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Quantitative Assessment of Relationship between Population Exposure to PM 2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Author

Listed:
  • Ling Yao

    (Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China)

  • Changchun Huang

    (School of Geography Science, Nanjing Normal University, Nanjing 210023, China)

  • Wenlong Jing

    (Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China)

  • Xiafang Yue

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China)

  • Yuyue Xu

    (School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China)

Abstract

Analyzing the association between fine particulate matter (PM 2.5 ) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m 3 in mainland China, and the highest population exposure to PM 2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM 2.5 was positively related to GDP. An apparent upward trend of population exposure to PM 2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM 2.5 increases by 1.05 person/km 2 at the 80th percentile, and 1.88 person/km 2 at the 95th percentile, respectively.

Suggested Citation

  • Ling Yao & Changchun Huang & Wenlong Jing & Xiafang Yue & Yuyue Xu, 2018. "Quantitative Assessment of Relationship between Population Exposure to PM 2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China," IJERPH, MDPI, vol. 15(9), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:2058-:d:170937
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    References listed on IDEAS

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    1. Mengjie Wang & Yanjun Wang & Fei Teng & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Estimation and Analysis of PM 2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China," IJERPH, MDPI, vol. 19(7), pages 1-18, April.

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