An economic decision framework using modeling for improving aquifer remediation design

Reducing cost is a critical challenge facing environmental remediation today. One of the most effective ways of reducing costs is to improve decision-making. This can range from choosing more cost- effective remediation alternatives (for example, determining whether a groundwater contamination plume should be remediated or not) to improving data collection (for example, determining when data collection should stoop). Uncertainty in site conditions presents a major challenge for effective decision-making. We present a framework for increasing the effectiveness of remedial design decision-making at groundwater contamination sites where there is uncertainty in many parameters that affect remediation design. The objective is to provide an easy-to-use economic framework for making remediation decisions. The presented framework is used to 1) select the best remedial design from a suite of possible ones, 2) estimate if additional data collection is cost-effective, and 3) determine the most important parameters to be sampled. The framework is developed by combining elements from Latin-Hypercube simulation of contaminant transport, economic risk-cost-benefit analysis, and Regional Sensitivity Analysis (RSA).