Using the geographical detector technique to explore the impact of socioeconomic factors on PM2.5 concentrations in China

By Yueting Ding, Ming Zhang, Xiangyan Qian, Chengren Li, Sai Chen, Wenwen Wang. Journal of Cleaner Production, 2019

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Abstract The purpose of this study is to explore the impact of socioeconomic factors on PM2.5 concentrations and to provide insights into air quality improvement. We firstly studied the spatial autocorrelations of PM2.5 concentrations using global Moran's I and Local Indicators of Spatial Association, then explained the spatial heterogeneity of regional PM2.5 concentrations and identify the driving factors on PM2.5 by geographical detector technique, using data extracted from satellite observations over the years from 2000 to 2015. The results showed that, the annual average PM2.5 concentration in China ranged from 11.5 μg/m³ to 18.7 μg/m³ with an upward trend in general, while PM2.5 pollution were relatively serious in Beijing-Tianjin-Hebei region and Yangtze River Delta. Regional PM2.5 concentrations showed significant global and local spatial autocorrelation. Regions of high PM2.5 concentrations tend to cluster with regions of similar PM2.5 concentrations. From a long-term perspective, population density has the greatest power of determinant on PM2.5, followed by electricity consumption, industry structure, coal consumption, number of vehicles per capita and GDP per capita. Over the study period, the impact of population density revealed a trend to first rise and then fall, and the impacts of GDP per capita showed a slightly upward trend. The impact trends of number of vehicle per capita and industry structure presented to be fluctuated. The determinant power of coal consumption and electricity consumption had significant downward trends.

Subfields: Economics and Econometrics, Health, Toxicology and Mutagenesis, Transportation

View Paper: https://www.semanticscholar.org/paper/0d15e597d1ae13321e69eddf0be18b088c1f300a