Temporal-Spatial Variations of Concentrations of PM10 and PM2.5 in Ambient Air
Liu Jie1,2, Hou Kepeng1,2, Wang Xiaodong1,2, Yang Peng3
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1Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming, China
2Postdoctorate Station of Mining Industry Engineering, Kunming University of Science and Technology, Kunming, China
3Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China
Publish date: 2016-11-24
Submission date: 2016-04-03
Final revision date: 2016-06-01
Acceptance date: 2016-06-11
Pol. J. Environ. Stud. 2016;25(6):2435–2444
Since the space points’ average concentrations of PM obtained by air quality automatic monitoring sites were less representative of PM pollution levels in the Beijing area, it was necessary to improve the spatial resolution of PM concentrations on the basis of continuous time series. In order to solve the problem, we used one-hour average concentrations of PM from March 2013 to February 2014 obtained by monitoring sites. Firstly, concentration variations with time scale of PM2.5 and PM10 were researched to find out their correlations and pollution grades in continuous time series. Secondly, in order to realize the spatial distribution characteristics from points to surfaces, MATLAB spatial interpolation tools were used to predict the average concentrations of PM on any latitude-longitude grid in regional surface, then spatial interpolation on longitudes and latitudes, and the PM concentrations were researched by radial basis functions based on biharmonic green function. Finally, by constructing decision functions and sample sets, the interpolation results were tested by k-fold cross validation to analyze the error distribution between monitoring values and fitted values, and then they were compared with Kriging interpolation results realized by DACE tool in MATLAB. The results showed there were periodical variations and significant correlations on the average concentrations of PM from March 2013 to February 2014 in Beijing. The PM pollutions also had obvious regional characteristics. Interpolation results of radial basis function interpolation on PM concentrations could represent their spatial distribution in Beijing, since the method had a certain precision to improve utilization of spatial information. Moreover, the analysis showed that the main factors of PM pollution were dust storms and strong winds in spring and autumn, rainfall and the warm wet climate in summer, and cold fronts and snowfall in winter. Pollution characteristics in the Beijing area were higher in the south and lower in the north, and the pollution sources might be regional transport as well as local anthropogenic sources. The conjoint analysis on time series and spatial interpolation of concentrations had significance for further research of time-space relationship of PM, and it also provided a method for understanding regional pollution characteristics.