Abstract
In recent years, with rapid industrialization and massive energy consumption, ground-level ozone () 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 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to 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.
Highlights
As one of the major pollutants, ground-level ozone (O3 ) has received a lot of public attention.Lots of studies have shown that O3 could have detrimental effects on human health, including exacerbation of cardiovascular and respiratory dysfunction, and even premature mortality [1,2].tropospheric ozone, as a greenhouse gas, plays an important role in climate change, and further affects, for example, agricultural crop production [3,4]
Considering the increasing public concern on ozone, we attempt to analyze the effects from other pollutants and meteorological variables on ozone pollution, and provide some insight into the diurnal cycle of O3, which peaks in the mid-day and reaches minimum at night-time
By comparing our proposed model with other models, we show the outstanding advantage of the functional spatio-temporal statistical model
Summary
As one of the major pollutants, ground-level ozone (O3 ) has received a lot of public attention. In addition to the dynamic random field and the hierarchical modeling, the third building block is based on the functional representation of daily profiles of atmospheric pollution through a functional data analysis approach, which is the main innovation of the method. The proposed model has the following advantages: (i) the dynamic random field is used to describe the spatio-temporal characterization of emissions of air pollution; (ii) and covariate effects are incorporated to analyze the underlying formation mechanism of atmospheric pollutants;.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
