Spatial Extreme Value Modeling for PFAS Source Apportionment

Thomas M. Menino Convention & Exhibition Center 

Source apportionment modeling to quantify contributors to water pollutant concentrations is crucial for targeted monitoring, remediation, and the evaluation of regulatory interventions. Per- and polyfluoroalkyl substances (PFAS) pose a unique challenge in this space due to highly skewed concentration distributions and the need to assess both background levels and consequential exceedances of health-based thresholds. To address this, we develop a Bayesian hierarchical model that utilizes the extended generalized Pareto distribution (EGPD) to flexibly characterize both the lower and upper tails of PFAS concentrations, enabling inference across the entire distribution. Spatial dependence between potential surface sources and groundwater receptors is modeled using river-network distances, accurately reflecting hydrologic connectivity and downstream transport pathways. We apply this modeling framework to groundwater monitoring data from California's GAMA program to demonstrate its utility in a complex, real-world regulatory environment. Ultimately, this approach allows for the identification of sparse, localized source contributions while accommodating the uncertainty in extreme concentrations that often drive compliance decisions.

Bayesian modeling

Spatial statistics

Extreme value modeling

Environmental monitoring 

Section on Statistics and the Environment