Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO_x ≡ NO + NO₂) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC⁴RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25° × 0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO_x from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC⁴RS observations of NO_x and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO₂ columns. Our results indicate that NEI NO_x emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NO_x emissions. Upper-tropospheric NO₂ from lightning makes a large contribution to satellite observations of tropospheric NO₂ that must be accounted for when using these data to estimate surface NO_x emissions. We find that only half of isoprene oxidation proceeds by the high-NO_x pathway to produce ozone; this fraction is only moderately sensitive to changes in NO_x emissions because isoprene and NO_x emissions are spatially segregated. GEOS-Chem with reduced NO_x emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO_x oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

A shifting lifetime for NO _x : Nitrogen oxides (NO _x ) have a central role in controlling air quality, so designing effective strategies to combat air pollution, especially by ozone and particulate matter, depends on knowing their atmospheric abundances. Those abundances depend not only on NO _x production rates but also on their lifetimes. Laughner and Cohen report that NO _x lifetimes can be measured directly from satellite observations of nitrogen dioxide. They used data on 49 North American cities to show how NO _x lifetimes there changed between 2005 and 2014. Accounting for this change in lifetime should help to reconcile conflicting trends between bottom-up and top-down emissions estimates. Science , this issue p. 723

The National Emission Inventory (NEI) of the US Environmental Protection Agency (EPA) reports a steady decrease in US NOx emissions over the 2005–2017 period at a rate of 0.1 Tg N a−1 (53 % decrease over the period), reflecting sustained efforts to improve air quality. Tropospheric NO2 columns observed by the satellite-based Ozone Monitoring Instrument (OMI) over the US show a steady decrease until 2009 but a flattening afterward, which has been attributed to a flattening of NOx emissions, contradicting the NEI. We show here that the steady 2005–2017 decrease in NOx emissions reported by the NEI is in fact largely consistent with observed network trends of surface NO2 and ozone concentrations. The OMI NO2 trend is instead similar to that observed for nitrate wet deposition fluxes, which is weaker than that for anthropogenic NOx emissions, due to a large and increasing relative contribution of non-anthropogenic background sources of NOx (mainly lightning and soils). This is confirmed by contrasting OMI NO2 trends in urban winter, where the background is low and OMI NO2 shows a 2005–2017 decrease consistent with the NEI, and rural summer, where the background is high and OMI NO2 shows no significant 2005–2017 trend. A GEOS-Chem model simulation driven by NEI emission trends for the 2005–2017 period reproduces these different trends, except for the post-2009 flattening of OMI NO2, which we attribute to a model underestimate of free tropospheric NO2. Better understanding is needed of the factors controlling free tropospheric NO2 in order to relate satellite observations of tropospheric NO2 columns to the underlying NOx emissions and their trends. Focusing on urban winter conditions in the satellite data minimizes the effect of this free tropospheric background.

The Total Carbon Column Observing Network (TCCON) measures column-average mole fractions of several greenhouse gases (GHGs), beginning in 2004, from over 30 current or past measurement sites around the world using solar absorption spectroscopy in the near-infrared (near-IR) region. TCCON GHG data have been used extensively for multiple purposes, including in studies of the carbon cycle and anthropogenic emissions, as well as to validate and improve observations from space-based sensors. Here, we describe an update to the retrieval algorithm used to process the TCCON near-IR solar spectra and to generate the associated data products. This version, called GGG2020, was initially released in April 2022. It includes updates and improvements to all steps of the retrieval, including but not limited to the conversion of the original interferograms into spectra, the spectroscopic information used in the column retrieval, post hoc air mass dependence correction, and scaling to align with the calibration scales of in situ GHG measurements. All TCCON data are available through https://tccondata.org/ (last access: 22 April 2024) and are hosted on CaltechDATA (https://data.caltech.edu/, last access: 22 April 2024). Each TCCON site has a unique DOI for its data record. An archive of all the sites' data is also available with the DOI https://doi.org/10.14291/TCCON.GGG2020 (Total Carbon Column Observing Network (TCCON) Team, 2022). The hosted files are updated approximately monthly, and TCCON sites are required to deliver data to the archive no later than 1 year after acquisition. Full details of data locations are provided in the “Code and data availability” section.

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.

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO₂) column-averaged dry-air mole fraction (XCO₂). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO₂ estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO₂ estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO₂ estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO₂ fields derived from atmospheric inversion systems that assimilated only near-surface CO₂ observations, i.e., did not assimilate satellite CO₂ measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO₂ over small areas, as well as XCO₂ biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO₂ measurements, suggesting they may be used together for scientific analyses.