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Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station

Author

Listed:
  • Liyang Liu

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hui Liu

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yiming Ma

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

With the increase in subway travelers, the air quality of underground enclosed spaces at subway stations has attracted much more attention. The study of pollutants exposure assessment, especially fine particulate matter, is important in both pollutant control and metro station design. In this paper, combining pedestrian flow analysis (PFA) and computational fluid dynamics (CFD) simulations, a novel surrogate-assisted particulate matter exposure assessment method is proposed, in which PFA is used to analyze the spatial-temporal movement characteristics of pedestrians to simultaneously consider the location and value of the pedestrian particulate generation source and their exposure streamline to particulate matter; the CFD model is used to analyze the airflow field and particulate matter concentration field in detail. To comprehensively consider the differences in the spatial concentration distribution of particulate matter caused by the time-varying characteristics of the airflow organization state in subway stations, surrogate models reflecting the nonlinear relationship between simulated and measured data are trained to perform accurate pedestrian exposure calculations. The actual measurement data proves the validity of the simulation and calculation methods, and the difference between the calculated and experimental values of the exposure is only about 5%.

Suggested Citation

  • Liyang Liu & Hui Liu & Yiming Ma, 2022. "Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station," IJERPH, MDPI, vol. 19(4), pages 1-25, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2295-:d:751971
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    References listed on IDEAS

    as
    1. Xin-Yi Song & Qing-Chang Lu & Zhong-Ren Peng, 2018. "Spatial Distribution of Fine Particulate Matter in Underground Passageways," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    2. Peng Mao & Jie Li & Lilin Xiong & Rubing Wang & Xiang Wang & Yongtao Tan & Hongyang Li, 2019. "Characterization of Urban Subway Microenvironment Exposure—A Case of Nanjing in China," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
    3. Yunhyung Hwang & Jaehoon An & Kiyoung Lee, 2018. "Characterization of a High PM 2.5 Exposure Group in Seoul Using the Korea Simulation Exposure Model for PM 2.5 (KoSEM-PM) Based on Time–Activity Patterns and Microenvironmental Measurements," IJERPH, MDPI, vol. 15(12), pages 1-14, December.
    4. Yang Wang & Ying Zhou & Jian Zuo & Raufdeen Rameezdeen, 2018. "A Computational Fluid Dynamic (CFD) Simulation of PM 10 Dispersion Caused by Rail Transit Construction Activity: A Real Urban Street Canyon Model," IJERPH, MDPI, vol. 15(3), pages 1-30, March.
    Full references (including those not matched with items on IDEAS)

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