Relationships between meteorological parameters and criteria air pollutants in three megacities in China

Meteorological conditions play a crucial role in ambient air pollution by affecting both directly and indirectly the emissions, transport, formation, and deposition of air pollutants. In this study, the relationships between meteorological parameters and ambient air pollutants concentrations in three megacities in China, Beijing, Shanghai, and Guangzhou were investigated. A systematic analysis of air pollutants including PM2.5, PM10, CO, SO2, NO2, and O-3 and meteorological parameters including temperature, wind speed (WS), wind direction (WD) and relative humanity (RH) was conducted for a continuous period of 12 months from March 2013 to February 2014. The results show that all three cities experienced severe air quality problems. Clear seasonal trends were observed for PM2.5, PM10, CO, SO2 and NO2 with the maximum concentrations in the winter and the minimum in the summer, while O-3 exhibited an opposite trend. Substantially different correlations between air pollutants and meteorological parameters were observed among these three cities. WS reversely correlated with air pollutants, and temperature positively correlated with O-3. Easterly wind led to the highest PM2.5 concentrations in Beijing, westerly wind led to high PM2.5 concentrations in Shanghai, while northern wind blew air parcels with the highest PM2.5 concentrations to Guangzhou. In Beijing, days of top 10% PM2.5, PM10, CO, and NO2 concentrations were with higher RH compared to days of bottom 10% concentrations, and SO2 and O-3 showed no distinct RH dependencies. In Guangzhou, days of top 10% PM2.5, PM10, CO, SO2, NO2 and O-3 concentrations were with lower RH compared to days of bottom 10% concentrations. Shanghai showed less fluctuation in RH between top and bottom 10%. These results confirm the important role of meteorological parameters in air pollution formation with large variations in different seasons and geological areas. These findings can be utilized to improve the understanding of the mechanisms that produce air pollution, enhance the forecast accuracy of the air pollution under different meteorological conditions, and provide effective measures for mitigating the pollution. (C) 2015 Elsevier Inc. All rights reserved.