Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 2. Key weather elements and emissions

A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM2.5 and O-3 in China. Following an accompanying paper on synoptic circulation impact (Gong et al., 2022), using a multi-linear regression model, the trends of key meteorological elements at local scale, i.e., temperature, relative humidity, solar radiation, PBL height, precipitation and wind speed, are analyzed and correlated with the trends of PM2.5 and O-3 levels to identify significantly influencing factors in seven Chinese cities. Furthermore, with additional emission surrogates introduced in the regression model, the impacts on the trends by meteorology and emission were separated and quantified. Results show that the increasing trends of O-3 at most Chinese cities were largely attributed to the trends of meteorological elements of temperature and solar radiation, while the trends of PM2.5 are mostly contributed by the emission reduction measures of PM2.5 and its precursors. The meteorology alone can explain approximately 57-80% of the O-3 variations and only 20-33% of the PM2.5 variations. With the addition of emission surrogates, this explanation percentage is increased to about 57-82% for O-3 but significantly enhanced to 71-83% for PM2.5.

Automated summary

A multiscale analysis was conducted to investigate the impacts of large-scale circulation types and local-scale key weather elements on complex air pollutants in China. The study found that the increasing trends of O-3 in most Chinese cities were largely influenced by temperature and solar radiation, while the trends of PM2.5 were mainly contributed by emission reduction measures. The meteorology alone explained approximately 57-80% of the variations in O-3 levels and only 20-33% of the variations in PM2.5 levels. However, with the addition of emission surrogates in the regression model, the explanation percentage increased significantly for both O-3 and PM2.5.