Groundwater contamination is the degradation of the natural quality of groundwater as a result of human activity. Landfills are one of the most common human activities threatening the groundwater quality. The objective of the monitoring systems is to detect the contaminant plumes before reaching the regulatory compliance boundary in order to prevent the severe risk to both society and groundwater quality, and also to enable cost-effective counter measures in case of a failure. The detection monitoring problem typically has a multi-objective nature. A multi-objective decision model (called MONIDAM) which links a classic decision analysis approach with a stochastic simulation model is applied to determine the optimal groundwater monitoring system given uncertainties due to the hydrogeological conditions and contaminant source characteristics. A Monte Carlo approach is used to incorporate uncertainties. Hydraulic conductivity and the leak location are the random inputs of the simulation model. The design objectives considered in the model are: (1) maximizing the detection probability, (2) minimizing the contaminated area and, (3) minimize the total cost of the monitoring system. The results show that the monitoring systems located close to the source are optimal except for the cases with very high unit installation and sampling cost and/or very cheap unit remediation cost.
Contaminant leaks released from landfills are a threat to groundwater quality. The groundwater monitoring systems installed in the vicinity of such facilities are vital. In this study the detection probability of a contaminant plume released from a landfill has been investigated by means of both a simulation and an analytical model for both homogeneous and heterogeneous aquifer conditions. Since the detection probability is a sensitive quantity, we first compare the two methods for homogeneous aquifer conditions to assess the errors that are encountered by performing simulations. The analysis shows that the simulation model yields the detection probabilities of a contaminant plume at a given monitoring well quite well in the homogeneous case. For heterogeneous aquifers we apply the approximated analytical model based on macro-dispersivities. Here we find that this model is insufficient in monitoring system design, since the obtained analytical values of the detection probabilities at a given monitoring well differ significantly from those computed by simulation.
The design of a ground water detection monitoring system at a lined landfill is complicated due to uncertainties in contaminant source characteristics and variability of hydrogeological conditions. Maximizing the likelihood of detecting contaminants and minimizing the contaminated area are the conflicting design objectives. Mostly, a large number of wells may be required to achieve the desired efficiency. However, the cost might be quite high from a practical point of view. Moreover, with the conventional monitoring approach, a widely applied three‐well monitoring system (minimum regulatory requirement) is more often inadequate to accomplish these objectives at lined landfills due to the limited capture zone of monitoring wells. Therefore, implementation of a new monitoring approach has been proposed in this study to design a highly efficient, cost‐effective, three‐well system. In this new approach, the main idea is to increase the interception of contaminant plumes at early stages by broadening the capture zone of monitoring well(s) simply by continuous pumping from the monitoring well(s) with a small pumping rate. A hypothetical problem is presented where a Monte Carlo framework is used to incorporate uncertainties due to subsurface heterogeneity and the leak location. A finite‐difference ground water model coupled with a random‐walk particle‐tracking model simulates a contaminant plume released from the landfill for each Monte Carlo realization. The efficiency and the cost of the three‐well monitoring network have been compared for conventional and proposed monitoring approaches (PMA). It has been observed that the efficiency of the monitoring system improves significantly by the application of the PMA.
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