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Utilization of a Bayesian Probabilistic Inferential Framework for Contamination Source Identification in River Environment

Author(s): Lijun Jing; Ruihui Chen; Xiaomei Bai; Fansheng Meng; Zhipeng Yao; Yanguo Teng; Haiyang Chen

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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.

DOI: https://doi.org/10.1051/matecconf/201824602035

Year: 2018

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