Spatio-temporal trends and influencing factors of PM 2.5 concentrations in urban agglomerations in China between 2000 an
- PDF / 657,711 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 108 Downloads / 223 Views
RESEARCH ARTICLE
Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016 Caihong Huang 1 & Kai Liu 1,2 & Liang Zhou 3 Received: 13 August 2020 / Accepted: 20 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract An urban agglomeration (UA), similar to a megalopolis or a metropolitan area, is a region where cities and people are concentrated, and where air pollution has adversely impacted on sustainable and high quality development. Studies on the spatiotemporal trends and the factors which influence PM2.5 concentrations may be used as a reference to support air pollution control policy for major UAs throughout the world. Nineteen UAs in China covering the years 2000–2016 were chosen as the research object, the PM2.5 concentrations being used to reflect air pollution and being estimated from analysis of remote sensing images. The Exploratory Spatial Data Analysis method was used to study the spatio-temporal trends for PM2.5 concentrations, and the Geodetector method was used to examine the factors influencing the PM2.5 concentrations. The results revealed that (i) the temporal trend for the average values of the PM2.5 concentrations in the UAs followed an inverted U-shaped curve and the inflection points of the curve occurred in 2007. (ii) The PM2.5 concentrations in the UAs exhibited significant global spatial autocorrelation with the high–high type and the low–low type being the main categories. (iii) The rate of land urbanization and the structure of energy consumption were the main factors which influenced the PM2.5 concentrations in the UAs. Keywords Spatio-temporal trend . Influencing factor . PM2.5 . Air pollution . Urban agglomeration . China
Highlights • Spatio-temporal trends for PM2.5 concentrations were studied using the Exploratory Spatial Data Analysis method. • Factors which influenced PM2.5 concentrations were examined using the Geodetector method. • Temporal trends for the average PM2.5 concentrations followed an inverted U-shaped curve. • PM 2 . 5 concentrations exhibited significant global spatial autocorrelation, the main categories being the high–high and the low–low type categories. • Main factors which influenced PM2.5 concentrations were the rate of land urbanization and the structure of energy consumption. Responsible Editor: Gerhard Lammel Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11356-02011357-z. * Kai Liu [email protected]
2
Collaborative Innovation Center of Human-Nature and Green Development in Universities of Shandong, Shandong Normal University, Jinan 250358, China
1
3
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
College of Geography and Environment, Shandong Normal University, Jinan 250358, China
Environ Sci Pollut Res
Introduction With the reform and opening up of China in 1978, the country has seen rapid urbanization. The resident population in urban areas increased from
Data Loading...
