Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method
Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modelling. A general Bayesian framework is proposed that c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Heidelberg
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44815/ http://umpir.ump.edu.my/id/eprint/44815/1/Source%20identification%20in%20river%20pollution%20incidents%20using%20a%20cellular.pdf |
| _version_ | 1848827189681717248 |
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| author | Wang, Wei Ji, Chao Li, Chuanqi Wu, Wenxin Anak Gisen, Jacqueline Isabella |
| author_facet | Wang, Wei Ji, Chao Li, Chuanqi Wu, Wenxin Anak Gisen, Jacqueline Isabella |
| author_sort | Wang, Wei |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modelling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources. |
| first_indexed | 2025-11-15T03:56:46Z |
| format | Article |
| id | ump-44815 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:56:46Z |
| publishDate | 2025 |
| publisher | Springer Heidelberg |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-448152025-06-18T02:18:05Z http://umpir.ump.edu.my/id/eprint/44815/ Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method Wang, Wei Ji, Chao Li, Chuanqi Wu, Wenxin Anak Gisen, Jacqueline Isabella QA Mathematics TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modelling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources. Springer Heidelberg 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/44815/1/Source%20identification%20in%20river%20pollution%20incidents%20using%20a%20cellular.pdf Wang, Wei and Ji, Chao and Li, Chuanqi and Wu, Wenxin and Anak Gisen, Jacqueline Isabella (2025) Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method. Environmental Science and Pollution Research, 32. pp. 12978-12991. ISSN 0944-1344. (Published) https://doi.org/10.1007/s11356-023-27988-x https://doi.org/10.1007/s11356-023-27988-x |
| spellingShingle | QA Mathematics TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering Wang, Wei Ji, Chao Li, Chuanqi Wu, Wenxin Anak Gisen, Jacqueline Isabella Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| title | Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| title_full | Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| title_fullStr | Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| title_full_unstemmed | Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| title_short | Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| title_sort | source identification in river pollution incidents using a cellular automata model and bayesian markov chain monte carlo method |
| topic | QA Mathematics TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering |
| url | http://umpir.ump.edu.my/id/eprint/44815/ http://umpir.ump.edu.my/id/eprint/44815/ http://umpir.ump.edu.my/id/eprint/44815/ http://umpir.ump.edu.my/id/eprint/44815/1/Source%20identification%20in%20river%20pollution%20incidents%20using%20a%20cellular.pdf |