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. 2022 Jul 25;19(15):9017.
doi: 10.3390/ijerph19159017.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Dan Yan  1   2 , Guoliang Chen  3 , Yu Lei  1 , Qi Zhou  1 , Chengjun Liu  4 , Fan Su  1
Affiliations

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Dan Yan et al. Int J Environ Res Public Health. .

Abstract

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the "high clustering club" and the "low clustering club" is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Keywords: AQI; Yangtze River Delta; geographically weighted regression; heterogeneity; spatiotemporal regularity.

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Conflict of interest statement

The authors declare no conflict of interest. They also declare no financial or personal relationships with other people or organizations that could inappropriately bias the results presented in this manuscript.

Figures

Figure 1
Figure 1
The monthly trend of the AQI value in the YRD from 2015 to 2018.
Figure 2
Figure 2
Spatial distribution of the AQI level in the YRD from 2015 to 2018.
Figure 3
Figure 3
Spatial trend distribution of the AQI in the YRD in 2015 and 2018.
Figure 4
Figure 4
Types of clustering of the AQI level in the YRD from 2015 to 2018.
Figure 5
Figure 5
Spatial distribution of hot and cold spots in the YRD from 2015 to 2018.
Figure 6
Figure 6
The spatial distribution of the regression coefficient for Ind.
Figure 7
Figure 7
The spatial distribution of the regression coefficient for Tran.
Figure 8
Figure 8
The spatial distribution of the regression coefficient for FDI.
Figure 9
Figure 9
The spatial distribution of the regression coefficient for Inno.
Figure 10
Figure 10
The spatial distribution of the regression coefficient for City.
See this image and copyright information in PMC

References

    1. Jiang W., Gao W., Gao X., Ma M., Zhou M., Du K., Ma X. Spatio-temporal heterogeneity of air pollution and its key influencing factors in the Yellow River Economic Belt of China from 2014 to 2019. J. Environ. Manag. 2021;296:113172. doi: 10.1016/j.jenvman.2021.113172. - DOI - PubMed
    1. Zhang Y.L., Cao F. Fine particulate matter (PM2.5) in China at a city level. Sci. Rep. 2015;5:14884. doi: 10.1038/srep14884. - DOI - PMC - PubMed
    1. Mu Q., Zhang S. An evaluation of the economic loss due to the heavy haze during January 2013 in China. China Environ. Sci. 2013;33:2087–2094. (In Chinese)
    1. World Health Organization . World Health Statistics 2021: Monitoring Health for the SDGs, Sustainable Development Goals. World Health Organization; Geneva, Switzerland: 2021.
    1. Zeng Y., Cao Y., Qiao X., Seyler B.C., Tang Y. Air pollution reduction in China: Recent success but great challenge for the future. Sci. Total Environ. 2019;663:329–337. doi: 10.1016/j.scitotenv.2019.01.262. - DOI - PubMed

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