Probabilistic risk assessment approaches better protect susceptible populations

Abstract

Across all of its program areas, the United States Environmental Protection Agency conducts risk assessments to evaluate the potential adverse effects of environmental hazards on ecological and human health. Traditionally, these assessments rely on deterministic methods that use point estimates for key parameters and incorporate uncertainty factors and precautionary assumptions to account for uncertainties in data and variability in environmental conditions, exposure pathways, and population characteristics. However, these approaches are unnecessarily conservative for the general population and yet fail to transparently account for the vulnerabilities of susceptible populations. Probabilistic risk assessment (PRA) offers a more refined approach that uses distributional data to better characterize uncertainty and response. By leveraging empirical data and probabilistic modeling, PRA allows for a more transparent, precise quantification of risk that ensures targeted protection for susceptible populations. This article examines how PRA enhances each phase of risk assessment—hazard identification, dose-response assessment, exposure assessment, and risk characterization—to produce a more scientifically robust assessment of risk for susceptible populations, including both human and ecological receptors. We review the literature and discuss practical applications of PRA in regulatory contexts to illustrate its advantages over deterministic approaches. Finally, we discuss key implementation challenges and propose future research directions to advance risk assessment methodologies and improve policy decision-making.