IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i2p1002-d726453.html
   My bibliography  Save this article

Analysis of the Interaction Mechanism of the Risk Factors of Gas Explosions in Chinese Underground Coal Mines

Author

Listed:
  • Jinjia Zhang

    (School of Public Administration, Northwest University, Xi’an 710069, China)

  • Yiping Zeng

    (Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518000, China)

  • Genserik Reniers

    (Faculty of Applied Economic Sciences, University of Antwerp, 2000 Antwerp, Belgium)

  • Jie Liu

    (Faculty of Public Security and Emergency Management, Kunming University of Science and Technology, Kunming 650093, China)

Abstract

Gas explosion accidents easily cause severe casualties in Chinese underground coal mines. Systematic analysis of accident causation is crucial for the prevention of gas explosions. This study identifies the representative risk factors of gas explosions and determines the interrelationship among these risk factors to highlight weak links and develop countermeasures. A total of 21 representative risk factors of gas explosions were identified through 128 case studies and front-line investigations. On this basis, a five-level hierarchical structure model of gas explosions was established to explore the complex interrelationships among the representative risk factors based on a combination of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods. Moreover, the Matrix of Cross Impact Multiplications Applied to Classification (MICMAC) method was applied to achieve risk factor classification into four clusters, namely, driving factors, linkage factors, dependent factors and autonomous factors. The results indicated that the interrelationships and emergence properties among the risk factors may cause gas explosions, which should give more attention to the interrelationships among multiple factors and multiple subsystems for coal enterprises. Meanwhile, the complex geological conditions, poor safety supervision, inadequate safety education and training, incomplete execution safety regulations and poor safety technology and input are the long-term focus of risk management for coal enterprises. Finally, 10 countermeasures were proposed to control these representative risk factors and interrelationships. The results are helpful to the development of gas explosion risk management policies and to the preferential allocation of limited resources to resolve these issues.

Suggested Citation

  • Jinjia Zhang & Yiping Zeng & Genserik Reniers & Jie Liu, 2022. "Analysis of the Interaction Mechanism of the Risk Factors of Gas Explosions in Chinese Underground Coal Mines," IJERPH, MDPI, vol. 19(2), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:2:p:1002-:d:726453
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/2/1002/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/2/1002/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    2. Arif Emre Dursun, 2020. "Statistical analysis of methane explosions in Turkey’s underground coal mines and some recommendations for the prevention of these accidents: 2010–2017," 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. 104(1), pages 329-351, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Ginda & Marta Szyba, 2023. "Identification of Key Factors for the Development of Agricultural Biogas Plants in Poland," Energies, MDPI, vol. 16(23), pages 1-19, November.
    2. Lixia Niu & Wende Xia & Yafan Qiao, 2022. "The Influence of Leader Bottom-Line Mentality on Miners’ Safety Behavior: A Moderated Parallel Mediation Model Based on the Dual-System Theory," IJERPH, MDPI, vol. 19(18), pages 1-21, September.

    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. Di Zhang & Xinping Yan & Zaili Yang & Jin Wang, 2014. "An accident data–based approach for congestion risk assessment of inland waterways: A Yangtze River case," Journal of Risk and Reliability, , vol. 228(2), pages 176-188, April.
    2. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    3. Jim Lewis & Kerrie Mengersen & Laurie Buys & Desley Vine & John Bell & Peter Morris & Gerard Ledwich, 2015. "Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-21, July.
    4. Nicholson, Ann E. & Flores, M. Julia, 2011. "Combining state and transition models with dynamic Bayesian networks," Ecological Modelling, Elsevier, vol. 222(3), pages 555-566.
    5. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
    6. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    7. Mostafa Shaaban & Carmen Schwartz & Joseph Macpherson & Annette Piorr, 2021. "A Conceptual Model Framework for Mapping, Analyzing and Managing Supply–Demand Mismatches of Ecosystem Services in Agricultural Landscapes," Land, MDPI, vol. 10(2), pages 1-19, January.
    8. Ye, Congliang & Zhang, Qi, 2022. "Chain explosion behaviors induced by discontinuous methane/air distribution," Energy, Elsevier, vol. 252(C).
    9. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    10. Dayong Li & Zengchuan Dong & Liyao Shi & Jintao Liu & Zhenye Zhu & Wei Xu, 2019. "Risk Probability Assessment of Sudden Water Pollution in the Plain River Network Based on Random Discharge from Multiple Risk Sources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4051-4065, September.
    11. Tiller, Rachel Gjelsvik & Hansen, Lillian & Richards, Russell & Strand, Hillevi, 2015. "Work segmentation in the Norwegian salmon industry: The application of segmented labor market theory to work migrants on the island community of Frøya, Norway," Marine Policy, Elsevier, vol. 51(C), pages 563-572.
    12. Leonel Lara-Estrada & Livia Rasche & L. Enrique Sucar & Uwe A. Schneider, 2018. "Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks," Land, MDPI, vol. 7(1), pages 1-13, January.
    13. Nguyen, Minh-Hoang, 2023. "Investigating urban residents' involvement in biodiversity conservation in protected areas: Empirical evidence from Vietnam," Thesis Commons z2hjv, Center for Open Science.
    14. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
    15. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    16. Meyer, Spencer R. & Johnson, Michelle L. & Lilieholm, Robert J. & Cronan, Christopher S., 2014. "Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA," Ecological Modelling, Elsevier, vol. 291(C), pages 42-57.
    17. Mastrangelo, Matias Enrique & Sun, Zhanli & Seghezzo, Lucas & Müller, Daniel, 2019. "Survey-based modeling of land-use intensity in agricultural frontiers of the Argentine dry Chaco," Land Use Policy, Elsevier, vol. 88(C).
    18. Anna Sperotto & Josè Luis Molina & Silvia Torresan & Andrea Critto & Manuel Pulido-Velazquez & Antonio Marcomini, 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks," Sustainability, MDPI, vol. 11(17), pages 1-34, August.
    19. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    20. Siddhartha Roy & Devi Prasad Mishra & Ram Madhab Bhattacharjee & Hemant Agrawal, 2022. "Genetic programming for prediction of heat stress hazard in underground coal mine environment," 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. 114(3), pages 2527-2543, December.

    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:2:p:1002-:d:726453. 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.