Bioavailability Models for Predicting Copper Toxicity to Freshwater

Each bioavailability model for a given toxicological endpoint e, a given effect .... pH levels and thus confirm the general trend observed for P. subc...
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Environ. Sci. Technol. 2006, 40, 4514-4522

Bioavailability Models for Predicting Copper Toxicity to Freshwater Green Microalgae as a Function of Water Chemistry KAREL A. C. DE SCHAMPHELAERE* AND COLIN R. JANSSEN Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University (UGent), Jozef Plateaustraat 22, B-9000 Gent, Belgium

We investigated whether an earlier-developed bioavailability model for predicting copper toxicity to growth rate of the freshwater alga Pseudokirchneriella subcapitata could be extrapolated to other species and toxicological effects (endpoints). Hardness and dissolved organic carbon did not significantly affect the toxicity of the free Cu2+ ion to P. subcapitata (earlier study) and Chlorella vulgaris (this study), but a higher pH resulted in an increased toxicity for both species. Regression analysis showed significant linear relationships between ECxpCu () “effect concentration” that produces x% adverse effect, expressed as pCu ) log of the Cu2+ activity) and pH. By linking these regression models with a geochemical metal speciation model, dissolved copper concentrations that elicit a given adverse effect (ECxdissolved) can be predicted. Within the pH range investigated (5.5-8.7), slopes of the linear ECxpCu vs pH regression models varied between 1.301 and 1.472 depending on the species and the effect level (10% or 50%) considered. In a statistical sense these slopes were all significantly different from one another (p < 0.05), suggesting that this empirical regression model does not yet capture the full complexity of toxicological copper bioavailability to algae. However, we demonstrated that regression models with an “average” slope of 1.354 had predictive power very similar to those of regression models with species and effectspecific slopes. Additionally, the “average” regression model was further successfully validated for other species (Chlamydomonas reinhardtii and Scenedesmus quadricauda) and for different toxicological effects/endpoints (growth rate, biomass yield, and phosphorus uptake rate). For all these toxicity datasets effect concentrations of copper could be predicted with this “average” model by errors of less than a factor of 2 in 94-100% of the cases. The success of this “average” model suggests the possibility that the pHbased linear regression model may form a sound conceptual basis for modeling the toxicological bioavailability of copper to green algae in regulatory assessments, although a full mechanistic understanding is lacking and should be the focus of future studies. * Corresponding author phone: +32 9 264 37 64; fax: +32 9 264 37 66; e-mail: [email protected]. 4514

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Introduction Toxicity of metals to freshwater organisms is influenced by water characteristics such as the concentration of dissolved organic carbon (DOC), water hardness, and pH (e.g., 1-6). One of our previous studies indicated that toxicity of dissolved copper to the green microalgae Pseudokirchneriella subcapitata was mainly affected by DOC and pH, whereas the effect of water hardness was not significant (7). The protective effect of DOC was a chemical speciation effect only, as suggested earlier by Sunda and Lewis (1), resulting from the decrease of the chemical activity of the Cu2+ ion at increased DOC concentrations. Indeed, toxic effect levels (e.g., 10% or 50% reduction of biomass yield) expressed as free Cu2+ ion activity did not vary with varying DOC (7). On the contrary, pH affected not only speciation (via increased complexation of Cu2+ with OH-, CO32-, and DOC at higher pH), but also the toxicity of the free Cu2+ ion itself (7). Several studies have demonstrated the increased toxicity of Cu2+ with increasing pH to species such as Pseudokirchneriella subcapitata (78), Chlorella sp. (8-9), C. reinhardtii (10), and Scenedesmus quadricauda (11). This effect has mostly been explained as competition between Cu2+ and H+ for binding sites on the algal surface (7-17). These sites may include Cu-transporters (sensu ref 17) or membrane proteins involved in other processes (e.g., phosphate transporters, 11, 16). Toxic effects may be induced at the plasma membrane by interference with membrane proteins (e.g., phosphate channels, 11, 16) or internally after transport into the cell (e.g., 17). Integrated knowledge about Cu2+ and H+ binding to the algal surface, Cu internalization, and toxic effects may eventually result in a mechanistic copper bioavailability and toxicity model for green microalgae, similar in concept to biotic ligand models developed for fish and invertebrate species (e.g., 19, 20). Until then more empirical approaches may be useful for regulatory applications if they are able to accurately predict copper toxicity. In this context we previously described a predictive toxicological bioavailability model for the effect of Cu on reducing biomass yield of P. subcapitata (7). This model consists of a linear regression model of ECxpCu () log of the Cu2+ activity resulting in x% adverse effect; EC ) “effective concentration”) vs pH. By linking this model to the speciation model WHAM-Model V (20), accurate predictions were obtained of toxic effect concentrations expressed as dissolved Cu (ECxdissolved) for a wide range of water characteristics (7). In the context of implementing the use of this bioavailability model into regulatory frameworks related to the potential environmental impact of copper, it is important to assess how variable the effects of water chemistry parameters on copper toxicity are across different algal species. Therefore we investigated whether the above-mentioned bioavailability model developed for P. subcapitata could be extrapolated to another green microalgal species, i.e., Chlorella vulgaris. We therefore evaluated (1) if the effect of DOC was a speciationonly effect and if the effect of water hardness was negligible too, as for P. subcapitata, and (2) if the pH effect was comparable to that for P. subcapitata. Further, we evaluated if this bioavailability model could be extrapolated to additional algal species and different toxicological effects (endpoints), based on literature data and on data for Chlamydomonas reinhardtii, also generated in the present study. The ultimate aim was thus to investigate whether a generalized model could be created that would reasonably predict toxicological effect concentrations of copper for different toxicological endpoints, different effect levels, and 10.1021/es0525051 CCC: $33.50

 2006 American Chemical Society Published on Web 06/08/2006

different species of green microalgae, or if separate models would be required.

Materials and Methods Toxicity Data for Pseudokirchneriella Subcapitata. The data on P. subcapitata used in the present study originate from the experiments described by De Schamphelaere et al. (7), with one modification. We have reanalyzed the data to derive concentrations of dissolved copper and Cu2+ that produce a given inhibition of growth rate rather than those that cause a given reduction in biomass yield (see ref 22 for information about both endpoints). The rationale for this is that effect concentrations based on growth rate are less dependent on test system parameters (23) and that algal growth rate is a dynamic parameter which can be directly incorporated in ecological models describing ecosystem dynamics, whereas biomass yield is not. However, previously generated effect concentrations, based on biomass yield and obtained in natural waters (data from ref 24) will also be evaluated for comparison (see Results and Discussion section). Toxicity Bioassays with Chlorella vulgaris and Chlamydomonas reinhardtii. Algal toxicity tests with C. vulgaris and Cu were conducted in 17 different test solutions in which DOC concentration (1.6-18.4 mg/L), pH (5.3-8.7), and hardness (0-500 mg CaCO3 L-1; molar Ca/Mg ratio of 4:1) were varied according to a central composite test design (25), identical to the one used for P. subcapitata (7). For C. reinhardtii only the pH effect was investigated (pH 6, 7, and 8) at a hardness of 250 mg CaCO3/L (molar Ca/Mg ratio of 4:1) and a nominal DOC concentration of 10 mg/L. Test solutions for both species were prepared as described in ref 7. DOC was added from a concentrated stock solution obtained by reverse osmosis from the Ankeveensche Plassen (Nederhorst-den-Berg, The Netherlands, see ref 7 for more details). The exact composition of all test waters is given as Supporting Information. C. vulgaris (CCAP 211/11B) and C. reinhardtii (CCAP 11/ 32B) were both obtained from the Culture Collection of Protozoans and Algae (CCAP, at the Scottish Association for Marine Science, Argyll, Scotland). Algae are continuously maintained in our laboratory at pH 8.3 in carbon-filtered and 0.45 µm filtered aerated tap water (Gent, Belgium) to which the modified Provasoli’s ES enrichment (26) at 1/2 strength and 5.03 µM FeSO4‚7H2O, 96.2 µM NaH2PO4‚2H2O, 567 µM NaNO3, and 11.9 µM MnCl2.4H2O were added. Cultures were kept at 20 °C under continuous light (240 µmol photons m-2 s-1). Culture media contained 1.0-1.5 µg Cu/L. Ecotoxicity tests with both species were in accordance with OECD Test Guideline No. 201 (22) and were conducted at 25 °C under continuous light at 120 µmol photons m-2 s-1. Exposure media were prepared by adding the correct amounts of stock solutions of CaCl2, MgSO4, and DOC to the standard medium described in the OECD guideline (22). Ethylenediamine-tetraacetic acid was replaced by natural DOC (7). MOPS (3-N-morpholino-propane-sulfonic acid, 750 mg L-1, Sigma-Aldrich, Steinheim, Germany) was added as a pH buffer except for solutions at pH > 8, which were buffered through NaHCO3 addition. MOPS is noncomplexing for metals (27) and does not change the toxicity of Cu to P. subcapitata and Daphnia magna (28). The medium was adjusted to the desired pH level with dilute NaOH or HCl and was spiked with five different copper concentrations (CuCl2). The difference between the lowest and highest copper concentration was 1 order of magnitude. Tests were performed in 100 mL borosilicate glass Erlenmeyer flasks containing 50 mL of test medium. Each test consisted of six control replicates and three replicates for each of the five copper concentrations tested. The spiked media were equilibrated for 48 h at 25 °C prior to testing. At the beginning of each test, each flask was inoculated with

104 cells mL-1. The cell density was measured after 24, 48, and 72 h with the aid of an electronic particle counter (Model DN, Harpenden., Herts, UK) in the case of C. vulgaris and using a Sedgewick-Rafter counting cell in the case of C. reinhardtii according to standard method 10200F (29). Concurrently, the pH of the medium was measured, and, if required, adjusted to the initial pH with NaOH or HCl. Chemical Analyses. Dissolved copper concentrations (filtered through a 0.45 µm filter, Gelman Sciences, Ann Arbor, MI) were determined at the beginning and at the end of the test using a flame-atomic absorption spectrophotometer (SpectroAA100, Varian, Mulgrave, Australia). Reported effect concentrations for Chlorella vulgaris and Chlamydomonas are based on averages. Dissolved copper concentrations at the end of the test were only slightly lower than those at the start of the test by on average 3.1% and 4.6% for C. vulgaris and C. reinhardtii, respectively. This decrease is similar to the decrease observed in Cu toxicity tests with P. subcapitata using the same natural organic matter from Ankeveen, i.e., 3.6% (7). Dissolved organic carbon (0.45 µm filtered) and inorganic carbon (IC) were measured using a TOC-analyzer (TOC-5000, Shimadzu, Duisburg, Germany). DOC was measured prior to addition of MOPS buffer; IC was measured immediately prior to testing. Concentrations of major cations (Na, K, Ca, Mg) and anions (Cl, SO4) were calculated as the sum of (1) ions added along with the DOC stock solution (composition reported in ref 7), (2) NaOH or HCl additions used to adjust the pH to the desired level, and (3) CaCl2 and MgSO4 additions. These ion concentrations were measured occasionally and were always within 10% of the reported calculated concentrations. pH measurements were performed using a benchtop pH meter (P407, Consort, Turnhout, Belgium). The pH glass electrode was calibrated before each use using pH 4 and 7 buffers (Merck, Darmstadt, Germany). Data Treatment. Exponential growth rates for all species and all treatments were calculated as the slope of a linear regression between the natural logarithm (loge) of the cell density and time (22). Control exponential growth rates were between 0.7 and 1.3 d-1 for P. subcapitata, between 1.0 and 1.3 d-1 for C. vulgaris, and between 1.2 and 1.3 d-1 for C. reinhardtii. The concentrations associated with 10% (ErC10) or 50% (ErC50) inhibition of the growth rate after 72 h of exposure were calculated with Statistica 6.0 (Statsoft, Tulsa, OK) using a logistic regression equation with the ErC10 and ErC50 as the fitting parameters (4). Effect concentrations were expressed as dissolved Cu. Cu2+ activities at ErC10 and ErC50 levels were calculated using BLM software (30), which uses the WHAM Model V speciation code for binding of metals to DOM (21). For the calculations we assumed Bihain, Ossenkolk, and Ankeveen DOM to consist of 65.2%, 64.8%, and 41.4% of active fulvic acid, respectively, with the remaining fraction considered inert, as determined earlier based on measured Cu speciation in the presence of these DOMs (7). In our earlier work (7), these assumptions resulted in predicted Cu2+ activities that matched very well with measured Cu2+ activities and the prediction errors were independent of pH, DOC concentration, and water hardness. Hence, these assumptions will not affect the interpretation of the effects of pH, DOC, and hardness on Cu2+ toxicity. Thermodynamic stability constants for inorganic complexes were taken from Martell et al. (31). The composition of all test solutions is given as Supporting Information. Terminology and Abbreviations. A brief explanation of some terminology, some abbreviations, and of the subscripting system used throughout this paper is given below. An ecotoxicological “endpoint” is a biological or physiological trait upon which the effect of a toxicant is determined; the endpoints considered in the present study are algal growth rate, biomass yield, and phosphorus uptake rate. VOL. 40, NO. 14, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Toxicity of Cu2+ vs pH as represented by the 10% and 50% effect concentration for growth rate, expressed as pCu units, for P. subcapitata (]) and C. vulgaris (+). Linear regression lines are the models described in Table 1. “Effect concentrations” of copper are abbreviated as EeCxz. This is the concentration, expressed on the basis of z (as dissolved Cu, z ) “dissolved”; Cu2+ activity, z ) “Cu2+”; or pCu, z ) “pCu”), which produces x% adverse effect on the endpoint e, compared to a control treatment (no Cu added to test solution). The term “species and effect-specific (pH regression) model” refers to linear regression models of EeCxpCu vs pH, characterized by a slope (SpH,x,y,e) and an intercept (Qx,y,e) for a specific species y and a specific adverse effect of x% for a specific endpoint e; throughout the text, whenever a specific endpoint is referred to, e is replaced with r (growth rate), b (biomass yield), or p (phosphorus uptake rate). The term “‘average (pH-regression) model” refers to a similar pH-based linear regression model, with an “average” slope of SpH,a but a variable species and effect specific intercept (Qa,x,y,e). The term “(Toxicological) bioavailability model” refers to a model that predicts toxicological effect concentrations on a dissolved basis (EeCxdissolved) for a given endpoint e; throughout the study, “bioavailability model” refers to toxicological effects of copper rather than copper uptake; here it consists of a pH-based regression model (see above) linked to the speciation model WHAM-Model V (21).

Results and Discussion Toxicity of Dissolved Cu and the Cu2+ Ion to P. subcapitata and C. vulgaris. Dissolved effect concentrations of copper in the test waters with different pH, hardness, and DOC varied by a factor of 20 to 27 for P. subcapitata and 16 for C. vulgaris. The 72-h ErC10dissolved for P. subcapitata ranged from 17 to 337 µg/L, while the 72-h ErC50dissolved ranged from 30 to 824 µg/L. The 72-h ErC10dissolved for C. vulgaris ranged from 31 to 510 µg/L and the 72-h ErC50dissolved ranged from 60 to 987 µg/L (see Supporting Information). This large variability stresses the importance of explicitly considering toxicological bioavailability when a regulatory assessment of the environmental risks associated with copper is performed. From a mechanistic point of view, copper toxicity to algae expressed as a dissolved effect concentration (ErC50dissolved) is the result of both copper speciation and interactions at 4516

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the cell surface (7, 8). Converting all dissolved effect concentrations to Cu2+ activities or pCu () - log of Cu2+ activity in M) is the first step toward understanding bioavailability of copper. All ErCxdissolved and ErCxpCu values are given in the Supporting Information (SI). ErC50pCu and ErC10pCu values for P. subcapitata varied between 5.46 and 10.41 and between 6.57 and 10.70, respectively (SI). ErC50pCu and ErC10pCu values for C. vulgaris varied between 5.49 and 9.59 and between 5.85 and 9.80, respectively (SI). The variability observed on the basis of Cu2+ activity is thus more than 4 orders of magnitude (>4 pCu units) for all instances. In the following section we will further investigate this variability and we will show we can deal with it from a modeling point of view. Understanding and Modeling the Toxicity of the Cu2+ ion to P. subcapitata and C. vulgaris. Stepwise forward multiple linear regression analysis revealed that pH did (p < 0.001), but water hardness and DOC concentration did not significantly affect ErC10pCu and ErC50pCu for P. subcapitata or C. vulgaris (p > 0.05). For DOC this is in agreement with other studies stating that complexes of Cu with DOC are typically not availablesor at least much less available than the free Cu2+ ion (1, 7; for exceptions see ref 15). DOC only affects toxicity expressed as dissolved Cu, through the reduction of the activity of Cu2+ at higher DOC levels. The protective effect of DOC is illustrated by a significant (p < 0.01) positive correlation between DOC (corrected for its percentage active fulvic acid as explained above) and ErC50dissolved and ErC10dissolved values for both species (r between 0.62 and 0.70, based on data in the Supporting Information). The fact that hardness in the present study does not affect toxicity of Cu2+ to both algal species corroborates a study of Markich et al. (32), but seemingly contradicts the data of Heijerick et al. (24). At a pH of 7.8, Heijerick et al. (24) demonstrated in a univariate experiment that increased Ca2+ and Mg2+ reduced the toxicity of Cu2+ to P. subcapitata by 2- to 3-fold. Possibly the effect of water hardness is too small to be detected in the presence of a much larger pH effect (4-5 orders of magnitude) in the present multivariate test design. Alternatively, the effect of water hardness on Cu2+ toxicity to P. subcapitata may be dependent on pH, which would also complicate the detection of it in our multivariate design. This may be supported by the fact that Markich et al. (32) did not find a protective effect of water hardness on Cu toxicity to Chlorella sp. at a pH of 6.5, a pH level different from the pH of 7.8 used by Heijerick et al. (24). Further investigation is needed to unravel the significance, the potential pH dependency, and interspecies variation of the hardness effect. As long as no further detailed information on the protective effects of Ca2+ and Mg2+ and its potential species and pH dependency is available, we suggest using the highly significant linear ECxpCu vs pH regression model as the conceptual basis for copper toxicity modeling, recognizing that the uncertainty about a potential hardness effect increases this model’s empirical nature. This seems a plausible approach as the data obtained with P. subcapitata and C. vulgaris clearly indicate that a linear regression with pH explains most (>90%) of the variability observed in ErC50pCu and ErC10pCu (r 2 between 0.93 and 0.96, Figure 1, Table 1). The increase of pH resulted in a marked increase of toxicity as indicated by increased ErC10pCu and ErC50pCu values with increased pH. This is evidenced by the slopes of the regression equations (parameter SpH inTable 1), which ranged from 1.271 (for C. vulgaris ErC50) to 1.475 (for P. subcapitata EC50) pCu units per pH unit. This is in a range similar to that of slopes previously observed for the P. subcapitata biomass yield endpoint based on the same dataset, which ranged between 1.140 and 1.472 (7), while for Chlorella sp. calculations from

TABLE 1. Summary of the Species- and Effect-Specific Linear Regression Models of ErCxpCu vs pH for P. Subcapitata and C. Vulgaris; r 2 and p Values are Given for the Regression Models and Also the p values for the Statistical Test of Equal Slopes of the Different Models species

P. subcapitata P. subcapitata C. vulgaris C. vulgaris a

endpoint/ intercept effect level model ID slope ( SEa SpH Q ErC50 ErC10 ErC50 ErC10

PSr50 PSr10 CVr50 CVr10

1.475 ( 0.067 1.301 ( 0.050 1.271 ( 0.089 1.371 ( 0.097

-2.576 -0.626 -1.893 -1.769

matrix of p values for test of equal slope N 34 35 17 17

r2 0.94 0.96 0.93 0.93

pregression

PSr50

PSr10

7.5 × 2.4 × 2.7 × 10-24 2.4 × 10-24 -10 -16 3.7 × 10 2.2 × 10 1.6 × 10-2 4.8 × 10-10 1.2 × 10-7 1.1 × 10-6 10-21

10-24

CVr50

CVr10

2.2 × 1.6 × 10-2

1.2 × 10-7 1.1 × 10-6 1.9 × 10-5

10-16

1.9 × 10-5

SE ) standard error.

references 8 and 9 give a slope of 1.4-1.65. In the present study there was thus up to a 30-fold decrease in ErCx, expressed as Cu2+ activity, for a 10-fold decrease in H+ activity (i.e., per pH unit). All slopes of the linear ECxpCu vs pH relationship for P. subcapitata and C. vulgaris determined in the present study were statistically significantly different from one another (p < 0.05, Table 1) according to an F-test for comparing linear regression slopes (33). This suggests that the effect of pH on copper toxicity may be dependent on both the species and the effect level under consideration (e.g., 10% or 50%). The species dependency may potentially be explained by species-specific differences of the binding of Cu2+ to Cutransport sites or other Cu-sensitive sites on the plasma membrane. Since binding of Cu2+ to theses sites precede a toxic response (see introduction and refs 8, 15) such differences could explain the observed differences between slopes of the ECxpCu vs pH relations. The affinity of a Cu2+ ion for binding to various algal surface sites may also depend on the number of copper ions already bound (14). This may be due to different types of complexing sites of the molecules making up the cell surface (polyfunctional effect), electrical charge density (polyelectrolyte effect), or conformational effects (14). Since the algal surface is also characterized by a variety of functional groups exhibiting a continuum of pKa values (12), it is plausible that the competition between H+ and Cu2+ may also vary with the amount of Cu already bound. This could potentially explain why slopes for ErC50s are (slightly) different from slopes for ErC10s. The above considerations (different pH slopes and uncertainty of hardness effect) clearly suggest that the empirical pH-based model for predicting Cu2+ toxicity does not capture the full complexity of toxicological copper bioavailability to algae. From an academic point of view, this justifies further study to understand and model toxicity of Cu to algae more mechanistically. From a regulatory point of view, regulators can only take into account the state-ofthe-science at the time they want to carry out an environmental assessment, while at the same time they should not disregard the uncertainties. The species- and effect-specific differences of the pH-based regression models can be used as the starting point for such an uncertainty assessment. The rationale for such an analysis is presented in the following paragraph. Regulatory Needs with Respect to Algal Bioavailability Models and an Approach to Meet These. Many jurisdictions, including the European Union, require that toxicity data with algae be taken into account in regulatory assessments of chemical substances, ideally with multiple species (34). If the bioavailability concept is to be incorporated in such assessments, appropriate bioavailability models are needed to normalize algal toxicity data from literature, obtained at a given water chemistry, to any water chemistry of interest (e.g., of a site, river, catchments, country, etc.). While it seems appropriate that species- and effect-specific models be used if available, regulators may be concerned about how to model

toxicological bioavailability of Cu to other algal species or for other toxicological endpoints for which no specific models have been developed. A possible solution would be to use an “average” model (see further) for all such species or endpoints, but it is obvious that regulators would want to know the uncertainty associated with such an extrapolation/ generalization. It was anticipated that such an uncertainty analysis could reveal generalities or discrepancies of toxicological copper bioavailability among different algal species and among different toxicological endpoints and effects. Hereafter, two types of uncertainty analyses will be described using the predictive capacity of the models as an inverse measure of uncertainty. First, we will determine for P. subcapitata and C. vulgaris how much predictive capacity is lost when using an average model instead of species- and effect-specific models. Second, we will determine the predictive capacity of an average model for other species and/ or toxicological effects/endpoints. Predictive capacity of different bioavailability models for different species and effects will be evaluated by three different statistical traits: (1) the determination coefficient r 2 of the ECxpCu vs pH regression, (2) the mean factor of prediction error about ECx values, and (3) the percentage of ECx values that were predicted by an error of less than factor 1.5 and 2.0. The latter two traits should preferably be determined on a dissolved Cu basis (ECxdissolved). Indeed, because dissolved Cu is more easily measurable on a routine basis than free Cu2+ ion activity, it is usually more useful in regulatory frameworks to predict concentrations of dissolved copper that elicit a given toxic response. All bioavailability models described below consist of linear regression models (Tables 1 and 2) of ECxpCu vs pH, linked to the speciation model WHAM-Model V (21), as explained in the Introduction and in the Terminology and Abbreviations section. Each bioavailability model for a given toxicological endpoint e, a given effect level x, and a given species y is different from any other model by two parameters only, i.e., the slope (SpH) and the intercept (Q) of the pH regression models. In each “species- and effect-specific” model both the slope (SpH,e,x,y) and intercept (Qe,x,y) are specific for the species (y), the endpoint (e), and the effect level (x) considered. Average models are defined to have the same average slope (SpH,a) but variable species- and effect-specific intercepts (Qa,e,x,y). The additional subscript a denotes average. We chose the pH-slope as the average model parameter based on the relative similarity of slopes observed for P. subcapitata and C. vulgaris in the present study. The use of average models parallels the approach followed for normalizing vertebrate and invertebrate copper toxicity data with the biotic ligand model (BLM) for setting site-specific water quality criteria in the United States (35). The pH-slope SpH,a fulfills the same role as the stability constants for binding of copper and competing ions to the gills, which are assumed identical across species. The intercept Qa fulfills the same role as the effect concentration of copper, expressed as gill-Cu, which is assumed to be independent of water chemistry and to reflect the “inherent” sensitivity of an organism (36). In other VOL. 40, NO. 14, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Overview of Slopes and Intercepts of the Different Linear Regression Models of ECxpCu vs pH Applied to Different Species and Endpoints/Effectsa P. subcapitata ErC50

C. vulgaris ErC50

C. reinhardtii ErC50

P. subcapitata NOEbC b

model

PSr50

average

CVr50

average

average

average

slope (SpH) intercept (Q) r2 mean % < 1.5 % < 2.0

1.475 -2.576 0.94 1.28 85 100

1.354 -1.706 0.93 1.30 82 94

1.271 -1.893 0.93 1.27 94 94

1.354 -2.473 0.93 1.28 88 94

1.354 -2.328 0.91 1.32 100 100

1.354 -1.097 0.94 1.39 70 90

C. reinhardtii ErC10

S. quadricauda EPC50 c

P. subcapitata ErC10

C. vulgaris ErC10

model

PSr10

average

CVr10

average

average

average

slope (SpH) intercept (Q) r2 mean % < 1.5 % < 2.0

1.301 -0.626 0.96 1.31 86 97

1.354 -1.000 0.96 1.32 86 97

1.371 -1.769 0.93 1.36 71 100

1.354 -1.651 0.93 1.36 71 94

1.354 -1.507 0.99 1.09 100 100

1.354 -1.465 0.99 1.35 63 100

a The subscript letter e in E Cx refers to the endpoints growth rate (r), biomass yield (b), or phosphorus uptake rate (p). The following indicators e of predictive capacity are given: r 2 of regression models, mean factor prediction error of the whole dataset (mean) and the percentage of effect concentrations predicted by an error of less than factor 1.5 (% < 1.5) and factor 2.0 (% < 2). Next to the species- and effect-specific models reported in Table 1 (PSr50, PSr10, CVr50, and CVr10), the average models with average slope SpH )1.354 are also given. b Data from ref 24. c Data from ref 11, factor prediction error is based on Cu2+ activity instead of dissolved Cu.

words, SpH,a is the parameter describing how toxicity of Cu2+ varies with pH for all species. Similar to the concentration of copper bound to the biotic ligand in the BLM, the parameter Qa reflects the relative sensitivity of different species and endpoints to Cu2+. In other words, it describes how the toxicity of Cu2+ varies among different algal species and toxicological endpoints for a fixed pH. We arbitrarily assumed SpH,a to be 1.354, which is the mean of the slopes determined for P. subcapitata and C. vulgaris and for ErC50s and ErC10s. For each dataset, the Qa,e,x,y was determined that resulted in the linear pH regression with the highest r 2 value when the fixed slope SpH,a was used (Table 2). Comparison of Average with Species- and Effect-Specific Models. Figure 2 visualizes that most effect concentrations for P. subcapitata and C. reinahrdtii are predicted by an error of less than a factor of 2 (dotted lines) and that predicted ErC50 and ErC10 values with average models (Figure 2C and D) are relatively similar to those predicted with the speciesand effect-specific models (Figure 2A and B). A quantitative analysis of the predictive capacity is given in Table 2. Considering both species and both the ErC50 and ErC10 effect levels, and using the species- and effect-specific models, r 2 values were between 0.93 and 0.96, and the mean error in predicted ECx levels was between 1.27- and 1.36-fold. ErC50 and ErC10 values were predicted by an error of less than a factor of 1.5 for 71-94% of the cases and less than a factor of 2 for 94-100% of the cases. Using the average model, r 2 values were between 0.93 and 0.96, and the mean error in predicted ECx levels was between 1.28- and 1.36-fold. ErC50 and ErC10 values were predicted by an error of less than a factor of 1.5 for 71-88% and less than a factor of 2 for 9497% of the cases. Thus, determination coefficients of the models (r 2 values), mean prediction errors, and the percentages of values predicted by an error of less than a factor of 1.5 and 2, were very similar across models (Table 2). It is thus concluded that, in general, the average pH regression model exhibits predictive properties quite similar to those of the speciesand effect-specific models. Thus, using an average generalized slope SpH,a and adjusting the species and effect-specific intercepts Qa by means of a best fit maintained a reasonable predictive capacity. This confirms that the relationship 4518

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between pH and Cu2+ toxicity is, although not identical, at least similar among species and for different effect levels. In a regulatory context, it indicates that they are sufficiently similar not to lose a substantial degree of predictive capacity when an average slope is used. Applicability of the Average Model to Reduction of Biomass Yield of P. subcapitata in Spiked Natural Surface Waters. Heijerick et al. (24) recently reported 72-h NOEbCdissolved values (no observed effect concentration) for P. subcapitata, expressed as dissolved Cu, in 13 spiked natural surface water samples from 10 locations in 5 EU countries. The subscript b stands for the biomass yield endpoint (sensu ref 22). From their dataset the test waters Bihain and Somerain are not considered in our current data analysis since pH shifted more than 0.5 pH units during the exposure (24), and Skarsjo¨n was not considered because of the previously reported suspected influence of high Fe and/or Al concentrations on the test results (7). As such, the data of 10 water samples from the 7 remaining locations were used in our analysis. These waters covered a pH range from 6.1 to 8.3, a DOC concentration range from 1.7 to 20.4 mg/L, and a NOEbCdissolved range from 16 to 164 µg Cu/L (10-fold variation). Using the water chemistry reported by Heijerick et al. (24) and the NOEbCdissolved values as input for the BLM-software, the NOEbCpCu values were calculated assuming the DOC to consist of 50% active fulvic acid (sensu refs 37, 38). The relation between pH and observed NOEbCpCu is depicted in Figure 3. Based on these data and using the average slope SpH,a the intercept Qa was derived as explained above. This resulted in the average model for predicting NOEbCdissolved levels (Table 2, Figure 3), the predictive capacity of which is summarized in Table 2 and Figure 2E. An r 2-value of 0.94 was obtained for this model, which is fairly similar to r 2-values of other models for P. subcapitata (Tables 1 and 2). NOEbCs were predicted by an error of less than factor 1.5 or factor 2 for 70% and 90% of the surface waters, respectively (Table 3, Figure 2E). The average prediction error was a factor of 1.39. Despite the potential differences in Cu-DOM binding characteristics across water bodies (37, 39) and considering the fact that we assumed these characteristics to be identical for all surface waters, the average algal Cu-toxicity model yields accurate predictions of dissolved NOEbC levels. This was also demonstrated for

FIGURE 2. Predictive capacity as shown by observed vs predicted effect levels (ECx, as dissolved Cu) of the species and endpoint specific models (Table 1, A and B) and of the “average” models (Table 2, C and D) for P. subcapitata (A, C) and C. vulgaris (B, D) growth rate inhibition, and of the “average” models for reduction of P. subcapitata biomass yield (in natural waters), and for C. reihardtii growth rate inhibition (Table 2, E). Full lines indicate a perfect match between observation and prediction; dashed lines indicate a 2-fold difference between prediction and observation. acute Cu toxicity to D. magna (37). This means that the average model, developed based on the growth rate endpoint in reconstituted laboratory test waters, can safely be extrapolated to the biomass yield endpoint in natural waters for P. subcapitata. The average model thus seems to be applicable for different toxicological endpoints/effects (growth rate and biomass yield) and among different water types (synthetic and natural) for P. subcapitata. Applicability of the Average Model for C. reinhardtii. Macfie et al. (10) reported 5-d EbC30s as free Cu2+concentration for pH 5.0 and pH 6.8 for two strains of C. reinhardtii, one containing and one lacking a cell wall. Based on the composition of their test solution (Table 1 in ref 10) we calculated the ionic strength to be about 30 mM. Using the Davies equation, we calculated the activity coefficient of

the Cu2+ ion to be about 0.52 and we converted their EbC30s (reported as free Cu2+ concentration) to free Cu2+ activity and pCu units. 5-d EbC30pCu was 5.84 at pH 5.0 vs 6.68 at pH 6.8 for the walled strain (SpH ) 0.47), and 6.17 at pH 5.0 vs 6.68 at pH 6.8 for the wall-less strain (SpH ) 0.28). These datasets point to a reduction of toxicity of Cu at lower pH levels and thus confirm the general trend observed for P. subcapitata and C. vulgaris in the present study. Two conclusions can be drawn from these data. First, a qualitatively similar pH effect is observed for walled and wallless algal strains, i.e., increased pH results in increased toxicity of Cu2+. MacFie et al. (10) suggested that “competitive” interactions between Cu2+ and H+ not only at plasma membrane sites but also at cell wall sites may be important in altering toxicity. Although the pH slope is higher for the VOL. 40, NO. 14, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Toxicity of Cu2+ vs pH as represented by the effect concentration expressed as pCu units for different endpoints and different species. Lines are the linear regression models for the different datasets with the “average” slope of 1.354 (see Table 2). The regression lines of S. quadricauda and C. reinahrdtii (ErC10) are nearly identical and cannot be distinguished from each other. Data from S. quadricauda were taken from Peterson et al. (11). Data for P. subcapitata are from Heijerick et al. (24). Data for C. reinhardtii are from the present study. walled strain, suggesting that the cell wall may play a role in the overall effect of pH on Cu2+ toxicity, it remains to be determined whether this really is a “competition” effect or whether another mechanism is involved. Second, the slopes of the 5-d EbC30pCu vs pH are considerably lower than the slopes observed for P. subcapitata and C. vulgaris in the present study, and than the assumed average slope SpH,a. However, two important differences between the present study and the MacFie et al. study (10) exist. First, the lowest pH (5.0) tested by MacFie et al. (10) is outside the pH range investigated in the present study (5.5-8.7). Since the cell wall and the plasma membrane may contain binding sites with a variety of pKa values (12) it is possible that the competitive effect of H+ ions on Cu2+ binding to the whole cell surface varies with the pH range considered. In other words, the slope of the pH regression models in the present study, which is an empirical approximation of H+ competition over the whole investigated pH range, may not be applicable outside this range. Second, MacFie et al. (10) acclimated the algae to the experimental pH for 4 days prior to the copper exposure, whereas this was not the case for P. subcapitata and C. vulgaris used in the present study. Future research is essential to determine how the slope of the algal pH regression models varies when a broader pH range is considered and to investigate if prior acclimation to pH affects the modifying effect of pH on Cu toxicity. Because of the differences in experimental setup an exact comparison between our data and the MacFie et al. (10) data was not possible. Hence, it was decided to perform limited additional testing of the effect of pH on copper toxicity to a walled strain of C. reinhardtii for a pH range which was within the range for which the average slope was developed, i.e., pH 6-8. As pH increased from 6 to 8, copper toxicity to C. reinhardtii increased (see Supporting Information and Figure 3). This is demonstrated by the approximately 2-fold decrease of the ErC10dissolved from 178 to 96 µg/L and of the ErC50dissolved from 380 to 146 µg/L (Supporting Information). It is also shown by the increase of ErC10pCu and ErC50pCu with increased pH, which is on average 1.3-1.4 pCu units per pH unit (Figure 3). This range is similar to the one observed for P. subcapitata and C. vulgaris (see above). The question was now if the use of average models, using average pH slopes SpH,a, would accurately predict the observed pH-induced variation in Cu effect levels to C. reinhardtii (See Table 3 for model parameters). Observations and predictions on a pCu basis are presented in Figure 3 and predictions on a dissolved Cu basis are presented in Figure 2. All ErCxpCu values were predicted by an error between 0.05 4520

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and 0.52 pCu units. ErC10dissolved and ErC50dissolved were predicted by an average error of 1.09 and 1.32, respectively. All effect levels were predicted by an error of less than factor 1.35 (Figure 2E, Table 3). It is thus concluded that the average pH regression model, developed based on data with P. subcapitata and C. vulgaris, can be extrapolated to another algal species, C. reinhardtii. Applicability of the Average Model to Phosphorus Uptake in Scenedesmus quadricauda. Peterson et al. (11) observed a linear relation between 1-h EpC50pCu (the pCu that causes a 50% inhibition of phosphate-uptake rate) and pH for P-limited S. quadricauda in a pH range of 5.5 to 8.5 (Figure 3). The slope of this regression was 1.44, which is again very similar to the values derived for growth rate inhibition P. subcapitata and C. vulgaris in the present study. We investigated the predictive capacity of their model (not shown) and the average model developed in the present study (Table 2). 1-h EPC50pCu values were predicted by an average error of 0.11 and 0.13 pCuunits using their original slope and the average slope, respectively. Converted to EpC50Cu2+ values this equals prediction errors of 1.29- and 1.34-fold, respectively. Hence, the average model developed in the present study also applies to another species and a toxicological endpoint other than growth rate, i.e., the physiological process of P uptake in S. quadricauda. Since P uptake has been shown to be directly inhibited by binding of Cu to plasma-membrane bound P-trasporters (16), one could speculate that the pH effect on Cu2+ toxicity to this endpoint is the result of competition between H+ and Cu2+ for these transporters. One could even speculate further that the pH slopes for the growth rate endpoint are similar because reduced growth rate is the result of reduced P uptake. However, Cu may also have a direct effect on growth rate by inhibition of cell division by internalized Cu (18). Thus, the above speculation does not necessarily explain why the pH slopes are similar for Cu inhibition of P uptake and growth rate. Therefore, and also because knowledge on the pH regression for Cu-induced reduction of P uptake is limited to S. quadricauda only, a parallel investigation of the effect of pH on the toxicity of Cu2+ on P uptake and growth rate, preferably with multiple species, is needed to resolve this issue. Nonetheless, the analyses presented in this study, using new and previously published experimental data with different algal species, clearly suggest that the linear relationship between ECxpCu and pH can be used successfully as a sound conceptual basis for Cu toxicity prediction models for green microalgae within the investigated pH range (roughly 5.5-8.7). Although the slopes of these relations slightly differ among species, toxicological endpoints, and effect levels, they appear to be sufficiently similar as to not substantially affect the predictive capacity of the model when an average slope is used. The fact that one such average slope is broadly applicable to multiple species suggests a similarity across species of the Cu2+/H+ binding characteristics of different cell surface ligands involved in Cu uptake and/or toxicity. On the other hand, the average pH regression model did not explain a dataset with pH-acclimated C. reinhardtii, including a test outside the presently investigated pH range. In addition, this model does neither capture the potential protective effects of water hardness on Cu2+ toxicity to algae, nor the potential dependency of this protective effect on pH. To mechanistically explain similarities and differences between ECxpCu vs pH relations and to improve the generality of the models, future research should focus on the parallel determination of the effect of pH on Cu2+ toxicity to several toxicological endpoints and Cu2+/H+ binding properties of algal cell surfaces and/or ligands on cell wall and plasma membrane. Additional research into the potential importance

of pH acclimation and the potential importance of water hardness is also needed. Until that time, the linear ECxpCu vs pH relationship forms a sound conceptual basis for taking into account toxicological bioavailability of copper to algae in regulatory frameworks. It is recommended that speciesand effect-specific pH regression models are used whenever available and that average models are used in other cases.

Acknowledgments Karel De Schamphelaere is currently supported by a postdoctoral fellowship from the Fund for Scientific Research Flanders (FWO-Vlaanderen, Belgium). Additional support was obtained form the Ghent University Research Fund (BOF No. 01110501) and by the European Copper Institute and the International Copper Association. We thank Jill Van Reybrouck and Emmy Pequeur (Ghent University) for valued assistance with the toxicity assays and the chemical analyses.

Supporting Information Available All data concerning chemical composition of all test media with the associated effect concentrations (as dissolved copper and as pCu). This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review December 14, 2005. Revised manuscript received April 28, 2006. Accepted May 4, 2006. ES0525051