Gridded National Inventory of U.S. Methane Emissions

Underestimation of Thermogenic Methane Emissions in New York City

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.

Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach

Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a-1, Montreal 111.54 Gg a-1, New York 144.38 Gg a-1, Los Angeles 207.03 Gg a-1, Houston 650.16 Gg a-1, and Mexico City 280.81 Gg a-1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts.

Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern Cities

Urban methane emissions estimated using atmospheric observations have been found to exceed estimates derived by using traditional inventory methods in several northeastern US cities. In this work, we leveraged a nearly five-year record of observations from a dense tower network coupled with a newly developed high-resolution emissions map to quantify methane emission rates in Washington, DC, and Baltimore, Maryland. Annual emissions averaged over 2018-2021 were 80.1 [95% CI: 61.2, 98.9] Gg in the Washington, DC urban area and 47.4 [95% CI: 35.9, 58.5] Gg in the Baltimore urban area, with a decreasing trend of approximately 4-5% per year in both cities. We also find wintertime emissions 44% higher than summertime emissions, correlating with natural gas consumption. We further attribute a large fraction of total methane emissions to the natural gas sector using a least-squares regression on our spatially resolved estimates, supporting previous findings that natural gas systems emit the plurality of methane in both cities. This study contributes to the relatively sparse existing knowledge base of urban methane emissions sources and variability, adding to our understanding of how these emissions change in time and providing evidence to support efforts to mitigate natural gas emissions.

Methane Emissions from Dairy Operations in California's San Joaquin Valley Evaluated Using Airborne Flux Measurements

State inventories indicate that dairy operations account for nearly half of California's methane budget. Recent analyses suggest, however, that these emissions may be underestimated, complicating efforts to develop emission reduction strategies. Here, we report estimates of dairy methane emissions in the southern San Joaquin Valley (SJV) of California in June 2021 using airborne flux measurements. We find average dairy methane fluxes of 512 ± 178 mg m-2 h-1 from a region of 300+ dairies near Visalia, CA using a combination of eddy covariance and mass balance-based techniques, corresponding to 118 ± 41 kg dairy-1 h-1. These values estimated during our June campaign are 39 ± 48% larger than annual average estimates from the recently developed VISTA-CA inventory. We observed notable increases in emissions with temperature. Our estimates align well with inventory predictions when parametrizations for the temperature dependence of emissions are applied. Our measurements further demonstrate that the VISTA-CA emission inventory is considerably more accurate than the EPA GHG-I inventory in this region. Source apportionment analyses confirm that dairy operations produce the majority of methane emissions in the southern SJV (∼65%). Fugitive oil and gas (O&G) sources account for the remaining ∼35%. Our results support the accuracy of the process-based models used to develop dairy emission inventories and highlight the need for additional investigation of the meteorological dependence of these emissions.

A Gridded Inventory of Annual 2012-2018 U.S. Anthropogenic Methane Emissions

Nationally reported greenhouse gas inventories are a core component of the Paris Agreement's transparency framework. Comparisons with emission estimates derived from atmospheric observations help identify improvements to reduce uncertainties and increase the confidence in reported values. To facilitate comparisons over the contiguous United States, we present a 0.1° × 0.1° gridded inventory of annual 2012-2018 anthropogenic methane emissions, allocated to 26 individual source categories, with scale-dependent error estimates. Our inventory is consistent with the U.S. Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI), submitted to the United Nations in 2020. Total emissions and patterns (spatial/temporal) reflect the activity and emission factor data underlying the GHGI, including many updates relative to a previous gridded version of the GHGI that has been extensively compared with observations. These underlying data are not generally available in global gridded inventories, and comparison to EDGAR version 6 shows large spatial differences, particularly for the oil and gas sectors. We also find strong regional variability across all sources in annual 2012-2018 spatial trends, highlighting the importance of understanding regional- and facility-level activities. Our inventory represents the first time series of gridded GHGI methane emissions and enables robust comparisons of emissions and their trends with atmospheric observations.

Co-Processing Agricultural Residues and Wet Organic Waste Can Produce Lower-Cost Carbon-Negative Fuels and Bioplastics

Scalable, low-cost biofuel and biochemical production can accelerate progress on the path to a more circular carbon economy and reduced dependence on crude oil. Rather than producing a single fuel product, lignocellulosic biorefineries have the potential to serve as hubs for the production of fuels, production of petrochemical replacements, and treatment of high-moisture organic waste. A detailed techno-economic analysis and life-cycle greenhouse gas assessment are developed to explore the cost and emission impacts of integrated corn stover-to-ethanol biorefineries that incorporate both codigestion of organic wastes and different strategies for utilizing biogas, including onsite energy generation, upgrading to bio-compressed natural gas (bioCNG), conversion to poly(3-hydroxybutyrate) (PHB) bioplastic, and conversion to single-cell protein (SCP). We find that codigesting manure or a combination of manure and food waste alongside process wastewater can reduce the biorefinery's total costs per metric ton of CO₂ equivalent mitigated by half or more. Upgrading biogas to bioCNG is the most cost-effective climate mitigation strategy, while upgrading biogas to PHB or SCP is competitive with combusting biogas onsite.

Life Cycle GHG Perspective on U.S. Natural Gas Delivery Pathways

Recent emission measurement campaigns have improved our understanding of the total greenhouse gas (GHG) emissions across the natural gas supply chain, the individual components that contribute to these emissions, and how these emissions vary geographically. However, our current understanding of natural gas supply chain emissions does not account for the linkages between specific production basins and consumers. This work provides a detailed life cycle perspective on how GHG emissions vary according to where natural gas is produced and where it is delivered. This is accomplished by disaggregating transmission and distribution infrastructure into six regions, balancing natural gas supply and demand locations to infer the likely pathways between production and delivery, and incorporating new data on distribution meters. The average transmission distance for U.S. natural gas is 815 km but ranges from 45 to 3000 km across estimated production-to-delivery pairings. In terms of 100-year global warming potentials, the delivery of one megajoule (MJ) of natural gas to the Pacific region has the highest mean life cycle GHG emissions (13.0 g CO₂e/MJ) and the delivery of natural gas to the Northeast U.S. has the lowest mean life cycle GHG emissions (8.1 g CO₂e/MJ). The cradle-to-delivery scenarios developed in this work show that a national average does not adequately represent the upstream GHG emission intensity for natural gas from a specific basin or delivered to a specific consumer.

Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States

Understanding, prioritizing, and mitigating methane (CH₄) emissions requires quantifying CH₄ budgets from facility scales to regional scales with the ability to differentiate between source sectors. We deployed a tiered observing system for multiple basins in the United States (San Joaquin Valley, Uinta, Denver-Julesburg, Permian, Marcellus). We quantify strong point source emissions (>10 kg CH₄ h^-1) using airborne imaging spectrometers, attribute them to sectors, and assess their intermittency with multiple revisits. We compare these point source emissions to total basin CH₄ fluxes derived from inversion of Sentinel-5p satellite CH₄ observations. Across basins, point sources make up on average 40% of the regional flux. We sampled some basins several times across multiple months and years and find a distinct bimodal structure to emission timescales: the total point source budget is split nearly in half by short-lasting and long-lasting emission events. With the increasing airborne and satellite observing capabilities planned for the near future, tiered observing systems will more fully quantify and attribute CH₄ emissions from facility to regional scales, which is needed to effectively and efficiently reduce methane emissions.

A Methane Emission Estimation Tool (MEET) for predictions of emissions from upstream oil and gas well sites with fine scale temporal and spatial resolution: Model structure and applications

In comparing observation based methane emission estimates for oil and gas well sites to routine emissions reported in inventories, the time scale of the measurement should match the time scale over which the inventoried emissions are estimated. Since many measurements are of relatively short duration (seconds to hours), a tool is needed to estimate emissions over these time scales rather than the annual totals reported in most emission inventories. This work presents a tool for estimating routine emissions from oil and gas well sites at multiple time scales; emissions at well sites vary over time due to changes in oil and gas production rates, operating practices and operational modes at the sites. Distributions of routine emissions (expected and inventoried) from well sites are generally skewed, and the nature and degree to which the distributions are skewed depends on the time scales over which emissions are aggregated. Abnormal emissions can create additional skew in these distributions. At very short time scales (emissions aggregated over 1 min) case study distributions presented in this work are both skewed and bimodal, with the modes depending on whether liquid storage tanks are flashing at the time of the measurement and whether abnormal emissions are occurring. At longer time scales (emissions aggregated over 1 day) distributions of routine emissions simulated in this work can have multiple modes if short duration, high emission rate events, such as liquid unloadings or large abnormal emissions, occur at the site. Multiple applications of the methane emission estimation tool (MEET), developed in this work, are presented. These results emphasize the importance of developing detailed emission inventories, which incorporate operational data, when comparing measurements to routine emissions. The model described in this work supports such comparisons and is freely available.

Hydrocarbon Tracers Suggest Methane Emissions from Fossil Sources Occur Predominately Before Gas Processing and That Petroleum Plays Are a Significant Source

We use global airborne observations of propane (C₃H₈) and ethane (C₂H₆) from the Atmospheric Tomography (ATom) and HIAPER Pole-to-Pole Observations (HIPPO), as well as U.S.-based aircraft and tower observations by NOAA and from the NCAR FRAPPE campaign as tracers for emissions from oil and gas operations. To simulate global mole fraction fields for these gases, we update the default emissions' configuration of C₃H₈ used by the global chemical transport model, GEOS-Chem v13.0.0, using a scaled C₂H₆ spatial proxy. With the updated emissions, simulations of both C₃H₈ and C₂H₆ using GEOS-Chem are in reasonable agreement with ATom and HIPPO observations, though the updated emission fields underestimate C₃H₈ accumulation in the arctic wintertime, pointing to additional sources of this gas in the high latitudes (e.g., Europe). Using a Bayesian hierarchical model, we estimate global emissions of C₂H₆ and C₃H₈ from fossil fuel production in 2016-2018 to be 13.3 ± 0.7 (95% CI) and 14.7 ± 0.8 (95% CI) Tg/year, respectively. We calculate bottom-up hydrocarbon emission ratios using basin composition measurements weighted by gas production and find their magnitude is higher than expected and is similar to ratios informed by our revised alkane emissions. This suggests that emissions are dominated by pre-processing activities in oil-producing basins.

Modeling air emissions from complex facilities at detailed temporal and spatial resolution: The Methane Emission Estimation Tool (MEET)

Recent attention to methane emissions from oil and gas infrastructure has increased interest in comparing measurements with inventory emission estimates. While measurement methods typically estimate emissions over a few periods that are seconds to hours in length, current inventory methods typically produce long-term average emission estimates. This temporal mis-alignment complicates comparisons and leads to underestimates in the uncertainty of measurement methods. This study describes a new temporally and spatially resolved inventory emission model (MEET), and demonstrates the model by application to compressor station emissions - the key facility type in midstream natural gas operations The study looks at three common facility measurement methods: tracer flux methods for measuring station emissions, the use of ethane-methane ratios for source attribution of basin-scale estimates, and the behavior of continuous monitoring for leak detection at stations. Simulation results indicate that measurement methods likely underestimate uncertainties in emission estimates by failing to account for the variability in normal facility emissions and variations in ethane/methane ratios. A tracer-based measurement campaign could estimate emissions outside the 95% confidence interval of annual emissions 30% of the time, while ethane/methane ratios could be mis-estimated by as much as 50%. Use of MEET also highlights the need to improve data reporting from measurement campaigns to better capture the temporal and spatial variation in observed emissions.

Artificial Intelligence Approach for Estimating Dairy Methane Emissions

California's dairy sector accounts for ∼50% of anthropogenic CH₄ emissions in the state's greenhouse gas (GHG) emission inventory. Although California dairy facilities' location and herd size vary over time, atmospheric inverse modeling studies rely on decade-old facility-scale geospatial information. For the first time, we apply artificial intelligence (AI) to aerial imagery to estimate dairy CH₄ emissions from California's San Joaquin Valley (SJV), a region with ∼90% of the state's dairy population. Using an AI method, we process 316,882 images to estimate the facility-scale herd size across the SJV. The AI approach predicts herd size that strongly (>95%) correlates with that made by human visual inspection, providing a low-cost alternative to the labor-intensive inventory development process. We estimate SJV's dairy enteric and manure CH₄ emissions for 2018 to be 496-763 Gg/yr (mean = 624; 95% confidence) using the predicted herd size. We also apply our AI approach to estimate CH₄ emission reduction from anaerobic digester deployment. We identify 162 large (90th percentile) farms and estimate a CH₄ reduction potential of 83 Gg CH₄/yr for these large facilities from anaerobic digester adoption. The results indicate that our AI approach can be applied to characterize the manure system (e.g., use of an anaerobic lagoon) and estimate GHG emissions for other sectors.

Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey

Limiting emissions of climate-warming methane from oil and gas (O&G) is a major opportunity for short-term climate benefits. We deploy a basin-wide airborne survey of O&G extraction and transportation activities in the New Mexico Permian Basin, spanning 35 923 km², 26 292 active wells, and over 15 000 km of natural gas pipelines using an independently validated hyperspectral methane point source detection and quantification system. The airborne survey repeatedly visited over 90% of the active wells in the survey region throughout October 2018 to January 2020, totaling approximately 98 000 well site visits. We estimate total O&G methane emissions in this area at 194 (+72/-68, 95% CI) metric tonnes per hour (t/h), or 9.4% (+3.5%/-3.3%) of gross gas production. 50% of observed emissions come from large emission sources with persistence-averaged emission rates over 308 kg/h. The fact that a large sample size is required to characterize the heavy tail of the distribution emphasizes the importance of capturing low-probability, high-consequence events through basin-wide surveys when estimating regional O&G methane emissions.

Mapping Agricultural Lands: From Conventional to Regenerative

In an era in which conventional agriculture has come under question for its environmental and social costs, regenerative agriculture suggests that land management practices can be organized around farming and grazing practices that regenerate interdependent ecological and community processes for generations to come. However, little is known about the geographies of ‘regenerative’ and ‘conventional’ agricultural lands—what defines them, where they are, and the extent to which actual agricultural lands interweave both or are characterizable by neither. In the context of the Midwest of the United States, we develop and map an index quantifying the degrees to which the agricultural lands of counties could be said to be regenerative, conventional, or both. We complement these results by using a clustering method to partition the land into distinct agricultural regions. Both approaches rely on a set of variables characterizing land we developed through an iterative dialogue across difference among our authors, who have a range of relevant backgrounds. We map, analyze, and synthesize our results by considering local contexts beyond our variables, comparing and contrasting the resulting perspectives on the geographies of midwestern agricultural lands. Our results portray agricultural lands of considerable diversity within and between states, as well as ecological and physiographic regions. Understanding the general patterns and detailed empirical geographies that emerge suggests spatial relationships that can inform peer-to-peer exchanges among farmers, agricultural extension, civil society, and policy formation.

Implications of Generation Efficiencies and Supply Chain Leaks for the Life Cycle Greenhouse Gas Emissions of Natural Gas-Fired Electricity in the United States

Uncertainties in supply chain emissions raise questions about the benefits of natural gas as a bridge fuel, but recent efficiency improvements in gas-fired electricity generation remain overlooked. Our comprehensive analysis of supply chain infrastructure and electricity generation across the United States informs spatially and temporally resolved estimates of life cycle greenhouse gas emissions. Results show decreasing life cycle emissions over each year examined: 629, 574, and 525 kg CO₂ eq MWh^-1 in 2005, 2010, and 2015, respectively. Electricity generation contributed 86% of emissions or greater for each year. Despite concerns over uncertain methane leaks, efficiency improvements make it much more likely that natural gas electricity has an unambiguous greenhouse gas benefit relative to coal. Methane leaks would have to be 4.4 times the Environmental Protection Agency (EPA) value in 2015 to reverse these benefits over 20-year time horizons. With retiring coal plants and scrutinized supply chain emissions, our results show that natural gas can provide a lower emissions option to coal in an increasingly decarbonized power sector.

Declining methane emissions and steady, high leakage rates observed over multiple years in a western US oil/gas production basin

Methane, a potent greenhouse gas, is the main component of natural gas. Previous research has identified considerable methane emissions associated with oil and gas production, but estimates of emission trends have been inconsistent, in part due to limited in-situ methane observations spanning multiple years in oil/gas production regions. Here we present a unique analysis of one of the longest-running datasets of in-situ methane observations from an oil/gas production region in Utah’s Uinta Basin. The observations indicate Uinta methane emissions approximately halved between 2015 and 2020, along with declining gas production. As a percentage of gas production, however, emissions remained steady over the same years, at ~ 6–8%, among the highest in the U.S. Addressing methane leaks and recovering more of the economically valuable natural gas is critical, as the U.S. seeks to address climate change through aggressive greenhouse emission reductions.

Majority of US urban natural gas emissions unaccounted for in inventories

Across many cities, estimates of methane emissions from natural gas (NG) distribution and end use based on atmospheric measurements have generally been more than double bottom-up estimates. We present a top-down study of NG methane emissions from the Boston urban region spanning 8 y (2012 to 2020) to assess total emissions, their seasonality, and trends. We used methane and ethane observations from five sites in and around Boston, combined with a high-resolution transport model, to calculate methane emissions of 76 ± 18 Gg/yr, with 49 ± 9 Gg/yr attributed to NG losses. We found no significant trend in the NG loss rate over 8 y, despite efforts from the city and state to increase the rate of repairing NG pipeline leaks. We estimate that 2.5 ± 0.5% of the gas entering the urban region is lost, approximately three times higher than bottom-up estimates. We saw a strong correlation between top-down NG emissions and NG consumed on a seasonal basis. This suggests that consumption-driven losses, such as in transmission or end-use, may be a large component of emissions that is missing from inventories, and require future policy action. We also compared top-down NG emission estimates from six US cities, all of which indicate significant missing sources in bottom-up inventories. Across these cities, we estimate NG losses from distribution and end use amount to 20 to 36% of all losses from the US NG supply chain, with a total loss rate of 3.3 to 4.7% of NG from well pad to urban consumer, notably larger than the current Environmental Protection Agency estimate of 1.4% [R. A. Alvarez et al., Science 361, 186-188 (2018)].

Theoretical Methane Emission Estimation from Volatile Fatty Acids in Bovine Rumen Fluid

Methane production from livestock farming is recognized as an important contributor to global GHGs. Volatile fatty acids (VFAs) found in bovine rumen may be utilized as a substrate for methanogens to form CH4, and thus improvement of quantitative VFA measurements can help facilitate greater understanding and mitigation of CH4 production. This study aims to contribute to the development of more accurate methods for the quantification and specification of VFAs in bovine rumen. The VFAs were analyzed using the conventional method and an alternative catalytic esterification reaction (CER) method. Substantial differences in the detected concentrations of the C3+ VFAs (chain length ≥ 3) were observed between both methods, especially for butyric acid. Evaluation of the sensitivity of both methods to detecting the VFA concentrations in standard solutions confirmed that the values resulting from the CER method were closer to the known concentrations of the standard solution than those from the conventional method. The results of this study provide the first quantitative proof to show the improved accuracy of the measurements of C3+ VFAs when using the CER method compared with the conventional method. Therefore, the CER method can be recommended to analyze the VFAs found in rumen, especially butyric acid and other C3+ VFAs.

Atmospheric Carbon and Transport - America (ACT-America) Data Sets: Description, Management, and Delivery

The ACT-America project is a NASA Earth Venture Suborbital-2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT-America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower-based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016-2019 over three regions of the US (Mid-Atlantic, Midwest and South) using two NASA research aircraft (B-200 and C-130). We performed three flight patterns (fair weather, frontal crossings, and OCO-2 underflights) and collected more than 1,140 h of airborne measurements via level-leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT-America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT-America data sets.

Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Submitting national inventory reports (NIRs) on emissions of greenhouse gases (GHGs) is obligatory for parties of the United Nations Framework Convention on Climate Change (UNFCCC). The NIR forms the basis for monitoring individual countries’ progress on mitigating climate change. Countries prepare NIRs using the default bottom–up methodology of the Intergovernmental Panel on Climate Change (IPCC), as approved by the Kyoto protocol. We provide tangible evidence of the discrepancy between official bottom–up NIR reporting (unit: tons) versus top–down XCO2 reporting (unit: ppm) within the European continent, as measured by the Greenhouse Gases Observing Satellite (GOSAT). Bottom–up NIR (annual growth rate of CO2 emission from 2010 to 2016: −1.55%) does not show meaningful correlation (geographically weighted regression coefficient = −0.001, R2 = 0.024) to top–down GOSAT XCO2 (annual growth rate: 0.59%) in the European countries. The top five countries within the European continent on carbon emissions in NIR do not match the top five countries on GOSAT XCO2 concentrations. NIR exhibits anthropogenic carbon-generating activity within country boundaries, whereas satellite signals reveal the trans-boundary movement of natural and anthropogenic carbon. Although bottom–up NIR reporting has already gained worldwide recognition as a method to track national follow-up for treaty obligations, the single approach based on bottom–up did not present background atmospheric CO2 density derived from the air mass movement between the countries. In conclusion, we suggest an integrated measuring, reporting, and verification (MRV) approach using top–down observation in combination with bottom–up NIR that can provide sufficient countrywide objective evidence for national follow-up activities.

Satellite-based survey of extreme methane emissions in the Permian basin

Industrial emissions play a major role in the global methane budget. The Permian basin is thought to be responsible for almost half of the methane emissions from all U.S. oil- and gas-producing regions, but little is known about individual contributors, a prerequisite for mitigation. We use a new class of satellite measurements acquired during several days in 2019 and 2020 to perform the first regional-scale and high-resolution survey of methane sources in the Permian. We find an unexpectedly large number of extreme point sources (37 plumes with emission rates >500 kg hour^-1), which account for a range between 31 and 53% of the estimated emissions in the sampled area. Our analysis reveals that new facilities are major emitters in the area, often due to inefficient flaring operations (20% of detections). These results put current practices into question and are relevant to guide emission reduction efforts.

Urban Emissions of Nitrogen Oxides, Carbon Monoxide, and Methane Determined from Ground-Based Measurements in Philadelphia

Nitrogen oxides (NO_X) and methane impact air quality through the promotion of ozone formation, and methane is also a strong greenhouse gas. Despite the importance of these pollutants, emissions in urban areas are poorly quantified. We present measurements of NO_X, CH₄, CO, and CO₂ made at Drexel University in Philadelphia along with NO_X and CO observations at two roadside monitors. Because CO₂ concentrations in the winter result almost entirely from combustion with negligible influence from photosynthesis and respiration, we are able to infer fleet-averaged fuel-based emission factors (EFs) for NO_X and CO, similar in some ways to how EFs are determined from tunnel studies. Comparison of the inferred NO_X and CO fuel-based EF to the National Emissions Inventory (NEI) suggests errors in NEI emissions of either NO_X, CO, or both. From the measurements of CH₄ and CO₂, which are not emitted by the same sources, we infer the ratio of CH₄ emissions (from leaks in the natural gas infrastructure) to CO₂ emissions (from fossil fuel combustion) in Philadelphia. Comparison of the CH₄/CO₂ emission ratios to emission inventories from the Environmental Protection Agency suggests underestimates in CH₄ emissions by almost a factor of 4. These results demonstrate the need for the addition of long-term observations of CH₄ and CO₂ to existing monitoring networks in urban areas to better constrain emissions and complement existing measurements of NO_X and CO.

Background conditions for an urban greenhouse gas network in the Washington, D.C. and Baltimore metropolitan region

As city governments take steps towards establishing emissions reduction targets, the atmospheric research community is increasingly able to assist in tracking emissions reductions. Researchers have established systems for observing atmospheric greenhouse gases in urban areas with the aim of attributing greenhouse gas concentration enhancements (and thus, emissions) to the region in question. However, to attribute enhancements to a particular region, one must isolate the component of the observed concentration attributable to fluxes inside the region by removing the background, which is the component due to fluxes outside. In this study, we demonstrate methods to construct several versions of a background for our carbon dioxide and methane observing network in the Washington, DC and Baltimore, MD metropolitan region. Some of these versions rely on transport and flux models, while others are based on observations upwind of the domain. First, we evaluate the backgrounds in a synthetic data framework, then we evaluate against real observations from our urban network. We find that backgrounds based on upwind observations capture the variability better than model-based backgrounds, although care must be taken to avoid bias from biospheric carbon dioxide fluxes near background stations in summer. Model-based backgrounds also perform well when upwind fluxes can be modeled accurately. Our study evaluates different background methods and provides guidance determining background methodology that can impact the design of urban monitoring networks.

Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

We apply airborne measurements across three seasons (summer, winter and spring 2017-2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16-23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14-17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature-hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with the Gridded EPA estimates during spring (to within 7 %) but are ∼25 % higher during summer and winter. Spatial analysis further shows good top-down and bottom-up agreement for beef facilities (with mainly enteric emissions) but larger (∼30 %) seasonal discrepancies for dairies and hog farms (with >40 % manure emissions). Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.

Eight-Year Estimates of Methane Emissions from Oil and Gas Operations in Western Canada Are Nearly Twice Those Reported in Inventories

The provinces of Alberta and Saskatchewan account for 70% of Canada's methane emissions from the oil and gas sector. In 2018, the Government of Canada introduced methane regulations to reduce emissions from the sector by 40-45% from the 2012 levels by 2025. Complementary to inventory accounting methods, the effectiveness of regulatory practices to reduce emissions can be assessed using atmospheric measurements and inverse models. Total anthropogenic (oil and gas, agriculture, and waste) emission rates of methane from 2010 to 2017 in Alberta and Saskatchewan were derived using hourly atmospheric methane measurements over a six-month winter period from October to March. Scaling up the winter estimate to annual indicated an anthropogenic emission rate of 3.7 ± 0.7 MtCH₄/year, about 60% greater than that reported in Canada's National Inventory Report (2.3 MtCH₄). This discrepancy is tied primarily to the oil and gas sector emissions as the reported emissions from livestock operations (0.6 MtCH₄) are well substantiated in both top-down and bottom-up estimates and waste management (0.1 MtCH₄) emissions are small. The resulting estimate of 3.0 MtCH₄ from the oil and gas sector is nearly twice that reported in Canada's National Inventory (1.6 MtCH₄).

Methane concentrations in streams reveal gas leak discharges in regions of oil, gas, and coal development

As natural gas has grown in importance as a global energy source, leakage of methane (CH₄) from wells has sometimes been noted. Leakage of this greenhouse gas is important because it affects groundwater quality and, when emitted to the atmosphere, climate. We hypothesized that streams might be most contaminated by CH₄ in the northern Appalachian Basin in regions with the longest history of hydrocarbon extraction activities. To test this, we searched for CH₄-contaminated streams in the basin. Methane concentrations ([CH₄]) for 529 stream sites are reported in New York, West Virginia and (mostly) Pennsylvania. Despite targeting contaminated areas, the median [CH₄], 1.1 μg/L, was lower than a recently identified threshold indicating potential contamination, 4.0 μg/L. [CH₄] values were higher in a few streams because they receive high-[CH₄] groundwaters, often from upwelling seeps. By analogy to the more commonly observed type of groundwater seep known as abandoned mine drainage (AMD), we introduce the term, "gas leak discharge" (GLD) for these waters where they are not associated with coal mines. GLD and AMD, observed in all parts of the study area, are both CH₄-rich. Surprisingly, the region of oldest and most productive oil/gas development did not show the highest median for stream [CH₄]. Instead, the median was statistically highest where dense coal mining was accompanied by conventional and unconventional oil and gas development, emphasizing the importance of CH₄ contamination from coal mines into streams.

Variability of Ammonia and Methane Emissions from Animal Feeding Operations in Northeastern Colorado

Concentrated animal feeding operations (CAFOs) are major emitters of both ammonia (NH₃) and methane (CH₄). However, current emission inventories have limited temporal resolution and use data derived from a small subset of farms. To this end, we deployed three mobile laboratories during the DISCOVER-AQ campaign in summer 2014 with a focus on northeastern Colorado. Observations of NH₃ and CH₄ plumes downwind of 43 CAFOs were used to investigate the diurnal and site-to-site variability of emissions with an inverse area source plume modeling approach. Ammonia emissions scaled to all permitted animals in Weld, Morgan, and Larimer counties were estimated at 1.9 Gg month^-1, 50% greater than the U.S. NEI 2014 and 360% greater than EDGAR for the month of August. Methane emissions were likewise estimated at 10.6 Gg month^-1, consistent with the U.S. GHGI but 99% greater than EDGAR. Significant differences between individual CAFOs with repeat observations were also observed for both CH₄ and NH₃ emissions. The large subfarm, site-to-site, and diurnal variabilities observed show the importance of measurements taken across these scales in order to derive representative emission factors.

Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations

Satellite observations of atmospheric methane plumes offer a means for global mapping of methane point sources. Here we use the GHGSat-D satellite instrument with 50 m effective spatial resolution and 9-18% single-pass column precision to quantify mean source rates for three coal mine vents (San Juan, United States; Appin, Australia; and Bulianta, China) over a two-year period (2016-2018). This involves averaging wind-rotated observations from 14 to 24 overpasses to achieve satisfactory signal-to-noise. Our wind rotation method optimizes the wind direction information for individual plumes to account for error in meteorological databases. We derive source rates from the time-averaged plumes using integrated mass enhancement (IME) and cross-sectional flux (CSF) methods calibrated with large eddy simulations. We find time-averaged source rates ranging from 2320 to 5850 kg h^-1 for the three coal mine vents, with 40-45% precision (1σ), and generally consistent with previous estimates. The IME and CSF methods agree within 15%. Our results demonstrate the potential of space-based monitoring for annual reporting of methane emissions from point sources and suggest that future satellite instruments with similar pixel resolution but better precision should be able to constrain a wide range of point sources.

Quantifying methane emissions from the largest oil-producing basin in the United States from space

Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world's most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a^-1, representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data-based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions.

Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC-Baltimore Metropolitan Area Using an Inverse Modeling Technique

Since greenhouse gas mitigation efforts are mostly being implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO2, CH4, and CO from aircraft over 5 days within an inverse model to estimate emissions from the DC-Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e., 17% for CO2, 24% for CH4, and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.

Daily Satellite Observations of Methane from Oil and Gas Production Regions in the United States

Production of oil and natural gas in North America is at an all-time high due to the development and use of horizontal drilling and hydraulic fracturing. Methane emissions associated with this industrial activity are a concern because of the contribution to climate radiative forcing. We present new measurements from the space-based TROPOspheric Monitoring Instrument (TROPOMI) launched in 2017 that show methane enhancements over production regions in the United States. In the Uintah Basin in Utah, TROPOMI methane columns correlated with in-situ measurements, and the highest columns were observed over the deepest parts of the basin, consistent with the accumulation of emissions underneath inversions. In the Permian Basin in Texas and New Mexico, methane columns showed maxima over regions with the highest natural gas production and were correlated with nitrogen-dioxide columns at a ratio that is consistent with results from in-situ airborne measurements. The improved detail provided by TROPOMI will likely enable the timely monitoring from space of methane emissions associated with oil and natural gas production.

Top-Down Constraints on Methane Point Source Emissions From Animal Agriculture and Waste Based on New Airborne Measurements in the U.S. Upper Midwest

Agriculture and waste are thought to account for half or more of the U.S. anthropogenic methane source. However, current bottom-up inventories contain inherent uncertainties from extrapolating limited in situ measurements to larger scales. Here, we employ new airborne methane measurements over the U.S. Corn Belt and Upper Midwest, among the most intensive agricultural regions in the world, to quantify emissions from an array of key agriculture and waste point sources. Nine of the largest concentrated animal feeding operations in the region and two sugar processing plants were measured, with multiple revisits during summer (August 2017), winter (January 2018), and spring (May-June 2018). We compare the top-down fluxes with state-of-science bottom-up estimates informed by U.S. Environmental Protection Agency methodology and site-level animal population and management practices. Top-down point source emissions are consistent with bottom-up estimates for beef concentrated animal feeding operations but moderately lower for dairies (by 37% on average) and significantly lower for sugar plants (by 80% on average). Swine facility results are more variable. The assumed bottom-up seasonality for manure methane emissions is not apparent in the aircraft measurements, which may be due to on-site management factors that are difficult to capture accurately in national-scale inventories. If not properly accounted for, such seasonal disparities could lead to source misattribution in top-down assessments of methane fluxes.

Satellite observations reveal extreme methane leakage from a natural gas well blowout

Methane emissions due to accidents in the oil and natural gas sector are very challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions.

Seasonally Resolved Excess Urban Methane Emissions from the Baltimore/Washington, DC Metropolitan Region

Urban areas are increasingly recognized as an important source of methane (CH₄), but we have limited seasonally resolved observations of these regions. In this study, we quantify seasonal and annual urban CH₄ emissions over the Baltimore, Maryland, and Washington, DC metropolitan regions. We use CH₄ atmospheric observations from four tall tower stations and a Lagrangian particle dispersion model to simulate CH₄ concentrations at these stations. We directly compare these simulations with observations and use a geostatistical inversion method to determine optimal emissions to match our observations. We use observations spanning four seasons and employ an ensemble approach considering multiple meteorological representations, emission inventories, and upwind CH₄ values. Forward simulations in winter, spring, and fall underestimate observed atmospheric CH₄ while in summer, simulations overestimate observations because of excess modeled wetland emissions. With ensemble geostatistical inversions, the optimized annual emissions in DC/Baltimore are 39 ± 9 Gg/month (1 δ), 2.0 ± 0.4 times higher than the ensemble mean of bottom-up emission inventories. We find a modest seasonal variability of urban CH₄ emissions not captured in current inventories, with optimized summer emissions ∼41% lower than winter, broadly consistent with expectations if emissions are dominated by fugitive natural gas sources that correlate with natural gas usage.

Reflecting on progress since the 2005 NARSTO emissions inventory report

Emission inventories are the foundation for cost-effective air quality management activities. In 2005, a report by the public/private partnership North American Research Strategy for Tropospheric Ozone (NARSTO) evaluated the strengths and weaknesses of North American emissions inventories and made recommendations for improving their effectiveness. This paper reviews the recommendation areas and briefly discusses what has been addressed, what remains unchanged, and new questions that have arisen. The findings reveal that all emissions inventory improvement areas identified by the 2005 NARSTO publication have been explored and implemented to some degree. The U.S. National Emissions Inventory has become more detailed and has incorporated new research into previously under-characterized sources such as fine particles and biomass burning. Additionally, it is now easier to access the emissions inventory and the documentation of the inventory via the internet. However, many emissions-related research needs exist, on topics such as emission estimation methods, speciation, scalable emission factor development, incorporation of new emission measurement techniques, estimation of uncertainty, top-down verification, and analysis of uncharacterized sources. A common theme throughout this retrospective summary is the need for increased coordination among stakeholders. Researchers and inventory developers must work together to ensure that planned emissions research and new findings can be used to update the emissions inventory. To continue to address emissions inventory challenges, industry, the scientific community, and government agencies need to continue to leverage resources and collaborate as often as possible. As evidenced by the progress noted, continued investment in and coordination of emissions inventory activities will provide dividends to air quality management programs across the country, continent, and world. Implications: In 2005, a report by the public/private partnership North American Research Strategy for Tropospheric Ozone (NARSTO) evaluated the strengths and weaknesses of North American air pollution emissions inventories. This paper reviews the eight recommendation areas and briefly discusses what has been addressed, what remains unchanged, and new questions that have arisen. Although progress has been made, many opportunities exist for the scientific agencies, industry, and government agencies to leverage resources and collaborate to continue improving emissions inventories.

A Multiplatform Inversion Estimation of Statewide and Regional Methane Emissions in California during 2014-2016

California methane (CH₄) emissions are quantified for three years from two tower networks and one aircraft campaign. We used backward trajectory simulations and a mesoscale Bayesian inverse model, initialized by three inventories, to achieve the emission quantification. Results show total statewide CH₄ emissions of 2.05 ± 0.26 (at 95% confidence) Tg/yr, which is 1.14 to 1.47 times greater than the anthropogenic emission estimates by California Air Resource Board (CARB). Some of differences could be biogenic emissions, superemitter point sources, and other episodic emissions which may not be completely included in the CARB inventory. San Joaquin Valley (SJV) has the largest CH₄ emissions (0.94 ± 0.18 Tg/yr), followed by the South Coast Air Basin, the Sacramento Valley, and the San Francisco Bay Area at 0.39 ± 0.18, 0.21 ± 0.04, and 0.16 ± 0.05 Tg/yr, respectively. The dairy and oil/gas production sources in the SJV contribute 0.44 ± 0.36 and 0.22 ± 0.23 Tg CH₄/yr, respectively. This study has important policy implications for regulatory programs, as it provides a thorough multiyear evaluation of the emissions inventory using independent atmospheric measurements and investigates the utility of a complementary multiplatform approach in understanding the spatial and temporal patterns of CH₄ emissions in the state and identifies opportunities for the expansion and applications of the monitoring network.

Large Fugitive Methane Emissions From Urban Centers Along the U.S. East Coast

Urban emissions remain an underexamined part of the methane budget. Here we present and interpret aircraft observations of six old and leak-prone major cities along the East Coast of the United States. We use direct observations of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), ethane (C2H6), and their correlations to quantify CH4 emissions and attribute to natural gas. We find the five largest cities emit 0.85 (0.63, 1.12) Tg CH4/year, of which 0.75 (0.49, 1.10) Tg CH4/year is attributed to natural gas. Our estimates, which include all thermogenic methane sources including end use, are more than twice that reported in the most recent gridded EPA inventory, which does not include end-use emissions. These results highlight that current urban inventory estimates of natural gas emissions are substantially low, either due to underestimates of leakage, lack of inclusion of end-use emissions, or some combination thereof.

Use of Light Alkane Fingerprints in Attributing Emissions from Oil and Gas Production

Spatially resolved emission inventories were used with an atmospheric dispersion model to predict ambient concentrations of methane, ethane, and propane in the Eagle Ford oil and gas production region in south central Texas; predicted concentrations were compared to ground level observations. Using a base case inventory, predicted median propane/ethane concentration ratios were 106% higher (95% CI: 83% higher-226% higher) than observations, while median ethane/methane concentration ratios were 112% higher (95% CI: 17% higher-228% higher) than observations. Predicted median propane and ethane concentrations were factors of 6.9 (95% CI: 3-15.2) and 3.4 (95% CI: 1.4-9) larger than observations, respectively. Predicted median methane concentrations were 7% higher (95% CI: 39% lower-37% higher) than observations. These comparisons indicate that sources of emissions with high propane/ethane ratios (condensate tank flashing) were likely overestimated in the inventories. Because sources of propane and ethane emissions are also sources of methane emissions, the results also suggest that sources of emissions with low ethane/methane ratios (midstream sources) were underestimated. This analysis demonstrates the value of using multiple light alkanes in attributing sources of methane emissions and evaluating the performance of methane emission inventories for oil and natural gas production regions.

Characterizing Regional Methane Emissions from Natural Gas Liquid Unloading

A "bottom-up" probabilistic model was developed using engineering first-principles to quantify annualized throughput normalized methane emissions (TNME) from natural gas liquid unloading activities for 18 basins in the United States in 2016. For each basin, six discrete liquid-unloading scenarios are considered, consisting of combinations of well types (conventional and unconventional) and liquid-unloading systems (nonplunger, manual plunger lift, and automatic plunger lift). Analysis reveals that methane emissions from liquids unloading are highly variable, with mean TNMEs ranging from 0.0093% to 0.38% across basins. Automatic plunger-lift systems are found to have significantly higher per-well methane emissions rates relative to manual plunger-lift or non-plunger systems and on average constitute 28% of annual methane emissions from liquids unloading over all basins despite representing only ∼0.43% of total natural gas well count. While previous work has advocated that operational malfunctions and abnormal process conditions explain the existence of super-emitters in the natural gas supply chain, this work finds that super-emitters can arise naturally due to variability in underlying component processes. Additionally, average cumulative methane emissions from liquids unloading, attributed to the natural gas supply chain, across all basins are ∼4.8 times higher than those inferred from the 2016 Greenhouse Gas Reporting Program (GHGRP). Our new model highlights the importance of technological disaggregation, uncertainty quantification, and regionalization in estimating episodic methane emissions from liquids unloading. These insights can help reconcile discrepancies between "top-down" (regional or atmospheric studies) and "bottom-up" (component or facility-level) studies.

Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon

Regional emissions of methane and their attribution to a variety of sources presently have large uncertainties. Measurements of radiocarbon (14C) in methane (CH4) may provide a method for identifying regional CH4 emissions from fossil versus biogenic sources because adding 14C-free fossil carbon reduces the 14C/C ratio (Δ14CH4) in atmospheric CH4 much more than biogenic carbon does. We describe an approach for estimating fossil and biogenic CH4 at regional scales using atmospheric Δ14CH4 observations. As a case study to demonstrate expected Δ14CH4 and Δ14CH4-CH4 relationships, we simulate and compare Δ14CH4 at a network of sites in California using two gridded CH4 emissions estimates (Emissions Database for Global Atmospheric Research, EDGAR, and Gridded Environmental Protection Agency, GEPA) and the CarbonTracker-Lagrange model for 2014, and for 2030 under business-as-usual and mitigation scenarios. The fossil fraction of CH4 (F) is closely linked with the simulated Δ14CH4-CH4 slope and differences of 2-21% in median F are found for EDGAR versus GEPA in 2014, and 7-10% for business-as-usual and mitigation scenarios in 2030. Differences of 10% in F for >200 ppb of added CH4 produce differences of >10‰ in Δ14CH4, which are likely detectable from regular observations. Nuclear power plant 14CH4 emissions generally have small simulated median influences on Δ14CH4 (0-7‰), but under certain atmospheric conditions they can be much stronger (>30‰) suggesting they must be considered in applications of Δ14CH4 in California. This study suggests that atmospheric Δ14CH4 measurements could provide powerful constraints on regional CH4 emissions, complementary to other monitoring techniques.

A new Tier 3 method to calculate methane emission inventory for ruminants

Livestock is the main source of methane (CH₄) emissions. It is important to accurately determine emissions from ruminants that meet standardized international guidelines for national greenhouse gas inventories. A new method to improve the accuracy of CH₄ emissions that complies with IPCC rules for a Tier 3 method is described and evaluated. This method, developed by INRA (French Institute for Agricultural Research), was applied to the French inventory of CH₄ emissions by ruminants and compared with the IPCC Tier 2 method. For enteric CH₄, depending on the animal category, the INRA CH₄ emission estimates lay between 88% and 114% of IPCC's. The INRA/IPCC ratio for enteric emission was close to unity and did not differ between methods (P = 0.43) for adult cows (i.e. most cattle). In France, feedlot manure is stored in aerobic conditions, and so the INRA/IPCC fit for manure emission was poorer (P < 0.05). The INRA/IPCC fit for enteric CH₄ was very close between methods to that for total CH₄ (P = 0.39), enteric CH₄ representing 93% of total emissions. The main improvement is the use of a robust equation (from numerous data and diets), based on digestible organic matter intake (DOMI) corrected for the digestive interactions, to predict CH₄ consistently from enteric and manure sources. It was developed for the French livestock inventory but is customizable for other countries. This new improved CH₄ estimation method, based on equations from a large literature database, complies with IPCC rules for a Tier 3 method.

Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted down-wind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted down-wind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Assessment of methane emissions from the U.S. oil and gas supply chain

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO₂ from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

Emissions of organic compounds from produced water ponds I: Characteristics and speciation

We measured fluxes of methane, a suite of non-methane hydrocarbons (C2-C11), light alcohols, and carbon dioxide from oil and gas produced water storage and disposal ponds in Utah (Uinta Basin) and Wyoming (Upper Green River Basin) United States during 2013-2016. In this paper, we discuss the characteristics of produced water composition and air-water fluxes, with a focus on flux chamber measurements. In companion papers, we will (1) report on inverse modeling methods used to estimate emissions from produced water ponds, including comparisons with flux chamber measurements, and (2) discuss the development of mass transfer coefficients to estimate emissions and place emissions from produced water ponds in the context of all regional oil and gas-related emissions. Alcohols (made up mostly of methanol) were the most abundant organic compound group in produced water (91% of total volatile organic concentration, with upper and lower 95% confidence levels of 89 and 93%) but accounted for only 34% (28 to 41%) of total organic compound fluxes from produced water ponds. Non-methane hydrocarbons, which are much less water-soluble than methanol and less abundant in produced water, accounted for the majority of emitted organics. C6-C9 alkanes and aromatics dominated hydrocarbon fluxes, perhaps because lighter hydrocarbons had already volatilized from produced water prior to its arrival in storage or disposal ponds, while heavier hydrocarbons are less water soluble and less volatile. Fluxes of formaldehyde and other carbonyls were low (1% (1 to 2%) of total organic compound flux). The speciation and magnitude of fluxes varied strongly across the facilities measured and with the amount of time water had been exposed to the atmosphere. The presence or absence of ice also impacted fluxes.

Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest

The methane (CH₄) budget and its source partitioning are poorly constrained in the Midwestern United States. We used tall tower (185 m) aerodynamic flux measurements and atmospheric scale factor Bayesian inversions to constrain the monthly budget and to partition the total budget into natural (e.g., wetlands) and anthropogenic (e.g., livestock, waste, and natural gas) sources for the period June 2016 to September 2017. Aerodynamic flux observations indicated that the landscape was a CH₄ source with a mean annual CH₄ flux of +13.7 ± 0.34 nmol m^-2 s^-1 and was rarely a net sink. The scale factor Bayesian inversion analyses revealed a mean annual source of +12.3 ± 2.1 nmol m^-2 s^-1. Flux partitioning revealed that the anthropogenic source (7.8 ± 1.6 Tg CH₄ yr^-1) was 1.5 times greater than the bottom-up gridded United States Environmental Protection Agency inventory, in which livestock and oil/gas sources were underestimated by 1.8-fold and 1.3-fold, respectively. Wetland emissions (4.0 ± 1.2 Tg CH₄ yr^-1) were the second largest source, accounting for 34% of the total budget. The temporal variability of total CH₄ emissions was dominated by wetlands with peak emissions occurring in August. In contrast, emissions from oil/gas and other anthropogenic sources showed relatively weak seasonality.

Discrepancies and Uncertainties in Bottom-up Gridded Inventories of Livestock Methane Emissions for the Contiguous United States

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

An Improved Approach to Estimate Methane Emissions from Coal Mining in China

China, the largest coal producer in the world, is responsible for over 50% of the total global methane (CH₄) emissions from coal mining. However, the current emission inventory of CH4 from coal mining has large uncertainties because of the lack of localized emission factors (EFs). In this study, province-level CH4 EFs from coal mining in China were developed based on the data analysis of coal production and corresponding discharged CH4 emissions from 787 coal mines distributed in 25 provinces with different geological and operation conditions. Results show that the spatial distribution of CH₄ EFs is highly variable with values as high as 36 m3/t and as low as 0.74 m3/t. Based on newly developed CH₄ EFs and activity data, an inventory of the province-level CH4 emissions was built for 2005-2010. Results reveal that the total CH₄ emissions in China increased from 11.5 Tg in 2005 to 16.0 Tg in 2010. By constructing a gray forecasting model for CH₄ EFs and a regression model for activity, the province-level CH₄ emissions from coal mining in China are forecasted for the years of 2011-2020. The estimates are compared with other published inventories. Our results have a reasonable agreement with USEPA's inventory and are lower by a factor of 1-2 than those estimated using the IPCC default EFs. This study could help guide CH₄ mitigation policies and practices in China.