Biogeochemical reactive–diffusive transport of heavy metals in Lake Coeur d’Alene sediments

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Applied Geochemistry Applied Geochemistry 22 (2007) 2569–2594 www.elsevier.com/locate/apgeochem

Biogeochemical reactive–diffusive transport of heavy metals in Lake Coeur d’Alene sediments S. Sevinc¸ S ß engo¨r a, Nicolas F. Spycher b, Timothy R. Ginn Brent Peyton c,2 a

a,*

, Rajesh K. Sani

c,1

,

University of California at Davis, One Shields Avenue, Civil and Environmental Engineering Department, Davis, CA 95616, USA b Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA c Washington State University, Department of Chemical Engineering, Center for Multiphase Environmental Research, Pullman, Washington 99164-2710, USA Available online 27 June 2007

Abstract Decades of runoff from precious-metal mining operations in the Lake Coeur d’Alene Basin, Idaho, have left the sediments in this lake heavily enriched with toxic metals, most notably Zn, Pb and Cu, together with As. The bioavailability, fate and transport of these metals in the sediments are governed by complex biogeochemical processes. In particular, indigenous microbes are capable of catalyzing reactions that detoxify their environments, and thus constitute an important driving component in the biogeochemical cycling of these metals. Here, the development of a quantitative model to evaluate the transport and fate of Zn, Pb and Cu in Lake Coeur d’Alene sediments is reported. The current focus is on the investigation and understanding of local-scale processes, rather than the larger-scale dynamics of sedimentation and diagenesis, with particular emphasis on metal transport through reductive dissolution of Fe hydroxides. The model includes 1-D inorganic diffusive transport coupled to a biotic reaction network including consortium biodegradation kinetics with multiple terminal electron acceptors and syntrophic consortium biotransformation dynamics of redox front. The model captures the mobilization of metals initially sorbed onto hydrous ferric oxides, through bacterial reduction of Fe(III) near the top of the sediment column, coupled with the precipitation of metal sulfides at depth due to biogenic sulfide production. Key chemical reactions involve the dissolution of ferrihydrite and precipitation of siderite and Fe sulfide. The relative rates of these reactions play an important role in the evolution of the sediment pore-water chemistry, notably pH, and directly depend on the relative activity of Fe and SO4 reducers. The model captures fairly well the observed trends of increased alkalinity, sulfide, Fe and heavy metal concentrations below the sediment–water interface, together with decreasing terminal electron acceptor concentrations with depth, including the development of anoxic conditions within about a centimeter below the lake bottom. This effort provides insights on important biogeochemical processes affecting the cycling of metals in Lake Coeur d’Alene and similar metal-impacted lacustrine environments.  2007 Elsevier Ltd. All rights reserved.

*

Corresponding author. E-mail address: [email protected] (T.R. Ginn). 1 Present address: South Dakota School of Mines and Technology, Chemical and Biological Engineering Department, 501 East St. Joseph Street, Rapid City, SD 57701-3995, USA. 2 Present address: Montana Sate University, Chemical and Biological Engineering Department, Bozeman, MT 59717, USA. 0883-2927/$ - see front matter  2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeochem.2007.06.011

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1. Introduction The rich mining history of the western United States has resulted in vast amounts of water and soil contaminated with toxic metals, leaving sediments at many sites heavily enriched with these metals, including in particular Zn, Pb and Cu. Such mining impact is seen in the Lake Coeur d’Alene Basin, where metal-enriched sediments and surface waters, mainly from the Bunker Hill Superfund mining site (located at the South Fork of the Coeur d’Alene River, Fig. 1) have been discharging for decades during periodic runoff events. This process has resulted in the transport of contaminated sediments and water continuously downstream to Lake Coeur d’Alene. Although the discharges from the mining activities are minimal today, periodic floods and erosion still continue to result in the transport of the metal-enriched sediments throughout Lake Coeur d’Alene. In this paper, a biogeochemical model of heavy metal fate and diffusive reactive transport in Lake Coeur d’Alene sediments is presented. The objective is to understand and quantify biogeochemical processes controlling the transport of Zn, Cu and Pb in the lake sediments. The model conceptualization is based primarily on field data and observations from the recent studies of Winowiecki (2002), Toevs et al. (2006), Balistrieri (1998), Balistrieri et al. (1999, 2003) and Cummings et al. (2000). The current model is somewhat simplified, as discussed later, but integrates priority biogeochemical pro-

cesses to seek a representation of these coupled processes that is consistent with observed trends. In this respect, the model is not intended to reproduce exactly measured data, the immediate goal being focused on the investigation and understanding of important biogeochemical processes at play at a local scale, rather than covering the entire lake ecosystem operating at a larger scale. The geochemical behavior of metals in contaminated sediments is the result of complex coupled physical, chemical and biological processes (e.g., Santschi et al., 1990), and thus quantification of metal cycling in Lake Coeur d’Alene sediments requires an understanding of the interactions between microbially mediated reactions and inorganic geochemical processes. The Lake Coeur d’Alene sediments are rich in hydrous Fe(III) oxides (mainly as ferrihydrite), making these sediments particularly prone to metal sorption (e.g., Tonkin et al., 2002; Balistrieri et al., 1999). Also, given that dissimilatory Fe reduction is an important process in Lake Coeur d’Alene (Cummings et al., 2000), the interest here focuses on modeling the mobilization of heavy metals sorbed onto hydrous Fe(III) oxides by microbial reductive dissolution of these oxides under redox disequilibrium conditions. The subsequent formation of metal aqueous complexes with biogenic sulfide, as well as the precipitation of sulfide minerals is also investigated. These processes are important controls on metal fluxes observed both into, and out of, the Lake Coeur d’Alene sediments (e.g., Kuwabara et al., 2003).

Fig. 1. Map of the Coeur d’Alene River basin (after Balistrieri et al., 2002) with sampling location for this study (N4728 0 W11643 0 ).

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Woods and Beckwith (1997) and Kuwabara et al. (2007) have indicated that the diversity and abundance of phytoplankton vary longitudinally in Lake Coeur d’Alene due to large differences in the composition of source water to the lake. Concentrations 2þ of O2, NO 3 and SO4 in bottom water, metal and organic C contents of surface sediment, as well as pore-water concentration profiles also vary spatially in the lake (Horowitz et al., 1993; Woods and Beckwith, 1997; Balistrieri, 1998; Toevs et al., 2006). The model presented in this study is not intended to capture this complex spatial variability, but rather focuses on local-scale processes affecting a typical sediment column in contaminated areas. A region around Harrison Slough (Fig. 1) was chosen because this area has been most heavily impacted by heavy metals from the Coeur d’Alene River inflow. Accordingly, biogeochemical data regarding sediment and pore-water samples collected from this region were taken as the basis for the conceptual model. Xue et al. (1997) presented data from two eutrophic lakes indicating large temporal variations in the settling fluxes of Cu and Zn in the lake sediments. Given that the goal of this study is not to model the entire dynamics of sedimentation, and also given the scarce data regarding seasonal variation of sedimentation rates in Lake Coeur d’Alene, temporal variations in fluxes of metals, organic matter and metal oxide phases related to changes in sedimentation rates are not currently considered. Reactive transport modeling of biogeochemical processes in benthic sediments has been carried out by others, primarily in marine sediments (e.g., Van Cappellen and Gaillard, 1996; Wang and Van Cappellen, 1996; Hunter et al., 1998; Giambalvo et al., 2002; Maher et al., 2006). The present model draws on these studies, and differs from them primarily in dealing with lacustrine sediments, under dilute oxic waters, and in a setting conducive to the mobilization of elevated metal concentrations through reductive dissolution of Fe(III) hydroxides. Gallon et al. (2004) examined the effects of reductive dissolution on Pb mobilization in lake sediments. Their modeling approach consisted of fitting a reaction–diffusion equation to observed Pb profiles, to determine Pb production (mobilization) and consumption (immobilization) zones in the sediments (net reaction rates). Smith and Jaffe (1998) developed a simplified steady state, one dimensional multicomponent reactive transport model and illustrated it for As cycling in a small lake. The

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study by Van Cappellen and Wang (1996) presented a multicomponent diagenetic model that explicitly accounted for the coupling of the redox cycles of Fe, Mn, O2, C, S and N. Their model included the oxidation of organic C by microbial guild members as well as the oxidation of secondary reduced species and precipitation and dissolution of sulfide and carbonate minerals, which is appropriate for modeling the overall large-scale dynamics of benthic sediments. Tromp et al. (1995) developed a diagenetic model based on sedimentation rate for the deposition of organic C and organic P in marine sediments to be included in global circulation models. The present model, however, differs from these works in that it focuses on small scale processes of heavy metal transport (Zn, Pb and Cu) in sediments. In particular, the release of metals sorbed onto Fe oxide surfaces by microbial reductive dissolution of ferrihydrite, a key process at Lake Coeur d’Alene, is investigated by implementing a reactive transport approach that explicitly models surface complexation. Several other investigations of lacustrine sediments have incorporated geochemical modeling (‘‘batch’’ models without transport) to evaluate metal speciation and pore-water saturation with respect to solid phases, including sulfide minerals (e.g., Huerta-Diaz et al., 1998). Others have used batch geochemical models to study metal sorption and precipitation in the Coeur d’Alene River basin (e.g., Balistrieri et al., 2003; Tonkin et al., 2004). The biogeochemical processes discussed in the above-mentioned papers are all likely important players for the large-scale cycling of metals, in the context of general lake dynamics. However, rather than performing a comprehensive ecosystem model of Lake Coeur d’Alene, the present study aims at incrementally building a model for heavy metal fate and transport in a diffusive benthic domain generally representative of a mining-impacted lacustrine environment. In doing so, this study represents the first attempt, to the authors’ knowledge, to model the reactive transport of heavy metals in sediments by integrating the coupled effects of microbial Fe oxide reductive dissolution, biogenic sulfide production, and metal sorption through the use of a full surface complexation model. An accurate characterization of solid phase and pore-water chemistry is critical to provide insight into the biogeochemical processes taking place within the sediments at Lake Coeur d’Alene and the extent and significance of heavy metal contamination. For this reason, available biogeochemical

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data regarding sediment and water samples collected from metal-contaminated regions of Lake Coeur d’Alene are discussed first. These data form the basis of the conceptual model. Next, the reactive transport model, including its conceptualization, boundary and initial conditions as well as transport and physical properties of the benthic sediments used in the simulations are introduced. The inorganic and microbially mediated reactions that govern the biogeochemical model are also discussed. Model results are then evaluated in context with available field data. 2. Lake Coeur d’Alene biogeochemistry The modeling work is based on analyses of sediments, pore waters and surface waters from Lake Coeur d’Alene and the Coeur d’Alene River, conducted mostly by other researchers. Some of the most recent and relevant published analytical data are given in Appendix A. As part of the present study, several sediment core, water and sediment samples, were also collected as well as a few pore water and lake water samples near the lake shore in the vicinity of Harrison (N4728 0 W11643 0 ; Fig. 1). The analyses of these samples are also reported in Appendix A. Lake Coeur d’Alene sediments and pore waters have been studied extensively (Maxfield et al., 1974a,b; Funk et al., 1975; Reece et al., 1978; Mok and Wai, 1990; La Force et al., 1998; Harrington et al., 1998; Horowitz et al., 1992, 1993, 1995; Balistrieri, 1998; Balistrieri et al., 2002; Cummings et al., 2000; Winowiecki, 2002; Kuwabara et al., 2003; Toevs et al., 2006). Many investigations have also been conducted on heavy and other trace metal discharges into the Lake Coeur d’Alene River and its tributaries (e.g., Paulson, 1997, 2001; Tonkin et al., 2002; Balistrieri et al., 1999, 2002, 2003). The US Geological Survey (USGS) also maintains an extensive database of water quality in the Coeur d’Alene River (http://nwis.waterdata.usgs.gov/ nwis). Of these studies, those most influential for the development of the model include the work of Cummings et al. (2000) on microbial Fe(III) reduction in Lake Coeur d’Alene sediments, the detailed characterization of these sediments by Toevs et al. (2006), and profiles of sediment pore-water compositions with depth reported by Winowiecki (2002) and Balistrieri (1998). The work of Winowiecki (2002) also provides an excellent summary of previous hydrogeochemical investigations conducted at

Lake Coeur d’Alene. Results of these investigations most relevant to this study are discussed below. 2.1. Sediment composition and mineralogy Maxfield et al. (1974a) showed that lake sediments collected within a 900 m radius from the mouth Coeur d’Alene River (Fig. 1) have been contaminated by heavy metals, with a roughly constant metal content down to a depth of 52 cm below the lake bottom. In comparison, Maxfield et al. (1974b) showed that lake sediments near the mouth of the St. Joe River (Fig. 1) were essentially unimpacted by mining activities. Reece et al. (1978) also observed that the sediments in the Coeur d’Alene River were highly enriched in Cd, Pb, Mn, Zn compared to the St. Joe River. Early X-ray diffraction (XRD) studies of sediment mineralogy in the Lake Coeur d’Alene delta and river include the work of Reece et al. (1978), who reported the presence of primarily quartz, siderite and small amounts of magnetite, and the study of Mok and Wai (1990) who identified primarily quartz, muscovite, siderite, and some kaolinite and K feldspar. Using Raman spectroscopy, Cummings et al. (2000) identified magnetite (FeO Æ Fe2O3 or Fe3O4) and pyrrhotite (Fe7S8) in addition to ferrihydrite and siderite in Lake Coeur d’Alene sediments. These authors observed minor amounts of quartz and small broad peaks corresponding to Fe sulfides, but did not report the presence of pyrite. Cummings et al. (2000) attribute the formation of magnetite and Fe sulfide to Fe- and SO4-reducing bacteria (SRB), respectively, and point to microbial Fe(III) reduction as being one of the dominant microbial processes taking place in Lake Coeur d’Alene sediments. Horowitz et al. (1992, 1993, 1995) reported that the top 17 to 119 cm of Lake Coeur d’Alene sediments were enriched in Ag, As, Cd, Hg, Pb, Sb, Cu and Zn. Mean total concentrations of metals and As (on dry weight basis) calculated from the data reported by Harrington et al. (1998) are 82,486 mg/kg Fe, 3820 mg/kg Pb, 2995 mg/kg Zn, 201 mg/kg As, 5953 mg/kg Mn, and the mean values (mg/kg dry weight sediment) for Cu and Cd reported by Horowitz et al. (1995) are 1.43 and 0.222, respectively. Horowitz et al. (1993, 1995) found that the bulk of Pb, Cd, Zn, As and Cu is associated with an operationally defined Fe oxide phase, with only minor amounts of metals associated with sulfide minerals. In contrast, Harrington

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et al. (1998) reported that 40–50% Pb and 55–65% Zn is associated with an operationally defined sulfidic phase. To solve this controversy and identify the dominant mineral phases along with diagenetic processes operating in Lake Coeur D’Alene sediments, Toevs et al. (2006) characterized sediments samples using X-ray absorption near-edge structure (XANES) spectroscopy, combined with in situ analyses of interstitial water composition. Their spectroscopic analysis indicates that pyrite is the main S mineral in contaminated areas, increasing with depth to 50% of total S at 36 cm, but that only 2–2.5% of the Fe is associated with pyritic minerals. Toevs et al. (2006) also note that siderite is an important gangue mineral in ore deposits in the Lake Coeur d’Alene basin, suggesting a detrital origin for this mineral in shallow parts of the sediment. Similar trends (in pyrite with depth, and in Fe association with pyrite) are reported in Winowiecki (2002), using acid volatile sulfur (AVS) and XANES methods on sediments from both contaminated and uncontaminated sites sampled in the lake between 2000 and 2002, who attribute the pyrite formation to diagenetic reactions occurring within the sediments, because samples at uncontaminated sites near the mouth of the St. Joe River also contain increasing amounts of pyrite with depth but are not impacted by mining activity. These results differ from the findings of Harrington et al. (1998) who, by selective sequential extractions, determined that 60–70% of the Fe is associated with a sulfidic phase. Toevs et al. (2006) thus question the accuracy of phase partitioning of metals using selective sequential extractions, given that such extractions are prone to reprecipitation or readsorption (Belzile et al., 1989; La Force et al., 2002). Toevs et al. (2006) show that Fe(III) as ferrihydrite (Fe2O3 Æ 3H2O) and Fe(II) as siderite (FeCO3) are present throughout the sediment column (down to at least 36 cm), with the proportion of ferrihydrite, relative to siderite, decreasing with depth. These authors also conclude that siderite is the dominant Fe(II) mineral phase, and suggest that the high Fe:S ratio observed in pore waters limits the formation of metal sulfides whereas the high carbonate content buffers pH and promotes siderite formation. This is again supported by data from Winowiecki (2002) that indicates that the majority of the Fe occurs in the form of oxides and carbonates. Data from Winowiecki (2002) show the solid Fe content (Fe(III) + Fe(II)) with depth in unimpacted areas is relatively constant around 3%. In contami-

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nated area, the Fe content generally decreases by a couple percent within the top 5 cm of sediments, then increases with depth to maximum amounts around 10%. The sediment analyses reported by Winowiecki (2002) are generally consistent with the data from Horowitz et al. (1993, 1995) and other studies discussed here. To further characterize the sediment composition and physical properties, a few core samples from a metal-contaminated area near Harrison were also collected in April 2005 (N4728 0 W11643 0 ; Fig. 1). These cores were collected using 15-cm long PVC tubes which, after sampling, were cut into 3 sections, each 5 cm in length. Each section was then analyzed by X-ray fluorescence microscopy (XRF) to obtain total metal concentrations (Table A.1), and to determine physical properties as discussed later. The analytical results are consistent with other published data, notably showing that the sediments are highly enriched in Fe, Pb, Zn, Cu and with other trace metals (Table A.2). One grab sediment sample was also analyzed by X-ray diffraction methods (Moberly et al., unpublished data). Standard XRD revealed the presence of mainly quartz and siderite, with minor mica and possibly jacobsite (MnO Æ Fe2O3) and dundasite (Pb, Al hydroxycarbonate). Synchrotron-light micro X-ray/XRF spot analysis was also used to identify associations of trace metal amounts with crystalline phases, revealing (tentatively) traces of Zn as smithsonite (ZnCO3), and Pb as bindhemimite (Pb2Sb2O7), mattheddleite (Pb10(SiO4)3.5(SO4)2Cl2), coronadite (PbMn8O16) and stolzite (PbWO4). Heavy metal sulfides and Fe/Mn hydroxides were not detected presumably because these occur as amorphous phases and, for the case of sulfides, possibly because of sample oxidation. 2.2. Sediment physical properties Besides mineralogy, particle size of the sediment has an important bearing on surface area, and thus on reactivity and metal sorption. In addition, recent studies have shown that as the size of amorphous Fe hydroxides such as ferrihydrite become smaller (in the nm range), surface-area-normalized reductive dissolution rates increase (Anschutz and Penn, 2006). Winowiecki (2002) summarizes trends observed by various authors between metal content and particle size in the Lake Coeur d’Alene sediments. As the particle size of the sediment increases, the heavy metal concentration decreases. Chemical

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variations with particle size were studied extensively by Horowitz et al. (1992, 1993), who reports that all the metal-enriched sediments are extremely finegrained (<63 lm). This correlation is attributed to sorption, with smaller sized sediment particles having larger specific surface area, thus more sorbing sites. As expected, the finer particles are carried further away than the coarser ones, downstream from the mouth of the Coeur d’Alene River. Horowitz et al. (1993) observed graded bands of sediment (0.1–2 cm thick) that they attributed to annual flood events. Based in part on ash from the 1980 Mount St Helen’s eruption, Horowitz et al. (1995) estimated the sedimentation rate at Lake Coeur d’Alene to range between 1.3 and 2.1 cm/a. Particle-size sieve analyses were carried out on some of the core samples collected in Harrison Slough (Fig. A1). In addition, pore size distribution and surface area was determined on these sediments, after centrifugation, using N2-BET analyses (Table A.3). Porosity and wet/dry density were also measured by gravimetric methods (Table 1). Particle size distribution within the top 15 cm of sediments (the length of collected cores) is not observed to vary significantly with depth, with about 40–65% of the particles smaller than 75 lm. Pore sizes measured on centrifuged sediment samples are extremely small, with approximately 10% of pores smaller than 6 nm, 1/3 smaller than 20 nm, and 2/ 3 smaller than 80 nm. 2.3. Water chemistry The Coeur d’Alene river and lake waters near Harrison are dilute Ca–SO4 waters typically with Ca and SO2 concentrations ranging 5–20 mg/L, 4 and with Mg, Na and Cl concentrations generally a few mg/L or less. The studies of Balistrieri Table 1 General model setup parameters Length of modeled column 45 cm Length of each discretized cell 0.5 cm for the 1st 8 cells and increasing gradually up to 1 cm throughout the rest of the column Maximum time step 3 h Diffusion coefficienta 4.27e6 cm2/s Porosityb 0.47 Wet bulk densityb 1.60 g/cm3 Dry bulk densityb 1.15 g/cm3 a

After Balistrieri (1998) (see text). Measured in this study for samples collected at location shown in Fig. 1. b

(1998), Winowiecki (2002) and Toevs et al. (2006) provide most of the available pore-water chemical analyses from Lake Coeur d’Alene sediments. For the most part, these data were collected using in situ using peepers (diffusion chambers) at various depths. Balistrieri (1998) also analyzed pore-water samples. The recent analyses by Winowiecki (2002), Toevs et al. (2006) and Balistrieri (1998) are presented later when comparing model results to measured data. The authors’ own analyses of Coeur d’Alene River water (‘‘shorewater’’) and of a centrifuged sediment pore-water sample average at 0–15 cm below the sediment/water interface collected near the shore in the vicinity of Harrison are given in Appendix A (Table A.4) and compared with riverwater analyses reported by Balistrieri et al. (2003), Tonkin et al. (2002) and Winowiecki (2002). The river/lake water is typically less acidic (neutral range) and exhibits lower concentrations of many dissolved species compared to sediment pore water, as discussed further below. Note that dissolved Fe in the authors’ pore-water sample apparently oxidized during sample collection and for this reason was below the detection limit. Seasonal water chemistry along the Coeur d’Alene River has been compiled by the USGS (http://nwis.waterdata.usgs.gov/nwis). Dissolved Pb and Zn concentrations for a location near Harrison are shown in Fig. 2 on a monthly time scale starting in March 1997. The periodic fluctuations in Pb and Zn concentrations increase in the rainy seasons evidently as a result of metal transport by seasonal runoff. During the dry season, water level fluctuations on levee banks of the Coeur d’Alene River affect the supply of O2 at the water/sediment interface and leads to the oxidation of sulfide minerals and production of sulfate crusts (Balistrieri et al., 2003). In the rainy season, these oxidized minerals are mobilized and/or dissolved vertically through the riverbank sediments providing dissolved SO2 4 and other species as well as acidity to the pore water (Balistrieri et al., 2003). Lower pH conditions lead to the dissolution of carbonate and other minerals (e.g., siderite) and thus increase the concentration of dissolved species in the levee banks (Balistrieri et al., 1999, 2003). La Force et al. (1998) also suggested that mobilization of heavy metals occurs as a result of biostimulation by flood events. Using sediments from the Coeur d’Alene River, these authors showed that Zn mobilization from nutrient-amended cores was much greater than when

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Zn

Pb

600 20

15

400 300

10

Pb (ug/L)

Zn (ug/L)

500

200 5 100 0 0 2/5/96 12/1/96 9/27/97 7/24/98 5/20/99 3/15/00 1/9/01 11/5/01 9/1/02 6/28/03 4/23/04 2/17/05 Date

Fig. 2. Seasonal Zn and Pb concentrations in the Coeur d’Alene River near Harrison, Idaho (US Geological Survey National Water Information System, ‘‘Coeur d’Alene River Near Harrison’’, http://nwis.waterdata.usgs.gov/nwis).

the cores were not amended. Because the nutrientamended cores showed high levels of biological activity, La Force et al. (1998) concluded the mobilization was related to microbially mediated reductive dissolution. The general trend of pore-water composition with depth is consistent with typical redox behavior transitioning from an oxic lake bottom (year-long in this case) to an anoxic environment with depth (e.g., Cook, 1984; Huerta-Diaz et al., 1998). A key feature of such transition is the sequential decrease with depth of terminal electron acceptor concentrations 2 (e.g., O2, NO 3 , Fe(III), SO4 ) and increase in bicarbonate alkalinity and chemically reduced species such as dissolved Fe(II) and sulfides (e.g., Van Cappellen and Gaillard, 1996). The pH at the lake bottom is typically in the neutral range. On average, the pH generally decreases with depth in the sediments (e.g., Winowiecki, 2002). An exception to this trend are measurements by Cummings et al. (2000) showing the pH to increase from around 5.8 at the top of the sediments to around 6.4 at a depth of 35 cm. The probable controls on pH and possible reasons for this trend reversal are discussed later. In the circum-neutral pH range, the solubility of Fe(III) minerals is extremely low (in the nanomolal range), such that total Fe concentrations are typically below the detection limits of standard analytical methods. For this reason, detectable total Fe concentrations (>micromolal) can generally be interpreted as representing Fe(II). The pore-water data of Toevs et al. (2006), Winowiecki (2002) and Balistrieri (1998) all show a clear trend of increasing soluble Fe concentration with depth, reaching in

some cases values near 100 mg/L. Using equilibrium speciation calculations with MINTEQ Visual (US EPA, 1999; Gustafsson, 2004), both Toevs et al. (2006) and Winowiecki (2002) confirmed the supersaturation of pore waters with respect to siderite at depth. Like Cummings et al. (2000), these authors conclude that Fe(II) is produced as a result of reductive dissolution of Fe(III) oxides. Dissolved Mn concentrations also increase with depth as the result of reductive dissolution of Mn(III/IV) (hydr)oxides to soluble Mn(II) species. All studies show a sharp decrease in SO2 4 concentrations with depth, approaching low or undetectable levels within about 10 cm from the lake bottom. This decrease is attributed to SO2 4 reduction. Increasing biogenic sulfide production with depth is evidenced by the difference between the concentrations of total dissolved S and SO2 4 measured by Winowiecki (2002), which increases with depth. The available pore-water data typically also show an increase in dissolved heavy and trace metals with depth attributed (e.g., McGeehan and Naylor, 1994; Balistrieri, 1998; Winowiecki, 2002) to the reductive dissolution of Fe and Mn hydroxides onto which these metals are sorbed. In some cases, the increase in heavy metal concentrations sharply reverses at depth, an effect attributed to the precipitation of metal sulfides (e.g., Winowiecki, 2002; Huerta-Diaz et al., 1998). 2.4. Microbial processes Among the many interactions between metals and microorganisms (Lloyd and Lovley, 2001;

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Gadd, 1992), microbes are well known for altering the chemistry of the environments in which they reside (Fredrickson and Gorby, 1996; Kostka et al., 1996). The oxidation of organic compounds within freshwater and marine sediments takes place with the thermodynamically favorable sequence of terminal electron acceptors: O2, NO 3 , Mn oxyhydroxides, Fe oxyhydroxides and SO2 4 , varying vertically with sediment depth. Suboxic conditions are characterized when O2 concentrations are very low and NO 3 , Mn and Fe oxides are used as alternative electron acceptors, whereas anoxic conditions occur when SO2 4 becomes the terminal electron acceptor (Balistrieri, 1998). The reduction of redox-sensitive metals Fe(III) and Mn(IV) are mediated by the dissimilatory metal reducing bacteria (DMRB) (Lovley, 1991), where Fe(III) oxide reduction is shown to alter the geochemistry of both soils, sediments and surface and subsurface water (Chapelle and Lovley, 1992; Fredrickson and Gorby, 1996; Kostka et al., 1996; Lovley, 1991, 1993; Ottow, 1970). The cycling and fate of Cd, Cu, Pb and Zn are also often microbially mediated because these metals can react with biogenic sulfide to form low-solubility metal sulfides (Sani et al., 2001, 2002, 2003), or sorb to microbially produced biopolymers (Beech and Cheung, 1995; Fang et al., 2002), and/or they can be mobilized by reductive dissolution of hydrous ferric oxides though the activity of DIRB (Ribet et al., 1995). Moreover, the biogenic sulfide produced as a result of the activity of SRB forms aqueous complexes with high concentrations of Fe(II), even as FeS precipitates (e.g., Rickard, 1995, 2006). The reaction of Fe(II) with sulfide may compete with the precipitation of other more toxic metals as sulfides, thus leaving these metals in solution (Cummings et al., 2000). The subsequent increase and decrease of dissolved metal concentrations as a result of biotic and abiotic reactions will ultimately affect the benthic fluxes of these metals. Recent studies have indicated that the chemical gradients created by such reactions in the Lake Coeur d’Alene sediments make these sediments generally act as a source of dissolved heavy metals into the overlying water (Balistrieri, 1998; Kuwabara et al., 2003; Toevs et al., 2006). Harrington et al. (1998) report that the Lake Coeur d’Alene sediments support SO4-reducing bacterial communities that range in concentration from 106 to 104 cells/g (wet weight) of sediment. Mostprobable-number analysis of the sediments also revealed the presence of Fe(III) reducing bacteria

with mean density of 8.3 · 105 cells/g (dry weight) of sediments (Cummings et al., 2000). Two new unique strains of dissimilatory Fe(III) reducing bacteria, CdA-2 and CdA-3, were isolated from the surface sediments, where both strains were placed in the Geobacteraceae family (Cummings et al., 2000). The two closely related species to CdA-2 and CdA-3 were characterized to be Pelobacter propionicus and Geobacter chapelleii (Cummings et al., 2000). As part of the present study, the microbial community present in LCA sediment was identified using rpoB gene amplification. Out of 98 rpoB sequences, 23 sequences were matched (>97% similarity) with Ralstonia eutropha and 9 sequences were matched (>93–98% similarity) with Pseudomonas sp. These results clearly showed that Ralstonia sp. (known metal-tolerant genus) was the dominant bacterium in LCdA sediment (Sani et al., unpublished data). 3. Biogeochemical reactive transport model The model is structured as a 1-D diffusive reactive transport model using PHREEQC (Parkhurst and Appelo, 1999) to simulate spatial and temporal distribution of metals through the benthic sediments. Inorganic reaction processes included in the model are aqueous speciation, surface complexation onto Fe hydroxides and mineral precipitation/dissolution reactions. The inorganic reaction system is coupled to microbially mediated redox reactions controlled by the multiple terminal electron acceptor (TEA) utility of the microbial consortium. Details on the modeling approach, modeled processes, input parameters, and rationale for the model conceptualization are given below. 3.1. Modeling approach The model is integrated with field and laboratory studies, and is being developed in a step-by-step fashion with an increasing degree of complexity. The current model includes the major biogeochemical processes of interest (Fig. 3), including sorption, microbial reductive dissolution and biogenic sulfide production. Bacterial growth, metal toxicity, and the effect of seasonal variations in lake water chemistry are not yet taken into account. The goal of the current model is to develop a basic understanding of these processes and their effects on pore-water chemistry and metal behavior, as a preliminary to long-term metal cycling predictions.

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Seasonal Aqueous Chemistry Biotic phase Diffusion Redox reactions Benthic porous media

z

O2

H2O

NO3-

N2

Fe(OH)3

Fe++

SO4-2

HS-

O2/H2O, FeIII/FeII, NO3/N2, SO4/HS-

Aquatic phase

surface ti complexation precipitation/ dissolutionn

Fe(OH)3, FeS, FeCO3 ZnS, PbS, Cu2S

Solid phase

Fig. 3. Illustration of the benthic porous media with primary processes governing the fate and transport of trace metals, also showing redox couples and solid phases considered in simulations presented in this paper.

The current model is used to simulate the diffusion of oxic lake water (referred to as ‘‘shorewater’’) vertically downwards into a sediment column initially liquid-saturated with a water composition representative of the same shorewater. The system is then left to evolve for a certain time, and trends of pore-water composition with depth are compared with actual data. As necessary, the model is refined and kinetic rates are adjusted (within expected ranges) in an iterative manner until reasonable model results are obtained. 3.2. Transport processes The mechanisms of solute transport in a lake environment may be represented by a number of transport processes including advection, diffusion/ dispersion. Kuwabara et al. (2003) conducted a study at Lake Coeur d’Alene to identify the potential significance of sediment–water interface on contaminant transport and to provide first direct measurements on benthic fluxes of dissolved constituents. The study indicated that the solute benthic fluxes were controlled by diffusion at the flux-chamber deployment sites and hence the benthic fluxes were not found to be significantly influenced by bioturbation, and/or biomixing and groundwater interactions (Kuwabara et al., 2003). Based on this observation, the biogeochemical model is focused purely on diffusive transport. Currently, diffusion in the model is expressed with an average diffusion coefficient applied to all dissolved species, an assumption that ignores the different tracer diffusivities of each species and elec-

trochemical migration effects due to the different charges of various ions (e.g., Boudreau et al., 2004; Van Cappellen and Gaillard, 1996; Giambalvo et al., 2002). Sediment transport and compaction are currently not incorporated into the model. The sedimentation rate at Lake Coeur d’Alene is fairly high (1–2 cm/a, as mentioned earlier) in comparison to the scale at which biogeochemical processes operate (cm scale within a depth of a few tens of cm). This is an indication that the biogeochemical processes defining the observed trends of pore-water composition with depth operate relatively quickly. As shown later, using reasonable microbial reaction rates, and ignoring sedimentation, observed trends of pore-water composition with depth can be reproduced fairly well after relatively short simulated time periods (around 2–5 a, after which time concentration trends remained reasonably steady). Therefore, for such short periods, the constant introduction of new unreacted detrital phases (by sedimentation) into the model was neglected and, as a result, simulations could not be carried out to a ‘‘true’’ steady state. For these reasons, although the reaction rates become fairly steady during the modeled period, time and space dependent changes in the amounts and reactivity of solid phases over the column are not captured. This simplification, however, is not expected to affect the general trends of modeled pore-water compositions, and can also be justified on the basis that ferrihydrite, the main reactive mineral in the system, is found present in large quantities throughout the entire modeled sediment column (e.g., Toevs et al., 2006).

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3.3. Model physical aspects and parameters The general physical and hydrological/transport parameters of the model are summarized in Table 1. The model is setup as a vertical 1-D column, 45 cm in length. The column is discretized into 45 cells, with a grid spacing of 0.5 cm along the column top 5 cm, then gradually increasing to 1 cm for the remainder of the column. The grid refinement at the top of the model is necessary to capture the sharp decrease in O2 and NO 3 concentrations below the sediment–water interface. Reducing the time step below 3 h was not found to significantly affect model results. Accordingly, a maximum transport time step of 3 h was specified, which also yielded reasonable computation speed. A Cauchy boundary condition is specified for diffusive flux at the bottom boundary. Physical sample properties are taken from the analyses of sediment samples discussed earlier. In situ diffusion coefficients for Lake Coeur d’Alene sediments were determined by Balistrieri (1998) for Zn2+, Pb2+, Cu2+ and Mn2+ species, using the molecular diffusion coefficients of these species at infinite dilution listed by Li and Gregory (1974) and considering the inorganic speciation of these metals in near neutral, oxic freshwater conditions. From these data, an average diffusion coefficient value was calculated (using Pb2+ as a representative ion) and assumed to be the same for all dissolved species. The diffusion coefficient is quite small (Table 1), consistent with the measurement of extremely small pore sizes (Table A.3). The water composition at the top boundary, and initially throughout the entire sediment column, is taken as the (oxic) shorewater composition shown in Table 2 (derived from the analysis of shorewater reported in Table A.4). As discussed later, disequilibrium is assumed for all redox couples. The dissolved Fe(III) concentration is calculated to be at equilibrium with ferrihydrite (3 nanomolal), and the initial concentrations of reduced species such as Fe(II), N2, and sulfide are given initial concentrations of essentially nil. Surface sites of sorbing minerals in the sediment (represented by ferrihydrite) are assumed initially equilibrated with the shorewater. This is an important aspect of the model, in which sorbed SO2 4 and metal ions are made available for reaction initially throughout the simulated sediment column. For the time being, metals other than Fe, Zn, Cu, and Pb are not considered. Minerals considered in

Table 2 Model input initial pore-water and top-boundary water compositiona Species

Units

Value used in the model

pH Total inorganic carbon Ca Mg Fe(2) Fe(3)b K S SO4 Na Cl O(0)c NO3d N2 Pbe Cu Zne Acetatef Brg

– M M M M M M M M M M M M M M M M M M

7.2 3.535e04 1.372e04 8.641e05 ffi0.0 3.009e09 1.279e05 ffi0.0 5.830e05 1.000e04 1.946e05 4.249e04 8.e-5 ffi0.0 5.309e08 1.180e08 8.717e06 7.e-3 1.e-6

a

Data from analyses (Table A.4) of shorewater from a location shown on Fig. 1, except as noted otherwise. b Calculated assuming equilibrium with ferrihydrite. c Calculated assuming equilibrium with atmospheric oxygen. d Estimated from data in Winowiecki (2002) for a similar location (Site A). e Taken from the data by USGS (Fig. 2). f Concentration in excess of values measured in pore water (on the order of 1 lmolal) to ensure unlimited supply during simulations. g Non-reactive tracer with arbitrary concentration.

the model are summarized in Table 3. The rationale for the selection of these minerals, and other details on the inorganic geochemical system and chemical input data are discussed below. Microbial reaction processes and kinetic data are summarized in Tables 4 and 5, with details given later in a separate subsection. Input thermodynamic data are discussed in Appendix B. 3.4. Inorganic geochemical system The mineralogy of the sediments was assigned on the basis of available field data reviewed earlier. The most abundant reactive minerals in Lake Coeur d’Alene sediments are ferrihydrite and siderite, which are found throughout the entire sediment column. Toevs et al. (2006) report that ferrihydrite and siderite in cores collected near Harrison account for about 50% and 40% of total Fe, respectively, near the sediment/water interface, and 20% and 40% of total Fe, respectively, at a depth of 36 cm. Both fer-

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Table 3 Minerals and kinetic rate laws included in the inorganic reaction network Mineral precipitation/dissolution reactions Ferrihydrite: Fe(OH)3 + 3H+ M Fe3+ + 3H2O Mackinawite: FeSm + H+ M Fe2+ + HS Siderite: FeCO3 $ Fe2þ þ CO2 3

Equilibrium Equilibrium SIFeCO3 RFeCO3 ¼ 1  1012 jSIFeCO jþ0:5 3

Sphalerite:

ZnS + H+ M Zn2+ + HS

SIZnS RZnS ¼ 1  106 ½Zn2þ ½H2 S jSIZnS jþ0:5

Galena:

PbS + H+ M Pb2+ + HS

SIPbS RPbS ¼ 1  106 ½Pb2þ ½H2 S jSIPbS jþ0:5

Chalcocite:

Cu2S + H+ M 2Cu+ + HS

2S RCu2 S ¼ 1  106 ½Cuþ 2 ½H2 S jCu2 Cu Sjþ0:5

SI

SI is the mineral saturation index, log(Q/K), where Q and K represent the activity product and equilibrium constant (Appendix B) for the reaction shown, respectively.

Table 4 Microbially mediated reactions and kinetic rate laws included in the biogeochemical model Microbially mediated reactions þ CH3 COO þ 2O2 ! 2CO2 3 þ 3H 

CH3 COO þ

1:6NO 3 3þ

!

SO2 4

2CO2 3

CH3 COO þ 8Fe 

CH3 COO þ

2CO2 3

R O2

þ 0:8N2 þ 1:4Hþ þ 0:8H2 O

þ þ 4H2 O ! 8Fe2þ þ 2CO2 3 þ 11H

!



þ HS þ 2H

þ

RFe3þ

RSO2 4

Kinetic rate laws

2 RO 2 ¼ V O m

logðQO2 =K O2 Þ ½O2  O2 ½O2  þ K s logðQO2 =K O2 Þ þ 0:5

3 RNO3 ¼ V NO m

RFe3þ ¼ V Fe m

K in O2 K in O2

4 RSO2 ¼ V SO m 4

K in logðQNO3 =K NO3 Þ ½NO O2 3  NO3 in logðQ ½NO3  þ K s K O2 þ ½O2  NO3 =K NO3 Þ þ 0:5 þ ½O2 

K in NO3 K in NO3

logðQFe3þ =K Fe3þ Þ logðQ þ ½NO3  Fe3þ =K Fe3þ Þ þ 0:5

K in K in logðQSO4 =K SO4 Þ ½SO2 K in NO3 O2 Fe 4  2 3þ in in in SO4 ½SO4  þ K s K O2 þ ½O2  K NO3 þ ½NO3  K Fe þ ½Fe  logðQSO4 =K SO4 Þ þ 0:5

V im , maximum substrate utilization rate constant using the ith terminal electron acceptor (TEA); K is , half saturation constant for the ith TEA; K in i , inhibition constants due the ith TEA; Qi, Ki, activity product and equilibrium constant for the corresponding TEA utilizing reaction. Values in brackets stand for the molal concentration of that particular species. See Table 5 and Appendix B for values and sources of these parameters.

rihydrite and siderite appear to have detrital origin, but there are strong indications that siderite at depth is also diagenetic (Toevs et al., 2006; Winowiecki, 2002). Because the siderite dissolution rate in oxic environments has been shown to be quite slow as the result of armoring by Fe(III) hydroxides (Duckworth and Martin, 2004), siderite in the model was considered only as a potential secondary phase (i.e., initially siderite is not allowed to react,

but this mineral is allowed to form in areas where pore water becomes supersaturated with respect to this mineral). Because actual pore-water analyses (e.g., Winowiecki, 2002) show fairly strong supersaturation with respect to siderite (saturation indices often ranging from about 1 to 2 log(Q/K) units), this mineral was set to precipitate under kinetic constraints that allow a significant supersaturation (Table 3).

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Table 5 Parameter values for the kinetic constants used in the biogeochemical model Parameter

Value used in the model

Source

1 2 VO m ðs Þ NO3 V m ðs1 Þ

5 · 109 2 · 1010

1 V Fe m ðs Þ 1 4 ðs Þ V SO m O2 K s ðMÞ 3 K NO ðMÞ s 4 ðMÞ K SO s K in O2 ðMÞ K in NO3 ðMÞ K in Fe ðMÞ

3 · 1012 3 · 109 2.41 · 105 1.13 · 104 1 · 103 1.61 · 108 1 · 107 1 · 108

Estimated from Russell (1973) Estimated from Parkhurst and Appelo (1999) Calibrated Calibrated Doussan et al. (1997) Doussan et al. (1997) Brugato (1999) Doussan et al. (1997) Calibrated Calibrated

Ferrihydrite was assumed to remain at equilibrium with the pore water, and because aqueous Fe(III) was coupled to a microbially-driven reductive reaction (Table 4), the reductive dissolution of ferrihydrite was constrained by the kinetics of that reaction. Intermediary Fe(III) reductive dissolution products, such as magnetite, or more crystalline Fe(III) phases that could develop from ferrihydrite, such as goethite and hematite, are not considered in the current model. Because the amount of ferrihydrite in the sediments is quite high, this mineral likely dominates Fe(III) chemistry such that exclusion of other Fe oxides in the model is not expected to significantly affect the modeling results. Metal sulfide minerals are included in the model as secondary phases (i.e., not initially present in the modeled sediment column). Toevs et al. (2006) report that the maximum total S in the Lake Coeur d’Alene sediments is less than 0.5% by weight, whereas, pyritic materials comprise about 2–2.5% of the Fe. Although these authors report pyrite to be the main Fe sulfide mineral, others have identified amorphous Fe sulfide phases instead (e.g., Cummings et al., 2000). It has been shown that at low temperatures, pyrite forms after an ‘‘amorphous’’ Fe monosulfide precursor, now generally accepted to be poorly crystalline or nanoparticulate mackinawite (composition ranging around Fe9S8, but typically expressed as FeS) (Harmandas and Koutsoukos, 1996; Butler and Rickard, 2000; Benning et al., 2000; Wolthers et al., 2003; Rickard, 2006). Disordered mackinawite (expressed as FeSm; Rickard, 2006; Wolthers et al., 2003) is therefore selected as the Fe sulfide phase in the model. The reaction rate of this phase being quite fast (Rickard, 1995), this mineral is allowed to form at equilib-

rium. For simplicity, the conversion of FeSm to pyrite is not considered, and this not anticipated to affect the trends of results presented here, which are intended to cover short-term processes. Heavy metal sulfides allowed to precipitate in the model were galena (PbS), sphalerite (ZnS) and chalcocite (Cu2S). Heavy metal sulfides may form other sulfide phases, and possibly amorphous precursors at low temperatures. However, thermodynamic data for such phases are limited. For this reason, galena, sphalerite, and chalcocite were deemed suitable proxies for the purpose of demonstrating the effect of heavy metal sulfide precipitation at depth. Other potential secondary minerals incorporating heavy metals include trace phases identified by microXRD analyses, as discussed earlier (smithsonite, ZnCO3, dundasite, Pb, Al hydroxycarbonate, as well as other trace Pb phases). Except for smithsonite, these minerals are not yet included in the model because of lack of suitable thermodynamic and field data. Smithsonite was considered in the simulations but was never calculated to precipitate, its saturation index values remaining below about 0.6. It should be noted that Cu can occur in Cu(II) or Cu(I) forms. It has been shown that Cu in copper sulfide phases such as covellite (CuS) or bornite (Cu5FeS4) is in the cuprous form, and that Cu(I) dominates the Cu chemistry in reducing sulfidic environments, except in the presence of very strong Cu(II) organic complexes (Mountain and Seward, 2003; Thompson and Helz, 1994; Young et al., 2003). Silvester et al. (1991) have also shown that Cu(II) reacts with sulfide to form chalcocite-like Cu(I) precipitates, which serve as precursors for the crystallization of covellite upon some degree of oxidation. For this reason, and because sulfide is produced within a few cm below the surface, the current model considers that Cu in the modeled sediment pore water consists only of Cu(I), with chalcocite as the solubility-controlling phase. In doing so, the reduction of Cu(II) in lake water to Cu(I) within the first cm or so of the modeled column is considered but not explicitly modeled. The non-redox mineral precipitation reactions for metal sulfides are assumed kinetically controlled, using rate laws in terms of metal and H2S concentrations (thus indirectly incorporating pH effects) following Rickard (1995) (Table 3). Rate constants were adjusted to provide reasonable model results. In doing so, the precipitation rates for heavy metal sulfides were decreased by about one order of magnitude compared to the precipitation rate of FeSm

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given by Rickard (1995). Note that the effect of surface area and surface roughness are included within the rate constants in Table 3. In order to ensure rate reversibility (and decrease to zero at equilibrium), the rate expressions for heavy metal sulfides and siderite were multiplied by an affinity term taking a similar shape as a Monod function, but in terms of the mineral saturation index instead of concentration (Table 3). Although such a function has so far only a speculative theoretical basis, it was found to be numerically better behaved for mineral precipitation far from equilibrium than transition-statetheory (TST)-based functions (e.g., Lasaga, 1981, 1984), which theoretically apply only to dissolution close to equilibrium. Iron and Mn oxides are considered to be the major sorbing phases in Lake Coeur d’Alene sediments. Comparatively to Fe and Mn oxides, silica and Al oxides are not significantly reactive in terms of sorbing metals from solution (Paulson and Balistrieri, 1999; Tonkin et al., 2002; Balistrieri et al., 2003). Although the Mn content of sediments is high (1.5% by weight as MnO, Table A.1), it is about 10 times lower than the Fe content, and thus sorption can reasonably assumed to be controlled by Fe hydroxides alone. Therefore, ferrihydrite was modeled as the sole sorbing solid. Surface complexation was modeled using a double layer model, and surface characteristics for hydrous ferric oxide (HFO) taken from Dzombak and Morel (1990). The diffuse double layer model is implemented in PHREEQC to simulate adsorption of Pb, Cu and Zn and other ions such as H+, Ca2+, Mg2+ and SO2 4 onto HFO. The model parameters for HFO included a strong-site density of 0.005 mol/mol Fe and a weak-site density of 0.2 mol/mol Fe (Dzombak and Morel, 1990). A value of 205 m2/g was taken for the surface area of ferrihydrite (Larsen and Postma, 2001; Bonneville et al., 2004). Note that a similar surface area is calculated using BET measurements of surface area for bulk sediment samples (4.15 m2/g, Table A.3) and measured total Fe concentration (15% by weight, Table A.1) assuming 2% of the Fe being reactive. Intrinsic surface complexation constants for the surface complexation model are discussed in Appendix B. It should be noted that recent studies have shown that sorption of Fe(II) on HFO has an impact on the reactivity of HFO (Roden, 2006), and that metal sulfides could also act as sorbing phases (e.g., Jong and Parry, 2004; Wolthers et al., 2005). These processes are not included in the present simulations

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but are currently being implemented for Phase II of the modeling effort. Besides primary components, minerals, and surface complexes, the inorganic reaction network is also determined by the number and types of secondary species (ion pairs and aqueous complexes) included in the model. These are read by the numerical model from a comprehensive multicomponent thermodynamic database (minteq.v4 database distributed with PHREEQC V2.12). This database was modified to allow redox disequilibrium, and to include recent data for potentially important Fe, Zn, Cu and Zn aqueous sulfide complexes, and the solubility of siderite and FeSm (Appendix B). 3.5. Microbially mediated reactions The inorganic reaction system is coupled to a microbially mediated redox reaction network including microbial consortium biodegradation kinetics with multiple terminal electron acceptors. Thus, redox is decoupled in the multicomponent reaction network (i.e., elements with different oxidation states are treated as separate components), consistent with observations in most natural systems at low temperatures (e.g., Stefansson et al., 2005). In this way, redox disequilibrium results naturally from the microbially mediated kinetic constraints of each of these reactions. In the model, no net growth of biomass is assumed (i.e., decay = growth) and all biomass is taken to be immobile for simplicity. Required guild members (i.e., aerobes, NO3-reducers, Fe-reducers, etc.) of the consortium are presumed available wherever TEA conditions favorable to their activity occur. For the microbial consortium, acetate is used as the ultimate energy source (and electron donor). Acetate occurs as a common end product of many fermentation processes in sedimentary environments (Lovley, 2002) and also many metal and SO2 4 reducing microorganisms present in sediments can use acetate as a C and energy source (Koretsky et al., 2003). Furthermore, acetate was also detected in the Lake Coeur d’Alene pore-water samples collected from the region around Harrison Slough (Moberly et al., unpublished data). The biodegradation of acetate with concomitant reduction of terminal electron acceptors is represented by single Michaelis-Menten kinetics, where acetate is treated as non-limiting (Table 4). The potential terminal electron acceptors used in the 2 model are O2, NO 3 , Fe(III) and SO4 . The sequential

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utilization of these terminal electron acceptors is implemented by including inhibiting factors (Kin in Table 4) (Van Cappellen and Gaillard, 1996) to impede the lower Gibb’s free energy biotic redox reactions when higher Gibb’s free energy electron acceptors are available. The amount of ferrihydrite in the sediment is very high and is assumed to serve as an unlimited source of Fe(III). Therefore, rather than using a rate law for Fe(III) depending on ferrihydrite concentration (e.g., Bonneville et al., 2004), the Fe(III) reduction reaction is written in terms of aqueous Fe(III) without including a Michaelis-Menten term for aqueous Fe(III) concentrations, and this species is ‘‘linked’’ to ferrihydrite by the imposed constraint of equilibrium between this mineral and the aqueous phase. As such, the reductive dissolution of ferrihydrite is directly constrained by the rate of the biotic Fe(III) reduction reaction in Table 4. Note that potential reoxidation of ferrihydrite is not currently allowed in the model. The kinetic expressions in Table 4 also include an affinity-limiting term, such that reaction rates drop to zero at thermodynamic equilibrium. This affinity term was chosen to take the same shape as the function used for mineral precipitation (i.e., a Monod-type function in terms log (Qi/Ki), where Qi and Ki represent the activity product and equilibrium constant for the corresponding TEA utilizing reactions, respectively). The reduction of NO 3 to aqueous dinitrogen has been implemented in the presence of low O2 levels. The further degradation of dinitrogen is not currently considered. Fermentation and utilization of other electron donors using electron acceptors of lower energy than SO2 4 are not currently considered for simplicity. Values for Michaelis-Menten kinetic parameters (Vm, Ks, and Kin) for the expressions shown in Table 4 are listed on Table 5. These were taken from the literature and/or adjusted within reasonable limits to reproduce observed concentration trends (i.e., not a formal calibration). 4. Results and discussion Model results are presented in Figs. 4–8. The results are shown as computed concentration profiles as a function of sediment depth, for simulated time periods of 2 and/or 5 years. When available, recent pore-water data from sediments in the vicinity of Harrison are included on these figures. Recall that the simulated profiles represent results of a numerical experiment designed to explore whether

observed trends of pore-water composition and redox conditions with depth can be reproduced starting from oxic conditions throughout the entire modeled sediment column. In this respect, the 2 to 5 year time frame is somewhat arbitrary, and represents the time after which the computed concentration trends with depth remain more or less steady, using microbial reaction rates (Vm in Table 5) adjusted within reasonable limits. Therefore, the time scale should be viewed more as a relative dimension in time, representing short-term processes, than ‘‘true’’ (absolute) values relating to the actual duration of processes in the field. It should be noted that kinetic data that were (loosely) calibrated to the field data (Table 5) must be considered as model parameters reflecting the assumptions and uncertainties of the model and its input data. As such, these parameters are not intended for use in studies elsewhere. Also, the available pore-water data are used to compare model results in a qualitative, rather than quantitative manner, as no pretension is made that the model is ‘‘validated’’ with these data. As stated by Balistrieri (1998), during deployment and retrieval of their pore-water samples, oxidation due to exposure to air may have occurred and thus, some sample integrity may have been compromised. Also, Winowiecki (2002) experienced some difficulties in obtaining charge balances <10% with her water analyses, which was attributed to underestimating alkalinity as a result of measurements in the laboratory instead of the field. Nevertheless, when examined in a qualitative context and as a set of data points rather than individual analyses, the analytical data reported by these authors provide an excellent basis for evaluating model results. The results of the numerical experiment provide important insights on the biogeochemical processes affecting the transport of Zn, Cu and Pb in the lake sediments; in particular, the characterization of redox disequilibrium conditions in the sediments, pH trends, mobilization of Fe and heavy metals by microbial reductive dissolution of ferrihydrite and complexation with biogenic sulfide, and their scavenging from solution by precipitation of metal sulfides. Each of these particular processes is described in detail below. 4.1. Redox disequilibrium conditions Redox conditions are one of the most important factors affecting the mobility of heavy metals in ben-

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Fig. 4. Computed concentrations of aqueous redox species as a function of sediment depth. Simulations start from initially fully oxic conditions, with an initial pore-water and constant top boundary composition shown in Table 2. (a) and (b) Model results for main redox species at a simulated time of 2 and 5 years, respectively. (c)–(e) Comparison of model predictions (dashed line, 5-year results) and 2 measured concentrations (symbols) for NO 3 , SO4 and total Fe (essentially entirely Fe(II), dissolved Fe(III) concentrations remaining negligible). (f) Calculated distribution of Fe(II) aqueous species. Measured concentrations are from 1–6 Winowiecki (2002), Site A, 2001: 1,2 Summer, 3,4 Spring, 5,6 Fall; 7 Balistrieri (1998), September 2002, Delta Site; 8 Toevs et al. (2006), May 2002, Harlow Point.

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Fig. 5. Comparison of model predictions (lines, 5-a results) and measured concentrations (symbols, see caption of Fig. 4 for data sources) as a function of sediment depth. (a) pH profile calculated using a ‘‘high’’ (solid line) and ‘‘low’’ (dashed line) Fe(III) reduction rate (Vm = 3 · 1012 and 1 · 1012 s1, respectively). (b) Alkalinity profile.

Fig. 6. Computed amounts of precipitating minerals with depth (5-year results).

thic sediments. Therefore, understanding the redox disequilibrium conditions and delineating the redox boundary is crucial in determining the cycling of Fe and heavy metals. Calculated concentration profiles of main redox couples with depth (Fig. 4a and b) show that O2 (the primary oxidant in the oxic zone and present above the sediment/water interface) is almost completely consumed by aerobes within the first 2 cm of the modeled column. The next thermodynamically favorable oxidant, NO 3 , becomes the  TEA by NO 3 -reducing bacteria. The NO3 consumption is followed by a steep increase in Fe(II) concentration, resulting from the reductive dissolution of ferrihydrite by dissimilatory Fe reducing bacteria (DIRB) (Fig. 4a, b, e). The model results

and field data plotted on Fig. 4c and d show that suboxic conditions occur within about the top 5 to 10 cm of the column. Prior researchers have observed various ranges of suboxic zone at Lake Coeur d’Alene; Horowitz et al. (1993) reported it occurs between 10 and 15 cm from the sediment/ lake-water interface whereas Harrington et al. (1998) reported it occurs at the interface. The transition from suboxic to anoxic conditions concentrais characterized by a decrease in SO2 4 tion, when SO2 becomes the terminal electron 4 acceptor by SRB. The model results and field data (Fig. 4) show that this anoxic zone develops at depths below about 5–10 cm. It is interesting to notice a slight rebound of computed SO2 4 concentrations at this depth, after the initial steep decrease (Fig. 4a, b and d). According to the implemented surface complexation model (Appendix B), at pH values around neutral, some SO2 4 is computed to sorb onto ferrihydrite in addition to metals, thus affecting the availability of this TEA upon reductive dissolution and pH changes. Whether sorbed SO2 4 could actually affect the availability of this TEA at depth would depend on the relative rates of SO2 4 reduction and sedimentation. The profile overall reflects the coupling between inorganic and biotic reaction subnetworks. 4.2. Alkalinity and pH Modeled pH values with depth show an increasing trend in the suboxic zone, followed by slowly decreasing values in the anoxic zone. This trend is

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Fig. 7. Comparison of model predictions (lines) and measured concentrations (symbols, see caption of Fig. 4 for data sources) as a function of sediment depth. The straight vertical dotted lines (between 0 and 15 cm) represent the average concentrations in pore water extracted from the 15-cm-long core samples collected near Harrison (Table A.4). (a) Total Zn at 2 years (dashed line) and 5 years a (solid line). (b) Calculated distribution of Zn aqueous species at 5 a. (c) Total Pb at 2 years (dashed line) and 5 years (solid line). (d) Calculated distribution of Pb aqueous species at 5 years. (e) Total Cu at 2 years (dashed line) and 5 years (solid line). (f) Calculated distribution of Cu aqueous species.

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Fig. 8. Comparison of model predictions (lines) and measured concentrations (symbols, see caption of Fig. 4 for data sources) as a function of sediment depth. (a) Total reduced S (as S2, with measured concentrations taken as the difference between total S and SO4 analyses). (b) Calculated distribution of S2 aqueous species. The bulk of dissolved sulfide is computed to occur as metal (bi)sulfide complexes.

captured by some but not all field data (Fig. 5a), those of Balistrieri (1998) showing a similar trend, albeit higher values, and those of Winowiecki (2002) showing more similar pH values but a generally slow, continuous, decrease with depth. Note that Cummings et al. (2000) reported the pH to increase continuously from around 5.8 at the top of the sediments to around 6.4 at a depth of 35 cm (not shown). The microbially mediated breakdown of organic acids, as expressed by the reactions in Table 4, would be expected to result in a pH decrease accompanied by a bicarbonate alkalinity increase. However, in this case, the pH initially increases with depth (Fig. 5a). This is because the main pH-controlling reaction in the suboxic zone is the reductive dissolution of ferrihydrite, which drives the pH up, as in the following reaction, 8FeðOHÞ3ðsÞ þ CH3 COOH þ 14Hþ ) 8Fe2þ þ 2HCO 3 þ 20H2 O

ð1Þ

This reaction, written for a neutral pH range, can also be recast to account for the precipitation of siderite (Fig. 6) from produced HCO 3, 8FeðOHÞ3ðsÞ þ CH3 COOH þ 12Hþ ) 2FeCO3ðsÞ þ 6Fe2þ þ 20H2 O;

ð2Þ

illustrating the siderite precipitation and Fe(II) concentration increase at depth, with concomitant pH increase. The modeled reversal in the pH trend at

a depth of about 10 cm can be explained by the precipitation of Fe(II) sulfide (Fig. 6), as illustrated by the following reaction, written to encompass both the effect of ferrihydrite reductive dissolution and biogenic sulfide production, 8FeðOHÞ3ðsÞ þ 9CH3 COOH þ 8SO2 4 ) 8FeSmðsÞ þ þ 18HCO 3 þ 20H2 O þ 2H :

ð3Þ

Note that reactions (2) and (3) are written with idealized stoichiometries to illustrate their competitive effect on pH, and that actual stoichiometries and overall pH effect depend on reaction rates. This competitive effect is evidenced on Fig. 5a, where model results are shown for both a ‘‘high’’ and ‘‘low’’ rate of Fe(III) reduction, using the same rate of SO2 4 reduction in both cases. As would be expected, when the Fe(III) reduction rate is lowered, the rise in pH in the suboxic zone corresponding to reaction (2) is less pronounced and the decrease attributed to reaction (3) is more pronounced. This suggests that differences in reported pH trends could be explained by the relative activity of Fe and SO2 4 reducers in the sediments. 4.3. Mobilization of metals The suboxic and anoxic zones within the benthic sediments are of particular interest, because the mobility of Fe and heavy metals is mostly controlled by the inorganic and microbially mediated reactions occurring within these zones. As shown earlier, one

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of the controlling reactions is the reductive dissolution of ferrihydrite by DIRB, evidenced by the increase in Fe(II) concentrations with depth (Fig. 4a, b, e, f). In the modeled pH range, the majority of Fe(II) increase is in the form of dissolved Fe(II) , whereas other Fe(II) complexes in the form of Fe(Acetate)+, FeS(aq) and FeHCOþ 3 are also present, but in minor quantities (Fig. 4f). The reductive dissolution of ferrihydrite promotes the release of sorbed heavy metals into the sediment pore water. This is particularly evident at earlier simulation times (2-year curves in Fig. 7a, c and e), when Zn, Pb, and to a lesser extent Cu concentrations increase significantly with depth as the direct result of ferrihydrite reductive dissolution, before significant precipitation of heavy metal sulfides occurs. As the precipitation of metal sulfides becomes more pronounced (Fig. 6), dissolved heavy metal concentrations sharply decrease, yielding computed concentrations reasonably close to measured values (5-year curves in Fig. 7a, c and e). For the case of Cu, however, the modeled increase in concentration with depth is not consistent with the measurements of Balistrieri (1998), which show an overall decrease with depth (Fig. 7e). This could be due to increased solubility at shallow depth caused by Cu(II) organic species (e.g., Skrabal et al., 2000), which were not considered in the present model. Nevertheless, this numerical experiment clearly shows the profound effect of reductive dissolution on metal mobilization in the suboxic zone, together with sharply decreased metal concentrations at depth from the precipitation of sulfide minerals. As mentioned earlier, such a concentration drop at depth has been reported for Fe and other metals in lacustrine sediments (e.g., Huerta-Diaz et al., 1998). The model results also show that the majority of dissolved heavy metals occur in the form of (bi)sulfide complexes (Fig. 7b, d and f), and that most of the soluble sulfide consists of these soluble (bi)sulfide species, as well as FeS(aq) (Fig. 8b). This shows the potential importance of biogenic sulfide in controlling the transport of Zn, Cu and Pb in the lake sediments. Hence, once the metals are solubilized by reductive dissolution, they form strong metal (bi)sulfide complexes, which may also further enhance their desorption from remaining ferrihydrite. The formation of Fe and heavy metal (bi)sulfide complexes also significantly reduces the activity of available HS (Fig. 8b), thus directly competing for the precipitation of Fe and other sulfide miner-

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als. Based on these observations, biogenic sulfide produced by SRB’s through SO2 4 reduction appears to be an important factor controlling the solubility and transport of heavy metals in the sediments. As shown earlier, the two major sinks for Fe(II) in the system are FeSm and siderite (FeCO3) (Fig. 6). The modeling work indicates that the biogeochemical system forms a delicate balance involving the competition of FeSm and FeCO3 precipitation for Fe(II), and the competition of soluble (bi)sulfide complexes and sulfide mineral precipitation for biogenic sulfide. When heavy metals are not included in the model and the minerals FeSm and siderite are allowed to precipitate at equilibrium, all the biogenic sulfide is available for the precipitation of FeSm and formation of FeS(aq) (the dominant Fe species in this case), and the porewater remains undersaturated with respect to siderite because of lowered Fe(II) activity. However, when the heavy metals are included in the simulations, the elevated metal concentrations as dominant Zn–HS and Pb–HS complexes compete for the available sulfide, this time making the pore water undersaturated with respect to FeSm, with most of the Fe now going to form siderite if this mineral is allowed to precipitate at equilibrium. However, if siderite precipitation is then constrained using kinetics to allow some degree of supersaturation (as suggested by field data), both FeSm and siderite are predicted to precipitate (Fig. 6), consistent with the field observations. Therefore, local variations in the extent of these competing mechanisms, as well as variations in the relative rate of ferrihydrite and SO2 4 reduction may explain why Fe sulfides are reported in some areas but not in others. 4.4. Diffusive transport and benthic fluxes As discussed throughout this paper, the mobility of heavy metals results from the coupled effects of inorganic and microbially mediated reaction processes resulting in a dynamic redox environment. These processes determine whether the benthic sediments act as a source or sink of dissolved heavy metals to the overlying shorewater. The simulations show that the mobilization of heavy metals by reductive dissolution in the suboxic zone results in a positive gradient of metal concentrations from the sediments to the overlying water (positive benthic fluxes), consistent with the findings of Balistrieri (1998), Kuwabara et al. (2003) and Toevs et al. (2006). Such positive benthic fluxes are expected to

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multicomponent reactive transport model. Results show that these processes can be modeled reasonably well, despite simplifications adopted. Obviously, there is room for improvement, notably the need to consider the potentially complex Mn biogeochemisry, currently ignored on the basis of large Fe dominance. The effect of organic ligands, most notably for Cu, also needs to be investigated further. Other planned future work also includes the incorporation of transient periodic variations of metal contamination through seasonally fluctuating boundary conditions, microbial growth, and metal/ product toxicity using dose–response relationships. These improvements are being implemented as part of a larger effort to develop a biogeochemical model capable of predicting temporal and spatial distributions of microbes, as well as describing microbiallydriven toxic metal cycling in lacustrine benthic sediments. Results of the currently simplified model show that the release of heavy metals into the interstitial pore water is highly promoted by the reductive dissolution of Fe(III) (hydr)oxides by DIRB, and the formation of strong (bi)sulfide complexes from the biogenic reduction of SO2 4 by SRB. As the precipitation of heavy metal sulfides significantly occurs, then dissolved metal concentrations sharply decrease at depth, resulting in computed concentrations reasonably close to measured values. The relative rate of Fe(III) reduction versus SO2 4 reduction

fluctuate with seasonal variations in lake water composition (e.g., Fig. 2), and to possibly reflect electrophoretic (charge balance) fluxes not considered here. Using the results of the present model, thus assuming the water composition above the sediments to remain constant (Table 2), benthic fluxes are calculated to be around 30, 3.2 and 0.003 lg/ cm2/yr, for Zn, Pb and Cu, respectively. These values are congruent with the benthic flux calculations by Balistrieri (1998) for the Delta site near Harrison (Fig. 1) and with the data of Kuwabara et al. (2000) for the main channel along Lake Coeur d’Alene, located 7 km down gradient of the mouth of Coeur d’Alene River. 5. Conclusions The sediments of Lake Coeur d’Alene, because of their elevated Fe(III) (hydr)oxide content, as well as elevated metal contamination from decades of runoff from adjacent mining areas, provide a unique environment for the study of metal mobilization resulting from microbial Fe(III) reductive dissolution coupled with inorganic reactions including especially metal–biogenic sulfide interactions. Previous studies have provided a wealth of field data suggesting that these biogeochemical processes play an important role in the transport of metals at Lake Coeur d’Alene. The present study is a first attempt to quantify these processes using a comprehensive

Mass of particles retained/mass of total sediment (%)

70

60

50 0-5 cm 40

5-10 cm 10-15 cm

30

20

10

0 1168

590

250

150

105

75

Below 75

Particle size (um)

Fig. A1. Particle size distribution in sediment cores with depth below Coeur d’Alene sediment/water interface.

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is observed to be a key factor controlling pH, as well as the relative amounts and types of Fe(II) mineral precipitation at depth. Numerical experiments also indicate that a delicate balance exists between the effects of FeSm and FeCO3 precipitation, which compete for Fe(II), and the mobilization of heavy metals as (bi)sulfide complexes, which competes with FeSm precipitation for biogenic sulfide. This

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balance is likely to be easily offset by periodic increases in toxicity (endured by microbes) associated with transient appearances of high concentrations of aqueous metals in the water column due to runoff events. However, preliminary investigation of recovery from such events is led by SRB (e.g., Sani et al., 2001, 2003) and suggests that consortium rebound occurs on timescales short with respect to

Table A.1 Major elements and trace metals in benthic sediments of Coeur d’Alene characterized using XRF collected from sediments near Harrison Slough on April 2005 Normalized major elements (weight %) Depth: SiO2 TiO2 Al2O3 FeO MnO MgO CaO Na2O K2O P2O5 Total

Unnormalized trace elements (ppm)

0–5 cm

5–10 cm

10–15 cm

70.51 0.441 7.67 15.80 1.564 0.98 0.42 0.55 1.96 0.100

70.99 0.434 7.32 15.72 1.665 0.97 0.41 0.51 1.90 0.095

71.56 0.418 6.81 15.80 1.665 0.93 0.44 0.43 1.83 0.103

100.00

100.00

100.00

Depth:

0–5 cm

5–10 cm

10–15 cm

Pb Zn Cu Cr Ni Sc V Ba Rb Sr

4372 5826 112 30 18 6 35 707 77 32

4357 6102 113 26 20 6 32 713 74 29

4265 5974 111 27 19 5 32 657 71 27

Zr

273

265

241

Table A.2 Comparison of element concentrations (mmol/kg) in benthic sediments of Coeur d’Alene collected from Harlow Point by Toevs et al. (2006) and Winowiecki (2002) Sample

Depth (cm)

As

Cd

Fe

Mn

Pb

S

Zn

a

0–3 3–6 6–12 12–18 18–24 24–30 5 10 15 20 5 10 15 20 5 10 15 20 5 10 15

1.90 ± 0.42 1.76 ± 0.45 1.67 ± 0.15 1.89 ± 0.28 2.11 ± 0.24 3.04 ± 1.58 1.76 1.74 1.74 2.03 2.15 1.76 1.78 1.91 1.95 1.78 1.88 2.00 1.92 1.82 1.92

0.206 ± 0.00 0.200 ± 0.00 0.223 ± 0.00 0.247 ± 0.02 0.315 ± 0.05 0.293 ± 0.03 0.256 0.259 0.260 0.290 0.258 0.262 0.274 0.278 0.246 0.255 0.267 0.274 0.255 0.258 0.264

1377 ± 38.61 1420 ± 101.34 1444 ± 39.25 1368 ± 41.79 1343 ± 108.84 1529 ± 411.86 1363 1266 1332 1472 1379 1273 1355 1398 1334 1296 1361 1418 1303 1334 1348

98.95 ± 11.32 104.29 ± 9.81 114.46 ± 2.09 96.67 ± 13.39 93.70 ± 21.98 124.67 ± 47.48 120.62 106.72 125.17 140.95 121.66 106.22 125.52 130.05 117.31 112.63 126.77 127.52 111.79 117.47 122.83

20.96 ± 1.67 21.49 ± 2.38 24.01 ± 1.54 22.35 ± 0.89 19.57 ± 2.45 18.76 ± 5.13 24.48 23.02 24.47 27.86 24.36 22.87 24.06 26.69 23.64 23.27 26.08 25.89 23.08 24.12 25.82

95.12 ± 17.0 109.04 ± 15.67 135.92 ± 2.98 135.32 ± 6.66 125.71 ± 12.60 140.87 ± 60.26 – – – – – – – – – – – – – – –

51.71 ± 2.65 51.85 ± 2.61 56.05 ± 1.18 56.05 ± 2.23 52.59 ± 3.53 49.88 ±10.82 57.20 55.51 56.40 61.98 57.04 55.80 58.77 59.95 55.41 55.15 57.93 59.37 55.56 57.58 57.84

a a a a a

H1–1b H1–2b H1–3b H1–4b H2–1b H2–2b H2–3b H2–4b H3–1b H3–2b H3–3b H3–4b H4–1b H4–2b H4–3b a b

Samples collected from Harlow Point by Toevs et al. (2006) in May 2002. Samples collected from Harlow Point and reported by Winowiecki (2002) in Spring 2001. Four different sediment cores were reported.

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annual cycles in pore water and/or sediment chemistry. Finally, the present study illustrates the complexity of biogeochemical processes affecting benthic sediments, and the challenges involved in quantification of these processes. A full understanding of the modeled systems cannot be achieved without accurate biogeochemical characterization, often at a very small scale. One clear conclusion from this effort is that such systems cannot be modeled assuming thermodynamic equilibrium, and that the modeling needs to be closely integrated with interdisciplinary laboratory and field investigations. Acknowledgements We gratefully acknowledge James Moberly for helping in collecting and analyzing our samples. We thank Gordon Toevs and Matthew Morra for

Table A.3 BET surface area and pore size distribution in sediment cores with depth below Coeur d’Alene sediment/water interface BET surface area: 4.15 m2/g Pore size range (nm)

%

Under 6 6–8 8–10 10–12 12–16 16–20 20–80 Over 80

12.08 5.63 4.64 4.61 6.01 6.22 32.47 28.33

kindly sharing their data with us. The analytical data from Leigh Winowiecki’s M.S. thesis also proved extremely valuable for this work. We are also grateful to Carl Steefel for thoughtful discussions and internal review comments, and John Apps for his help with siderite thermodynamic data.

Table A.4 Analysis of Lake Coeur d’Alene River and pore-water data on the core samples and comparison with reported data by Tonkin et al. (2002), Balistrieri et al. (2003) and Winowiecki (2002) Composition of Coeur d’Alene River and pore water April 2005 Coeur d’Alene Species

Units

River

Pore water

Temperature pH Alkalinity

C

5.5 7.2 19

–

1.6 * 0.69 ND ND 5.6 * 0.018 0.00081 5.5 * * 0.00075 0.03 0.0032 2.1 0.032 0.00059 * * 0.16

170 1.7 2.8 0.81 6.8 59 0.0088 0.16 0.018 25 0.0087 0.023 0.0025 * 0.13 7.3 27 0.013 5.2 0.0022 2.7

TOC F Cl NO2–N NO3–N SO4 As Ba Cd Ca Cr Co Cu Fe Pb Mg Mn Ni K V Zn

mg CaCO3/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L

*Below method detection limit. –Not reported.

6.4 57

September 1999

November 2000

Summer 2001

Tonkin et al. LCdA River

Balistrieri et al. LCdA River

Winowiecki Site A porewater at depth 5 cm

22.1 7.36 1.4

– 7.21 0.54

– 7.1 1.3797

– 0.1 3 – 0.9 24 0.00078 0.067 0.0089 21 0.00012 0.00011 0.015 0.2 0.012 6.3 0.00003 0.00085 1.29 – 1.07

0.7 – 18.081 – – 19.213 0.00045 0.02884 0.00236 10.020 – 0.00018 – 0.01117 0.00104 3.646 0.10988 0.00053 21.113 – 0.458

– – 0.702 – 0.0 0.462 0.0212 – 0.0 28.04 – – – 36.12 0.1537 9.516 13.89 – 2.733 – 0.4292

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Table B.1 Equilibrium constants (log(K)) for minerals, aqueous sulfide metal complexes, and Cu+ surface complexes used in this study Reaction

Log(K) 25 C

Source

Siderite FeCO3ðsÞ ¼ Fe2þ þ CO2 3 Ferrihydrite Fe(OH)3(s) + 3H+ = Fe3+ + 3H2O 2+  FeSm FeS + H+ = Fe + HS Chalcocite Cu2S(s) + H+ = 2Cu+ + HS Sphalerite ZnS(s) + H+ = Zn2+ + HS Galena PbS(s) + H+ = Pb2+ + HS Fe2+ + HS = FeS0 + H+ Cuþ þ 2HS ¼ CuðHSÞ 2 þ 2Cuþ þ 3HS ¼ Cu2 SðHSÞ2 2 þH +  0 Cu + HS = Cu(HS) þ Zn2þ þ 3HS ¼ ZnSðHSÞ2 2 þH 2  2þ Zn þ 4HS ¼ ZnðHSÞ4 Zn2+ + 2HS = ZnS(HS) + H+ Pb2+ + 2HS = Pb(HS)2 Pb2þ þ 3HS ¼ PbðHSÞ 3 HFO_sOH + Cu+ = Hfo_sOCu + H+ (strong sites) + HFO_wOH + Cu = Hfo_wOCu + H+ (weak sites)

10.59 3.191 3.5 34.92 11.45 13.97 2.2 17.3 29.87 13.0 6.12 14.64 6.81 14.71 16.01 2.21 5.44

Preis and Gamsja¨ger (2002) NIST46.4 and NIST13.1a,b Rickard (2006)c NIST46.4a,d Daskalakis and Helz (1993)a Dyrssen and Kremling (1990)a,e Rickard (2006) Mountain and Seward (2003) Mountain and Seward (1999) Mountain and Seward (1999) Daskalakis and Helz (1993)f Daskalakis and Helz (1993)f Daskalakis and Helz (1993)f g g

Calculatedh Calculatedh

Data for all other aqueous species and surface complexes were taken from the database minteq.v4.dat 85 2005-02-02 (see text). a Values in PREEQC V2.12 database minteq.v4.dat 85 2005-02-02. NIST stands for National Institute of Standards and Technology (with database number) (http://www.nist.gov/). b Within the range of Majzlan et al. (2004) who report values from 3 ± 0.5 to 3.4 ± 0.5 for 6-line ferrihydrite and 3.4 ± 0.5 to 4 ± 0.5 for 2-line ferrihydrite. c Disordered mackinawite (Wolthers et al., 2003). d Consistent with the values of reported by Dyrssen and Kremling (1990) (34.65), and Mountain and Seward (1999) (34.62). e In the range of literature data reviewed by Gallon et al. (2004), which suggest this value could be more representative of amorphous PbS than galena. f 3-complex model, replacing the 5-complex model by the same authors in minteq.v4.dat database. g Sverjensky et al. (1997), after Giordano and Barnes (1979). h Intrinsic sorption constants calculated using an estimated 1st hydrolysis constant for Cu+ determined from its ionization potential (using the method of Chang et al., 2003), and a correlation between 1st hydrolysis constants and intrinsic sorption constants (hydrous ferric oxide, Dzombak and Morel, 1990) for other metal ions from the minteq.v4 database.

Thoughtful comments by two anonymous reviewers are also acknowledged. This work has been supported by National Science Foundation, NSF, under Grant No. 0420374, ‘‘Metal toxicity and Microbial Consortia: Response to Acid-Mine Drainage at Lake Coeur d’Alene, Idaho’’ and by the University of California Toxic Substances Research and Training Program. Appendix A. Sediment and water chemistry of Coeur d’Alene benthic sediments See Fig. A1 and Tables A.1–A.4. Appendix B. Thermodynamic data For the most part, thermodynamic data were taken from the database minteq.v4.dat 85 2005-0202 distributed with PHREEQC V2.12, with some exceptions as noted in Table B.1. This database

was originally developed for MINTEQA2 (US EPA, 1999; Gustafsson, 2004). A complete evaluation of the sources of these data was beyond the scope of this study. Nevertheless, data for a few critical components were reviewed and updated when deemed most appropriate. Note that in the simulations presented in this paper, heavy metal sulfides were significantly supersaturated but their precipitation was kinetically constrained, such that uncertainties on log(K) values for these minerals did not affect the model results. References Anschutz, A.J., Penn, L.R., 2006. Reduction of crystalline iron(III) oxyhydroxides using hydroquinone: influence of phase and particle size. Geochem. Trans. 6, 60–66. Balistrieri, L.S., 1998. Preliminary estimates of benthic fluxes of dissolved metals in Coeur d’Alene Lake, Idaho 98-793. US Geological Survey, Seattle, WA. Balistrieri, L.S., Box, S.E., Bookstrom, A.A., Ikramuddin, M., 1999. Assessing the influence of reacting pyrite and carbonate

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