Risk-based in situ bioremediation design using a noisy genetic algorithm

Risk-based corrective action (RBCA) is rapidly becoming the method of choic e for remediating contaminated groundwater. In this paper, a management mod el is presented that simultaneously predicts risk and proposes cost-effecti ve options for reducing risk to acceptable levels under conditions of uncer tainty. The model combines a noisy genetic algorithm with a numerical fate and transport model and an exposure and risk assessment model. The noisy ge netic algorithm uses sampling from parameter distributions to assess the pe rformance of candidate designs. Results from an application to a site from the literature show that the noisy genetic algorithm is capable of identify ing highly reliable designs from a small number of samples, a significant a dvantage for computationally intensive groundwater management models. For t he site considered, time-dependent costs associated with monitoring and the remedial system were significant, illustrating the potential importance of allowing variable cleanup lengths and a realistic cost function.