Exploring the Spatial Variation Characteristics and Influencing Factors of PM 2.5 Pollution in China: Evidence from 289 Chinese Cities

Shen Zhao and Yong Xu
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Shen Zhao: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Yong Xu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Sustainability, 2019, vol. 11, issue 17, 1-17

Abstract: Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 pollution; secondly, the spatial agglomeration state of PM 2.5 pollution was characterized by Moran’s I ; finally, several regression models were used to quantitatively analyze the correlation between PM 2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM 2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM 2.5 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 PM 2.5 concentration ( p < 0.01), while VE is the most critical driver of increasing PM 2.5 concentration ( p < 0.01). Results from the local regression model show reliable evidence that the relation between PM 2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM 2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM 2.5 pollution in Chinese cities.

Keywords: PM 2.5 concentration; spatial variation; natural environmental conditions; socio-economic factors; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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