IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i24p16713-d1001755.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/24/16713/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/24/16713/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Fuyu Wang & Xuefei Ge & Yan Li & Jingjing Zheng & Weichen Zheng, 2023. "Optimising the Distribution of Multi-Cycle Emergency Supplies after a Disaster," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    3. Zhang, Haoyuan & Marsh, D. William R, 2021. "Managing infrastructure asset: Bayesian networks for inspection and maintenance decisions reasoning and planning," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    5. Wang, Haijun & Du, Lijing & Ma, Shihua, 2014. "Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 160-179.
    6. Zhaoping Tang & Jianping Sun, 2018. "Multi objective optimization of railway emergency rescue resource allocation and decision," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(3), pages 696-702, June.
    7. Cailin Wang & Jidong Wu & Xin He & Mengqi Ye & Wenhui Liu & Rumei Tang, 2018. "Emerging Trends and New Developments in Disaster Research after the 2008 Wenchuan Earthquake," IJERPH, MDPI, vol. 16(1), pages 1-19, December.
    8. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    9. A. Anaya-Arenas & J. Renaud & A. Ruiz, 2014. "Relief distribution networks: a systematic review," Annals of Operations Research, Springer, vol. 223(1), pages 53-79, December.
    10. Dilsu Binnaz Ozkapici & Mustafa Alp Ertem & Haluk Aygüneş, 2016. "Intermodal humanitarian logistics model based on maritime transportation in Istanbul," 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. 83(1), pages 345-364, August.
    11. Zheng Tang & Yijia Li & Xiaofeng Hu & Huanggang Wu, 2019. "Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    12. Park, Jae-Hyun, 2017. "Time-dependent reliability of wireless networks with dependent failures," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 47-61.
    13. Lu, Chung-Cheng & Ying, Kuo-Ching & Chen, Hui-Ju, 2016. "Real-time relief distribution in the aftermath of disasters – A rolling horizon approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 1-20.
    14. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    15. Rafiei, Rezvan & Huang, Kai & Verma, Manish, 2022. "Cash versus in-kind transfer programs in humanitarian operations: An optimization program and a case study," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    16. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    17. Yagci Sokat, Kezban & Dolinskaya, Irina S. & Smilowitz, Karen & Bank, Ryan, 2018. "Incomplete information imputation in limited data environments with application to disaster response," European Journal of Operational Research, Elsevier, vol. 269(2), pages 466-485.
    18. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    19. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    20. Eghbal Akhlaghi, Vahid & Campbell, Ann Melissa, 2022. "The two-echelon island fuel distribution problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 999-1017.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16713-:d:1001755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.