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Exploring Spatio-temporal Patterns of Air Quality Index Data in China

Haokun Tang
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

Yulin Xie
Jiangsu Tianyi High School, Wuxi, China

Binbin Lu
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

Abstract. With the rapid urbanization and economy development happening in China, air pollution has been becoming a hot topic with intensive concerns. using the historical air quality index (AQI) data, this study explores the spatial-temporal distribution of AQI value to examine the distribution air pollution in China. Combined with economic and AQI data, the researches explored the spatial heterogeneity of Chinese air pollution via spatial autocorrelation analysis and Geographically Weighted Regression (GWR) technique. The air pollution data was obtained from China Air Quality Online Monitoring and Analysis Platform. The following conclusions are reached according to the results: (1) Air quality in the northern part of China is generally worse than that in the southern part. The Northeastern three province's air pollution are good. The air quality of coastal cities is better than that of inland. (2) Air quality in winter time is generally worse than that in summer time. The primary pollutant is different between summer and winter, the summer is PM10 and the winter is PM2.5. (3) Chinese air pollution has strong positive spatial autocorrelation. The aggregation pattern is high-high concentration in the north, low-low concentration in the south. (4) The GWR model fits the data set significant better than OLR model. Fossil consumption and industrial production seem to be the main causes of air pollution.

Keywords: Air pollution • AQI • GIS • GWR.

DOI: https://doi.org/10.35566/isdsa2019c7

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