Development and Evaluation of Aquatic and Terrestrial Food Web Bioaccumulation Models for Per- and Polyfluoroalkyl Substances.

There is a need for reliable models to predict the food web bioaccumulation and assess ecological and human health risks of per- and polyfluoroalkyl substances (PFAS). This present study presents (i) the development of novel mechanistic aquatic and terrestrial food web bioaccumulation models for PFAS and (ii) an evaluation of model performance using available laboratory and field data. Model predictions of laboratory-measured bioconcentration factors and field-based bioaccumulation factors of PFAS in fish were in good agreement with observed data as measured by the mean model bias (MB), representing systematic over- or under-estimation and the standard deviation of the MB, representing general uncertainty.

The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities.

Purpose Of Review: This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water.

A food web bioaccumulation model for the accumulation of per- and polyfluoroalkyl substances (PFAS) in fish: how important is renal elimination?

Per- and polyfluoroalkyl substances (PFAS) are a large class of highly fluorinated anthropogenic chemicals. Some PFAS bioaccumulate in aquatic food webs, thereby posing risks for seafood consumers. Existing models for persistent organic pollutants (POPs) perform poorly for ionizable PFAS.