Exploring the Impacts of Interpolation Methods on Groundwater Monitoring Optimization

Long-term groundwater monitoring (LTM) after active remediation is required to assess the concentration of residual contaminants and to protect human health and environment. As a monitored site becomes stable, the contamination extent diminishes and monitored parameters become more predictable, it may be feasible to reduce the monitoring frequency, locations, and analytical requirements while meeting monitoring goals and data sufficiency. LTM optimization offers an opportunity to reduce costs while assuring that monitoring network collects sufficient data. The optimization includes identifying the redundancy or insufficiency in the monitoring network, and recommending changes to protect against potential impacts to human health and the environment.This study develops an ant colony optimization (ACO) method, using ordinary kriging (OK), or inverse distance weighting (IDW) for data interpolation, to identify redundant wells in LTM networks while satisfying data accuracy requirements. ACO is a global stochastic search method inspired by the collective problem-solving ability of a colony of ants as they search for the most efficient routes from their nest to food sources.The performance of ACO combined with either IDW or OK interpolation is evaluated separately in two test cases for LTM network optimization. One is a synthetic data set generated from a groundwater transport simulation model with 73 data locations, and the other is a field site with 53 monitoring locations. Results of the test cases within the ACO-LTM optimization framework are discussed.