The optimal design of groundwater quality monitoring networks under conditions of uncertainty

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 both incorporates system uncertainty in monitoring network design and provides network alternatives that are noninferior with respect to several objectives. Monte Carlo simulation is the method of uncertainty analysis. The random inputs to the simulation are the hydraulic conductivity field and the contaminant source location. A transport model is used to generate a series of random plumes representing equally likely contamination scenarios. For each plume, the method finds the set of potential monitoring locations at which the plume is detectable. In addition, the area of each plume is recorded at the time when it reaches each potential monitoring location. This information is used to formulate one of two optimization models. 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 method is applied to a generic problem. Results illustrate the tradeoffs between objectives and the configurations of noninferior network solutions. The probability of detection is increased 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 plumes. 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 is required to provide a high probability of detection. These tradeoffs are an important factor in network design unless the cost (as expressed by the number of monitoring wells) is of limited concern. The importance of using a sufficiently large number of plume realizations in the Monte Carlo simulation is demonstrated. Finally, a sensitivity analysis illustrates the importance of several model parameters, including the hydraulic conductivity field variance and correlation scale, the transverse dispersivity, and the size of the contaminant source.U of I OnlyETDs are only available to UIUC Users without author permissio