[Impacts of Meteorology and Emission Variations on PM2.5 Concentration Throughout the Country During the 2020 Epidemic Period].

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.

[Contributors to Air Pollutant Emission Changes in Autumn and Winter in Beijing-Tianjin-Hebei and Surrounding Areas].

Based on the air pollution emission inventory technical methodology, this study conducted a quantitative analysis on the changes in major air pollutant emissions in Beijing-Tianjin-Hebei and its surrounding areas from the 'New Year Haze' in the autumn and winter of 2016-2017 to the 'Pandemic Haze' in the autumn and winter of 2019-2020. The contributions of the implementation of air pollution prevention and control policies and the COVID-19 pandemic to major air pollutant emission reductions were studied, and their impacts on the regional air quality under adverse meteorological conditions were simulated using an air quality model. The results showed that from the 'New Year Haze' in Dec 2016-Jan 2017 to the 'Pandemic Haze' in Jan-Feb 2020, the major air pollutant emissions in the region had dropped by approximately 50%, and the average concentration of PM2.5 was potentially reduced by more than 40% under adverse meteorological conditions. The most effective emission reduction measures included the clean heating project and raising the standards in key industrial sectors, such as the iron and steel industry, coal-fired boilers, and power plants, which contributed 67.1% and 53.4% of the emission reductions in SO2 and PM2.5, respectively. The COVID-19 pandemic predominantly affected the mobile sources and light industry, which contributed 71.9% and 68.2% of the emission reductions in NOx and VOCs, respectively. The implementation of air pollution prevention and control policies contributed substantially to the improvement of regional air quality, which effectively reduced the intensity and extent of the heavy pollution process under unfavorable meteorological conditions. The regional average PM2.5 concentration was reduced by 26%, and the number of days experiencing heavy pollution decreased by 44%. Due to the impacts of the COVID-19 pandemic, the average PM2.5 concentration in the region was reduced by an additional 24%, and the duration and extent of heavy pollution decreased even further.