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.

Bioavailability-based approaches have been developed for the regulation of metals in freshwaters in several countries. Empirical multiple linear regression (MLR) models have been developed for nickel that can be applied to aquatic organisms. The MLR models have been compared against the use of previously developed biotic ligand models (BLMs) for the normalization of an ecotoxicity dataset compiled for the derivation of a water quality guideline value that could be applied in Australia and New Zealand. The MLR models were developed from data for a number of specific species and were validated independently to confirm their reliability. An MLR modeling approach using different models for algae, plants, invertebrates, and vertebrates performed better than either a pooled MLR model for all taxa or the BLMs, in terms of its ability to correctly predict the results of the tests in the ecotoxicity database based on their water chemistry and a fitted species-specific sensitivity parameter. The present study demonstrates that MLR approaches can be developed and validated to predict chronic nickel toxicity to freshwater ecosystems from existing datasets. The MLR approaches provide a viable alternative to the use of BLMs for taking account of nickel bioavailability in freshwaters for regulatory purposes.

There has been an increased emphasis on incorporating bioavailability-based approaches into freshwater guideline value derivations for metals in the Australian and New Zealand water quality guidelines. Four bioavailability models were compared: the existing European biotic ligand model (European Union BLM) and a softwater BLM, together with 2 newly developed multiple linear regressions (MLRs)-a trophic level-specific MLR and a pooled MLR. Each of the 4 models was used to normalize a nickel ecotoxicity dataset (combined tropical and temperate data) to an index condition of pH 7.5, 6 mg Ca/L, 4 mg Mg/L, (i.e., approximately 30 mg CaCO 3 /L hardness), and 0.5 mg DOC/L. The trophic level-specific MLR outperformed the other 3 models, with 79% of the predicted 10% effect concentration (EC10) values within a factor of 2 of the observed EC10 values. All 4 models gave similar normalized species sensitivity distributions and similar estimates of protective concentrations (PCs). Based on the index condition water chemistry proposed as the basis of the national guideline value, a protective concentration for 95% of species (PC95) of 3 µg Ni/L was derived. This guideline value can be adjusted up and down to account for site-specific water chemistries. Predictions of PC95 values for 20 different typical water chemistries for Australia and New Zealand varied by >40-fold, which confirmed that correction for nickel bioavailability is critical for the derivation of site-specific guideline values.

We studied biotic ligand model (BLM) predictions of the toxicity of nickel (Ni) and zinc (Zn) in natural waters from Illinois and Minnesota, USA, which had combinations of pH, hardness, and dissolved organic carbon (DOC) more extreme than 99.7% of waters in a nationwide database. We conducted 7‐day chronic tests with Ceriodaphnia dubia and 96‐hour acute and 14‐day chronic tests with Neocloeon triangulifer and estimated median lethal concentrations and 20% effect concentrations for both species. Toxicity of Ni and Zn to both species differed among test waters by factors from 8 (Zn tests with C. dubia) to 35 (Zn tests with N. triangulifer). For both species and metals, tests with Minnesota waters (low pH and hardness, high DOC) showed lower toxicity than Illinois waters (high pH and high hardness, low DOC). Recalibration of the Ni BLM to be more responsive to pH‐related changes improved predictions of Ni toxicity, especially for C. dubia. For the Zn BLM, we compared several input data scenarios, which generally had minor effects on model performance scores (MPS). A scenario that included inputs of modeled dissolved inorganic carbon and measured Al and Fe(III) produced the highest MPS values for tests with both C. dubia and N. triangulifer. Overall, the BLM framework successfully modeled variation in toxicity for both Zn and Ni across wide ranges of water chemistry in tests with both standard and novel test organisms. Environ Toxicol Chem 2021;40:3049–3062. © 2021 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

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.

A review of nickel (Ni) toxicity to aquatic organisms was conducted to determine the primary water quality factors that affect Ni toxicity and to provide information for the development and testing of a biotic ligand model (BLM) for Ni. Acute and chronic data for 66 aquatic species were compiled for the present review. The present review found that dissolved organic carbon (DOC) and hardness act as toxicity-modifying factors (TMFs) because they reduced Ni toxicity to fish and aquatic invertebrates, and these effects were consistent in acute and chronic exposures. The effects of pH on Ni toxicity were inconsistent, and for most organisms there was either no effect of pH or, in some cases, a reduction in toxicity at low pH. There appears to be a unique pH effect on Ceriodaphnia dubia that results in increased toxicity at pHs above 8, but otherwise the effects of TMFs were consistent enough across all organisms and endpoints that a single set of parameters in the Ni BLM worked well with all acute and chronic toxicity data for fish, amphibians, aquatic invertebrates, and aquatic plants and algae. The unique effects of pH on C. dubia may be due to mixture toxicity involving both Ni and bicarbonate. The implications of this mixture effect on BLM modeling and a proposed set of BLM parameters for C. dubia are addressed in the review. Other than this exception, the Ni BLM with a single set of parameters could successfully predict toxicity to all acute and chronic data compiled in the present review.

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Per‐ and polyfluoroalkyl substances (PFAS) have been around for decades and have been the subject of laboratory and field research studies for nearly as long. Although attention to PFAS has grown in recent years, many unanswered questions remain. Accordingly, the number of research projects designed to improve our understanding of PFAS toxicity, bioaccumulation, and biomagnification, and to improve our ability to predict the interactions of PFAS mixtures, is also increasing. The growing number of ongoing and future research projects focusing on these chemicals will benefit from lessons learned in previous studies. This perspectives article discusses available approaches to mixture risk assessment with specific focus on application to PFAS mixtures. We discuss ongoing research as well as lessons learned from approaches to handling mixtures of other groups of chemicals. Many of these approaches require some detailed understanding of a manageable number of representative chemicals, yet only limited toxicological data are available for most PFAS. With the limited amount of published data currently available, the need for single‐chemical and binary‐mixture studies persists. Based on our previous work with single‐chemical exposures for understudied PFAS, we explored some of the challenges of trying to design and interpret an aquatic concentration–response experiment for a binary mixture of two commonly detected PFAS: perfluorooctane sulfonate (PFOS) and perfluorohexane sulfonic acid (PFHxS). Based on our findings, we propose that addressing risk from PFAS mixtures will likely require combinations of approaches incorporating findings from traditional whole‐organism toxicity studies and novel methods relying on in vitro assays, read‐across, or quantitative structure activity relationships. Complicating a limited understanding of PFAS mixture toxicity is that relative concentrations of PFAS mixtures vary across sites and time, even at one site. Thus, reliance on empirical data, such as site‐specific toxicity sampling or community structure studies, may be needed to fully understand potential impacts of mixtures. Integr Environ Assess Manag 2021;17:697–704. © 2021 SETAC