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Open AccessArticle

Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones

1
Shandong Province “3S” Engineering Research Center, Shandong University of Science and Technology, Qianwangang Road, Huangdao Zone, Qingdao 266590, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100049, China
3
Qingdao Environmental Monitoring Center Station, Yan’an No.1 Road, Qingdao 266003, China
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Qingdao Meteorological Observation, Fulongshan Road, Qingdao 266003, China
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MS GIS Program, University of Redlands, East Colton Avenue, Redlands, CA 92373, USA
*
Authors to whom correspondence should be addressed.
Atmosphere 2018, 9(4), 135; https://doi.org/10.3390/atmos9040135
Received: 2 March 2018 / Revised: 22 March 2018 / Accepted: 31 March 2018 / Published: 4 April 2018
(This article belongs to the Section Air Quality)
Air pollution has impacted people’s lives in urban China, and the analysis of the distribution and driving factors behind air quality has become a current research focus. In this study, the temporal heterogeneity of air quality (AQ) and the dominant air pollutants across the four seasons were analyzed based on the Kruskal-Wallis rank-sum test method. Then, the spatial heterogeneity of AQ and the dominant air pollutants across four sites were analyzed based on the Wilcoxon signed-rank test method. Finally, the copula model was introduced to analyze the effect of relative factors on dominant air pollutants. The results show that AQ and dominant air pollutants present significant spatiotemporal heterogeneity in the study area. AQ is worst in winter and best in summer. PM10, O3, and PM2.5 are the dominant air pollutants in spring, summer, and winter, respectively. The average concentration of dominant air pollutants presents significant and diverse daily peaks and troughs across the four sites. The main driving factors are pollutants such as SO2, NO2, and CO, so pollutant emission reduction is the key to improving air quality. Corresponding pollution control measures should account for this heterogeneity in terms of AQ and the dominant air pollutants among different urban zones. View Full-Text
Keywords: air quality (AQ); dominant air pollutants; spatiotemporal heterogeneity; Kruskal-Wallis rank-sum test; Wilcoxon signed-rank test; copula model air quality (AQ); dominant air pollutants; spatiotemporal heterogeneity; Kruskal-Wallis rank-sum test; Wilcoxon signed-rank test; copula model
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MDPI and ACS Style

Zhao, X.; Gao, Q.; Sun, M.; Xue, Y.; Ma, R.; Xiao, X.; Ai, B. Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones. Atmosphere 2018, 9, 135. https://doi.org/10.3390/atmos9040135

AMA Style

Zhao X, Gao Q, Sun M, Xue Y, Ma R, Xiao X, Ai B. Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones. Atmosphere. 2018; 9(4):135. https://doi.org/10.3390/atmos9040135

Chicago/Turabian Style

Zhao, Xiangwei; Gao, Qian; Sun, Meng; Xue, Yunchuan; Ma, RuiJin; Xiao, Xingyuan; Ai, Bo. 2018. "Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones" Atmosphere 9, no. 4: 135. https://doi.org/10.3390/atmos9040135

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