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

ABSTRACT

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.

KEYWORDS:

• PM_2.5
• urban and nonurban regions
• random forest model
• weather normalization
• clean air actions

Acknowledgments

This work was partially supported by the Special Project on National Science and Technology Basic Resources Investigation of China (No. 2021FY100702), Field Station Basic Research Project of the Chinese Academy of Sciences (Grant KFJ-SW-YW043-3), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020303). All the authors thank for the above funding.

Data availability statement

The data that support the findings of this study are openly available in the Open Science Framework data repository.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was partially supported by the Special Project on National Science and Technology Basic Resources Investigation of China (No. 2021FY100702), Field Station Basic Research Project of the Chinese Academy of Sciences (grant KFJ-SW-YW043-3), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020303).