Development of Empirical Bioavailability Models for Metals

Abstract

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.

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

“…Species‐specific models will obviously perform better for the species on which they are based compared with a pooled model. However, there are advantages to using a pooled model for criteria development over species‐specific models that may outweigh the reduced performance of pooled models in predicting toxicity for a given species (Brix et al 2020). Specifically, use of a pooled model ensures that all taxa in the GSD will respond to TMFs in the same way.…”

“…Lastly, we note that recently developed methods for comparing the performance of different models for predicting metals toxicity as a function of varying TMFs could also have been applied to the comparison of individual and pooled species models (Brix et al 2020; Garman et al 2020). The primary components of the model comparison metrics from those studies are based on adjusted and predicted R 2 values and slopes of the residuals for TMFs.…”

“…This updated dataset was used to develop new MLR models following the methods described in DeForest et al (2018) and Brix et al (2020), which employed the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) to determine which combination of terms resulted in the most parsimonious models for predicting Al EC10s or EC20s (Burnham and Anderson 2004).…”

“…Pooled MLR models based on the combined C. dubia and P. promelas datasets were developed using the approach described for copper in Brix et al (2017, 2020). In addition, the possibility of developing a pooled model using Al toxicity data for C. dubia , P. promelas , and the alga R. subcapitata was evaluated.…”

Multiple Linear Regression Modeling Predicts the Effects of Surface Water Chemistry on Acute Vanadium Toxicity to Model Freshwater Organisms

“…Consequently, an empirical approach (multiple linear regression [MLR]) is gaining popularity and could become a valuable resource for the development of CWQGs for V (Esbaugh et al 2011; Brix et al 2017, 2020; DeForest et al 2018). The MLR model has recently been applied in developing a CCME CWQG‐PAL for zinc (Zn) to account for the simultaneous influence of pH, water hardness, and dissolved organic carbon (Canadian Council of Ministers of the Environment 2018).…”