Simultaneous identification of the location and release history of pollutant sources in river networks is an ill-posed and complicated problem, particularly in the case of multiple sources with time-varying release patterns. This study presents an innovative method for solving this problem using minimum observational data. To do so, a procedure is proposed in which, the number and the suspected reaches to the existence of pollutant sources are determined. This is done by defining two different types of monitoring stations with an adaptive arrangement in addition to real-time data collection and reliable flow and transport mathematical models. In the next step, the sources’ location and their release history are identified by solving the inverse source problem employing a geostatistical approach. Different scenarios are discussed for different conditions of number, release history and location of pollutant sources in the river network. Results indicated the capability of the proposed method in identifying the characteristics of the sources in complicated cases. Hence, it can be effectively used for the comprehensive monitoring of river networks for different purposes.
An innovative framework for real-time monitoring of pollutant point sources in river networks / Barati Moghaddam, M.; Mazaheri, M.; Mohammad Vali Samani, J.; Boano, F.. - In: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT. - ISSN 1436-3240. - ELETTRONICO. - (2022), pp. 1-28. [10.1007/s00477-022-02233-y]
An innovative framework for real-time monitoring of pollutant point sources in river networks
Boano F.
2022
Abstract
Simultaneous identification of the location and release history of pollutant sources in river networks is an ill-posed and complicated problem, particularly in the case of multiple sources with time-varying release patterns. This study presents an innovative method for solving this problem using minimum observational data. To do so, a procedure is proposed in which, the number and the suspected reaches to the existence of pollutant sources are determined. This is done by defining two different types of monitoring stations with an adaptive arrangement in addition to real-time data collection and reliable flow and transport mathematical models. In the next step, the sources’ location and their release history are identified by solving the inverse source problem employing a geostatistical approach. Different scenarios are discussed for different conditions of number, release history and location of pollutant sources in the river network. Results indicated the capability of the proposed method in identifying the characteristics of the sources in complicated cases. Hence, it can be effectively used for the comprehensive monitoring of river networks for different purposes.File | Dimensione | Formato | |
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BaratiMoghaddam2022_preprint.pdf
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BaratiMoghaddam2022_Article_AnInnovativeFrameworkForReal-t.pdf
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https://hdl.handle.net/11583/2964951