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.

A Review of Water Quality Factors that Affect Nickel Bioavailability to Aquatic Organisms: Refinement of the Biotic Ligand Model for Nickel in Acute and Chronic Exposures.

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. Environ Toxicol Chem 2021;40:2121-2134. © 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.

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.