Monitoring network design to provide initial detection of groundwater contamination

Abstract

The design of a monitoring network to provide initial detection of groundwater contamination at a waste disposal facility is complicated by uncertainty in both the characterization of the subsurface and the nature of the contaminant source. In addition, monitoring network design requires the resolution of multiple conflicting objectives. A method is presented that incorporates system uncertainty in monitoring network design and provides network alternatives that are noninferior with respect to several objectives. Monte Carlo simulation of groundwater contaminant transport is the method of uncertainty analysis. The random inputs to the simulation are the hydraulic conductivity field and the contaminant source location. The design objectives considered are (1) minimize the number of monitoring wells, (2) maximize the probability of detecting a contaminant leak, and (3) minimize the expected area of contamination at the time of detection. The network design problem is formulated as a multiobjective, integer programming problem and is solved using simulated annealing. An application of the method illustrates the configurations of noninferior network solutions and the trade‐offs between objectives. The probability of detection can be increased either by using more monitoring wells or by locating the wells farther from the source. The latter case results in an increase in the average area of the detected contaminant plumes at the time of initial detection. If monitoring is carried out very close to the contaminant source to reduce the expected area of a detected plume, a large number of wells are required to provide a high probability of detection. A sensitivity analysis showed that the predicted performance of a given number of wells decreases significantly as the heterogeneity of the porous medium increases. In addition, a poor estimate of hydraulic conductivity was shown to result in optimistic estimates of network performance. In general, the trade‐offs between monitoring objectives are an important factor in network design unless the cost (as expressed by the number of monitoring wells) is of limited concern.

Risk-Informed Monitoring, Verification and Accounting (RI-MVA). An NRAP White Paper Documenting Methods and a Demonstration Model for Risk-Informed MVA System Design and Operations in Geologic Carbon Sequestration

“…In an application to groundwater monitoring, Meyer et al (1994) developed a design optimization method which, when linked to a groundwater flow and transport model, identified preferred well monitoring networks against three objectives. The objectives were (1) minimizing network cost; (2) maximizing probability of detecting a contaminant leak; and (3) minimizing the extent of contamination when the leak is first detected.…”

Determination of Optimal Water Quality Monitoring Points in Sewer Systems using Entropy Theory

“…Meyer et al used a simulated annealing approach to solve the multi-objective integer programming of optimal monitoring network design [10]. Hudak defined the configuration of monitoring wells for a solid water landfill in Tarrant County, TX, USA.…”

Detection of contaminant plumes released from landfills: numerical versus analytical solutions

“…Most of these studies do not incorporate all the relevant factors due to the complexity of the issue. In general approaches based on geostatistical methods (i.e., Rouhani and Hall 1988;Haug et al 1989), optimization methods (i.e., Hudak and Loaiciga 1993;Meyer et al 1994;Storck et al 1997), methods based on extensive simulation (Massmann and Freeze 1987;Meyer et al 1994;Storck et al 1997) and graphical methods (Hudak 2001(Hudak , 2002) are used to design monitoring systems.…”