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Real-Time Estimation of Population Exposure to PM 2.5 Using Mobile- and Station-Based Big Data

Author

Listed:
  • Bin Chen

    (Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
    Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA)

  • Yimeng Song

    (Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Tingting Jiang

    (Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China)

  • Ziyue Chen

    (State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China)

  • Bo Huang

    (Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Bing Xu

    (Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
    State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
    Department of Geography, University of Utah, 260 S. Central Campus Dr., Salt Lake City, UT 84112, USA)

Abstract

Extremely high fine particulate matter (PM 2.5 ) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM 2.5 exposure assessments have practical limitations, due to the assumption that population distribution or air pollution levels are spatially stationary and temporally constant and people move within regions of generally the same air quality throughout a day or other time periods. To deal with this challenge, we propose a novel method to achieve the real-time estimation of population exposure to PM 2.5 in China by integrating mobile-phone locating-request (MPL) big data and station-based PM 2.5 observations. Nationwide experiments show that the proposed method can yield the estimation of population exposure to PM 2.5 concentrations and cumulative inhaled PM 2.5 masses with a 3-h updating frequency. Compared with the census-based method, it introduced the dynamics of population distribution into the exposure estimation, thereby providing an improved way to better assess the population exposure to PM 2.5 at different temporal scales. Additionally, the proposed method and dataset can be easily extended to estimate other ambient pollutant exposures such as PM 10 , O 3 , SO 2 , and NO 2 , and may hold potential utilities in supporting the environmental exposure assessment and related policy-driven environmental actions.

Suggested Citation

  • Bin Chen & Yimeng Song & Tingting Jiang & Ziyue Chen & Bo Huang & Bing Xu, 2018. "Real-Time Estimation of Population Exposure to PM 2.5 Using Mobile- and Station-Based Big Data," IJERPH, MDPI, vol. 15(4), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:573-:d:137636
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    References listed on IDEAS

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    1. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
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    Cited by:

    1. Zhen Yang & Weijun Gao & Jiawei Li, 2022. "Can Economic Growth and Environmental Protection Achieve a “Win–Win” Situation? Empirical Evidence from China," IJERPH, MDPI, vol. 19(16), pages 1-21, August.
    2. Eun-hye Yoo & Qiang Pu & Youngseob Eum & Xiangyu Jiang, 2021. "The Impact of Individual Mobility on Long-Term Exposure to Ambient PM 2.5 : Assessing Effect Modification by Travel Patterns and Spatial Variability of PM 2.5," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    3. Zhuoran Shan & Hongfei Li & Haolan Pan & Man Yuan & Shen Xu, 2022. "Spatial Equity of PM 2.5 Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China," IJERPH, MDPI, vol. 19(19), pages 1-22, October.
    4. Mingxiao Li & Song Gao & Feng Lu & Huan Tong & Hengcai Zhang, 2019. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
    5. Bin Chen & Shengbiao Wu & Yimeng Song & Chris Webster & Bing Xu & Peng Gong, 2022. "Contrasting inequality in human exposure to greenspace between cities of Global North and Global South," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Wanting Liu & Ning Sun & Jingyu Guo & Zhenhua Zheng, 2022. "Campus Green Spaces, Academic Achievement and Mental Health of College Students," IJERPH, MDPI, vol. 19(14), pages 1-10, July.
    7. Sang-Hyeok Lee & Jung Eun Kang, 2022. "Spatial Disparity of Visitors Changes during Particulate Matter Warning Using Big Data Focused on Seoul, Korea," IJERPH, MDPI, vol. 19(11), pages 1-16, May.

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