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Quantifying Individual PM 2.5 Exposure with Human Mobility Inferred from Mobile Phone Data

Author

Listed:
  • Zhaoping Hu

    (MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
    Data-Driven Management Decision Making Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China
    These authors contributed equally to this work.)

  • Le Huang

    (MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
    Data-Driven Management Decision Making Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China
    These authors contributed equally to this work.)

  • Xi Zhai

    (Shanghai Urban and Rural Construction and Traffic Development Research Institute, Shanghai 200240, China)

  • Tao Yang

    (Shanghai Urban and Rural Construction and Traffic Development Research Institute, Shanghai 200240, China)

  • Yaohui Jin

    (MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
    Data-Driven Management Decision Making Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yanyan Xu

    (MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
    Data-Driven Management Decision Making Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM 2.5 exposure by combining residents’ mobility traces and PM 2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM 2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM 2.5 concentration, we quantify individual PM 2.5 exposure and find that the average PM 2.5 exposure of residences in Shanghai is 60.37 ug·h·m − 3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM 2.5 exposure in Shanghai. Our PM 2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development.

Suggested Citation

  • Zhaoping Hu & Le Huang & Xi Zhai & Tao Yang & Yaohui Jin & Yanyan Xu, 2023. "Quantifying Individual PM 2.5 Exposure with Human Mobility Inferred from Mobile Phone Data," Sustainability, MDPI, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:184-:d:1306937
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    References listed on IDEAS

    as
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    2. Marshall, Julian D. & Granvold, Patrick W. & Hoats, Abigail S. & McKone, Thomas E. & Deakin, Elizabeth & Nazaroff, William W., 2006. "Inhalation intake of ambient air pollution in California's South Coast Air Basin," University of California Transportation Center, Working Papers qt92w972mb, University of California Transportation Center.
    3. Timothy W. Collins & Sara E. Grineski, 2019. "Environmental Injustice and Religion: Outdoor Air Pollution Disparities in Metropolitan Salt Lake City, Utah," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(5), pages 1597-1617, September.
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