NDLI: The Functional Spatio-Temporal Statistical Model with Application to O3 Pollution in Beijing, China

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The Functional Spatio-Temporal Statistical Model with Application to O3 Pollution in Beijing, China

Content Provider	MDPI
Author	YaQiong, Wang Xu, Ke Li, Shaomin
Copyright Year	2020
Description	In recent years, with rapid industrialization and massive energy consumption, ground-level ozone ( $O3$ ) 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 $O3$ pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to $O3$ 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.
Starting Page	3172
e-ISSN	16604601
DOI	10.3390/ijerph17093172
Journal	International Journal of Environmental Research and Public Health
Issue Number	9
Volume Number	17
Language	English
Publisher	MDPI
Publisher Date	2020-05-02
Access Restriction	Open
Subject Keyword	International Journal of Environmental Research and Public Health Industrial Engineering Spatio-temporal Statistical Model Functional Data Analysis O3 Pollution
Content Type	Text
Resource Type	Article

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