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

Yaqiong Wang, Ke Xu and Shaomin Li
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Yaqiong Wang: Guanghua School of Management, Peking University, Beijing 100871, China
Ke Xu: School of Statistics, University of International Business and Economics, Beijing 100029, China
Shaomin Li: Guanghua School of Management, Peking University, Beijing 100871, China

IJERPH, 2020, vol. 17, issue 9, 1-15

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.

Keywords: spatio-temporal statistical model; functional data analysis; O 3 pollution (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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