Air pollution is one consequence of rapid, massive, and disorganized urbanization. In China, pollution with PM2.5 has caused widespread government and public concern. Many studies report that the PM2.5 increase is associated with the increase of other molecules. Using statistics and spatial autocorrelation analysis methods, this study analyzes the temporal-spatial characteristics and agglomeration distribution of PM2.5 concentrations in 21 cities of Anhui Province, China (one of the most polluted provinces in the Yangtze River Delta) over 2015 and 2016. In Hefei, the capital city of Anhui Province, a Pearson correlation coefficient was used to identify the relationship between PM2.5 and SO2, CO, NO2, and O3. The concentration levels show that (1) daily average PM2.5 concentrations fluctuated wildly in different seasons of both years, (2) monthly average PM2.5 concentrations were low in the spring and summer but high in the winter and autumn, and (3) PM2.5 concentrations in 21 cities exceeded the annual PM2.5 standards (≤ 35 μg/m³) and daily limit (≤ 75 μg/m³) with the second year showing lower levels. The annual average PM2.5 had significant spatial autocorrelation and clustering characteristics with high concentrations of PM2.5 expanding in northern Anhui, whereas lower concentrations occurred in the south. Additionally, SO2, CO, and NO2 had significant correlations with PM2.5, but O3 had no significant correlation with PM2.5. This study provides a scientific basis for understanding the impact mechanisms of atmospheric pollution. It also outlines an understanding of pollution characteristics, which enables government to make efficient policies that jointly prevent and control pollution. Copyright © 2018 Elsevier Ltd. All rights reserved.
CitationMi, K., Zhuang, R., Zhang, Z., Gao, J., & Pei, Q. (2019). Spatiotemporal characteristics of PM₂.₅ and its associated gas pollutants, a case in China. Sustainable Cities and Society, 45, 287-295. doi: 10.1016/j.scs.2018.11.004
- Spatiotemporal characteristics
- Gas pollutants