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Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state, and national levels. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop appropriately focused emission reduction strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and inform policy decisions. This study presents a new high-resolution (0.02 × 0.02°) methane emission inventory for New York City and its surrounding area, constructed using the latest activity data, emission factors, and spatial proxies. The new high-resolution inventory estimates of methane emissions for the New York-Newark urban area are 1.3 times larger than those for the gridded Environmental Protection Agency inventory. We used aircraft mole fraction measurements from nine research flights to optimize the high-resolution inventory emissions within a Bayesian inversion. These sectorally optimized emissions show that the high-resolution inventory still significantly underestimates methane emissions within the New York-Newark urban area, primarily because it underestimates emissions from thermogenic sources (by a factor of 2.3). This suggests that there remains a gap in our process-based understanding of urban methane emissions.

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO 2 ) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries., (© 2022. The Author(s).)

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO 2 ) emissions. Here, we detect and estimate decreases in CO 2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO 2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO 2 reduction activities efficiently., (© 2021. The Authors. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

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.

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 Ridge National Laboratory Distributed Active Archive Center (Gurney, Liang, et al., 2019, https://doi.org/10.3334/ORNLDAAC/1741)., Competing Interests: The authors declare that they have no conflict of interest., (©2020. The Authors.)

Background: Cities contribute more than 70% of global anthropogenic carbon dioxide (CO 2 ) emissions and are leading the effort to reduce greenhouse gas (GHG) emissions through sustainable planning and development. However, urban greenhouse gas mitigation often relies on self-reported emissions estimates that may be incomplete and unverifiable via atmospheric monitoring of GHGs. We present the Hestia Scope 1 fossil fuel CO 2 (FFCO 2 ) emissions for the city of Baltimore, Maryland-a gridded annual and hourly emissions data product for 2010 through 2015 (Hestia-Baltimore v1.6). We also compare the Hestia-Baltimore emissions to overlapping Scope 1 FFCO 2 emissions in Baltimore's self-reported inventory for 2014., Results: The Hestia-Baltimore emissions in 2014 totaled 1487.3 kt C (95% confidence interval of 1158.9-1944.9 kt C), with the largest emissions coming from onroad (34.2% of total city emissions), commercial (19.9%), residential (19.0%), and industrial (11.8%) sectors. Scope 1 electricity production and marine shipping were each generally less than 10% of the city's total emissions. Baltimore's self-reported Scope 1 FFCO 2 emissions included onroad, natural gas consumption in buildings, and some electricity generating facilities within city limits. The self-reported Scope 1 FFCO 2 total of 1182.6 kt C was similar to the sum of matching emission sectors and fuels in Hestia-Baltimore v1.6. However, 20.5% of Hestia-Baltimore's emissions were in sectors and fuels that were not included in the self-reported inventory. Petroleum use in buildings were omitted and all Scope 1 emissions from industrial point sources, marine shipping, nonroad vehicles, rail, and aircraft were categorically excluded., Conclusions: The omission of petroleum combustion in buildings and categorical exclusions of several sectors resulted in an underestimate of total Scope 1 FFCO 2 emissions in Baltimore's self-reported inventory. Accurate Scope 1 FFCO 2 emissions, along with Scope 2 and 3 emissions, are needed to inform effective urban policymaking for system-wide GHG mitigation. We emphasize the need for comprehensive Scope 1 emissions estimates for emissions verification and measuring progress towards Scope 1 GHG mitigation goals using atmospheric monitoring.

The bottom-up (BU) approach has been used to develop spatiotemporally resolved, sectorally disaggregated fossil fuel CO 2 (FFCO 2 ) emission data products. These efforts are critical constraints to atmospheric assessment of anthropogenic fluxes in addition to offering the climate change policymaking community usable information to guide mitigation. In the United States, there are two high-resolution FFCO 2 emission data products, Vulcan and the Anthropogenic Carbon Emissions System (ACES). As a step toward developing improved, accurate, and detailed FFCO 2 emission landscapes, we perform a comparison of the two data products. We find that while agreeing on total FFCO 2 emissions at the aggregate scale (relative difference = 1.7%), larger differences occur at smaller spatial scales and in individual sectors. Differences in the smaller-emitting sectors are likely errors in ACES input data or emission factors. ACES advances the approach for estimating emissions in the gas and oil sector, while Vulcan shows better geocoordinate correction in the electricity production sector. Differences in the subcounty residential and commercial building sectors are driven by different spatial proxies and suggest a task for future investigation. The gridcell absolute median relative difference, a measure of the average gridcell-scale relative difference, indicates a 53.5% difference. The recommendation for improved BU granular FFCO 2 emission estimation includes review, assessment, and archive of point source geolocations, CO emission input data, CO and CO 2 emission factors, and uncertainty approaches including those due to spatial errors. Finally, intensives where local utility data are publicly available could test the spatial proxies used in estimating residential and commercial building emissions. These steps toward best practices will lead to more accurate, granular emissions, enabling optimal emission mitigation policy choices.