Estimates Of Methane Emissions Research Articles

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.

Assessing the circularity status of waste management among manufacturing, waste management, and recycling companies in Kigali, Rwanda

A high quantity of waste is generated from industrial activities by manufacturers across metropolitan cities compared to smaller cities with fewer industrial activities. Adopting Circular Economy principles in waste management has the potential for sustainable waste management to reduce the quantity of waste at dumpsites and harmful emissions from wastes. The study was conducted to assess circularity status and challenges for attaining higher circularity by Kigali’s manufacturing, waste collection, and waste recycling companies using the 10-R framework. The study also sought to build estimates for industrial solid waste quantity and methane emissions for the next 10 years (2020–2030) in Rwanda. A mixed method approach was utilized where primary data was collected from manufacturing, waste collection, and waste recycling companies using a validated questionnaire and an in-depth interview guide. Thematic, descriptive analysis and First Order Decay methods were used to analyze the qualitative, quantitative, and secondary data. Results show that adopting the 10-R principles of circular economy was low and varies across manufacturing, waste collection and recycling companies. There were instances of high adoption of R3-Reuse and R8-Recycle with a mean score of 7.17 and 7.21 among manufacturing companies. R7-Repurpose (8.80) recorded a high adoption rate among waste collection companies and a medium level of adoption for R7-Repurpose (3.50) and R9-Recover (3.00) among recycling companies. The overall low adoption toward attaining circularity was attributed to various economic, institutional, infrastructural, operational, attitudinal, and technological challenges. In 2030, it is projected that 922 Gg of industrial waste will be deposited in solid waste disposal sites in Rwanda; with an estimated methane emission of 157 Gg Carbon dioxide equivalence. It was recommended among others, that human resource capacity development should be prioritized to drive the adoption of the 10-R principles across manufacturing, waste collection, and waste recycling companies in Kigali, which can reduce the quantity of wastes that ends at dumpsites and methane emissions that contribute significantly to global warming.

Methane Emission Estimation Tools as a Basis for Sustainable Underground Mining of Gas-Bearing Coal Seams

Underground coal mining of gas-bearing coal seams is accompanied by the emission of large amounts of methane, which increases with depth. Coal seam methane is not only a major cause of major accidents in coal mines, but is also a greenhouse gas that has a significant negative impact on the Earth’s atmosphere. Analysis of the efficiency of underground coal mining suggests that as the depth of mining increases, the productivity of a longwall decreases by a factor of 3–5 or more, while the specific volume of methane emitted increases manifold and the efficiency of methane management decreases. Effective management of coal seam methane can only be achieved by monitoring its content at key points in a system of workings. Monitoring of methane not only eliminates the risk of explosions, but also lets us assess the effectiveness of using methane management techniques and their parameters to improve efficiency and reduce the cost of methane management (including a methane drainage) for ensuring sustainable underground coal mining. The aim of this article is to develop a software and hardware complex for monitoring methane in a coal mine by creating a simulation model for monitoring methane. The Arduino Uno board and the methane sensor MQ-4 were used for this purpose. In this article, the causes of methane emissions in coal mines, gas control systems, the structure of the mine monitoring system, and the causes of risks and occurrence of accidents in coal mines are considered. As a result of the work, the mathematical model of the methane measurement sensor was developed; the Arduino Uno board developed a simulation system for methane monitoring; and the numerical results of the research are presented in the graphs.

In vitro nutritional assessment and estimation of methane emissions from Kikuyu grass pastures overseeded with rye

Objective: To determine the in vitro digestibility and gas production, and to estimate the methane emissions from Kikuyu grass pastures, and Kikuyu grass overseeded with rye. Design/methodology/approach: Two pastures were assessed. One was the subtropical grass Kikuyu Cenchrus clandestinus) (KY), and the other was Kikuyu grass plus overseeding with rye (Secale cereale) (KYCEN), both associated with white clover (Trifolium repens cv. Ladino). Sample collection was in June and July 2021. In vitro digestibility of dry matter (MS), organic matter (MO), and Neutral Detergent Fibre (FDN), as well as the methane emissions were estimated. The in vitro digestibility and gas production variables were analysed with a split-plot experimental design, and the methane emission variables were analysed with a doble cross-over design. Results: There were no significant differences between treatments for dry matter (MS), organic matter (MO) or Neutral Detergent Fibre (FDN) in vitro digestibility, nor in methane emissions (P>0.05). Limitations on study/implications: The in vitro assessment of digestibility, gas production and the estimation of methane emissions of Kikuyu grass pastures and Kikuyu plus rye enable the implementation of feeding strategies for small-scale livestock production systems that do not only benefit the farmers but also the environment. Findings/conclusions: It is concluded that Kikuyu grass pastures and Kikuyu with rye are a viable feeding option for small-scale dairy systems.

Satellite-Derived Estimate of City-Level Methane Emissions from Calgary, Alberta, Canada

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the emission rate to contextualize mitigation actions and track progress. Herein, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emission rates in a case study of the City of Calgary, Alberta, Canada. Due to low and variable annual observational coverage, we integrated valid TROPOMI observations over three years (2020–2022) and used mass balance modeling to derive a long-term mean estimate of the emission rate. The resulting column-mean dry-air mole fraction (XCH4) enhancement over Calgary was small (4.7 ppb), but within the city boundaries, we identified local hot spots in the vicinity of known emission sources (wastewater treatment facilities and landfills). The city-level emission estimate from mass balance was 215.4 ± 132.8 t CH4/d. This estimate is approximately four times larger than estimates from Canada’s gridded National Inventory Report of anthropogenic CH4 emissions and six times larger than the Emissions Database for Global Atmospheric Research (EDGAR v8.0). We note that valid TROPOMI observations are more common in warmer months and occur during a narrow daily overpass time slot over Calgary. The limited valid observations in combination with the constrained temporal observational coverage may bias the emission estimate. Overall, the findings from this case study highlight an approach to derive a screening-level estimate of city-level methane emission rates using TROPOMI data in settings with low observational coverage.

Assessing Methane Emissions from Rice Fields in Large Irrigation Projects Using Satellite-Derived Land Surface Temperature and Agronomic Flooding: A Spatial Analysis

Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels.

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

8 Opportunities in enteric methane emissions mitigation in beef cattle

Abstract Enteric fermentation is the largest source of methane emissions in the United States, with beef cattle representing 71 percent of total enteric methane emissions in 2021. Approximately 87% of the enteric methane produced from US beef cattle comes from animals not housed in feedlots, thus significantly opportunity lies in development and delivery of mitigation solutions to grazing situations. Mitigating methane emissions can be expressed in two ways: on an emissions intensity basis (i.e., methane per kg of beef) or in absolute terms (i.e., methane emissions from the entire beef system). Mitigation solutions that reduce absolute or total emissions consistently are currently limited or in the case of higher concentrate diets in finishing cattle, already widely used. Genetic selection is a promising solution to mitigate enteric methane emissions; however, standardization of phenotype collection is required, as methane emissions estimates can be influenced by a wide array of measurement errors and methodological choices by investigators. Economically viable, practical, and scalable solutions are needed. Researchers and extension professionals should provide objective guidance to cattle producers and others in the cattle industry on the costs and benefits of the variety of potential enteric methane mitigation solutions.

Procedures to combine estimators of greenhouse gases emission factors.

This article describes a new procedure to estimate the mean and variance of greenhouse gases (GHG) emission factors based on different, possibly conflicting, estimates for these emission factors. The procedure uses common information such as mean and standard deviation usually reported in IPCC (Intergovernmental Panel on Climate Change) database and other references in the literature that estimate emission factors. Essentially, it is a procedure in the class of meta-analysis, based on the computation of [Formula: see text], a new estimator for the variance of the emission factor. We discuss the quality of this estimator in terms of its probability distribution and show that it is unbiased. The resulting confidence interval for the mean emission factor is tighter than those that would have resulted from using other estimators such as pooled variance and thus, the new procedure improves the accuracy in estimating GHG emissions. The application of the procedure is illustrated in a case study involving the estimation of methane emissions from rice cultivation. The estimation of emission factors using [Formula: see text] was demonstrated to be more accurate because it is not biased and more precise than alternative methods.

Estimating methane emissions from swine waste treatment lagoons and the reduction through solid-liquid separation: A multiscale case evaluation

With an estimated global warming potential of 27 to 30 over 100 years, methane (CH4) is a significant greenhouse gas emitted by livestock operations across the United States. In particular, the swine production industry of North Carolina where agglomeration of the production and the use of open-air lagoon systems for waste treatment, cause substantial emissions of CH4 and alter the environmental quality. Yet the possibility of reducing CH4 emissions from these lagoons represents an avenue to sustain environmental quality. However, quantitative estimates of methane emissions potentials at county, and regional levels are needed to envision effective emissions reduction initiatives. In this study, updated guidelines of the Intergovernmental Panel on Climate Change (IPCC) and fifteen-month full-scale data of two scenarios of waste treatment systems were used for a multiscale estimation of CH4 emissions from swine farms in North Carolina. Scenario 1 (baseline) was the conventional lagoon-based swine waste treatment system. Scenario 2 (project activity) was the conventional lagoon-based system retrofitted with a solid-liquid separation module that diverted organic compounds from reaching the anaerobic lagoon and processed with aerobic in-vessel composting. Both scenarios are feeder-to-finish swine operations and the related in-situ data utilized are monthly animal population and weight, liquid waste temperature, and 5-day biological oxygen demand (BOD5). The farm-scale estimates showed an annual emission of 332.2 kg CH4 per 1000 kg swine mass for scenario 1 and 114.7 kg CH4 per 1000 kg swine mass for scenario 2 sustaining an overall 65.5% CH4 abatement ascribable to the solid-liquid separation system. Methane emissions potentials were upscaled at the county, and state levels. The multiscale estimates highlighted hotspots of swine lagoons CH4 emissions in the North Carolina region. Two counties, Duplin and Sampson, contributed 49% of the state-level swine lagoon emissions estimated as 117,227 Mg CH4/year. However, the solid-liquid separation, as a mitigation practice, could reduce the total emissions at the state-level by 76,723 Mg CH4/year. Hence, the spatial disparities highlighted through the multiscale scenarios analyses could help to plan strategies for CH4 emission avoidance and abatement through technology improvement such as solid-liquid separation modules.

Methods for Spatial Extrapolation of Methane Measurements in Constructing Regional Estimates from Sample Populations.

Methane emission estimates for oil and gas facilities are typically based on estimates at a subpopulation of facilities, and these emission estimates are then extrapolated to a larger region or basin. Basin-level emission estimates are then frequently compared with basin-level observations. Methane emissions from oil and gas systems are inherently variable and intermittent, which make it difficult to determine whether a sample population is sufficiently large to be representative of a larger region. This work develops a framework for extrapolation of emission estimates using the case study of an operator in the Green River Basin. This work also identifies a new metric, the capture ratio, which quantifies the extent to which sources are represented in the sample population, based on the skewness of emissions for each source. There is a strong correlation between the capture ratio and extrapolation error, which suggests that understanding source-level emissions distributions can mitigate error when sample populations are selected and extrapolating measurements. The framework and results from this work can inform the selection and extrapolation of site measurements when developing methane emission inventories and establishing uncertainty bounds to assess whether inventory estimates are consistent with independent large spatial-scale observations.

Single-blind test of nine methane-sensing satellite systems from three continents

Abstract. Satellite-based remote sensing enables detection and mitigation of large point sources of climate-warming methane. These satellites will have the greatest impact if stakeholders have a clear-eyed assessment of their capabilities. We performed a single-blind test of nine methane-sensing satellites from three continents and five countries, including both commercial and government satellites. Over 2 months, we conducted 82 controlled methane releases during satellite overpasses. Six teams analyzed the resulting data, producing 134 estimates of methane emissions. Of these, 80 (58 %) were correctly identified, with 46 true positive detections (34 %) and 34 true negative non-detections (25 %). There were 41 false negatives, in which teams missed a true emission, and 0 false positives, in which teams incorrectly claimed methane was present. All eight satellites that were given a nonzero emission detected methane at least once, including the first single-blind evaluation of the EnMAP, Gaofen 5, and Ziyuan 1 systems. In percent terms, quantification error across all satellites and teams is similar to aircraft-based methane remote sensing systems, with 55 % of mean estimates falling within ±50 % of the metered value. Although teams correctly detected emissions as low as 0.03 metric tons of methane per hour, it is unclear whether detection performance in this test is representative of real-world field performance. Full retrieval fields submitted by all teams suggest that in some cases it may be difficult to distinguish true emissions from background artifacts without a known source location. Cloud interference is significant and appears to vary across teams and satellites. This work confirms the basic efficacy of the tested satellite systems in detecting and quantifying methane, providing additional insight into detection limits and informing experimental design for future satellite-focused controlled methane release testing campaigns.

Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies

This review examines the significant role of methane emissions in the livestock industry, with a focus on cattle and their substantial impact on climate change. It highlights the importance of accurate measurement and management techniques for methane, a potent greenhouse gas accounting for 14–16% of global emissions. The study evaluates both conventional and AI-driven methods for detecting methane emissions from livestock, particularly emphasizing cattle contributions, and the need for region-specific formulas. Sections cover livestock methane emissions, the potential of AI technology, data collection issues, methane’s significance in carbon credit schemes, and current research and innovation. The review emphasizes the critical role of accurate measurement and estimation methods for effective climate change mitigation and reducing methane emissions from livestock operations. Overall, it provides a comprehensive overview of methane emissions in the livestock industry by synthesizing existing research and literature, aiming to improve knowledge and methods for mitigating climate change. Livestock-generated methane, especially from cattle, is highlighted as a crucial factor in climate change, and the review underscores the importance of integrating precise measurement and estimation techniques for effective mitigation.

Aircraft-based mass balance estimate of methane emissions from offshore gas facilities in the southern North Sea

Abstract. Atmospheric methane (CH4) concentrations have more than doubled since the beginning of the industrial age, making CH4 the second most important anthropogenic greenhouse gas after carbon dioxide (CO2). The oil and gas sector represents one of the major anthropogenic CH4 emitters as it is estimated to account for 22 % of global anthropogenic CH4 emissions. An airborne field campaign was conducted in April–May 2019 to study CH4 emissions from offshore gas facilities in the southern North Sea with the aim of deriving emission estimates using a top-down (measurement-led) approach. We present CH4 fluxes for six UK and five Dutch offshore platforms or platform complexes using the well-established mass balance flux method. We identify specific gas production emissions and emission processes (venting and fugitive or flaring and combustion) using observations of co-emitted ethane (C2H6) and CO2. We compare our top-down estimated fluxes with a ship-based top-down study in the Dutch sector and with bottom-up estimates from a globally gridded annual inventory, UK national annual point-source inventories, and operator-based reporting for individual Dutch facilities. In this study, we find that all the inventories, except for the operator-based facility-level reporting, underestimate measured emissions, with the largest discrepancy observed with the globally gridded inventory. Individual facility reporting, as available for Dutch sites for the specific survey date, shows better agreement with our measurement-based estimates. For all the sampled Dutch installations together, we find that our estimated flux of (122.9 ± 36.8) kg h−1 deviates by a factor of 0.64 (0.33–12) from reported values (192.8 kg h−1). Comparisons with aircraft observations in two other offshore regions (the Norwegian Sea and the Gulf of Mexico) show that measured, absolute facility-level emission rates agree with the general distribution found in other offshore basins despite different production types (oil, gas) and gas production rates, which vary by 2 orders of magnitude. Therefore, mitigation is warranted equally across geographies.

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

Long-term continuous monitoring of methane emissions at an oil and gas facility using a multi-open-path laser dispersion spectrometer

A method for methane emissions monitoring at industrial facility level was developed based on a high precision multi-open-path laser dispersion spectrometer combined with Bayesian analysis algorithms using Monte Carlo Markov Chain (MCMC) inference. From the methane path-averaged concentrations spatially distributed over the facility under study, together with the wind vector, the analysis allows detection, localization and quantification of fugitive methane emissions. This paper describes the very first long term (3 months), continuous (24 h/7 days) deployment of this monitoring system at an operational gas processing and distribution facility. The continuous monitoring system, made of the combination of the open-path high-precision (<10 ppb) methane concentration analyser and the data analysis method, was evaluated with controlled releases of methane of about 5 kg/h for short periods of time (30–60 min). Quantification was successful, with actual emission rates lying well within the quoted uncertainty ranges. Source localisation was found to lack accuracy, with biases of 30–50 m in the direction of the line of sight of the spectrometer, due to the short duration of the controlled releases, the limited wind vector diversity, and complications from air flows around buildings not accounted for by the transport model. Using longer-term data from the deployment, the MCMC algorithm led to the identification of unexpected low intensity persistent sources (<1 kg/h) at the site. Localisation of persistent sources was mostly successful at equipment level (within ~20 m) as confirmed by a subsequent survey with an optical gas imaging (OGI) camera. Quantification of these individual sources was challenging owing to their low intensity, but a consistent estimate of the total methane emission from the facility could be derived using two different inference approaches. These results represent a stepping stone in the development of continuous monitoring systems for methane emissions, pivotal in driving greenhouse gas reduction from industrial facilities. The demonstrated continuous monitoring system gives promising performance in early detection of unexpected emissions and quantification of potentially time-varying emissions from an entire facility.

Effect of animal and diet parameters on methane emissions for pasture-fed cattle

Context Estimates of enteric methane emissions for agricultural emissions trading schemes or national inventories can be a simple single emission factor, but the accuracy of the predictions may be affected by other diet- and animal-related parameters. Aims Determine the animal and dietary factors that affect methane yield (methane per unit of dry-matter intake) in pasture-fed cattle. Methods Methane emissions and dry-matter intake (DMI) of cattle of various ages and in different physiological stages that were fed different-quality fresh-cut pastures were quantified in respiration chambers. The animals used in the various trials were post-weaned calves, heifers and steers of various ages and some older lactating dairy cows. Diet quality of the pastures offered was determined using near-infrared spectroscopy. Mixed linear modelling was used to assess the impacts of animal and diet parameters on methane emissions. Key results Our results indicated that diet quality does not have a major effect on methane production. For individual composition parameters, the correlation (Pearson’s r) with methane production was less than 0.25. Only estimates of metabolisable energy (ME) content showed a higher correlation (r = 0.43). However, despite this correlation, ME, like the other diet composition variables, was not a useful parameter to predict daily methane production, as indicated by the Akaike’s information criterion (AICc). Including data on concentrate supplementation at a level of 30% of the DMI did not improve the prediction of methane production. The resulting model indicated that besides DMI, bodyweight, physiological state and sex significantly affected methane production. Methane production was mostly explained by DMI. This was illustrated by the observation that when methane production is expressed per kilogram DMI (methane yield, g kg−1 DMI) none of the diet or animal related characteristics showed a significant correlation with methane yield. The model performed well, but needs to be validated with an independent dataset. Conclusions For ryegrass-based pasture dry-matter intake is the single most important factor that affects methane yield, while pasture composition has no effect and animal-related factors such as physiological stage and age only appear to modulate methane emissions. Implications Our findings have implications for methane accounting and national inventories in pastoral agricultural systems, which account for the majority of ruminant production systems.

A Practical Framework for Oil and Gas Operators to Estimate Methane Emission Duration Using Operational Data

Summary Methane management is a key greenhouse gas (GHG) reduction focus within the oil and gas industry. While there are a variety of techniques for methane detection and measurement, aircraft-mounted sensors have become popular for both academic studies and operators in onshore regions in North America due to their ability to screen many sites in a relatively short amount of time. Many traditional leak detection and repair (LDAR) techniques, like optical gas imaging (OGI), and emerging approaches, such as aircraft-based or satellite-based screenings, are periodic, which means that they can provide information about the presence of emissions and approximate rates during the observation period. Scaling periodic observations to annual emission estimates needed for regulatory and corporate reporting requires information on emission duration, which is not generally provided by the same screening techniques. In the literature to date, there have been several statistical approaches proposed for the annualization of basin-level methane observations, but these techniques may struggle to downscale to individual operators, across which methane emission intensities are expected to vary. In this paper, we propose a novel, conceptual framework for using other sources of data, such as records from operator inspections and parametric monitoring, to help define the duration of detected methane emissions. Such information may prove to be a useful input to measurement-informed methane emission protocols that are under development by multistakeholder groups, like GTI Energy Veritas and federal agencies in the United States.

Impact of unrecovered shale gas reserve on methane emissions from abandoned shale gas wells

Shale gas, with its abundance and lower carbon footprint compared to other fossil fuels, is an important bridge fuel in the ongoing energy transition. However, a notable concern in shale gas exploration is fugitive methane emissions during the extraction, development, and transport of natural gas. While most existing works evaluate methane emissions released by well fracking, completion and operation, the greenhouse footprint of unproductive shale gas wells (often abandoned or orphaned) has received little scrutiny. A large fraction of these emissions from abandoned shale gas wells are due to the diffusive transport of methane trapped in nanoporous shale matrix, which is poorly understood. Here, we develop a theoretical kinetic approach to predict methane diffusive flux from heterogeneous shale matrix. Our theoretical model is based on a layer sequence formulation and accurately considers multiple flow mechanisms, including viscous flow, gas slippage, and Knudsen diffusion and their mutual interactions. The model is validated against the observed methane diffusion data obtained from high-pressure and high-temperature experimental measurements on Marcellus shale. We find that methane diffusive flux increases as reservoir pressure decreases. We estimate methane emission due to diffusive transport up to 20 × 103 m3 per well per day, which is comparable to emissions from flowback fluid. For the first time, unrecovered natural gas in the shale matrix is demonstrated to be the main source of methane emissions from abandoned shale gas wells. Given the long-lasting nature of diffusive transport to shale gas seepage, it is suggested that regulatory requirements should be implemented to provide long-term monitoring of methane emissions from abandoned shale gas wells.

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates

AbstractLake methane emissions are commonly upscaled from lake area, with recognition that smaller, non‐inventoried lakes emit more per unit area. There is also growing awareness of the importance of lake aquatic vegetation and potential “double‐counting” with wetlands, but lack of consensus on which is most impactful. Here, we combine high‐resolution data with the comprehensive lake inventory HydroLAKES to rank these three variables based on emissions sensitivity. Including non‐inventoried small lakes &lt;0.1 km2 (+30 [range: 9.0 to 82]% change) is greatest, followed by double‐counting (−20 [−11 to −34]%) and lake aquatic vegetation (+14 [2.7 to 43]%). Significantly, emissions from non‐inventoried lakes contribute far less than the ∼40% previously determined globally through statistical area extrapolation. We produce a first pan‐Arctic estimate of lake aquatic vegetation in 1.37 million km2 of lakes, but after correcting for persistent double‐counting, its net effect is to decrease emissions estimates by 9%. Thus, previous global emissions estimates are likely too high.