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Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

Author

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  • Wenxuan Xu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China)

  • Yongzhong Tian

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China)

  • Yongxue Liu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Bingxue Zhao

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Yongchao Liu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China)

  • Xueqian Zhang

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China)

Abstract

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O 3 , wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

Suggested Citation

  • Wenxuan Xu & Yongzhong Tian & Yongxue Liu & Bingxue Zhao & Yongchao Liu & Xueqian Zhang, 2019. "Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China," IJERPH, MDPI, vol. 16(16), pages 1-23, August.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:16:p:2820-:d:255599
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    References listed on IDEAS

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    Cited by:

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    2. Wei Xue & Qingming Zhan & Qi Zhang & Zhonghua Wu, 2019. "Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China," IJERPH, MDPI, vol. 17(1), pages 1-23, December.
    3. Chao Hu & Jin Fan & Jian Chen, 2022. "Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    4. Ruyin Long & Qin Zhang & Hong Chen & Meifen Wu & Qianwen Li, 2020. "Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors," IJERPH, MDPI, vol. 17(1), pages 1-21, January.

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