New approach for point pollution source identification in rivers based on the backward probability method

Wang, Jiabiao; Zhao, Jianshi; Lei, Xiaohui; Wang, Hao

doi:10.1016/j.envpol.2018.05.093

Cited by 64 publications

(33 citation statements)

References 32 publications

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“…The traceability simulation module calls the pollution source information inversed by the sudden water pollution accidents traceability model depending on abnormal data of some cross-section water quality and then predicts the pollutant diffusion process by using a hydrodynamic water quality model [4]. For details, see Table 5.…”

Section: Traceability Simulationmentioning

confidence: 99%

Emergency Management System for Sudden Water Pollution Accidents

Li¹,

Liao²,

Wang³

et al. 2018

Emergency Operation Technologies for Sudden Water Pollution Accidents in the Middle Route of South-to-North Water Diversion Pro

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The emergency management system for sudden water pollution accidents of the main canal is the integrated application of the aforesaid three key technologies and is the key to verify the effect of practical application of these technologies. The emergency management system is formed by integrating basic information, measured data, and professional models through the communication mode of network transmission. The system can provide support for emergency response in case of emergency conditions including sudden water pollution accidents and technical support for security operations of the MRP.

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Section: Traceability Simulationmentioning

confidence: 99%

Emergency Management System for Sudden Water Pollution Accidents

Li¹,

Liao²,

Wang³

et al. 2018

Emergency Operation Technologies for Sudden Water Pollution Accidents in the Middle Route of South-to-North Water Diversion Pro

Self Cite

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“…To precisely predict the probability of water inrush and provide scientific evidence to prevent water inrush in 2 of 17 mine districts, correct and effective methods to identify water sources are urgently required [6][7][8][9]. Hydrochemical analysis, which is simple, efficient, and inexpensive, is usually used to identify water sources in rivers and mine operations [10][11][12]. On the basis of the hydrochemical data set, principle component analysis (PCA), cluster analysis, factor analysis, and discriminant analysis are multivariate methods that are extremely helpful for characterizing groundwater.…”

Section: Introductionmentioning

confidence: 99%

Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Liu

et al. 2019

Sustainability

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Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

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“…Once occurring, quick and accurate identification of the contamination sources is essential to manage the emergency response and mitigate environmental consequences in the river system [3]. However, identifying source terms in environments faces challenge since it is a problem to find what happened in the past with finite set of monitoring information, such as, a limited number of observations [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…In the past two decades, several deterministic and stochastic methods have been developed successively to facilitate the solution to the source identification problem for identifying the release locations of unknown sources, and estimate the release time and emission loads [2][3][4][5][6][7][8][9][10][11]. Among them, Bayesian approach has been applied widely for source identification in recent years as it has a number of distinctive attributes [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Among them, Bayesian approach has been applied widely for source identification in recent years as it has a number of distinctive attributes [10,11]. The method converts contamination source identification into the reiterative computation of posterior probability distribution of the source parameters and can quantify random errors in the concentration data and their uncertainties [5]. For example, Yang et al [10] constructed differential evolution algorithm based on Bayesian inference to identify multi-point sudden water pollution sources.…”

Section: Introductionmentioning

confidence: 99%

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Utilization of a Bayesian probabilistic inferential framework for contamination source identification in river environment

et al. 2018

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In the environmental event of hazardous release into river, quick and accurate identification of the contamination source is important for emergence response. Generally, given a noisy and finite set of monitoring information, determining the source items (i.e. location, strength and release time) is an ill-posed inverse problem. In this study, a Markov chain Monte Carlo method combined with advection-dispersion equation (ADE) was proposed for the source identification of contamination event in river system based on a Bayesian probabilistic inferential framework. Case study with analytical solution for one-dimensional ADE showed that the proposed methodology was effective and the mean posterior errors for all source parameters were lower than 3%. Case simulation based on two-dimensional ADE with numerical solution obtained similar results and further demonstrated the utility of the proposed approach for source identification. We hope the study will provide a helpful guidance to develop approach for contamination event source identification to support environmental risk management of river system.

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New approach for point pollution source identification in rivers based on the backward probability method

Cited by 64 publications

References 32 publications

Emergency Management System for Sudden Water Pollution Accidents

Emergency Management System for Sudden Water Pollution Accidents

Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Utilization of a Bayesian probabilistic inferential framework for contamination source identification in river environment

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