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The Scenario Construction and Evolution Method of Casualties in Liquid Ammonia Leakage Based on Bayesian Network

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  • Pengxia Zhao

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology (Beijing Municipal Institute of Labour Protection), Beijing 100077, China)

  • Tie Li

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Biao Wang

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Ming Li

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Yu Wang

    (Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology (Beijing Municipal Institute of Labour Protection), Beijing 100077, China)

  • Xiahui Guo

    (Safety Culture Research Center, Beijing Academy of Emergency Management Science and Technology, Beijing 100052, China)

  • Yue Yu

    (Institute of Smart Ageing, Beijing Academy of Science and Technology, Beijing 100000, China)

Abstract

In China, food-freezing plants that use liquid ammonia, which were established in the suburbs in the 1970s, are being surrounded by urban built-up areas as urbanization progresses. These plants lead to extremely serious casualties in the event of a liquid ammonia leakage. The purpose of this thesis was to explore the key factors of personnel protection failure through the scenario evolution analysis of liquid ammonia leakage. The chain of emergencies and their secondary events were used to portray the evolutionary process of a full scenario of casualties caused by liquid ammonia leakage from three dimensions: disaster, disaster-bearing bodies, and emergency management. A Bayesian network model of liquid ammonia leakage casualties based on the scenario chain was constructed, and key nodes in the network were derived by examining the sensitivity of risk factors. Then, this model was applied to a food-freezing plant in Beijing. The results showed that inadequate risk identification capability is a key node in accident prevention; the level of emergency preparedness is closely related to the degree of casualties; the emergency disposal by collaborative onsite and offsite is the key to avoiding mass casualties. A basis for emergency response to the integration of personnel protection is provided.

Suggested Citation

  • Pengxia Zhao & Tie Li & Biao Wang & Ming Li & Yu Wang & Xiahui Guo & Yue Yu, 2022. "The Scenario Construction and Evolution Method of Casualties in Liquid Ammonia Leakage Based on Bayesian Network," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16713-:d:1001755
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    References listed on IDEAS

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    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Ling He & Qing Yang & Xingxing Liu & Lingmei Fu & Jinmei Wang, 2021. "Exploring Factors Influencing Scenarios Evolution of Waste NIMBY Crisis: Analysis of Typical Cases in China," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    3. Zhi Yuan & Nima Khakzad & Faisal Khan & Paul Amyotte, 2015. "Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 35(2), pages 278-291, February.
    4. Kui Huang & Wen Nie & Nianxue Luo, 2020. "A Method of Constructing Marine Oil Spill Scenarios from Flat Text Based on Semantic Analysis," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
    5. G Barbarosoǧlu & Y Arda, 2004. "A two-stage stochastic programming framework for transportation planning in disaster response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 43-53, January.
    6. M. Peng & L. Zhang, 2012. "Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(1), pages 903-933, October.
    7. Zhang, N. & Ni, X.Y. & Huang, H. & Duarte, M., 2017. "Risk-based personal emergency response plan under hazardous gas leakage: Optimal information dissemination and regional evacuation in metropolises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 237-250.
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