Modeling Arsenic in the Patuxent Estuary - American

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Environ. Sci. Technol. 2008, 42, 4804–4810

Modeling Arsenic in the Patuxent Estuary A L E X J . N I C E , † W U - S E N G L U N G , * ,† A N D GERHARDT F. RIEDEL‡ Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, Virginia 22904, and Smithsonian Environmental Research Center, Maryland 21037

Received November 8, 2007. Revised manuscript received March 25, 2008. Accepted April 1, 2008.

A water quality model was developed to track the fate and transport of four arsenic species in the Patuxent Estuary: arsenate (As(V)), arsenite (As(III)), methylarsonate (MMA), and dimethylarsinate (DMA). Processes simulated include mass transport, solid–liquid partitioning with suspended solids, uptake and transformation of As(V) by phytoplankton, oxidation of As(III), demethylation of MMA and DMA, and settling/deposition/ resuspension of particulate arsenic in the water column. A sediment module was also developed and linked with the water column to generate fluxes of inorganic arsenic from the sediment bed. The arsenic model was calibrated using water quality data from the Patuxent Estuary over a period ranging from May 24, 1995 to October 29, 1997. Model results indicated that transformation of arsenic by phytoplankton is not a significant source of DMA to the lower Patuxent. Instead, results suggested that the primary source of methylated arsenic (DMA and MMA) to the lower estuary is beyond the downstream boundary (Chesapeake Bay). However, model results supported the hypothesis that flux of arsenic from the sediment is a significant source of inorganic arsenic to the lower estuary.

Introduction There has been increasing interests in understanding the role of arsenic in estuarine ecosystems. In addition, the overall impact of arsenic when acting alone or in conjunction with other stressors is not completely understood. While studies have attempted to define actual concentrations and trends of certain contaminants in estuaries (1) and other studies have investigated possible interaction with other stressors, like nutrients and low dissolved oxygen (2, 3), a quantitative tool to assess the fate and transport of arsenic in estuaries is lacking. Data for a suite of trace elements (including four species of arsenic) for the Patuxent Estuary in Maryland at 15 monitoring sites over approximately a two and half-year period (1) provided an excellent opportunity to quantify the cycling and transport of arsenic species in the ecosystem. The purpose of this study is to develop a water quality modeling framework for the Patuxent to track the fate and transport of arsenic. The calibrated model could then be used to extract additional physical insights into the ecosystem, i.e., testing hypotheses of arsenic attenuation in the estuary and to assess the estuarine response to land use changes in the watershed. * Corresponding author fax: 434-982-2951; e-mail: [email protected]. † University of Virginia. ‡ Smithsonian Environmental Research Center. 4804

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Past Arsenic Modeling. Arsenic modeling has been attempted in a number of systems using several approaches, including geochemical and 1-D mass balance models (4–10). By far the most comprehensive model of arsenic uptake and transformation by phytoplankton was developed by Hellweger et al. (11) with successful application to Lake Biwa, Japan by Hellweger and Lall (12). Four species of arsenic (As(V), As(III), MMA, and DMA) were tracked by the model both within phytoplankton cells and in the water column. The model simulated starved and luxury uptake of phosphorus and arsenate, and competitive uptake between phosphorus and arsenate. Arsenate reduction and transformation was simulated within the phytoplankton cell and the type of arsenic species excreted was partially dependent on phosphorus being the limiting nutrient for algal growth. While including uptake and transformation of arsenate by phytoplankton, the model does not account for adsorption of arsenic to suspended solids (which may be an important feature for estuaries), nor does it include an interactive sediment component. Hypotheses Tested by the Model. One hypothesis is that substantial amounts of methylated arsenic observed in the lower estuary were generated by the uptake and methylation of arsenic by algal blooms (1, 13). Many studies have shown that phytoplankton will readily uptake As(V) and reduce, methylate and release it back into the water (13, 14). Additionally, As(V) competitively inhibits algal growth and phosphate uptake (15). For the Patuxent Estuary, higher concentrations of dimethylarsinate (DMA) have been associated with observations of wintertime dinoflagellate blooms (1, 13, 16). Arsenic is believed to compete for uptake with phosphorus when the arsenic to phosphate ratio is high and phosphate concentrations are limited, thus, also inhibiting phytoplankton growth and increasing the possibility of toxicity (7, 17). To evaluate arsenic transformation by phytoplankton in the estuary, a formulation for uptake and methylation of arsenic by phytoplankton was implemented and incorporated into the model. The other hypothesis is that summertime maximums of arsenic observed in the lower estuary were due to release of arsenic from sediments during anoxic conditions in the bottom waters (1). Many studies have shown that flux from the sediment could be a large source, if not the major source of arsenic to a surface water body, especially during anoxic or reducing conditions (4, 5, 7, 18–20). Therefore, a twolayer sediment module was developed to simulate accumulation and flux of arsenic from the sediment.

Materials Modeling Approach. In this study, the modeling framework is essentially a mass balance of arsenic based on arsenic environmental chemistry, of which a detailed discussion is presented by Nice (21). Fate and transport of four arsenic species were tracked by the model: arsenate (As(V)), arsenite (As(III)), methylarsonate (MMA), and dimethylarsinate (DMA). Development of the model for arsenic started with an existing eutrophication model for the Patuxent (22). In addition, a sediment transport and solid-liquid partitioning model (21), accounting for arsenic sorption to sediments (23, 24), was incorporated with the fate and transport processes for arsenic in the water column and sediment. Interaction of arsenic with the eutrophication model and with suspended solids is presented in Figure 1. Arsenic fate and transport processes tracked by the model in the water column included advective and dispersive mass transport, solid–liquid partitioning of As(V) and As(III) with suspended solids, uptake and trans10.1021/es702452e CCC: $40.75

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FIGURE 1. Processes modeled for Arsenic and interaction with eutrophication model. formation of As(V) by phytoplankton, oxidation of As(III), demethylation of MMA and DMA, settling/deposition/ resuspension of particulate inorganic arsenic, and fluxes of inorganic arsenic from the sediment bed. A two-layer sediment component (aerobic and anaerobic) simulate arsenic in the sediment and its interaction with the overlying water. Species considered in the sediment were limited to inorganic arsenic: As(V) and As(III). Previous studies have found little of the methyl arsenic species present in sediment (18, 25, 26), and the relationship between phosphate and arsenic methylation suggests that high concentrations of phosphorus in sediment probably inhibit methylation. Processes simulated by the model for arsenic in the sediment included sorption/desorption, oxidation/reduction, particle mixing, burial, and diffusion/flux to the overlying water column (Figure 1). Data to Support the Modeling Analysis. Ambient water quality data for arsenic includes dissolved concentrations of As(V), As(III), MMA, and DMA collected at 15 stations (see map in the online Supporting Information) on the Patuxent Estuary in nine surveys from May 24, 1995 to October 29,

1997 (1). Spatial profiles of arsenic species (graphs included with online Supporting Information) clearly demonstrate substantial amounts of methylated arsenic (primarily DMA) in the lower estuary during winter (1, 13), and high arsenate levels in the lower estuary during summer (1). Limited data for particulate concentrations of total arsenic and suspended solids was also collected during this period. Two sediment cores in the Patuxent provided data for particulate and porewater arsenic concentrations to establish average concentrations for use with the sediment component of the model. Ambient water quality data for temperature, salinity, chlorophyll a (chl a), dissolved oxygen (DO), nutrients, CBOD (dissolved organic carbon), and total suspended solids for eleven monitoring stations in the Patuxent Estuary were obtained or derived from the Chesapeake Bay Program database (27). The data were utilized to establish tributary inputs, downstream boundary conditions, and for comparison with model results. There are two major upstream inputs to the model: the upper Patuxent River at Station TF1.0 (also U.S. Geological Survey (USGS) gaging station 01594440) and Western Branch (USGS gaging station 01594526). Flowrate VOL. 42, NO. 13, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Calibrated Rates and Coefficients for Arsenic Model rate or coefficient Water Column Kd values for As(V) and As(III) in estuary (L/g) Kd value for As(V) and As(III) for tributary input at Western Branch (L/g) Kd value for As(V) and As(III) for tributary input at Patuxent near Bowie (L/g) oxidation rate for As(III) in the water column, ko,AsIII (1 L/d) demethylation rate for MMA in the water column, k,DM,MMA (L/d) demethylation rate for DMA in the water column, k,DM,DMA (L/d) temperature correction coefficient for oxidation and demethylation maximum As(V) uptake rate, Vmax,AsV (µg As/mg C/day) limitation constant for uptake of phosphate by phytoplankton, Km,PO4 (µmol/L) limitation constant for uptake of As(V) by phytoplankton, Km,AsV (µmol/L) Sediment initial concentration of total As(V) in sediment (mg/L) initial concentration of total As(III) in sediment (mg/L) porosity active aerobic layer depth for arsenic flux model (cm) (top layer) active anaerobic layer depth for arsenic flux model (cm) (bottom layer) burial (only from aerobic layer to anaerobic layer) (cm/year) partition coefficient for As(V) and As(III) in anaerobic layer, πAs,2 (L/kg) coefficient for partition coefficient for As(V) and As(III) in aerobic layer in upper estuary ∆πAs,2 coefficient for partition coefficient for As(V) and As(III) in aerobic layer in lower estuary ∆πAs,2 critical threshold concentration of dissolved oxygen which determines calculation of partition coefficient for As(V) and As(III) in aerobic layer (mg O2/L) solids concentration in active sediment layers for arsenic flux model (kg/L) particle mixing dissolved oxygen half-saturation constant for arsenic flux model, K,M,Dp (mg O2/L) first-order decay rate for accumulated benthic stress for arsenic flux model, ks, (1/d) diffusion coefficient for particle mixing in arsenic flux model, Dp (cm2/d) temperature correction coefficient for diffusion coefficient for particle mixing diffusion coefficient for dissolved phase mixing in arsenic flux model, Dd (cm2/d) temperature correction coefficient for diffusion coefficient for dissolved phase mixing reference concentration of POC in reactivity class G1 for particle mixing calculation in arsenic flux model, mg C/g oxidation rate for As(III) in porewater of aerobic sediment layer (1/d) reduction rate for As(V) in porewater of anaerobic sediment layer (1/d)

value 21-50 49 21 0.03 0.02 0.02 1.047 2.5 0.16 0.16

4.0 0.8 0.8 0.1 9.9 0.25 100 150 300 2.0

0.52 4.0 0.03 1.2 1.117 5.0 1.08 0.1 0.03 0.02

and nutrient loadings from other minor tributaries were estimated based on a ratio of estimated watershed area for each tributary to that of Western Branch (21). Inputs for dissolved arsenic were assumed to only occur from the upper Patuxent River and the Western Branch tributary and were linearly interpolated between data points to obtain time variant values for the model. Solid-Liquid Partitioning. To be consistent with species considered in the sediment flux model, As(V) and As(III) were assumed to participate in solid-liquid partitioning processes. While data from adsorption experiments for As(V) and As(III) were found to fit Langmuir isotherms (28, 29), plotting values of particulate versus dissolved for various combinations of field data from different monitoring stations did not reveal a significant trend. Thus, arsenic adsorption was simulated using a linear isotherm based on average distribution coefficients (Kd) at different monitoring stations. Particulate arsenic data collected for the Patuxent River was not subdivided into four species, but was recorded as total arsenic. 4806

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Kd values were determined for total arsenic using particulate and dissolved data (see online Supporting Information). Kd values derived from collected data were within the range of values reported for arsenic in recent literature (30). For nonboundary segments, Kd values were assigned to vary longitudinally in the estuary, but to remain constant temporally; average values (derived from data) were assigned to segments at monitoring station locations, while linearly interpolated values were assigned to segments between monitoring stations. For tributary inputs to the model, constant Kd values (average values derived from data) were assigned to tributary inputs and to segments at downstream boundary conditions. Total values for As(V) and As(III) for tributary inflow and the downstream boundary conditions are calculated by the model using a linear isotherm with observed dissolved concentrations, suspended solids concentrations, and appropriate Kd values (discussion of linear isotherms and solid-liquid partitioning is included in online Supporting Information). For the lack of better information, Kd values for As(V) and As(III) are identical, based on those calculated for total arsenic. Arsenic Kinetics in the Water Column. As(III), MMA, and DMA in the water column are all converted to As(V) following first-order kinetics in this sequence: DMA f MMA f As(III) f As(V). The potential temperature dependence of these bacterially mediated reactions such as oxidation and demethylation was simulated using an Arrhenius type correction factor (31, 32). Uptake and transformation of As(V) was linked to phytoplankton growth, hence, also to phosphate uptake, which is subject to Michaelis-Menten limitation using a limitation/ inhibitor term for phosphate and the phosphate concentration. Arsenate is also thought to interfere or inhibit the uptake of phosphate by phytoplankton (11, 15). Similarly, phosphate uptake also incorporates a limitation/inhibitor term for As(V) and the As(V) concentration. Since rapid transformation of arsenic has been observed (13), instantaneous transformation of As(V) was adopted for this model. A fundamental assumption adopted from Hellweger et al. (11) was that when phosphorus is the limiting nutrient for phytoplankton growth, As(V) consumed by phytoplankton is reduced to As(III), then methylated to MMA and DMA, then excreted. During phosphorus replete conditions (nonlimiting), algae uptake phosphate at a higher rate than needed (luxury uptake), and more As(V) is also consumed. As(V) is reduced to As(III) within the phytoplankton cell at a rate higher than methylation, causing overaccumulation and subsequent excretion of As(III), hence bypassing the methylation process (11). Within the current model, As(V) consumed by phytoplankton is immediately excreted as either DMA or As(III), depending on whether phosphate is limiting or replete. Equations utilized by the model to simulate arsenic in the water column are provided in the online Supporting Information. Arsenic in the Sediment. The two layer (aerobic and anaerobic) sediment flux model for arsenic was inspired by a conceptual model presented by Aggett and O’Brien (18) and by more recent sediment phosphorus flux modeling work by Di Toro (33), as implemented by Hunt (34). Only inorganic forms of arsenic were simulated since many experimental studies have revealed only low levels of DMA and MMA in sediments (18, 19, 25). The sediment flux model for arsenic tracks concentrations of As(V) and As(III) in both the aerobic and anaerobic layers. The arsenic cycle in the sediment begins with deposition of particulate arsenic from the water column. Particle mixing and further deposition moves particulate arsenic between the aerobic and anaerobic layers. In the anaerobic layer, sorbed arsenic is released into solution with the reduction of Fe(III) to Fe(II). Some of the As(V) released is reduced following first order kinetics similar to those

FIGURE 2. Spatial results of calibrated model for two summer and two winter sampling dates.

previously discussed for As(V) reduction in the water column. A portion of total arsenic is buried and lost to the system. Diffusion will carry some of the dissolved As(V) and As(III) into the aerobic layer where oxidation of As(III), sorption of arsenic to iron hydroxides, and escape into the overlying water column will occur. The oxidation of As(III) and sorption to iron hydroxides in the aerobic layer will compete with the diffusion of arsenic to the overlying water column. If the overlying water column becomes anoxic, oxidation of As(III) and sorption of arsenic to iron hydroxides in the aerobic layer will be greatly reduced, thus increasing diffusive flux of arsenic to the water column. As illustrated in Figure 1, oxidation of As(III) was assumed to only occur in the aerobic layer, while reduction of As(V) was assumed to only occur in the anaerobic layer. Dissolved and particulate fractions of As(V) and As(III) in the aerobic and anaerobic layers are calculated based on porosity, sediment concentrations, and partition coefficients (21). Because As(V) has demonstrated adsorptive characteristics that are very similar to phosphate (29), the partition coefficients for As(V) and As(III) were assigned to be the same as those used for phosphate in the sediment. Arsenic Sediment-Water Interactions. To simulate flux of As(V) and As(III) from the sediment during hypoxic conditions, the partition coefficient for the aerobic layer is calculated based on the dissolved oxygen levels in the overlying water column (21). Mechanisms to account for the effects of sediment particle mixing by macrobenthos and benthic stress due to anoxia as described by Di Toro (33)

were also included in the arsenic sediment flux model (21), of which the mass balance equations are provided in the online Supporting Information. Model Calibration and Parameter Value Assignment. There are a number of model coefficients and constants for which values were derived from reported studies, e.g., by Hellweger (11) and literature values (8, 31, 32, 35–37). While a comprehensive discussion on model parameter derivation and assignment is provided in the online Supporting Information, Table 1 lists the key model coefficients for the arsenic model of the Patuxent Estuary.

Results and Discussion Model Calibration Results. Longitudinal plots of model results, as compared to observed data, for two summer sampling dates and for two winter sampling dates are shown in Figure 2. Temporal plots for the four species of arsenic, as compared to observed data at Nottingham (upper estuary, approximately 63 km from the mouth) and Broomes Island (lower estuary, approximately 24 km from the mouth), are depicted in Figure 3. Overall, the model reproduced concentrations of the four forms of arsenic reasonably well when compared to observed data. Most importantly, specific trends, which were evident in the observed data, were reproduced by the model. For instance, as shown in Figure 2, a maximum of As(V) in the lower estuary during the summer (August 23, 1995 and August 29, 1996) was shown in both the results and the observed data. This phenomenon was also reproduced temporally at Broomes Island (lower estuary) as shown in VOL. 42, NO. 13, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Temporal results of calibrated model at Nottingham (upper estuary) and Broomes Island (lower estuary). Figure 3. The general trend of methylated forms being present in the lower estuary was also reproduced by the model. As shown in Figure 3, the peak of MMA at Broomes Island (lower estuary) on August 29, 1996 was predicted well by the model. Conversely, peaks of DMA on August 23, 1996 at Nottingham (upper estuary) and on February 26, 1996 at Broomes Island (lower estuary) were not predicted well by the model. However, when viewing spatial results for DMA as shown in Figure 2, results compared reasonably well to observed data (bearing in mind that analytical results have a repeatability of circa ( 10%). Model results for sediment flux of As(V) and As(III) along with sediment oxygen demand and nutrient fluxes at Broomes Island (lower estuary) are shown in Figure 4. Because the phosphate and arsenic flux models are similar in mechanistic structure, both models produce a pattern of flux that primarily occurs during hypoxic conditions in the water column. In a study using sediments collected from Baltimore Harbor, researchers found that an initial pulse of arsenic occurred when the water column changed from high to low dissolved oxygen, averaging 0.127 mg/m2/d for long-term experiments and 0.0674 mg/m2/d for the short-term experiments (20). The flux decreased substantially with time, but continued to be higher than sediments overlain by oxic water columns. In another study, researchers evaluated the effect of biological and physical disturbances on flux of arsenic from sediment collected from the lower Patuxent River, measuring fluxes 4808

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ranging from 1.08 to 2.81 mg/m2/d during bioturbation and from 1.06 to 3.56 mg/m2/d during resuspension experiments (19). Arsenic concentrations in the sediment for both research studies were generally higher than those concentrations utilized in the model. As shown in Figure 4, arsenic fluxes predicted by the model are higher than those reported by experimental studies. However, the model did predict the initial pulse of arsenic followed by a lower, steadier flux as observed in the experiments. In the model, the initial pulse is reproduced mechanistically through buildup of arsenic in the aerobic layer when conditions in the overlying water column are oxic, and then released when conditions become anoxic. As concentrations in the aerobic layer are suddenly reduced, the steady flux of arsenic to the water column is limited through particle and diffusive mixing from the anaerobic layer. Concentrations of As(V) in the water column, which may be heavily influenced by sediment flux, were reproduced well by the model. In any case, additional data for arsenic, including vertical column profile data and sediment data, should be collected to further verify results of the model. Hypotheses Testing. To evaluate the hypothesis that substantial amounts of methylated arsenic observed in the lower estuary are caused by uptake and methylation of arsenic by phytoplankton, calibrated model results were compared to results from the model after turning off all kinetic processes in the water column, including oxidation of As(III), demethylation of MMA and DMA, and uptake and transformation of As(V). There were no changes to calibrated parameters between the two scenarios. For a summer sampling date (August 29, 1995), concentrations of DMA were vastly over predicted in the lower estuary when no kinetic processes were applied; indicating that the lower estuary was a net sink rather than a source of DMA. For a late fall sampling date (November 27, 1995), results for DMA using no kinetics processes compared more favorably to observed data, possibly indicating that demethylation of DMA is hindered by lowering temperatures. Upon inspection of both spatial and temporal plots, results for MMA using no kinetic processes compared only slightly better to observed data than results from the calibrated model, indicating that the primary process influencing concentrations of MMA in the estuary was mass transport. Any improvement in results for As(III) was debatable while very little difference was observed in results for As(V). Overall, the comparison of results indicates that while some uptake and transformation of As(V) may be occurring in the estuary, the major source of methylated species to the lower estuary was mass transport from the downstream boundary (tidal exchange with Chesapeake Bay). These results are not surprising when considering the large net tidal exchange occurring at the mouth of the estuary as discussed by Lung and Nice (22). Taking into account the concentration and mass transport of salinity in the estuary, the location of significant arsenic methylation could be in the Chesapeake Bay or even as far as the Atlantic Ocean. Additional data collection and modeling of the downstream water bodies will be required to obtain a definitive answer. Thus, model results do not support the hypothesis that the majority of DMA and MMA in the lower estuary are produced from the uptake and methylation of arsenic by phytoplankton in situ. However, this inference does not rule out that some sources of methylated species are generated in the estuary, otherwise longitudinal concentration profiles would be identical to salinity, showing zero concentration upstream. To evaluate the hypothesis that high concentrations of arsenic observed in the lower estuary during the summer are due to release of arsenic from sediments, calibrated model results were compared to results from the model after disabling all routines pertaining to solid-liquid partitioning

FIGURE 4. Sediment flux for calibrated model. with suspended solids and sediment flux (in addition to disabling kinetic processes in the water column). In essence, the only processes influencing the fate and transport of arsenic in the estuary for this model simulation were mass transport (advection and dispersion). There were no changes to calibrated parameters between the two scenarios. Upon comparison of results, the most striking difference was that As(V) was vastly under-predicted when considering mass transport alone. This discrepancy emphasizes the significance of sediment flux of As(V) during the summer and supports the hypothesis that sediment flux was responsible for higher summer concentrations. Another smaller difference was noticeable for As(III) in comparison of results for a late fall sampling date (November 27, 1995). Slightly lower concentrations for As(III) were predicted by the calibrated model and better compare to observed data, implying that solid-liquid partitioning processes are important in the lower estuary to scavenge dissolved arsenic from the water column.

Acknowledgments This study was funded by the National Oceanic and Atmospheric Administration as part of the multidisciplinary research study, Complexity and Stressors in Estuarine Systems (COASTES).

Supporting Information Available Additional details about our analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) Riedel, G. F.; Williams, S. A.; Riedel, G. S.; Gilmour, C. C.; Sanders, J. G. Temporal and spatial patterns of trace elements in the Patuxent River: a whole watershed approach. Estuaries 2000, 23, 521–535. (2) Riedel, G. F.; Sanders, J. G. The interrelationships among trace element cycling, nutrient loading, and system complexity in estuaries: A mesocosm study. Estuaries 2003, 26 (2A), 339–351. VOL. 42, NO. 13, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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(3) Riedel, G. F.; Sanders, J. G.; Breitburg, D. L. Seasonal variability in response of estuarine phytoplankton communities to stress: Linkages between toxic trace elements and nutrient enrichment. Estuaries 2003, 26 (2A), 323–338. (4) Diamond, M. L. Application of a mass balance model to assess in-place arsenic pollution. Environ. Sci. Technol. 1995, 29 (1), 29–42. (5) Cornett, J.; Chant, L.; Risto, B. Arsenic transport between water and sediments. Hydrobiologia 1992, 235 (6), 533–544. (6) Tempel, R. N.; Shevenell, L. A.; Lechler, P.; Price, J. Geochemical modeling approach to predicting arsenic concentrations in a mine pit lake. Appl. Geochem. 2000, 15, 475–492. (7) Anderson, L. C. D.; Bruland, K. W. Biogeochemistry of arsenic in natural waters: The importance of methylated species. Environ. Sci. Technol. 1991, 25 (3), 420–427. (8) Holm, T. R.; Anderson, M. A.; Stanforth, R. R.; Iverson, D. G. The influence of adsorption on the rates of microbial degradation of arsenic species in sediments. Limnol. Oceanogr. 1980, 25 (1), 23–30. (9) Fanger, H.-U.; Kunze, B.; Michaelis, W.; Muller, A.; Riethmuller, R. Suspended Matter and Heavy Metal Transport in the Tidal River Elbe River. In Coastal and Estuarine Pollution; Kyushu University, Fukuoka, Japan, October 18-21, 1987; Awaya, Y., Kusuda, T., Eds.; Kyushu University: Fukuoka, Japan, 1987; pp 302-309. (10) Cutter, G. A. Kinetic controls on metalloid speciation in seawater. Mar. Chem. 1992, 40 (1-2), 65–80. (11) Hellweger, F. L.; Farley, K. J.; Lall, U.; Di Toro, D. M. Greedy algae reduce arsenate. Limnol. Oceanogr. 2003, 48 (6), 2275– 2288. (12) Hellweger, F. L.; Lall, U. Modeling the effect of algal dynamics on arsenic speciation in Lake Biwa. Environ. Sci. Technol. 2004, 38 (24), 6716–6723. (13) Sanders, J. G.; Riedel, G. F., Trace element transformation during the development of an estuarine algal bloom. Estuaries 1993, 16, (3A). (14) Andreae, M. O.; Klumpp, D. Biosynthesis and release of organoarsenic compounds by marine algae. Environ. Sci. Technol. 1979, 13 (6), 738–741. (15) Blum, J. J. Arsenate uptake by phosphate starved Euglena. J. Gen. Physiol. 1966, 49, 1125–1136. (16) Riedel, G. F. The annual cycle of arsenic in a temperate estuary. Estuaries 1993, 16 (3A), 533–540. (17) Sanders, J. G.; Riedel, G. F.; Osman, R. W. Arsenic Cycling and Its Impact in Estuarine and Coastal Marine Ecosystems. In Arsenic in the Environment, Part I: Cyclingand Characterization; Nriagu, J. O., Ed.; John Wiley and Sons: New York, 1994; pp 289-308. (18) Aggett, J.; O’Brien, G. A. Detailed model for the mobility of arsenic in lacustrine sediments based on measurements in Lake Ohakuri. Environ. Sci. Technol. 1985, 19 (2), 231–238. (19) Riedel, G. F.; Sanders, J. G.; Osman, R. W. Effect of biological and physical disturbances on the transport of arsenic from contaminated estuarine sediments. Estuarine Coastal Shelf Sci. 1987, 25 (6), 693–706.

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(20) Riedel, G. F.; Sanders, J. G.; Osman, R. W. Biogeochemical control on the flux of trace elements from estuarine sediments: effects of seasonal and short-term hypoxia. Mar. Environ. Res. 1999, 47, 349–372. (21) Nice, A. J. Developing a Fate and Transport Model for Arsenic in Estuaries. PhD Dissertation, University of Virginia, Charlottesville, VA, 2006. (22) Lung, W. S.; Nice, A. J. A Eutrophication model for the Patuxent Estuary: Advances in predictive capabilities. J. Environ. Eng. 2007, 133 (9), 917–930. (23) Inskeep, W. P.; McDermott, T. R.; Fendorf, S. Arsenic(V/III) cycling in soils and natural waters: Chemical and microbiological processes. In Environmental Chemistry of Arsenic; Frankenberger, W. T., Ed. Marcel Dekker Inc.: New York, NY, 2002; pp 183-215. (24) Mok, W. M.; Wai, C. M., Mobilization of Arsenic in Contaminated River Waters. In Arsenic in the Environment, Part I: Cycling and Characterization; Nriagu, J. O., Ed.; John Wiley and Sons: New York, 1994; pp 99-115. (25) Andreae, M. O. Arsenic speciation in seawater and interstitial waters: The influence of biological-chemical interactions on the chemistry of a trace element. Limnol. Oceanogr. 1979, 24 (3), 440–452. (26) Riedel, G. F.; Sanders, J. G.; Osman, R. W. Biogeochemical control on the flux of trace elements from estuarine sediments: Water column oxygen concentrations and benthic infauna. Estuarine Coastal Shelf Sci. 1997, 44 (1), 23–38. (27) Chesapeake Bay Program Water Quality Data. www. chesapeakebay.net. (28) Anderson, M. A.; Ferguson, J. F.; Gavis, J. Arsenate adsorption on amorphous aluminum hydroxide. J. Colloid Interface Sci. 1976, 54, 391–399. (29) Pierce, M. L.; Moore, C. B. Adsorption of arsenite and arsenate on amorphous iron hydroxide. Water Res. 1982, 16, 1247–1253. (30) Allison, J. D.; Allision, T. L. Partition Coefficients for Metals in Surface Water, Soil, and Waste; EPA/600/R-05/074; Environmental Protection Agency: Washington, DC, 2005. (31) Scudlark, J. R.; Johnson, D. L. Biological oxidation of arsenite in seawater. Estuarine Coastal Shelf Sci. 1982, 14, 693–706. (32) Peterson, M. L.; Carpenter, R. Biogeochemical Processes affecting total arsenic and arsenic species distributions in an intermittently anoxic fjord. Mar. Chem. 1983, 12, 295–321. (33) Di Toro, D. M., Sediment Flux Modeling; Wiley-Interscience: New York, NY, 2001. (34) Hunt, G. A. Harmful Algal Bloom Modeling Framework for the Pocomoke River Estuary, PhD Dissertation, University of Virginia, 2004. (35) Cutter, G. A. Dissolved arsenic and antimony in the Black Sea. Deep Sea Res. 1991, 38 (Suppl. 2), S825-S843. (36) Sanders, J. G. Microbial role in the demethylation and oxidation of methylated arsenicals in seawater. Chemosphere 1979, 3, 135– 137. (37) Brockbank, C. I.; Bately, G. E.; Low, G. K.-C. Photochemical decomposition of arsenic species in natural waters. Environ. Technol. Letters 1988, 9, 1361–1366.

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