Abstract

Optimal groundwater pollution monitoring network design models are developed to prescribe optimal and efficient sampling locations for detecting pollution in groundwater aquifers. The developed methodology incorporates a two dimensional flow and transport simulation model to simulate the pollutant concentrations in the study area. Different realizations of the pollutant plume are randomly generated by incorporating the uncertainty in both source and aquifer parameters. These concentration realizations are incorporated in the optimal monitoring network design models. Two different objectives are considered separately. The first objective function minimizes the summation of unmonitored concentrations at different potential monitoring locations. This objective function in effect minimizes the probability of not monitoring the pollutant concentrations at those locations where the probable concentration value is large. Although this probability is not explicitly incorporated in the model, a surrogate form of this objective is included as the objective function. The second objective function considered is the minimization of estimation variances of pollutant concentrations at various unmonitored locations. This objective results in a design that chooses optimal monitoring locations where the uncertainties in simulated concentrations are large. The developed optimization models are solved using Genetic Algorithm. The variances of estimated concentrations at potential monitoring locations are computed using the geostatistical tool, kriging. The designed monitoring network is dynamic in nature, as it provides time varying network designs for different management periods, to account for the transient pollutant plumes. Such a design can eliminate temporal redundancy and is therefore, economically more efficient. The optimal design incorporates budgetary constraints in the form of limits on the number of monitoring wells installed in any particular management period. The solution results are evaluated for an illustrative study area comprising of a hypothetical aquifer. The performance evaluation results establish the potential applicability of the proposed methodology for optimal design of the dynamic monitoring network for detection and monitoring of pollutant plumes in contaminated aquifers.

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