA).
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Experimental section
2.1.
Materials
Analytical methods
Analysis of the four HDA isomers was done on a Rainin Dynamax HPLC with a Dynamax UV-1 absorbance detector. Benzoic acid was added to samples as an internal standard. A 200 mL volume of sample was injected onto an Inertsil ODS-3 C18 column (250 mm 4.6 mm, 5 mm particle size). The
10000
0.20 0.15
1000 0.10 0.05
100
0.00 290
300
310
320
330
Wavelength (nm)
b 0.15 10000 0.10 1000 0.05
Lamp Intensity Log Scale
2.2.
a
Lamp intensity Log Scale
Suwannee River natural organic matter from the International Humic Substances Society (SRNOM; St. Paul, MN) had a specific UV absorbance at 254 nm (SUVA254) of 4.1 L mg1 m1. Acros sorbic acid (trans-trans-hexadienoic acid or t,t-HDA, 99%) and benzophenone (99%), JT Baker 2propanol, Acros analytical grade NaBr (99.5%), and Fluka ultra grade NaCl, which had previously been found to contain 0.0038 mol percent Br- (Grebel et al., 2010), were used without further purification. Experimental solutions consisted of 4 mg-C/L of SRNOM in 20 mM phosphate buffer at pH 8.1. t,t-HDA was spiked at 8e12 different concentrations ranging from 3 to 1000 mM. Solutions were de-oxygenated by bubbling with N2 gas for 15 min prior to their addition to irradiation vials; however, traces of oxygen likely remained. Each solution was prepared and irradiated in duplicate. A non-irradiated solution was prepared and sampled to determine the precise quantity of t,t-HDA added to irradiated solutions, correcting for potential errors during sample handling. Irradiation experiments were conducted in 25-mL headspace-free borosilicate vials (Fig. 1). Vials contained glass beads to promote mixing during irradiation. The samples were illuminated with 4 General Electric F15T8/BLB lamps (Fairfield, CT, USA) emitting between 280 and 430 nm with a peak at 350 nm to simulate the lower emission wavebands of sunlight (Fig. 1). Samples were mounted on rotating disks below the lamps at w25 angle from horizontal. Fans maintained sample temperatures at 23 C. Photon flux (2.56 105 E L1 s1) (with the plastic filters in place in the majority of experiments to cutoff light below 315 nm; see below) and vial pathlength (1.1 cm) were characterized periodically by potassium ferrioxalate actinometry (Rabek, 1982; Zepp, 1978). The photon flux did not vary significantly during the study.
Sorbic Acid Absorbance
2.
isocratic mobile phase consisted of 15% acetonitrile and 85% 30 mM acetate buffer at pH 4.75 at 1 mL/min. Full resolution of all four sorbic acid isomers (Fig. 2) required the mobile phase to be buffered at a pH equal to the pKa of HDA (4.75). After 25 min, the acetonitrile content was increased to 90% for 5 min to purge the column, and then decreased back to 15%. The total run time was 42 min, and the detection wavelength was 254 nm. An instrumental calibration curve was determined for t,tHDA. As isomer standards of cis,cis-, cis,trans-, and trans,cisHDA were not commercially available, molar absorption coefficient correction factors at 254 nm relative to t,t-HDA were used to determine corrected calibration curves for isomer products. These correction factors were calculated from UV absorbance (l ¼ 254 nm) and proton NMR data on et al. (2001). The mixed HDA isomer solutions provided in Cigic NMR data were used to quantify the relative concentrations of isomers, enabling the calculation of molar absorption coefficients from the UV254 data: 3t,t ¼ 2.38 102 mM1 cm1, 2 3c,t ¼ 2.18 10 mM1 cm1, 3c,c ¼ 1.36 102 mM1 cm1, 2 3t,c ¼ 1.95 10 mM1 cm1. Isomer peaks were assigned based upon analogy to the results of Velosa et al. (2007), who resolved c,t-HDA, t,t-HDA and t,c-HDA with a similar HPLC chromatographic system. Further confirmation of these
4 ppm NOM Absorbance
all in the visible region, it does not compete with the target sensitizer for photons. Although triplet quenching rate constants have been determined by other techniques (e.g., SterneVolmer or laser techniques), little data exists on triplet formation and steadystate concentrations. The method was validated by comparing the quenching rate constant of benzophenone triplets by tyrosine determined by the probe method against literature values determined by SterneVolmer and laser techniques. The method was applied to quantify the formation, scavenging and steady-state concentrations of Suwannee River NOM triplets as a function of increasing NOM concentrations.
100
0.00 300
350
400
450
Wavelength (nm) Fig. 1 e Lamp radiation spectrum versus solution component absorption spectra. The top panel shows absorption spectrum of 62.5 mM sorbic acid (solid line, left axis) and UVB/UVA lamp irradiation spectrum (dashed line, right axis). Bottom panel shows the absorbance (1 cm pathlength spectrophotometric cell) of a 4 mg-C/L SRNOM solution (solid line, left axis) and the full lamp irradiation spectrum with 315 nm cutoff filter (dashed line, right axis).
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Fig. 2 e HPLC chromatogram of a) benzoic acid (internal standard, retention time [ 13.6 min), b) cis-trans-HDA (retention time [ 17.8 min), c) cis-cis-HDA (retention time [ 19.2 min), d) trans-trans-HDA (retention time [ 21.1 min), and e) transcis-HDA (retention time [ 23.2 min). Vertical axis is detector response in mV and horizontal axis is time in min.
identifications, and assignment of the c,c-HDA peak, was conducted by matching the yields of each isomer, calculated using the molar extinction coefficients provided above, to et al. (2001) at the photostabilization those observed by Cigic point. Yield calculations related to the photostabilization point are further discussed in Section 3.1.
2.3.
Kinetic analysis
The kinetic analysis used is similar to previously developed probe analyses of transient species (Blough and Zafiriou, 1985; Mopper and Zhou, 1990; Zafiriou et al., 1990). It was assumed that bimolecular self-reaction between triplets is negligible. Chromophore excited triplet (T) formation (FT) is via intersystem crossing from the excited singlet (S1), with kisc as the rate constant for intersystem crossing (Eq. (1)). In the presence of the probe, the triplet removal rate (Eq. (2)) will be the sum of removal rates with solution scavengers, RS (Eq. (3)), and with the probe, Rp (Eq. (4)). At steady-state, triplet formation and removal rates are equal (Eq.(5)). Formation Rate ¼ FT ¼
Decay Rate ¼ RS þ Rp
d½T obs ¼ kisc ½S1 dt
(1)
(2)
RS ¼ kS ½Scavengers½T0SS ¼ k0S ½T0SS
(3)
RP ¼ kP ½Probe½T0SS ¼ k0P ½T0SS
(4)
FT ¼ RS þ RP
(5)
Here kS is the second-order rate constant for reaction between solution scavengers and T, and kP is the second-order rate constant for reaction between the probe and T, while k0S and k0P are pseudo-first order rate constants, assuming that both scavenger and probe concentrations are sufficiently large that their concentrations are effectively constant during the experimental time-scale. Combining Eqs. (3)e(5) and rearranging gives ½T0SS , the adjusted steady-state concentration of excited triplet in the presence of both scavengers and probe (Eq. (6)). ½T0SS ¼
FT k0S þ kP ½Probe
(6)
Substitution of Eq. (6) into Eq. (4) gives Eq. (7), a non-linear expression. Re-arrangement leads to the HaneseWoolf or Freundlich linearized form (Eq. (8)):
Rp ¼
FT kp ½probe kp ½probe þ k0S
(7)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 3 5 e6 5 4 4
½Probe ½Probe k0 ¼ þ S FT kP RP FT
(8)
Regressing [Probe]/Rp against [Probe] yields FT, k0S , and ½TSS , the steady-state concentration of triplets in the absence of the probe: FT ¼
1 slope
k0S ¼ kP $
½TSS ¼
(9)
intercept slope
(10)
FT 1 ¼ k0S kp $intercept
(11)
One can either use non-linear regression of Eq. (7) or linear regression of Eq. (8) to obtain the parameters. We have found linear regression of Eq. (8) to be slightly more reliable. Computations on simulated datasets of 17 points evenlyspaced on the abscissa were carried out in which Rp was assigned a randomized error of within 10% or 20% (using the random number generator in Microsoft Excel), and no error in [probe], prior to regression. If the error was kept within 10%, satisfactory results (i.e., accurate prediction of, and reasonable standard errors in the parameters) were obtained with either non-linear or linear regression provided that kp[probe] ranged up to at least 50% the value of k’s. If the error was kept within 20%, kp[probe] must range up to at least twice the value of k’s in the case of Eq. (8) and four times the value of k’s in the case of Eq. (7) to obtain satisfactory results. These results may differ depending on the number of points, how they are spread out, and the randomization process. When excited HDA relaxes back to the ground state, four isomers are possible, including reformation of the original t,tHDA (Scheme 1). For simplicity, only one product isomer (cis,trans-HDA or c,t-HDA) was used for quantification. Rp was therefore corrected for relative isomer formation rates (Eqn. (12)). The rate of c,t-HDA formation, Fc;tHDA , was determined experimentally. The yield of c,t-HDA compared to other isomer products,Yc;tHDA (Eqn. (13)), was 0.18 (see below). RP ¼ kP ½Probe½T0SS ¼
Yc;tHDA ¼
Fc;tHDA Yc;tHDA
Fc;tHDA Fc;tHDA þ Ft;cHDA þ Fc;cHDA þ Ft;tHDA
(12)
(13)
At high diene concentrations, the probability increases of an excited-state diene chemically reacting with a second groundstate diene to form a non-isomer product (Haller and Srinivasan, 1964; Hammond et al., 1964; Lamola and Hammond, 1965), reducing the observed quantum yields of isomerization resulting from interactions with the target sensitizers (i.e., SFisomers < 1). At lower diene concentrations this reaction becomes insignificant and isomerization dominates (i.e., SFisomers ¼ 1). Additionally, minimizing the concentration of t,t-HDA reduces the chance of overlap between the lamp irradiation spectrum and the absorbance spectrum of t,t-HDA which would result in direct photoisomerization and interference with the analysis of triplet concentrations. Because of these two potential interferences,
6539
it is desirable to avoid extremely high diene concentrations, while achieving concentrations high enough to characterize the saturation levels of Rp in Eq. (7); this issue is further examined below using the data on biacetyl triplet interactions with 2-propanol. If the t,t-HDA probe concentration is sufficiently small, all energy-transfer interactions between the excited sensitizer and t,t-HDA can be accounted for by monitoring HDA isomer yields. Although other types of interactions between excited chromophoretriplets and t,t-HDA are possible, these interactions do not lead to isomerization. The rate constant for interaction of t,t-HDA with NOM (kp) has not been determined, and likely varies with the identity and prevalence of difference chromophores. We estimated it from the average of 11 s-order rate constants for energy transfer from a wide range of excited triplet sensitizers to simple dienes, equal to 4.4 109 M1 s1 (4.29 109 M1 s1 standard deviation) (Table SI-1; Montalti et al., 2006).
3.
Results and discussion
3.1.
Method development
Experiments evaluated the possibility of direct photoisomerization of t,t-HDA. Small overlaps between the absorption spectrum of t,t-HDA and the lamp emission spectrum (Fig. 1) resulted in some direct photo-isomerization. Addition of plastic film filters (3 M, overhead projection slides) suspended in window frame-style holders between the lamps and samples, with a sharp transmittance cutoff at 315 nm (Fig. 1), were employed to minimize direct photoisomerization of t,t-HDA. Although t,t-HDA exhibited some absorbance above 315 nm, the associated direct photoisomerization was generally unimportant as long as probe concentrations were <1000 mM. Sensitized isomer formation was investigated in the initial rate kinetics regime, fulfilling two method requirements: i) that t,t-HDA would be the major isomer interacting with triplets, because other isomers would be at much smaller concentrations; and ii) that bleaching or degradation of the sensitizer would not significantly perturb the pseudo-steadystate conditions. Fig. 3 shows formation of c,t-HDA, the isomer used for quantification, with reaction time for several conditions using SRNOM as a triplet source. c,t-HDA formation was linear with time up to 40 h and then leveled off. Isomerization is a reversible process. At prolonged irradiation times, a stabilization point is reached where the formation and loss rates of each isomer become equal. Experiments with solution constituents that slowed or increased isomerization rates using SRNOM or biacetyl as triplet sources under a variety of conditions revealed that c,t-HDA formation was consistently linear for times 20 h. Even using 15 mg-C/L SRNOM, the yield of c,t-HDA was only w4% of the initial t,t-HDA concentration after 20 h. Formation rates were measured over times <30 h where formation was linear. Accounting for all interactions between triplet-excited organics and t,t-HDA is complicated by t,t-HDA reformation by relaxation of the excited HDA reaction intermediate formed during isomerization (Scheme 1). Yields of all isomer
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et al. (2001). As both direct UVC-UVA photon absorpof Cigic tion and energy transfer from triplet sensitizers should yield the same excited HDA intermediate, the relative relaxation rates should be similar in the two systems. Accordingly, the relaxation rate of t,t-HDA can be calculated relative to the directly quantifiable relaxation rate of c,t-HDA. With the relaxation rates of all 4 HDA isomers it was possible to determine the yield (Y; Eq. (13)) of c,t-HDA, the isomer used for quantification, as 0.18 during isomerization induced by energy transfer reactions with triplet sensitizers; the yields of t,cHDA, c,c-HDA, and reformation of t,t-HDA were 0.13, 0.12 and 0.57, respectively. The validity of this calculation was checked by a kinetic analysis of 6 replicate experiments containing only SRNOM, phosphate buffer and t,t-HDA, to calculate the rate constant for t,t-HDA reformation relative to that of c,t-HDA, using multiple linear regression in Matlab (see Supporting information). The relative standard deviations of values produced by this method were large (125%); however, the average value of t,t-HDA reformation relative to c,t-HDA formation was within 15% of the value determined using the photostabilization point. Experiments were performed to verify that common solution constituents would not affect isomer yields. Experimental yields of three HDA isomer products (c,t-HDA, t,c-HDA, c,cHDA) were compared for the different solution chemistries shown in Table 1. Addition of chloride, bromide, iodide and bicarbonate did not significantly impact isomer yields. The presence of dissolved oxygen did not significantly affect the yield of t,c-HDA relative to c,t-HDA; however, c,c-HDA formation was reduced by 19% compared to de-aerated conditions, likely due to reactions of singlet oxygen produced by excited NOM; singlet oxygen undergoes [4 þ 2] cyclo-addition to cis,cis1,3-dienes (Foote and Clennan, 1995). The relative yields of the other two isomers were not affected, indicating that quantification of triplets based on the c,t-HDA isomer was still valid in the presence of oxygen. Most of the experiments described involved 4 mg-C/L SRNOM under de-aerated conditions for reaction times of
[c,t - HDA] (µM)
0.5 0.4 0.3 0.2 0.1 0.0 0
10
20
30
40
50
60
Irradiation Time (h) Fig. 3 e cis, trans-HDA isomer formation with time. Black circles represent solutions containing 4 mg-C/L SRNOM, de-aerated. Open triangles represent solutions containing 4 mg-C/L SRNOM, aerated. Open squares represent solutions containing 15 mg-C/L SRNOM, de-aerated. 7 mM t,t-HDA in 20 mM phosphate buffer at pH 8.1.
products, including reformed t,t-HDA, are necessary to quantify triplet species. The yield of t,t-HDA reformation relative to the other isomers was estimated based on the stabilization point associated with direct photo-isomerization et al. (2001). induced by UVC-UVA light as determined by Cigic For photo-isomerization, the relative concentrations of isomers at the stabilization point would match their relative yields due to isomerization (i.e., yields excluding other potential degradation pathways). These yields would be a factor of both the tendency of the isomers to absorb light (i.e., their molar absorption coefficients), and the relative relaxation rates of the excited HDA intermediate to each ground state isomer (Carroll, 1998). However, the molar absorption coefficients of t,t-HDA and c,t-HDA within the UVA et al., 2001). Therefore differences range are within 10% (Cigic in relaxation rates controlled the relative concentrations of these two isomers at the stationary point in the experiments
Table 1 e Relative yields of HDA isomers with varying water constituents and percent change relative to solutions containing only NOM. Conditiona
NOM only þ100 mM NaCl þ100 mMNaBr þ1 mMKIb þ2.3 mM NaHCO3 þ1.3 mM O2c Averaged S.D.d RSD a b c d
c,t-HDA
t,c-HDA
c,c-HDA
Ratio to c,t e HDA
% Change
Ratio to c,t e HDA
% Change
Ratio to c,t e HDA
% Change
1 1 1 1 1 1 1 e e
e e e e e
0.82 0.83 0.80 0.79 0.81 0.85 0.81 0.014 1.69%
0.00 1.05 2.14 3.13 1.44 4.25
0.80 0.77 0.74 0.72 0.76 0.65 0.76 0.031 4.06 %d
0.00 2.95 7.04 10.10 5.32 18.72
S(isomer formation)/ t,t-HDA loss
All solutions contain 4 mg-C/L SRNOM þ 10 mM phosphate buffer, pH 8.1 and were de-aerated unless otherwise noted. Maximum KI concentration which did not induce direct photo-isomerization. Oxygen-saturated solution. Excluding data for 1.3 mM O2, dissolved oxygen-saturated, condition.
0.6 0.6 1.0 0.6 0.6 0.6
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0.0
2.0x10
5
[trans,trans-HDA] (M)
-3
1.4x10
-3
1.2x10
1.4x10
-3
-3
1.2x10
-3
1.0x10
-4
8.0x10
-4
6.0x10
-4
4.0x10
-4
2.0x10
0.0
0.0 -3
-10
5
1.0x10
3.0x10
4.0x10
-4
6.0x10
-10
8.0x10
-10
5
-4
9.0x10
6.0x10
6.0x10
-9
-4
1.2x10
5
4.0x10
-9
8.0x10
-4
1.5x10
To validate the t,t-HDA probe method, the rate constant for quenching of benzophenone triplets by tyrosine determined by the probe method was compared to literature values determined using either SterneVolmer plots of phosphorescence decay (Encinas et al., 1985) or laser flash photolysis (Canonica et al., 2000) as functions of tyrosine concentration. Following the procedure of Canonica et al. (2000), aqueous solutions buffered at pH 7 (50 mM phosphate) and containing 50e300 mM tyrosine, 50e1500 mM of t,t-HDA, and 50 mM benzophenone (BP, in methanol carrier; 0.5% v/v final methanol concentration) were irradiated for 21.5 h. The kinetic analysis requires input of kp, the rate constant for benzophenone triplet (3BP) reaction with t,t-HDA. Although this rate constant is not available, rate constants are available for 3BP quenching by the alkenes fumaronitrile (1.2 109 M1 s1) and (E )-stilbene (6.0 109 M1 s1) (Montalti et al., 2006). Fig. 5 presents the results of the kinetic analysis using the average of these quenching rate constants, 3.6 109 M1 s1, for kp. Tyrosine concentration had no significant effect on 3BP formation rate (Fig. 5A), consistent with previous research based on SterneVolmer plots of phosphorescence decay (Encinas et al., 1985) indicating that the 3BP quantum yield is 1.0, already maximal. However, increasing tyrosine concentrations increased the overall solution scavenging rate of 3BP (Fig. 5B), such that 3BP steadystate concentrations declined (Fig. 5C). Because solutions had been equilibrated with air, both oxygen and tyrosine were important 3BP scavengers in these solutions. A rate constant for 3BP scavenging by oxygen in water of 3 109 M1 s1 had been determined by laser flash photolysis (Canonica et al., 2000). A regression of the data in Fig. 5B was used to extract a 3BP scavenging rate constant by tyrosine, yielding 3.8 109 M1 s1 (0.33 109 M1 s1 standard error based upon the regression). This result compares favorably with the 3.6 109 M1 s1 value determined by a SterneVolmer plot of phosphorescence decay (Encinas et al., 1985), and the 2.6 109 M1 s1 value determined by laser flash photolysis (Canonica et al., 2000). The largest source of uncertainty in the calculated parameters is the estimate of the 3BP quenching rate constant by t,t-HDA. Substituting the corresponding rate constant for fumaronitrile or (E)-stilbene results in 3BP
2.0x10
-9
Method validation
0.0
1.8x10
3.2.
[trans,trans-HDA]/Rp (s)
-1
RP (M s )
18 h. Under these conditions, t,t-HDA was reduced by 13% on average, but the sum of all HDA isomer products accounted for only 65% of this loss. The most likely sink for the other 35% would be reaction with secondary photo-oxidants produced during photo-excitation of NOM. Although previous results (Velosa et al., 2007) indicated that reaction of t,t-HDA with HO or 1O2 was not significant, the very large reaction rate constants of alkenes with HO and 1O2 as reported in literature (NDRL, 2002), suggest that if these oxidants are present, they would degrade t,t-HDA. Addition of chloride, iodide, oxygen and bicarbonate did not significantly alter isomer yields versus t,t-HDA loss (Table 1). In the presence of 100 mM Br, all reacted t,t-HDA could be accounted for by isomer formation, suggesting that Br- may react with oxidants (i.e., HO (Grebel et al., 2009)) preventing HDA destruction. An example of the data used to quantify triplet behavior is shown in Fig. 4 for a de-aerated 15 mg-C/L SRNOM solution, the highest SRNOM concentration employed in this study. As [t,t-HDA] increases to 1000 mM, Rp levels off as the probe chemical scavenges nearly all NOM triplets. The values of FT, k’S and [T]ss obtained from a non-linear regression of Eq. (7) were 2.51 (0.07) 109 M s1, 2.68 (0.17) 106 s1, and 9.37 (0.65) 1016 M, respectively, using equation 11 to derive [T]ss. Using the linearized HaneseWoolf regression plot according to Eqn. (8) yielded FT, k’S and [T]ss values of 2.67 (0.19) 109 M s1, 3.13 (0.26) 106 s1, and 8.55 (0.38) 1016 M according to Eqs. (9)e(11). Note that the parameters derived by the non-linear and linear regressions agreed within 15%, indicating that no significant bias was introduced by use of the linear regression. The FT value compares favorably with the 1.3 108 M s1 value derived for illumination of a de-aerated 20 mg-C/L Elliott soil fulvic acid solution using a trimethylphenol probe (Halladja et al., 2007); k’s and [T]ss were not calculated. Using the linear regression, uncertainty increases in the order FT < ½TSS < k0S owing to the calculation procedure. For FT, the variance is based on the error in the slope, while for [T]SS, the variance is based on the larger error in the y-intercept. For the data presented in Fig. 4, the relative standard error of the y-intercept and slope were <7%. Because calculation of k0S depends on both the slope and y-intercept, the associated error propagation increases the uncertainty in this value, but the relative standard error was typically no greater than 10%.
-1
[trans,trans-HDA] (M )
Fig. 4 e Example of raw data and graphical data analysis. The solution contained 15 mg-C/L SRNOM, de-aerated, in 20 mM phosphate buffer, pH 8.1.
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6.0x10
-9
4.5x10
-9
3.0x10
-9
1.5x10
-9
3
BP Formation Rate (M/s)
a
b
0.0
0.05 0.10 0.15 0.20 0.25 0.30
Tyrosine (mM) 2.5x10
SRNOM triplet formation rates were not available. Accordingly, we evaluated whether the SRNOM triplet formation rates (F3NOM) determined by the probe method matched expectations. As anticipated, the value of F3NOM increased linearly with SRNOM concentrations ( p-value ¼ 0.009; Fig. 6), as the increasing concentration of chromophores enhanced triplet formation. This formation rate data was employed to derive the quantum yield for SRNOM triplet formation. The formation rate of excited singlets is equivalent to the rate of light absorption by NOM, kabs, as described by Eq. (14) (Turro et al., 2009), where I0 (A/V) is the incident light intensity in EinsteinsL1 s1, 3 is the absorption coefficient per cm and per unit mg-C/L of SRNOM, [ is the pathlength in cm, and [S0] is the
6 -1
-1
-1
0.0
0.05 0.10 0.15 0.20 0.25 0.30
-15
4x10
-15
3x10
-15
2x10
-15
1x10
-15
b
-1
5x10
k'S (s )
3
[ BP]ss (M)
c
3
Tyrosine (mM)
-9
2.5x10
-9
2.0x10
-9
1.5x10
-9
1.0x10
-9
5.0x10
-8
4.0x10
-8
3.5x10
-8
3.0x10
-8
2.5x10
-8
5.0x10
5
3.0x10
2.0x10
6
-8
1.0x10
a
1.5x10
6
-8
1.5x10
1.0x10
6
NOM Formation Rate (M s )
2.0x10
-1
k's (s )
kabs (Einsteins L s )
-10
0.0 4.0x10
6
3.5x10
6
3.0x10
6
2.5x10
6
2.0x10
6
1.5x10
6
1.0x10
6
4
6
8
10
12
14
16
14
16
14
16
[NOM] (mg-C/L)
5
0
5.0x10 0.0
0.05 0.10 0.15 0.20 0.25 0.30
4
6
Tyrosine (mM)
c
3
[ NOM]SS (M)
Fig. 5 e Effect of tyrosine on a) formation rate, b) scavenging rate constant, k0s and c) steady-state concentrations of benzophenone triplets (3BP). Solutions contained 50 mM phosphate at pH 7. Error bars are based upon the standard error of the regression analysis.
quenching rate constant by tyrosine of 1.3 (0.1) 109 M1 s1 or 6.3 (0.6) 109 M1 s1, respectively.
3.3.
Application to Suwannee River NOM
The t,t-HDA method was then used to investigate triplets of Suwanee River natural organic matter (SRNOM) as a function of SRNOM concentration in de-aerated solution. Data on
8
10
12
[NOM] (mg-C/L) 1.0x10
-15
8.0x10
-16
6.0x10
-16
4.0x10
-16
2.0x10
-16
0.0
4
6
8
10
12
[NOM] (mg-C/L) Fig. 6 e Effect of NOM concentration on triplet a) formation rate (F3NOM) b) scavenging rate constant ðk0s Þ c) steady-state concentration ([3NOM]SS) for Suwannee River NOM. Solutions were de-aerated and contained 20 mM phosphate at pH 8.1.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 3 5 e6 5 4 4
chromophore concentration as mg-C/L of SRNOM. At steadystate, the rate of singlet formation is equal to the sum of all singlet decay pathways, including isc, fluorescence, internal conversion and chemical reactions. The quantum yield of triplets, VT, is defined as the rate of their formation (FT) by isc from the excited singlet state (kisc[S1], see Eq. (1)), divided by the sum of all singlet decay pathways. Absorption coefficients per unit mg-C/L of SRNOM (3) were determined and using known irradiation characteristics of the photoreactor (i.e., I0 (A/V) and [, see Materials and Methods section), kabs for each SRNOM concentration were calculated and plotted against F3NOM to obtain VT (Eq. (15)) as 0.062. This quantum yield is likely similar to triplet yields from natural sunlight, although wavelengths <315 nm were excluded. This value is higher than previous estimates of triplet yields in natural waters based upon yields of singlet oxygen (VT ¼ 0.004e0.016; Zepp et al., 1985). However, the values are not directly comparable, as the triplet yield derived by the t,t-HDA probe was determined for SRNOM in de-aerated, synthetic solutions in a photoreactor, while those of Zepp et al. (1985) were for natural waters in equilibrium with atmospheric oxygen in natural sunlight. kabs
A ¼ I0 1 103 [½S0 V
(14)
FT ¼ FT kabs
laser flash photolysis methods require knowledge of triplet state absorbance spectra, which are difficult to obtain in the presence of a polychromaphoric substance like NOM and can exhibit interference from additional products, such as the ketyl radicals formed from benzophenone triplets (Canonica et al., 2000). The probe method provides additional benefits of simultaneous quantification of triplet formation rates and steady-state concentrations, and compatibility with polychromatic illumination. Despite the uncertainties resulting from kp estimates, the method can be applied to evaluate trends in triplet behavior associated with the systematic variation of solution components.
Acknowledgments This research was partially supported by a grant from the National Science Foundation (CBET-1066526).
Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.048.
(15) k0S
Increasing NOM concentrations also increased for SRNOM slightly; however this trend was not significant ( pvalue > 0.05, Fig. 6B). Overall, increased formation rates dominated over increased scavenging, leading to increased steady-state concentrations of SRNOM triplets ( p ¼ 0.004, Fig. 6C).
4.
6543
Conclusions
A novel method was developed using t,t-HDA as a probe to simultaneously quantify organic triplet formation rates, scavenging rate constants and steady-state concentrations under conditions relevant to natural systems. The method allows study of triplet behavior using polychromatic light without exact knowledge of the absorbance characteristics of the triplet chromophores, facilitating the examination of complex chromophores such as NOM under natural conditions. The method was validated against a literature value for the quenching of benzophenone triplets by tyrosine obtained by other methods. Additionally, the increase in formation rates of SRNOM triplets with increasing SRNOM concentrations matched expectations. The quantum yield of triplet formation of SRNOM under de-aerated conditions was found to be FT ¼ 0.062 for the 315e430 nm irradiation spectrum used in this study. A drawback for the probe method is the paucity of rate constants for triplet reactions with t,t-HDA (kp). For benzophenone, the largest single contributor to the uncertainty in the parameters calculated by the probe method is the estimate of the 3BP quenching rate constant by t,t-HDA. However,
references
Blough, N.V., Zafiriou, O.C., 1985. Reaction of superoxide with nitric oxide to form peroxonitrite in alkaline aqueous solution. Inorg. Chem. 24, 3502e3504. Canonica, S., Hellrung, B., Wirz, J., 2000. Oxidation of phenols by triplet aromatic ketones in aqueous solution. J. Phys. Chem. A 104, 1226e1232. Carroll, F.A., 1998. Perspectives on Structure and Mechanism in Organic Chemistry. Brooks/Cole Publishing Company, Pacific Grove, CA. , I.K., Plavec, J., Moz ic -Kralj, L., 2001. ina, S.S., Zupanc Cigic Characterization of sorbate geometrical isomers. J. Chrom. A 905, 359e366. Encinas, M.V., Lissi, E.A., Olea, A.F., 1985. Quenching of triplet benzophenone by vitamins E and C and by sulfur containing aminoacids and peptides. Photochem.Photobio. 42, 347e352. Foote, C.F., Clennan, E.L., 1995. Properties and reactions of singlet oxygen. In: Foote, C.S. (Ed.), Active Oxygen in Chemistry. Chapman and Hall, London, UK. Grebel, J.E., Pignatello, J.J., Song, W., Cooper, W.J., Mitch, W.A., 2009. Impact of halides on the photobleaching of dissolved organic matter. Marine Chem. 115, 134e144. Grebel, J.E., Pignatello, J.J., Mitch, W.A., 2010. Effect of halide ions and carbonates on organic contaminant degradation by hydroxyl radical-based advanced oxidation processes in saline waters. Environ. Sci. Technol. 44 (17), 6822e6828. Halladja, S., Ter Halle, A., Aguer, J.-P., Boulkamh, A., Richard, C., 2007. Inhibition of humic substances mediated photooxygenation of furfuryl alcohol by 2,4,6trimethylphenol. Evidence for reactivity of the phenol with humic triplet excited states. Environ. Sci. Technol. 41, 6066e6073. Haller, I., Srinivasan, R., 1964. Photochemistry of 1,3-butadiene: details of the primary processes and mechanism of photopolymerization. J. Chem. Phys. 40 (7), 1992e1997.
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Hammond, G.S., Saltiel, J., Lamola, A.A., Turro, N.J., Bradshaw, J.S. , Cowan, D.O., Counsell, R.C., Vogt, V., Dalton, C., 1964. Mechanisms of photochemical reactions in solution. XXII. Photochemical cis-trans isomerization. J. Am. Chem. Soc. 86, 3197e3217. Koziar, J.C., Cowan, D.O., 1978. Photochemical heavy-atom effects. Accts. Chem. Res. 11, 334e341. Kuzmin, V.A., Chibisov, A.K., 1971. One-electron photo-oxidation of inorganic anions by 9,10-anthraquinone-2,6-disulfonic acid in the triplet state. J. Chem. Soc. D. Chem. Comm. 23, 1559e1560. Lamola, A.A., Hammond, G.S., 1965. Mechanisms of photochemical reactions in solution. XXXIII. Intersystem crossing efficiencies. J. Chem. Phys. 43 (6), 2129e2135. Loeff, I., Treinin, A., Linschitz, H., 1984. The photochemistry of 9,10-anthroquinone-2-sulfonate in solution. 2. Effects of inorganic anions: quenching vs. radical formation at moderate and high anion concentrations. J. Phys. Chem. 88, 4931e4937. Montalti, M., Credi, A., Prodi, L., Gandolfi, M.T., 2006. Handbook of Photochemistry, third ed.. CRC Press, Boca Raton, FL. Mopper, K., Zhou, X., 1990. Hydroxyl radical photoproduction in the sea and its potential impact on marine processes. Science 250, 661e664. Notre Dame Radiation Laboratory, 2002. Kinetics Database. Radiation Chemistry Data Center. www.rcdc.nd.edu (accessed 26.05.2010).
Rabek, J.F., 1982. Experimental Methods in Photochemistry and Photophysics, Part 2. John Wiley & Sons Publishing, New York. 945e947. Treinin, A., Loeff, I., Hurley, J.K., Linschitz, H., 1983. Chargetransfer interactions of excited molecules with inorganic anions: the role of spin-orbit coupling in controlling net electron transfer. Chem. Phys. Lett. 95, 333e338. Turro, N.J., Engel, R., 1969. Quenching of biacetyl fluorescence and phosphorescence. J. Am. Chem. Soc. 91, 7113e7121. Turro, N.J., Ramamurthy, V., Scaiano, J.C., 2009. Principles of Molecular Photochemistry: An Introduction. University Science Books, Sausalito, CA. Velosa, A.C., Baader, W.J., Stevani, C.V., Mano, C.M., Bechara, E.J. H., 2007. 1,3-Diene probes for detection of triplet carbonyls in biological systems. Chem. Res. Toxicol. 20, 1162e1169. Zafiriou, O.C., Blough, N.V., Micinski, E., Dister, B., Kieber, D., Moffett, J., 1990. Molecular probe systems for reactive transients in natural waters. Marine Chem. 30, 45e70. Zepp, R.G., 1978. Quantum yields for reaction of pollutants in dilute aqueous solution. Environ. Sci. Technol. 12 (3), 327e329. Zepp, R.G., Schlotzhauer, P.F., Sink, R.M., 1985. Photosensitized transformations involving electronic energy transfer in natural waters: role of humic substances. Environ. Sci. Technol. 19, 74e81.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
Available online at www.sciencedirect.com
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Formation of halogenated organic byproducts during medium-pressure UV and chlorine coexposure of model compounds, NOM and bromide Quan Zhao a,1, Chii Shang a,*, Xiangru Zhang a, Guoyu Ding a, Xin Yang b a
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong b School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, China
article info
abstract
Article history:
When chlorine is applied before or during UV disinfection of bromide-containing water,
Received 29 April 2011
interactions between chlorine, bromide and UV light are inevitable. Formation of haloge-
Received in revised form
nated organic byproducts was studied during medium-pressure UV (MPUV) and chlorine
25 August 2011
coexposure of phenol, nitrobenzene and benzoic acid and maleic acid, chosen to represent
Accepted 27 September 2011
electron-donating aromatics, electron-withdrawing aromatics, and aliphatic structures in
Available online 15 October 2011
natural organic matter (NOM), respectively. All were evaluated in the presence and absence of bromide. MPUV and chlorine coexposure of phenol produced less total organic halogen
Keywords:
(TOX, a collective parameter for halogenated organic byproducts) than chlorination in the
Medium pressure
dark, and more haloacetic acids instead of halophenols. Increases in TOX were found in the
Ultraviolet
coexposure of nitrobenzene and benzoic acid, but maleic acid was rather inert during
Total organic halogen (TOX)
coexposure. The presence of bromide increased the formation of brominated TOX but did
Photolysis
not significantly affect total TOX formation, in spite of the fact that it reduced hydroxyl
Disinfection
radical levels. MPUV and chlorine coexposure of NOM gave a higher differential UV absorbance of NOM and a larger shift to lower molecular weight compounds than chlorination in the dark. However, TOX formation with NOM remained similar to that observed from dark chlorination. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Ultraviolet (UV) irradiation is a promising “byproduct-free” alternative to achieve primary disinfection in drinking water treatment. It can be used together with chlorine to achieve multiple-barrier disinfection and give residual protection. When chlorine is added before or during UV disinfection (e.g., for preoxidation), interactions between chlorine and UV light are inevitable, creating a new spectrum in alternation of
natural organic matter (NOM) and formation of disinfection byproducts (DBPs). Chlorine photolysis has been studied over several decades. Depending on the pH, exposure of aqueous chlorine (HOCl/ OCl, pKa of 7.5) to sunlight, UV or electron sources initiates a series of radical-type chain reactions in which hydroxyl radicals (OH) and chlorine radicals (Cl) serve as major intermediates, and chloride, chlorate and oxygen are the final products (Buxton and Subhani, 1972; Nowell and Hoigne´, 1992):
* Corresponding author. Tel.: þ86 852 2358 7885; fax: þ86 852 2358 1534. E-mail address:
[email protected] (C. Shang). 1 Present address: Water & Urban Development, AECOM Asia Co. Ltd., Shatin, New Territories, Hong Kong. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.053
6546
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
HOCl OCl
þ hv/OH O þ Cl
OH þ HOCl OCl
Cl þ HOCl OCl
/ClO þ H2 O OH
/ClO þ HCl Cl
2ClO/Cl2 O2 /products
(1)
(2) (3)
(4)
With typical disinfection doses (less than 500 mJ/cm2), photolysis of chlorine under UV irradiation from low-pressure (LP) or medium-pressure (MP) Hg lamps has been found efficient in producing OH with a quantum yield (FOH) ranging from 1.2 to 1.7 mol/Es (Watts and Linden, 2007). Such a high free radical yield indicates potential for using UV and chlorine coexposure for both disinfection and advanced oxidation to achieve control of pathogens and micropollutants simultaneously. When bromide exists in the source water, bromine will be rapidly formed upon chlorination (Kumar and Margerum, 1987). Although the presence of bromide during UV and chlorine coexposure does not affect the overall photolysis rate of free halogen, it may result in a considerable decrease in OH concentration compared to that produced from UV and chlorine coexposure in the absence of bromide (Zhao et al., 2009). So the presence of bromide may hinder OH-related reaction pathways during UV and chlorine coexposure. NOM is ubiquitous in natural water and serves as the major precursor of DBPs. Upon UV and halogen coexposure, NOM may react simultaneously with free halogen species and their radical descendents (OH and X). In consequence, the DBPs formed may differ significantly from those upon chlorination in the dark. The presence of bromide further complicates the chemistry. Information on DBP formation during combined UV and chlorine treatment is scarce and mainly focused on sequential exposure to UV and free chlorine/chloramine. Insignificant DBP increases in sequential exposure to UV and chlorine have been reported in some investigations (Machey et al., 2000; Kashinkunti et al., 2004; Bukhari et al., 2004). Liu et al. (2006) found statistically significant enhancements in the formation of chloroform, dichloroacetic acid, trichloroacetic acid and cyanogen chloride from additional UV irradiation compared to chlorination or chloramination alone. More recently, Reckhow et al. (2010) has observed increases in chloropicrin and 1,1,1-trichloropropoanone as a result of medium but not low-pressure UV-induced photonitration. With respect to the residual impact of UV irradiation on the DBPs precursors, it is generally agreed that UV irradiation at disinfection doses can itself degrade NOM from large molecules to smaller ones (Frimmel, 1998; Magnuson et al., 2002; Lehtola et al., 2003) and so can result in increased levels of assimilable organic carbon (Shaw et al., 2000). But to our knowledge, the impacts of UV and chlorine coexposure on changes in NOM and DBP formation have not yet been reported. The UV and chlorine coexposure process, particularly using medium-pressure UV (MPUV) involving high free radical concentrations, is fundamentally different from sequential exposure and deserves to be better understood. This study was designed to compare the formation of halogenated organic byproducts during MPUV and chlorine
coexposure (hereafter referred to as “coexposure”) and during chlorination in the dark (hereafter referred to as “dark chlorination”). Selected model organic compounds and NOM were treated in the presence or absence of bromide, and total organic halogen (TOX) was observed as a collective variable. The model organic compounds were phenol, nitrobenzene, benzoic acid and maleic acid. Among the four model organic compounds studied: benzoic acid and nitrobenzene have aromatic structures with electron-withdrawing groups, phenol has an electron-rich aromatic structure typical of NOM molecules, and maleic acid represents alky structures and derivatives from ring-opened aromatic structures. UV absorbance, high performance size exclusion chromatography (HPSEC), ultra-performance liquid chromatography (UPLC) and electrospray ionization-triple quadruple mass spectrometry (ESI-tqMS) were used to track the changes in NOM molecules resulting from coexposure and dark chlorination. It should be noted that such coexposure in the presence of bromide may also favor the formation of halate due to the formation of hydroxyl or halogen radicals from UV photolysis of halogen. Increases in chlorate and bromate formation were observed in our parallel study, which are discussed in another paper under revision (unpublished data).
2.
Materials and methods
2.1.
Chemicals
Solutions were prepared from reagent-grade chemicals and ultrapure water (18.2 MU/cm) supplied by a NANOpure system (Barnstead). A dilution of NaOCl from 15% active chlorine (Allied Signal) was used as a free chlorine stock solution (15002000 mg/L as Cl2). Phenol, nitrobenzene, benzoic acid, and maleic acid (structures shown in Fig. S1 in the Supporting information) were of analytical grade or better. They were obtained from SigmaeAldrich. Suwannee River NOM (Cat. No. 1R101N) obtained from the International Humic Substances Society was dissolved in ultrapure water and filtered through a 0.45-mm membrane filter to make a stock NOM solution.
2.2.
Irradiation system
A collimated beam UV apparatus with one MPUV lamp (UVV5, Hanovia) was used as the UV source. During irradiation, a glass Petri dish (30 mm ID) containing the test solution (5 mL, approximately 7 mm in depth) was placed in the beam center at the end of the collimating tube and completely mixed using a mini magnetic stirrer. At the point of application, the incidence of the MPUV irradiation was measured using a spectroradiometer (RPS900-R, International light) and fixed at 0.37 mW/cm2 in all experiments. It should be noted that the germicidal fluence rate of this MPUV setup as measured by MS2 biodosimetry (Bolton and Linden, 2003) was equivalent to 0.20 mW/cm2. The exposure time was controlled using a manual shutter system above the irradiation vessel and measured with a stopwatch. The UV fluence (in mJ/cm2) was calculated as the product of the incidence and the exposure time (in seconds).
6547
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
2.3.
Experimental procedures
The coexposure experiments were initiated by NaOCl addition (0.28 mM) and simultaneous exposure to MPUV irradiation using a model compound solution (20 mM) buffered at pH 6.5 or 8.5 with 50 mM phosphate in the absence or presence of bromide (50 mM). Once a desired reaction time was reached, the test solution was quenched with 120% of the requisite stoichiometric amount of sulfite and withdrawn for analyses of TOX and ESI-tqMS analyses. In parallel batches, hydroxyl radical production tests were carried out using a similar approach in phosphate buffer predosed with 4 mM para-chlorobenzoic acid ( pCBA). The dark chlorination experiments were performed in amber bottles at the same concentrations of chlorine, bromide and the organic compounds and the same reaction times. In addition to the model compounds, NOM solution (5 mg/L as DOC), pH buffered, was subjected to the same parallel coexposure and dark chlorination. The treated solutions were analyzed using UV absorbance, HPSEC, TOX and ESI-tqMS.
2.4.
Analytical methods
DOC was quantified using a TOC analyzer (TOC-5000A, Shimadzu). Free chlorine and free bromine were measured by the DPD colorimetric method (4500-Cl G) (APHA, 1995). UVevis spectra were recorded with a UVevis spectrophotometer (Lambda25, PerkinElmer) with 1-nm span. The concentration of pCBA was determined by HPLC (VP series, Shimadzu) with a C18 column (4.9 150 mm, Alltech) using an acetonitrilephosphate (10:90, v/v) buffered (pH 6.8, 40 mM) eluent at 1 mL min1 and a detection wavelength of 233 nm. The steady-state concentration of OH ([OH]SS) was calculated as follows (Elovitz and Von Gunten, 1999): d½pCBA= ¼ kOH;pCBA ½OHSS ½pCBA kOH;pCBA ¼ 5 109 M1 s1 dt
(5)
TOX was determined using a precombustion station (AQF100, Mitsubishi) with an on-line ion chromatograph (ICS-90, Dionex) as the halide detector and a method modified from Standard Method 5320B (APHA, 1995). The TOX was reported as the equivalent sum of total organic chlorine (TOCl) and total organic bromine (TOBr). HPSEC was conducted in accordance to Chin et al. (1994) using a HPLC (VP series, Shimadzu) equipped with a photodiode array UVevis detector (SPD-M10A). A TSK-GEL column (G3000PWxl, 7.8 300 mm, Tosoh Bioscience) was calibrated with polystyrene sulfonate standards (American Polymer Standard) with molecular weights (MWs) of 1, 3, 7, 15 and 41 KDa and salicylic acid (MW 138 Da). The HPSEC mobile phase was a 0.004 M phosphate buffer adjusted to an ionic strength of 0.1 M with NaCl at pH 6.8. The flow rate was 1 mL/min, the sample injection volume was 25 mL, and the detector wavelength was set at 280 nm. A Waters ACQUITY TQD, consisting of an Acquity UPLC system, a diode array detector (DAD), and an ESI-tqMS, was used to identify the reaction products using model organic compounds, and to obtain the full scan mass spectra of the treated model compounds and NOM. Chromatographic separation was achieved using an Acquity HSS T3 column (2.1 50 mm, 1.8 mm particle size, Waters) and an acetonitrilewater (50:50, v/v) isocratic mobile phase at a flow rate of
0.40 mL/min. The sample injection volume was 10 mL. The ESItqMS was operated in the negative ion mode for all sample analyses. Its detailed operating conditions are described in Table S1 in the Supporting information.
2.5.
Sample pretreatment for mass spectrometry
Liquidliquid extraction with methyl tert-butyl ether was used to pretreat the samples to simultaneously exclude inorganic ions and concentrate the organic compounds, following the method developed by Zhang’s group (Zhang et al., 2008). In brief, the samples were extracted with a tenth of the sample volume of methyl tert-butyl ether. The organic phase was concentrated to 0.5 mL on a rotary evaporator. Ten mL of acetonitrile was mixed with the concentrate and rotavapored back to 0.5 mL. The 0.5 mL of solution was diluted with water to 1.0 mL and filtered through a 0.22-mm membrane filter prior to ESI-tqMS or UPLC/ESI-tqMS analysis.
3.
Results and discussion
3.1.
Coexposure products of model compounds
The key feature making UV and chlorine coexposure distinct from dark chlorination is the presence of various reactive radicals. For the coexposure in the absence of bromide, Nowell and Hoigne´ (1992) demonstrated that both OH and Cl serve as primary photooxidants during the photolysis of aqueous chlorine at visible and ultraviolet wavelengths. When bromide ion was introduced into the coexposure, photolysis of free bromine also formed Br. According to Haag and Yao (1992), OH is highly reactive and fairly nonselective toward CH bonds. The reactivity of Cl toward organic compounds is comparable to that of OH (Buxton et al., 2000), while the reactions between Br and organic compounds are much Nyi and Lind, 1994). slower (Merec However, in the presence of chloride ions, an equilibrium (Eq. (6)) is established:
Cl þ Cl 4Cl2
K ¼ 1:4 105 M1
(6)
(Buxton et al., 1998) The NaOCl stock solution used in this study contained a high concentration of chloride ions (about 10 times the concentration of the reactive chorine species), so Cl 2 could act as an effective sink for Cl. Although Cl 2 itself is also a strong oxidant (E(Cl 2 /2Cl ) ¼ 2.0 V, (Beitz et al., 1998)), the reactivity of Cl2 toward CeH bonds is very low as compared to Cl. For example, the rate constants for Cl 2 and Cl with benzene are <1 105 M1s1 and 6 109 to 1.2 1010 M1s1, respectively (Alegre et al., 2000). So OH would serve as the major radical initiating chain reactions in the coexposure process explored. Fig. 1 shows the TOX formed from benzoic acid after dark chlorination and coexposure at pH 6.5 and 8.5 in the absence and presence of bromide. There was negligible reaction between benzoic acid and either chlorine or bromine (formed from chlorination of bromide) in the dark, since the presence of an electron-withdrawing carboxyl group deactivated the ring structure of benzoic acid and hindered electrophilic
6548
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
attacks by halogen species (Larson and Rockwell, 1979). Coexposure, on the other hand, significantly enhanced halogen incorporation reactions, as indicated by the increases in TOX formation. Such photochlorination of thermally chlorine-resistant organic compounds (e.g., benzoic acid and n-butanol) has also been reported in the literature (Oliver and Carey, 1977). Depending on the pH, the presence of bromide either promoted or retarded the enhancement. The TOX formation from nitrobenzene after dark chlorination and coexposure in the absence or presence of bromide is also presented in Fig. 1. Coexposure increased TOX formation compared to dark chlorination. In fact, the nitro group also serves as an electron-withdrawing group, so the overall
a
TOX ( M)
6
pH 6.5
5
5
4
4
3
3
2
2
TOX ( M)
0 Nitrobenzene
3
2
2
1
1
0
0
N.D.
N.D.
N.D. N.D.
6
Maleic acid
5
5
4
4
3
3
2
2
1
1
N.D. N.D.
0 100
Dark chlorination MPUV and chorine coexposure
4
3
6
pH 8.5
1 N.D.
0 4
TOX ( M)
b 6
Bezoic acid
1
TOX (µM)
trend can be expected, as with benzoic acid. TOX formation was, however, less significant than with benzoic acid. At pH 8.5 and without bromide, no TOX was formed from dark chlorination or coexposure with nitrobenzene. The enhancement of TOX formation from benzoic acid and nitrobenzene during the coexposure can be explained by OH attack. Once exposed to OH, salicylic acid and o-, m-, and pnitrophenol have been identified as the major intermediates generated from benzoic acid and nitrobenzene, respectively (Oliver and Carey, 1977; Rodriguez et al., 2003). These intermediates, carrying electron-donating groups, are much more reactive toward electrophilic halogenation than their precursors. For example, the reaction rate constants between
0
N.D. N.D.
100
Phenol
80
80
60
60
40
40
20
20
0
0
NaOCl
NaOCl:Br5.6
NaOCl
NaOCl:Br5.6
Fig. 1 e TOX formation (TOCl, TOBr) from benzoic acid, nitrobenzene, maleic acid, and phenol after dark chlorination and the MPUV and chlorine coexposure at (a) pH 6.5 and (b) pH 8.5 (initial chlorine dosage 0.28 mM, initial organic compound dosage 20 mM, contact time 10 min, N.D.: undetectable).
6549
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
chlorine and salicylic acid are orders of magnitude higher than that between chlorine and benzoic acid (Deborde and Von Gunten, 2008). Hence the OH attack during coexposure may significantly activate the ring structures with low electron density and accelerate TOX formation. As also shown in Fig. 1, maleic acid was inert to chlorine as well as to coexposure at pH 6.5 and 8.5. On the other hand, TOBr only was formed in the bromine cases (chlorination in the presence of bromide) at pH 6.5 and 8.5. The results from the coexposure trials in the presence of bromide were merely the same. Maleic acid is readily reactive toward OH with a rate constant of 6 109 M1s1 (Buxton et al., 1988) leading to the formation of malic acid, oxalic acid, formic acid (HCOOH), and finally CO2 (Scheck and Frimmel, 1995). Since maleic acid itself cannot generate any TOCl and these degradation products are even less reactive toward electrophilic halogenation,
it is not surprising that no TOCl was formed upon coexposure (Fig. 1). In the presence of bromide, TOBr formed from dark chlorination was comparable to that formed during coexposure and higher than that formed in the absence of bromide, which can be attributed to the greater reactivity of free bromine species toward organic precursors compared to their chlorine counterparts (Westerhoff et al., 2004). Dark chlorination and coexposure of phenol in the presence and absence of bromide yielded much higher TOX than did the other three model precursors (Fig. 1). Unexpectedly, the coexposure led to less TOX formation from phenol than dark chlorination. Because of the much higher production of halogenated byproducts from phenol, we were able to explore the different products formed during the coexposure and dark chlorination using UPLC and ESI-tqMS. Fig. 2(a) shows the direct infusion
a 195 197
Scan ES-
%
100
199
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
m/z 300
b 255
100
Scan ES-
195 105 97 157 61
227
89
241
%
121
199
145
0
20
40
60
80
100
120
140
289
173
143
160
283
187 205
180
200
220
240
260
280
m/z 300
Fig. 2 e Full scan spectra of phenol after (a) dark chlorination (Y-axis maximum intensity 3.06 3 109) (Initial phenol dosage 20 mM, free chlorine dosage 0.28 mM, contact time 10 min, pH 6.5) and (b) the MPUV and chlorine coexposure (Y-axis maximum intensity 6.28 3 107).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
OH þ RH/R þ H2 O
(7)
OH þ AR/AROH
(8)
For aliphatic structures, the alkylperoxyl radicals formed will go through unimolecular decay by eliminating O 2 (yielding radical cation) or HO2 (yielding a C]C or C]O bond) (Von Sonntag et al., 1997). For aromatic structures, the peroxyhydroxycyclohexadienyl radicals formed (in a mixture of ortho and para isomers) could either eliminate HO2 and form phenol-like structures or undergo an intramolecular peroxide forming reaction that eventually leads to ring opening (Pan et al., 1993). Ultimately all the radical chain reactions will be terminated by bimolecular decay leading to final products (e.g., alcohols, ketones, esters, and acids) depending on their precursors (Von Sonntag et al., 1997). It is however worth mentioning that the organic radicals generated from OH-induced chain reactions, though bearing unpaired electrons, did not seem to contribute to distinguishable TOX formation. This may be due to the very short lifetimes of these reactive organic radicals. It suggests that stable intermediates and final degradation products, rather than organic radicals, play key roles in the formation of TOX during this coexposure.
a
pH 6.5 16 14
Dark chlorination MPUV and chlorine coexposure
12 TOX (µM)
ESI-tqMS full scan spectrum of phenol after dark chlorination. Trichlorophenol (ion cluster at m/z 195/197/199/201 with a ratio of 3:3:1:0.1) was the major product detected. Upon coexposure, trichlorophenol was no longer the major product (Fig. 2(b)); instead, a broader range of molecular ions showed up, suggesting diversified fragments. It is worth mentioning that the fragments located near the high MW end are very likely to be adducts of low molecular ions considering the mild ionization conditions employed (Table S1 in the Supporting information) (Zhai and Zhang, 2009). In the presence of bromide, a similar shift from tribromophenol to diversified fragments was observed after coexposure (Figure S2 in the Supporting information). With precursor ion scans of m/z 79 and 81, the bromine-containing products could be further identified. During dark chlorination in the presence of bromide, besides tribromophenol (m/z 327/ 329/331, 329/331/333), the pair of ion fragments with m/z of 189 and 191 indicated the presence of another brominecontaining product (Figure S3 in the Supporting information). Product iron scans of m/z 189 and 191 indicated that the compound contains a carboxylic group and one bromine atom (Figure S4 in the Supporting information), which could correspond to OOCCH]CHCBr]CHCH3 or OOCCBr]CHCH]CHCH3. After coexposure in the presence of bromide (Figure S5 in the Supporting information), the pairs (m/z 171/173, 173/175) and (m/z 215/217, 217/219) can readily be attributed to bromochloroacetic acid (BrClCHCOO) and dibromoacetic acid (Br2CHCOO), respectively. A small amount of tribromophenol (m/z 327/329/331, 329/331/333) can be observed as well. The pairs m/z (141/143) (193/195), and (349/351) with ratios close to 1 to 1 represent unknown ion fragments that may contain a single bromine atom. All these evidences suggest that the coexposure significantly shifted the aromatic structures (not only phenol but also halophenol) into low MW polar products, which eluted earlier in the UPLC than aryl structures. The UPLC/ESI-tqMS analysis confirmed the enhanced ring opening of phenol during MPUV/chlorine coexposure and it can be considered as a deactivation process (Fig. 1), because phenol is much more reactive toward electrophilic halogen attack than its ring-opened fragment products are. Such a deactivation process would significantly hinder TOX formation kinetically and cause a structural shift among the halogenated products. In addition, should loss of unsaturated bonds occur during OH attack and the chain reactions, e.g. formation of saturated carboxylic acids, it would eventually jeopardize the TOX formation potential of phenol. It is also well known that OH readily reacts with organic compounds by either H-abstraction from the CeH bond (Eq. (7)) or addition to a C]C bond (Eq. (8)), where the addition reaction is generally the faster (Von Sonntag, 2007).
10 8 6 4 2 0 NaOCl:Br5.6
NaOCl
b
pH 8.5 16 14
Dark chlorination MPUV and chlorine coexposure
12
TOX (µM)
6550
10 8 6 4 2 0
Following initiation of the chain reactions, peroxyl radicals (i.e., ROO) would be formed at diffusion-controlled rates in the presence of dissolved oxygen (Foote et al., 1995): R þ O2 /ROO
(9)
NaOCl
NaOCl:Br5.6
Fig. 3 e TOX formation (TOCl, TOBr) from dark chlorination and the MPUV and chlorine coexposure of NOM at (a) pH 6.5 and (b) pH 8.5 (initial chlorine dosage 0.28 mM, initial NOM dosage 5 mg/L, contact time 10 min).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
6551
Fig. 4 e Full scan mass spectra of NOM solution from direct infusion: (a) NOM blank (Y-axis maximum intensity 6.34 3 107), (b) NOM after dark chlorination with Br- (Y-axis maximum intensity 2.21 3 107), (c) NOM after the MPUV and chlorine coexposure with Br- (Y-axis maximum intensity 5.18 3 107), (Initial chlorine dosage 0.28 mM, bromide dosage 0.05 mM, contact time 10 min).
6552
3.2.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
Coexposure products of NOM
Dark chlorination and coexposure of the NOM were conducted to compare TOX formation with that using the model organic compounds. Fig. 3 shows TOX formation from NOM during coexposure and dark chlorination in the presence and absence of bromide. No significant difference in TOX formation was observed between the coexposed samples and the corresponding dark chlorination controls in the absence of bromide. In the presence of bromide, coexposure led to a slightly less TOX formation compared to the dark control. In addition, to explore any possible residual effect of the coexposure, the NOM samples after 10 min of coexposure were stored for 24 h and then quenched for TOX analysis in parallel experiments. Compared with dark chlorination, no significant residual effect from coexposure on 24-h TOX formation was found (data not shown). Fig. 4 compares the impact of the different treatment schemes on the full scan mass spectra of the NOM solution obtained by direct infusion to ESI-tqMS. Fig. 4(a) displays the continuously distributed ion fragments from the untreated NOM solution, which centered at m/z of 300. Dark chlorination with Br (Fig. 4(b)) did not affect the pattern of the full scan spectra of the NOM much. The MPUV/chlorine coexposure with Br (Fig. 4(c)), however, led to observable changes in the full scan spectra of the NOM, including a new peak cluster around m/z of 100 and reduction in the relative abundance of the peak cluster at m/z of 300, both of which indicate a shift in the MW distribution toward lower MWs. It is worth mentioning that such results were applicable only to the polar and ionizable fractions of NOM, considering the extraction procedures and the ionization mode employed. The HPSEC results with NOM after dark chlorination and the coexposure (Figure S6 in the Supporting information) also confirmed that the MW distribution of chromophores in the NOM slightly shifted toward lower MWs. The differential UV absorbance (ΔUVA) obtained with NOM from MPUV irradiation, dark halogenation, and the MPUV and chlorine coexposure is shown in Figure S7 in the Supporting information. At a fluence of 220 mJ/cm2, MPUV irradiation alone caused negligible change in the UV absorbance of the NOM solution. Coexposure at the same UV fluence and chlorine concentrations increased ΔUVA as compared to dark chlorination. Figure S8 in the Supporting information compares the [OH]SS resulting from MPUV/chlorine coexposure of NOM at pH 6.5 with that at 8.5 in the absence and presence of bromide. The presence of bromide resulted in decreased [OH]SS as compared with bromide-free coexposure but the difference is not significant. This finding suggests that bromide does not significantly affect the photochemical process. On the other hand, the [OH]SS at pH 6.5 was always higher than that at pH 8.5, regardless of the presence or absence of bromide. It can be seen that the scavenging rate of OH and the consequent [OH]SS was controlled predominantly by protonation and deportation of halogen species. In addition, the amount of available chromophores (e.g., phenol-like structures) present in NOM may limit OH-induced chain reactions. The MPUV and chlorine coexposure may serve as an accelerator for the oxidative transformation of NOM and TOX, most of which would also happen during dark halogenation but take much longer.
In parallel with the photochemical processes, thermal halogenation occurred simultaneously (Oliver and Carey, 1977; Rodriguez et al., 2003). As discussed in Section 3.1, in the MPUV and chlorine coexposure of model organic compounds, TOX formed through halogenation of the precursors, intermediates and final products resulting from the chain reactions. However, depending on the competition between removal of structures responsible for TOX formation (e.g., phenol) and the formation of new reactive structures (e.g. aldehydes and ketones), OH attack during the coexposure could activate or deactivate some structures responsible for TOX formation, while other structures remained rather refractory (e.g., maleic acid in the absence of bromide). So during coexposure of NOM, transformation of precursors may alter the kinetics of TOX formation and shift the composition of the halogenated products. The shift from large MW NOM to lower MW NOM (Fig. 4 and Fig. S6) supports this assertion. Another reason responsible for the small changes in TOX from the MPUV/chlorine coexposure of NOM could be OHinduced dehalogenation of TOX. A supplementary experiment was conducted applying photolysis of H2O2 as a halogen-free OH source. After 24-h of dark halogenation of NOM by 28 mM NaOCl or NaOBr, the test solution (free of residual halogen) was subjected to MPUV/H2O2 (10 mg/L) coexposure for 10 min. Such a condition yields an [OH]SS of 1.5 1013 M. At pH 6.5, 18% of the preformed TOCl and 28% of the TOBr were destroyed by the coexposure. At pH 8.5, 54% and 37% of the preformed TOCl and TOBr were destroyed. Since the OH generated from the coexposure was independent of solution pH, the enhanced TOX decomposition at higher pH may be attributed to the deprotonation of TOX molecules. Higher pH favored the deprotonation of TOX molecules, which made them more susceptible to electrophilic OH attack.
4.
Conclusions
Compared to dark chlorination, TOX formed more during MPUV and chlorine coexposure of benzoic acid and nitrobenzene, similar of maleic acid, and less of phenol. The presence of bromide shifted the TOX to brominated species, but did not result in a significant impact on TOX formation during coexposure. During coexposure, NOM underwent significant loss in UVA due to preferential removal of high MW chromophoric structures accompanied by a shift in the MW distribution toward lower MWs. The TOX formed during MPUV and halogen coexposure did not vary significantly from that formed by halogenation alone for NOM. With the UV fluence applied in the current study (i.e. up to 440 mJ/cm2 for disinfection purposes), there was no observable drop in DOC during the UV and halogen coexposure process, which indicates no mineralization occurred. In such a mild photochemical process, the activated electron-rich structures (e.g., phenol) were efficiently decomposed, yielding mainly low MW carboxylic acids. Structures with low electron density might be activated by chain reactions (e.g., benzoic acid and nitrobenzene), while others remained rather refractory (e.g., maleic acid). Therefore, the changes of the TOX formation during coexposure of NOM, compared to
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 4 5 e6 5 5 4
halogenation alone, depend on the structures of the organic matter.
Acknowledgments This study was supported in part by the Hong Kong Research Grants Council under grant 619108. The National Natural Science Foundation of China (grant 51008316) and China’s Fundamental Research Funds for the Central Universities (grant 11lgzd15) also provided partial support.
Appendix. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011.09.053.
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Foote, C.S., Valentine, J.S., Greenberg, A., Liebman, J.F., 1995. Active oxygen. In: Chemistry. Chapman & Hall, London, p. 342. Frimmel, F.H., 1998. Impact of light on the properties of aquatic natural organic matter. Environ. Int. 24 (5e6), 559e571. Haag, W.R., Yao, D.C.C., 1992. Rate constants for reaction of hydroxyl radicals with several drinking water contaminants. Environ. Sci. Technol. 26 (5), 1005e1013. Kashinkunti, R.D., Linden, K.G., Shin, G.A., Metz, D.H., Sobsey, M. D., Moran, M.C., Samuelson, A.M., 2004. Investigating multibarrier inactivation for Cincinnati–UV, by-products, and biostability. J. Am. Water Works Assoc. 96 (6), 114e127. Kumar, K., Margerum, D.W., 1987. Kinetics and mechanism of general-acid-assisted oxidation of bromide by hypochlorite and hypochlorous acid. Inorg. Chem. 26, 2706e2711. Larson, R.A., Rockwell, A.L., 1979. Chloroform and chlorophenol production by decarboxylation of natural acids during aqueous chlorination. Environ. Sci. Technol. 13 (3), 325e329. Lehtola, M.J., Miettinen, I.T., Vartiainen, T., Rantakokko, P., Hirvonen, A., Martikaine, P.J., 2003. Impact of UV disinfection on microbially available phosphorus, organic carbon, and microbial growth in drinking water. Water Res. 37 (5), 1064e1070. Liu, W., Cheung, L., Yang, X., Shang, C., 2006. THM, HAA and CNCl formation from UV irradiation and chlor(am)ination of selected organic waters. Water Res. 40 (10), 2033e2043. Machey, E.D., Cushing, R.S., Crozes, G.F., 2000. Practical Aspects of UV Disinfection. Awwarf Awwa, West Explorer Drive, Suite 200, Boise. Id 83713. Magnuson, M.L., Kelty, C.A., Sharpless, C.M., Linden, K.G., Fromme, W., Metz, D.H., Kashinkunti, R., 2002. Effect of UV irradiation on organic matter extracted from treated Ohio River water studied through the use of electrospray mass spectrometry. Environ. Sci. Technol. 36 (23), 5252e5260. Mere´nyi, G., Lind, J., 1994. Reaction mechanism of hydrogen abstraction by the bromine atom in water. J. Am. Chem. Soc. 116 (17), 7872e7876. Nowell, L.H., Hoigne´, J., 1992. Photolysis of aqueous chlorine at sunlight and ultraviolet wavelengths: II. hydroxyl radical production. Water Res. 26 (5), 599e605. Oliver, B.G., Carey, J.H., 1977. Photochemical production of chlorinated organics in aqueous solutions containing chlorine. Environ. Sci. Technol. 11 (9), 893e895. Pan, X., Schuchmann, M.N., von Sonntag, C., 1993. Oxidation of benzene by the OH radical: a product and pulse radiolysis study in oxygenated aqueous solution. J. Chem. Soc. Perkin Trans. 2 (3), 289e297. Reckhow, D.A., Linden, K.G., Kim, J., Shermer, H., Makdissy, G., 2010. Impact of UV treatment on the formation of disinfection byproducts. J. Am. Water Works Assoc. 102 (6), 100e113. Rodriguez, M.L., Timokhin, V.I., Contreras, S., Chamarro, E., Esplugas, S., 2003. Rate equation for the degradation of nitrobenzene by ‘Fenton-Like’ reagent. Adv. Environ. Res. 7 (2), 583e595. Scheck, C.K., Frimmel, F.H., 1995. Degradation of phenol and salicylic acid by ultraviolet radiation-hydrogen peroxideoxygen. Water Res. 29 (10), 2346e2352. Shaw, J.P., Malley Jr., J.P., Willoughby, S.A., 2000. Effects of UV irradiation on organic matter. J. Am. Water Works Assoc. 92 (4), 157e167. von Sonntag, C., 2007. The basics of oxidants in water treatment Part A: OH radical reactions. Water Sci. Technol. 55 (12), 19e23. von Sonntag, C., Dowideit, P., Fang, X., Mertens, R., Pan, X., Schuchmann, M.N., Schuchmann, H.P., 1997. The fate of peroxyl radicals in aqueous solution. Water Sci. Technol. 35 (4), 9e15. Watts, M.J., Linden, K.G., 2007. Chlorine photolysis and subsequent OH radical production during UV treatment of chlorinated water. Water Res. 41 (13), 2871e2878.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 5 5 e6 5 6 3
Available online at www.sciencedirect.com
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Spectrometric characterization of effluent organic matter of a sequencing batch reactor operated at three sludge retention times M. Esparza-Soto*, S. Nu´n˜ez-Herna´ndez, C. Fall Universidad Auto´noma del Estado de Me´xico, Centro Interamericano de Recursos del Agua, Carretera Toluca-Atlacomulco km 14.5, Unidad San Cayetano, Toluca, Estado de Me´xico, C.P. 50200, Me´xico
article info
abstract
Article history:
Effluent organic matter (EfOM) from activated sludge systems is composed primarily of
Received 12 April 2011
influent refractory compounds, residual degradable substrate, intermediate products and
Received in revised form
soluble microbial products (SMPs). Depending on operational conditions (hydraulic and
27 September 2011
sludge retention time (SRT)), the quantity and quality of EfOM significantly changes. The
Accepted 28 September 2011
main objective of this research was to quantify and characterize the EfOM of a lab-scale
Available online 6 October 2011
activated sludge sequencing batch reactor (SBR), which was operated at three SRTs and fed glucose, an easily biodegradable substrate. EfOM was followed with two direct-
Keywords:
quantification methods (chemical oxygen demand (COD) and dissolved organic carbon
Soluble microbial products (SMPs)
(DOC)), three spectrometric methods (ultraviolet absorbance at 254 nm (UVA254),
Effluent organic matter (EfOM)
excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC)) and three organic matter (OM) indices (specific UVA254 (SUVA), SUVAeCOD, COD/DOC ratio).
Sequencing batch reactor (SBR) Ultraviolet
light
absorbance
at
The significant increment of UVA254 and OM indices after treatment indicated an accu-
254 nm (UVA254)
mulation of refractory high-molecular-weight humic-like compounds in the EfOM, which
Specific UVA254 (SUVA)
demonstrated that EfOM was composed mainly by SMPs and not glucose. On the other
Excitation-emission matrix fluores-
hand, as the SRT increased, the amount of EfOM decreased, but SUVA, SUVAeCOD and
cence (EEM)
fluorescence intensity increased; these trends indicated the accumulation of SMPs of
Parallel factor analysis (PARAFAC)
increased molecular weight and aromaticity. Increasing SRT in the SBRs reduced the amount of EfOM, but increased its aromaticity and reactivity. Visual analysis of EfOM EEMs showed two protein- and one humic-like peak, which were attributed to SMPs generated within the SBRs. PARAFAC determined that a two-component model best represented EfOM EEMs. The two-components from PARAFAC were mathematically correlated to the visually identified protein- and humic-like SMPs peaks. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
The effluent from aerobic biological wastewater (WW) treatment systems, such as the activated sludge, contains a heterogeneous mix of soluble organic compounds. This effluent organic matter (EfOM) is comprised by influent
refractory compounds, residual degradable substrate, biodegradation products, and soluble microbial products (SMPs) (Barker and Stuckey, 1999). Some researchers have concluded that most of the EfOM in aerobic biological WW treatment systems is comprised by SMPs (Barker and Stuckey, 1999; Jarusutthirak and Amy, 2007). SMPs are composed
* Corresponding author. Tel.: þ52 722 296 5550; fax: þ52 722 296 5555. E-mail addresses:
[email protected],
[email protected] (M. Esparza-Soto). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.057
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mainly of proteins, polysaccharides, organic acids and organic colloids (Barker and Stuckey, 1999). Depending on the biological systems operational conditions (hydraulic retention time (HRT) and sludge retention time (SRT)), the quantity and quality of EfOM, and therefore SMPs, may significantly change and adversely affect downstream treatment processes (sand and membrane filtration, chlorination, ozonation) (Li et al., 2000; Weishaar et al., 2003). The literature reports several easy-to-use organic matter (OM) indices to determine its refractability and biodegradability. Specific ultraviolet absorbance at 254 nm (SUVA), defined as the ratio of ultraviolet absorbance at 254 nm (UVA254) and dissolved organic carbon (DOC) of a water sample, is a well established surrogate parameter to determine humic content (aromaticity) and molecular weight of DOC (Chin et al., 1994; Weishaar et al., 2003); whereas the chemical oxygen demand/DOC (COD/DOC) ratio indicates the grams of oxygen chemically consumed during the COD test by each gram of carbon present in the sample (Vogel et al., 2000). Literature also reports several fluorescence methods to characterize natural OM and EfOM. Three-dimensional excitationemission matrix (3-D EEM) fluorescence is a rapid and selective spectrometric method that has been used either to differentiate natural OM transformations in the environment or to evaluate the influence of EfOM on river or marine water (Coble, 1996; Baker, 2001). Literature reports on characterizing EfOM or SMPs using 3-D EEM fluorescence showed three fluorescence peaks during visual analysis, which were attributed to protein- and humic-like fluorophores (EsparzaSoto and Westerhoff, 2001; Sheng and Yu, 2006); due to the complexity of the OM present in the studied WW, more fluorophores may be present in the mixture, but they were not visually detected. Parallel factor analysis (PARAFAC), a threeway analysis method, decomposes 3-D EEMs into their individual fluorescent components (Stedmon and Bro, 2008). PARAFAC have been successfully used to decompose 3-D EEMs from surface water natural OM (Stedmon et al., 2003), soil OM (Ohno and Bro, 2006) and extracellular polymeric substances (EPS) from aerobic biological reactors (Li et al., 2008), therefore, PARAFAC could be helpful to extract all fluorophores present in complex EfOM EEMs. The main objective of this research was to quantify and characterize the EfOM of a lab-scale activated sludge sequencing batch reactor (SBR), which was operated at three SRT and fed solely glucose and nutrients. EfOM was followed using two direct-quantification methods (COD and DOC), two spectrometric methods (UVA254, 3-D EEM fluorescence/PARAFAC) and three OM indices (SUVA, SUVAeCOD and COD/ DOC ratio).
in the effluent during the experiments. The other operational parameters (influent COD, nutrients, HRT, mixing and aeration) were kept constant during each SRT. The experiments were performed in series, starting and stabilizing the SBR with the shortest SRT, and then continuing with the next SRT. The SBR had a total volume of 2 L (1.5 L working volume and 0.5 L head space) for filling (1 min), mixing and aeration (23 h), sludge wasting (1 min), sedimentation (55 min) and decanting (3 min). The SBR was operated manually by decanting 1-L of treated WW and adding 1-L of synthetic WW. The SBR was monitored since the first day of seeding, during the acclimatizing and steady state phases in order to detect any changes in the effluent’s spectrometric signature. It was assumed that the steady state was reached when the effluent COD varied less than 10% during at least 10e15 days. The SRT of each SBR was controlled by daily wasting activated sludge directly from the reactor before the end of the mixing/aeration phase. The waste volume was estimated with the equation: Qw ¼ V/SRT (Jarusutthirak and Amy, 2007) where: Qw is the suspended solids wasting rate, L/d; V is the working volume of the reactor, L; SRT is the solids retention time, d. According to this equation, the volume of waste sludge needed to maintain the SRT at 5, 10 and 30 days was 0.4, 0.2 and 0.6 L/d. A mixing table was used to agitate the mixed liquor volatile suspended solids (MLVSS). A fish tank air pump was used to aerate the systems (Elite 802 Model, Hagen, UK) and to keep the dissolved oxygen at 2 mg/L. The air was bubbled through air stone diffusers (Aqua Fizzz Airstone, Hagen, UK). The SBR was initially inoculated with activated sludge from a pilotscale WW treatment plant. The decanted WW was considered as the treated effluent for analysis purposes. The treated effluent was filtered with a glass-fiber filter (GF/C, Whatman) previously cleaned (ashed at 550 C for 1 h and rinsed with distilled water). In this research the DOC was defined as the organic carbon that remains in the sample after filtration with a 1.2 mm pore filter (McKnight et al., 2001). The filtrate was collected in amberglass vials previously cleaned to eliminate trace carbon (rinsed with tap and distilled water, ashed at 550 C for 1 h, cooled and stored), acidified to a pH of 4 (20% HCl), and stored at 4 C until analysis. All samples were analyzed within a week of collection. COD, DOC, UVA254 were measured daily in all treated WW samples. Effluent and mixed liquor volatile suspended solids (VSS) were also measured daily. The average concentration of each measured parameter for each SRT was calculated during steady state for statistical analysis. Analysis of variance at a 95% of confidence was performed on each set of averages in order to determine if the averages were statistically different.
2.
Materials and methods
2.2.
2.1.
Lab-scale sequencing batch reactor operation
Glucose, an easily biodegradable organic compound, was the sole carbon source for bacterial growth (2000 mg/L) in the synthetic WW. Nutrients were added from the following four stock solutions prepared in 1-L of distilled water: Stock A: phosphate buffer with 8.5 g KH2PO4, 21.75 g K2HPO4, 33.4 g Na2HPO4$7H2O, and 1.7 g NH4Cl; Stock B: 22.5 g MgSO4·7H2O; Stock C: 27.5 g CaCl2; Stock D: 0.25 FeCl3$6H2O. The four stock
A lab-scale SBR was used to simulate an activated sludge process and to generate treated effluent for characterization. The lab-scale SBR was operated at three theoretical SRTs (5, 10 and 30 days), assuming no sludge loss in the effluent, however, each SRT was re-calculated because sludge was lost
Synthetic wastewater
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solutions were prepared according the SM 5210 (APHA, 1995), which describes the dilution water for the 5-day biochemical oxygen demand test. One liter of synthetic WW was prepared daily by adding 1 ml of each stock solution and 2000 mg of glucose in distilled water.
2.3.
Analytical methods
The soluble COD was determined with a closed-reflux commercial colorimetric kit (High range 0e1500 mg/L COD, Hach, Co.). The DOC was determined with a total organic carbon analyzer with autosampler (1020a Model, O-I Analytical). UVA254 was measured using an ultravioletevisible spectrophotometer (Cary 1E, Varian) at a wavelength of 254 nm using a 1 cm quartz cuvette according to Standard Method 5910B (APHA, 1995; Eaton, 1995). SUVA was calculated as the ratio of UVA254 and DOC of the samples (Chin et al., 1994). SUVAeCOD was calculated as the ratio of UVA254 and COD of the samples. All 3-D EEMs were measured in a fluorescence spectrometer (Model LS-55, Perkin Elmer, USA) equipped with a 150-Watt xenon lamp as excitation source. The spectrometer was controlled with the software WINLAB from a desktop computer. EEMs consisted of 45 emission scans (250e600 nm) collected over excitation wavelengths ranging from 200 to 420 nm at 5 nm increments. The excitation and emission slit width were set at 10 nm. The scanning speed was set to 1200 nm/min. A 290-nm excitation filter was used to eliminate secondary Rayleigh scattering. Inner filtering correction was not necessary because the average UVA254 for each run was between 0.048 and 0.064 cm1 (Table 1), which was lower than the minimum of 0.1 and 0.3 cm1 recommended in literature (Mobed et al., 1996; Ohno, 2002). Samples were not corrected for the excitation response of the lamp source and the detection of the emission spectra. Because all data were collected from a single instrument in a 3.5-months period, the results are internally comparable (Chen et al., 2003). The Raman peak of dionized water was collected at a excitation/emission
wavelength (lexc/lem) of 350/394.5 nm and averaged 39.4 2.1 A. U. (n ¼ 23) during the experimentation period. This Raman peak was used to test the instrument stability and to permit interlaboratory comparison (Baker, 2001). The low standard deviation of the Raman peak of water indicated that the instrument signal did not drift during the experimentation period. Results shown herein can be normalized by dividing fluorescence intensity by 39.4 if interlaboratory comparisons need to be done (Baker, 2001). All WINLAB EEMs files were exported to an Excel spreadsheet with the in-house software MIGRACION. Exported EEMs were corrected with an Excel spreadsheet specially designed to subtract a water-blank EEM and eliminate the primary Rayleigh and Raman scattering. Visual analysis, which is the detection of the number and fluorescence intensity of easily-identifiable peaks and their location within the EEMs, was performed on the corrected EEMs. PARAFAC is a mathematical method that decomposes the corrected EEMs into all the available peaks, visible and overlapped. PARAFAC decomposes corrected EEMs into tri-linear components and residuals (Stedmon et al., 2003; Stedmon and Bro, 2008). The algorithm used was the N-way Toolbox for MATLAB (Andersson and Bro, 2000). The algorithm was run assuming non-negativity constraints on each component loading. The number of components (fluorophores) that best fit a model for each 3-D EEM set was determined by minimizing the sum of squared residuals. The model was validated with the core consistency diagnostic (CORCONDIA) provided in the N-way Toolbox and split-half analysis as recommended by Andersson and Bro (2000) and Stedmon and Bro (2008).
3.
Results and discussion
3.1.
SBR performance
Each SBR reached steady state after approximately 21 days of operation. Steady state averages and standard deviations
Table 1 e Sludge retention time and water quality of the SBR operated at three different SRTs. SRT, days
MLVSS, mg/L
Influent Avg. S.D. n SBR 1 Effluent, SRT ¼ 3.3 d Avg. 3.3 968 S.D. 0.3 117 n 22 25 SBR 2 Effluent, SRT ¼ 7.8 d Avg. 7.8 1470 S.D. 1.2 172 n 15 16 SBR 3 Effluent, SRT ¼ 27.7 d Avg. 27.7 2905 S.D. 6.9 321 n 18 18
UVA254, cm1
SUVA, m1/[mg-C/L]
SUVAeCOD, m1/[mg-O2/L]
COD/DOC, mg-O2/mg-C
0.0201 0.0054 56
0.0026 0.0008 45
0.0009 0.0002 55
2.78 0.14 45
96.5 0.4 18
0.0482 0.0070 20
0.1820 0.0258 20
0.0682 0.0144 20
2.73 0.46 20
96.1 0.3 17
95.51 0.38 16
0.0649 0.0134 17
0.1883 0.0382 17
0.0777 0.0131 17
2.42 0.20 17
97.8 0.3 13
97.8 0.3 6
0.0646 0.0114 14
0.3361 0.0470 14
0.1443 0.0255 12
2.36 0.51 14
COD, mg/L
DOC, mg/L
% COD Removal
2149 75 55
784 33 47
72.6 13.1 20
26.7 3.5 20
96.7 0.5 17
83.5 8.0 17
34.5 2.7 17
44.9 7.9 14
19.3 2.8 14
% DOC Removal
Avg. ¼ Average; S.D. ¼ Standard deviation; n ¼ number of samples.
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y = 1.8985x + 0.0477 0.40
2
R = 0.9208
0.30
-1
SUVA, m /[mg-C/L]
0.50
0.20 0.10 0.00 0.00
0.05
0.10
0.15
0.20
0.25
-1
SUVA-COD, m /[mg-O2 /L]
Fig. 1 e Correlation between SUVAeCOD and SUVA with steady state data obtained at the SBR operated at three different SRT.
of different SBRs parameters are presented in Table 1. The lab-scale SBRs were operated at different SRTs by daily wasting MLVSS directly from the reactor, however, approximately 5e18% of the MLVSS were daily lost in the effluent; therefore, the SRT had to be re-calculated considering these losses (Metcalf and Eddy, 2003). The measured SRT for each run was 3.3 0.3, 7.8 1.2 and 27.7 6.9 days (Table 1). The MLVSS increased as the SRT increased, which indicated that sludge wasting successfully developed three completely different SBR in terms of amount of MLVSS and SRT. The effluent COD and DOC decreased from 72.6 to 44.9 mgO2/L, and from 26.7 to 19.3 mg-C/L, respectively, as the SRT increased from 3.3 to 27.7 days. The effluent COD and DOC decreased as the SRT and MLVSS increased in the SBR, but this inverse linear correlation between COD or DOC with SRT was not statistically significant. The removal efficiency of the SBRs was higher than 95.5%, which indicated that most of the glucose was consumed. The first indication that SMPs were produced and were present in the SBR effluent was given by
UVA254, which increased from 0.020 cm1 in the influent to approximately 0.065 cm1 after treatment (Table 1). UVA254 also increased as the SRT increased. Higher effluent UVA254 after biological treatment and its increase with SRT indicated an accumulation of aromatic humic-like compounds in the EfOM, which can be attributed to SMPs (Barker and Stuckey, 1999; Imai et al., 2002; Jarusutthirak and Amy, 2007). The high COD and DOC removal indicated that most of the glucose was removed, but the significant UVA254 increment could indicate that most of the EfOM could be associated to SMPs.
3.2.
OM indices
The OM indices were analyzed to qualitatively differentiate the influent OM from the EfOM and to identify the SRT effect on the EfOM quality. If the SUVA of a sample increases the humic content and the molecular weight of the sample DOC also increases (Chin et al., 1994). SUVA and SUVAeCOD significantly increased more than 16,000 and 140 times (a ¼ 0.05), respectively, after biological treatment at each SBR (Table 1), which indicated a large accumulation of refractory high-molecular-weight humic-like compounds in the EfOM (Imai et al., 2002; Saadi et al., 2006; Jarusutthirak and Amy, 2007). This demonstrated that the EfOM was composed mainly by SMPs and not glucose. On the other hand, effluent SUVA and SUVAeCOD increased as the SRT increased (Table 1), which indicated the accumulation of SMPs of higher molecular weight and aromaticity (Chin et al., 1994; Imai et al., 2002; Weishaar et al., 2003). Higher SUVA at longer SRT can cause problems with the formation of disinfection by-products if the treated water is chlorinated (Li et al., 2000; Weishaar et al., 2003). Increasing SRT in biological rectors can significantly reduce the amount of EfOM, but it may increase EfOM aromaticity and THM reactivity.
Fig. 2 e Representative corrected EEM of EfOM from SBR 2. Each contour line represent 10 A. U. of fluorescence, a total of 39 contour lines are shown from 0 to 420 A. U. Fluorescence intensity can be normalized by dividing by the Raman peak of dionized water (39.4 A. U., lexc/lem [ 350/394.5 nm).
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Table 2 e Average steady state fluorescente properties of each sequencing batch reactor. Peak A lexc, nm SBR 1, SRT ¼ 3.3 d Avg. 221.1 S.D. 3.6 C.V. 1.6 n 19 SBR 2, SRT ¼ 7.8 d Avg. 220.9 S.D. 2.0 C.V. 0.9 n 16 SBR 3, SRT ¼ 27.7 d Avg. 225.0 S.D. 0.0 C.V. 0.0 N 4
Peak B
Peak C
Peak ratios
lem, nm
F. I., A. U.
lexc, nm
lem, nm
F. I., A. U.
lexc, nm
lem, nm
F. I., A. U.
A/B
A/C
B/C
343.6 2.3 0.7 19
443.9 152.4 34.3 19
275.5 1.5 0.6 20
344.4 2.1 0.6 20
135.8 38.0 28.0 20
333.8 2.8 0.8 20
415.8 4.3 1.0 20
46.9 14.7 31.3 20
3.3 0.3 8.7 19
9.7 1.4 14.4 19
3.0 0.5 15.4 20
342.2 4.0 1.2 16
439.2 99.1 22.6 16
276.6 2.4 0.9 16
343.3 3.0 0.9 16
148.7 31.0 20.8 16
335.0 0.0 0.0 16
426.5 5.4 1.3 16
45.0 8.8 19.5 16
3.0 0.2 8.4 16
9.8 1.5 15.7 16
3.3 0.6 16.6 16
344.8 0.5 0.1 4
820.7 178.8 21.8 4
283.2 2.5 0.9 14
345.2 1.0 0.3 14
230.6 40.4 17.5 14
340.0 2.8 0.8 14
433.4 1.7 0.4 14
68.7 6.4 9.3 14
4.2 0.6 15.1 4
12.5 4.1 32.9 4
3.4 0.6 17.5 14
F. I. ¼ Fluorescence intensity; A. U. ¼ Arbitrary units; Avg. ¼ Average; S.D. ¼ Standard deviation; n ¼ number of samples. Fluorescence intensity can be normalized by dividing by the Raman peak of dionized water (39.4 A. U., lexc/lem ¼ 350/394.5 nm).
The influent COD/DOC ratio (glucose) was close to its theoretical value (Baker et al., 1999) (Table 1). If the COD/DOC ratio decreases, the carbon molecule becomes refractory because the same amount of carbon consumes less oxygen to get chemically oxidized (Vogel et al., 2000). The COD/DOC ratio decreased after biological treatment indicating that the effluent was more refractory than the influent. The COD/DOC ratio decreased as the SRT increased, indicating an increase on EfOM refractability (Table 1). The reduction of the COD/DOC ratio after biological treatment and as the SRT increased may also indicate the accumulation of refractory SMPs at the EfOM. SUVA is a well established surrogate parameter to determine humic content (aromaticity) and molecular weight of DOC (Chin et al., 1994; Weishaar et al., 2003), however, not all laboratories has the equipment and economical resources to routinely measure it. In this research, a significant correlation was found between SUVA and SUVAeCOD (R2 ¼ 0.9208) (Fig. 1). To the authors’ knowledge, a similar correlation has not been reported in literature; therefore, it is proposed that SUVAeCOD can also be used as a surrogate of DOC aromaticity.
3.3.
3-D EEM fluorescence visual analysis
The use of synthetic non-fluorescent WW allowed to monitor the generation of fluorescent dissolved OM in the SBR effluent and to analyze the effect of SRT on its fluorescence properties. Fig. 2 shows a representative corrected 3-D EEM of the SBR effluent, whereas Table 2 shows the steady state average of the fluorescence properties for all identified fluorescence peaks. All of the effluent EEMs showed three fluorescence peaks (Peaks A, B and C, Fig. 2). Since the influent was nonfluorescent, the observed effluent fluorescence signal can be attributed solely to SMPs generated within the SBR, confirming observations previously stated. The observed peaks were identified by comparing their fluorescence properties (lexc/ lem, and fluorescence intensity) with those of pure compounds reported in literature. Fluorescence signature of pure compounds always shows a pair of well-rounded symmetrical peaks, which fluoresce at the same lem, but two different lexc, with the shortest lexc peak having the highest fluorescence intensity of the two (Coble, 1996; Wolfeis, 1985).
Fig. 3 e PARAFAC components for the 3.3-d SRT SBR. a) Component 1 with 19 contour lines; b) Component 2 with 9 contour lines. Each contour line represents 0.002 A. U. of fluorescence.
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Fig. 4 e PARAFAC components for the 7.8-d SRT SBR. a) Component 1 with 18 contour lines; b) Component 2 with 10 contour lines. Each contour line represents 0.002 A. U. of fluorescence.
Peaks A and B were located at the same lem (342.2e345.2 nm), but had two different lexc (220.9e225.0 nm and 275.5e283.2 nm, respectively), and had a peak ratio A/B of 3.0e3.4 (Fig. 2, Table 2). Based on the location of Peaks A and B within the EEMs and the peak ratio A/B larger than 3, they were identified as protein-like SMP peaks attributed to the amino acid tryptophan (Baker, 2001; Wolfeis, 1985). Peak C was identified as visible humic acid-like peak based on its location within the 3-D EEM (lexc/lem ¼ 333.8e340.0/ 415.8e433.4 nm) (Table 2) (Coble, 1996). The humic acid-like Peak C should have second short-lexc peak to the right of Peak A, however, this accompanying peak appeared in the 3-D EEMs as Shoulder A due to overlapping with the fluorescence intensity of Peak A (Fig. 2). Literature reporting EfOM EEMs have found several types of visible humic acid-like peaks, but have never determined the location and intensity of its accompanying short-lexc peak (Baker, 2001; Esparza-Soto and Westerhoff, 2001; Li et al., 2008; Ni et al., 2009). Visual determination of the location of the short-lexc humic acid-like peak is not possible, so mathematical analysis of EEMs is necessary to resolve the overlapped peak. The identification of the shortlexc humic acid-like peak was done using PARAFAC, as it will
be explained latter. Similar protein- and humic acid-like peaks have been visually detected in EfOM and EPS 3-D EEMs from different activated sludge systems (Baker, 2001; Esparza-Soto and Westerhoff, 2001; Saadi et al., 2006; Sheng and Yu, 2006; Li et al., 2008; Ni et al., 2009). Table 2 shows that the fluorescence intensity increased as the SRT increased, which may indicate the generation and accumulation of organic fluorescent molecules, attributed to protein- and humic-like SMPs, during the aerobic biodegradation of glucose (Li et al., 2008). This trend is similar to that observed for SUVA and SUVAeCOD, which confirms the accumulation of refractory proteinic and humic SMPs as the SRT increased. The movement of humic-like peaks location within the 3-D EEM (lexc/lem) to longer wavelengths (red-shifting) indicates their possible transformation and humification (Coble, 1996; Saadi et al., 2006). Coble (1996) assumed that red-shifted humic substances from marine environments belonged to weathered natural OM because it had been physical, chemical and biologically processed more times than a natural OM of recent incorporation. The location of Peak C did not statistically varied for the 3.3- and 7.8-days SRT SBRs, whereas, as the
Fig. 5 e PARAFAC components for the 27.7-d SRT SBR. a) Component 1 with 20 contour lines; b) Component 2 with 9 contour lines. Each contour line represents 0.002 A. U. of fluorescence.
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Table 3 e Linear regression equations of the correlation between the maximum fluorescence intensity of the PARAFAC peaks and visual analysis peaks for the 7.8-days SRT SBR. lexc/lem, nm
Peak A 220.9/342.2 Peak B 276.6/343.3 Peak C 335.0/426.5
Comp. 1 e Peak 1
Comp. 1 e Peak 2
Comp. 2 e Peak 1
Comp. 2 e Peak 2
220.0/341.5
275/341.5
215.0/431.5
300.0/431.5
y [ 0.89373 D 29.201 R2 [ 0.9668 y ¼ 2.6737 þ 29.179 R2 ¼ 0.9549 y ¼ 7.3091 þ 53.541 R2 ¼ 0.5941
y ¼ 0.298 þ 10.004 R2 ¼ 0.967 y [ 0.89123 D 10.025 R2 [ 0.9546 y ¼ 2.4435 þ 17.83 R2 ¼ 0.5989
y ¼ 0.1637 þ 34.771 R2 ¼ 0.4195 y ¼ 0.4643 þ 37.907 R2 ¼ 0.3725 y ¼ 2.0723 þ 7.5791 R2 ¼ 0.8077
y ¼ 0.0659 þ 15.96 R2 ¼ 0.3477 y ¼ 0.1852 þ 17.432 R2 ¼ 0.3031 y [ 0.88323 D 2.9026 R2 [ 0.7478
Bold regression equations represent the best fit between PARAFAC peaks and visually identified peaks.
SRT increased to 27.7 days, the lexc and lem statistically moved to longer wavelengths (a ¼ 0.05). The red-shift to longer wavelengths of Peak C as the SRT increased indicated that the humic-like fluorophores were concentrating, as the fluorescence intensity also increased, and becoming more refractory. This observation supports the observation regarding the SUVA and SUVAeCOD increment. Because the EfOM fluorescence is attributed to SMPs, it can be concluded that SMPs became more aromatic and refractory as the SRT increased. Therefore, it can be concluded that 3-D EEM fluorescence helped to determine that the significant increase of UVA254, SUVA and SUVAeCOD after biological treatment and after increasing the SRT can be attributed to an accumulation of protein- and humic-like SMPs in the EfOM.
3.4.
PARAFAC modeling
The PARAFAC algorithm, through its CORCONDIA, least squares and half-split analysis validation, determined that the two-component model best represented the 3-D EEMs databases for the three SBRs. Each component consisted of a pair of peaks (e.g., component 1, peak 1 (C1 e P1) and component 1, peak 2 (C1 e P2)) located at the same lem, but different lexc (Figs. 3, 4 and 5). The P-1 of each component was located at the shortest lexc and had the highest fluorescence intensity, as described for pure compounds (Wolfeis, 1985) and recommended for PARAFAC modeling (Stedmon and Bro, 2008). The order of the calculated components indicates its relative importance in the EEMs, being the first component, C1, the most important and the one that contributes the most with the total EEM fluorescence. Based on their location within the EEMs, C1 and C2 peaks were identified as
tryptophan- (lexc/lem ¼ 220 and 275/341.5e344.5 nm) and humic-like (lexc/lem ¼ 215 and 300/430e433.5 nm) peaks, respectively (Wolfeis, 1985). PARAFAC results showed that the relative proportion of C1 and C2 fluorophores remained the same, even though their fluorescence intensity increased, as the SRT increased (Table 2 and Figs. 3e5). This can indicate that regardless of the SRT, the same proportion of SMPs (proteinic followed by humic) was always produced. In order to determine which PARAFAC peak better correlated to the visually detected peaks, the maximum fluorescence intensity (MFI) of each PARAFAC peak was correlated with that of each visually identified peak (Fig. 6 and Tables 3 and 4). Fig. 6 shows the figures and equations of the correlations for the 3.3-d SRT SBR, whereas Tables 3 and 4 only shows the equations of such correlations for the 7.8- and 27.7-d SRT SBRs, respectively. The first row of Fig. 6 and Table 3 shows the correlation between the MFI of Peak A and the MFI of C1 e P1 and C1 e P2 on the left-side columns and the MFI of C2 e P1, C2 e P2 on the right-side columns; whereas the second and third rows shows the correlation between the MFI of Peak B and C and that of each PARAFAC peak, respectively. The best fit of a measured peak, indicated in Fig. 6 and Tables 3 and 4 in bold letters, was the regression with the highest R2 and the slope closest to 1. For all SBRs, Peak A was correlated with C1 e P1 (R2 ¼ 0.8462e0.9757), whereas Peak B was correlated with C1 e P2 (R2 ¼ 0.7498e0.9757), which indicated that both visual analysis and PARAFAC agreed that Peaks A and B were attributed to tryptophan. Peak C was correlated with C2 e P2 (R2 ¼ 0.7478 0.9802), which corroborated that Peak C can be attributed to a humic-like fluorophore. Since C2 was identified as a humic-like fluorophore and had two peaks emitting at the same lem, but two different lexc, it can be concluded that the
Table 4 e Linear regression equations of the correlation between the maximum fluorescence intensity of the PARAFAC peaks and visual analysis peaks for the 27.7-days SRT SBR. lexc/lem, nm
Peak A 225.0/344.8 Peak B 283.2/345.2 Peak C 340.0/433.4
Comp. 1 e Peak 1
Comp. 1 e Peak 2
Comp. 2 e Peak 1
Comp. 2 e Peak 2
220.0/341.5
275/341.5
215.0/431.5
300.0/431.5
y [ 0.56683 D 251.11 R2 [ 0.8462 y ¼ 3.2086 16.349 R2 ¼ 0.7464 y ¼ 0.0971 þ 594.81 R2 ¼ 0.0007
y ¼ 0.1776 þ 80.252 R2 ¼ 0.8433 y [ 1.00933 L 4.3226 R2 [ 0.7498 y ¼ 0.0471 þ 186.47 R2 ¼ 0.0016
y ¼ 0.0024 þ 182.27 R2 ¼ 5E-05 y ¼ 0.4761 þ 91.801 R2 ¼ 0.0597 y ¼ 1.9152 þ 15.779 R2 ¼ 0.972
y ¼ 0.011 þ 88.562 R2 ¼ 0.0046 y ¼ 0.1751 þ 47.748 R2 ¼ 0.0307 y [ 0.98653 L 4.9415 R2 [ 0.9802
Bold regression equations represent the best fit between PARAFAC peaks and visually identified peaks.
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250
800 y = 0.7919x + 78.712 700 R2 = 0.9868 600 500 + 23.71 400 y = 0.2451x R2 = 0.9873 300 200 100 0 0
200
M. F. I. of Component 2
M. F. I. of Component 1
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200 150 100
y = 0.2262x + 0.9298 R2 = 0.836 y = 0.0945x - 1.4955 R2 = 0.7773
50 0
400
600
800
1000
0
200
50
200
800
1000
y = 0.8833x - 16.232 R2 = 0.7511
150
y = 0.3652x - 8.1498 R2 = 0.6838
100 50 0
100
150
200
250
0
Maximum Fluorescence Intensity of Peak B, A. U C1-P1
50
100
150
200
250
Maximum Fluorescence Intensity of Peak B, A. U
C1-P2
C2-P1
C2-P2
250
800 700 600 500 400 300 200 100 0
y = 7.4405x + 87.788 R2 = 0.7945
M. F. I. of Component 2
M. F. I. of Component 1
600
250
800 700 y = 3.2478x - 2.0173 R2 = 0.9778 600 500 y = 1.0038x - 1.0959 400 R2 = 0.9757 300 200 100 0 0
400
Maximum Fluorescence Intensity of Peak A, A. U
M. F. I. of Component 2
M. F. I. of Component 1
Maximum Fluorescence Intensity of Peak A, A. U.
y = 2.3154x + 25.933 R2 = 0.8037
200
y = 2.5479x - 15.939 R2 = 0.9675
150 y = 1.1014x - 10.244 R2 = 0.9632
100 50 0
0
20
40
60
80
100
Maximum Fluorescence Intensity of Peak C, A. U C1-P1
0
20
40
60
80
100
Maximum Fluorescence Intensity of Peak C, A. U
C1-P2
C2-P1
C2-P2
Fig. 6 e Linear regressions of the correlation between the maximum fluorescence intensity (MFI) of the visual analysis peaks and PARAFAC peaks for the 3.3-days SRT SBR. First row correspond to the correlation of Peak A versus both components (C1 on left and C2 on right). Second row correspond to the correlation of Peak B versus both components (C1 on left and C2 on right). C1 e P1 [ Component 1, Peak 1; C1 e P2 [ Component 1, Peak 2. C2 e P1 [ Component 2, Peak 1; C2 e P2 [ Component 2, Peak 2. Bold regression equations represent the best fit between PARAFAC peaks and visually identified peaks.
C2 e P1 is the humic-like peak that appeared overlapped with Peak A and identified as Shoulder A during visual analysis.
4.
Conclusions
The EfOM of a lab-scale SBR operated at three SRTs (3.3, 7.8 and 27.7 days) and fed solely a non-fluorescent, easily biodegradable substrate (glucose) was quantified and characterized by different methods. After biological treatment, the parameters UVA254, SUVA, SUVAeCOD increased and three fluorescence peaks appeared in EfOM, which indicated that EfOM was composed mainly by SMPs and not the fed substrate. On the other hand, as the SRT increased in the SBRs, the effluent COD and DOC decreased, but the SUVA, SUVAeCOD and fluorescence intensity increased, indicating the accumulation of EfOM of increased aromaticity and THM reactivity. A significant correlation was found between SUVA and
SUVAeCOD (R2 ¼ 0.9208), so it is proposed that SUVAeCOD can also be used as a surrogate of DOC aromaticity. Visual analysis of EfOM EEMs showed two protein-like (tryptophan, lexc/lem ¼ 225/345 and 280/345 nm), one humiclike peak (lexc/lem ¼ 340/433 nm) and a shoulder (lexc/ lem ¼ 220/433 nm), which were attributed to SMPs generated within the SBRs because the substrate was not fluorescent. PARAFAC determined that a two-component model best represented the EfOM EEMs. The two-component model was mathematically correlated to the visually identified shoulder and protein- and humic-like peaks.
Acknowledgements The authors would like to thank the Consejo Nacional de Ciencia y Tecnologı´a (Grant 53058-Y “Identificacio´n de productos microbianos solubles en un reactor de lodos activados
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 5 5 e6 5 6 3
por espectrometrı´a de fluorescencia”) for the economical support of this study. The authors also would like to thank MSE Daniel Esparza-Soto for writing the software MIGRA´ N, which speeded the EEMs exportation process to Excel. CIO
references
Andersson, C.A., Bro, R., 2000. The N-way analysis toolbox for MATLAB. Chemometrics and Intelligent Laboratory Systems 52 (1), 1e4. APHA, 1995. Standard Methods for the Examination of Water and Wastewater, nineteenth ed. American Public Health Association, Washington, DC. Baker, J.R., Milke, M.W., Mihelcic, J.R., 1999. Relationship between chemical and theoretical oxygen demand for specific classes of organic chemicals. Water Research 33 (2), 327e334. Baker, A., 2001. Fluorescence excitation-emission matrix characterization of some sewage-impacted rivers. Environmental Science & Technology 35 (5), 948e953. Barker, D.J., Stuckey, D.C., 1999. A review of soluble microbial products (SMP) in wastewater treatment systems. Water Research 33 (14), 3063e3082. Chen, W., Westerhoff, P., Leenheer, J.A., Booksh, K., 2003. Fluorescence excitation-emission matrix regional integration to quantify spectra for dissolved organic matter. Environmental Science & Technology 37 (24), 5701e5710. Chin, Y., Aiken, G., y O’Loughlin, E., 1994. Molecular weight, polydispersity and spectroscopic properties of aquatic humic substances. Environmental Science & Technology 28 (11), 1853e1858. Coble, P., 1996. Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy. Marine Chemistry 51 (4), 325e346. Eaton, A., 1995. Measuring UV-absorbing organics: a standard method. Journal of the American Water Works Association 87 (2), 86e90. Esparza-Soto, M., Westerhoff, P., 2001. Fluorescence spectroscopy and molecular weight distribution of extracellular polymers from full-scale activated sludge biomass. Water Science and Technology 43 (3), 87e95. Imai, A., Fukushima, T., Matsishige, K., Kim, Y.-H., Choi, K., 2002. Characterization of dissolved organic matter in effluents from wastewater treatment plants. Water Research 36 (4), 859e870. Jarusutthirak, C., Amy, G., 2007. Understanding soluble microbial products (SMP) as a component of effluent organic matter (EfOM). Water Research 41 (12), 2787e2793. Li, C.-W., Benjamı´n, M.M., Korshin, G., 2000. Use of UV spectroscopy to characterize the reaction between NOM and free chlorine. Environmental Science & Technology 34 (12), 2570e2575. Li, W.H., Sheng, G.P., Liu, X.W., Yu, H.Q., 2008. Characterizing the extracellular and intracellular fluorescent products of
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activated sludge in a sequencing batch reactor. Water Research 42 (12), 3173e3181. McKnight, D.M., Boyer, E.W., Westerhoff, P.K., Doran, P.T., Kulbe, T., Andersen, D., 2001. Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity. Limnology and Oceanography 46 (1), 38e48. Metcalf & Eddy, Inc, 2003. Wastewater Engineering: Treatment and Reuse, fourth ed. McGraw Hill, New York, N.Y. Mobed, J.J., Hemmingsen, S.L., Autry, J.L., McGown, L.B., 1996. Fluorescence characterization of IHSS humic substances: total luminescence spectra with absorbance correction. Environmental Science & Technology 30 (10), 3061e3065. Ni, B.J., Fang, F., Xie, W.M., Sun, M., Sheng, G.P., Li, W.H., Yu, H.Q., 2009. Characterization of extracellular polymeric substances produced by mixed microorganisms in activated sludge with gel-permeating chromatography, excitation-emission matrix fluorescence spectroscopy measurement and kinetic modeling. Water Research 43 (5), 1350e1358. Ohno, T, 2002. Fluorescence inner-filtering correction for determining the humification index of dissolved organic matter. Environmental Science and Technology 36 (4), 742e746. Ohno, T., Bro, R., 2006. Dissolved organic matter characterization using multiway spectral decomposition of fluorescence landscapes. Soil Science Society of America Journal 70 (6), 2028e2037. Saadi, I., Borisover, M., Armon, R., Laor, Y., 2006. Monitoring of effluent DOM biodegradation using fluorescence, UV and DOC. Water Research 63, 530e539. Sheng, G.P., Yu, H.Q., 2006. Characterization of extracellular polymeric substances of aerobic and anaerobic sludge using three-dimensional excitation and emission matrix fluorescence spectroscopy. Water Research 40 (6), 1233e1239. Stedmon, C.A., Markager, S., Bro, R., 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Marine Chemistry 82 (3e4), 239e254. Stedmon, C.A., Bro, R., 2008. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods 6, 572e579. Vogel, F., Harf, J., Hug, A., von Rohr, P.R., 2000. The mean oxidation number of carbon (MOC) e A useful concept for describing oxidation processes. Water Research 34 (10), 2689e2702. Weishaar, J.L., Aiken, G.R., Bergamaschi, B.A., Fram, M.S., Fujii, R., Mopper, K., 2003. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environmental Science & Technology 37 (20), 4702e4706. Wolfeis, O.S., 1985. The fluorescence of organic natural products. In: Shulman, S.G. (Ed.), Molecular Luminescence Spectroscopy. Part I: Methods and Applications. Wiley, New York, pp. 167e370.
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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Chemical dosing for sulfide control in Australia: An industry survey Ramon Ganigue a, Oriol Gutierrez a,b, Ray Rootsey a, Zhiguo Yuan a,* a
Advanced Water Management Centre, Building 60, Research Road, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia Catalan Institute for Water Research (ICRA), Scientific and Technological Park of the University of Girona, Emili Grahit 101-17003, Girona, Spain b
article info
abstract
Article history:
Controlling sulfide (H2S) production and emission in sewer systems is critical due to the
Received 28 April 2011
corrosion and malodour problems that sulfide causes. Chemical dosing is one of the most
Received in revised form
commonly used measures to mitigate these problems. Many chemicals have been reported
21 September 2011
to be effective for sulfide control, but the extent of success varies between chemicals and is
Accepted 30 September 2011
also dependent on how they are applied. This industry survey aims to summarise the
Available online 6 October 2011
current practice in Australia with the view to assist the water industry to further improve their practices and to identify new research questions. Results showed that dosing is
Keywords:
mainly undertaken in pressure mains. Magnesium hydroxide, sodium hydroxide and
Sewer systems
nitrate are the most commonly used chemicals for sewers with low flows. In comparison,
Corrosion
iron salts are preferentially used for sulfide control in large systems. The use of oxygen
Sulfide control
injection has declined dramatically in the past few years. Chemical dosing is mainly
Chemical dosing
conducted at wet wells and pumping stations, except for oxygen, which is injected into the pipe. The dosing rates are normally linked to the control mechanisms of the chemicals and the dosing locations, with constant or profiled dosing rates usually applied. Finally, key opportunities for improvement are the use of mathematical models for the selection of chemicals and dosing locations, on-line dynamic control of the dosing rates and the development of more cost-effective chemicals for sulfide control. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
The production of sulfide is a critical problem in sewer systems due to the corrosion of sewer infrastructure and the generation of malodours (Boon, 1995; Pomeroy, 1959). Sulfide also has a detrimental effect on human health (USEPA, 1974). One of the main approaches adopted to date to mitigate such issues has been the dosage of chemicals to the wastewater (Melbourne, 1989). Among them, the addition of air or oxygen to prevent anaerobic conditions and to oxidise sulfide to sulfate (SO2 4 ) has
been widely used (Hvitved-Jacobsen, 2002; USEPA, 1992). Nitrate, another thermodynamically favourable electron acceptor, has also been employed over the last 70 years to prevent anaerobic conditions and thus to control odours and sulfide concentration in many environments (Bentzen et al., 1995; Bertra´n de Lis et al., 2007). However, Mohanakrishnan et al. (2009) and Jiang et al. (2009) recently demonstrated that the actual mechanism leading to sulfide mitigation is the via elemental sulfur by nitrate oxidation of H2S to SO2 4 reducing-sulfide oxidising bacteria (NR-SOB). Strong oxidants
* Corresponding author. E-mail addresses:
[email protected] (R. Ganigue),
[email protected] (O. Gutierrez),
[email protected] (R. Rootsey),
[email protected] (Z. Yuan). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.054
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such as H2O2, NaOCl or KMnO4 have also been applied for chemical sulfide oxidation (Tomar and Abdullah, 1994; USEPA, 1992). Another widely used strategy for H2S mitigation is the addition of iron salts including ferrous chloride, ferric chloride and in some cases ferrous sulfate (Jameel, 1989; Padival et al., 1995). Ferrous ions (Fe2þ) precipitate sulfide by forming highly insoluble metallic sulfide precipitates (WERF, 2007). On the other hand, Ferric ions (Fe3þ) oxidise sulfide to elemental sulfur while being reduced into Fe2þ, which precipitates with sulfide to form ferrous sulfide precipitants (Dohnalek and Fitzpatrick, 1983). pH elevation is another strategy to control H2S. A pH increase to 8.5e9, typically achieved through the continuous addition of magnesium hydroxide, prevents sulfide release to the gas phase (WERF, 2007). A recent study showed that pH elevation to 8.5e9 diminishes sulfate reducing bacteria (SRB) activity by 30e50% (Gutierrez et al., 2009), further enhancing the controlling effect. On the other hand, pulse addition of sodium hydroxide to increase pH to 12.5e13 for a short time (20e30 min) has been shown to inactivate SRB in the slime layer for a period of a few days to 2 weeks, effectively suppressing sulfide production (USEPA, 1992; WERF, 2007). The wastewater pulse or slug with high pH has to be isolated at the wastewater treatment plant (WWTP) and fed slowly into the system if it is not diluted in the collection system (USEPA, 1992). Finally, the use of microbial inhibitors such as formaldehyde (Zhang et al., 2008) or nitrite (Jiang et al., 2010a, b; Mohanakrishnan et al., 2008) have also been proposed, successfully demonstrated at laboratory and field-scale systems and in the near future may represent a more cost-effective methods for sulfide mitigation. While many different chemicals are being used in practice for sulfide control and widely reported in literature, the extent of their use and the way how they are applied are not well known. The present work aims to establish the state of the art of chemical dosing for sulfide control in Australia, to summarise the key experiences of industry partners, and to identify ways to improve the dosing practices. Due to its warm climate in general, sewer corrosion and odour problems are widespread in Australia and are of major importance, and consequently sulfide control is receiving strong attention, with tens of millions spent each year. Nevertheless, it is important to note that sewer corrosion and odours are widespread problems, and chemical dosing for sulfide control has been conducted in many other countries as well, including Austria (Bertra´n de Lis et al., 2007; Matsche´ et al., 2005), EUA (USEPA, 1992), Germany
(Barjenbruch, 2003), Spain (Delgado et al., 1999) and United Arab Emirates (Vollertsen et al., 2011), among others. The article aims to answer four main questions: (i) what chemicals are being used or have been used in the recent past to mitigate hydrogen sulfide production in sewer systems; (ii) to what extent each chemical is being used in terms of number of sites and wastewater flows treated; (iii) where each chemical is added; and (iv) how the dosing is carried out and controlled. These dosing practices are discussed in the context of the current understanding of the mechanisms by which each chemical works and improvement opportunities are identified. Questions for future research are also raised.
2.
Methodology
2.1.
Industrial survey
2.1.1.
Design of the survey
The survey sought to identify the different chemicals dosed by the industry for sulfide control and the extent of their uses in terms of the number of locations, and the flow treated. The main characteristics of the pipes (dimensions, flow, hydraulic retention time, dosing location) where various chemicals are/ were dosed were also collected. Other aspects covered by the survey included the selection or control of the chemical dosing rate, which is critical for successful sulfide control performance and costs. The survey was composed of four parts. Parts 1 and 2 covered general information of the systems, and specially aimed to identify the different chemical products used or in use. Part 3 targeted specific details of past and on-going sites, such as complete information of the installation and the dosing system, available instrumentation and off-line monitoring, as well as operation performance. Finally, Part 4 of the survey sought to identify dosing sites under design or construction, and collect detailed information of the site characteristics and dosing system. For further details see Appendix A.1, where the survey form is provided.
2.1.2.
Conduct of the survey
The survey was conducted with seven water utilities across Australia, which provide sewage services to 13 million people (60% of the total population of the country). Table 1 lists all
Table 1 e Water utilities surveyed, location and main climate characteristics of the areas. Water utility
Allconnex Water Barwon Water Melbourne Water South East Water Limited Sydney Water United Water International South Australian Water Water Corporation
State
Queensland
Mean minimum temperature in winter ( C)
Mean maximum temperature in summer ( C)
Annual rainfall (mm)
9.3 6.5
29.3 25.3
1443.6 648.9
8.7 7.9
25.6 28.5
1211.9 546.7
8.0
30.4
734.0
Victoria New South Wales South Australia Western Australia
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these utilities, together with their location and main climate features (obtained from the Australian Bureau of Meteorology, www.bom.gov.au). After the collection and preliminary analysis of the data, supplementary information was obtained with direct enquiries to the representatives of the water utilities to clarify specific issues.
3.
Results and discussion
3.1.
Dosing sites
The first step of the survey was cataloguing the different field sites where chemical dosage has been or will be carried out. Results are shown in Fig. 1. In June 2010, a total of 165 dosing sites were identified through the survey. From these, 38 sites were reported to be no longer operative (in some of these, dosing was not required anymore, while others had been decommissioned because the dosing system had been proven not to be suitable to control sulfide production/release). At the time of the survey, chemical dosing was on-going at 114 sites and a further 13 dosing sites were due to be started in the near future. The vast majority of dosing systems were located in pressure mains (162 sites), with only three gravity sites having been dosed with chemicals. These results clearly show a preference for control in rising mains where sulfide is normally generated in relatively large quantities, thus preventing possible adverse effects of H2S at the discharge point or in the gravity section. Sulfide production in a sewer pipe highly depends on factors like pipe dimensions (length and diameter), sewage flow and detention time. The characteristics of the surveyed pipes are presented in Fig. 2. As can be observed in Fig. 2A, around 80% of the surveyed sites had pipe diameters between 0.15 and 0.5 m, while only 6% of the sites had a large pipe diameter (>0.5 m). 15% of the sites corresponded to very small pipes, with diameters below 0.15 m. The area to volume ratio (A/V) of a pipe is inversely proportional to the diameter of the pipe (A/V ¼ 4/D, where D is the pipe diameter). Higher A/V values (i.e. smaller diameter
pipes) generally present a more significant biofilm contribution because sulfide production occurs mainly in biofilms attached to sewer walls (WERF, 2007). In addition, pipe diameter also has an impact on sewage hydraulic retention time (HRT). With regards to the pipe length (Fig. 2B), almost half of the sites (44%) had a length below 1 km. Pipes between 1 and 5 km were 37% of the total, whereas longer pipes represented only around 20% (5e10 km, 16%; >10 km, 3%). Similarly to pipe diameter, pipe length also impacts on HRT. The larger the diameter and the longer the pipe, the larger the HRT will be for a given flow. According to the average dry weather flow (ADWF) results presented in Fig. 2C, the majority of the dosing sites were located in pipes with very low flows (<0.5 ML d1, 34%). About 20% and 26% of the pipes had an ADWF between 0.5e1 ML d1 and 1e5 ML d1, respectively, whereas only 20% of the pipes had a large flow (>5 ML d1). With regards to the HRT (Fig. 2D), about 70% of the pipes with chemical dosing had an average HRT lower than 6 h. From the remaining systems, 19% have retention times in the range of 6e12 h, and only 12% of the pipes had HRT longer than 12 h. Theoretically, the longer the HRT of the sewage, the higher the sulfide production at the end of the pipe (Boon, 1995; Delgado et al., 1999; Sharma et al., 2008a, b). However, other parameters such as the A/V ratio (linked to the pipe diameter) and the wastewater characteristics also have an impact on sulfide production.
3.2.
Chemicals applied
Seven chemicals are in use or were used in the recent past. Fig. 3 presents the number of sites that each of the chemicals are/were applied. The most used chemical to mitigate the effects of sulfide, by number of sites, is presently magnesium hydroxide, which is applied at 30 locations. The second chemical in the ranking is sodium hydroxide, used for caustic shock to suppress activity of sulfate reducing bacteria (SRB) due to high pH. It is important to point out that sodium hydroxide is dosed all year round at only 16 of these 27 sites, while at the rest of the sites, sodium hydroxide is dosed during spring and summer
Fig. 1 e (A) Status of identified sites; (B) types of pipes.
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Fig. 2 e Main characteristics of the systems surveyed. (A) Pipe diameter; (B) pipe length; (C) average dry weather flow (ADWF); (D) hydraulic retention time (HRT).
seasons only. Nitrate and iron salts are used to a similar extent (25 and 22 sites, respectively). These two chemicals are also commonly used in Europe (Barjenbruch, 2003; Bertra´n de Lis et al., 2007; Matsche´ et al., 2005). Oxygen was widely applied in the past to prevent anaerobic conditions, but its utilisation has significantly decreased due to its poor performance in some systems (27 decommissioned sites) and is now used in only 21 locations. Odour neutralizers are currently dosed at three sites, while biomaterials had been used in the past but were reported to be no longer applied at any site. As a final remark, a large number of biomaterials and bio-products are currently available in the market. However, the survey has shown that they are not gaining wide application in Australia. The effectiveness of several such products was tested recently in the laboratory of the Advanced Water Management Centre
at The University of Queensland. All the chemicals tested so far did not show any positive results (Gutierrez et al., 2010). The number of application sites may not represent a full picture of the extent of usage of each product, and sewage flows receiving chemical dosing should be included in the analysis. Therefore the use of each chemical was also quantified based on the total flow treated for H2S mitigation (calculated from the individual average flows). Only on-going and future sites have been considered in this analysis. Results are presented in Fig. 4. Fig. 4 clearly illustrates the lower contribution of some of the most commonly used chemicals (in terms of number of sites). Only about 10% of the sewage was treated by sites with sodium hydroxide or magnesium hydroxide dosing (4.3 and 5.7%, respectively), while low percentages were also observed
Fig. 3 e Chemical products used for sulfide control.
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Fig. 4 e Contribution of each chemical to the overall sulfide control.
for nitrate (7.9%). Although iron salts and oxygen are dosed at only a fewer sites, these two chemicals have a much higher contribution than the others in terms of sewage flow treated. Around 16% of the total sewage is treated with oxygen, while the sewage receiving iron salts dosing accounts for about 66% of the total sewage with chemical dosing. Fig. 5 presents the analysis of the results based on the average ADWF (A) and pipe diameters (B) for the five most 2þ 3þ and O2. used chemicals: NaOH, Mg(OH)2, NO 3 , Fe /Fe It can be seen that sodium hydroxide is only used in systems with low flows (ADWF lower than 0.5 ML d1) and
small pipe diameters (more than 95% of the dosing is conducted in pipes with diameters smaller than 0.3 m). The mechanism for the caustic shock relies on the suppression of SRB from the biofilm attached to the sewer walls. In this respect, pipes with high surface area to volume ratios (A/V) are more favourable for this treatment since less amount of NaOH is required per volume of sewage. Therefore sodium hydroxide represents a cost-effective solution for sulfide control in small systems with low flow rates and high A/V ratios. Similarly to sodium hydroxide, magnesium hydroxide is mainly applied in sewers with low flows and small diameters. About 80% of the magnesium hydroxide dosed sites are applied in sewers with average dry weather flows below 1 ML d1 and pipe diameters between 0.15 and 0.3 m (Fig. 5B). Magnesium hydroxide effectiveness does not depend on the pipe size, but relies only on the pH and volume of sewage to treat (Parsons et al., 2003) and therefore could be suitable for sulfide control in systems of all sizes. Fig. 5A shows that iron salts are preferentially used in medium and large systems (around 80% of the sites with flows larger than 1 ML d1). Accordingly, these systems have big pipe diameters (Fig. 5B). Iron salts dosing is a simple and costeffective method for H2S control. Precipitation reactions occur in the bulk liquid phase and iron salts dosing in the past has been considered not to have any interaction with the biofilm. Thus, iron dosing is appropriate for both small and large systems. It has gained a wider application in large systems because probably other chemicals are less suitable for sulfide control in such pipes. It should be noted that recent laboratory studies have shown that iron salts do interfere with biofilm activities. Zhang et al. (2009) showed that the activity of sulfide reducing bacteria in biofilms receiving FeCl3 dosage was lowered by approximately 50% in comparison to that not
Fig. 5 e Site classification based on: (A) average daily flow rate of the sewer; (B) pipe diameter.
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receiving FeCl3 dosage. Zhang et al. (2010) further revealed that the interactions between iron salts and biofilm activity lowered the demand for FeCl2 for sulfide control. Both of the above effects are positive, and may further enhance the prospect of the use of FeCl3 or FeCl2 for sulfide control in sewers. Based on the results depicted in Fig. 5, oxygen is injected in systems of all sizes, but is mainly used in medium and high flow systems (pipes with ADWF between 1 and 5 ML d1) with large pipes (most of the pipes have diameters larger than 0.3 m). Oxygen relies on sulfide oxidation as the mechanism for H2S control, although this can be chemical or biological (Gutierrez et al., 2008), i.e. this oxidation happens in both the biofilm and in the bulk liquid. Oxygen also promotes biological organic matter oxidation, with the subsequent consumption of oxygen and volatile fatty acids. Because this process primarily occurs in the biofilm (Sharma et al., 2011), oxygen is used more efficiently in pipes with large diameters (i.e. low A/ V ratios) which usually deal with larger flows. Fig. 6 presents the theoretical O2 utilisation efficiency for sulfide oxidation for different pipe diameters, assuming an oxygen consumption rate for biological sulfide oxidation of 5.6 gO2/m2 d (Gutierrez et al., 2008), an oxygen consumption rate for chemicals sulfide oxidation of 107 gO2/m2 d (Sharma and Yuan, 2010) and an oxygen consumption rate due to other heterotrophic microorganisms of 16 gO2/m2 d (Gutierrez et al., 2008). Oxygen consumption due to biological sulfide oxidation, chemical sulfide oxidation and heterotrophic consumption was calculated for different pipe diameters and expressed as a percentage of the total oxygen consumption. Fig. 6 clearly shows that the larger the diameter, the higher the efficiency of oxygen utilisation for sulfide oxidation purposes. By analysing the contribution of each process, it can be seen that this is mostly due to the major role of chemical sulfide oxidation in the total sulfide oxidation, whereas the reduction of the A/V ratio for larger pipe diameters also implies the decline in the contribution of the biofilm processes (biological sulfide oxidation and also the heterotrophic consumption of O2 for purposes other than sulfide oxidation).
With regards to nitrate, it is also preferentially used in small systems, with only around 25% of the sites located in pipes with ADWF higher than 1 ML d1 (Fig. 5A). Concerning the pipe dimensions, more than 60% of the sites where nitrate is dosed have a diameter between 0.15 and 0.3 m. Nitrate is an expensive chemical (de Haas et al., 2008), usually dosed as NaNO3 or Ca(NO3)2 (Zhang et al., 2008). It does not inhibit SRB activity in the short- or long-term, and does not decrease the abundance of SRB in the sewer biofilm. Instead, nitrate addition to the start of rising mains increase SRB activity in downstream biofilms (Mohanakrishnan et al., 2009). Sulfide can be oxidised biologically by nitrate reducing-sulfide oxidising bacteria (NR-SOB), but chemical sulfide oxidation is almost negligible (unpublished results from the authors’ group). Similar to oxygen, nitrate is also used as an electron acceptor by other heterotrophic bacteria, with subsequent nitrate consumption. Both the biological processes described above occur mainly in the biofilm (Mohanakrishnan et al., 2009). Hence, the effectiveness of nitrate does not rely on the A/V ratio. Therefore, nitrate addition is suitable for both small and large pipes, unlike oxygen, which is more suitable for large systems, However, the use of nitrate in large systems may be cost prohibitive due to its high price (Sharma et al., 2011). As stated before, sewage HRT has a significant impact on sulfide production (Sharma et al., 2008b). HRT in systems with chemical dosing was also assessed to complete the analysis and identify if any of the five main chemicals is preferentially used to deal with certain conditions. Results are shown in Fig. 7. It can be observed from Fig. 7 that the distribution of the HRT for four of the five chemicals, namely magnesium hydroxide, nitrate, iron salts and oxygen, was similar, indicating that these were not preferentially used for certain retention times or sulfide levels. Only sodium hydroxide is the exception to this. Sodium hydroxide is dosed to pipes with average HRT between 3 and 12 h. This may be due to the specific characteristics of these systems; small size pipes with low flows, as depicted in Fig. 5.
3.3.
Fig. 6 e Oxygen utilisation efficiency as a function of the pipe diameter for: biological sulfide oxidation, chemical sulfide oxidation, sulfide oxidation (total) and heterotrophic consumption.
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Dosing location
The location where a chemical is dosed may determine the effectiveness of treatment and the operational cost. The survey showed that chemicals are dosed at the wet wells, pumping stations, an intermediate location along the rising main or at the discharge point of a rising main. These results are depicted in Fig. 8, where only current and future sites are considered. In general terms, the preferred dosing location for the majority of the chemicals is before the wastewater enters the rising main (either at the wet well or the pumping station). Sodium hydroxide was in all cases added at the wet well. This is in agreement with the theory that it should be dosed upstream of the rising main to effectively inhibit/kill all the SRB attached to the walls of the pipe. Dosing of magnesium hydroxide is conducted primarily (60%) at either wet wells or the pumping stations. However, this chemical is also dosed, albeit to a less extent, directly into the main. It was even applied in one site at the discharge point of the rising main. Magnesium hydroxide is used to increase
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Fig. 7 e Site classification based on HRT for the different chemicals.
the pH to prevent sulfide transfer to the gas phase at rising main discharging points and in gravity sections. Therefore, the dosing location should not have a critical impact on the control of H2S transfer. However, a recent study showed that high pH (pH > 8.5) can partially inhibit SRB activity, diminishing sulfide production (Gutierrez et al., 2009). As such, adding this chemical upstream would reduce its consumption to achieve the same level of control of sulfide transfer (Gutierrez et al., 2009). In this respect, the dosing of magnesium hydroxide upstream should be beneficial. Around 70% of iron dosing was conducted at upstream locations (mainly at the wet well). The dosing location of iron salts is not important in terms of effectiveness of the chemical, as long as the hydraulic retention time (HRT) in the pipe after dosing allows sufficient time for sulfide precipitation (in the order of seconds according to Wei and Osseo-Asare (1995)). However, a recent lab-scale study demonstrated that the addition of Fe3þ significantly inhibits the SRB activity of anaerobic sewer biofilms (Zhang et al., 2009), although the same phenomenon is yet to be observed in real sewers. If this inhibitory effect is verified in real sewers, iron salts should preferably be added at upstream locations. A further benefit of
upstream dosage of iron salts is that sulfide would be controlled along the entire pipe. While iron salts may initially react with some other anions (e.g. phosphate and hydroxide) in the absence of sulfide, iron ions will be made available for sulfide precipitation when the latter is produced due to the lower solubility of FeS in comparison to iron phosphate and iron hydroxide precipitates (Zhang et al., 2009). The survey also showed that nitrate is normally added upstream (95% of the dosing was conducted at the wet well or the pumping station), whereas oxygen was dosed at 90% of the sites along the rising main. Neither oxygen nor nitrate inhibit SRB activity in the short- or long-term, but their actual mechanism for H2S control relies on sulfide oxidation (either chemical or biological) (Gutierrez et al., 2008; Mohanakrishnan et al., 2009). If oxygen was supplied at the beginning of the pipe, complete control of sulfide at the discharging points would require the entirety of the pipe to be oxic, which is not possible with the existing method of dosing (i.e. into wastewater when the pump is running) (Gutierrez et al., 2008). On the other hand, dosing nitrate upstream at a very high rate may ensure anoxic conditions along the whole pipe, although at a very high cost (Mohanakrishnan et al., 2009). In this
Fig. 8 e Dosing location of the chemicals.
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respect, locating dosing points for oxygen or nitrate downstream (close to the end of the sewer or target point for sulfide control, but allowing adequate wastewater retention time downstream of dosing for complete oxidation of sulfide) is a more optimal solution in terms of performance and costs (Gutierrez et al., 2008; Mohanakrishnan et al., 2009).
3.4.
Dosing rate control
Chemicals are dosed continuously or just during pumping events or periodically for several days/weeks. This mainly depends on the characteristics of the chemical and the dosing location. The dosing rate also has a critical impact on the effectiveness of sulfide mitigation and chemical consumption. According to the survey, four different dosing strategies are used by the industry: (i) intermittent dosing, where the same amount of product is dosed periodically; (ii) constant dosing, where the chemical is dosed at the same rate independent of the flow and wastewater characteristics; (iii) flow-paced dosing, where the amount of chemical delivered is proportional to the wastewater flow, and (iv) profiled dosing, where the dosing rate is variable according to a pre-defined profile. The reported dosing strategies used by industry are shown in Fig. 9. As can be observed in Fig. 9, sodium hydroxide is only added intermittently and for a short period (one to five pumping cycles) to reach pH 10e11 (O’Gorman et al., 2011). This is because of its unique mechanism e SRB activity suppression by caustic shock. Magnesium hydroxide, nitrate and oxygen sites are mostly operated at pre-defined dosing rates. Around 50% of the iron dosing sites used pre-defined profile dosing rates, while the other half used constant rates. Constant rates were also used at about 30% of the nitrate and oxygen dosing stations. Finally, flow-paced dosing was seldom applied and was only of relative importance for sites with magnesium hydroxide dosing (25e30%). This is reasonable, given the fact that magnesium hydroxide dosing relies mainly on the volume of sewage and not on the sulfide concentration. Finally, Table 2 summarise the current industry practice in terms of dosing rates and costs for the different chemicals. The effectiveness related to these dosing rates is however not known.
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As can be clearly observed, dosing levels differ from one chemical to another and present a wide range. This is due to the different characteristics of the systems where chemical dosing is conducted. Taking into account the dosing frequency and flow, sodium hydroxide dosing rates from 11 to 28.6 L of NaOH (50% w/w) per ML of sewage were reported in the survey. This chemical relies on the suppression of SRB from the pipe biofilm due to the caustic shock. Hence, the pipe diameter plays a critical role and dosing requirements dramatically increase with pipe diameter. Magnesium hydroxide dosing levels ranged from 48 to 157 kg Mg(OH)2/ML of sewage. The effectiveness of this chemical is independent of the sulfide levels or the pipe diameter, depending mainly on the buffer capacity of the sewage. In addition, biological acidifying reactions could take place along the pipe so longer pipes may require additional Mg(OH)2 dosing. Nitrate, oxygen and iron salts present also a wide dosing rate range. Nitrate and oxygen oxidise sulfide, while the principle behind iron salts is the sulfide precipitation. In this respect, the dosing of all three chemicals is critically governed by sulfide levels in the pipe. Other aspects such as the dosing location or the pipe length and diameter may also partly explain the disparity among the results. When the costs are analysed, nitrate dosing is found to be the most expensive option, with a maximum cost of up to $483.6/ML. Similar chemical costs were reported for Magnesium hydroxide (48.7e159.3 $/ML) and iron salts dosing (10.9e170.6 $/ML). Chemical costs for iron salts dosing were comparable to those reported by Matsche´ et al. (2005) for sulfide control in Austria (42e77 $/ML). The dosing costs for intermittent NaOH dosing were slightly lower, 39.6e99.1 $/ML, although the constraints of this technology have to be taken into consideration. Finally, these figures show oxygen to be the least expensive chemical with a maximum dosing cost of 74 $/ML of sewage.
3.5.
Chemical dosing performance
Assessing the performance of chemical dosing was one of the objectives of the survey (see Appendix A.1, sections 3.C and 4.C). However, results showed that systems with chemical dosing were seldom monitored and little data was available. For most of the sites, neither on-line nor periodical
Fig. 9 e Dosing rate control of chemicals.
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Table 2 e Chemical dosing current industry practice. Chemical Sodium hydroxidea Magnesium hydroxide Nitrate Iron salts Oxygen
Dosing levels 11.4e28.6 L NaOH/MLa 47.9e156.6 kg Mg(OH)2/ML 1.33e15.5 kg N-NO 3 /ML 3e47 kg Fe/MLc 15.8e91.5 kg O2/ML
Chemical costb 39.6e99.1 $/MLa 48.7e159.3 $/ML 41.3e483.6 $/ML 10.9e170.6 $/ML 12.8e74.0 $/ML
a To reach pH 10e11 using NaOH 50% w/w. b Based on the following chemical prices in Australian dollars: $69.3/20L NaOH (50% w/w); $590/tonne Mg(OH)2 58% w/w; $1010/m3 Ca(NO3)2 50% w/w; $526/tonne FeCl3 42% w/w; $1.15/m3 O2 99.5% w/w. c Dosing levels of up to 705 kg Fe/ML were reported, but were excluded from this comparison as non-representative (abnormally high).
effective as the dynamics of each sewer system is different, and even for the same sewer system the conditions vary from day to day (Sharma and Yuan, 2009). Recent development in on-line sensor technology has made the on-line measurement of dissolved sulfide levels possible (Sutherland-Stacey et al., 2008). In this respect, dynamic dosing of chemicals based upon on-line measurement of parameters such as flow rate and liquid phase sulfide concentration may achieve better sulfide control with lower chemical costs. However, the current high price of the dissolved sulfide sensors makes online control economically non-viable for small systems. Future work should address this issue by developing more affordable and reliable sensors for dissolved sulfide measurement. In addition, due to the different mechanisms of the chemicals, specific control algorithms need to be developedfor each chemical to ensure optimised dynamic control.
4.3. monitoring of dissolved sulfide or gas phase H2S was conducted. Lack of odour complaints were, in most of the cases, used as an indication of the effectiveness.
4. Opportunities for operational improvement and future research Based on the results of the survey, several opportunities for improvement as well as future research questions have been identified.
4.1. Model-based support for the selection of chemicals, dosing locations and dosing rates The current selection of chemicals and the design of its dosing locations and rates are mainly based on experience (i.e. iron salts are more suitable for large systems, or oxygen has to be dosed directly into the pipe). However, there are many factors to be considered. The selection and design should be made based on specific features of each site and characteristics of the sewage. In recent years, powerful mathematical models such as the SeweX model (Sharma et al., 2008b) and the WATS model (Hvitved-Jacobsen et al., 1998) have been developed to support the decision making and have indeed been demonstrated in several cases (de Haas et al., 2008; Sharma et al., 2008a). However, these models are yet to be taken up widely.
4.2.
On-line control of the dosing rate
Currently constant, flow-paced and profiled dosing rates are used to control chemical dosing. These are commonly based on empirical guidelines developed, again, through experience. However, sulfide concentration and flows have a dynamic behaviour over a day, and constant or flow-paced dosage could lead to over dosage during periods with low levels of sulfide production, and under dosage during some other periods. In some cases, profiled dosing with variable dosing rates is applied based upon historical data (flow rate, sulfide concentration, etc.). However, this strategy may not be
More cost-effective chemicals
All the above-mentioned chemicals (except for NaOH) require continuous dosing of the product, which results in high chemical supply costs. In this respect, research is needed to develop more chemicals to directly target the activities of sewer biofilms. Some biomaterial products are available, but there has been little peer-reviewed evidence showing their effectiveness. However, recent lab studies reported the inhibitory effect of nitrite/free nitrous acid (FNA) on SRB and methanogenic activities in sewer biofilms (Jiang et al., 2010a, b, 2011). FNA as low as 0.18 mg-N/L can suppress sulfide production after 24-h of exposure. The suppression is followed by a slow recovery (several days to several weeks) after stopping the FNA addition (Jiang et al., 2011). FNA has appears to be promising chemical, but its dosage requires further optimisation.
5.
Conclusions
An industrial survey was conducted among seven major Australian water utilities, cataloguing the different field sites where chemical dosage was/is/will be carried out. A total of 165 sites were identified, the vast majority of them being located in pressure mains. Seven different chemicals were or are in use: Magnesium hydroxide, sodium hydroxide (caustic shock), nitrate, iron salts, oxygen, odour neutralizers and other biomaterials. Magnesium hydroxide is the chemical applied at most sulfide control sites (30), followed by sodium hydroxide (27) and nitrate (25). However, when taking into account the flow treated, the dosing of iron salts is used to control sulfide in approximately 66% of the sewage treated. Magnesium hydroxide, sodium hydroxide and nitrate are used mainly for small pipes with low flows and their total contribution is only around 18% of the total flow treated with chemicals for sulfide control. Chemicals are normally dosed at wet wells or pumping stations, except for oxygen, which is mainly injected directly into rising mains. The dosing of nitrate in these upstream locations is likely to result in either low H2S control efficiency
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or high chemical costs, while the dosage of other chemicals at such locations appear appropriate. Constant dosing and profiled dosing are the two methods predominantly used for dosing rate control. Flow-paced control is also in use. None of the 165 sites use or used online control of the dosing. Finally, there are several key opportunities to achieve more effective and cost-effective sulfide control. Mathematical models are now available to support the selection of the most suitable chemical, dosing location and dosing control for each case. On-line control of dosing rate may also improve the effectiveness of the dosing and reduce chemical costs. Also, more cost-effective chemicals need to be developed, which does appear to be possible.
Acknowledgements The authors acknowledge the Sewer Corrosion and Odour Research (SCORe) Project LP0882016 funded by an Australian Research Council Industry Linkage Project Grant and by many key members of the Australian water industry and acknowledge our Research Partners on this Project (for more details see: www.score.org.au).
Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011.09.054.
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Sharma, K.R., Yuan, Z., de Haas, D., Hamilton, G., Corrie, S., Keller, J., 2008b. Dynamics and dynamic modelling of H2S production in sewer systems. Water Research 42 (10, 11), 2527e2538. Sharma, K.R., Gutierrez, O., Corrie, S., O’Halloran, K., Capati, B., Keller, J., Yuan, Z., 2011. Impact of chemical dosing of sewers on WWTP performance. Water 38 (2), 88e91. Sutherland-Stacey, L., Corrie, S., Neethling, A., Johnson, I., Gutierrez, O., Dexter, R., Yuan, Z., Keller, J., Hamilton, G., 2008. Continuous measurement of dissolved sulfide in sewer systems. Water Science and Technology 57 (3), 375e381. Tomar, M., Abdullah, T.H.A., 1994. Evaluation of chemicals to control the generation of malodorous hydrogen-sulfide in waste-water. Water Research 28 (12), 2545e2552. USEPA, 1974. Process Design Manual for Sulfide Control in Sanitary Sewerage Systems. U.S. Environmental Protection Agency, Washington, DC. USEPA, 1992. Detection, Control and Correction of Hydrogen Sulfide Corrosion in Existing Wastewater Systems. U.S. Environmental Protection Agency, Washington, DC. Technical Report, 832-R-92-001.
Vollertsen, J., Nielsen, L., Blicher, T.D., Hvitved-Jacobsen, T., Nielsen, A.H., 2011. A sewer process model as planning and management tool e hydrogen sulfide simulation at catchment scale. Water Science and Technology 64 (2), 348e354. Wei, D., Osseo-Asare, K., 1995. Formation of iron monosulfide: a spectrophotometric study of the reaction between ferrous and sulfide ions in aqueous solutions. Journal of Colloid and Interface Science 174 (2), 273e282. WERF, 2007. Minimization of Odors and Corrosion in Collection Systems, Phase I. Water Environment Research Foundation WERF, IWA, Alexandria, VA 22314-1177. Zhang, L., De Schryver, P., De Gusseme, B., De Muynck, W., Boon, N., Verstraete, W., 2008. Chemical and biological technologies for hydrogen sulfide emission control in sewer systems: a review. Water Research 42 (1, 2), 1e12. Zhang, L., Keller, J., Yuan, Z., 2009. Inhibition of sulfate-reducing and methanogenic activities of anaerobic sewer biofilms by ferric iron dosing. Water Research 43 (17), 4123e4132. Zhang, L., Keller, J., Zhiguo, Y., 2010. Ferrous salt demand for sulfide control in rising main sewers: tests on a laboratory scale sewer system. Journal of Environmental Engineering.
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Synergistic effect of coupling zero-valent iron with iron oxide-coated sand in columns for chromate and arsenate removal from groundwater: Influences of humic acid and the reactive media configuration Mark S.H. Mak, Irene M.C. Lo*, Tongzhou Liu 1 Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
article info
abstract
Article history:
A column study was conducted using a combination of zero-valent iron (Fe0) and iron oxide-
Received 13 April 2011
coated sand (IOCS) for removing Cr(VI) and As(V) from groundwater. The removal efficiency
Received in revised form
and mechanism of Cr(VI) and As(V), the effects of humic acid (HA), and the various config-
27 September 2011
urations of Fe0 and IOCS were investigated. The results showed that the use of an Fe0 and
Accepted 1 October 2011
IOCS mixture in a completely mixed configuration can achieve the highest removal of both
Available online 8 October 2011
Cr(VI) and As(V), whilst the effects of HA were marginal in using these reactive materials. The solid phase analysis revealed the occurrence of the synergistic effect in these reactive
Keywords:
materials as Fe2þ can be adsorbed onto the IOCS and transform the iron oxides to magnetite,
As(V) adsorption
providing more reactive surface area for Cr(VI) reduction and reducing the passivation on the
Cr(VI) reduction
Fe0. As(V) can then be removed by adsorption onto these iron corrosion products. HA can be
Humic acid
adsorbed onto the IOCS so that the impacts of the deposition of HA aggregates on the Fe0
Iron oxide-coated sand
surface can be reduced, thus enhancing the Fe0 corrosion.
Zero-valent iron
1.
Introduction
Cr(VI) and As(V) contamination in groundwater has raised health concerns due to their toxic, carcinogenic and mutagenic properties (Smedley and Kinniburgh, 2002; USEPA, 2000a). The toxicity of Cr(VI) is caused by free diffusion across cell membranes and a strong oxidative potential (Kotas and Stasicka, 2000), while the toxicity of As(V) arises from an ability in inhibiting the energy-linked functions of mitochondria (Bissen and Frimmel, 2003). Since Cr(VI) and As(V) can cause adverse health impacts, effective remediation technology is required for cleaning up these pollutants. Permeable reactive barriers (PRBs) have been recognized as a cost-effective and environmental friendly technology for in-
ª 2011 Elsevier Ltd. All rights reserved.
situ groundwater remediation (Higgins and Olson, 2009). Zerovalent iron (Fe0) has been frequently used as the reactive media in PRBs. Fe0 has proven to be capable of removing Cr(VI) and As(V) from groundwater (Blowes et al., 2000; Manning et al., 2002). The removal mechanisms of Cr(VI) by Fe0 mainly involve chemical reduction and subsequent Cr(III) precipitation as Cr(III) hydroxides and Fe(III)/Cr(III) (oxy) hydroxides (Alowitz and Scherer, 2002), while the mechanism of As(V) removal is predominantly through the adsorption onto or by co-precipitation with the iron corrosion products (Lackovic et al., 2000; Su and Puls, 2008). Although many studies, including field studies have shown the capability of Fe0 in removing Cr(VI) and As(V) individually, numerous problems were encountered when using Fe0 for
* Corresponding author. Tel.: þ852 23587157; fax: þ852 23581534. E-mail address:
[email protected] (I.M.C. Lo). 1 Present address: Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.10.002
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removing both Cr(VI) and As(V). Inhibitory effects on As(V) removal by Fe0 has been shown in the presence of Cr(VI), as Cr(VI) would compete with As(V) for the adsorption sites of the iron corrosion products (Liu et al., 2009). As(V) removal rate by Fe0 was decreased by 51.2%e78.5%, in a solution containing 0.1e1.0 mM of Cr(VI) and 2 mg/L of As(V) (Su and Puls, 2001). Another problem is that some geochemical constituents, such as natural organic matter (NOM), can impact the performance of an Fe0 system. A column study was reported when using Fe0 to treat Cr(VI) with the presence of humic acid (HA), which is one of the major components of NOM, HA was aggregated and deposited on Fe0 surfaces, and as a result, the reactivity of Fe0 was reduced (Liu and Lo, 2011). This may also result in inhibitory effects of As(V) removal as the production of the iron corrosion products can be reduced. Apart from these problems, passivation of iron corrosion products always occurs on Fe0 surfaces. Passivation of Cr(III) and carbonate precipitates can reduce the corrosion rate of Fe0 (Jeen et al., 2006). The accumulation of the passivation layers eventually diminishes the reactivity of Fe0 and inactivates the Fe0 (Noubactep, 2008). Coupling the iron oxides with Fe0 could provide solutions for the problems of using Fe0 alone. The iron oxides can provide additional adsorption sites for As(V), thus reducing the inhibitory effects of As(V) removal by Fe0 (Mak et al., 2011). Besides, iron oxides can adsorb NOM by forming inner sphere complexes (Gu et al., 1994; Saito et al., 2004), which can reduce the impacts of NOM on Fe0 systems. Furthermore, Fe2þ, which is produced from Fe0 corrosion, can be catalyzed by iron oxides for the contaminant reduction, such as Cr(VI), U(VI) and NO 3 (Buerge and Hug, 1999; Jang et al., 2008; Tai and Dempsey, 2009). Among numerous types of iron oxides, iron oxide-coated sand (IOCS) could be suitable for enhancing the co-removal of Cr(VI) and As(V). Since IOCS is commonly in granular form, it is suitable for packing in columns or PRBs. USEPA has proposed the use of IOCS in fixed-bed filtration for As removal (USEPA, 2000b; Mohan et al., 2007). Batch studies using an Fe0eIOCS combination have shown better performance on the co-removal Cr(VI) and As(V), in both the absence and presence of HA, compared with using Fe0 alone (Mak et al., 2011). Nevertheless, such batch studies cannot show relatively long term impacts such as the passivation of iron corrosion products and the deposition of HA aggregates which usually occur in columns or PRBs. Additionally, the configuration of reactive material in PRBs can cause different effects on the removal performance. A previous study using three configurations of reactive material (Fe0 only, a granular mixture of Fe0 and pumice, and pumice and Fe0 in series) for Ni and Cu removal has shown that the effects of the passivation on the Fe0 surface were remarkably varied (Moraci and Calabro`, 2010). In order to have a better understanding on the use of an Fe0eIOCS combination for Cr(VI) and As(V) removal, a series of column experiments was conducted in this study to evaluate the performance of various Fe0eIOCS systems in As(V) and Cr(VI) removal in a relatively long-term operation. The aims were to investigate the mechanisms of As(V) and Cr(VI) removal by Fe0 and IOCS in a stimulated environment of PRBs, to assess the relatively long-term impacts of HA on As(V) and Cr(VI) removal by Fe0 and IOCS, and to study the effects of the Fe0 and IOCS configuration.
2.
Materials and methods
2.1.
Materials
Fe0 filings (ETC-CC-1004; Connelly-GPM Inc) of size of 0.5e1 mm were used without chemical pretreatment. The characteristics of Fe0 have been addressed in detailed in Liu et al. (2008). The IOCS, of size 0.7e0.75 mm, was obtained from the waste generated from a fluidized and air-aerated bed reactor, which was used to remove iron ions that were produced in the process 0 of NO 3 reduction by Fe (Hsu et al., 2008). The IOCS characteristics have been well addressed by Hsu et al. (2008). Quartz sand of size 0.71e0.85 mm (BS 18/22; Goodwill Co.) was used for adjusting the porosity of the column when using Fe0 alone. The chemical stock solutions were prepared by dissolving chemicals (K2Cr2O7, Na2HAsO4$7H2O, CaCl2$2H2O, NaHCO3, Na2SO4 and NaCl) into ultrapure water (>18.1 MU-cm). Aldrich HA was used in this study and dissolved organic carbon (DOC) was used for representing the HA concentration. The solutions fed to the columns were prepared by diluting the stock solutions to certain concentrations using ultrapure water.
2.2.
Column experiments
The columns used in this study were 20 cm long with 3.6 cm internal diameter, and made of rigid PVC pipe. Sampling ports were located at the both ends and at distances of 5, 10, and 15 cm from the influent end (Supplementary Information (SI) Fig. S1). The column fillings (Fe0, IOCS and quartz sand) were carefully packed into the columns to obtain a homogeneous distribution with a porosity of 0.4 by adding and compacting the fillings for a 2 cm increment each time. Thirteen columns were set up for studying the removal performance of Cr(VI) and As(V) by the reactive media (i.e. Fe0 and IOCS), and the effects of HA and reactive media configuration (Table 1). The synthetic groundwater consisted of 0.8 mM CaCl2 (representing moderate hardness; Todd and Mays, 2005), 3 mM HCO 3 (representing medium alkalinity; Todd and Mays, 2005), 1 mM Na2SO4, 5 mM NaCl, 20 mg/L Cr(VI), and 10 mg/L As(V), with/without 8 mg/L HA as DOC. The pH was adjusted to 7 by adding 0.01 N HCl. Duplicate columns were set up for the columns with the Fe0 and IOCS mixture, and Fe0 and IOCS in series which were fed with the solution containing HA (columns 7, 11 and 13), for ensuring the reliabilities of the experimental results in those columns. The synthetic groundwater was fed into the columns in an upflow manner at a velocity of 100 m/year (27.4 cm/d) and a flow rate of 0.1 mL/min using a peristaltic pump (MasterFlex). This velocity was reported to be the seepage velocity in the PRB installed at the Vapokon site, Denmark (Lai et al., 2006). The required time for one pore volume (PV) was about 17.6e17.7 h. Groundwater samples (around 5 ml) were collected regularly (every 5 PV) from the sampling ports of the columns to determine the Cr and As concentrations, and for pH measurement. A flow cell was used for measuring the redox potential of the effluent.
2.3.
Analytical methods
The concentration of Cr(VI) was measured by 1,5diphenylcarbazide colorimetric methods (American Public
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Table 1 e Experimental conditions of the column set up and the corresponding removal capacities of Cr(VI) and As(V). Column ID
Material
Configuration of reactive media
Initial Duration Total Total input Cr(VI) As(V) removal concentration (d) input of As(V) (mg) removal capacity of HA of Cr(VI) capacity (mg/column) (mg/L as DOC) (mg) (mg/column)
IOCS n.a.e 0 Fe and quartz Completely mixed sanda Fe0 and IOCSa Completely mixed
0 0
24 243
69.1 699.7
34.6 349.8
2.2 486.9
25.5 246.4
0
243
699.7
349.8
608.9
275.1
8 8
24 243
69.1 699.7
34.6 349.8
2.2 408.2
20.9 149.0
6 7b
IOCS n.a.e Fe0 and quartz Completely mixed sanda Fe0 and IOCSa Completely mixed Fe0 and IOCSa Completely mixed
8 8
243 243
699.7 699.7
349.8 349.8
567.5 582.8
271.0 256.9
8 9
Fe0 and IOCSa Fe0 and IOCSa
Fe0 followed by IOCS IOCS followed by Fe0
0 0
218 218
627.7 627.7
313.8 313.8
348.1 230.0
206.3 107.0
10 11c 12 13d
Fe0 Fe0 Fe0 Fe0
Fe0 followed by IOCS Fe0 followed by IOCS IOCS followed by Fe0 IOCS followed by Fe0
8 8 8 8
218 218 218 218
627.7 627.7 627.7 627.7
313.8 313.8 313.8 313.8
209.7f 209.7f 200.7f 200.7f
114.7f 110.1f 111.6f 113.9f
1 2 3 4 5
and and and and
IOCSa IOCSa IOCSa IOCSa
a Fe0 and quartz sand/IOCS were filled into the columns in 1:1 (w:w). b Duplicate of column 6. c Duplicate of column 10. d Duplicate of column 12. e No configuration can be varied as only one type of reactive media existed. f Since the migration rates in the Fe0 and IOCS layers are different, the removal capacity was estimated by using the effluent breakthrough curve only.
Health Association et al., 2005), using an UV/visible spectrophotometer (Ultrospec 4300 Pro) at a wavelength of 540 nm. The As(V) concentration was determined by a graphite furnace-atomic absorption spectrometer (Hitachi Z-8200). The concentrations of the total dissolved iron and chromium were determined using a flame atomic absorption spectrometer (Varian 220FS). The solution pH was measured using a pH meter (Orion Model 420A). The redox potential was measured using a redox potential probe.
IOCS was analyzed using X-ray photoelectron spectroscopy (XPS, Perkin Elmer PHI 5600). The reacted Fe0, quartz sand and IOCS were immersed in methanol and sonicated for removal of the surface coatings (Lee and Wilkin, 2010). These surface coatings were collected by centrifugation and subsequently freeze-dried, prior to analysis by X-ray diffraction (XRD, Philips PW1825) for identifying the mineral phase on the reacted Fe0, quartz sand and IOCS surfaces.
2.4.
3.
Results and discussion
3.1.
Removal capacity of Cr(VI) and As(V)
Solid phase analysis
At the end of the column experiments, the columns were dismantled and the reacted column fillings (about 20 g) at a location of 15 cm from the influent end were collected immediately. The reacted column fillings collected were then freeze-dried prior to fractionation by a 150 mm sieve (Phillips et al., 2003; Bartzas and Komnitsas, 2010). The reacted Fe0, quartz sand and IOCS remained on the sieve as their sizes were larger than 150 mm. Magnetic separation was used for separating the Fe0 and sand/IOCS (Zolla et al., 2009). The samples were then stored under N2 at 4 C. The surface and the cross-section of the reacted Fe0, quartz sand and IOCS were examined using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX, JEOL 6390). The cross-section samples were obtained by embedding the reacted Fe0, quartz sand and IOCS in resins, followed by grinding and polishing (Phillips et al., 2003). The valence of chemical species on the surface of the Fe0, quartz sand and
Fig. 1 a and b show the breakthrough curves of Cr(VI) and As(V) of column 3, which consisted of completely mixed Fe0 and IOCS as reactive media and received the synthetic groundwater without HA. When the column was continuously fed with the solution, the migration fronts of Cr(VI) and As(V) moved toward the column end with time. The removal capacities of Cr(VI) and As(V) can be estimated by using the migration rates of the Cr(VI) and As(V) fronts. This approach has been commonly adopted for calculating the removal capacities of contaminants in a Fe0 column (Lai and Lo, 2008; Liu and Lo, 2011). The migration rates of the Cr(VI) and As(V) fronts at their relative concentrations of 0.5 can be obtained from the slopes of the fitting lines of the distance-PV plots (Fig. 1c and d). The removal capacities of Cr(VI) and As(V) can then be calculated by using the following equations:
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Fig. 1 e (a) Cr(VI) and (b) As(V) breakthrough curves in column 3 at different sampling ports: A port 1 (5 cm); - port 2 (10 cm); : port 3 (15 cm); C effluent (20 cm). Migration distances of the (c) Cr(VI) and (d) As(V) fronts at C/C0 [ 0.5 in column 3, the slopes of the lines indicate the Cr(VI) and As(V) front migration rates along the column. Column 3 received Cr(VI) and As(V) solution with background electrolytes. Initial concentrations of Cr(VI) and As(V) were 20 mg/L and 10 mg/L, respectively. Background electrolyte consisted of 0.8 mM CaCl2, 3 mM NaHCO3, 1 mM Na2SO4 and 5 mM NaCl. Lines are for guidance only.
Removal Capacity½mg CrðVIÞ=column ¼
½CrðVIÞ L M 1000
(1)
Removal Capacity½mg AsðVÞ=column ¼
½AsðVÞ L M 1000
(2)
where [Cr(VI)] and [As(V)] (mg/L) are the initial concentrations of Cr(VI) and As(V), respectively; M (cm/cm3)is the normalized migration rate at the relative concentrations of 0.5, which is the migration rate (cm/PV) divided by the pore volume of the column (cm3/PV); and L (cm) is the column length (i.e. 20 cm). The removal capacities of Cr(VI) and As(V) in each of the columns are shown in Table 1. The breakthrough curves of other columns are shown in SI Figs. S2-S5. The effluent pH of all the columns and the effluent redox potential of columns 2e3, and 5e13 were shown in SI Figs. S6 and S7. The effluent pH gradually decreased from about 10 to 8, while the redox potential gradually increased. The highest removal capacities of Cr(VI) and As(V) in the absence and presence of HA were obtained from the columns with the Fe0 and IOCS mixture (columns 3, and 6 and 7, respectively), while the lowest removal capacities of Cr(VI) and As(V) in the absence and presence of HA were obtained from the columns with IOCS only (columns 1, and 4, respectively), as shown in Table 1. The removal capacities of Cr(VI) and As(V) of the columns with the Fe0 and quartz sand mixture (columns 2 and 5) are significantly higher, compared with those of the IOCS columns (columns 1 and 4). This indicates that IOCS has only contributed a small amount in the
Cr(VI) and As(V) removal, when using IOCS alone. The Cr(VI) and As(V) removal capacities of the columns with the Fe0 and IOCS mixture (columns 3, 6 and 7) were not merely equal to, but higher than, the sum of the removal capacities of the columns with IOCS only (columns 1 and 4) and that with the Fe0 and quartz sand mixture (columns 2 and 5). For example, the Cr(VI) removal of the column with the Fe0 and IOCS mixture (column 3) was far higher than the sum of the Cr(VI) removal of the columns with IOCS only (column 1) and that with the Fe0 and quartz sand mixture (column 2). Besides, the removal capacities of Cr(VI) and As(V) of the columns with the Fe0 and IOCS mixture (columns 3, 6 and 7) were noticeably higher than the columns with Fe0 and IOCS in series (columns 8e13), in solutions with or without HA. These results imply that a synergistic effect occurred when using the Fe0 and IOCS mixture for removing Cr(VI) and As(V). Nevertheless, such synergistic effect could not be observed in the previous batch studies when using the Fe0eIOCS combination for Cr(VI) and As(V) removal, in which the removal capacities of Cr(VI) and As(V) by the Fe0eIOCS combination were lower than the sum of the removal capacities of Cr(VI) and As(V) by only using Fe0 and those by only using IOCS (Mak et al., 2011).
3.2.
Removal mechanisms
Compared with IOCS, Fe0 contributed a larger portion in the removal of Cr(VI) and As(V). Cr(VI) was removed by Fe0 mainly through reductive precipitation (Alowitz and Scherer, 2002) while As(V) was removed via adsorption/co-precipitation with
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 7 5 e6 5 8 4
iron corrosion products (Lackovic et al., 2000; Su and Puls, 2008). By examining the surface morphology of the reacted Fe0, some secondary iron corrosion products such as botryoidal clusters and boulder-like precipitates were commonly observed, and others were observed to consist of euhedral tabular structures and hexagonal forms of precipitates as well. These findings are in agreement with the previous studies on the surface morphology of the Fe0 surface (SI Fig. S8; Lai and Lo, 2008; Liu and Lo, 2011). Most of these
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secondary products were FeeCr precipitates as determined by EDX, which can lead to a reduction of the reactivity of Fe0 by preventing the electron transfer from the Fe0 core to the solution (Lai and Lo, 2008). Fig. 2 shows the cross-sections of reacted Fe0 from different columns. The iron corrosion product layers of the reacted Fe0 from the columns with the Fe0 and IOCS in series (columns 8, 9, 10 and 12) were generally thicker than those from the columns with the Fe0 and quartz sand/IOCS mixture (columns 2, 3, 5 and 6). This indicates that
Fig. 2 e SEM images of the cross-section of the reacted Fe0 collected from (a) column 2, (b) column 3, (c) column 5, (d) column 6, (e) column 8, (f) column 9, (g) column 10 and (h) column 12.
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the passivation of the iron corrosion products was more severe in the columns with the Fe0 and IOCS in series. The EDX analysis indicated that the passivated layers were mainly Fe, Cr, and O, possibly due to the existence of Fe(III)/Cr(III) (oxy) hydroxides. These findings are consistent with those in the surface morphology of the Fe0. The electron transfers can be inhibited by these hydroxides, while the rate of electron transfers could be much reduced in Fe0 with thicker passivated layers (Noubactep, 2008). Therefore, the reactivity of the Fe0 from the columns with the Fe0 and IOCS in series was lower than that from the columns with the Fe0 and quartz sand/IOCS mixture, causing an earlier breakthrough of the Cr(VI) and As(V). On the other hand, the iron corrosion products can deposit onto the quartz sand and IOCS in the columns with the Fe0 and quartz sand/IOCS mixture (Fig. 3). The thickness of the original iron oxide layer on IOCS was about 1 mm (SI Fig. S9) and therefore the deposited layers of the IOCS were estimated to be about 2.7 mm and 9.7 mm from the columns fed by the solution without and with HA, respectively. The deposited layers of the IOCS were thicker than those of the quartz sand, implying that more corrosion products were deposited on the IOCS. The synergistic effect occurred in the columns with the Fe0 and IOCS mixture, leading to the higher reactivity of Fe0 and the production of more corrosion products. This increased the breakthrough time for Cr(VI) and As(V). The deposition of the iron corrosion products onto the quartz sand/IOCS can be initiated by the adsorption of Fe2þ. It has been reported that Fe2þ can be adsorbed onto the iron oxides via forming inner sphere complexes (Buamah et al., 2009; Liger et al., 1999), and Fe2þ can also be adsorbed onto the quartz sand through forming outer sphere complexes
(Barry et al., 1994). Fe2þ was produced from Fe0 corrosion (Melitas et al., 2001). In the columns with the Fe0 and IOCS in series, where there was no extensive contact of Fe0 and IOCS, the Fe2þ would only be adsorbed onto the indigenous iron oxides on the Fe0 surface (Charlet et al., 1998). The adsorbed Fe2þ can then be transformed into structural Fe2þ as magnetite (Fe3O4) (Odziemkowski et al., 1998). Cr(VI) was adsorbed onto the magnetite and subsequently reduced to Cr(III) by accepting the electrons from Fe2þ (Melitas et al., 2001). Cr(III)/ Fe(III) (oxy)hydroxides were passivated on the Fe0 surface, progressively reducing the reactivity of Fe0. In the columns with the Fe0 and quartz sand/IOCS mixture, the quartz sand/ IOCS surface can be available for Fe2þ adsorption, in addition to the indigenous iron oxides on the Fe0 surface. Similar to the adsorption onto the indigenous iron oxide on Fe0, the Fe2þ adsorbed can reduce the Cr(VI) to Cr(III), and the Cr(III)/Fe(III) (oxy)hydroxides were deposited on the quartz sand/IOCS surface. Unlike the deposition of the iron corrosion products on the Fe0 surface, the deposition on the quartz sand/IOCS surface did not affect the electron transfer on the quartz sand/ IOCS as the electrons originated from the Fe0 rather than the quartz sand/IOCS itself. The IOCS can have more deposited products on its surface in the column with the Fe0 and IOCS mixture because the original iron oxides on IOCS can enhance the electron transfer from Fe2þ to Cr(VI). The Fe2þ innerspherically bound to the iron oxides is more reducing than the Fe2þ(aq) or the Fe2þ outer-spherically bound due to the decrease in the redox potential of the Fe2þ/Fe3þ couples by forming inner sphere complexes with the original iron oxides (Stumm et al., 1992). Similar effects have been observed for the reduction of Cr(VI), U(VI) and TCE by Fe2þ in the presence of iron oxides (Elsner et al., 2004; Jang et al., 2008; Tai and
Fig. 3 e SEM images of the cross-sections of the quartz sand collected from (a) column 2 and (c) column 5, and the IOCS collected from (b) column 3 and (d) column 6.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 7 5 e6 5 8 4
Dempsey, 2009). In addition, the original iron oxide on IOCS is in amorphous form (Hsu et al., 2008), leading to a higher surface area than the quartz sand, which is in crystalline form. Therefore the IOCS can provide more adsorption sites for the Fe2þ than quartz sand. For As(V) removal, it can be adsorbed onto these deposited products on the quartz sand/ IOCS. Since more deposited iron corrosion products can be formed on IOCS, a higher removal capacity of As(V) can be reached. These interactions between Fe0 and IOCS can contribute to the synergistic effect of using the Fe0 and IOCS mixture. These can also explain the discrepancies between the previous batch study (Mak et al., 2011) and the current column study in which the synergistic effect occurred in the column studies only. In this column study, the flow of the solution was laminar and the solid/solution ratio was high. In comparison, mixing (turbulent flow) was allowed and the solid/solution ratio was lower in the previous batch study (Mak et al., 2011). As a result, the Fe2þ released from Fe0 was quickly brought away from the Fe0 surface and the iron corrosion products were formed mainly in suspensions instead of passivating onto the Fe0 surfaces and being deposited onto the IOCS surfaces (Noubactep, 2008). Therefore, the synergistic effect observed in the column study cannot occur in the batch study. XPS analysis shows that the Fe3þ predominantly exists on the surface of quartz sand and IOCS (712.0 eV; Fig. 4a), while Cr(III) was found on the quartz sand/IOCS (577.3 and 586.8 eV;
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Fig. 4b). This shows that Cr(VI) was removed by reduction to Cr(III) and the deposition was as mixed Fe(III)/Cr(III) precipitates on the quartz sand/IOCS, rather than by adsorption onto the quartz sand/IOCS. The peak at 1326.8 eV shows that the As(V) was dominant on the quartz sand and IOCS, and As(III) was not detected (Fig. 4c), indicating that As(V) was removed by adsorption and that no reduction of As(V) occurred. Besides, the XRD pattern shows that the deposited corrosion products of the quartz sand and IOCS consisted of Fe3O4 which was transformed from the adsorption of the Fe2þ (Fig. 5). Other minerals, including hematite and goethite, could be the oxidation product of Fe3O4. The existence of hematite and goethite is consistent with the findings of some studies which used the Fe3O4 for the chemical reduction of pollutants (Legrand et al., 2004; Jung et al., 2007). SEM images show that hexagonal-shaped mineral exist on the quartz sand/IOCS (Fig. 6). This mineral is suspected to be the carbonate green rust, which consists of structural Fe2þ, and has been shown to have Cr(VI) reducing power (Legrand et al., 2004).
3.3.
Effects of HA
In the presence of HA, the removal capacities of Cr(VI) and As(V) of all columns were reduced, compared to those in the absence of HA (Table 1). The removal capacities of Cr(VI) and As(V) of the columns with the Fe0 and IOCS mixture were only slightly reduced by 5.5% and 4.1%, respectively (columns 6 and
Fig. 4 e XPS spectra of (a) Fe, (b) Cr, (c) As, and (d) C on the surface of the quartz sand collected from columns 2 and 5, and the surface of the IOCS collected from columns 3 and 6.
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Fig. 5 e XRD pattern of the corrosion products of the quartz sand collected from columns 2 and 5, and the corrosion products of IOCS collected from columns 3 and 6. M: magnetite (Fe3O4); A: aragonite (CaCO3); G: goethite [FeO(OH)]; H: hematite (a-Fe2O3); Q: quartz (SiO2).
7 compared with column 3; Table 1) while other columns fed with the HA solution could be reduced by up to 40% (columns 10 and 11 compared with column 8; Table 1). In comparison to the column with the Fe0 and quartz sand mixture (column 5), the removal capacities of Cr(VI) and As(V) in the columns with the Fe0 and IOCS mixture (columns 6 and 7) were significantly higher by 38.9% and 77.1%, respectively (Table 1). The decrease in the removal capacities of the Cr(VI) was due to the deposition of HA aggregates on the Fe0 surface (Liu and Lo, 2011). HA can form aggregates with cations such as Fe2þ, Fe3þ and Ca2þ, deposited on the iron surface. The electron transfer from Fe0 to the solution was inhibited, thereby affecting the
removal of Cr(VI). Since the Fe0 reactivity was reduced, less iron corrosion products were produced and hence the As(V) removal was inhibited. XPS analysis has shown a shoulder at 288.5 eV which corresponded to carboxylic groups (Fig. 4d). This corresponded to HA adsorption onto the quartz sand/ IOCS. Since the iron corrosion products (mainly iron (oxy) hydroxides) were deposited onto the quartz sand/IOCS, the iron corrosion products could adsorb HA by forming inner sphere complexes (Gu et al., 1994). In the columns with the Fe0 and IOCS mixture, however, the original iron oxides on the IOCS can adsorb HA (Mak et al., 2011). Furthermore, owing to the synergistic effect of the Fe0 and IOCS mixture, more corrosion products can be deposited onto the IOCS, which can adsorb more HA. As a result, more HA can be adsorbed compared to the column with the Fe0 and quartz sand mixture, thereby mitigating the impacts of HA on the removal of Cr(VI) and As(V).
3.4.
Effects of the Fe0 and IOCS configuration
By comparing the removal capacities of Cr(VI) and As(V) in different Fe0 and IOCS configurations, the order of the removal capacities is: completely mixed Fe0 and IOCS > Fe0 followed by IOCS z> IOCS followed by Fe0, in the solutions with or without HA. The Fe0 and IOCS mixture has the highest removal capacity as a synergistic effect occurred so that the IOCS surface can mediate the Cr(VI) reduction by adsorbing the Fe2þ produced from Fe0 corrosion. The columns with IOCS followed by Fe0 (columns 9, 12 and 13) have the least removal capacity for Cr(VI) as the pH was increased to around 8 when the solution passed through the IOCS layer (SI Fig. S10). This caused the decrease in the corrosion rate in the Fe0 layer which contributed to most of the Cr(VI) removal.
Fig. 6 e SEM images of the hexagonal-shaped mineral existing on the surface of the quartz sand collected from (a) column 2 and (c) column 5, and the IOCS collected from (b) column 3 and (d) column 6.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 7 5 e6 5 8 4
4.
Conclusions
This study has demonstrated that a mixture of Fe0 and IOCS shows the highest removal capacities of Cr(VI) and As(V) compared to IOCS alone, an Fe0 and quartz sand mixture, or in arranging the Fe0 and IOCS in series. This is because a synergistic effect occurred in the Fe0 and IOCS mixture. Fe2þ produced from Fe0 corrosion was adsorbed onto the IOCS and transformed to a better reducing agent for Cr(VI) reduction. This not only reduced the passivation on the Fe0 and increased the longevity of the Fe0, but also increased the size of the reactive surface for the redox process. Therefore, the removal capacities of Cr(VI) and As(V) can be increased by using an Fe0-IOCS combination. The impacts of HA towards the removal capacities of Cr(VI) and As(V) by using an Fe0 and IOCS mixture were marginal. HA can be adsorbed onto the original iron oxides on the IOCS so that the deposition of HA aggregates on the Fe0 surface can be reduced, resulting in a lower reactivity decrease of Fe0. This study suggests that the Fe0 and IOCS mixture could well perform as reactive media for PRBs in both the absence and presence of NOM. This also implies that the thickness of PRBs can be reduced, as well as using Fe0. The combination can enhance the sustainability of PRBs by avoiding the disposal of the waste (i.e. IOCS) and reducing the use of natural resources (i.e. Fe0).
Acknowledgments The authors wish to thank the Research Grants Council of the Hong Kong SAR Government for providing financial support under the General Research Fund for this research study (account RPC 03/04.EG01). The authors are grateful to Prof. C.H. Liao (Department of Environmental Resources Management, Chia Nan University of Pharmacy and Science, Taiwan) for providing the IOCS.
Appendix. Supplementary information Supplementary Information associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011. 10.002.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 8 5 e6 5 9 2
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Reasons for the lack of chemical stability of treated water rich in magnesium a, Joanna Swietlik *, Urszula Raczyk-Stanisławiak a, Paweł Piszora b, Jacek Nawrocki a a b
, Poland Department of Water Treatment Technology, Faculty of Chemistry, A. Mickiewicz University, Drzymały st. 24, 60-613 Poznan , Poland Department of Chemistry of Materials, Faculty of Chemistry, A. Mickiewicz University, Grunwaldzka 6, 60-780 Poznan
article info
abstract
Article history:
Chemical stability of water should be high enough to ensure that the water reaching the
Received 13 April 2011 Received in revised form
consumers would have the same composition as at the treatment plant. The drinking was observed to water supplied by one of the water treatment plants for the city of Poznan
1 October 2011
produce periodically white non-sedimenting precipitate on boiling, deteriorating its
Accepted 6 October 2011
organoleptic properties. The phenomenon was found to be related to a high content of
Available online 15 October 2011
magnesium in the water taken for treatment and low content of other ions besides bicarbonates. XRD and SEM analyses have shown that a low ratio of calcium ions to
Keywords:
magnesium ions leads to formation of calcite crystals on water boiling in which a fraction
Drinking water
of cationic crystallographic sites are substituted with Mg2þ ions giving (Ca1xMgx)CO3
High concentration of Mg Low Ca/Mg ratio
crystallites. Such crystallites have smaller size than those of calcite formed on boiling suppliers. The smaller size of the crystallites is responwater coming from other Poznan
Calcite
sible for their slower sedimentation and hence the observed increase in the water turbidity
Aragonite
on its boiling. It has been proved that the appearance of precipitates in drinking water at the consumers can be achieved by reduction of the Mg/(Mg þ Ca) ratio to below 3, which would inhibit peptisation of the precipitate and prevent water opacity and/or adjustment of pH of the raw water and removal of the carbon dioxide released to convert some carbonate hardness into non-carbonate one. These measures will limit the amount of the precipitate forming upon water boiling and change its microcrystalline type into an easier sedimenting one. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
The water treatment is undertaken to produce water of parameters describing the composition, chemical and biological stability in conformity with the current regulations. The chemical stability should be high enough to guarantee that the composition of water supplied to the consumers is the same as that exiting from the water treatment plant. The city of Poznan is supplied in drinking water by three water treatment stations: Water Treatment Plant Mosina (WTP-M), Water Treatment
Plant De˛bina (WTP-D) and Water Treatment Plant Gruszczyn (WTP-G). Despite all the current norms being satisfied by the water provided by WTP-G (Directive of MHCS, 2007), the consumers periodically reported the appearance of white precipitate after the water boiling, deteriorating the organoleptic properties of water. The suspended precipitate did not sediment and was felt while drinking. The complaints came from consumers at different distances from the WTP-G station. On the basis of analysis of the raw water subjected to treatment some factors potentially responsible for causing the
, Poland. Tel.: þ48618293430; * Corresponding author. Department of Water Treatment Technology, UAM, ul. Drzymały 24, 60-613 Poznan fax: þ48618293400. E-mail addresses:
[email protected],
[email protected] (J. Swietlik). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.10.003
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appearance of suspension were indicated. Detail investigation was undertaken to verify the following two hypotheses. According to the first hypothesis, the precipitate could be calcium carbonate of a crystallographic form different from calcite commonly precipitating from water on boiling and characterised by different physical properties. According to the second hypothesis the precipitate appearing on water boiling could be magnesium hydroxide. The water from WTPG is very rich in magnesium, which occurs in some wells in unusually high and rarely met concentrations. Magnesium hydroxide comes from hydrolysis of magnesium bicarbonate. It is a strong base hardly soluble in water making it strongly alkaline. Magnesium hydroxide is characterised by very low solubility product, and its water solubility decreases with increasing temperature. In the hitherto literature on treatment of water for everyday consumption the phenomenon of water turbidity appearance on boiling of treated water rich in magnesium has not been covered. The starting point for formulation of the above hypotheses was the model study on correct crystallisation of calcium carbonate and the studies of seawater. As follows from the results of these studies, in the presence of elevated concentrations of magnesium the process of calcite crystallisation can be disturbed. Magnesium inhibits the growth of calcite crystals by the incorporation mechanism (Davis et al., 2000) leading to formation of calcium and magnesium carbonates of the general formula (Ca1xMgx)CO3 (Davis et al., 2000; Katz, 1973; Tynan and Opdyke, 2011; Xu and Higgins, 2011; Deleuze and Brantley, 1997; Lopez et al., 2009; Gutjahr et al., 1996). Mg2þ ions are not only adsorbed on the calcite surface but are also incorporated into the crystal lattice (Davis et al., 2000; Gutjahr et al., 1996) increasing the solubility of growing crystals (Davis et al., 2000). In the model studies it has been shown that the concentration of magnesium ions in the crystallising calcite increases with increasing temperature (Katz, 1973; Xu and Higgins, 2011; Lopez et al., 2009) and with increasing concentration of Mg2þ ions in solution (Davis et al., 2000; Katz, 1973; Tynan and Opdyke, 2011; Xu and Higgins, 2011; Deleuze and Brantley, 1997; Lopez et al., 2009; Gutjahr et al., 1996). The presence of magnesium in concentrations <104 M L1 in water has practically no effect on the growth rate of calcite (Xu and Higgins, 2011; Gutjahr et al., 1996), while an increase in the content of Mg2þ ions > 103 M L1 inhibits the growth of calcite crystals and the precipitated crystals have unique distorted rhombic shape (Xu and Higgins, 2011; Gutjahr et al., 1996). The presence of magnesium ions in seawater and in model water can also affect the crystallisation of aragonite e another crystalline form of calcium carbonate. According to Deleuze and Brantley (1997) the presence of dissolved magnesium ions favours precipitation of CaCO3 in the form of aragonite and the amount of this crystal species increases with increasing concentration of Mg2þ. These authors have shown that even at a low-Mg/Ca ratio the recrystallisation of aragonite into calcite is inhibited. Conversely, Guttjahr et al. (1996) have proved that the presence of magnesium in concentrations over 104 M L1 has no effect on crystallisation of aragonite. The aim of this study was verification of the above hypotheses and identification of the reasons for the formation of white non-sedimenting suspension on boiling of treated water rich in magnesium.
2.
Experimental
2.1. Water treatment plant and water intakes description is supplied in drinking water from three The city of Poznan water treatment stations WTP-M, WTP-D and WTP-G. The appearance of white suspended precipitation after the water boiling was observed periodically by consumers in the water produced at WTP-G. The water treated at WTP-G comes from two underground intakes: Water Intake Gruszczyn (WIG) and Water Intake Promienko (WIP). At the WIG there are presently nine deep wells H1, H2 H3, H4, H5, H6, H7, H8 and H9. The wells take water from the water bearing horizons in the Greater Poland fossil valley at the depths of 46.0e73.7 m ppt. The exploitation yield of a single well varies from 75.0 to 125.0 m3 h1. The mean values of quality parameters for mixed raw water from WIG are: colour 23.6 mgPt L1, pH 7.32e7.48, Fe 2.48 mg L1, Mn 0.14 mg L1, alkalinity 4.94 mval L1, hardness 5.56 mval L1, DOC 1.97 mgCorg L1, 6.64e83.60 mg L1 and NHþ Cl 2.64e33.30 mg L1, SO2 4 4 1 0.3 mg L . The WIP uses five wells numbered as I, II, III, IV and V, driller in a barrier of about 820 m in length, recently three more drills have been made numbered as VI, VII, VIII and the current length of the barrier is 1350 m. The yields of individual wells vary from 75 to 125 m3 h1. The mean values of quality parameters for mixed raw water from WIG are: colour 28.5 mgPt L1, pH 7.16e7.53, Fe 2.41 mg L1, Mn 0.14 mg L1, alkalinity 6.48 mval L1, hardness 6.16 mval L1, DOC 5.01 1 and mgCorg L1, Cl 4.34e10.50 mg L1, SO2 4 1.17e40.80 mg L þ 1 NH4 0.69 mg L . The maximum yield of the both intakes is 24,000 m3 day1. Waters from the wells working at both intakes are mixed in variable proportions prior to entering WTP-G. As a consequence, the composition of raw water changes every 5 h. Water treatment at WTP-G involves the following stages: water aeration and two-step filtration including I fast filtration through a two-layer bed and II filtration through granular carbon filters. Water aeration is realised in an open system with the use of spray nozzles, which ensures not only high content of oxygen dissolved in water but also water deacidification and removal of hydrogen sulphide. The two-stage water treatment system ensures highly effective water purification and therefore the disinfectant demand is constant and stable. Water disinfection is carried out with the use of two reagents: chlorine which is introduced to the pure water reservoirs localised at the water treatment station and chloride dioxide which is introduced into the pipeline distributing water. Even though, the waters from the two intakes differ in composition the pilot study has not shown negative consequences of the mixing on final water quality that meets applicable standards (Directive of MHCS, 2007) with turbidity <0.6 NTU, pH 7.22e7.85, Fe <0.01 mg L1, Mn <0.05 mg L1, alkalinity 4.9e5.8 mval L1, hardness 5.7e6.0 mval L1, Cl 1 <20 mg L1 and SO2 4 <40 mg L . In practice it has been shown that the mixing makes no problems for treated, cold water but on boiling some problems appeared, especially in the periods when the dominant contribution of water came from WIP intake.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 8 5 e6 5 9 2
2.2.
Sample preparation
Water samples from raw water taken from all wells at WIG and WIP and from treated water were subjected to boiling to generate the precipitates for further analyses. The precipitate was collected and analysed. Water samples were analysed on boiling and after boiling. The samples were characterised as to turbidity, conductivity, pH (PN-ISO 6332:2001), hardness (PNISO 6059:1999), alkalinity (PN-EN ISO 9963-1:2001/Ap1:2004), concentrations of Ca, Mg (PN-ISO 6059:1999), Mn (PN-92/C04590/02, 2002) and total Fe ions (PN-ISO 6332:2001), concentrations of chloride and sulphate ions. Also the Larson index was calculated from the formula: þ Cl = HCO IL ¼ 2 SO2 4 3 where: 1 ½SO2 4 e concentration of sulphate ions, [mval L ], 1 [Cl ] e concentration of chloride ions, [mval L ], 1 ½HCO 3 e concentration of bicarbonate ions, [mval L ]. For the sake of comparison, analogous analyses were performed for the water from the water supply system for Poznan produced by WTP-M. Moreover, samples of the precipitate formed on boiling water taken from individual wells from WTP-G were compared with the precipitates obtained upon many times repeated boiling of water from the other two water treatment stations WTP-M and WTP-D, these samples were labelled as WTP-Mp and WTP-Dp.
2.3.
Metal ion determination
To determine metal ion concentrations (Fe, Mn, Ca, Mg,) in the precipitates the samples were treated with 35% HCl (POCH Gliwice, Poland) and then the ions were determined by the Inductively Coupled Plasma Spectroscopy. The emission was measured on Varian ICP-OES analyser model Vista-MPX (CCD simultaneous). The method was described in details elsewhere (Nawrocki et al., 2010). ICP-OES provided concentrations of metal ions at the level of mg L1.
2.4.
Cl and SO2 4 determination
The inorganic ions Cl and SO2 4 in water were determined by ion chromatography on DIONEX ICS-2500 system with an IonPac AS19eHC analytical column (4 250 mm) and an IonPac AG19eHC guard column (4 50 mm), connected with a conductivity detector ED 50A (Dionex, USA) e the details of the analysis method are described elsewhere (Swietlik et al., 2009). Calibration curves were linear from the detection limit 5 mg L1 to at least 100 mg L1. RDS was less than 10% for each of the ions monitored. The standard solution of inorganic ions was obtained from Dionex (USA). Before analysis all samples were filtered through 0.45 mm filters (Fisherbrand, Fisher Scientific).
2.5.
X-ray diffraction (XRD) measurements
Diffraction measurements of polycrystalline materials were performed using an X-ray diffractometer BRUKER D8 Advance in the Bragg-Brentano configuration and using a strip detector
6587
LynxEye. Measurements were performed for 22 samples in the angular range 20e120 2q on sample rotation. The diffractograms were analysed with refinement by the Rietveld method using the program Topas (Bruker AXS, 2003).
2.6.
Scanning electron microscopy (SEM)
The morphology of precipitates was examined using a scanning electron microscope Carl ZEISS EVO 40 SEM operating at 20 kV. The samples were dehydrated with acetone and then were mounted on stubs and coated with gold in a BALZERS SCD 050 sputter coater.
3.
Results and discussion
3.1.
Characterisation of water from WIG and WIP
The water treated at WTP-G comes from two underground water intakes WIG and WIP. The water from WIG and WIP differ in the alkalinity and hardness (Fig. 1). The water from WIG has nominally higher hardness than alkalinity, which means that some Ca and Mg ions occur in complexes with anions other than bicarbonates, such as e.g. sulphates and chlorides. The situation is different for the water intake in the barrier, at WIP as for this water the nominal values of water alkalinity are higher than those of hardness, which implies that a certain contribution to alkalinity comes from another bicarbonate, e.g. sodium bicarbonate. The water from WIG differs from that provided by WIP in a slightly higher content of calcium, while the water from WIP is richer in magnesium (particularly high content of Mg was found in the water from well WIP-V). Because of these differences the water from WIG and WIP significantly differ in the Ca/Mg ratio. As illustrated in Fig. 2, for the water from WIG, the Ca/Mg ratio varies from 5.5 to 8, while for the water from WIP, the Ca/Mg ratio varies in the range 3.5e5.0. The water samples from wells H1eH9 show diverse levels of chlorides (2.64e33.30 mg L1) and sulphates (6.64e83.60 mg L1) but with the dominant presence of sulphates. The samples from wells IeVIII show a very low content of sulphates (1.17e24.40 mg L1) and chlorides (4.34e10.50 mg L1), and both anions occur at comparable levels. Particularly low contents of sulphates and chlorides were determined for the water sample from well V at WIP (1.17e4.20 mg L1). The water samples from WIP are characterised by higher contents of sodium than those from WIG. Particularly high concentrations of sodium were detected in the sample from well V at WIP. Moreover, the sample from well V also shows very low concentrations of chlorides and sulphates, which implies the presence of sodium bicarbonate responsible for the high alkalinity of the water from WIP.
3.2.
Changes in the water quality on boiling
To explain the appearance of white precipitate on boiling water at the consumers, water samples were collected from all wells and observed on boiling. The parameters monitored on boiling were: hardness, alkalinity, pH, turbidity and conductivity, paying particular attention to changes in the
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Fig. 1 e The alkalinity and hardness of the water from individual wells at a) WIP and b) WIG.
concentrations of calcium and magnesium ions. The precipitates obtained were subjected to thorough analysis. For the sake of comparison analogous experiments were performed for the tap water collected from the supply network in Poznan and produced by WTP-M. On boiling the water samples from WIG and WIP their electrolytic conductivity and hardness significantly decreased. With increasing temperature the pH value of these samples increased. These changes can be easily explained by removal of CO2 from water with increasing temperature. This process was most violent on boiling, so at about 100 C. By extending the duration of water boiling it was established that the removal of CO2 takes place within about 30 min and is accompanied by a continuous increase in pH, even up to above 9.1. The increase in pH was accompanied by a significant increase in the water turbidity, much higher than that observed for the water from WTP-M. Another phenomenon observed on water boiling was a significant decrease in alkalinity. The alkalinity of raw water at the WIG intake varied from 5.4 to 3.8 mval L1, while after boiling it varied from 2.5 to 1.8 mval L1. The alkalinity of raw water at the WIP intake varied from 7.4 to 5.5 mval L1 and after boiling it decreased to 3.9e2.6 mval L1. However, the most significant were the changes in calcium and magnesium ions concentrations. The drop in calcium concentration observed on boiling of the samples from wells
H1eH9 and IeVIII was much greater (by 60e85%) than that in the water from WTP-M (40%). The decrease in calcium concentration in the water from the above wells was interpreted as related to their high carbonate hardness. The samples of water from WIG and WIP also showed a decrease in the magnesium concentration after boiling, by 5e20% depending on the well, while for the water from WTP-M the concentration of magnesium dissolved after boiling did not change. It should be emphasised that the water intaken at WTP-G are rich in magnesium, and the water from WIP contains magnesium in unusually high concentrations (19.4e33.1 mg L1, i.e. 8.08 104e1.38 103 M L1). We initially assumed that on boiling of water magnesium precipitates in the form of magnesium carbonate which quickly undergoes hydrolysis according to the reaction:
MgCO3 þ 2H2O ¼ Mg(OH)2 þ CO2 Magnesium hydroxide formed is characterised by a low solubility product in water, whose value decreases with increasing temperature. However, magnesium hydroxide can remain in solution in the form of fine-crystalline suspension. The hypothesis of suspended magnesium hydroxide presence was supported with an increase in pH and a small decrease in Mg ions in water after its boiling.
3.3.
Fig. 2 e Weight ratio of Ca/Mg for the waters from WIG and WIP.
Characterisation of precipitates
All water samples from the two intakes WIG and WIP were boiled in order to generate the precipitates that were subjected to detailed analysis to determine their elemental composition and calcium to magnesium ratio. The Ca/Mg ratio in the precipitates was found to be five times higher than in water (Fig. 3, Table 1). This result means that only a small part of Mg ions goes to the coarse-crystalline sediment appearing on water boiling and its majority remains in the soluble form and in the form of fine-crystalline suspension in water causing an increase in water turbidity and pH value. As follows from the data presented in Table 1, the precipitates appearing in the water from WIG and WIP contained more magnesium than the precipitates formed in the water
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multiple boiling has indicated a possibility of dissolving the co-precipitated magnesium carbonate by its hydrolysis.
3.4.
Fig. 3 e The Ca/Mg ratio in the precipitate appearing in water samples on boiling as a function of the Ca/Mg ratio in water.
produced at WTP-D and WTP-M. Moreover, the precipitate from the water intake at WIP had much higher content of Mg than that from the WIG water. Relatively high amounts of Mg in the water samples from WIP and WIG were responsible for the appearance of precipitates which were hardly sedimenting. The fact that the precipitates do not sediment can be related to the instability of magnesium carbonate and its susceptibility to hydrolysis. A comparison of the precipitates collected for some time (WTP-Dp and WTP-Mp) with the freshly made ones has shown that the fresh precipitates contain more Mg than the former ones. On the other hand, a comparison of the content of Mg in the fresh precipitates from the tap water (WTP-M) with the precipitates aged upon
Diffractometric analysis of the precipitate samples
The final verification of the hypotheses was made on the basis of investigation of the morphology and crystalline structure of the precipitates. The precipitates generated in the experiment and the sediment collected from kettles were subjected to XRD study. The results showed that in all precipitates the main crystalline phase is that of calcite. The samples generated for the sake of comparison in the water coming from WTP-M and WTP-D were composed exclusively of calcite. In 80% of the samples from WIG and WIP also the presence of crystalline phase of the aragonite structure was detected in various amounts. This observation is in agreement with the results reported by other authors for seawater and other waters rich in magnesium, which showed that the presence of Mg2þ in water favours precipitation of calcium carbonate in the form of aragonite (Deleuze and Brantley, 1997; Lippmann, 1973; Kitano, 1962; Simkiss, 1964). According to some other reports (Deleuze and Brantley, 1997; Taft, 1967; Bischoff and Fyfe, 1968) recrystallisation of aragonite into calcite is inhibited even at low ratios of dissolved magnesium to dissolved calcium. Deleuze and Brantley (1997) proved that with increasing concentration of magnesium in solution, the weight percent of aragonite in the precipitate increased; it was 6% at Mg/Ca ¼ 1/3 and 25% at Mg/Ca ¼ 2/3. For all samples analysed the lattice parameters of the dominant phase of the calcite structure were measured. Because of the untypical XRD profiles for some samples it was impossible to describe their shapes by typical analytical
Table 1 e Elemental composition of the precipitates formed in the water from WIGeWIP on boiling. Content [%] Well label WIP-I WIP-II WIP-III WIP-IV WIP-V WIP-VI WIP-VII WIP-VIII WIP-VI decanteda WIP-VII decanteda WIG-H1 WIG-H2 WIG-H3 WIG-H4 WIG-H5 WIG-H6 WIG-H7 WIG-H8 WIG-H9 WTP-M WTP-Dp WTP-Mp
Ca
Fe
Mg
Mn
39.59 37.09 38.00 37.73 35.37 36.58 36.52 35.90 36.24 34.66 38.57 38.63 38.63 35.83 37.71 34.24 35.67 35.30 36.49 37.05 38.69 39.38
0.476 0.503 0.572 0.701 0.566 0.745 0.453 1.043 0.300 0.164 1.67 0.85 0.70 0.944 1.018 1.661 1.045 1.038 1.24 0.11 0.024 0.0207
2.01 2.11 2.48 2.21 3.17 2.36 2.39 2.038 2.04 2.66 1.22 1.75 1.30 1.39 1.488 1.041 1.240 1.237 1.39 0.94 0.768 0.741
0.060 0.057 0.055 0.070 0.055 0.056 0.055 0.066 0.053 0.046 0.102 0.090 0.080 0.081 0.079 0.100 0.081 0.080 0.076 0.00 0.011 0.017
Ca/Mg ratio in precipitate
Ca/Mg ratio in water
Amount of precipitate [g 15 L1 of water]
19.70 17.58 15.32 17.07 11.16 15.50 15.28 17.17 17.76 13.03 31.61 22.07 29.72 25.78 25.34 32.89 28.65 28.54 26.25 39.41 50.37 53.14
5.12 3.47 3.76 3.27 3.11 4.72 4.42 4.52 2.43 4.47 7.19 6.17 7.16 6.36 5.69 6.35 6.56 6.11 6.33 9.33 10.2 10.2
2.228 2.304 2.136 1.877 2.987 2.367 2.330 1.462 2.001 2.426 1.082 1.423 2.429 1.339 1.392 1.564 1.809 2.421 1.975 1.256 e e
a water samples decanted form above sediments containing oxidised Fe species.
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Table 2 e Lattice parameters of calcite taken from literature. a 4.988 4.990 4.991 4.988 4.991 4.992 4.990
C
Literature
17.061 17.0615 17.062 17.068 17.068 17.069 17.065
[23] [24] [25] [26] [27] [27] Mean value
functions and then the lattice parameters were determined on the basis of the centres of gravity of the reflexes. At the first stage, on the basis of literature data (Markgraf and Reeder, 1985; Goldsmith et al., 1961; Maslen et al., 1993, 1995; Sitepu et al., 2005) the mean values of lattice parameters of calcite were calculated (Table 2). Then on the basis of literature data for calcite substituted with magnesium (Table 3) the following equation was derived y ¼ 0.4017x þ 4.991 (R2 ¼ 0.9956), where x is the molar fraction of magnesium ions replacing calcium ions in the crystal (Mg/(Mg þ Ca)) and y is the lattice parameter a of the calcite crystal (Paquette and Reeder, 1990). The above relation was applied for determination of the (Mg/(Mg þ Ca)) ratio in the samples studied on the basis of the diffraction data. For the majority of samples, there was a correlation between the analytically found Mg/(Mg þ Ca) ratio and that found from the Vegard law on the basis of changes in the lattice parameter a of the phase of calcite structure. The calculations were made taking into account the correction for the content of iron determined analytically for each precipitate (Table 4). Broadenings of the diffraction signals permit comparison of the crystallite size and bring information on the stress in the crystal lattice. The signals in the diffractograms of the samples from WIP were found broadened with respect to those of the reference samples from WTP-M, WTP-Mp and WTP-Dp as well as from WIG. On the basis of full width at half maximum (FWHM) values of the 113 diffraction line of calcite and applying the Scherrer equation, the sizes of crystallites were estimated (Table 4). The reference sample WTP-Dp contained relatively large crystallites of calcite, of 177 nm. From among the nine samples of precipitates in the water from WIG, the calcite crystallites have sizes from the range 50e100 nm. The sizes of calcite crystallites in all samples from WIP were below 50 nm (the mean size was 32 nm). The presence of crystallites of such a small size favours peptisation of the precipitates formed and causes water turbidity. In the process of precipitation upon water evaporation, smaller magnesium ions were incorporated into the calcite structure
Table 4 e The Mg/(Mg D Ca) ratio found analytically and calculated from the Vegard law using the lattice parameter a of the calcite structure phase determined from Rietveld analysis and the sizes of precipitate crystallites estimated from the Scherrer equation on the basis of FWHM of 113 line of calcite. Well label
WIP-I WIP-II WIP-III WIP-IV WIP-V WIP-VI WIP-VII WIP-VIII WIP-VI decanteda WIP-VII decanteda WIG-H1 WIG-H2 WIG-H3 WIG-H4 WIG-H5 WIG-H6 WIG-H7 WIG-H8 WIG-H9 WTP-M WTP-Mp WTP-Dp
Mg/(Mg þ Ca) Mg/(Mg þ Ca) Size of analytical from crystallites Vegard law on the basis of FWHM of 113 line of calcite [nm] 3.066 4.334 6.126 3.735 8.225 6.061 6.142 5.372 5.329 7.128 3.066 4.334 3.256 3.735 3.796 2.951 3.360 3.386 3.669 2.474 1.847 1.946
5.960 6.812 7.965 7.193 9.542 6.945 6.088 6.600 5.329 6.916 2.669 4.240 3.592 3.425 3.298 6.916 2.688 3.434 3.596 4.756 3.948 1.267
40 34 26 36 24 27 34 30 48 24 93 58 61 71 53 88 80 85 63 111 93 177
a water samples decanted form above sediments containing oxidised Fe species.
replacing calcium ions, which led to decreasing lattice parameters of crystal precipitated with respect to those of stoichiometric calcite. The incorporation of magnesium ions led to formation of crystals of the general formula (Ca1xMgx) CO3. The appearance of structures of that type has been
Table 3 e Lattice parameters of calcite substituted with magnesium taken from literature.
Mean value [28] [28]
Mg/(Mg þ Ca)
a
C
0 0.064 0.129
4.990 4.967 4.938
17.065 16.963 16.832
Fig. 4 e The size of precipitate crystallites determined from the Scherrer equation on the basis of FWHM values of 113 line of calcite versus the analytically determined Mg/ (Mg D Ca) ratio in the precipitates.
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Fig. 5 e SEM images (scale of 10 mm) of the precipitates appearing on boiling of water samples: A) WTP-M, B) WTP-G.
described in (Deleuze and Brantley, 1997) whose authors determined the content of Mg2þ built in the calcite structure at the level of 1.2e1.6 wt % at a simultaneous small (w3%) decrease in the concentration of Mg2þ ions in the solution. In the samples we studied the decrease in magnesium ions concentration was also small and varied from 5 to 20%. The Xray diffractograms revealed a shift of reflexes towards higher 2q angles. The asymmetry of the reflexes was particularly well seen for the precipitate from the water samples from WIP, which suggests that such a substitution took place on evaporation and during the appearance of the precipitates. However, the ratio of Mg/(Mg þ Ca) varied in time leading to formation of crystals with different content of magnesium. Such a phenomenon has been described for seawater and model solutions in which the presence of high-Mg calcite and aragonite has been evidenced. These structures are metastable with respect to those of low-Mg calcite and calcite (Tynan and Opdyke, 2011; Lopez et al., 2009; Gutjahr et al., 1996). In the samples analysed in (Lopez et al., 2009) increasing temperature of the solutions favoured the formation of instable high-Mg calcite, and the amount of ions incorporated into the calcite structure increased from 6 to 18% mol with temperature increasing from 5 to 70 C (Lopez et al., 2009). The increase in concentration of magnesium ions in the crystallising calcite in correlation to the temperature increase from 25 to 90 C and the increase in Mg/Ca ratio has been also reported by Katz (1973). A shift of the system equilibrium towards the conditions favouring precipitation of calcite results in a decrease in alkalinity which we also observed for our samples. According to Tynan and Opdyke (2011) the decrease in alkalinity causes a secondary increase in the concentration of magnesium ions in solution caused by a decomposition of high-Mg calcite and precipitation of more stable low-Mg calcite. According to Tynan and Opdyke (2011) this process is very fast and the calcium ions liberated on dissolution of high-Mg calcite are incorporated again into lowMg calcite. In our samples we also observed a great reduction in the calcium ions concentration, reaching even up to 85% at a small decrease in the concentration of magnesium ions. Another reason for the appearance of non-sedimenting precipitate can be the changes in Mg/(Mg þ Ca) ratio. Fig. 4 presents the dependence of the size of the precipitate crystallites on the Mg/(Mg þ Ca) ratio in the precipitate. As follows
from this dependence, a decrease in the Mg/(Mg þ Ca) ratio below 3 will inhibit the peptisation of precipitates and prevent the water turbidity. Moreover it shows that the Mg/(Mg þ Ca) ratio <3 should ensure that the parameters of water from WTP-G will be close to those of the water produced at WTP-M and WTP-D.
3.5.
SEM analyses
The samples of the precipitates studied were also subjected to SEM analysis. As shown in the SEM images presented in Fig. 5, the morphology of the crystallites found in the precipitates was much different. The crystallites from the water samples WTP-M and WTP-D developed in the trigonal system, hexagonal scalenohedral, characteristic of calcite (Fig. 5A) (http:// webmineral.com/data/Calcite.shtml, 2011), whereas those in the precipitates from the water samples produced at WTP-G developed mainly in the form of elongated needless (Fig. 5B). The crystals in the form of needles of orthorhombic dipyramidal structure are made by aragonite variety CaCO3, whose presence was evidenced by XDR in 80% of the samples of precipitates. As shown by SEM images, the freshly precipitates in the samples from WTP-G can contain a higher percent of aragonite than implied by the XRD results. This fact can indicate a continuous process of aragonite recrystallisation into calcite taking place in the precipitates. The SEM photograph of the precipitate from the water produced at WTP-G shows besides aragonite crystals also clear crystallites of trigonal structure, characteristic of calcite, but their amount and morphology differ from those present in the sample from the water produced at WTP-M (Fig. 5A). This observation confirms the results of XRD study suggesting that on precipitates formation smaller magnesium ions were incorporated at the sites of calcium ions in the calcite crystals causing a reduction in the crystallite size and consequently their limited sedimentation and increase in water turbidity on boiling.
4.
Conclusions
According to the above presented and discussed results, the periodically observed phenomenon of the appearance of
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white precipitate on boiling of water produced at WTP-G should be attributed to: Relatively high content of magnesium in the raw water to be treated at WTP-G, particularly in the water from the well barrier at WIP; Low content of ions other than bicarbonate ones in the water treated at WTP-G. The low ratio of calcium to magnesium ions in the raw waters from WIP and WIG well barriers is responsible for the formation of crystals of calcite structure in which some of the cationic crystallographic sites is substituted with Mg2þ thus the forming crystals can be described as (Ca1xMgx)CO3. Such crystallites have smaller size than those of calcite appearing in the water from the other sources (WTP-M and WTP-D). The smaller sizes of the crystallites mean that their sedimentation is much slower and hence the increased water turbidity on boiling. The appearance of this phenomenon can be restricted by the following measures: Reduction of the Mg/(Mg þ Ca) ratio to below 3, which will hinder the peptisation of precipitates and prevent water turbidity. This measure should also ensure that the parameters of the water produced at WTP-G should be close to those of the water produced by WTP-M and WTP-D. Adjustment of pH of the raw water and removal of the liberated carbon dioxide in order to transform some of the carbonate hardness into the non-carbonate hardness. This measure will restrict the amount of precipitate forming upon water boiling and will change its crystalline form into an easier sedimenting one. The choice of water parameters ensuring its chemical stability at the consumers should be made on studies in semitechnological or technological scale.
references
Bischoff, J.L., Fyfe, W.S., 1968. The aragonite-calcite transformation. Am. J. Sci. 266, 65e79. Bruker AXS, 2003. TOPAS V2.1: General Profile and Structure Analysis Software for Powder Diffraction Data. e User’s Manual. Bruker AXS, Karlsruhe, Germany. Davis, K.J., Dove, P.M., De Yoreo, J.J., 2000. The role of Mg2þ as an impurity in calcite growth. Science 290, 1134e1137. Deleuze, M., Brantley, S.L., 1997. Inhibition of calcite crystal growth by Mg2þ at 100 C and 100 bars: influence of growth regime. Geochim. Cosmochim. Acta 61 (7), 1475e1485. Directive of the Minister of Health Care Services of 29.03.2007 on the quality of drinking water, Dz.U. No 61 item 417, 2007.
Goldsmith, J.R., Graf, D.L., Heard, H.C., 1961. Lattice constants of the calcium magnesium carbonates. Am. Mineral 46, 453e457. Gutjahr, A., Dabringhaus, H., Lacmann, R., 1996. Studies of the growth and dissolution kinetics of the CaCO3 polymorphs calcite and aragonite. II. The influence of divalent cation additives on the growth and dissolution rates. J. Cryst. Growth. 158, 310e315. http://webmineral.com/data/Calcite.shtml, 16.02.2011. Katz, A., 1973. The interaction of magnesium with calcite during crystal growth at 25e90 C and one atmosphere. Geochim. Cosmochim. Acta 37, 1563e1586. Kitano, Y., 1962. The behaviour of various inorganic ions in the separation of calcium carbonate from a bicarbonate solution. B. Chem. Soc. Jpn. 35, 1973e1980. Lippmann, F., 1973. Sedimentary Carbonate Minerals. SpringerVerlag. Lopez, O., Zuddas, P., Faivre, D., 2009. The influence of temperature and seawater composition on calcite crystals growth mechanisms and kinetics: implications for Mg incorporation in calcite lattice. Geochim. Cosmochim. Acta. 73, 337e347. Markgraf, S.A., Reeder, R.J., 1985. High-temperature structure refinements of calcite and magnesite. Am Mineral. 70, 590e600. Maslen, E.N., Streltsov, V.A., Streltsova, N.R., 1993. X-ray study of the electron density in calcite, CaCO3. Acta. Crystallogr. B49, 636e641. Maslen, E.N., Streltsov, V.A., Streltsova, N.R., Ishizawa, N., 1995. Electron density and optical anisotropy in rhombohedral carbonates. III. Synchrotron X-ray studies of CaCO3, MgCO3 and MnCO3. Acta. Crystallogr. B51, 929e939. Nawrocki, J., Raczyk-Stanisławiak, U., Swietlik, J., Olejnik, A., Sroka, M., 2010. Corrosion in a distribution system: steady water and its composition. Wat. Res. 44, 1863e1872. Paquette, J., Reeder, R.J., 1990. Single-crystal X-ray structure refinements of two biogenic magnesium calcite crystals. Am Mineral. 75, 1151e1158. PN-92/C-04590/02, 2002. PN-EN ISO 9963-1:2001/Ap1:2004, 2004. PN-ISO 6059:1999, 1999. PN-ISO 6332:2001, 2001. Simkiss, K., 1964. Variation in the crystallization form of calcium carbonate from artificial sea water. Nature 201, 492e493. Sitepu, H., O’Connor, B.H., Li, D., 2005. Comparative evaluation of the March and generalized spherical harmonic preferred orientation models using X-ray diffraction data for molybdite and calcite powders. J. Appl. Cryst. 38, 158e167. Swietlik, J, Raczyk-Stanisławiak, U., Nawrocki, J., 2009. The influence of disinfection on aquatic biodegradable organic carbon formation. Water. Res. 43, 463e473. Taft, W.H., 1967. Physical chemistry of formation of carbonates. In: Chilingar, G.V., et al. (Eds.), Developments in Sedimentology, 9b Carbonate Rock. Elsevier, pp. 151e168. Tynan, S., Opdyke, B.N., 2011. Effects of lower surface ocean pH upon the stability of shallow water carbonate sediments. Sci. Total. Environ. 409, 1082e1086. Xu, M., Higgins, S.R., 2011. Effects of magnesium ions on nearequilibrium calcite dissolution: step kinetics and morphology. Geochim. Cosmochim. Acta 75, 719e733.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 9 3 e6 6 0 1
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Autohydrogenotrophic perchlorate reduction kinetics of a microbial consortium in the presence and absence of nitrate Mara R. London*, Susan K. De Long 1, Mark D. Strahota 2, Lynn E. Katz, Gerald E. Speitel Jr. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, 1 University Station C1786, Austin, TX 78712-0273, USA
article info
abstract
Article history:
This is the first study to model the effects of nitrate on autohydrogenotrophic perchlorate
Received 5 April 2011
biokinetics. Batch experiments demonstrated that the presence of nitrate significantly
Received in revised form
inhibited perchlorate degradation by a hydrogen-oxidizing, perchlorate-reducing microbial
4 August 2011
consortium. However, the consortium was capable of significant perchlorate reduction
Accepted 8 October 2011
while the bulk of the nitrate was still present. Results showed that a modified competitive
Available online 19 October 2011
inhibition model successfully predicted autohydrogenotrophic perchlorate degradation in the presence of nitrate (initial concentrations of w230 mg ClO 4 /L and 2.2e4.6 mg NO3 -N/L).
Keywords:
The model describes perchlorate degradation as a function of the biomass, perchlorate,
Perchlorate
hydrogen, and nitrate concentrations, as well as the single-component perchlorate (28 mg/L),
Nitrate
hydrogen (2.3 106 M (aq)), and nitrate (0.15 mg/L as N) half-saturation coefficients (Ks)
Hydrogen
and perchlorate maximum substrate utilization rate (k) (1.8 mg ClO 4 /mg TSS-hr). Single-
Kinetics
component parameters were obtained through a series of batch experiments performed
Competitive inhibition
under perchlorate-, nitrate-, and hydrogen-limiting conditions with initial concentrations
Biological treatment
of 80e340 mg ClO 4 /L, 2.7e3.6 mg NO3 -N/L, and 1%e3% H2 (g) by volume.
ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Concern regarding the presence of perchlorate (ClO-4) in drinking water has increased dramatically over the past decade and at least 35 states have known perchlorate contamination (USEPA, 2005). Perchlorate is of particular concern because of its ability to inhibit thyroid functionality, potentially affecting growth, mental development, and metabolism (NRC, 2005). Perchlorate tends to persist in the aqueous environment under typical ground and surface water conditions because it sorbs weakly to most soil minerals, is highly soluble, and nonreactive (Urbansky, 1998). Contamination is frequently attributed to perchlorate’s use as
a propellant in the defense and aerospace industries and in products such as fireworks and road flares (Gullick et al., 2001). Additionally, it has been hypothesized that naturallyoccurring perchlorate in the environment is a result of atmospheric deposition (Rajagopalan et al., 2006; Rao et al., 2007). Furthermore, perchlorate has been found in the drinking water disinfectant sodium hypochlorite (Greiner et al., 2008), as well as produce and milk (USFDA, 2006). Contaminated groundwater concentrations are typically in the low hundreds of mg/L (Gullick et al., 2001; Urbansky, 1998) and most drinking water source concentrations are less than 20 mg/L (Hatzinger, 2005; Wang et al., 2002). While currently there are no Federal perchlorate requirements of public
* Corresponding author. Department of Civil Engineering, Gonzaga University, 502 E. Boone Ave., Spokane, WA 99258-0026, USA. Tel.: þ1 509 313 3521; fax: þ1 509 313 5871. E-mail address:
[email protected] (M.R. London). 1 Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA. 2 Hazen and Sawyer, State College, PA, USA. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.10.007
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drinking water systems, in 2011 the EPA announced its decision to regulate perchlorate under the Safe Drinking Water Act (USEPA, 2011). The national primary drinking water regulation for perchlorate will likely be based on the National Academy of Science’s recommended perchlorate reference dose of 0.0007 mg/kg/day (NRC, 2005). Presently, several states, such as California (6 mg/L) and Massachusetts (2 mg/L), have set their own maximum contaminant levels for perchlorate in drinking water (CDEP, 2008; MDEP, 2009). Microorganisms capable of degrading perchlorate are ubiquitous in the environment (Coates and Achenbach, 2004), and autohydrogenotrophic microbial reduction of perchlorate to chloride has proven effective in the treatment of perchlorate-contaminated ground and drinking water (Giblin et al., 2000; Logan and LaPoint, 2002; Nerenberg et al., 2002; Sanchez, 2003; Padhye et al., 2007; Yu et al., 2006). However, to fully take advantage of these treatment systems for perchlorate-contaminated water, nitrate co-contamination must be taken into account. Not only is nitrate often a co-contaminant with perchlorate, it is usually present at concentrations several orders of magnitude greater than that of perchlorate (Gu et al., 2002; Kimbrough and Parekh, 2007; van Ginkel et al., 2008). Furthermore, nitrate has been shown to inhibit or slow biological perchlorate reduction in autohydrogenotrophic treatment systems (Nerenberg et al., 2002; van Ginkel et al., 2008; Yu et al., 2006; Ziv-El and Rittmann, 2009). Although research suggests separate pathways are responsible for perchlorate and nitrate reduction, potential exists for shared enzymes in the reduction pathway of perchlorate and nitrate for some perchlorate-reducing bacteria (Chaudhuri et al., 2002; Kengen et al., 1999; Rikken et al., 1996; Xu et al., 2004). Because both hydrogen and perchlorate could be ratelimiting and nitrate co-contamination likely, suitable biokinetic models and parameters that incorporate hydrogen, perchlorate, and nitrate utilization must be identified. Nearly all the reported hydrogen-oxidizing, perchlorate-limiting kinetic parameters are for pure cultures (Miller and Logan, 2000; Nerenberg et al., 2006; Yu et al., 2006) with initial perchlorate concentrations in the mg/L range. All parameters were estimated using simple Monod or MichaeliseMenten kinetics without taking into account potential inhibitory/ competitive effects of nitrate. In our laboratory, Sanchez (2003) demonstrated the potential for autohydrogenotrophic perchlorate biodegradation using a microbial consortium by determining perchlorate-limiting kinetic parameters with perchlorate concentrations in the mg/L range. This work extends previous research by developing a model to account for the impact of nitrate co-contamination on autohydrogenotrophic perchlorate degradation, with the aim of facilitating and enhancing autohydrogenotrophic treatment technologies for perchlorate-contaminated water.
2.
Materials and methods
2.1.
Bacterial strain and culture conditions
Plant (WWTP) in Austin, Texas, an anaerobic digester at the Hornsby Bend WWTP in Austin, Texas, and from a dormant perchlorate-degrading microbial consortium (stored at 80 C) previously cultured by Sanchez (2003). All three cultures were maintained in the laboratory and capable of degrading perchlorate anaerobically using hydrogen gas as the electron donor. Because all three cultures behaved similarly and provided good perchlorate degradation, they were mixed together. The resultant microbial consortium was maintained in the laboratory for use in experiments. Initially, cultures were grown in autoclaved 250-mL amber glass bottles capped with Mininert valves (VICI, TX). Bottles were stored in a Plexiglas housing (Bellco Biotechnology, NJ) under a N2(g) atmosphere on top of a lateral shaker. Bottles were monitored and fed both H2(g) (5%) and ClO 4 (50 mg/L, NaClO4, reagent grade) in fresh deaerated buffer medium (6.4 104 M NH4Cl, 6.0 103 M NaHCO3, 2.3 102 M KH2PO4, 2.3 102 M K2HPO4, 1 mL/L trace mineral solution (0.0367 g/L CuSO4, 0.2880 g/L ZnSO4$7H2O, 0.0232 g/L NiCl2$7H2O, 0.7016 g/L FeCl2$4H2O, 0.2000 g/L AlCl3$6H2O, 0.2807 g/L MnCl2$4H2O, 0.0382 g/L CoCl2$6H2O, 0.0254 g/L Na2MoO4$2H2O, 0.0382 g/L H3BO4, 0.1420 g/L Na2SO4), all reagent grade). Phosphate buffer was included in the media to maintain neutral pH. To produce large amounts of bacteria, a Bio-Flo 3000 or BioFlow III Batch/Continuous Bioreactor (New Brunswick Scientific, NJ) with a 2.5-L working volume was operated in batch mode at 28 C, using deaerated bioreactor buffer medium (3.2 104 M NH4Cl, 8.9 104 M NaHCO3, 2.9 103 M KH2PO4, 2.9 103 M K2HPO4, 0.1e1 mg/L NaClO4, 1 mL/L trace mineral solution, all reagent grade). The bioreactor was inoculated from bottle cultures. The bioreactor diffuser tube with an oxygen trap (Agilent, CA) was exploited for use in delivering H2(g) and CO2(g), to maintain anaerobic conditions.
2.2.
Terminal restriction fragment length polymorphism (T-RFLP) was performed to characterize the microbial consortium. DNA was extracted from samples of the bioreactor inoculum using the UltraClean Soil DNA Isolation Kit (MoBio Laboratories, CA). T-RFLP was conducted as described previously (Egert and Friedrich, 2005; Marsh, 1999). Briefly, Bacterial 16S rRNA genes were amplified using primers 8F and 1492R; the forward primer was fluorescently labeled with 5-carboxyfluorescein. DNA was PCR-amplified for 16 cycles. Duplicate PCR reactions were combined and subjected to post-amplification treatment with Klenow to fill in partially single-stranded amplicon (Egert and Friedrich, 2005). The PCR amplicon was digested with one of three restriction enzymes: HhaI, MspI, or RsaI. Restriction fragments were separated by size on an ABI 3130 DNA analyzer. T-RFs were putatively identified using the TAP T-RFLP software (Marsh et al., 2000).
2.3.
Originally, seed cultures were taken from raw wastewater and activated sludge at the Walnut Creek Wastewater Treatment
Phylogenetic characterization
Batch microbial kinetic experiments
A series of batch kinetic experiments was performed to model autohydrogenotrophic perchlorate degradation in the absence and presence of nitrate. The experimental conditions were:
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(1) perchlorate provided in excess relative to hydrogen (hydrogen-limiting) (2) hydrogen provided in excess relative to perchlorate (perchlorate-limiting) (3) hydrogen provided in excess relative to nitrate (nitratelimiting) (4) hydrogen provided in excess relative to perchlorate and nitrate; nitrate concentration several orders of magnitude greater than the perchlorate concentration (perchloratein-the-presence-of-nitrate) Kinetic experiments were conducted in bottles (condition 1) or in a 2.5-L bioreactor (conditions 2, 3, 4). In each set of experiments all microbial nutritional requirements were provided in excess except for the limiting substrate under study.
2.3.1.
Hydrogen-limiting
During the hydrogen-limited experiments, conducted over several months, perchlorate was provided in excess at a concentration of 50 mg/L (hundreds of orders of magnitude greater than the perchlorate half-saturation coefficient determined during perchlorate-limiting experiments described later). A modified deaerated buffer media was used as follows: 6.4 104 M NH4Cl, 6.0 104 M NaHCO3, 1.7 104 M KH2PO4, 1.7 104 M K2HPO4, 1 mL/L trace mineral solution, all reagent grade. The initial H2(g) concentration in the headspace was 1%, 2%, or 3% by volume. Each experiment included a noninoculated control and was performed in duplicate. Glass amber vials (125-mL) with Teflon septa and purged with N2(g) were seeded with microorganisms from the bioreactor. Experiments lasted less than 12 h, and 1-mL samples were taken from the vial headspace using a gas-tight syringe. Liquid samples were taken at the beginning and end of each hydrogen-limiting experiment (and all subsequent experiments outlined later), to determine the biomass concentration (i.e., total suspended solids [TSS]). Initial and final biomass concentration values in these and subsequent
experiments were always found to be within 10% of one another. Therefore, the biomass concentration was considered constant throughout each experiment.
2.3.2. Perchlorate-limiting, nitrate-limiting, perchlorate-inthe-presence-of-nitrate Perchlorate-limiting, nitrate-limiting, and perchlorate-in-thepresence-of-nitrate kinetic experiments were each conducted in the bioreactor buffer medium described previously. Hydrogen gas was provided to the bioreactor in excess (the initial aqueous H2 concentration was several orders of magnitude greater than the hydrogen half-saturation coefficient determined in the hydrogen-limiting experiments) and was continually replenished at a flow rate of w20 mL/min (headspace 1e1.5 L). The experiment duration varied from 1 to 268 h. For perchlorate-limiting experiments, initial perchlorate concentrations varied between 80 and 340 mg/L. For nitrate-limiting experiments, initial nitrate concentrations varied between 2.7 and 3.6 mg/L as N. Finally, for perchloratein-the-presence-of-nitrate experiments, initial perchlorate concentrations varied between 220 and 230 mg/L and initial nitrate concentrations between 2.2 and 4.6 mg/L as N. After filtration (0.2-mm filter (Pall Life Sciences, NY)), samples not immediately analyzed were stored at 4 C.
2.4.
Determination of kinetic parameters
Microbial kinetic parameters were estimated by nonlinear regression using the kinetic models shown in Table 1. For hydrogen-limiting, perchlorate-limiting, and nitrate-limiting kinetic experiments, the half-saturation coefficient (Ks) and maximum substrate utilization rate (k) were estimated simultaneously using the Solver routine in Excel. A fourthorder Runge-Kutta numerical approximation of the microbial kinetic rate expression was fit to the data by minimizing the normalized residual sum of squares between predicted and measured concentrations. Normalization was achieved by dividing the residual sum of squares by the measured
Table 1 e Kinetic models for hydrogen, perchlorate, nitrate, and perchlorate-in-the-presence-of-nitrate biodegradation. Chemical
Model
Rate equation
Hydrogen
Modified Monoda
dSH ðKs;H þ SH ÞSH Hb4 þ kH XSH ¼ dt ðKs;H þ SH Þð1 þ HbÞ
Perchlorate
Monod
dSP kP XSP ¼ dt Ks;P þ SP
Nitrate
Monod
dSN kN XSN ¼ dt Ks;N þ SN
Perchlorate-in-the-presence-of-nitrate
Modified competitive inhibitionb
dSP ¼ dt
Ks;P
kP XSP SN 1þ þ SP Ks;N
H ¼ hydrogen Henry’s constant (dimensionless), b ¼ ratio of the volume of gas to the volume of liquid, 4 ¼ volumetric flow fraction representing the mass of hydrogen lost during sampling; product of the volume of sample and the number of samples taken, divided by the product of the elapsed experimental time and the headspace volume [1/T], X ¼ biomass concentration [M/V], kP ¼ perchlorate maximum substrate utilization rate [M/M-T], kH ¼ hydrogen maximum substrate utilization rate [M/M-T], kN ¼ nitrate maximum substrate utilization rate [M/M-T], Ks,P ¼ perchlorate half-saturation coefficient [M/V], Ks,H ¼ hydrogen half-saturation coefficient [M/V], Ks,N ¼ nitrate half-saturation coefficient [M/V], SP ¼ perchlorate concentration [M/V], SH ¼ aqueous hydrogen concentration [M/V], SN ¼ nitrate concentration [M/V]. a Sanchez, 2003. b Adapted from Rittmann and McCarty, 2001.
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2.5.
Analytical methods
Hydrogen gas concentrations were analyzed on a Gow-Mac, Series 580 gas chromatograph equipped with a thermal conductivity detector and a Suppelco molecular sieve 13X column. Column, injector, and detector were set at 60 C. Perchlorate concentrations were analyzed using a 1000-mL loop injection on a Dionex DX-600 ion chromatograph (IC) equipped with an AMMS III 2-mm conductivity detector suppressor, AS-16 2 250 mm column, AG-16 2 50 mm guard column, EGC II KOH eluent cartridge, and AS-40 autosampler (detection limit 1.19 mg/L). The column was regenerated using 50 mN sulfuric acid. An Agilent 8453 UVevisible spectrophotometer was used to measure NO 3 -N during the perchlorate-in-the-presence-ofnitrate experiments at 410 nm using the HACH Test ‘N Tube Reactor/Cuvette Tubes with NitraVer X Reagent (Chromotropic Acid method). Alternatively, NO 3 -N during the nitrate-limiting experiments was measured using a 500-mL loop injection on a Dionex DX-600 IC, equipped with an ASRS Ultra II 4-mm conductivity detector suppressor, AS-11 4 250 mm column, AG-11 4 50 mm guard column, EGC II KOH eluent cartridge, and AS-40 auto-sampler (detection limit 0.0065 mg/L as N). To avoid nitrate peak interference from phosphate, iron(III) chloride was used to precipitate out the phosphate. OnGuard II H cartridges (Dionex) were used to remove soluble iron from solution prior to IC analysis. A PerkineElmer Lambda 3b UVevisible spectrophotometer at a wavelength of 600 nm and analysis of TSS (Clesceri et al., 1998) were used to measure biomass concentration.
3.
Results and discussion
3.1.
Hydrogen-limiting kinetics
losses of hydrogen gas due to sampling; and (b) mass transfer of hydrogen between the aqueous and gas phases. Fig. 1 presents the simultaneous fit to the results of the three experiments using the modified Monod model. Table 2 lists the best-fit Ks value and associated 95% joint CL. The mean Ks value from the simultaneous fit was found to be 2.3 106 M, approximately two orders of magnitude lower than the solubility of hydrogen in water. Miller and Logan (2000) determined the hydrogen Ks value for the hydrogen-oxidizing, perchlorate-reducing culture Dechloromonas sp. JM (JM) to be 3.6 105 0.00014 M. The maximum concentration of hydrogen in water is 7.8 104 M at 25 C and 1 atm (Lide, 1994). The Ks value determined for the consortium used in this research and for JM are at least one order of magnitude lower than the solubility concentration of hydrogen. In treatment scenarios where the hydrogen concentration is well below the saturation value, the microbial consortium may have a competitive advantage over the pure culture JM because the Ks value of the consortium is an order of magnitude less than that of JM. The best-fit k value and corresponding 95% joint CL for the hydrogen-limited experiments are listed in Table 2. The mean value of k was found to be 5.0 106 mol H2/mg TSS-hr. It was not possible to compare the k value to the work of Miller and Logan (2000) because only a maximum uptake rate (Vmax) was reported. Vmax is not normalized by the biomass concentration, and the biomass concentration was not reported.
3.2.
Perchlorate-limiting kinetics
Perchlorate-limiting kinetic parameters were determined after the autohydrogenotrophic microbial consortium had become acclimated to perchlorate concentrations of 1 mg/L or less over a period of several months. The Monod model (Table 1) was fit simultaneously to three perchlorate-limiting experiments shown in Fig. 2. Table 2 lists the Ks value and
2.00 x 10-5 1% Hydrogen 2% Hydrogen
Hydrogen (aq), M
concentration at that time (Aziz et al., 1999). Additional adjustable parameters included the initial concentration of hydrogen, perchlorate, and nitrate. Error in the parameter estimates was determined by approximating 95% joint confidence limits (CL) for all experimental scenarios (Aziz et al., 1999; Robinson, 1985; Smith et al., 1997, 1998; Wahman et al., 2005). A modified competitive inhibition model (Table 1) was used to predict perchlorate degradation in the presence of nitrate using the software package AQUASIM. The modified competitive inhibition model uses the perchlorate and nitrate concentrations, single-component perchlorate Ks and k values, and single-component nitrate Ks value to predict perchlorate degradation.
3% Hydrogen Monod fit
1.00 x 10-5
0 0
Hydrogen-limiting kinetic parameters were determined after the microbial consortium had acclimated to autohydrogenotrophic degradation of perchlorate for several months. Kinetic parameters and 95% joint CL for hydrogen-limited degradation were determined for experiments conducted at 1%, 2%, and 3% H2(g) by volume, by simultaneously fitting the modified Monod model to all experimental results (Table 1). The modified Monod model (Sanchez, 2003) incorporates two corrections to the general Monod equation to account for (a)
2
4
6
8
10
12
Time, hours Fig. 1 e Hydrogen-limiting batch kinetic experiments using the modified Monod model simultaneously fit to all three experimental data sets. (Initial perchlorate concentration of 50 mg/L; initial hydrogen concentrations: 7.21 3 10L6 M H2 (aq) (1%), 1.49 3 10L5 M H2 (aq) (2%), 1.97 3 10L5 M H2 (aq) (3%); biomass concentrations: 14.6 mg TSS/L (1%), 18.3 mg TSS/L (2%), 19.3 mg TSS/L (3%)).
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Table 2 e Half-saturation coefficients (Ks), maximum substrate utilization rates (k), and 95% joint confidence limits for hydrogen-limiting, perchlorate-limiting, and nitrate-limiting batch kinetic experiments. Parameter Hydrogen Ks k Perchlorate Ks k Nitrate Ks k
Unit
Lower 95%
Mean
Upper 95%
M (aq) mol/mg TSS-hr
1.5 106 4.5 106
2.3 106 5.0 106
3.3 106 5.6 106
mg/L mg/mg TSS-hr
23 1.7
28 1.8
34 1.9
mg/L as N mg as N/mg TSS-hr
0.039 0.013
0.15 0.014
0.39 0.017
350 80 µg/L Perchlorate
Perchlorate, µg/L
300
195 µg/L Perchlorate
250
340 µg/L Perchlorate
200
3.3.
Monod fit
Phylogenetic characterization
Because the hydrogen and perchlorate kinetic parameters for the consortium were one to three orders of magnitude
100 Perchlorate, µg/L
95% joint CL. The mean Ks value was found to be 28 mg/L. The mean Ks value is on the same order of magnitude of that found by Sanchez (2003) for a microbial consortium from a similar source (mean: 72.2 mg/L, 95% joint CL: 49.6e86.8 mg/L). A low mg/L perchlorate Ks value indicates the microorganisms are able to reach their maximum degradation rate at low mg/L perchlorate concentrations. A low mg/L Ks value for perchlorate is desirable for the treatment of perchloratecontaminated waters with initial perchlorate concentrations in the low hundreds of mg/L or lower and the existing/expected mg/L perchlorate regulations. The perchlorate Ks value found for the microbial consortium was approximately an order of magnitude less than that found by Nerenberg et al. (2006) for the hydrogen-oxidizing pure culture Dechloromonas sp. PC1 (PC1) (Ks PC1: 140 mg/L), around two orders of magnitude less than that found by Yu et al. (2006) (Ks HZ: 8900 mg/L) for the hydrogen-oxidizing pure culture Dechloromonas sp. HZ (HZ), and approximately three orders of magnitude less than that found by Miller and Logan (2000) (Ks JM: w15,000 mg/L) for the hydrogen-oxidizing pure culture JM. The lower value of Ks for the consortium, especially compared to those of HZ and JM, suggests that the consortium has a greater affinity for
perchlorate and could provide a competitive advantage when perchlorate concentrations are in the low mg/L range, as is the case with most contaminated waters (Gullick et al., 2001; Urbansky, 1998) and regulations (CDPH, 2008; MDEP, 2009). The k values and 95% joint CL for the perchlorate-limited experiments are presented in Table 2. The mean k value was found to be 1.8 mg/mg TSS-hr. The mean k value is on the same order of magnitude of that found by Sanchez (2003) (1.29 mg/mg TSS-hr) for a microbial consortium from a similar source. The k values reported by Nerenberg et al. (2006) and Yu et al. (2006) for their hydrogen-oxidizing pure cultures were 130 (PC1) and 9.2 (HZ) mg/mg dry weight-hr respectively. Because the k values of the consortium and HZ are approximately two orders of magnitude less than that reported for PC1, the consortium and HZ may degrade perchlorate more slowly than PC1. Because of the differences in kinetic parameters between the consortium used here and the aforementioned pure cultures, further analyses were performed to determine if the consortium could potentially have a competitive advantage during treatment. Biodegradation of perchlorate by each consortium/pure culture was evaluated using AQUASIM (Reichert, 1994, 1995) under the following conditions: (1) an initial perchlorate concentration of 100 mg/L, (2) using the corresponding set of Monod kinetic parameters for each consortium/pure culture, and (3) assuming hydrogen is available in excess. Fig. 3 shows the microbial consortium and PC1 are predicted to require less time (<1.5 h) to completely degrade perchlorate as compared to HZ (w45 h). As expected, based on the k values and evidenced by Fig. 3, PC1 is able to completely degrade perchlorate slightly faster than the microbial consortium.
80 60
HZ
40
PC1
20
Microbial consortium
150
0
100
0
50
1
2
3
4
5
Time, hours
0 0
2
4
6
8
10
Time, hours Fig. 2 e Perchlorate-limiting batch kinetic experiments using the Monod model fit simultaneously to all three experimental data sets. (Biomass concentrations: 58.4 mg TSS/L (80 mg/L), 42.2 mg TSS/L (195 mg/L), 34.7 mg TSS/L (340 mg/L)).
Fig. 3 e Predictions of perchlorate biodegradation using Monod model and kinetic parameters for various autohydrogenotrophic strains/consortium. (Assumptions: initial perchlorate concentration 100 mg/L; biomass concentration 100 mg TSS/L, hydrogen available in excess; Monod kinetic parameters for microbial consortium studied here, Dechloromonas sp. HZ (Yu et al., 2006), Dechloromonas sp. PC1 (Nerenberg et al., 2006)).
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3.4.
3 Nitrate measured
Nitrate, mg/L as N
different than those of previously-documented, pure culture, perchlorate-reducing, hydrogen-oxidizing microorganisms (Dechloromonas spp. HZ, JM, and PC1 strains), it was hypothesized that genera other than Dechloromonas may be present and responsible for perchlorate reduction in the bioreactor. T-RFLP was performed to characterize the microbial community present in the consortium (see supplementary data). The microbial community present in the kinetic experiments appeared to contain strains related to previously documented perchlorate-reducing microorganisms. Restriction fragments for perchlorate-reducing bacteria Dechloromonas spp. and the chlorate-reducing bacteria Ideonella dechloratans were putatively identified, although the putatively identified peaks may also correspond to other closely related strains. No other genera of known perchlorate-reducing bacteria were detected via T-RFLP. A small number of other restriction fragments (w4e10, range for the three enzymes) were present that may be associated with unknown perchlorate-reducing bacteria or non-perchlorate-reducing bacteria. Since T-RFLP putatively identified Dechloromonas spp. strains were present in the bioreactor and Dechloromonas spp. strains are known perchlorate-reducers, it follows that Dechloromonas spp. strains were most likely responsible for at least a portion of the perchlorate reduction seen in the kinetic experiments. However, because of the differences in kinetic parameters, it is possible that the microbial consortium contains Dechloromonas spp. strains other than HZ, JM, and PC1. The different enrichment conditions for the three hydrogen-oxidizing pure cultures and the microbial consortium may have led to the selection of different strains, which in turn could have led to the variations among the four sets of kinetic parameters.
Monod fit
2
1
0 0
1
2
3
4
5
6
Time, hours Fig. 4 e Typical nitrate-limiting batch kinetic experiment using Monod model fit. (Initial nitrate concentration: 2.71 mg/L as N; biomass concentration: 53.2 mg TSS/L).
for a short period. Typical results can be seen in Fig. 5. During the course of the experiment, when perchlorate was present, the nitrate concentration was high enough, such that the consortium exhibited zero-order degradation kinetics, with a rate constant of 0.0207 mg/L-hr.
Nitrate-limiting kinetics
Since nitrate typically is a co-contaminant present at concentrations several orders of magnitude greater than that of perchlorate, the potential for competition or inhibition was explored for the consortium. To quantify the impact of nitrate on perchlorate, it was necessary to determine consortium kinetic parameters for nitrate at concentrations typical of those found in contaminated water. Nitrate-limiting kinetic parameters were estimated after the microbial consortium had been acclimated to a nitrate concentration of 5 mg/L as N for a period of several months. During this period no perchlorate was fed to the microbial consortium. Nitrate-limiting kinetic experiments were fit to the Monod model (Table 1); a typical experiment can be seen in Fig. 4. Table 2 lists the mean nitrate-limiting Ks (0.15 mg/L as N) and k (0.014 mg as N/mg TSS-hr) values and their 95% joint CL.
3.5.
Perchlorate kinetics in the presence of nitrate
The impact of nitrate on perchlorate degradation was examined in batch experiments in which both nitrate and perchlorate were present. Prior to the perchlorate-in-thepresence-of-nitrate kinetic experiments, the autohydrogenotrophic microbial consortium had been consistently degrading perchlorate (1 mg/L or less) for several months and was exposed to nitrate (5 mg/L as N) in addition to perchlorate
Fig. 5 e (A) Typical nitrate degradation and zero-order fit during a perchlorate-in-the presence-of-nitrate kinetic experiment. (B) Typical perchlorate degradation with modified competitive inhibition and Monod model predictions during a perchlorate-in-the-presence-ofnitrate kinetic experiment. Monod model prediction was made assuming the absence of nitrate. (Note: different time scales; initial nitrate concentration 4.6 mg/L as N; initial perchlorate concentration 221 mg/L; biomass concentration 25.1 mg TSS/L).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 9 3 e6 6 0 1
The modified competitive inhibition model (Table 1) was able to successfully predict perchlorate degradation in the presence of nitrate (Fig. 5A). The modified competitive inhibition model used the perchlorate, biomass, and nitrate concentrations, as well as the single-component perchlorate Ks and k values and the single-component nitrate Ks value to predict perchlorate degradation in the presence of nitrate. Although the competitive inhibition model is an enzyme competition model, it is used here more empirically, as no mechanistic basis for applying the model has been demonstrated. The perchlorate-in-the-presence-of-nitrate experiments demonstrated the significant competitive/inhibitory effect of nitrate on perchlorate degradation by the microbial consortium. The dashed line in Fig. 5B shows a predicted perchlorate degradation profile by the Monod model in the absence of nitrate. In the absence of nitrate, complete perchlorate degradation would be expected in approximately 10 h, whereas approximately 90 h were actually required at the nitrate concentrations present in these experiments. However, despite the additional time required for complete perchlorate removal, the consortium demonstrated it is capable of significant perchlorate reduction in the presence of nitrate. Additionally, the ability to degrade perchlorate in the presence of nitrate could be important from a culture sustainability perspective. If the perchlorate concentration is low enough, alternative electron acceptors, like nitrate, may be required to sustain growth of perchlorate-reducers. Effectively, in the modified competitive inhibition model (Table 1), the single-component perchlorate Ks value is altered by the presence of nitrate (effective Ks value for perchlorate). The larger the ratio of the initial nitrate concentration to the single-component nitrate Ks value, the greater its impact on the effective Ks value for perchlorate. Therefore, perchlorate degradation for increasing ratios of the initial nitrate concentration to the single-component nitrate Ks value (effective Ks ratio) were analyzed to determine if a threshold effective Ks ratio exists, at which nitrate’s impact on perchlorate degradation becomes apparent. Model predictions for various effective Ks ratios (0e33) for typical perchlorate concentrations were conducted. An initial biomass concentration of 25 mg TSS/L was assumed. Endogenous decay and biomass yield of the consortium were accounted for using a decay coefficient of 0.008 d1 and yield coefficient of 0.3 mg/mg for perchlorate reduction (Sanchez, 2003). Additionally, a yield coefficient of 0.7 mg/mg (Rittmann and McCarty, 2001) for nitrate reduction was input into the model. As seen in Fig. 6, the time required to achieve 99% perchlorate removal (initial perchlorate concentration 100 mg/L) for effective Ks ratios of 0.33 and 3.3, increased by approximately 25 and 200 percent respectively, relative to the time required for 99% perchlorate removal in the absence of nitrate (effective Ks ratio ¼ 0). Overall, no significant threshold was discernable. A linear relationship was observed between the effective Ks ratio and its associated percent increase in time required for 99% removal of perchlorate relative to the time required for 99% removal in the absence of nitrate (effective Ks ratio ¼ 0).
6599
Fig. 6 e Effect of the ratio of initial nitrate concentration to nitrate single-component half-saturation coefficient, and thus effective perchlorate Ks value in the competitive inhibition model, on rates of perchlorate biodegradation in the presence of nitrate. Regression line based on ratios of 0e33 (ratios greater than 15 not shown). (Assumptions: competitive inhibition model applies; initial perchlorate concentration 100 mg/L; 99% removal e final perchlorate concentration 1 mg/L; initial biomass concentration of 25 mg TSS/L).
3.6.
Practical implications of kinetic parameters
The kinetic parameters and models determined could be used to aid in the design of autohydrogenotrophic perchlorate treatment systems, such as biofilm reactors or zero-valent iron permeable reactive barriers (PRB). In practice, in different parts of the reactor or PRB, different substances could be limiting. Hydrogen is expected to be limiting as a result of its limited aqueous solubility, while perchlorate is expected to be limiting because its concentration in contaminated ground and drinking water is typically in the low hundreds of mg/L (Urbansky, 1998; Gullick et al., 2001). Potentially, the simplest way to account for dual substrate limitation is use of a multiplicative term. Eqs. (1) and (2) below are a proposed multiplicative Monod model for autohydrogenotrophic perchlorate degradation and a proposed multiplicative modified competitive inhibition model for autohydrogenotrophic perchlorate degradation in the presence of nitrate, respectively. All abbreviations are defined in Table 1. Use of the multiplicative Monod models would allow for the prediction of not only perchlorate and nitrate concentrations, but hydrogen concentrations as well. dSP kP XSP SH ¼ dt Ks;P þ SP Ks;H þ SH
(1)
dSP ¼ dt
(2)
4.
k XS SH P P SN Ks;H þ SH þ SP Ks;P 1 þ Ks;N
Conclusions
The kinetic parameter values obtained indicate that the microbial consortium may have a competitive advantage over
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some pure cultures when treating perchlorate-contaminated water with initial concentrations of perchlorate in the low hundreds of mg/L or less and when perchlorate treatment goals/regulations are in the low mg/L range. Additionally, perchlorate-contaminated water is often co-contaminated with nitrate. Results demonstrated the significant competitive/inhibitive effect of nitrate on autohydrogenotrophic perchlorate degradation by the microbial consortium; however, the consortium demonstrated it is capable of significant perchlorate reduction. Analysis showed that a modified competitive inhibition model was able to successfully predict autohydrogenotrophic degradation of perchlorate by the consortium in the presence of nitrate using the perchlorate, biomass, and nitrate concentrations, as well as the single-component Ks value for nitrate and the singlecomponent Ks and k values for perchlorate. As part of a larger design tool, the multiplicative modified competitive inhibition model and biokinetic parameters presented here could aid in predicting performance and enhancing designs of autohydrogenotrophic perchlorate treatment systems.
Funding Partial funding was provided by the Gulf Coast Hazardous Substance Research Center (GCHSR). GCHSR had no involvement in the writing of the report.
Appendix. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.watres.2011. 10.007.
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