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Analysis of dust pollution characteristics in the respiratory risk zone of the roadway under multiple factors

Author

Listed:
  • Wang, Haoyu
  • Jiang, Bingyou
  • Lin, Hanyi
  • Zheng, Haotian
  • Wang, Yifan
  • Ji, Ben
  • Zhou, Yu

Abstract

To comprehend the dust pollution characteristics of the respiratory risk zone (RRZ) in the roadway, experiments and numerical simulations were conducted to investigate the effects of ventilation parameters (Air supply vent-to-dust source horizontal distance (Ls), Supply air velocity (Vs), Exhaust-to-supply air volume ratio (Rv)), and the dust emission time (Te) on the behavior, number, and percentage of dust particle parcels within the RRZ. Regression forest models were also constructed for the number and percentage of particle parcels. The results show that, as Te increases, the number of total particle parcels in the RRZ under different ventilation parameters initially experiences rapid growth, followed by a gradual decrease in the growth rate until the number reaches saturation. The Hill function effectively describes this pattern. Moreover, the percentage of delicate dust parcels (PM1, PM7, and PM10) exhibites the same trend as Te increases. Random forest analysis reveals that the order of factor importance affecting the percentage of delicate dust parcels in the RRZ is Ls > Vs > Rv > Te. Additionally, affecting the number of total particle parcels in the RRZ is Ls > Rv > Te > Vs. The research can provide a theoretical basis for targeted dust reduction.

Suggested Citation

  • Wang, Haoyu & Jiang, Bingyou & Lin, Hanyi & Zheng, Haotian & Wang, Yifan & Ji, Ben & Zhou, Yu, 2024. "Analysis of dust pollution characteristics in the respiratory risk zone of the roadway under multiple factors," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224020723
    DOI: 10.1016/j.energy.2024.132298
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    References listed on IDEAS

    as
    1. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    2. Hua, Yun & Nie, Wen & Liu, Qiang & Yin, Shuai & Peng, Huitian, 2020. "Effect of wind curtain on dust extraction in rock tunnel working face: CFD and field measurement analysis," Energy, Elsevier, vol. 197(C).
    3. Jiang, Bingyou & Yu, Chang-Fei & Yuan, Liang & Lu, Kunlun & Tao, Wenhan & Lin, Hanyi & Zhou, Yu, 2023. "Investigation on oxidative pyrolysis characteristics of bituminous coal through thermal analysis and density functional theory," Applied Energy, Elsevier, vol. 349(C).
    4. Lu, Xin-xiao & Wang, Cheng-yan & Shen, Cong & Wang, Ming-yang & Xing, Yun, 2022. "Verisimilar research on the dust movement in the underground tunneling at the roadheader cutterhead dynamic rotation," Energy, Elsevier, vol. 238(PC).
    5. Jiang, Bingyou & Ji, Ben & Yuan, Liang & Yu, Chang-Fei & Tao, Wenhan & Zhou, Yu & Wang, Haoyu & Wang, Xiao-Han & Liao, Maolin, 2023. "Experimental and molecular dynamics simulation study of the ionic liquids’ chain-length on wetting of bituminous coal," Energy, Elsevier, vol. 283(C).
    6. Nie, Wen & Jiang, Chenwang & Sun, Ning & Guo, Lidian & Xue, Qianqian & Liu, Qiang & Liu, Chengyi & Cha, Xingpeng & Yi, Shixing, 2023. "Analysis of multi-factor ventilation parameters for reducing energy air pollution in coal mines," Energy, Elsevier, vol. 278(PA).
    7. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    8. Hu, Zhenqi & Li, Gensheng & Xia, Jianan & Feng, Zhanjie & Han, Jiazheng & Chen, Zanxu & Wang, Wenjuan & Li, Guodong, 2023. "Coupling of underground coal mining and mine reclamation for farmland protection and sustainable mining," Resources Policy, Elsevier, vol. 84(C).
    Full references (including those not matched with items on IDEAS)

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