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Particulate Matter Pollution and Population Exposure Assessment over Mainland China in 2010 with Remote Sensing

Author

Listed:
  • 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)

  • Ning Lu

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

The public is increasingly concerned about particulate matter pollution caused by respirable suspended particles (PM 10 ) and fine particles (PM 2.5 ). In this paper, PM 10 and PM 2.5 concentration are estimated with remote sensing and individual air quality indexes of PM 10 and PM 2.5 (IPM 10 and IPM 2.5 ) over mainland China in 2010 are calculated. We find that China suffered more serious PM 2.5 than PM 10 pollution in 2010, and they presented a spatial differentiation. Consequently, a particulate-based air quality index (PAQI) based on a weighting method is proposed to provide a more objective assessment of the particulate pollution. The study demonstrates that, in 2010, most of mainland China faced a lightly polluted situation in PAQI case; there were three areas obviously under moderate pollution (Hubei, Sichuan-Chongqing border region and Ningxia-Inner Mongolia border region). Simultaneously, two indicators are calculated with the combination of population density gridded data to reveal Chinese population exposure to PM 2.5 . Comparing per capita PM 2.5 concentration with population-weighted PM 2.5 concentration, the former shows that the high-level regions are distributed in Guangdong, Shanghai, and Tianjin, while the latter are in Hebei, Chongqing, and Shandong. By comparison, the results demonstrate that population-weighted PM 2.5 concentration is more in line with the actual situation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:5:p:5241-5250:d:36076
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    References listed on IDEAS

    as
    1. 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.
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    Cited by:

    1. 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.
    2. Mike Z. He & Xiange Zeng & Kaiyue Zhang & Patrick L. Kinney, 2017. "Fine Particulate Matter Concentrations in Urban Chinese Cities, 2005–2016: A Systematic Review," IJERPH, MDPI, vol. 14(2), pages 1-14, February.
    3. 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.
    4. Bao-Linh Tran & Ching-Cheng Chang & Chia-Sheng Hsu & Chi-Chung Chen & Wei-Chun Tseng & Shih-Hsun Hsu, 2019. "Threshold Effects of PM 2.5 Exposure on Particle-Related Mortality in China," IJERPH, MDPI, vol. 16(19), pages 1-18, September.
    5. Shenxin Li & Sedra Shafi & Bin Zou & Jing Liu & Ying Xiong & Bilal Muhammad, 2022. "PM 2.5 Concentration Exposure over the Belt and Road Region from 2000 to 2020," IJERPH, MDPI, vol. 19(5), pages 1-16, March.
    6. Ping Zhang & Bo Hong & Liang He & Fei Cheng & Peng Zhao & Cailiang Wei & Yunhui Liu, 2015. "Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural N," IJERPH, MDPI, vol. 12(10), pages 1-25, September.

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