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Estimates of Daily PM 2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model

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  • Jinhuang Lin

    (College of Geographical Science, Fujian Normal University, Fuzhou 350007, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • An Zhang

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

  • Wenhui Chen

    (College of Geographical Science, Fujian Normal University, Fuzhou 350007, China)

  • Mingshui Lin

    (College of Tourism, Fujian Normal University, Fuzhou 350117, China)

Abstract

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM 2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM 2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM 2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM 2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM 2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM 2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.

Suggested Citation

  • Jinhuang Lin & An Zhang & Wenhui Chen & Mingshui Lin, 2018. "Estimates of Daily PM 2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2772-:d:162122
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    References listed on IDEAS

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    1. Lijian Han & Weiqi Zhou & Weifeng Li, 2018. "Growing Urbanization and the Impact on Fine Particulate Matter (PM 2.5 ) Dynamics," Sustainability, MDPI, vol. 10(6), pages 1-9, May.
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    Cited by:

    1. Yaqiong Wang & Ke Xu & Shaomin Li, 2020. "The Functional Spatio-Temporal Statistical Model with Application to O 3 Pollution in Beijing, China," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
    2. Mingshui Lin & Juan Lin & Caibin Lin & An Zhang & Kaiyong Wang, 2018. "Spatial Diffusion of Taiwanese Enterprises in Mainland China under the Vision of Rural Industrial Vitalization," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    3. Qingbin Wei & Lianjun Zhang & Wenbiao Duan & Zhen Zhen, 2019. "Global and Geographically and Temporally Weighted Regression Models for Modeling PM 2.5 in Heilongjiang, China from 2015 to 2018," IJERPH, MDPI, vol. 16(24), pages 1-20, December.

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