GROUNDWATER DETECTION MONITORING USING COMBINED INFORMATION FROM MULTIPLE CONSTITUENTS

A Bayesian methodology is developed for groundwater leak detection at waste disposal sites. The method requires specification of the prior p robability of a leak at the facility and updates this probability base d on the proportion of constituent observations above a threshold valu e (e.g., the analytical detection limit) in the monitoring versus the background samples. The information content of a chemical measurement is characterized by the probability of occurrence of elevated concentr ations of the chemical in a monitoring well, given the presence or abs ence of a facility leak. The evidence from multiple constituents is co mbined, assuming independence of errors, into a single aggregate estim ate for the posterior probability of a leak. The procedure allows expe rt knowledge on site conditions and chemical behavior in the groundwat er to be systematically incorporated in the regulatory evaluation of l eak detection monitoring data. Applications using hypothetical and act ual monitoring data from a hazardous waste facility demonstrate the pr ocedure and highlight needs for further research.