Location and release time identification of pollution point source in river networks based on the Backward Probability Method

Ghane, Alireza; Mazaheri, Mehdi; Samani, Jamal Mohammad Vali

doi:10.1016/j.jenvman.2016.05.015

Cited by 57 publications

(28 citation statements)

References 14 publications

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“…Cheng and Jia () introduced a backward location probability density function method to invert a longitudinal dispersion coefficient. Ghane, Mazaheri, and Jamal () developed a numerical model on the basis of adjoint analysis through the backward probability method in surface water pollution source identification and verified the developed model on the basis of an analytical solution and certain real data. Jiang, Han, Zheng, Wang, and Yuan () conducted an empirical investigation on generic inverse uncertainty characteristics under a well‐accepted uncertainty analysis framework using stochastic analysis approaches, including regional sensitivity analysis, identifiability plot, and perturbation methods.…”

Section: Introductionmentioning

confidence: 99%

Inversion of multiple parameters for river pollution accidents using emergency monitoring data

Jing

Yang

Zhou

et al. 2019

Water Environment Research

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This study presented a new inversion algorithm on the basis of least squares method for river point pollution sources. A series of numerical experiments was conducted to verify the accuracy of the proposed inversion algorithm. The general solution of the one‐dimensional (1D) pollutant transport equation is the governing equation of this method. Pollutant concentrations at various hypothetical monitoring points should be observed at two moments to obtain the point pollution source parameters, namely, velocity, longitudinal dispersion coefficient, emission moment of the pollution source, emission location, and emission intensity. Monitoring error was considered a random noise in the numerical experiments. The inversion result error was 3.69% when the monitoring error was 5%. Although the monitoring error reached 20%, the maximum error of inversion parameters was 8.58%. In addition, the effects of river flow velocity, contaminant decay rates, monitoring point setting, and time intervals between two sampling groups were analyzed in hypothetic cases. Practitioner points Present a new inversion algorithm for river point pollution sources. Multiple parameters can be obtained by inversion. The unknown river velocity does not affect the result of parameter inversion. Different levels of pollutant concentration monitoring errors are considered.

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Section: Introductionmentioning

confidence: 99%

Inversion of multiple parameters for river pollution accidents using emergency monitoring data

Jing

Yang

Zhou

et al. 2019

Water Environment Research

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“…Therefore, the first task of urgently coping with sudden water pollution is to determine the source of pollution at the first time after such event, make a reasonable emergency handling plan on the basis of pollution source intensity and the occurrence place and time which have been determined, and meanwhile, provide preconditions for sudden water pollution prediction and early warning [1,2]. The aforesaid traceability technology infers the position and time of occurrence as well as pollution source intensity through research on the transfer and conversion laws of pollutants in a river and on the basis of a monitored pollutant concentration process, realizing the reconstitution of the pollution event and playing an important role in the emergent regulation and control process of sudden water pollution events [3].…”

Section: Introductionmentioning

confidence: 99%

Traceability Technology for Sudden Water Pollution Accidents in Rivers

Liao¹,

Gan²,

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 traceability technology for sudden water pollution accidents can be used for fast, accurate identification of a pollution source in the river. A correlation optimization model with the pollution source position and release time as its parameters is established based on hydrodynamic calculation and on the coupling relationship between forward concentration probability density and backward position probability density; and the solution of the model is realized by using a differential evolution algorithm (DEA). A coupled probability density method is to convert the traceability of a sudden water pollution accident into the optimization of two minimum values. This method is simple in principle and easy in solution, realizing the decoupling of parameter of the pollution source. The concept of gradient is introduced to the differential evolution algorithm, improving the efficiency of searching process. The proposed method of traceability was applied to the emergency demonstration project of the SNWDMRP. The results indicate that the model has good efficiency of traceability and high simulation precision and that traceability results have a certain guiding significance to the emergent regulation and control of sudden water pollution events in a river.

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“…For instance, in November 2005, approximately 100 tons of benzene were spilled into Songhuajiang River, China and caused severe social and ecological problems [2]. 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%

“…Protecting ecological safety of river system has become a security issue because the demand for clean water is increasing in the world. Although contaminant source identification has been studied in river and lake pollution cases [3,4], limited publications have been found for quickly identifying contamination event source in river system with a very finite set of monitoring information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Location and release time identification of pollution point source in river networks based on the Backward Probability Method

Cited by 57 publications

References 14 publications

Inversion of multiple parameters for river pollution accidents using emergency monitoring data

Inversion of multiple parameters for river pollution accidents using emergency monitoring data

Traceability Technology for Sudden Water Pollution Accidents in Rivers

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

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