Bayesian Method for Groundwater Quality Monitoring Network Analysis

Abstract

A new methodology is developed to analyze existing monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy reduction. A conceptual framework for groundwater quality monitoring is formulated to represent the methodology's context. Relevance vector machine RVM plays a basic role in this conceptual framework, and is employed to reduce redundancy and to create probability map of contaminant distribution, and accordingly to estimate the expected value of sample information. Disability adjusted life years approach of the global burden of disease is used for quantifying the health risk consequences. This is demonstrated through a case study application to nitrate contamination monitoring in the West Bank, Palestine. The results obtained from the RVM analysis showed that an overlap error of less than 30% were obtained based on using around 30% of the monitoring sites 170 relevance vectors. This reflects the importance of the RVM as a useful model for improving the efficiency of monitoring systems, both in terms of reducing redundancy and increasing the information content of the collected data. However, in this application, the results of health risk assessment and the evaluation of monitoring investments were less encouraging due to the minimal elasticity of the nitrate health effect with respect to monitoring information and uncertainty.