An Improved Contaminant Source Identification Method for Sudden Water Pollution Accident in Coaster Estuaries

Li, Jing; Kong, Jun; Wang, Qing; Yao, Ying

doi:10.2112/si85-190.1

Cited by 7 publications

(4 citation statements)

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“…Given that the SWP was characterized by uncertainties regarding multiple and complex pollution sources (Cheng et al, 2018;Jing et al, 2018;Long et al, 2019), quick source identification information was vital for providing early warnings and emergency measures. This information included discharge time, discharge location, and discharge amount or discharge rate (Liao et al, 2018;Wang, Zhao, et al, 2018) (Jing et al, 2018). The primary challenge in preventing and managing abrupt pollution incidents involved acquiring relevant information on the origin (Zhang, Ding, et al, 2021).…”

Section: Source Identification Of Swpmentioning

confidence: 99%

“…Given that hydrodynamic models effectively provided the basis for proactive and reactive measures against SWP, this model was the most optimal approach to address the causes of SWP incidents in recent times (Kim et al, 2022; Yinge Liu et al, 2020). The hydrodynamic model was crucial in identifying potential SWP sources and determining the most effective management strategy to avert the incidence or mitigate its possible risk (Jing et al, 2018; Tang et al, 2018). This model also simulated the optimal water treatment and disposal options after an SWP incident (Lyu, 2018).…”

Section: Review Outputmentioning

confidence: 99%

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A management framework for sudden water pollution: A systematic review output

Bello,

Jamil,

Looi

et al. 2024

Water Environment Research

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Numerous sudden water pollution (SWP) incidents have occurred frequently in recent years, constituting a potential risk to human, socio‐economic, and ecological health. This paper systematically reviews the current literature, with the view to establishing a management framework for SWP incidents. Only 39 of the 327 downloaded articles were selected, and the ROSES protocol was utilized in this review. The results indicated industries, mining sites, and sewage treatment plants as key SWP contributors through accidental leakages, traffic accidents, illegal discharge, natural disasters, and terrorist attacks. These processes also presented five consequences, including the contamination of drinking water sources, disruption of drinking water supply, ecological damage, loss of human life, and agricultural water pollution. Meanwhile, five mitigation strategies included reservoir operation, real‐time monitoring, early warning, and chemical and biological treatments. Although an advancement in mitigation strategies against SWP was observed in this review, previous studies reported only a few prevention strategies. Considering that this review provided an SWP‐based management framework and a hydrodynamic model selection guideline, which provide a foundation for implementing proactive measures against the SWP. These guidelines and the SWP‐based management framework require practical field trials for future studies.Practitioner Points Sudden water pollution increases with industrial growth but decrease with awareness. Human and ecosystem health and social economy are the endpoint receptacles. Mitigation strategies include reservoir dispatch, early warning, and treatments. DPSIR model forms the basis for proving proactive measures against sudden pollution. This review provides a guideline for the selection hydrodynamic models application.

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Section: Source Identification Of Swpmentioning

confidence: 99%

Section: Review Outputmentioning

confidence: 99%

A management framework for sudden water pollution: A systematic review output

Bello,

Jamil,

Looi

et al. 2024

Water Environment Research

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show abstract

“…This identification problem can be also reformulated as an optimization problem where the cost function depends on the differences between the observed and the predicted values for the different pollutant concentrations at the sampling points. There exists a large variety of proposed methods for solving the pollution source identification problem, but they can be categorized into three main groups, according to their approach: the probability-based approach (including Bayesian inference [21], backward probability method [22], the minimum relative entropy [23], and many others), the classification approach [24,25], and, finally, the optimization-based approach where the differences between simulated and observed pollutant concentrations at several points -obtained by solving a numerical model for pollutant concentrations -are minimized by means of a large range of optimization algorithms of derivative, derivative-free, or hybrid type [26][27][28]. In our case, we have chosen this linked simulation-optimization option, employing the gradient-free Nelder-Mead algorithm [29] for the minimization process and a convection-diffusionreaction equation for the simulation step.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Design of an Estuarine Water Quality Monitoring Network by Optimal Control Techniques

Álvarez-Vázquez

Martínez

Rodríguez

et al. 2023

Environ Model Assess

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In this work, we propose a novel methodology in order to automatically optimize the location of the sampling points for a water quality monitoring network in an estuary, in such a way that any unknown pollution source can be identified (both in intensity and location) from the data supplied by those sampling points. In the central part of the article, after a rigorous mathematical formulation of the environmental problem, the full details of its numerical implementation are given. Finally, we present and analyze the results when applying the above proposed technique to study a real case in Ría of Vigo (northwestern Spain).

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“…Zhang and Xin (2017) studied the source identification problem of single-and multipoint source SWPAs on the basis of the genetic algorithm (GA). Jing et al (2018) studied the source identification problem of SWPAs that occur in rivers by using an improved GA. Sun et al (2019) studied the emergency identification problem of SWPAs that occur in rivers on the basis of the variable decoupling method and GA. Pan et al (2020) studied the emergency identification problem of SWPAs that occur in groundwater, including the intensity and the permeability coefficient, by using a simulation-optimisation method. The source identification problem of SWPAs is an ill-posed problem; however, if a small error in observation value or identification model exists, the identification result can be obtained with a larger error by using the deterministic method (Hazart et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis–Hastings sampling

Yang

Liu

2021

Environ Sci Pollut Res

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A quick and accurate identification of source information on sudden hazardous chemical leakage accident is crucial for early accident warning and emergency response. This study firstly regards source identification problem of sudden hazardous chemical leakage accidents as an inverse problem and constructs a source identification model based on the Bayesian framework. Secondly, a new identification method is designed on the basis of particle swarm optimisation (PSO), differential evolution (DE) and the Metropolis-Hastings (M-H) sampling method. Lastly, the designed method, i.e. PSO-DE-MH, is verified by an outdoor experiment analyses in a section of the South-North Water Transfer Project. Results show that the number of iterations, the average absolute error, the average relative error and the average standard deviations of the identification results obtained by PSO-DE-MH are less than those of PSO-DE and DE-MH. Moreover, the relative error and the sampling relative error of the identification results under five different measurement errors (MEs) (σ = 0.01, 0.05, 0.1, 0.15, 0.2) are less than 9.5% and 0.2%, respectively. The designed method is effective even when the standard deviation of the ME increases to 0.2. Therefore, the designed method can effectively and accurately obtain the source information of sudden hazardous chemical leakage accidents. This study provides a new idea and method to solve the difficult problems of emergency management.

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An Improved Contaminant Source Identification Method for Sudden Water Pollution Accident in Coaster Estuaries

Cited by 7 publications

References 0 publications

A management framework for sudden water pollution: A systematic review output

A management framework for sudden water pollution: A systematic review output

Optimizing the Design of an Estuarine Water Quality Monitoring Network by Optimal Control Techniques

Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis–Hastings sampling

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