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The COVID‐19 pandemic led to widespread reductions in mobility and induced observable changes in atmospheric emissions. Recent work has employed novel mobility data sets as a proxy for trace gas emissions from traffic by scaling CO(2) emissions linearly with those near‐real‐time mobility data. Yet, there has been little work evaluating these emission numbers. Here, we systematically compare these mobility data sets to traffic data from local governments in seven diverse urban and national/state regions to characterize the magnitude of errors that result from using the mobility data. We observe differences in excess of 60% between these mobility data sets and local traffic data. We could not find a general functional relationship between the mobility data and traffic flow over all the regions and observe higher deviations from using such general relationships than the original data. Finally, we give an overview of the potential errors that come from estimating CO(2) emissions using (mobility or traffic) activity data. Future work should be cautious while using these mobility metrics for emission estimates.

The COVID-19 pandemic led to widespread reductions in mobility and induced observable changes in atmospheric emissions. Recent work has employed novel mobility data sets as a proxy for trace gas emissions from traffic by scaling CO2 emissions linearly with those near-real-time mobility data. Yet, there has been little work evaluating these emission numbers. Here, we systematically compare these mobility data sets to traffic data from local governments in seven diverse urban and national/state regions to characterize the magnitude of errors that result from using the mobility data. We observe differences in excess of 60% between these mobility data sets and local traffic data. We could not find a general functional relationship between the mobility data and traffic flow over all the regions and observe higher deviations from using such general relationships than the original data. Finally, we give an overview of the potential errors that come from estimating CO2 emissions using (mobility or traffic) activity data. Future work should be cautious while using these mobility metrics for emission estimates. ; ISSN:0148-0227 ; ISSN:2169-897X

Transportation represents the largest sector of anthropogenic CO 2 emissions in urban areas. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important. However, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at a neighborhood scale. The BErkeley Air Quality and CO 2 Network (BEACO 2 N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ~1300 km 2 multi-city spatial domain. Here, we focus on a 5 km, high volume, stretch of highway in the SF Bay area. We show that inversion of the BEACO 2 N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO 2 emission rate of the average vehicle (g/vkm) are shown to vary by as much as 27 % at different times of a typical weekday because of changes in vehicle speed and fleet composition. The BEACO 2 N-derived emissions estimates are consistent to within ~3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the Emissions FACtor model (EMFAC) of vehicle fuel efficiency.

Transportation represents the largest sector of anthropogenic CO 2 emissions in urban areas in the United States. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important; however, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at the abovementioned scale. The BErkeley Air Quality and CO 2 Network (BEACO 2 N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ∼ 1300 km 2 multicity spatial domain. Here, we focus on a 5 km, high-volume, stretch of highway in the San Francisco Bay Area. We show that inversion of the BEACO 2 N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO 2 emission rate of the average vehicle (in grams per vehicle kilometer) is shown to vary by as much as 27 % at different times of a typical weekday because of changes in these two factors. The BEACO 2 N-derived emission estimates are consistent to within ∼ 3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the EMission FACtor model (EMFAC) of vehicle fuel efficiency.

An increase of 0.31 ± 0.03 % year−1 of atmospheric methane is reported using 10 years of solar observations performed at 10 ground-based stations since 2005. These trend agree with a GEOS-Chem-tagged simulation that accounts for the contribution of each emission source and one sink in the total methane. The GEOS-Chem simulation shows that anthropogenic emissions from coal mining and gas and oil transport and exploration have played a major role in the increase methane since 2005. ; W. Bader has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 704951, and from the University of Toronto through a Faculty of Arts & Science Postdoctoral Fellowship Award. E. Mahieu is a Research Associate with the F.R.S.–FNRS. The F.R.S.–FNRS further supported this work under Grant no. J.0093.15 and the Fédération Wallonie Bruxelles contributed to supporting observational activities. The Centre for Atmospheric Chemistry at the University of Wollongong involvement in this work is funded by Australian Research Council projects DP1601021598 and LE0668470.

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.

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 CO(2) and a likely increase in CH(4) lifetime from reduced NO(x) emissions. Second, the response of O(3) 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 CH(4) and CO(2), background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.