During the COVID‐19 outbreak that took place in early 2020, the economic activities in China were drastically reduced and accompanied by a strong reduction in the emission of primary air pollutants. On the basis of measurements made at the monitoring stations operated by the China National Environmental Monitoring Center, we quantify the reduction in surface PM2.5, NO2, CO, and SO2 concentrations in northern China during the lockdown, which started on 23 January 2020. We find that, on the average, the levels of surface PM2.5 and NO2 have decreased by approximately 35% and 60%, respectively, between the period 1 and 22 January 2020 and the period 23 January and 29 February 2020. At the same time, the mean ozone concentration has increased by a factor 1.5–2. In urban area of Wuhan, where drastic measures were adopted to limit the spread of the coronavirus, similar changes in the concentrations of PM2.5, NO2, and ozone are found.

Abstract. The relationship between aerosol optical depth (AOD) and PM 2.5 is often investigated in order to obtain surface PM 2.5 from satellite observation of AOD with a broad area coverage. However, various factors could affect the AOD-PM 2.5 regressions. Using both ground and satellite observations in Beijing from 2011 to 2015, this study analyzes the influential factors including the aerosol type, relative humidity (RH), planetary boundary layer height (PBLH), wind speed and direction, and the vertical structure of aerosol distribution. The ratio of PM 2.5 to AOD, which is defined as η, and the square of their correlation coefficient (R 2 ) have been examined. It shows that η varies from 54.32 to 183.14, 87.32 to 104.79, 95.13 to 163.52, and 1.23 to 235.08 µg m −3 with aerosol type in spring, summer, fall, and winter, respectively. η is smaller for scattering-dominant aerosols than for absorbing-dominant aerosols, and smaller for coarse-mode aerosols than for fine-mode aerosols. Both RH and PBLH affect the η value significantly. The higher the RH, the smaller the η, and the higher the PBLH, the smaller the η. For AOD and PM 2.5 data with the correction of RH and PBLH compared to those without, R 2 of monthly averaged PM 2.5 and AOD at 14:00 LT increases from 0.63 to 0.76, and R 2 of multi-year averaged PM 2.5 and AOD by time of day increases from 0.01 to 0.93, 0.24 to 0.84, 0.85 to 0.91, and 0.84 to 0.93 in four seasons respectively. Wind direction is a key factor for the transport and spatial-temporal distribution of aerosols originated from different sources with distinctive physicochemical characteristics. Similar to the variation in AOD and PM 2.5 , η also decreases with the increasing surface wind speed, indicating that the contribution of surface PM 2.5 concentrations to AOD decreases with surface wind speed. The vertical structure of aerosol exhibits a remarkable change with seasons, with most particles concentrated within about 500 m in summer and within 150 m in winter. Compared to the AOD of the whole atmosphere, AOD below 500 m has a better correlation with PM 2.5 , for which R 2 is 0.77. This study suggests that all the above influential factors should be considered when we investigate the AOD-PM 2.5 relationships.

Abstract. In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1 × 0.1 latitude/longitude degree resolution, on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs is provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC are estimated. As discussed in the paper, reductions vary highly across regions and sectors, due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).

Abstract. In order to fight the spread of the global COVID-19 pandemic, most of the world's countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to current global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1×0.1 latitude–longitude degree resolution on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs are provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first 6 months of 2020. Maximum decreases in the total emissions are found in February in eastern China, with an average reduction of 20 %–30 % in NOx, NMVOCs (non-methane volatile organic compounds) and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20 %–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30 %–50 %) in South America. In India and African regions, NOx and NMVOC emissions are reduced on average by 15 %–30 %. For the other species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC (black carbon) are estimated. As discussed in the paper, reductions vary highly across regions and sectors due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid-19 adjustmeNt Factors fOR eMissions) (https://doi.org/10.25326/88; Doumbia et al., 2020). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/, last access: 23 August 2021).

With the development of the COVID-19 pandemic and the resulting slowdown in economic activity, first in China and then in the rest of the world, anthropogenic emissions of primary pollutants were significantly altered after January 2020. This unanticipated planet-wide experiment allows us to examine the response of the atmosphere's chemical system and in particular, the formation of secondary compounds such as