Bayesian Statistics-Based Procedure for the Groundwater Long-Term Monitoring Temporal Optimization Problem

Groundwater long-term monitoring (LTM) is required for groundwater remediation projects to assess compliance of active remedial systems and natural attenuation sites where groundwater contamination is still present. LTM can be costly given the large number of sampling locations, frequency of monitoring, and number of constituents monitored at given site. This study uses a Bayesian statistics-based methodology to optimize the scheduling of groundwater long-term monitoring. This method does not rely on pollutant simulated transport models. The technique combines information from different sets of observations over multiple sampling periods with spatial sampling optimization by ant colony optimization algorithm. Instead of binary results (0/1), this methodology will suggest future monitoring schedule of each individual monitoring well on fuzzy probabilistic scale (0–1) and thus facilitating its inclusion into risk assessment procedures.