You are here:
Development of empirical bioavailability models for metals
Citation:
Brix, K., D. DeForest, W. Peijnenburg, A. Peters, E. Traudt, AND R. Erickson. Development of empirical bioavailability models for metals. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 39(1):85-100, (2020).
Impact/Purpose:
This manuscript is a review of methods for developing empirical mathematical models for the dependence of metals toxicity on “toxicity modifying factors” (TMFs) in freshwater. These types of models have been used in USEPA water quality criteria for over 35 years and continue to be developed. This review identifies numerous considerations important in model development, regarding issues of biological species selection, identifying 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. By summarizing these considerations, this manuscript should be a useful resource for future model development.
Description:
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 this paper, 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 non-linear responses to TMFs and should encompass a large fraction (e.g., 90%) of the TMF range while avoiding TMF combinations that are unlikely to occur in natural waters. 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.