Revista Águas Subterrâneas (Feb 2015)
Uncertainty analysis for simulating pump-and-treat in contaminated areas
Abstract
Due to the growing demand for project execution remediation of contaminated aquifers, the pump and treat technique is being used significantly. This remediation technique consists in groundwater pumping in strategically points placed to generate a hydraulic barrier to prevent the advance of a contamination plume. In areas with high lithological heterogeneity, the spatial organization of the hydraulic conductivity (K) values in the subsurface introduces uncertainties associated with the variability in the distribution of facies. In this study these uncertainties were evaluated using geostatistical methods (indicator kriging and stochastic simulations) and also from numerical simulations of flow and contaminant transport. These tools enable the analysis of the contamination plumes areas, as well as scenarios for remediation, due to different random fields of local conductivity according to the geological complexity. By indicator kriging, was possible to provide a geostatistical facies model which was used as a tool to validate the hydraulic conductivity as a function of each lithology, thus defining the possible hydrofacies groups in the area. The stochastic simulations generated twenty distinct hydraulic conductivity fields, which represented different spatial distributions of contamination plumes, as well as scenarios optimistic, intermediate and pessimistic to offset these. Three scenarios for pump and treat were simulated resulting nine situations of this remediation technique. Therefore the effects of heterogeneity associated with hydraulic conductivity showed that, for the remediation design in areas with certain geological heterogeneity, the analysis of uncertainties linked to hydrogeological studies is needed. When considering models with homogeneous conductivity and most simplistic, the remediation project may prove to be inefficient increasing the time to the project and consequently increasing costs.
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