Utilization of a Bayesian probabilistic inferential framework for contamination source identification in river environment

¹ College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing, 100875, China
² Chinese Research Academy of Environmental Sciences, No 8, Dayangfang, Chaoyang District, Beijing, 100012, China
³ State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing, 100012, China

^a Corresponding author: superbg@163.com; yaozp@cnemc.cn

Abstract

In the environmental event of hazardous release into river, quick and accurate identification of the contamination source is important for emergence response. Generally, given a noisy and finite set of monitoring information, determining the source items (i.e. location, strength and release time) is an ill-posed inverse problem. In this study, a Markov chain Monte Carlo method combined with advection-dispersion equation (ADE) was proposed for the source identification of contamination event in river system based on a Bayesian probabilistic inferential framework. Case study with analytical solution for one-dimensional ADE showed that the proposed methodology was effective and the mean posterior errors for all source parameters were lower than 3%. Case simulation based on two-dimensional ADE with numerical solution obtained similar results and further demonstrated the utility of the proposed approach for source identification. We hope the study will provide a helpful guidance to develop approach for contamination event source identification to support environmental risk management of river system.