Abstract:In order to compare and analyze the factors affecting the two heavy pollution processes during the heating season in Beijing, the meteorological conditions of the two processes from 2 to 5 November 2016 and from 11 to 14 March 2018 are analyzed by using the meteorological conventional and unconventional data and the observation data of the environmental monitoring station. The results are as follows. 1) In the 2018 process and the 2016 process, the upper and lower synoptic systems are similar, both of the mean surface wind speed are around 1.5 m·s-1, the horizontal atmospheric diffusion conditions are basically similar, and the distribution of wind field in the boundary layer and the wind speed are basically the same. However, the height of the lower warm layer in the 2018 process is above 2 km, the temperature inversion intensity is high, and the atmospheric diffusion conditions are more disadvantageous for the vertical diffusion of pollutants. 2) Compared with the 2016 process, the peak PM2.5 concentration in the 2018 process is 30.2% lower, the city average concentration is also slightly lower, and there is no explosive increase phase, but slow concentration growth. 3) In the 2016 process, the carbon monoxide (CO) increases explosively, which increases by 1 000 μg·m-3 in 4 hours, and the peak concentration is 3 179 μg·m-3. The concentration of black carbon (BC) is continuously high and the peak concentration is 19 939 ng·m-3. 4) In the 2018 process, the peak concentration of CO decreases by 24.6% compared to the 2016 process and there are no explosive increase phase. There are diurnal variations for BC, the peak concentration of which is 4 228 ng·m-3, far lower than that of the 2016 process. Though the two heavy pollution processes occur under similar meteorological conditions and the vertical diffusion conditions in the 2018 process are more disadvantageous, the concentration of various pollutants decreases significantly in the 2018 process and the average concentration decreases obviously. The air quality improvement may be closely related to the reduction of pollutant emissions.