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 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. The annual dynamics-based estimates of PM2.5 emissions averaged over mainland China for the years 2016–2020 without biomass burning emissions are 7.66, 7.40, 7.02, 6.62, and 6.38 Tg, respectively, which are very closed to the values of the Multi-resolution Emission Inventory (MEIC). Annual PM2.5 emissions in China have consistently decreased by 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. Compared to the diurnal variations of PM2.5 emission fractions estimated based on diurnal variation profiles from the US and EU, the estimated PM2.5 emission fractions are 1.25 % larger during the evening, the morning peak is 0.57 % smaller in winter and 1.05 % larger in summer, and the evening peak is 0.83 % smaller. Improved representations of PM2.5 emissions across timescales 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 pollution or rare human events with significant socioeconomic impacts.