Enhancing water pollution numerical source tracking performance using information fusion

WANG Hantao; YANG Ruiyi; PING Yang; LI Hongming; CHEN Yuan; JIANG Jiping

doi:10.13205/j.hjgc.202512010

Volume 43 Issue 12

Dec. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(12): 81-91

WANG Hantao, YANG Ruiyi, PING Yang, LI Hongming, CHEN Yuan, JIANG Jiping. Enhancing water pollution numerical source tracking performance using information fusion[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(12): 81-91. doi: 10.13205/j.hjgc.202512010

Citation:

WANG Hantao, YANG Ruiyi, PING Yang, LI Hongming, CHEN Yuan, JIANG Jiping. Enhancing water pollution numerical source tracking performance using information fusion[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(12): 81-91. doi: 10.13205/j.hjgc.202512010

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Enhancing water pollution numerical source tracking performance using information fusion

doi: 10.13205/j.hjgc.202512010

1. Power China Eco-Environmental Group Co., Ltd., Shenzhen 518101, China;
2. Southern University of Science and Technology, Shenzhen 518000, China;
3. Power China Water Environmental Technology Co., Ltd., Shenzhen 518101, China;
4. Shenzhen Zhishu Environmental Protection Technology Co., Ltd., Shenzhen 518000, China

Received Date: 2024-09-29
Accepted Date: 2024-11-18
Rev Recd Date: 2024-11-02

Available Online: 2026-01-09

Abstract

Abstract

Water pollution source tracing is an important aspect of environmental monitoring. In recent years， pollution source tracing technologies based on numerical methods have gained widespread attention. However， existing numerical source tracing methods often consider only the information of a single pollutant， neglecting the simultaneous discharge of different types of pollutants. It is entirely possible to effectively utilize information from various pollutants to enhance the reliability and accuracy of numerical source tracing. To this end， this study used the AM-MCMC Bayesian inference source tracing model as the baseline numerical tracing algorithm and simulated a scenario of three pollutants being discharged simultaneously and at the same location. Two pollutant information fusion paths were designed， and the effect of combining data assimilation on the performance enhancement of numerical source tracing was investigated. The results showed that information fusion significantly improved the inversion performance of two source parameters， the emission time and location， which have relatively large errors. In particular， the information fusion path weighted by the Markov chain yielded significant improvements. For example， the uncertainty range （by 95% confidence interval） of the emission location inversion result was reduced from an average of 10% to 1%， and the accuracy （relative mean error） improved from 9.6%~18.6%， to 2.9%. The framework of information fusion combined with data assimilation didn't increase the monitoring burden. It only required additional existing water quality parameters to significantly enhance the robustness of the source tracing， demonstrating high value in practice.
- water environmental pollution,
- numerical source tracking,
- data assimilation,
- information fusion,
- AM-MCMC

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References(27)

References

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