Health scientists often rely on simulation models to reconstruct groundwater contaminant exposure data for retrospective epidemiologic studies. Due to the nature of historical reconstruction process, there are inevitably uncertainties associated with the input data and, therefore, with the final results of the simulation models, potentially adversely impacting related epidemiologic investigations. This study examines the uncertainties associated with the historically reconstructed contaminant fate and transport simulations for an epidemiologic study conducted at U.S. Marine Corps Base Camp Lejeune, North Carolina. To achieve an efficient uncertainty analysis, sensitivity analysis was first conducted to identify the critical uncertain variables, which were then adopted in the uncertainty analysis using an improved Monte Carlo simulation (MCS) method. Particularly, uncertainties associated with the historical contaminant arrival time were evaluated. To quantify the uncertainties in an efficient manner, a procedure identified as Pumping Schedule Optimization System (PSOpS) was developed to obtain the extreme (i.e., earliest and latest) contaminant arrival times caused by pumping schedule variations. Two improved nonlinear programming methods Rank-and-Assign (RAA) and Improved Gradient (IG) are used in PSOpS to provide computational efficiency. Furthermore, a quantitative procedure named Pareto Dominance based Critical Realization Identification (PDCRI) was developed to screen out critical realizations for contaminant transport in subsurface system, so that the extreme contaminant arrival times under multi-parameter uncertainties could be evaluated efficiently.
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Sensitivity and uncertainty analyses of contaminant fate and transport in a field-scale subsurface system
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