Evaluating urban and nonurban PM_2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset

The air quality in China has changed due to the implementation of clean air actions since 2013. Evaluating the spatial pattern of PM_2.5 and the effectiveness of reducing anthropogenic emissions in urban and nonurban areas is crucial. Therefore, the China Long-term Air Pollutant dataset for PM_2.5 (CLAP_PM_2.5) 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 PM_2.5 measurements, visibility, satellite retrievals, surface and upper-level meteorological data and other ancillary data. The CLAP_PM_2.5 dataset is more reliable and accurate than other public datasets. Analysis of CLAP_PM_2.5 from 2010 to 2022 reveals the decrease in positive urban-nonurban PM_2.5 differences and higher decreasing rates of PM_2.5 in most city clusters in eastern China. Furthermore, separating meteorological and emission contributions to the PM_2.5 variability by a meteorological normalization approach indicates that meteorological contribution gradually changed from unfavorable to PM_2.5 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 PM_2.5 concentrations highlights China's significant achievements in terms of comprehensive clean air actions.