Evaluating urban and nonurban PM2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset

Abstract

ABSTRACTThe air quality in China has changed due to the implementation of clean air actions since 2013. Evaluating the spatial pattern of PM2.5 and the effectiveness of reducing anthropogenic emissions in urban and nonurban areas is crucial. Therefore, the China Long-term Air Pollutant dataset for PM2.5 (CLAP_PM2.5) was generated from 2010 to 2022 with a daily 0.1° resolution using the random forest model and integrating multiple data sources, including extensive in-situ PM2.5 measurements, visibility, satellite retrievals, surface and upper-level meteorological data and other ancillary data. The CLAP_PM2.5 dataset is more reliable and accurate than other public datasets. Analysis of CLAP_PM2.5 from 2010 to 2022 reveals the decrease in positive urban-nonurban PM2.5 differences and higher decreasing rates of PM2.5 in most city clusters in eastern China. Furthermore, separating meteorological and emission contributions to the PM2.5 variability by a meteorological normalization approach indicates that meteorological contribution gradually changed from unfavorable to PM2.5 reduction during 2013–2017 to favorable to decline enhancement during 2018–2022, and in urban regions, meteorological contribution is higher than that in nonurban areas. Overall, the reduction in deweathered PM2.5 concentrations highlights China's significant achievements in terms of comprehensive clean air actions.

Keywords

• PM2.5
• urban and nonurban regions
• random forest model
• weather normalization
• clean air actions