Dynamics-based estimates of decline trend with fine temporal variations in China's PM_2.5 emissions

Abstract. Timely, continuous, and dynamics-based estimates of PM_2.5 emissions with a high temporal resolution can be objectively and optimally obtained by assimilating observed surface PM_2.5 concentrations using flow-dependent error statistics. Annual PM_2.5 emissions in China have consistently decreased of approximately 3 % to 5 % from 2017 to 2020. Significant PM_2.5 emission reductions occurred frequently in regions with large PM_2.5 emissions. COVID-19 could cause a significant reduction of PM_2.5 emissions in the north China plain and northeast of China in 2020. The magnitudes of PM_2.5 emissions were greater in the winter than in the summer. PM_2.5 emissions show an obvious diurnal variation that varies significantly with the season and urban population. Improved representations of PM_2.5 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.