On uncertainty in remediation analysis: variance propagation from subsurface transport to exposure modeling

Abstract

Addressing long-term potential human exposures to, and health risks from contaminants in the subsurface environment requires the use of models. Because these models must project contaminant behavior into the future, and make use of highly variable landscape properties, there is uncertainty associated with predictions of long-term exposure. Many parameters used in both subsurface contaminant transport simulation and health risk assessment have variance owing to uncertainty and/or variability. These parameters are best represented by ranges or probability distributions rather than single values. Based on a case study with information from an actual site contaminated with trichloroethylene (TCE), we demonstrate the propagation of variance in the simulation of risk using a complex subsurface contaminant transport simulation model integrated with a multi-pathway human health risk model. Ranges of subsurface contaminant concentrations are calculated with the subsurface transport simulator T2VOC (using the associated code ITOUGH2 for uncertainty analysis) for a three-dimensional system in which TCE migrates in both the vadose and saturated zones over extended distances and time scales. The subsurface TCE concentration distributions are passed to CalTOX, a multimedia, multi-pathway exposure model, which is used to calculate risk through multiple exposure pathways based on inhalation, ingestion and dermal contact. Monte Carlo and linear methods are used for the propagation of uncertainty owing to parameter variance. We demonstrate how rank correlation can be used to evaluate contributions to overall uncertainty from each model system. In this sample TCE case study, we find that although exposure model uncertainties are significant, subsurface transport uncertainties are dominant.