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Spatial distribution and determinants of PM2.5 in China’s cities: fresh evidence from IDW and GWR

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Abstract

While numerous studies have explored the spatial patterns and underlying causes of PM2.5 at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM2.5 factors. In order to enrich this research field, we collected PM2.5 monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM2.5 at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM2.5. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM2.5 in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM2.5 governance. (2) The effects of various factors of socio-economic activities on the concentration of PM2.5 have significant spatial heterogeneity among Chinese cities. (3) There is an inverted “U” curve between economic growth and PM2.5. When the per capita income reaches 47,000 yuan, the PM2.5 emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM2.5 governance measures.

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Acknowledgments

We express our gratitude to Dr. Bin Su in the national university of Singapore for his valuable comments and suggestions to improve significantly this work.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos.71963030 and 71974188), the Autonomous Region Postgraduate Research and Innovation Project (Grant No.XJ2020G003), and the Doctoral Student Science and Technology Innovation Project of Xinjiang University (Grant No.XJUBSCX-201920).

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Authors and Affiliations

  1. School of Economic and Management, Xinjiang University, Urumqi, 83000, People’s Republic of China

    Kuiying Gu & Hui Sun

  2. Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi,, 83000, People’s Republic of China

    Kuiying Gu & Hui Sun

  3. Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China

    Yi Zhou

  4. School of Economics and Management, China University of Mining and Technology, Xuzhou, 221116, People’s Republic of China

    Feng Dong

  5. Center of Innovation on Industrial Cloud Big Data of Xinjiang, Urumqi, 830000, People’s Republic of China

    Lianming Zhao

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  1. Kuiying Gu
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Gu, K., Zhou, Y., Sun, H. et al. Spatial distribution and determinants of PM2.5 in China’s cities: fresh evidence from IDW and GWR. Environ Monit Assess 193, 15 (2021). https://doi.org/10.1007/s10661-020-08749-6

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