Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature..
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) modeling. 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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
|---|---|
| Enthalten in: |
Environmental science and pollution research international - (2023) vom: 03. Juni |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wang, Wei [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Bayesian inference |
|---|
| Anmerkungen: |
Date Revised 03.06.2023 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1007/s11356-023-27988-x |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM357715535 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM357715535 | ||
| 003 | DE-627 | ||
| 005 | 20231226073206.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11356-023-27988-x |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1192.xml |
| 035 | |a (DE-627)NLM357715535 | ||
| 035 | |a (NLM)37269522 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wang, Wei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 03.06.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. | ||
| 520 | |a 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) modeling. 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bayesian inference | |
| 650 | 4 | |a Cellular automata | |
| 650 | 4 | |a Contaminant source identification | |
| 650 | 4 | |a MCMC | |
| 650 | 4 | |a River pollution | |
| 700 | 1 | |a Ji, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chuanqi |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Wenxin |e verfasserin |4 aut | |
| 700 | 1 | |a Gisen, Jacqueline Isabella Anak |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Environmental science and pollution research international |d 1994 |g (2023) vom: 03. Juni |w (DE-627)NLM093849869 |x 1614-7499 |7 nnns |
| 773 | 1 | 8 | |g year:2023 |g day:03 |g month:06 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s11356-023-27988-x |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 03 |c 06 | ||