Chronic Toxicity of Iron to Aquatic Organisms under Variable pH, Hardness, and Dissolved Organic Carbon Conditions.

A series of chronic toxicity tests was conducted exposing three aquatic species to iron (Fe) in laboratory freshwaters. The test organisms included the green algae Raphidocelis subcapitata, the cladoceran Ceriodaphnia dubia, and the fathead minnow Pimephales promelas. They were exposed to Fe (as Fe (III) sulfate) in waters under varying pH (5.9-8.5), hardness (10.3-255 mg/L CaCO3 ), and dissolved organic carbon (DOC; 0.3-10.9 mg/L) conditions. Measured total Fe was used for calculations of biological effect concentrations because dissolved Fe was only a fraction of nominal and did not consistently increase as total Fe increased. This was indicative of the high concentrations of Fe required to elicit a biological response and that Fe species that did not pass through a 0.20- or 0.45-µm filter (dissolved fraction) contributed to Fe toxicity. The concentrations frequently exceeded the solubility limits of Fe(III) under circumneutral pH conditions relevant to most natural surface waters. Chronic toxicity endpoints (10% effect concentrations [EC10s]) ranged from 442 to 9607 µg total Fe/L for R. subcapitata growth, from 383 to 15 947 µg total Fe/L for C. dubia reproduction, and from 192 to 58,308 µg total Fe/L for P. promelas growth. Toxicity to R. subcapitata was variably influenced by all three water quality parameters, but especially DOC. Toxicity to C. dubia was influenced by DOC, less so by hardness, but not by pH. Toxicity to P. promelas was variable, but greatest under low hardness, low pH, and low DOC conditions. These data were used to develop an Fe-specific, bioavailability-based multiple linear regression model as part of a companion publication. Environ Toxicol Chem 2023;42:1371-1385. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Development of Multiple Linear Regression Models for Predicting Chronic Iron Toxicity to Aquatic Organisms.

We developed multiple linear regression (MLR) models for predicting iron (Fe) toxicity to aquatic organisms for use in deriving site-specific water quality guidelines (WQGs). The effects of dissolved organic carbon (DOC), hardness, and pH on Fe toxicity to three representative taxa (Ceriodaphnia dubia, Pimephales promelas, and Raphidocelis subcapitata) were evaluated. Both DOC and pH were identified as toxicity-modifying factors (TMFs) for P. promelas and R. subcapitata, whereas only DOC was a TMF for C. dubia. The MLR models based on effective concentration 10% and 20% values were developed and performed reasonably well, with adjusted R2 of 0.68-0.89 across all species and statistical endpoints. Differences among species in the MLR models precluded development of a pooled model. Instead, the species-specific models were assumed to be representative of invertebrates, fish, and algae and were applied accordingly to normalize toxicity data. The species sensitivity distribution (SSD) included standard laboratory toxicity data and effects data from mesocosm experiments on aquatic insects, with aquatic insects being the predominant taxa in the lowest quartile of the SSD. Using the European Union approach for deriving WQGs, application of MLR models to this SSD resulted in WQGs ranging from 114 to 765 µg l-1 Fe across the TMF conditions evaluated (DOC: 0.5-10 mg l-1 ; pH: 6.0-8.4), with slightly higher WQGs (199-910 µg l-1 ) derived using the US Environmental Protection Agency (USEPA) methodology. An important uncertainty in these derivations is the applicability of the C. dubia MLR model (no pH parameter) to aquatic insects, and understanding the pH sensitivity of aquatic insects to Fe toxicity is a research priority. An Excel-based tool for calculating Fe WQGs using both European Union and USEPA approaches across a range of TMF conditions is provided. Environ Toxicol Chem 2023;42:1386-1400. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms.

Multiple linear regression (MLR) models for predicting zinc (Zn) toxicity to freshwater organisms were developed based on three toxicity-modifying factors: dissolved organic carbon (DOC), hardness, and pH. Species-specific, stepwise MLR models were developed to predict acute Zn toxicity to four invertebrates and two fish, and chronic toxicity to three invertebrates, a fish, and a green alga. Stepwise regression analyses found that hardness had the most consistent influence on Zn toxicity among species, whereas DOC and pH had a variable influence. Pooled acute and chronic MLR models were also developed, and a k-fold cross-validation was used to evaluate the fit and predictive ability of the pooled MLR models. The pooled MLR models and an updated Zn biotic ligand model (BLM) performed similarly based on (1) R2 , (2) the percentage of effect concentration (ECx) predictions within a factor of 2.0 of observed ECx, and (3) residuals of observed/predicted ECx versus observed ECx, DOC, hardness, and pH. Although fit of the pooled models to species-specific toxicity data differed among species, species-specific differences were consistent between the BLM and MLR models. Consistency in the performance of the two models across species indicates that additional terms, beyond DOC, hardness, and pH, included in the BLM do not help explain the differences among species. The pooled acute and chronic MLR models and BLM both performed better than the US Environmental Protection Agency's existing hardness-based model. We therefore conclude that both MLR models and the BLM provide an improvement over the existing hardness-only models and that either could be used for deriving ambient water quality criteria. Environ Toxicol Chem 2023;42:393-413. © 2022 SETAC.

Evaluation of Sublethal Toxicity of Nitrite to a Suite of Aquatic Organisms in Support of the Derivation of a Chronic Environmental Water Quality Benchmark.

Nitrite is a naturally-occurring inorganic compound that occurs in aquatic environments as an intermediary between nitrate and ammonia in the nitrogen cycle. It is a contaminant of potential concern resulting from anthropogenic activities in some cases. While the acute toxicity of nitrite has been characterized in previous studies, its sublethal toxicity is less understood. To determine the sublethal toxicity of nitrite on freshwater organisms, a suite of organisms was tested including: two salmonids (Oncorhynchus mykiss and O. kisutch), an alga (Pseudokirchneriella subcapitata), an aquatic macrophyte (Lemna minor), and three invertebrates (Ceriodaphnia dubia, Chironomus dilutus, and Neocloeon triangulifer). Test organisms were exposed to nitrite concentrations ranging between 0.02 and 1.28 mg/L nitrite (NO2-N). The toxicity tests were conducted according to procedures specified in standardized methods, allowing for the estimation of multiple endpoints for each test species. Species sensitivity distributions (SSDs) were generated using endpoints from the toxicity testing results, as well as data from previous studies, from which water chemistry approximated that used in the tests (i.e., < 5 mg/L chloride, an important toxicity-modifying factor for nitrite). The mayfly, N. triangulifer, was the most sensitive species, followed by the two salmonids (which represented the second and third most sensitive species), although they were not as sensitive to nitrite exposure as reported in previous studies. The fifth percentile hazard concentration (HC5) generated from the SSD could be used for derivation of regulatory benchmarks and threshold values for site-specific aquatic risk assessments.

Sediment toxicity data and excess simultaneously extracted metals from field-collected samples: Comparison to United States Environmental Protection Agency benchmarks.

US Environmental Protection Agency (USEPA) Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: Metal Mixtures are based on the principle that metals toxicity to benthic organisms is determined by bioavailable metals concentrations in porewater. One ESB is based on the difference between simultaneously extracted metal (SEM) and acid volatile sulfide (AVS) concentrations in sediment (excess SEM). The excess SEM ESBs include a lower uncertainty bound, below which most samples (95%) are expected to be "nontoxic" (defined as a bioassay mortality rate ≤24%), and an upper uncertainty bound, above which most samples (95%) are expected to be "toxic" (defined as a mortality rate >24%). Samples that fall between the upper and lower bounds are classified as "uncertain." Excess SEM ESBs can, in principle, be improved by normalizing for organic carbon (OC). OC is a binding phase that reduces metals bioavailability. OC normalization should improve the accuracy of bioavailable metal concentration estimates, thus tightening uncertainty bounds. We evaluated field-collected sediments from 13 studies with excess SEM, OC, and bioassay data (n = 740). Use of the OC-normalized excess SEM benchmarks did not improve prediction accuracy. The ESB model predicts OC-normalized excess SEM exceeding the upper benchmark even when toxicity is not observed, because error in the OC normalization model increases at low OC concentrations. To minimize the likelihood of incorrectly identifying nontoxic samples as toxic, we recommend that OC normalization of excess SEM should not be considered for sediments with an OC concentration <1% and is questionable for sediments with an OC concentration of 1%-4%. Additional focused studies are needed to confirm or refine the minimum sediment OC concentrations that are applicable for reducing uncertainty in toxicity predictions due to excess SEM. Integr Environ Assess Manag 2022;18:174-186. © 2021 SETAC.

Collection and use of porewater data from sediment bioassay studies for understanding exposure to bioavailable metals.

The US Environmental Protection Agency Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: Metal Mixtures (Cadmium, Copper, Lead, Nickel, Silver and Zinc) equilibrium partitioning approach causally link metal concentrations and toxicological effects; they apply to sediment and porewater (i.e., interstitial water). The evaluation of bioavailable metal concentrations in porewater, using tools such as the biotic ligand model, provides an advancement that complements sediment-based evaluations. However, porewater characterization is less commonly performed in sediment bioassays than sediment chemistry characterization due to the difficulty and expense of porewater collection as well as concerns about interpretation of porewater data. This study discusses the advantages and disadvantages of different porewater extraction methods for analysis of metals and bioavailability parameters during laboratory sediment bioassays, with a focus on peepers and centrifugation. The purpose is to provide recommendations to generate bioassay porewater data of sufficient quality for use in risk-based decision-making, such as for regulated cleanup actions. Comparisons of paired data from previous bioassay studies indicate that metal porewater concentrations collected via centrifugation tend to be higher than those collected via peepers. However, centrifugation disrupts the redox status of the sediment; also, metal concentrations can vary markedly based on centrifugation conditions. Data to compare the concentrations of peeper- and centrifugation-collected bioavailability parameters (e.g., major ions, pH) are much more limited, but indicate smaller differences than those observed for metal concentrations. While peepers can be sampled without altering the redox status of the porewater, the small volume of porewater peepers collected is enough for metal concentration analysis, but insufficient for analysis of all metal bioavailability parameters. Given the benefits of metal collection via peepers, it is optimal to use centrifugation and peepers in tandem for bioassay porewater collection to improve bioavailability predictions. Environ Assess Manag 2022;18:1321-1334. © 2021 SETAC.

Refining our understanding of metal bioavailability in sediments using information from porewater: Application of a multimetal biotic ligand model as an extension of the equilibrium partitioning sediment benchmarks.

The equilibrium partitioning sediment benchmarks (ESBs) derived by the US Environmental Protection Agency (USEPA) in 2005 provide a mechanistic framework for understanding metal bioavailability in sediments by considering equilibrium partitioning (EqP) theory, which predicts that metal bioavailability in sediments is determined largely by partitioning to sediment particles. Factors that favor the partitioning of metals to sediment particles, such as the presence of acid volatile sulfide (AVS) and sediment organic matter, reduce metal bioavailability to benthic organisms. Because ESBs link metal bioavailability to partitioning to particles, they also predict that measuring metals in porewater can lead to a more accurate assessment of bioavailability and toxicity to benthic organisms. At the time of their development, sediment ESBs based on the analysis of porewater metal concentrations were limited to comparison with hardness-dependent metals criteria for the calculation of interstitial water benchmark units (IWBUs). However, the multimetal biotic ligand model (mBLM) provides a more comprehensive assessment of porewater metal concentrations, because it considers factors in addition to hardness, such as pH and dissolved organic carbon, and allows for interactions between metals. To evaluate the utility of the various sediment and porewater ESBs, four Hyalella azteca bioassay studies were identified that included sediment and porewater measurements of metals and porewater bioavailability parameters. Evaluations of excess simultaneously extracted metals, IWBUs, and mBLM toxic units (TUs) were compared among the bioassay studies. For porewater, IWBUs and mBLM TUs were calculated using porewater metal concentrations from samples collected using centrifugation and peepers. The percentage of correct predictions of toxicity was calculated for each benchmark comparison. The mBLM-based assessment using peeper data provided the most accurate predictions for the greatest number of samples among the evaluation methods considered. This evaluation demonstrates the value of porewater-based evaluations in conjunction with sediment chemistry in understanding toxicity observed in bioassay studies. Integr Environ Assess Manag 2022;18:1335-1347. © 2021 SETAC.

Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.

Toxicity-modifying factors can be modeled either empirically with linear regression models or mechanistically, such as with the biotic ligand model (BLM). The primary factors affecting the toxicity of nickel to aquatic organisms are hardness, dissolved organic carbon (DOC), and pH. Interactions between these terms were also considered. The present study develops multiple linear regressions (MLRs) with stepwise regression for 5 organisms in acute exposures, 4 organisms in chronic exposures, and pooled models for acute, chronic, and all data and compares the performance of the Pooled All MLR model to the performance of the BLM. Independent validation data were used for evaluating model performance, which for pooled models included data for organisms and endpoints not present in the calibration data set. Hardness and DOC were most often selected as the explanatory variables in the MLR models. An attempt was also made at evaluating the uncertainty of the predictions for each model; predictions that showed the most error tended to show the highest levels of uncertainty as well. The performances of the 2 models were largely equal, with differences becoming more apparent when looking at the performance within subsets of the data. Environ Toxicol Chem 2021;40:2189-2205. © 2021 SETAC.

Comparative Performance of Multiple Linear Regression and Biotic Ligand Models for Estimating the Bioavailability of Copper in Freshwater.

An increasing number of metal bioavailability models are available for use in setting regulations and conducting risk assessments in aquatic systems. Selection of the most appropriate model is dependent on the user's needs but will always benefit from an objective, comparative assessment of the performance of available models. In 2017, an expert workshop developed procedures for assessing metal bioavailability models. The present study applies these procedures to evaluate the performance of biotic ligand models (BLMs) and multiple linear regression (MLR) models for copper. We find that the procedures recommended by the expert workshop generally provide a robust series of metrics for evaluating model performance. However, we recommend some modifications to the analysis of model residuals because the current method is insensitive to relatively large differences in residual patterns when comparing models. We also provide clarification on details of the evaluation procedure which, if not applied correctly, could mischaracterize model performance. We found that acute Cu MLR and BLM performances are quite comparable, though there are differences in performance on a species-specific basis and in the resulting water quality criteria as a function of water chemistry. In contrast, the chronic Cu MLR performed distinctly better than the BLM. Observed differences in performance are due to the smaller effects of hardness and pH on chronic Cu toxicity compared to acute Cu toxicity. These differences are captured in the chronic MLR model but not the chronic BLM, which only adjusts for differences in organism sensitivity. In general, we continue to recommend concurrent development of both modeling approaches because they provide useful comparative insights into the strengths, limitations, and predictive capabilities of each model. Environ Toxicol Chem 2021;40:1649-1661. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Effects of Maternally Transferred Egg Selenium on Embryo-Larval Survival, Growth, and Development in Arctic Grayling (Thymallus arcticus).

Selenium (Se) toxicity to fish is primarily manifested via maternal transfer to the eggs, which may result in adverse effects on larval survival and development. The present study assessed the effects of egg Se concentrations derived via maternal transfer on early life-stage development, survival, and growth of Arctic grayling (Thymallus arcticus), a salmonid species not previously assessed for Se sensitivity. Fish gametes were collected from 4 streams in Alaska known to exhibit a range of egg Se concentrations. Eggs were fertilized and reared in the laboratory from hatch through post-swim-up. Larvae were assessed for survival, length, and weight, as well as deformities (skeletal, craniofacial, fin-fold) and edema based on a graduated severity index. Eggs from a total of 47 females were collected, with egg Se concentrations ranging from 3.3 to 33.9 mg kg-1 dry weight. No relationships were observed between larval endpoints evaluated and parent females' egg, muscle, or whole-body Se concentrations. Therefore, Se 10% effective concentrations (EC10s) were defined as the maximum measured Se concentrations: >33.9, >17.6, and >19.7 mg kg-1 dry weight for eggs, muscle, and whole-body tissue, respectively. Collectively, these data indicate that Arctic grayling are relatively insensitive to maternally transferred Se compared to other fish species. Environ Toxicol Chem 2021;40:380-389. © 2020 SETAC.

Updated Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines.

Multiple linear regression (MLR) models for predicting chronic aluminum toxicity to a cladoceran (Ceriodaphnia dubia) and a fish (Pimephales promelas) as a function of 3 toxicity-modifying factors (TMFs)-dissolved organic carbon (DOC), pH, and hardness-have been published previously. However, the range over which data for these TMFs were available was somewhat limited. To address this limitation, additional chronic toxicity tests with these species were subsequently conducted to expand the DOC range up to 12 mg/L, the pH range up to 8.7, and the hardness range up to 428 mg/L. The additional toxicity data were used to update the chronic MLR models. The adjusted R2 for the C. dubia 20% effect concentration (EC20) model increased from 0.71 to 0.92 with the additional toxicity data, and the predicted R2 increased from 0.57 to 0.89. For P. promelas, the adjusted R2 increased from 0.87 to 0.92 and the predicted R2 increased from 0.72 to 0.87. The high predicted R2 relative to the adjusted R2 indicates that the models for both species are not overly parameterized. When data for C. dubia and P. promelas were pooled, the adjusted R2 values were comparable to the species-specific models (0.90 and 0.88 for C. dubia and P. promelas, respectively). This indicates that chronic aluminum EC20s for C. dubia and P. promelas respond similarly to variation in DOC, pH, and hardness. Overall, the pooled model predicted EC20s that were within a factor of 2 of observed in 100% of the C. dubia tests and 94% of the P. promelas tests. Environ Toxicol Chem 2020;39:1724-1736. © 2020 SETAC.

Development of Empirical Bioavailability Models for Metals.

Recently, there has been renewed interest in the development and use of empirical models to predict metal bioavailability and derive protective values for aquatic life. However, there is considerable variability in the conceptual and statistical approaches with which these models have been developed. In the present study, we review case studies of empirical bioavailability model development, evaluating and making recommendations on key issues, including species selection, identifying toxicity-modifying factors (TMFs) and the appropriate environmental range of these factors, use of existing toxicity data sets and experimental design for developing new data sets, statistical considerations in deriving species-specific and pooled bioavailability models, and normalization of species sensitivity distributions using these models. We recommend that TMFs be identified from a combination of available chemical speciation and toxicity data and statistical evaluations of their relationships to toxicity. Experimental designs for new toxicity data must be sufficiently robust to detect nonlinear responses to TMFs and should encompass a large fraction (e.g., 90%) of the TMF range. Model development should involve a rigorous use of both visual plotting and statistical techniques to evaluate data fit. When data allow, we recommend using a simple linear model structure and developing pooled models rather than retaining multiple taxa-specific models. We conclude that empirical bioavailability models often have similar predictive capabilities compared to mechanistic models and can provide a relatively simple, transparent tool for predicting the effects of TMFs on metal bioavailability to achieve desired environmental management goals. Environ Toxicol Chem 2019;39:85-100. © 2019 SETAC.

An analysis of potential bias in the sensitivity of toxicity data used to construct sensitivity distributions for copper.

Since the mid-1970s, thousands of studies have evaluated the toxicity of various chemicals to aquatic organisms. Results from many of these studies have been used to develop species sensitivity distributions (SSDs) or genus sensitivity distributions (GSDs) for deriving water quality guidelines. Recently, there has been more emphasis on evaluating the toxicity of chemicals to sensitive organisms rather than the entire range of sensitivities. The SSD approach is intended to inform the derivation of guidelines for the protection of all species, not just those that were included in the SSD. The overemphasis of the more sensitive end of the SSD can contribute to a skew in the observed distribution such that the shape of the distribution is distorted from what it would be if all species could be tested, which ultimately affects the derived guideline value. The freshwater acute Cu GSD derived by the US Environmental Protection Agency (USEPA) is one that exemplifies this trend, with one-third of the genera in the GSD belonging to only 3 taxonomic families, all of which are nearer to the sensitive end of the distribution. The stronger representation of the more sensitive families does not seem to mirror the overall abundance of species within those families in nature. This tendency toward testing sensitive organisms is not seen in the chronic Cu SSD. In the present study, Cu toxicity literature is reviewed and long-term trends in the availability of toxicity information for species of varying sensitivity are examined. As part of the present review, the apparent bias that favors the publication of toxicity data for sensitive taxa is demonstrated, and implications for the representativeness of SSDs and their use in developing water quality guidelines are discussed. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

Effects of copper on olfactory, behavioral, and other sublethal responses of saltwater organisms: Are estimated chronic limits using the biotic ligand model protective?

There is concern over whether regulatory criteria for copper (Cu) are protective against chemosensory and behavioral impairment in aquatic organisms. We compiled Cu toxicity data for these and other sublethal endpoints in 35 tests with saltwater organisms and compared the Cu toxicity thresholds with biotic ligand model (BLM)-based estimated chronic limits (ECL values, which are 20% effect concentrations [EC20s] for the embryo-larval life stage of the blue mussel [Mytilus edulis], a saltwater species sensitive to Cu that has historically been used to derive saltwater Cu criteria). Only 8 of the 35 tests had sufficient toxicity and chemistry data to support unequivocal conclusions (i.e., a Cu EC20 or no-observed-effect concentration could be derived, and Cu and dissolved organic carbon [DOC] concentrations were measured [or DOC concentrations could be inferred from the test-water source]). The BLM-based ECL values would have been protective (i.e., the ECL was lower than the toxicity threshold) in 7 of those 8 tests. In the remaining 27 tests, this meta-analysis was limited by several factors, including 1) the Cu toxicity threshold was a "less than" value in 19 tests because only a lowest-observed-effect concentration could be calculated and 2) Cu and/or DOC concentrations often were not measured. In 2 of those 27 tests, the ECL would not have been protective if based only on a conservatively high upper-bound DOC estimate. To facilitate future evaluations of the protectiveness of aquatic life criteria for metals, we urge researchers to measure and report exposure-water chemistry and test-metal concentrations that bracket regulatory criteria. Environ Toxicol Chem 2018;37:1515-1522. © 2018 SETAC.

Protectiveness of Cu water quality criteria against impairment of behavior and chemo/mechanosensory responses: An update.

A meta-analysis was conducted of studies that reported behavior and chemo/mechanosensory responses by fish, amphibians, and aquatic invertebrates in Cu-containing waters and also reported sufficient water chemistry for calculation of hardness-based and biotic ligand model (BLM)-based water quality criteria (WQC) for Cu. The calculated WQC concentrations were then compared with the corresponding 20% impairment concentrations (IC20) of Cu for those behavior and chemo/mechanosensory responses. The hardness-based acute and chronic WQC for Cu would not have been protective (i.e., the IC20 would have been lower than the WQC) in 33.6 and 26.2%, respectively, of the 107 combined behavior- and chemo/mechanosensory-response cases that also had adequate water chemistry data for BLM-based WQC calculations (32.7% inconclusive). In comparison, the BLM-based acute and chronic WQC for Cu would not have been protective in only 10.3 and 4.7%, respectively, of the same 107 cases (29.9% inconclusive). To improve evaluations of regulatory effectiveness, researchers conducting aquatic Cu toxicity tests should measure and report complete BLM-input water chemistry and bracket the hardness-based and BLM-based WQC concentrations for Cu that would be applicable in their exposure waters. This meta-analysis demonstrates that, overall, the BLM-based WQC for Cu were considerably more protective than the hardness-based WQC for Cu against impairment of behavior and chemo/mechanosensory responses. Environ Toxicol Chem 2018;37:1260-1279. © 2018 SETAC.

Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines.

The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [EC10s and EC20s]), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). Hazardous concentrations to 5% of the species or genera were then derived in 2 ways: 1) fitting a log-normal distribution to species-mean EC10s for all species (following the European Union methodology), and 2) fitting a triangular distribution to genus-mean EC20s for animals only (following the US Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC.