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

Abstract

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.

MCFP: A Monte Carlo Simulation-based Fuzzy Programming Approach for Optimization under Dual Uncertainties of Possibility and Continuous Probability

“…The integration of fuzzy programming with Monte Carlo simulation could be a promising solution (Sadeghi et al, 2010;Li et al, 2013). However, due to the difficulties in integrating fuzzy programming with Monte Carlo simulation, only a few studies were reported and they were all used to assess health risk issues (Guyonnet et al, 2003;Li et al, 2004;Chen et al, 2003;Liu et al, 2004;Li et al, 2007;Sadeghi et al, 2010;Ping et al, 2010b). In addition, because of the complex iterations in optimization algorithm, the integration of fuzzy programming and Monte Carlo simulation becomes challenging, and none such study is applied in optimization.…”

DIPIP: Dual Interval Probabilistic Integer Programming for Solid Waste Management

“…Environmental protection and resources conservation are of major concerns along with increasing waste generation and decreasing waste-disposal capacity. In response to these, various optimization techniques were used for supporting effective management of the systems (Chang and Wang, 1997;Huang et al, 2007;Ahluwalia and Nema, 2007;Li, 2007;He et al, 2008). At the same time, uncertainties exist in many system components (e.g.…”