Probabilistic approaches for risk assessment and regulatory criteria development: current applications, gaps, and opportunities
Camille Flinders, Brad Barnhart, Emily Morrison, Paul Anderson, Wayne G. Landis
Abstract
Traditional ecological and human health risk assessment often relies on deterministic frameworks that preclude the presence of variability or uncertainty among input parameters characterizing exposure, effects, and risk. To promote increased realism and generate more robust risk management decisions, probabilistic risk assessment (PRA) has been introduced as a foundational grouping of techniques that seeks to broadly characterize variability among its components. Although multiple methods exist (e.g., Monte Carlo simulations, Bayesian networks), along with some federal and state regulatory guidance, gaps remain in prescriptive regulatory recommendations for the implementation of PRA methods. This article describes specific probabilistic approaches for risk characterization and assessment, regulatory support of PRA, challenges that may limit more widespread use, and opportunities for its expanded use in regulatory areas where it is not currently applied. Taken together, we hope to advance the understanding of probabilistic methodologies and their versatility for robust, transparent, data-based environmental risk assessment and standards derivation across a range of media that align with regulatory objectives to protect aquatic and terrestrial biota, human health, and vulnerable populations.