Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set

Abstract

AbstractIn the context of China's clean air policy, the meteorological impacts on improved particulate matter (PM2.5) air quality during 2016–2019 are investigated based on a four‐year high‐resolution atmospheric composition reanalysis data‐set, which has been produced by the Joint Data Assimilation System to resolve long‐term fine‐scale air quality variability over China. The reanalysis assimilates surface air quality observations using the Weather Research and Forecasting model coupled with Chemistry and an ensemble‐based assimilation algorithm, and simultaneous assimilations of meteorological observations, chemical initial conditions (ICs) and emissions are applied to help reduce the uncertainty in meteorology, ICs and the emissions inventory. Further, objective weather classification method is applied to quantitatively explore synoptic circulation pattern changes and associated PM2.5 variability over North China by using this unique reanalysis data‐set. PM2.5 reanalysis data are also investigated according to different circulation types, and results indicate that temporal and spatial variations of PM2.5 are found to be closely connected with weather and circulation patterns. The northerly types correspond to the lower PM2.5 levels, while the southerly and easterly types correspond to the higher PM2.5 concentration due to favorable local meteorological conditions. According to the quantitative evaluation on circulation pattern changes, meteorological contribution have played a positive role in improving air quality in the context of China's clean air policy during 2016–2019. This study serves as a basis for future retrospective assessments of air pollutant variation and emissions regulation measures.