title: Risk analysis of emergent water pollution accidents based on a Bayesian Network.
abstract: To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents.
authors.affiliation: Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China.
authors.forename: Caihong
authors.initials: C
authors.lastname: Tang
authors.affiliation: Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China. Electronic address: yiyujun@bnu.edu.cn.
authors.forename: Yujun
authors.initials: Y
authors.lastname: Yi
authors.affiliation: Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China.
authors.forename: Zhifeng
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Sun
mesh_terms: D000059:Accidents; D001499:Bayes Theorem; D004784:Environmental Monitoring; D006801:Humans; D008962:Models, Theoretical; D018986:Motor Vehicles; D018570:Risk Assessment; D014876:Water Pollution / Q000032:analysis*; D060753:Water Quality
keywords: Bayesian Network; Risk analysis; Sensitivity parameter; Water pollution