The Relationships between PM(2.5) and Meteorological Factors in China: Seasonal and Regional Variations

The interactions between PM(2.5) and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM(2.5) concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM(2.5) concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM(2.5) exist. Spatially, RH is positively correlated with PM(2.5) concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM(2.5) everywhere except for Hainan Island. PS has a strong positive relationship with PM(2.5) concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM(2.5) concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM(2.5) in autumn and the opposite in winter. PS is more positively correlated with PM(2.5) in autumn than in other seasons. Our study investigated the relationships between PM(2.5) and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM(2.5) and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM(2.5) concentration prediction and haze control to improve the prediction accuracy and policy efficiency.