Spatial and Temporal Distribution of PM 2.5 Pollution in Xi’an City, China

Ping Huang, Jingyuan Zhang, Yuxiang Tang and Lu Liu
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Ping Huang: State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Jingyuan Zhang: State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Yuxiang Tang: State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Lu Liu: State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China

IJERPH, 2015, vol. 12, issue 6, 1-18

Abstract: The monitoring data of the 13 stations in Xi’an city for the whole years of 2013 and 2014 was counted and analyzed. Obtaining the spatial and temporal distribution characteristics of PM 2.5 was the goal. Cluster analysis and the wavelet transform were utilized to discuss the regional distribution characteristics of PM 2.5 concentration ( ? (PM 2.5 )) and the main features of its yearly changes and sudden changes. Additionally, some relevant factors were taken into account to interpret the changes. The results show that ? (PM 2.5 ) in Xi’an during 2013 was generally higher than in 2014, it is high in winter and low in summer, and the high PM 2.5 concentration centers are around the People’s Stadium and Caotan monitoring sites; For the regional PM 2.5 distribution, the 13 sites can be divided into three categories, in which Textile city is Cluster 1, and High-tech Western is Cluster 2, and Cluster 3 includes the remaining 11 monitoring sites; the coefficient of goodness of the cluster analysis is 0.6761, which indicates that the result is acceptable. As for the yearly change, apart from June and July, the average ? (PM 2.5 ) concentration has been above the normal concentration criteria of Chinese National Standard (50 g/m 3 ); cloudy weather and low winds are the major meteorological factors leading to the sudden changes of ? (PM 2.5 ).

Keywords: PM 2.5; spatial and temporal distribution; cluster analysis; wavelet transform (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
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