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Spatio-Temporal Variation of PM 2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China

Author

Listed:
  • Gang Lin

    (College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China
    These authors contributed equally to this work.)

  • Jingying Fu

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
    University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing 100049, China
    These authors contributed equally to this work.)

  • Dong Jiang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China)

  • Wensheng Hu

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China)

  • Donglin Dong

    (College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China)

  • Yaohuan Huang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China)

  • Mingdong Zhao

    (College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China)

Abstract

The air quality in China, particularly the PM 2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM 2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM 2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM 2.5 concentrations and the factors impacting those concentrations in China for the years of 2001–2010. The contributions of urban areas, high population and economic development to PM 2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM 2.5 concentrations in China remained stable during the period 2001–2010; high concentrations of PM 2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM 2.5 concentrations.

Suggested Citation

  • Gang Lin & Jingying Fu & Dong Jiang & Wensheng Hu & Donglin Dong & Yaohuan Huang & Mingdong Zhao, 2013. "Spatio-Temporal Variation of PM 2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China," IJERPH, MDPI, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:gam:jijerp:v:11:y:2013:i:1:p:173-186:d:31538
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    References listed on IDEAS

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    1. An Zhang & Qingwen Qi & Lili Jiang & Fang Zhou & Jinfeng Wang, 2013. "Population Exposure to PM2.5 in the Urban Area of Beijing," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-9, May.
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    Cited by:

    1. Dongsheng Zhan & Mei-Po Kwan & Wenzhong Zhang & Shaojian Wang & Jianhui Yu, 2017. "Spatiotemporal Variations and Driving Factors of Air Pollution in China," IJERPH, MDPI, vol. 14(12), pages 1-18, December.
    2. Junming Li & Meijun Jin & Honglin Li, 2019. "Exploring Spatial Influence of Remotely Sensed PM 2.5 Concentration Using a Developed Deep Convolutional Neural Network Model," IJERPH, MDPI, vol. 16(3), pages 1-11, February.
    3. Wenting Wang & Lijun Zhang & Jun Zhao & Mengge Qi & Fengrui Chen, 2020. "The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM 2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas," IJERPH, MDPI, vol. 17(9), pages 1-16, April.
    4. Lisha Luo & Junfeng Jiang & Ganshen Zhang & Lu Wang & Zhenkun Wang & Jin Yang & Chuanhua Yu, 2017. "Stroke Mortality Attributable to Ambient Particulate Matter Pollution from 1990 to 2015 in China: An Age-Period-Cohort and Spatial Autocorrelation Analysis," IJERPH, MDPI, vol. 14(7), pages 1-17, July.
    5. 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.
    6. Huilin Yang & Rui Yao & Peng Sun & Chenhao Ge & Zice Ma & Yaojin Bian & Ruilin Liu, 2023. "Spatiotemporal Evolution and Driving Forces of PM 2.5 in Urban Agglomerations in China," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    7. Lei Yao & Wentian Xu & Ying Xu & Shuo Sun, 2022. "Examining the Potential Scaling Law in Urban PM2.5 Pollution Risks along with the Nationwide Air Environmental Effort in China," IJERPH, MDPI, vol. 19(8), pages 1-18, April.
    8. Silver Onyango & Beth Parks & Simon Anguma & Qingyu Meng, 2019. "Spatio-Temporal Variation in the Concentration of Inhalable Particulate Matter (PM 10 ) in Uganda," IJERPH, MDPI, vol. 16(10), pages 1-12, May.
    9. 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.
    10. 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.
    11. Haoran Zhao & Sen Guo & Huiru Zhao, 2019. "Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM 2.5 Concentration: A Provincial Panel Data Model Analysis of China," IJERPH, MDPI, vol. 16(16), pages 1-18, August.
    12. Zeng Li & Jingying Fu & Dong Jiang & Gang Lin & Donglin Dong & Xiaoxi Yan, 2017. "Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China," IJERPH, MDPI, vol. 14(11), pages 1-12, November.
    13. Jianhua Wang & Susumu Ogawa, 2015. "Effects of Meteorological Conditions on PM 2.5 Concentrations in Nagasaki, Japan," IJERPH, MDPI, vol. 12(8), pages 1-13, August.
    14. Jin-Wei Yan & Fei Tao & Shuai-Qian Zhang & Shuang Lin & Tong Zhou, 2021. "Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt," IJERPH, MDPI, vol. 18(5), pages 1-25, February.
    15. Haiou Yang & Wenbo Chen & Zhaofeng Liang, 2017. "Impact of Land Use on PM 2.5 Pollution in a Representative City of Middle China," IJERPH, MDPI, vol. 14(5), pages 1-14, April.

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