Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO_x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO₂ columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO_x emissions in March 2020 (14 %–31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %–51 %) in April. However, NO_x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO₂. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO₂ reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r=0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by −4.83 ppbv, while ozone production rates dampened by largely negative $J_{{\mathrm{NO}}_{\mathrm{2}}}$ [NO₂]-$k_{\mathrm{NO}+{\mathrm{O}}_{\mathrm{3}}}$ [NO][O₃] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

Atmospheric aerosols are significant sources of uncertainty in air mass factor (AMF) calculations for trace gas retrievals using ultraviolet measurements from space. Current trace gas retrievals typically do not consider aerosols explicitly as cloud products partially account for aerosol effects. Here, we propose a new measurement‐based approach to correct for aerosols explicitly in the AMF calculation, apply it to Ozone Monitoring Instrument (OMI) formaldehyde (HCHO) retrievals and quantify the aerosol‐induced HCHO vertical column density difference for three aerosol types (smoke, dust, and sulfate) during 2006–2007. We use OMI aerosol retrievals for aerosol optical properties and vertical profiles to construct lookup tables of scattering weights as functions of geometry, surface pressure, surface albedo, and aerosol information. The average difference between the National Aeronautics and Space Administration operational OMI HCHO product (not considering aerosols) and the results obtained in this study on a global scale are 27%, 6%, and −0.3% for smoke, dust, and sulfate aerosols, respectively. The region with the largest aerosol effects is East China, where the explicit smoke aerosol correction enhances mean HCHO vertical column densities by 35%, with corrections to individual observations sometimes larger than 100%. The quantified aerosol effects are applicable under clear‐sky conditions. This study highlights the need to implement aerosol corrections in the AMF calculation for HCHO retrievals. This is particularly relevant in regions with high levels of pollution where aerosols interfere the most with formaldehyde satellite observations.

The ground-based ozone observation instrument, Brewer spectrophotometer (Brewer), was used to evaluate the quality of the total ozone column (TOC) produced by multiple polar-orbit satellite measurements at three stations in Antarctica (King Sejong, Jang Bogo, and Zhongshan stations). While all satellite TOCs showed high correlations with Brewer TOCs (R = ~0.8 to 0.9), there are some TOC differences among satellite data in austral spring, which is mainly attributed to the bias of Atmospheric Infrared Sounder (AIRS) TOC. The quality of satellite TOCs is consistent between Level 2 and 3 data, implying that “which satellite TOC is used” can induce larger uncertainty than “which spatial resolution is used” for the investigation of the Antarctic TOC pattern. Additionally, the quality of satellite TOC is regionally different (e.g., OMI TOC is a little higher at the King Sejong station, but lower at the Zhongshan station than the Brewer TOC). Thus, it seems necessary to consider the difference of multiple satellite data for better assessing the spatiotemporal pattern of Antarctic TOC.

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. This approach has another advantage in that we can, to a certain extent, disentangle the chemical and physical processes involved in the formation of ozone. Accordingly, we jointly constrain NO_x and VOC emissions using well-characterized TROPOMI HCHO and NO₂ columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO_x emissions in March 2020 (14–31 %) in several major cities including Paris, London, Madrid, and Milan expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34–51 %) in April. The large variability of changes in NO_x emissions is indicative of different dates and the degree of restrictions enacted to prevent the spread of the virus. For instance, NO_x emissions remain at somewhat similar values or even higher in northern Germany and Moscow in March 2020 compared to the baseline. Comparisons against surface monitoring stations indicate that the model estimate of the NO₂ reduction is underestimated, a picture that correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO₂ reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). Changes in VOC emissions are dominated by eastern European biomass burning activities and biogenic isoprene emissions. In March, however, TROPOMI HCHO sets an upper limit for HCHO changes such that the chemical feedback of NO_x on HCHO constrained by TROPOMI NO₂ reveals a non-negligible decline in anthropogenic VOC emissions in Paris (−9 %), Milan (−29 %), London (−5 %), and Rome (−5 %). Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r = 0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). Results of integrated process rates of MDA8 surface ozone over central Europe in the same month suggest that physical processes (dry deposition, advection and diffusion) decrease ozone on average by −4.83 ppbv, while ozone production rates dampened by largely negative J_NO2[NO₂]-k_NO+O3[NO][O₃] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology (mainly as air temperature and photolysis) contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture the essential character of ozone changes induced by abrupt emission anomalies.

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. This approach has another advantage in that we can, to a certain extent, disentangle the chemical and physical processes involved in the formation of ozone. Accordingly, we jointly constrain NO_x and VOC emissions using well-characterized TROPOMI HCHO and NO₂ columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO_x emissions in March 2020 (14–31 %) in several major cities including Paris, London, Madrid, and Milan expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34–51 %) in April. The large variability of changes in NO_x emissions is indicative of different dates and the degree of restrictions enacted to prevent the spread of the virus. For instance, NO_x emissions remain at somewhat similar values or even higher in northern Germany and Moscow in March 2020 compared to the baseline. Comparisons against surface monitoring stations indicate that the model estimate of the NO₂ reduction is underestimated, a picture that correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO₂ reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). Changes in VOC emissions are dominated by eastern European biomass burning activities and biogenic isoprene emissions. In March, however, TROPOMI HCHO sets an upper limit for HCHO changes such that the chemical feedback of NO_x on HCHO constrained by TROPOMI NO₂ reveals a non-negligible decline in anthropogenic VOC emissions in Paris (−9 %), Milan (−29 %), London (−5 %), and Rome (−5 %). Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r = 0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). Results of integrated process rates of MDA8 surface ozone over central Europe in the same month suggest that physical processes (dry deposition, advection and diffusion) decrease ozone on average by −4.83 ppbv, while ozone production rates dampened by largely negative J_NO2[NO₂]-k_NO+O3[NO][O₃] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology (mainly as air temperature and photolysis) contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture the essential character of ozone changes induced by abrupt emission anomalies.