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A spatio‐temporal model for the analysis and prediction of fine particulate matter concentration in Beijing

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  • Yating Wan
  • Minya Xu
  • Hui Huang
  • Song Xi Chen

Abstract

Effective air quality management and forecasting in Beijing is urgently needed as the region suffers from the worst air pollution in any standards. However, the statistical mechanism of the PM2.5 formation with respect to various factors is underexplored in this region and China in general. Through an elaborate application with refinement of a spatio‐temporal model with varying coefficients to the dynamics of PM2.5 around Beijing based on a large dataset, we provide a comprehensive interpretation for the dynamics of PM2.5 concentration with respect to its gaseous precursors, meteorological conditions and geographical variables. Furthermore, we conduct multistep temporal forecasts on a rolling basis for both the PM2.5 concentration and the pollution levels. With the help of the expectation‐maximization algorithm, the proposed models estimated for eight seasons from March 2015 to February 2017 around Beijing provide satisfactory in‐sample fits and generate more accurate out‐of‐sample forecasts, compared with Finazzi and Fassò's original model as well as other alternative models. Valuable insights in tackling the excessive air pollution in Beijing are suggested from the comprehensive application of our model.

Suggested Citation

  • Yating Wan & Minya Xu & Hui Huang & Song Xi Chen, 2021. "A spatio‐temporal model for the analysis and prediction of fine particulate matter concentration in Beijing," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:1:n:e2648
    DOI: 10.1002/env.2648
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    References listed on IDEAS

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    1. Wenju Cai & Ke Li & Hong Liao & Huijun Wang & Lixin Wu, 2017. "Weather conditions conducive to Beijing severe haze more frequent under climate change," Nature Climate Change, Nature, vol. 7(4), pages 257-262, April.
    2. Gao, Zhaoxing & Ma, Yingying & Wang, Hansheng & Yao, Qiwei, 2019. "Banded spatio-temporal autoregressions," Journal of Econometrics, Elsevier, vol. 208(1), pages 211-230.
    3. G. Y. Yi & W. Liu & Lang Wu, 2011. "Simultaneous Inference and Bias Analysis for Longitudinal Data with Covariate Measurement Error and Missing Responses," Biometrics, The International Biometric Society, vol. 67(1), pages 67-75, March.
    4. 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.
    5. R.M. Boaz & A.B. Lawson & J.L. Pearce, 2019. "Multivariate air pollution prediction modeling with partial missingness," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    6. O. Nicolis & M. Díaz & S. K. Sahu & J. C. Marín, 2019. "Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    7. Sam Clifford & Samantha Low‐Choy & Mandana Mazaheri & Farhad Salimi & Lidia Morawska & Kerrie Mengersen, 2019. "A Bayesian spatiotemporal model of panel design data: Airborne particle number concentration in Brisbane, Australia," Environmetrics, John Wiley & Sons, Ltd., vol. 30(7), November.
    8. Crescenza Calculli & Alessandro Fassò & Francesco Finazzi & Alessio Pollice & Annarita Turnone, 2015. "Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy," Environmetrics, John Wiley & Sons, Ltd., vol. 26(6), pages 406-417, September.
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    Cited by:

    1. Xiangyu Zheng & Bin Guo & Jing He & Song Xi Chen, 2021. "Effects of corona virus disease‐19 control measures on air quality in North China," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    2. Daniel Cirkovic & Thomas J. Fisher, 2021. "On testing for the equality of autocovariance in time series," Environmetrics, John Wiley & Sons, Ltd., vol. 32(7), November.
    3. Ying Zhang & Song Xi Chen & Le Bao, 2023. "Air pollution estimation under air stagnation—A case study of Beijing," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    4. Sanying Feng & Menghan Zhang & Tiejun Tong, 2022. "Variable selection for functional linear models with strong heredity constraint," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 321-339, April.

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