A Case Study of Cu–Al–DOM Interactions - ACS Publications

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Integrating Empirically Dissolved Organic Matter Quality for WHAM VI using the DOM Optical Properties: A Case Study of Cu−Al−DOM Interactions Anthony Chappaz*,† and P. Jeff Curtis‡ †

Institute for Great Lakes Research, Department of Chemistry, Department of Earth and Atmospheric Sciences, Central Michigan University, Mount Pleasant, Michigan, United States ‡ Department of Earth and Environmental Sciences, The University of British Columbia Okanagan, Kelowna, BC, Canada S Supporting Information *

ABSTRACT: Metal speciation is important for understanding the toxicity of metals in aquatic systems, and can be predicted for mixtures of metals in presence of dissolved organic matter (DOM) with thermodynamic models such as WHAM VI. The influence of the DOM source (quality) has been demonstrated, but is presently neglected in predicting Cu activity (WHAM VI). Here we determined the effect of aluminum (Al) competition on copper (Cu) complexation for four different DOMs, from a high-colored DOM (more humic) to a low-colored DOM (less humic). In presence of Al, free Cu activities (defined as free ion activity) increased, consistent with competition between Cu and Al for the same binding sites on all DOM. The apparent competition decreased with increasing DOM color. Equilibrium modeling of Cu speciation with WHAM VI explained 49% of the variance in measured Cu activity. When modified to integrate DOM quality using a new empirical coefficient F related to DOM optical properties, Cu activities predicted from WHAM VI were significantly improved to about 80% of the observed variance explained. The effects of Al on Cu activity were well predicted by WHAM VI. Subsequently, we compared the relative effects of DOM concentration, DOM quality, and Al competition with other determinants of Cu activity represented in legislation and scientific literature (pH and hardness), and qualitatively ranked them by their influence on Cu activity for normal ranges encountered in fresh waters using WHAM VI. Our experimental results indicate that DOM quality is an important variable that should be included in predictive models of ion speciation (WHAM VI) and eco-toxicological models such as the biotic ligand model (BLM).

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INTRODUCTION Dissolved organic matter (DOM, measured as C mg/L) is composed of a heterogeneous mixture of organic molecules, ranging in size from macromolecules (e.g., proteins, humic substances) to smaller molecules (e.g., lipids, amino acids).1 pH and ionic strength can significantly modify the physical and chemical properties of DOM, and therefore the complexation capacity and affinity for metals.2 The more abundant functional groups in DOM include carboxyl and phenolic (weak metal binding), however less abundant functional groups containing nitrogen and sulfur form strong metal bonds.3,4 Qualitative properties of DOM in aquatic systems depend on source materials for DOM, and transformation and fractionation that might occur over time.5 The range in source composition extends from high-colored DOM (more humic, imported from the watershed including wetlands) to lowcolored DOM (less humic, produced within aquatic systems). High-colored DOM is considered to be enriched in aromatic humic and fulvic substances. Low-colored DOM is commonly composed of aliphatic compounds enriched in carbohydrate and nitrogen content.2 © 2013 American Chemical Society

DOM concentration and qualitative properties can evolve over time and along hydrologic flow paths from high-color to low-color by degradation, transformation, and fractionation processes, and by aquatic production.6,7 Such evolution is correlated with hydrologic residence time (HRT),7−9 with short HRT being associated with high-colored DOM whereas low-colored DOM being found in aquatic systems with higher HRT (>5 years). Linking DOM quality to hydrologic residence time enables extrapolation of DOM quality to lakes using climatic and watershed data as suggested by Mueller et al.10 DOM can form complexes with some metals, including Cu and Al, and thus reduce their bioavailability and toxicity, and affect their fate and transport in aquatic systems.11,12 Chemical speciation of copper (Cu) in aquatic systems and the importance of DOM interactions have been well demonstrated.13−23 Free Cu2+ is an essential micronutrient but can be Received: Revised: Accepted: Published: 2001

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acutely toxic at higher concentrations approaching 2 × 10−8 M.24 Recent studies have shown that Cu(II) can form chelate ring structures with DOM involving carboxyl functional groups.25−27 Moreover, it has been established that DOM reduces free Cu ion (Cu2+) activity, and that high-colored DOM binds Cu more efficiently that does low-colored DOM.28−31 Similar to copper, binding of aluminum (Al) with DOM leads to the decrease of free Al activity.32,33 Indeed, Al complexation by DOM can significantly inhibit the binding of other metals such as lead and cadmium.34,35 To our knowledge, competition between Al and Cu for DOM binding sites on DOM has only been shown theoretically using the thermodynamic model WHAM VI36 with fixed DOM quality. WHAM VI is a discrete site/electrostatic model that simulates the interactions of metals with humic substances using sites presenting two types of affinities for cations (types A and B) and three types of binding capacities (monodentate, bidentate, and tridentate).37−39 It has been extensively used to predict speciation of metals in presence of DOM in diverse aquatic systems. Simulations predict that Al(III) competes significantly with Cu for DOM over a wide pH range (4−9), especially at very low Cu concentrations (∼ 1 nM). Neither WHAM VI nor the new WHAM VII have the capability to represent the influence of different types of DOM on metal speciation (WHAM, www.ceh.ac.uk). We undertook this series of experiments to address three important questions of DOM−Cu−Al interactions that could determine free Cu activity. First, is apparent Al−Cu competition independent of DOM quality? Second, by using WHAM VI, can we predict free Cu activities similar to the ones measured in our experiments? Finally, can prediction of free Cu activity be improved by using qualitative properties of DOM through an empirical approach (e.g., specific absorption coefficient, specific UV absorbance, and fluorescence index) to account for differences in apparent Al−Cu competition, and integrated to WHAM VI?

and Unnamed Creek) to a low-colored DOM (Wood Lake), by sampling sites having different HRT from days to decades from within a watershed, as done previously by Luider et al.7 Approximately 20 L (using polyethylene containers) was collected at each site, the same day, and transported in darkness to the nearby lab where it was pumped through 144mm diameter glass fiber filters (nominal pore size 1 μm; Geotech Environmental Equipment, Denver, CO) using a peristaltic pump with silicon tubing, within the next two days following the sampling. DOM was quantified as dissolved organic carbon (DOC) using a Shimadzu TOC-5050A (Tokyo, Japan). DOM was characterized by the three following optical properties: (a) specific absorption coefficient (SAC340), (b) specific UV absorbance (SUVA254), and (c) fluorescence index (FI). We calculated SAC340 using the following equation:40,41 SAC340 =

MATERIALS AND METHODS Sampling and DOM Characterization. Four different surface water samples were collected near Kelowna (British Columbia, Canada) in July 2007 from sites along a hydrologic flowpath (Unnamed Creek, Swalwell Lake, Duck Lake, and Wood Lake; Table 1). The aim was to collect a range of DOM spanning a gradient from a high-colored DOM (Swalwell Lake

Table 1. Characteristics of DOM Sourcesa DOM source

location

Unnamed Creek

50°02′ W 50°03′ N 119°14′ W 119°14′ W HRT (year) ≤1 week 0.1 diluted DOM solution (∼10 mg C/L) SAC340 22.0 13.0 SUVA254 3.69 2.80 FI 1.40 1.45

Duck Lake

Wood Lake

50°00′ N 119°24′ W 0.5

50°05′ N 119°23′ W 20

9.9 2.49 1.46

4.3 1.63 1.50

[DOM] 1000 cm 3

(1)

where Abs340 represents the absorbance of the DOM samples, measured on a Cary 50 spectrophotometer using paired quartz cuvettes (1 cm width) at 340 nm (SD for SAC340 < 0.2). − SUVA254 (L mg 1 m−1) was calculated by dividing the absorbance of a sample at 254 nm per meter of path length by DOC concentration in C mg/L42 (SD for SUVA254 < 0.02). Finally, we calculated the DOM fluorescence index (FI = the dimensionless ratio of fluorescence 450 nm/fluorescence 500 nm using an excitation wavelength of 340 nm43). Fluorescence measurements were made on filtered and diluted DOM samples of less than 0.3 absorbance units (254 nm). Further, the fluorescence index was corrected for inner filter effects by the method described by Ohno.44 Fluorescence measurements were made on samples that were further diluted to minimize absorbance of fluorescence.43 All measurements of fluorescence were made using a Shimadzu 1555 Spectrofluorimeter (Tokyo, Japan) (SD for FI < 0.02). Standardization of DOM. Filtered water from each site was concentrated using a stainless steel portable reverse osmosis unit (Limnological Research Corporation, Kelowna, BC) with a molecular weight cutoff of about 400 Da (FilmTec FT30 US Filters thin composite RO membrane, Minneapolis, MN). Once concentrated to approximately 500 mL, DOM was treated with a H+-cation exchange resin (Amberlite IR-118H; Sigma) to a final pH ≤ 2 to remove metals. The quality of DOM may have been affected by short-term acidification (precipitation of less soluble organic acids and loss of lowmolecular-weight organic acids) according to Serkiz and Perdue,45 and Sun et al.46 This method has been applied in many DOM studies29,41 and is similar to the techniques used to produce standard reference DOM (e.g., Suwanee River NOM). Standardization changed optical properties on average by 9.0, 4.5, and 0.5% for SAC340, SUVA254, and FI, respectively. Experimental Protocol. All glassware was cleaned and rinsed thoroughly with ultrapure water (≥18 MΩ·cm). For DOM from each site, subsamples were diluted to concentrations of 2, 4, 6, 8, and 10 mg/L as dissolved organic carbon. The subsamples were adjusted to pH 6.5 with NaOH and HNO3; ionic strength was adjusted to I = 0.01 N with KNO3; and total Cu concentration was adjusted to 2 μM with CuCl2. For each DOM source, aliquots (100 mL) of each subsample were amended with different amounts of Al (as Al(NO3)3) to

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Swalwell Lake

(2.303 × Abs340) (pathlength)

The absorbance and fluorescence measurements were run on filtered and diluted DOM solutions. Fluorescence measurements were made on samples that were further diluted to minimize absorbance of fluorescence.43,44 a

2002

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DOM combined with some recruitment of aquatic DOM over time. DOM−Cu−Al Interactions. The combined treatments reduced free Cu activities from a total of 2 μM to levels between 0.5 and 0.01 μM (Figure 1). Variability in measured

produce the following total Al concentrations: 0, 0.1, 0.5, 1.0, and 5.0 μM. Experimental solutions were under-saturated for Cu and Al minerals. These samples were equilibrated for 24 h before measuring free copper concentration. Twenty-four hours was chosen because the equilibration of Cu and Al complexation by different DOM are not known, but likely much less than 24h,47 and so that the time required to do the measurements (∼ 2 h) was a small portion of the total equilibration time so that differences in total equilibration time (24−26 h) were small. Free Copper Activities Measurements with Ion Selective Electrode (ISE). Measurements of free copper activities were made with an Orion solid state ion selective electrode (ISE) and a Ag/AgCl double junction reference electrode. The Cu electrode was prepared according to Avdeef et al.48 Each day, before measurement, the electrode was polished to optimize the electrode and minimize carry-over of contaminants, and rinsed with ultrapure water. The outer solution of the reference electrode was replaced daily, and the inner solution was replaced weekly. Free copper activities were determined directly from the millivolt response. A calibration check was performed each day. Calibration slopes were always within 1% of the Nernstian value. According to the manufacturer (Orion), Al(III) interferences are negligible. See Supporting Information for more details on the Cu measurements, specifically Figure S1 and Table S1. WHAM VI Calculations. We used WHAM VI to compare free Cu activities for all samples. As inputs, we introduced the total Cu concentration (2 μM), DOM concentrations (2, 4, 6, 8, and 10 mg C/L), Al concentrations (0, 0.1, 0.5, 1.0, and 5.0 μM), K+ (0.01 M), Na+ (0.01 M), NO3− (0.01 M), and pH (6.5). Cl− was not introduced in WHAM VI because its concentration, as CuCl2, (4 μM) is several orders of magnitude lower than our electrolyte concentration (KNO3, 0.01 M). We used the ratio 9:1 for the split between humic and fulvic acids, respectively. DOM Quality Factor: F. To represent DOM quality when using WHAM VI, we introduced a new empirical coefficient, F, which allows us to adjust the DOM concentration to account for differences in binding site density but not site affinity on DOM (eq 2). The same approach has already been successfully applied in other studies.29 DOMWHAM = DOM measured × F

Figure 1. Frequency distribution of the free Cu measurements. Each bar represents the sum of the samples for all DOM sources for a 0.5 × 10−7 M of free Cu range. Each sample represents a specific condition of DOM sources (Swalwell, Unnamed, Duck, and Wood), DOM concentration (≈ 2, 4, 6, 8, and 10 mg C/L), and Al concentrations (0, 0.1, 0.5, 1, and 5 μM). Each color is associated with a DOM source: Swalwell (black), Unnamed (red), Duck (blue), and Wood (gray).

Cu activity depended on DOM origin (ANOVA P < 0.05). Lowest concentrations were measured for high-colored DOM (Swalwell Lake) and highest concentrations were for lowcolored DOM (Wood Lake, Figure 1). These results support previous studies28−31 showing that Cu is more efficiently complexed by high-colored DOM than low-colored DOM. Al increased the free Cu activities for all conditions of DOM concentrations and sources tested (Figure 2). Added Al increased free Cu by factors of 1.2 to 2.1 over levels measured in samples with no added Al, consistent with competition between Cu and Al for DOM binding sites. The effect of added aluminum decreased with increasing DOM concentration for all of the DOM (Figure 2), consistent with a higher number of binding sites available to complex both Al and Cu. Interestingly, the displacement of Cu by added aluminum depended strongly on DOM quality, with high-colored DOM (Swalwell Lake and Unnamed Creek) showing less displacement of Cu by Al than the low-colored DOM (ANOVA P < 0.05). The effect of increasing Al concentration, for DOM of different origin, was less significant for Duck Lake DOM and nonsignificant for Wood Lake (ANOVA, P < 0.2 and, P < 0.6, respectively). Modeled Cu using WHAM VI. Predicted free Cu activity in all DOM using WHAM VI explained 49% of the total variability in measurements of free Cu by ISE (Figure 3a). Specifically, for high-colored DOM from low HRT sources (Swalwell Lake, Unnamed Creek, and Duck Lake), WHAM VI consistently overestimated free Cu activity by factors between 1.5 and 2.9; while it underestimated free Cu activity by a factor of 1.3 for the low-colored DOM long HRT (Wood Lake). These observations are consistent with transformation, fractionation, and recruitment to less colored/less metal

(2)

where DOMWHAM and DOMmeasured are DOM concentrations in mg/L used for WHAM simulation and measured, respectively, and F is dimensionless.

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RESULTS Optical Properties of DOM. HRT ranged from less than 1 week (Unnamed Creek) to 20 years (Wood Lake) (Table 1). The specific absorbance (SAC340) and the specific UV absorbance (SUVA254) decreased significantly with increasing hydraulic residence time (HRT); see Figure S2 in the Supporting Information. In contrast, FI increased slightly from 1.40 to 1.50 as HRT increased, indicating a slight tendency toward low-colored and less humic DOM (Figure S2). However, FI values are close to 1.4 and suggest that DOM sources are predominantly terrestrial sources.43 Taken together, these data indicate that the sources of DOM are mostly highcolored and humic, with qualitative differences in NOM optical properties consistent with transformation and fractionation of 2003

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Figure 2. Influence of the DOM and Al concentrations. Data points correspond to different experimental conditions of DOM and Al concentrations (the five points of the same color at a specific DOM concentration represent different Al concentrations and the arrow indicates an increasing Al concentration). Each color is associated with a DOM source: Swalwell (black), Unnamed (red), Duck (blue), and Wood (gray).

reactive DOM over time. Mechanisms responsible for these changes cannot be inferred from our study (or any published data) but this is certainly an important area for future research. To account for qualitative differences in DOM, we calculated an “optimized” F iteratively by trial and error to obtain the best correlation (slope of the linear regression = 1.0; Figure 3b) between predicted and measured free Cu activities using WHAM VI (“optimized” F = 1.8, 1.5, 1.3, and 0.8 for Swalwell Lake, Unnamed Creek, Duck Lake, and Wood Lake, respectively, with r2 between 0.75 and 0.82). We adjusted F until it no longer improved our model with a ± 5% confidence interval (0.95 < slope of the linear regression < 1.05). For all treatments, using WHAM VI with “optimized” F values explained 85% of the variability in free Cu measurements (Figure 3b). Optimized values of F were strongly linearly correlated (Figure 4) with the specific absorption coefficient (SAC340), specific UV absorbance (SUVA254), and fluorescence index (FI) of the DOM (eq 3, r2 = 0.93; eq 4, r2 = 0.98; eq 5, r2 = 0.95). F = 0.055 × SAC340 + 0.677

(3)

F = 0.488 × SUVA 254 + 0.055

(4)

F = − 9.95 × FI + 15.80

(5)

Figure 3. (a) Relationship between free Cu activities calculated with WHAM (VI) and free Cu activities measured (p < 0.0001). (b) Relationship between free Cu activities calculated with WHAM (VI) using a factor F for each DOM to optimize predicted and measured free Cu activities (p < 0.0001). (c) Relationship between free Cu activities calculated with WHAM (VI) using model F and measured free Cu activities (p < 0.0001). Each color is associated with a DOM source: Swalwell (black), Unnamed (red), Duck (blue), and Wood (gray). Data points with the same color represent different DOM and Al concentrations tested with a same DOM source. The dashed line represents the 1:1 line.

By applying eq 3, 4, or 5, we determined almost identical F values for each DOM (Table 2) that were quite similar to the “optimized” F values. These new F values, calculated via eq 3, 4, or 5, were used to modify the DOM concentrations (eq 2), and we found a high correlation between the predicted free Cu activities and the measured free Cu activities that explained 80% of free Cu variability (Figure 3c). WHAM VI with the DOM quality factor (F) explained significantly more variance than did WHAM VI without F (F = 4.18, p = 0.042). Equations 2, 3, 4, and 5 proposed within this study should be used only for freshwater systems with circumneutral pH. This new approach is an empirical model (a “patch” for WHAM based on observations and experiments), and not a rigorous test of

involved mechanisms in Cu−DOM complexation (mechanistic model).

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DISCUSSION Importance of DOM Quality for Al−Cu Competition. DOM quality in surface waters can vary independently of Cu 2004

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and Al. For example, surface waters having any DOM quality can be contaminated with leachates or effluents from mining, industry, or municipal wastewater.49,50 High levels of Al can cooccur with Cu in mining wastewaters where Cu is associated with alumino-silicate host rock, and especially where mine tailings leachate is acidic.51 Where such contaminated leachates are diluted in surface waters, strong competition for DOM binding sites can occur. The competition will depend on the quality and quantity of the DOM. Here it is important to remember than Cu2+ LC50 levels as low as 2 × 10−8 M24 are harmful for many aquatic species, and is within our Cu2+ range chosen for our experiments (1−50 × 10−8 M). To illustrate the influence of differences in DOM quality but with the same DOM quantity ([DOM] ∼8 mg C/L), free Cu activities for Swalwell Lake differed from Wood Lake by a factor of 4.5 (1.9 × 10−8 M and 9.1 × 10−8 M for Swalwell Lake and Wood Lake, respectively) for different DOM quality ([Cu]total = 2 μM and [Al] = 5 μM). Such disparity for the same metals and DOM concentrations but different DOM sources is much too large to ignore. Differences in DOM quality can be accounted for by experimentally estimating the F factor to adjust the DOM binding site density in WHAM VI (eq 2). Alternatively, F can be estimated from any of SAC340, SUVA254, or FI (eqs 3, 4, and 5). For individuals and populations of lakes, optical properties including SAC340 can be predicted from watershed properties, climate, and lake morphometry.10 In our case, Cu activity was predicted equally well from F calculated individually for each DOM as from F predicted from the empirical relationship between F and SAC340. Environmental Implications and Further Developments. To predict and manage Cu activity and potential toxicity in aquatic systems, variables that interact with Cu have to be assessed. From our study, the relative importance (i.e., a qualitative ranking) of the variables we tested on the Cu activity was DOM concentration, DOM quality, followed by Al concentration. Increasing DOM concentration from 2 to 10 mg C/L decreased the Cu activity by factors between 7.4 and 16.3 for the different DOMs. At constant DOM concentration, increasing the SAC340 from 4.3 to 22.0 decreased the Cu activity by factors between 3.4 and 9.4. For constant DOM concentration and DOM quality, increasing Al concentration from 0 to 5 μM increased the Cu activity by factors between 1.2 and 2.5. pH and hardness have also been shown to affect Cu activity and potential toxicity.30,52−56 While pH increase (1 unit) has been shown to significantly decrease the Cu activity by an order of magnitude, the effect of hardness change on Cu activity is small but toxicity can be reduced by competition between Cu and Ca at membrane surfaces (Cu2+ LC50 varying by factors between 1 and 1.8). To contextualize the effect of pH and hardness with Al, DOM quality and quantity into relative rank, we used WHAM VI to predict Cu activity for our samples including a range of pH and hardness typical of Canadian freshwaters.57 For our Alfree experimental solutions, we simulated conditions of pH and hardness (expressed as CaCO3 in mg/L) given by Gandhi et al.57 Increasing pH concentration from 5.3 to 8.5 decreased the Cu activity by factors between 70 and 7840 for the different DOMs. For constant pH, DOM concentration, and DOM quality, increasing hardness from 4.7 to 378.3 mg/L of CaCO3 increased the Cu activity by factors between 2.9 and 9.8 for lower DOM concentrations (2 and 4 mg C/L) and decreased

Figure 4. Empirical relationship between SAC340, SUVA254, FI, and the optimal F coefficient for each DOM source (p = 0.049). Each data point (color) is associated with a DOM source: Swalwell (black), Unnamed (red), Duck (blue), and Wood (gray). The solids thin lines represent a 95% confidence interval.

Table 2. F Values Calculated from Equations 3, 4, and 5

Swalwell Unnamed Duck Wood

F calculated via eq 3

F calculated via eq 4

F calculated via eq 5

1.8 1.4 1.2 0.9

1.9 1.4 1.3 0.9

1.9 1.4 1.3 0.9

average and standard deviation 1.9 1.4 1.3 0.9

± ± ± ±

0.1 0.0 0.1 0.0

2005

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(5) Jaffé, R.; McKnight, D.; Maie, N.; Cory, R.; McDowell, W. H; Campbell, J. L. Spatial and temporal variations in DOM composition in ecosystems: The importance of long-term monitoring of optical properties. J. Geophys. Res. 2008, 113, G04032. (6) Moran, M. A.; Sheldon, W. M., Jr.; Zepp, R. G. Carbon loss and optical property changes during long-term photochemical and biological degradation of estuarine dissolved organic matter. Limnol. Oceanogr. 2000, 45, 1254−1264. (7) Luider, C.; Pettigrew, E.; Curtis, P. J. Scavenging of dissolved organic matter (DOM) by amorphous iron hydroxide particles Fe(OH)3(S). Hydrobiologia 2003, 494, 37−41. (8) Cory, R. M.; McKnight, D. M.; Chin, Y-P; Miller, P.; Jaros, C. L. Chemical characteristics of fulvic acids from Arctic surface waters: Microbial contributions and photochemical transformations. J. Geophys. Res. 2007, 112, 10.1029/2006JG000343. (9) Duan, S.; Bianchi, T. S; Sampere, T. P. Temporal variability in the composition and abundance of terrestrially-derived dissolved organic matter in the lower Mississippi and Pearl Rivers. Mar. Chem. 2007, 103, 172−184. (10) Mueller, K. K.; Fortin, C.; Campbell, P. G. C. Spatial Variation in the Optical Properties of Dissolved Organic Matter (DOM) in Lakes on the Canadian Precambrian Shield and Links to Watershed Characteristics. Aquat. Geochem. 2012, 18, 21−44. (11) Campbell, P. G. C. In Metal Speciation and Bioavailability in Aquatic Systems; Tessier, A., Turner, D., Eds.; John Wiley: Chichester, 1995; Ch. 2, pp 42−102. (12) Kantar, C. Heterogeneous processes affecting metal ion transport in the presence of organic ligands: reactive transport modeling. Earth Sci. Rev. 2007, 81, 175−198. (13) Buffle, J. A critical comparison of studies of complex formation between copper(II) and fulvic substances of natural waters. Anal. Chim. Acta 1980, 118, 29−44. (14) McKnight, D. M.; Feder, G. L.; Thurman, E. M.; Wershaw, R. L.; Westall, J. C. Complexation of copper by aquatic humic substances from different environments. Sci. Total Environ. 1983, 28, 65−76. (15) Cabaniss, S. E.; Shuman, M. S. Copper binding by dissolved organic matter: II. Variation in type and source of organic matter. Geochim. Cosmochim. Acta 1988, 52, 195−200. (16) Xue, B.; Sigg, L. Free cupric ion concentration and Cu(II) speciation in a eutrophic lake. Limnol. Oceanogr. 1993, 38, 1200−1213. (17) Breault, R. F.; Colman, J. A.; Aiken, G. R.; McKnight, D. M. Copper Speciation and Binding by Organic Matter in CopperContaminated Streamwater. Environ. Sci. Technol. 1996, 30, 3477− 3486. (18) Rozan, T. F.; Benoit, G. Geochemical factors controlling free Cu ion concentrations in river water. Geochim. Cosmochim. Acta 1999, 63, 3311−3319. (19) Kogut, M. B.; Voelker, B. M. Strong Copper-Binding Behavior of Terrestrial Humic Substances in Seawater. Environ. Sci. Technol. 2001, 35, 1149−1156. (20) Shank, G. C.; Skrabal, S. A.; Whitehead, R. F.; Kieber, R. J. Strong copper complexation in an organic-rich estuary: The importance of allochthonous dissolved organic matter. Mar. Chem. 2004, 88, 21−39. (21) Guthrie, J. W.; Hassan, N. M.; Salam, M. S. A.; Fasfous, I. I.; Murimboh, C. A.; Murimboh, J.; Chakrabarti, C. L.; Gregoire, D. C. Complexation of Ni, Cu, Zn, and Cd by DOC in some metal-impacted freshwater lakes: A comparison of approaches using electrochemical determination of free-metal-ion and labile complexes and a computer speciation model, WHAM V and VI. Anal. Chim. Acta 2005, 528, 205−218. (22) Brooks, S. J.; Bolam, T.; Tolhurst, L.; Bassett, J.; La Roche, J.; Waldock, M.; Barry, J.; Thomas, K. V. Effects of dissolved organic carbon on the toxicity of copper to the developing embryos of the pacific oyster (Crassostrea gigas). Environ. Toxicol. Chem. 2007, 26, 1756−1763. (23) Brooks, M. L.; McKnight, D. M.; Clements, W. H. Photochemical control of copper complexation by dissolved organic matter

the Cu activity by factors between 1.1 and 3.3 for higher DOM concentrations (6, 8, and 10 mg C/L). Simulations indicated the following qualitative ranking: pH ≫ DOM concentration > DOM quality > hardness (CaCO3) > Al. DOM quality needs to be characterized because both the binding capacity of DOM for Cu and the relative affinities for Cu and Al depend on DOM quality. In applying the F factor, we adjusted the site density for representing the DOM quality and we improved the variance between results measured and predicted from 49% to 80%. Optical property can be related empirically to metal complexation for a suite of natural DOM probably because metal complexation covaries with optical propertiesthere is no necessary mechanistic connection between absorbance of photons and complexation of metals. Further empirical improvements may be possible if binding site affinities are assessed and related to DOM quality. This approach is especially suited to predicting and regulating toxic levels of copper in aquatic systems, notably the scientific community working with the Biotic Ligand Model58 that is WHAM based. Our studies were intended to explore Cu speciation at levels typically toxic for aquatic organismsfuture studies should extend the range of copper concentration to lower levels and possibly to levels where Cu becomes an essential nutrient. Similarly, interactive effects among trace metals should be extended to other metals having relatively high concentration and affinity for DOM (e.g., Fe). Broadly, the same approach needs to be applied to other metals (Cd, Ni, Zn) known for forming complexes with DOM, particularly if they use the same sites for binding DOM.

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ASSOCIATED CONTENT

S Supporting Information *

Details about the Cu measurements, in particular the method used for the ISE calibration (including SD), and a figure presenting the relationships between the DOM optical properties with the hydrological residence time. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: (989) 774-4388; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Financial support from the Natural Sciences and Engineering Research Council of Canada is acknowledged. We thank Dave Arkinstall and Chad Luider for their technical assistance. We also acknowledge the helpful comments made by three anonymous reviewers.

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REFERENCES

(1) Morel, F. M. M.; Hering, J. G. Principles and Applications of Aquatic Chemistry; John Wiley & Sons: New York, 1993. (2) Buffle, J. Complexation Reactions in Aquatic Systems: An Analytical Approach, 2nd ed.; Ellis Horwood: New York, 1990. (3) Lenhart, J. J.; Honeyman, B. D. Uranium(VI) sorption to hematite in the presence of humic acid. Geochim. Cosmochim. Acta 1999, 63, 2891−2901. (4) Santschi, P. H.; Lenhart, J. J.; Honeyman, B. D. Heterogeneous processes affecting trace contaminant distibution in estuaries: The role of natural organic matter. Mar. Chem. 1997, 58, 99−125. 2006

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dx.doi.org/10.1021/es3022045 | Environ. Sci. Technol. 2013, 47, 2001−2007