Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

This study aims to improve the accuracy of groundwater pollution source identification using concentration measurements from a heuristically designed optimal monitoring network. The designed network is constrained by the maximum number of permissible monitoring locations. The designed monitoring network improves the results of source identification by choosing monitoring locations that reduces the possibility of missing a pollution source, at the same time decreasing the degree of non uniqueness in the set of possible aquifer responses to subjected geo-chemical stresses. The proposed methodology combines the capability of Genetic Programming (GP), and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. The GP models are trained using large number of simulated realizations of the pollutant plumes for varying input flux scenarios. A selected subset of GP models are used to compute the impact factor and frequency factor of pollutant source fluxes, at candidate monitoring locations, which in turn is used to find the best monitoring locations. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the efficiency in source identification when concentration measurements from the designed monitoring network are utilized.     

Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources

The identification of unknown pollution sources is a prerequisite for designing of a remediation strategy. In most of the real world situations, it is difficult to identify the pollution sources without a scientifically designed efficient monitoring network. The locations of the contaminant concentration measurement sites would determine the efficiency of the unknown source identification process to a large extent. Therefore coupled and iterative sequential source identification and dynamic monitoring network design framework is developed. The coupled approach provides a framework for necessary sequential exchange of information between monitoring network and source identification methodology. The preliminary identification of unknown sources, based on limited concentration data from existing arbitrarily located wells provides the initial rough estimate of the source fluxes. These identified source fluxes are then utilized for designing an optimal monitoring network for the first stage. Both the monitoring network and source identification process is repeated by sequential identification of sources and design of monitoring network which provides the feedback information. In the optimal source identification model, the Jacobian matrix which is the determinant for the search direction in the nonlinear optimization model links the groundwater flow-transport simulator and the optimization method. For the optimal monitoring network design, the integer programming based optimal design model requires as input, simulated sets of concentration data. In the proposed methodology, the concentration measurement data from the designed and implemented monitoring network are used as feedback information for sequential identification of unknown pollution sources. The potential applicability of the developed methodology is demonstrated for an illustrative study area.     

Dynamic Optimal Monitoring Network Design for Transient Transport of Pollutants in Groundwater Aquifers

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.