Pollution source identification for river chemical spills by modular-bayesian approach: A retrospective study on the 'Landmark' spill incident in China

Jiang，Jiping1; Chen，Yasong2,3; Wang，Baoyu4

2019-09-01

10.3390/hydrology6030074

Hydrology   影响因子和分区

2306-5338

2306-5338

It is important to identify source information after a river chemical spill incident occurs. Among various source inversion approaches, a Bayesian-based framework is able to directly characterize inverse uncertainty using a probability distribution and has recently become of interest. However, the literature has not reported its application to actual spill incidents, and many aspects in practical use have not yet been clearly illustrated, e.g., feasibility for large scale pollution incidents, algorithm parameters, and likelihood functions. This work deduced a complete modular-Bayesian approach for river chemical spills, which combined variance assumptions on a pollutant concentration time series with Adaptive-Metropolis sampling. A retrospective case study was conducted based on the 'landmark' spill incident in China, the Songhua River nitrobenzene spill of 2005. The results show that release mass, place, and moment were identified with biases of -26.9%, -7.9%, and 16.9%, respectively. Inverse uncertainty statistics were also quantified for each source parameter. Performance, uncertainty sources, and future work are discussed. This study provides an important real-life case to demonstrate the usefulness of the modular-Bayesian approach in practice and provides valuable references for the setting of parameters for the sampling algorithm and variance assumptions.

Dynamic risk warning Emergency response Inverse source problem Modular Bayesian Approach Songhua river spill

ESCI

英语

第一 ; 通讯

Special Funds of Shenzhen Water Science and Technology Development[SZCG2018160615] ; China Postdoctoral Science Foundation[2014M551249] ; National Natural Science Foundation of China[51509061]

Water Resources

Water Resources

WOS:000488111900018

MDPI

2-s2.0-85077144265

Scopus

1.Shenzhen Municipal Engineering Lab of Environmental IoT Technologies,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Huayue Institute of Ecological Environment Engineering,Chongqing,401122,China
3.School of the Environment,Nanjing University,Nanjing,210093,China
4.School of Environment,Harbin Institute of Technology,Harbin,150090,China

Jiang，Jiping,Chen，Yasong,Wang，Baoyu. Pollution source identification for river chemical spills by modular-bayesian approach: A retrospective study on the 'Landmark' spill incident in China[J]. Hydrology,2019,6(3).

Jiang，Jiping,Chen，Yasong,&Wang，Baoyu.(2019).Pollution source identification for river chemical spills by modular-bayesian approach: A retrospective study on the 'Landmark' spill incident in China.Hydrology,6(3).

Jiang，Jiping,et al."Pollution source identification for river chemical spills by modular-bayesian approach: A retrospective study on the 'Landmark' spill incident in China".Hydrology 6.3(2019).