Extension and field application of an integrated DNAPL source identification algorithm that utilizes stochastic modeling and a Kalman filter

The design of an effective groundwater remediation system involves the determination of the source zone characteristics and subsequent source zone removal. The work presented in this paper focuses on the three-dimensional extension and field application of a previously described source zone identification and delineation algorithm. The three-dimensional search algorithm defines how to achieve an acceptable level of accuracy regarding the strength, geographic location and depth of a dense non-aqueous phase liquid (DNAPL) source while using the least possible number of water quality samples. Target locations and depths of potential sources are identified and given initial importance measures or weights using a technique that exploits expert knowledge. The weights reflect the expert’s confidence that the particular source location is the correct one and they are updated as the investigation proceeds. The overall strategy uses stochastic groundwater flow and transport modeling assuming that hydraulic conductivity is known with uncertainty (Monte Carlo approach). Optimal water quality samples are selected according to the degree to which they contribute to the total concentration uncertainty reduction across all model layers and the proximity of the samples to the potential source locations. After a sample is taken, the contaminant concentration plume is updated using a Kalman filter. The set of optimal source strengths is determined using linear programming by minimizing the sum of the absolute differences between modeled and measured concentration values at sampling locations. The Monte Carlo generated suite of plumes emanating from each individual source is calculated and compared with the updated plume. The scores obtained from this comparison serve to update the weights initially assigned by the expert, and the above steps are repeated until the optimal source characteristics are determined. The algorithm’s effectiveness is demonstrated by performing a ‘blind test’ at a field site in the Anniston Army Depot (ANAD) in Alabama.