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A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents

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Abstract

A real-time, dynamic, early-warning model (EP-risk model) is proposed to cope with sudden water quality pollution accidents affecting downstream areas with raw-water intakes (denoted as EPs). The EP-risk model outputs the risk level of water pollution at the EP by calculating the likelihood of pollution and evaluating the impact of pollution. A generalized form of the EP-risk model for river pollution accidents based on Monte Carlo simulation, the analytic hierarchy process (AHP) method, and the risk matrix method is proposed. The likelihood of water pollution at the EP is calculated by the Monte Carlo method, which is used for uncertainty analysis of pollutants’ transport in rivers. The impact of water pollution at the EP is evaluated by expert knowledge and the results of Monte Carlo simulation based on the analytic hierarchy process. The final risk level of water pollution at the EP is determined by the risk matrix method. A case study of the proposed method is illustrated with a phenol spill accident in China.

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Acknowledgments

This work was funded by the National Major Projects on Control and Rectification of Water-Body Pollution of China (no. 2008ZX07420-004) “Research and Application of Water Quality Security Evaluation and Early-warning Technologies,” the National Natural Science Foundation of China (no. 41101508) “Research on Water Quality Event Detection Methods based on Time-Frequency Analysis and Multi-sensor Data Fusion,” and the Fundamental Research Funds for the Central Universities (no. 2013FZA5011) “Research on Intelligent Detection and Evaluation of Water Quality Contamination Events.”

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Authors and Affiliations

  1. Department of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Dibo Hou, Xiaofan Ge, Pingjie Huang & Guangxin Zhang

  2. Department of Geography/UCSB, Santa Barbara, CA, 93106, USA

    Hugo Loáiciga

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  1. Dibo Hou
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  2. Xiaofan Ge
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  3. Pingjie Huang
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  4. Guangxin Zhang
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  5. Hugo Loáiciga
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Correspondence to Dibo Hou.

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Responsible editor: Michael Matthies

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Hou, D., Ge, X., Huang, P. et al. A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents. Environ Sci Pollut Res 21, 8878–8892 (2014). https://doi.org/10.1007/s11356-014-2936-2

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