A risk analysis framework is proposed for the optimum remediation of a contaminated aquifer under hydrogeological uncertainty. The limited information and the spatial variation of hydraulic conductivity in a real-world large-scale aquifer create uncertain conditions for decision-making when remediation schemes ought to be accompanied by the minimum possibility of failure. The primary concern is focused on safeguarding public health when groundwater is used for urban drinking purposes from a contaminated aquifer. The proposed framework is based on the conjunctive use of stochastic simulation–optimization modelling followed up by a risk analysis application on remediation trade-offs. The framework includes three main steps/procedures: (i) the model formulation of multiple realizations of groundwater flow and contaminant transport, (ii) the optimal positioning and operation of the clean-up wells determined by the method of stochastic optimization, and (iii) the risk analysis of the optimum remediation strategies through a proposed decision model, so as the one with the minimum cost and risk of failure is chosen as the most appropriate. The proposed framework is tested for two scenarios of nitrogen fertilizer application in the cultivated areas. The strategic target is the groundwater nitrate concentration minimization in an area where exceedances of nitrate concentrations have been observed and water supply wells have been operating for the last twenty years satisfying domestic needs. The results demonstrate that, when decision-making is under hydrogeological uncertainty, the combined use of stochastic optimization and risk-based decision analysis can commend the remediation strategy with the minimum cost and the highest possibility of success.
CITATION STYLE
Sidiropoulos, P., Mylopoulos, N., Lyra, A., Tziatzios, G. A., & Loukas, A. (2023). Risk analysis framework for the optimum remediation of a contaminated aquifer under uncertainty: application in Lake Karla aquifer, Thessaly, Greece. Stochastic Environmental Research and Risk Assessment, 37(4), 1281–1302. https://doi.org/10.1007/s00477-022-02341-9
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