A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation

Groundwater contaminant remediation poses a significant challenge due in large part to ubiquitous uncertainties and unknowns. A number of remedial options are typically available at a given contamination site. However, choosing the best option is challenging, because it is not typically known beforehand how effective each remediation will be. Nonetheless, choices must be made. We present an approach to decision support consisting of three coupled layers accounting for model/parametric uncertainties and unknowns. The inner and outer layers employ an information-gap approach to uncertainty quantification, while the middle layer employs Bayes' theorem. Two synthetic remedial scenarios are explored to demonstrate the efficacy of the approach. Considered remedial actions are natural attenuation (NA) and an enhanced attenuation (EA).