MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||6|
|Section||Parallel Session I: Water Resources System|
|Published online||07 December 2018|
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
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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