Estimates of high-resolution greenhouse gas (GHG) emissions have become a critical component of climate change research and an aid to decision makers considering GHG mitigation opportunities. The "Vulcan Project" is an effort to estimate bottom-up carbon dioxide emissions from fossil fuel combustion and cement production (FFCO 2) for the U.S. landscape at space and time scales that satisfy both scientific and policy needs. Here, we report on the Vulcan version 3.0 which quantifies emissions at a resolution of 1 km 2 /hr for the 2010-2015 time period. We estimate 2011 FFCO 2 emissions of 1,589.9 TgC with a 95% confidence interval of 1,367/1,853 TgC (−14.0%/+16.6%), implying a one-sigma uncertainty of~±8%. Per capita emissions are larger in states dominated by electricity production and industrial activity and smaller where onroad and building emissions dominate. The U.S. FFCO 2 emissions center of mass (CoM) is located in the state of Missouri with mean seasonality that moves on a near-elliptical NE/SW path. Comparison to ODIAC, a global gridded FFCO 2 emissions estimate, shows large total emissions differences (100.4 TgC for year 2011), a spatial correlation of 0.68 (R 2), and a mean absolute relative difference at the 1 km 2 scale of 104.3%. The Vulcan data product offers a high-resolution estimate of FFCO 2 emissions in every U.S. city, obviating costly development of self-reported urban inventories. The Vulcan v3.0 annual gridded emissions data product can be downloaded from the Oak

Cities dominate greenhouse gas emissions. Many have generated self-reported emission inventories, but their value to emissions mitigation depends on their accuracy, which remains untested. Here, we compare self-reported inventories from 48 US cities to independent estimates from the Vulcan carbon dioxide emissions data product, which is consistent with atmospheric measurements. We found that cities under-report their own greenhouse gas emissions, on average, by 18.3% (range: −145.5% to +63.5%) – a difference which if extrapolated to all U.S. cities, exceeds California’s total emissions by 23.5%. Differences arise because city inventories omit particular fuels and source types and estimate transportation emissions differently. These results raise concerns about self-reported inventories in planning or assessing emissions, and warrant consideration of the new urban greenhouse gas information system recently developed by the scientific community.

Within the last decade, unconventional oil and gas exploration in the US has become a new source of atmospheric hydrocarbons. Although a geographically dispersed source, field measurements in and downwind of a number of shale basins demonstrate the impact exploration activities have on ambient levels of hydrocarbons. Due to concerns related to ozone production, regulatory agencies are adding monitoring stations to better understand the potential influence of emissions from areas with increased oil and gas related activities. The Eagle Ford shale in south Texas is a rapidly developing shale play producing both oil and natural gas, providing 10% and 5% of US domestic oil and gas production, respectively, in 2013. We analyzed the first year of measurements from a newly established monitoring site at its central north edge. The data reveal median ethane mixing ratios-used as a marker for oil and gas exploration related emissions-at five times its typical clean air background. Ethane mixing ratios above ten times the background occurred regularly. Saturated hydrocarbons with likely origin in oil and gas exploration explain half of the data set's variability. They dominate OH radical reactivity at levels both similar to other shale areas and similar to Houston's ship channel area a decade ago. Air advecting slowly across the shale area from east-southeast and southwest directions shows the most elevated hydrocarbon concentrations, and evidence is presented linking elevated alkene abundances to flaring in the shale area. A case study is presented linking high emissions from an upwind facility to hydrocarbon plumes observed at the monitor.

Responses to public health threats presented by the global COVID-19 pandemic dramatically altered daily activities in cities around the world, including in the Los Angeles and Washington DC/Baltimore metropolitan areas. Researchers have attempted to determine the extent to which CO 2 emissions were impacted by the pandemic, linking changes in emissions to processes and sectors using different types of activity data and baselines for comparisons (Le Quéré et al., 2020;Liu et al., 2020;Zheng et al., 2020). One study shows that CO 2 emissions declined by 3.9% globally in the first 4 months in 2020, attributing half of this decline to changes in traffic and mobility (Le Quéré et al., 2020). Unlike these studies, which use only activity data to estimate declines, here we also use atmospheric CO 2 observations to detect when and how emissions were impacted, and focus on CO 2 emissions reductions at the city scale.Our analysis relies on high-accuracy atmospheric CO 2 observations from urban networks, building on a recently published study that used lower-accuracy CO 2 sensors to estimate COVID-19 related impacts for the San Francisco Bay area (Turner et al., 2020). Here, we evaluate impacts in two separate metropolitan areas: Los Angeles and Washington DC/Baltimore, allowing for an inter-comparison between two large urban regions. In Los Angeles and Washington DC/Baltimore, traffic congestion and commuting play dominant

Cities are greenhouse gas emission hot spots, making them targets for emission reduction policies. Effective emission reduction policies must be supported by accurate and transparent emissions accounting. Top-down approaches to emissions estimation, based on atmospheric greenhouse gas measurements, are an important and complementary tool to assess, improve, and update the emission inventories on which policy decisions are based and assessed. In this study, we present results from 9 research flights measuring CO2 and CH4 around New York City during the nongrowing seasons of 2018–2020. We used an ensemble of dispersion model runs in a Bayesian inverse modeling framework to derive campaign-average posterior emission estimates for the New York–Newark, NJ, urban area of (125 ± 39) kmol CO2 s–1 and (0.62 ± 0.19) kmol CH4 s–1 (reported as mean ± 1σ variability across the nine flights). We also derived emission estimates of (45 ± 18) kmol CO2 s–1 and (0.20 ± 0.07) kmol CH4 s–1 for the 5 boroughs of New York City. These emission rates, among the first top-down estimates for New York City, are consistent with inventory estimates for CO2 but are 2.4 times larger than the gridded EPA CH4 inventory, consistent with previous work suggesting CH4 emissions from cities throughout the northeast United States are currently underestimated.