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

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ID: 106493
2020
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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.
Reference Key
wang2020theinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Yaqiong;Xu, Ke;Li, Shaomin;
Journal International journal of environmental research and public health
Year 2020
DOI
E3172
URL
Keywords
functional data analysis o3 pollution spatio-temporal statistical model

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