Stochastic Risk Assessment of Groundwater Contamination under Uncertainty: A Canadian Case Study

Abstract

This study presents a stochastic approach for risk assessment of groundwater contamination through incorporating a stochastic subsurface contaminant transport and fate modeling system within a general risk assessment framework. The uncertainties associated with soil properties (e.g. soil porosity and permeability) were addressed through the probabilistic method, and the resulting uncertainties in risks of groundwater contamination were then identified under different remediation scenarios. The method was applied to a petroleum-contaminated site in western Canada. Three remediation scenarios with different clean-up efficiencies (0, 60, and 90%) were examined for a planning period of 10 years to study the risks introduced by xylene. The concept of hazard index was applied to characterize the associated non-carcinogenic risk. Three risky zones with different mean hazard index distributions were identified, and the probabilities of violating assumed hazard index criteria at the study site were also presented. The obtained results indicated that the proposed method could effectively identify risky zones with different risk levels and violation probabilities under various remediation actions, and they are directly useful for the decision maker to gain insight into the study site and to make remediation decisions.