Heuristic space–time design of monitoring wells for contaminant plume characterization in stochastic flow fields

An optimization methodology for designing groundwater quality monitoring networks applicable to stochastic flow fields is presented and evaluated. The approach sets itself apart from previous techniques by incorporating the time dimension directly into the objective function. This function is extremized using a directed partial enumeration strategy guided by physical considerations related to transport processes. The result is a set of monitoring well locations and a sampling schedule that minimizes plume characterization error while satisfying constraints on the maximum number of wells and allowable number of active wells. The method is evaluated using hypothetical plumes with varying degrees of heterogeneity. Results indicate that the proposed approach is successful in generating near-optimal sampling networks that satisfy all imposed constraints. Monitoring networks with as little as three active wells and a total of 12 wells are found to provide adequate plume characterization for low toxicity contaminants.