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PM 2.5 , Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data

Author

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  • Yonglin Shen

    (College of Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Ling Yao

    (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)

Abstract

This paper adopts the PM 2.5 concentration data obtained from 1497 station-based monitoring sites, population and gross domestic product (GDP) census data, revealing population exposure and economic effects of PM 2.5 in four typical urban agglomerations of China, i.e., Beijing-Tianjin-Hebei (BTH), the Yangtze River delta (YRD), the Pearl River delta (PRD), and Chengdu-Chongqing (CC). The Cokriging interpolation method was used to estimate the PM 2.5 concentration from station-level to grid-level. Next, an evaluation was conducted mainly at the grid-level with a cell size of 1 × 1 km, assisted by the urban agglomeration scale. Criteria including the population-weighted mean, the cumulative percent distribution and the correlation coefficient were applied in our evaluation. The results showed that the spatial pattern of population exposure in BTH was consistent with that of PM 2.5 concentration, as well as changes in elevation. The topography was also an important factor in the accumulation of PM 2.5 in CC. Moreover, the most polluted urban agglomeration based on the population-weighted mean was BTH, while the least was PRD. In terms of the cumulative percent distribution, only 0.51% of the population who lived in the four urban agglomerations, and 2.33% of the GDP that was produced in the four urban agglomerations, were associated with an annual PM 2.5 concentration smaller than the Chinese National Ambient Air Quality Standard of 35 µg/m 3 . This indicates that the majority of people live in the high air polluted areas, and economic development contributes to air pollution. Our results are supported by the high correlation between population exposure and the corresponding GDP in each urban agglomeration.

Suggested Citation

  • Yonglin Shen & Ling Yao, 2017. "PM 2.5 , Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data," IJERPH, MDPI, vol. 14(7), pages 1-15, July.
  • Handle: RePEc:gam:jijerp:v:14:y:2017:i:7:p:716-:d:103497
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    References listed on IDEAS

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    1. Hsin-Ling Yeh & Shang-Wei Hsu & Yu-Chia Chang & Ta-Chien Chan & Hui-Chen Tsou & Yen-Chen Chang & Po-Huang Chiang, 2017. "Spatial Analysis of Ambient PM 2.5 Exposure and Bladder Cancer Mortality in Taiwan," IJERPH, MDPI, vol. 14(5), pages 1-14, May.
    2. Ling Yao & Ning Lu, 2014. "Particulate Matter Pollution and Population Exposure Assessment over Mainland China in 2010 with Remote Sensing," IJERPH, MDPI, vol. 11(5), pages 1-10, May.
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    Cited by:

    1. Yi Yang & Jie Li & Guobin Zhu & Qiangqiang Yuan, 2019. "Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM 2.5 in China During 1998–2016," IJERPH, MDPI, vol. 16(7), pages 1-24, March.
    2. Dan Xu & Wenpeng Lin & Jun Gao & Yue Jiang & Lubing Li & Fei Gao, 2022. "PM 2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS," IJERPH, MDPI, vol. 19(10), pages 1-24, May.
    3. Hongbin He & Yonglin Shen & Changmin Jiang & Tianqi Li & Mingqiang Guo & Ling Yao, 2020. "Spatiotemporal Big Data for PM 2.5 Exposure and Health Risk Assessment during COVID-19," IJERPH, MDPI, vol. 17(20), pages 1-19, October.
    4. Yazhu Wang & Xuejun Duan & Lei Wang, 2019. "Spatial-Temporal Evolution of PM 2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014–2017," IJERPH, MDPI, vol. 16(6), pages 1-18, March.
    5. 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.
    6. Yu Chen & Qianqian Miao & Qian Zhou, 2022. "Spatiotemporal Differentiation and Driving Force Analysis of the High-Quality Development of Urban Agglomerations along the Yellow River Basin," IJERPH, MDPI, vol. 19(4), pages 1-21, February.

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