The socioeconomic factors influencing the PM2.5 levels of 160 cities in China

Abstract

• 44 underlying socioeconomic drivers of PM 2.5 examined in 160 cities in China. • Most northern cities face more PM 2.5 concentration on weekends than weekdays. • PM 2.5 pollution in the southern areas during the winter is more deteriorating. • Larger areas of landscaping and urban construction associate with higher PM 2.5 . • Influential factors reveal highly urbanised cities to have higher PM 2.5 pollution. PM 2.5 pollution has emerged in Chinese cities due to the acceleration of its social economy; changes in socioeconomic factors result in variations in urban PM 2.5 levels. As the detection of the extensive socioeconomic factors influencing PM 2.5 concentration in literature is insufficient, questions regarding whether there are neglected factors and the relationships among various socioeconomic factors and PM 2.5 pollution remain unexplored. Benefiting from the data mining technique, this study employs association rule mining to identify the critical factors from 44 socioeconomic factors, and their underlying associative relationships with PM 2.5 pollution are extracted. Considering the annual, seasonal, and weekly scenarios of 160 cities with varying PM 2.5 levels, the hidden patterns reflecting the difference of factors’ diverse contributions to different types of cities are further revealed. The main results reveal the following: most northern cities face more PM 2.5 concentration on weekends; PM 2.5 pollution in the southern areas during the winter is more deteriorating; Beijing exhibits high PM 2.5 levels but extremely low seasonal variance. Further, the urban land utilization structure, electricity consumption and its structure, industrial soot emissions, and traffic of passengers and freight are confirmed as critical factors. These findings present practical implications for PM 2.5 pollution control and sustainable development of Chinese cities. Summary of the main finding: Critical socioeconomic factors influencing PM 2.5 levels are identified from 44 potential factors under annual, seasonal, and weekly scenarios.