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The Functional Spatio-Temporal Statistical Model with Application to O 3 Pollution in Beijing, China

Author

Listed:
  • Yaqiong Wang

    (Guanghua School of Management, Peking University, Beijing 100871, China
    These authors contributed equally to this work.)

  • Ke Xu

    (School of Statistics, University of International Business and Economics, Beijing 100029, China
    These authors contributed equally to this work.)

  • Shaomin Li

    (Guanghua School of Management, Peking University, Beijing 100871, China)

Abstract

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone ( O 3 ) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O 3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O 3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3172-:d:353357
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    References listed on IDEAS

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    1. 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.
    2. Huang, Hsin-Cheng & Cressie, Noel, 1996. "Spatio-temporal prediction of snow water equivalent using the Kalman filter," Computational Statistics & Data Analysis, Elsevier, vol. 22(2), pages 159-175, July.
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