28 Apr 2023
 | 28 Apr 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Dynamics-based estimates of decline trend with fine temporal variations in China's PM2.5 emissions

Zhen Peng, Lili Lei, Zhe-Min Tan, Meigen Zhang, Aijun Ding, and Xingxia Kou

Abstract. Timely, continuous, and dynamics-based estimates of PM2.5 emissions with a high temporal resolution can be objectively and optimally obtained by assimilating observed surface PM2.5 concentrations using flow-dependent error statistics. Annual PM2.5 emissions in China have consistently decreased of approximately 3 % to 5 % from 2017 to 2020. Significant PM2.5 emission reductions occurred frequently in regions with large PM2.5 emissions. COVID-19 could cause a significant reduction of PM2.5 emissions in the north China plain and northeast of China in 2020. The magnitudes of PM2.5 emissions were greater in the winter than in the summer. PM2.5 emissions show an obvious diurnal variation that varies significantly with the season and urban population. Improved representations of PM2.5 emissions across time scales can benefit emission inventory, regulation policy and emission trading schemes, particularly for especially for high temporal resolution air quality forecasting and policy response to severe haze pollutions or rare human events with significant socioeconomic impacts.

Zhen Peng et al.

Status: open (until 09 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Zhen Peng et al.

Zhen Peng et al.


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Short summary
Annual PM2.5 emission in China consistently decreases about 3 % to 5 % from 2017 to 2020 with spatial variations and seasonal dependences. High temporal-resolution and dynamics-based PM2.5 emission estimates provide quantitative diurnal variations for each season. Significant reduction of PM2.5 emissions in the north China plain and northeast of China in 2020 are caused by COVID-19.