Optimal dynamic monitoring network design for reliable tracking of contaminant plumes in an aquifer system.

Monitoring groundwater aquifers for possible sources of contamination is an important aspect of water resource management. The design of monitoring networks has been one of the key concerns of researchers who deal with the management of groundwater quality. To control, prevent, and remediate groundwater contamination, large number of monitoring well locations is required in a 3-dimensional transient system. This is associated with significant installation, operational and implementation costs. Therefore, a method, which can identify an optimal number of monitoring wells, is useful in saving costs and for effective monitoring of the plume concentration and movement. A state of the art groundwater monitoring network design, which combines groundwater flow and transport results with a Genetic Algorithm (GA) optimization procedure to identify optimal monitoring well location is presented in this study. The proposed sequential network design approach differs from other monitoring network designs by placing the emphasis on maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. It also addresses the issue of enhancing modelling accuracy when the hydrogeologic and hydrochemical data such as contaminant concentration measurement data are sparse. The proposed methodology aims at tracking the pollutant plume movement by sequentially designing and then implementing an optimal monitoring network. Each design and its implementation are followed by a limited period of additional concentration measurements at the monitoring locations. This additional information is later used for a new design of the monitoring network.