TROPOMI satellite data show substantial drops in nitrogen dioxide (NO₂) during COVID‐19 physical distancing. To attribute NO₂ changes to NO_x emissions changes over short timescales, one must account for meteorology. We find that meteorological patterns were especially favorable for low NO₂ in much of the United States in spring 2020, complicating comparisons with spring 2019. Meteorological variations between years can cause column NO₂ differences of ~15% over monthly timescales. After accounting for solar angle and meteorological considerations, we calculate that NO₂ drops ranged between 9.2% and 43.4% among 20 cities in North America, with a median of 21.6%. Of the studied cities, largest NO₂ drops (>30%) were in San Jose, Los Angeles, and Toronto, and smallest drops (<12%) were in Miami, Minneapolis, and Dallas. These normalized NO₂ changes can be used to highlight locations with greater activity changes and better understand the sources contributing to adverse air quality in each city.

Observing the spatial heterogeneities of NO₂ air pollution is an important first step in quantifying NO_X emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO₂ pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine‐scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO₂ columns by day‐of‐the‐week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2‐m temperatures and NO₂ column amounts and find that NO₂ is larger on the hottest days (>32°C) as compared to warm days (26°C–32°C), which is in contrast to a general decrease in NO₂ with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO₂ data to annual surface‐level concentrations yields relatively strong correlation (R² = 0.66). These new developments make TROPOMI NO₂ satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO₂ and a likely increase in CH₄ lifetime from reduced NO_x emissions. Second, the response of O₃ to decreased NO_x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH₄ and CO₂, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty. Significance The COVID-19 pandemic and associated lockdowns caused significant changes to human activity that temporarily altered our imprint on the atmosphere, providing a brief glimpse of potential future changes in atmospheric composition. This event demonstrated key feedbacks within and between air quality and the carbon cycle: Improvements in air quality increased the lifetime of methane (an important greenhouse gas), while unusually hot weather and intense wildfires in Los Angeles drove poor air quality. This shows that efforts to reduce greenhouse gas emissions and improve air quality cannot be considered separately.

In this work, we investigate the NO_x emissions inventory in Seoul, South Korea, using a regional ozone monitoring instrument (OMI) NO₂ product derived from the standard NASA product. We first develop a regional OMI NO₂ product by recalculating the air mass factors using a high-resolution (4 km × 4 km) WRF-Chem model simulation, which better captures the NO₂ profile shapes in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the subpixel variability. These two modifications yield OMI NO₂ values in the regional product that are 1.37 times larger in the Seoul metropolitan region and >2 times larger near substantial point sources. These two modifications also yield an OMI NO₂ product that is in better agreement with the Pandora NO₂ spectrometer measurements acquired during the South Korea–United States Air Quality (KORUS-AQ) field campaign. NO_x emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO₂ products. We first apply the top-down approach to a model simulation to ensure that the method is appropriate: the WRF-Chem simulation utilizing the bottom-up emissions inventory yields a NO_x emissions rate of 227±94 kt yr⁻¹, while the bottom-up inventory itself within a 40 km radius of Seoul yields a NO_x emissions rate of 198 kt yr⁻¹. Using the top-down approach on the regional OMI NO₂ product, we derive the NO_x emissions rate from Seoul to be 484±201 kt yr⁻¹, and a 353±146 kt yr⁻¹ NO_x emissions rate using the standard NASA OMI NO₂ product. This suggests an underestimate of 53 % and 36 % in the bottom-up inventory using the regional and standard NASA OMI NO₂ products respectively. To supplement this finding, we compare the NO₂ and NO_y simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NO_x emissions in the WRF-Chem model are increased by a factor of 2.13 in the Seoul metropolitan area, there is better agreement with KORUS-AQ aircraft observations and the recalculated OMI NO₂ tropospheric columns. Finally, we show that by using a WRF-Chem simulation with an updated emissions inventory to recalculate the air mass factor (AMF), there are small differences (∼8 %) in OMI NO₂ compared to using the original WRF-Chem simulation to derive the AMF. This suggests that changes in model resolution have a larger effect on the AMF calculation than modifications to the South Korean emissions inventory. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality region-specific top-down NO_x emissions estimates.