English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry’s proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities