Risk assessment for per-and polyfluoroalkyl substances (PFAS) is complicated by the fact that PFAS include several thousand compounds. While new analytical methods have increased the number that can be identified in environmental samples, a significant fraction of them remain uncharacterized. Perfluorooctane sulfonate (PFOS) is the PFAS compound of primary interest when evaluating risks to humans and wildlife due to consumption of aquatic organisms. The exposure assessment for PFOS is complicated by the presence of PFOS precursors and their transformation, which can occur both in the environment and within organisms. Thus, the PFOS to which wildlife or people are exposed may consist of PFOS that was discharged directly into the environment and/or other PFOS precursors that were transformed into PFOS. This means that exposure assessment and the development of remedial strategies may depend on the relative concentrations and properties not only of PFOS, but also of other PFAS that are transformed into PFOS. A bioaccumulation model was developed to explore these issues.The model embeds toxicokinetic and bioenergetic components within a larger food web calculation that accounts for uptake from both food and water, as well as predator/prey interactions. Multiple chemicals are modeled, including parent/daughter reactions. A series of illustrative simulations explores how chemical properties can influence exposure assessment and remedial decision-making.

Groundwater professionals require tools to evaluate a variety of technical issues related to per-and polyfluoroalkyl substances (PFAS). These include the potential impact of PFAS precursors on groundwater plumes of perfluoroalkyl acids (PFAAs). Numerical modeling results show that, by adjusting the mass loading rate, source zones with or without a precursor can produce similar PFAA plumes. However, if a precursor is present, it can impact PFAA plume concentrations and extend PFAA plume durations by decades. Additional research regarding in situ precursor transformation rates-and improvements in source area characterization-will further advance the predictive value of modeling.