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<p>The global use of freshwater has increased six-fold over the past 100 years and has been growing by about 1% per year since the 1980s. By 2021, 40% of the water bodies on which Spain depends were in poor condition due to overexploitation, pollution, or ecological deterioration. To address this challenging situation, a <b>Monitoring and Mitigation Framework</b> (<b>MMF</b>) was developed to minimize damage from contaminant spills, identify the location of contamination sources, and estimate the <b>Chemical Oxygen Demand</b> (<b>COD</b>) flow concentration rates. The framework is designed to detect environmental crimes, identify the companies responsible for contamination spills, and schedule mitigation actions in coordination with law enforcement authorities for future events. Although the framework includes real-time modules to identify contamination sources and estimate COD concentration rates, this manuscript focuses solely on the offline module, where <b>Inverse Estimation (IE)</b> algorithms form the core. The IE algorithms are based on Neural Networks: a classification algorithm to locate the contamination source and a regression algorithm to estimate the COD flow concentration at the source location over time. These algorithms provide evidence to help local police specifically the Malaga City Council (Spain) identify potential companies involved in environmental crimes. The MMF was developed and tested at a Llobregat river reach (Barcelona, Spain), and it was considered 2621 test cases with different spill location and COD concentrations ranging from 0.1 to 80,000&#xa0;mg/l. In those tests, IE algorithms were able to estimate COD concentration at source with a mean absolute error (MAE) lower than 2.6&#xa0;mg/l or a Mean Square Relative Error (MSRE) lower than 10&#xa0;mg/l, and it correctly identified the contamination source location in 85% of cases.</p>

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Monitoring and mitigation framework for contaminant spills in a river

  • 期刊论文

  • 发表日期：

    2025年4月11日

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  • Enrique Bonet,
  • Maria Teresa Yubero,
  • Lluis Sanmiquel,
  • Marc Bascompta

摘要

The global use of freshwater has increased six-fold over the past 100 years and has been growing by about 1% per year since the 1980s. By 2021, 40% of the water bodies on which Spain depends were in poor condition due to overexploitation, pollution, or ecological deterioration. To address this challenging situation, a Monitoring and Mitigation Framework (MMF) was developed to minimize damage from contaminant spills, identify the location of contamination sources, and estimate the Chemical Oxygen Demand (COD) flow concentration rates. The framework is designed to detect environmental crimes, identify the companies responsible for contamination spills, and schedule mitigation actions in coordination with law enforcement authorities for future events. Although the framework includes real-time modules to identify contamination sources and estimate COD concentration rates, this manuscript focuses solely on the offline module, where Inverse Estimation (IE) algorithms form the core. The IE algorithms are based on Neural Networks: a classification algorithm to locate the contamination source and a regression algorithm to estimate the COD flow concentration at the source location over time. These algorithms provide evidence to help local police specifically the Malaga City Council (Spain) identify potential companies involved in environmental crimes. The MMF was developed and tested at a Llobregat river reach (Barcelona, Spain), and it was considered 2621 test cases with different spill location and COD concentrations ranging from 0.1 to 80,000 mg/l. In those tests, IE algorithms were able to estimate COD concentration at source with a mean absolute error (MAE) lower than 2.6 mg/l or a Mean Square Relative Error (MSRE) lower than 10 mg/l, and it correctly identified the contamination source location in 85% of cases.