320
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Spatial and temporal evolution of air pollution and verification of the environmental Kuznets curve in the Yangtze River Basin during 1980—2019

, , , , &
Article: 2265157 | Received 03 Jun 2023, Accepted 26 Sep 2023, Published online: 30 Oct 2023
 

ABSTRACT

Continuously high concentrations of haze pollution can hinder urban economic development. In order to improve the quality of the environment in the Yangtze River Economic Belt, it is necessary to investigate the spatio-temporal characteristics and impact factors of smog. This study, relying on multi-source remote sensing data, conducted a comprehensive study on the concentration of haze pollution based on long-term data, multiple spatial scales and pollution indicators. The results showed that the concentrations of seven air pollutants (PM2.5, SO4, SO2, BC, OC, SS and dust) in the Yangtze River Basin appeared to first increase and then decreased from 1980 to 2019. Dust pollution and sea salt pollution were concentrated in the upper reaches of the Yangtze River and the coastal areas of the Yangtze River Delta, while other pollutants were higher in the Sichuan Basin and northeast of the Yangtze River. Of the socioeconomic factors, the significance of different factors on pollutant concentration was obviously different. In addition, the environmental Kuznets curve relationship between economic gain and air pollution depended on the type of pollutant, and there were certain regional differences. This study provided a scientific basis for considering innovations in haze control in the urban agglomeration of the Yangtze River Economic Belt.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this study was obtained from the National Aeronautics &Space Administration (NASA): http://disc.sci.gsfc.nasa.gov/mdisc/. Social and economic data come from China Statistical Yearbook.

Additional information

Funding

This work was funded by National Natural Science Foundation of China Grant (No.42201031, No.41901285), Natural Science Foundation of Hubei Province (No.2022CFB754).