An optimization approach for groundwater quality monitoring network design

The optimal sampling plan for groundwater quality monitoring is formulated as a mixed integer programming (MIP) problem. A sampling plan consists of the number and locations of sampling sites as well as the temporal sampling frequency. The MIP network problem is defined by the minimization of the variance of estimation error subject to resource and unbiasedness constraints. The mean and covariance of the spatial/temporal variable (chloride concentration measurements) are derived from the advection‐dispersion equation governing mass transport. The solution for the optimal sampling proceeds in two stages: (1) parameter estimation and (2) network optimization. The MIP model was successfully tested with a network design problem in a buried valley aquifer in Butler County, Ohio. The application illustrates the role of objective function, resource constraint, mass transport processes, and hydrogeologic setting in groundwater quality monitoring network design.