Spatio-temporal Variation of PM2.5 Pollution and its Relationship with Meteorology among Five Megacities in China

Fine particles are a crucial air pollutant in terms of their impact on the ambient environment, citizens’ health and traffic visibility. In this study, temporal and spatial variations in PM 2.5 were analyzed in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang in China from June 2013 till May 2017 using hourly data collected from the U.S. Embassy and Consulate monitoring system. The distributions of the annual, seasonal, monthly and diurnal concentration were illustrated by the attainment rate and the severity rate, as well as the length of time. After that, the coefficient of divergence (COD) was adopted to study the spatial heterogeneity among five typical megacities. Additionally, the relationship between PM 2.5 and meteorology was calculated by Pearson’s correlation and stepwise multiple linear regression. The results show that annual PM 2.5 concentrations were overall downward trends in all areas. Clear seasonal variations were identified, with the least pollution in summer and the most in winter. The hourly distribution was dramatically different, while the average concentration during the daytime was higher than at night except in Shanghai, and the weekends had higher pollution than the weekdays except in Chengdu. Also, the monthly attainment rate displayed an inverted-U distribution; oppositely, the severity rate revealed a U-shaped distribution. With the increasing pollution levels, the duration of the PM 2.5 pollution was observed to be continuously declining in Guangzhou, Shanghai and Beijing but fluctuating in the other two cities. Furthermore, COD values indicated that there was an obvious spatial heterogeneity between Beijing—Chengdu—Shenyang and Shanghai—Guangzhou. As for meteorology, the pressure had a significantly positive impact on the PM 2.5 concentration, while the temperature and rainfall had a negative influence. These results show the pollution levels of PM 2.5 in different cities at distinct times and confirm the important role of meteorological conditions in air quality. The findings also provide forecast models of PM 2.5 for the five cities.