This session will highlight measurement studies at all scales and from ground-based to satellites, that focus on quantification and source attribution of methane emissions from human activities. We especially encourage submissions from both IMEO and non-IMEO funded work that focus on the following topics: (1) new technologies / methods to provide accurate and repeatable emissions measurements, (2) demonstration of affordable and reliable quantification methods for mitigation tracking, (3) attribution of emissions to specific sources and, (4) methods for upscaling measurements into inventories and creating policy relevant datasets.
An increasing amount of empirical methane data is being published, drawing on a variety of measurement platforms. However, efforts to track global methane emissions are hampered by fragmentation and scale inconsistency in these diverse data streams. Integrating empirical data across scales and sensor technologies is essential to reveal an accurate picture of emissions, support effective mitigation, monitor progress and drive accountability across anthropogenic activities. Methane data processing consists of compiling and standardizing fragmented measurements collected worldwide. To-date, no comprehensive methane data repository exists. To address this challenge, UNEP’s International Methane Emissions Observatory (IMEO) is developing a new Repository of Empirical Methane Emissions for Data Integration (hereafter REMEDI), a dedicated database designed as a one-stop-shop for using and exchanging empirical, science-grade methane emissions measurements.
REMEDI is a geospatially referenced repository that compiles methane emission data derived from a variety of scientific activities, such as satellite remote sensing, aerial surveys, ground-based measurements, and mobile campaigns globally. The system accommodates diverse data types spanning multiple spatial scales—from facility-level point sources to basin-scale flux estimates—while preserving metadata necessary for uncertainty characterization, temporal attribution, and methodological traceability. Data ingested into REMEDI undergo pre-screening by IMEO to ensure that all data fit pre-defined eligibility criteria.
While containing only methane emission flux rates based on empirical measurements at site level and beyond (e.g., basin or country level), REMEDI does not consider data from super-emitters thus is complementary to IMEO’s flagship Methane Alert and Response System (MARS) where satellite imagery is used to pinpoint highly emissive sources. The first version of REMEDI, focusing on peer-reviewed data, is available through UNEP’s Eye on Methane platform and aims to support the future of methane data integration products. By filtering empirical measurements according to locations and source types among others, this new repository provides a standardized data backbone and enables users to comparatively mine diverse input streams. This presentation will describe REMEDI’s data architecture, ingestion and review processes, an overview of its content, and potential applications.
How to cite: Bartosiewicz, M., Bradley, A., Dane, G., Ikegwuonu, O., Petrova, I., Stavropolou, F., Powel, B., and Schwietzke, S.: Repository of Empirical Methane Emissions for Data Integration (REMEDI), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18281, https://doi.org/10.5194/egusphere-egu26-18281, 2026.
From 2016 to 2024, multiple flights were performed over managed landfills in the US, with successful quantification of emissions. The publicly available data from Carbon Mapper as well as not yet published Methane AIR data cover 170 managed landfills with 2202 methane plumes quantifications. Most analyses interpret instantaneous plumes against annual emission rates, assuming the average of the measured flux representative of the yearly emissions. We deliberately avoided this framing, and used reported fluxes in kg CH₄ h⁻¹ normalized by the added amounts of waste only as indicators of the “leak intensity” on a specific year. Operational parameters were extracted from yearly reports under the EPA Greenhouse Gas reporting framework, and the facilities of interest were classified in three aridity levels based on meteorological data.
Our approach allowed us to analyze drivers of change in emissions by taking into account multiple factors. We find that the humidity of a region is one of the main causes for inefficient gas capture in US landfills. We have followed the evolution of the magnitude of CH4 emissions as well as the improvement of the gas collecting infrastructure as a case study from the highest emitting climate category.
In relatively wet climates, gas collection is more difficult because of higher generation and increased soil humidity. We show that collection can improve with more wells, but reaching the efficiency of dryer climates landfills is very challenging. It is also important, as previous studies have shown, to implement gas collection as soon as possible after the disposal of organic waste.
On the global scale, the emissions of large dump sites in lower income countries can be reduced with appropriate gas collection systems, but best results are to achieve in relatively dry climates. The disposal of organic waste in landfills is even more to be avoided in tropical countries with relatively high precipitations. We emphasize that collaboration with local operators to investigate landfill specific parameters such as organic content is of major importance to design the best mitigation strategies, together with the analysis of environmental data.
How to cite: Menoud, M., Abichou, T., Aguilera, M. S., and Warren, J.: Compilation of remote sensing emission estimates of CH4 with environmental parameters and operational practices at US sanitary landfills: what works for mitigation?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14435, https://doi.org/10.5194/egusphere-egu26-14435, 2026.
Mitigation of CH4 emissions represents an effective short-term strategy for reducing climate change impacts. However, anthropogenic CH4 emission estimates remain highly uncertain due to the complex and heterogeneous distribution of sources and the strong temporal variability of emission processes. Emission factors for biogas plants and for the waste and wastewater treatment sector are still subject to large uncertainties.
This study focuses on the quantification of CH4 emission rates from biogas plants and wastewater treatment facilities in Germany using mobile methane measurements conducted at street level with cars and bicycles. Localized CH4 concentration enhancements are detected and converted into emission rates using a Gaussian plume model. Based on more than seven years of controlled CH4 release experiments, we developed a best-practice methode for mobile measurements, including optimized driving strategies and data processing procedures, reducing the overall uncertainty of derived emission rates to below 30%.
Mobile measurements were performed at more than 60 biogas plants, with one facility monitored continuously since 2016 to assess long-term emission behavior. Derived methane emission rates ranged from 0.1 to 46 kg CH4 h-1, corresponding to relative CH4 losses of approximately 0.2- 42.7% of the produced CH4. Methane emissions from wastewater treatment plants (WWTPs) in Germany have been investigated only in a limited number of studies, and current national inventories rely largely on emission factors derived from measurements in other European countries. To address this gap, a systematic survey was conducted at 13 WWTPs in Germany. Measurements were used to identify dominant CH4 emission sources, investigate diurnal emission patterns, and quantify facility-level CH4 emission rates and emission factors. At the surveyed sites, sludge treatment units and screening facilities were identified as the main emission sources. Determined CH4 emission rates ranged from 0.42 to 12.96 kg CH4 h-1.
The derived emission rates and emission factors are statistically analyzed, and compared with values currently used in regional and national emission inventories. These findings help to improve inventory accuracy and to target methane mitigation strategies.
How to cite: Schmidt, M., Wietzel, J., and Zeleny, M.: National-Scale Quantification of Methane Emissions from Biogas Facilities and Wastewater Treatment Plants in Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12712, https://doi.org/10.5194/egusphere-egu26-12712, 2026.
Bio-waste composting is environmentally favourable to landfilling; however, this practice has been shown to emit methane (CH4) and nitrous oxide (N2O), both of which contribute to climate change. Measurement-based studies are necessary to quantify these emissions accurately, revise emission factors, define composting best practices and monitor mitigation strategies. This study investigated CH4 and N2O emissions, as well as associated parameters (i.e., gas composition, temperature, material age and composition), at garden waste windrow composting facilities in Denmark. We report measured CH4 andN2O emissions and emissions factors at 11 full-scale composting facilities and one farm in Denmark. In addition, gas concentrations and temperatures were measured inside material piles present, including windrows and stored mounds of untreated garden waste, biofuel and compost products. Methane and N2O fluxes on the surface of the windrows and material piles were measured using flux chambers. Total facility emissions were quantified using a tracer gas-based method. Finally, large scale experimental studies were performed to investigate if CH4 and N2O emissions and CO2 emissions from energy consumption could be reduced from composting facilities by improvement in operating conditions such as reducing windrow size, increase turning frequency, implement active aeration and pre-treatment of the garden waste. The outcomes include the establishment of a revised national emission baseline for current composting practices in Denmark; an improvement in the robustness and representativeness of emission factors, thereby enabling an update of the values applied in the Danish National Inventory Report (NIR); and the development of evidence‑based best‑practice recommendations for composting, directed at the Danish Environmental Protection Agency, municipal authorities, and the waste management sector. Collectively, these outputs are anticipated to support forthcoming revisions of the national composting guidelines and to strengthen the scientific foundation for emissions reporting and regulatory decision‑making.
How to cite: Scheutz, C., Klotz, L. A., and Fredenslund, A.: Methane and nitrous oxide emission from garden waste composting facilities – emission factors and mitigation potential, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15901, https://doi.org/10.5194/egusphere-egu26-15901, 2026.
Quantifying methane emissions from distributed, non-point sources remains a critical barrier to effective mitigation in the waste, agriculture, and natural systems sectors. Unlike oil and gas infrastructure that produces well-defined plumes from point sources at the local scale, emissions from landfills, wetlands, and agricultural operations are often diffuse, cover large spatial extents (100s–1000s m2), and produce weaker local enhancements. Despite potential difficulties in quantification, these sources remain globally significant contributors to the methane budget and require accurate characterization for effective mitigation. The next generation of emissions quantification requires new measurement approaches specifically designed for distributed sources.
We present a Bayesian inversion framework designed to quantify distributed, area-source fluxes from open-path laser spectroscopy concentration measurements. This method simultaneously retrieves spatially-resolved surface fluxes and time-varying background concentrations. We eliminate the common practice of discarding measurements when unenhanced background concentrations are unavailable by relaxing the constraint of requiring explicit background subtraction or favorable wind conditions. This approach enables quantification in systems characterized by networks of diffuse and nearby sources. By accumulating observations over extended periods (hours to weeks), the method achieves sensitivity to weak signals below traditional detection thresholds while providing detailed uncertainty diagnostics through the Bayesian framework. These diagnostics inform how well our measurements constrain inferred emission rates, providing actionable guidance for observing system design and establishing emission detection thresholds.
We demonstrate these methods with continuous multi-month observations at an active municipal solid waste landfill, revealing linkages between operational activities and emission patterns. Combining new inversion methods with open-path laser measurements enables reliable mitigation tracking in the distributed-source sectors where closing the gap between atmospheric observations and inventory estimates is most critical.
How to cite: Miller, E., Coburn, S., Rozmiarek, K., Alden, C., Jones, T., Henze, D., and Rieker, G.: Bayesian inversion methods for quantifying diffuse methane emissions with open-path laser measurements: Enabling landscape-scale tracking of distributed sources , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16182, https://doi.org/10.5194/egusphere-egu26-16182, 2026.
While thermal coal is being phased out, metallurgical coal is likely to remain essential for steel production for several more decades. Reducing the climate impact of ongoing coal mining requires accurate quantification and mitigation of methane emissions. Reliable emission estimates are crucial for designing effective mitigation strategies and for reporting under frameworks such as the UNFCCC and the Global Methane Pledge.
Traditional approaches to estimating coal mine methane emissions rely on generalized models, such as Langmuir isotherms, which consider only coal rank and mine depth. These first-order approximations fail to capture the considerable variability in emissions across individual mines, mining methods, production regimes, and operational practices such as ventilation and methane drainage. Recent advances in satellite remote sensing now allow for inversion-based measurement of methane emissions at the scale of individual ventilation shafts and drainage stations. The International Methane Emissions Observatory (IMEO) Steel Methane Programme (SMP) leverages these observations by integrating satellite measurements with aircraft campaigns, published studies, and a comprehensive bottom-up inventory. The SMP applies a Bayesian inference framework to effectively integrate incomplete and heterogeneous data, delivering the first empirically grounded global dataset of methane emission estimates from metallurgical coal mines. Supported by a transparent deterministic methodology, the SMP framework will produce a publicly accessible database of coal mine methane emissions, alongside IMEO’s best estimate of annual mine-level emissions. By providing a transparent, empirically grounded framework, this work also establishes a scalable approach that can be applied to thermal coal production and integrated into global greenhouse gas monitoring initiatives.
How to cite: Bradley, A., Tibrewal, K., Castañeda, C., Irakulis-Loitxate, I., Bartosiewicz, M., Field, R., Kasprzak, M., van Niekerk, L., and Schwietzke, S.: Quantifying methane emissions from metallurgical coal production at mine-level using empirical measurements in a Bayesian inference framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19629, https://doi.org/10.5194/egusphere-egu26-19629, 2026.
Emissions from coal production represent one of the major anthropogenic sources of atmospheric methane, particularly CH4 released through underground mine degassing and discharged via the ventilation system. Ventilation air methane (VAM) is of great interest in terms of safe mining exploitation and GHG emission reduction targets. The direct measurements and quantification of CH4 emission from mining activities and its controlling factors are often subject to considerable uncertainty. Therefore, it is necessary to perform continuous monitoring of CH4 content and estimate methane emission rates across mine ventilation shafts into the atmosphere.
This study investigates methane concentration variability in ventilation air, evaluates the performance of a low-cost TDLAS analyzer (Axetris LGD), and estimates a site-specific methane emission factor for an underground coal mine ventilation system. The analysis is based on continuous one-year VAM measurements conducted at the exhaust ventilation shaft of a hard coal mine located in the western part of Upper Silesian Coal Basin (USCB), Poland. During the study period, the mine operated three active longwall panels at a depth of ⁓700 m b.g.l.
Methane monitoring conducted between July 2024 and August 2025 revealed pronounced temporal variability in concentration and volume of exhausted VAM in the ventilation shaft on daily and weekly timescales. These variations reflect episodic gas release events, changes in airflow rates, and operational dynamics associated with mining activities. Seasonal fluctuations in shaft methane concentrations, ranging from 0.15 to 0.45 %, were generally associated with intensified mining activity, particularly during the pre-winter period, whereas downward trends corresponded to a reduced number of active longwalls.
A comparison of the Axetris LGD analyzer with in-mine thermocatalytic Pellistor gas detector at 1-minute resolution revealed systematic offsets in the Pellistor measurements, which consistently underestimated CH4 content under low-concentration conditions. Following recalibration of the Pellistor sensor using the higher-resolution Axetris measurements as a reference, a strong agreement between the two instruments was achieved, characterized by convergent concentration trends and a substantially reduced measurement bias (relative RMSE of ⁓7 %). These results demonstrate the necessity of regular low-range calibration to ensure the reliability of long-term Pellistor-based CH4 monitoring in mine ventilation air.
Analysis of methane concentrations from coal mine ventilation shaft identified three distinct emission trends. Period 1 (August–December 2024) exhibited the highest CH4 emission rates, averaging 1060 ± 140 tons ∙ month–1, Period 2 (January–March 2025) showed slightly lower emissions, with an average of 1034 ± 80 tons ∙ month–1, while Period 3 (April–June 2025) was characterized by the lowest methane release, averaging 720 ± 40 tons ∙ month–1. Hourly emission rates ranged from 1.0 to 2.5 tons CH4 ∙ h–1. Methane emission rates correlated with mining activity indicators, including longwall advance (R2 = 0.55) and coal production (R2 = 0.38). A site-specific methane emission factor of 5.1 ± 1.0 m3 ∙ ton–1 coal was determined for the studied mine.
Acknowledgment:
This work was funded by the Polish Ministry of Science and Higher Education under Grant No. 2022/44/C/ST10/00112. The authors want to thank the coal mine company for permission to access the Pellistor sensor and airflow records.
How to cite: Bezyk, Y., Góra, A., Szurgacz, D., Bartyzel, J., Jagoda, P., Swolkien, J., and Nęcki, J.: Towards accurate methane emission reporting from coal mine ventilation: a direct measurement approach at the shaft , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14130, https://doi.org/10.5194/egusphere-egu26-14130, 2026.
Three years have passed since the United Nations Environment Programme’s International Methane Emissions Observatory (IMEO) launched the Methane Alert and Response System (MARS), with one year in a pilot phase (2023) and two years now in nominal operations (2024–present). MARS leverages the capabilities of more than a dozen methane-sensitive satellites to detect emissions worldwide and to drive mitigation actions. Since its launch, MARS has focused on the rapid detection and notification (within 15 days from the observation date) of oil and gas point-source methane emissions. This has resulted in the notification of more than 5,600 plumes from over 1,500 oil and gas point sources across 34 countries, as well as feedback on the cause and current status of emissions from operators and governments for more than 170 sources in 18 countries and the effective mitigation of nearly 25 sources.
While the number of mitigated sources may appear low compared to the number of notifications, feedback received to date from governments and companies indicates that most detected plumes are linked to permitted, short-duration operational events, including planned activities and emergency releases. Other cases involve temporary mitigation measures that do not ensure long-term emission prevention, or mitigation actions requiring substantial economic and logistical efforts, with implementation timelines of several months to years; therefore, they cannot yet be classified as fully mitigated.
In the meantime, engagement with notified countries has increased significantly, with a growing number of formal responses received. This has led to an increasing number of confirmed mitigation cases and is enabling more robust and representative statistics on emission sources, causes, and mitigation status.
In parallel, MARS has also detected and monitored a large number of emissions from the coal, waste, and other sectors over the years, building a multi-sectoral data set with more than 20,000 plumes. Recognizing that mitigation in these other sectors is equally critical, and that satellite data can be a powerful tool to support action, MARS will start notifying emissions in the coal and waste sectors in 2026, adopting different notification approaches depending on the nature and mitigation potential of the source.
In this presentation, we will provide an update on the current status of MARS, highlighting key results and conclusions, as well as lessons learned to date. We will also provide an overview of upcoming measures and new products to be introduced as part of MARS' expansion.
How to cite: Irakulis-Loitxate, I., Demeter, M., Montesino-San Martín, M., Raunak, A., Castañeda Martínez, C., Mateo-García, G., Bonazzi, G., Johnson, J. E., Mokalled, T., Carreras, F., Inbeess, H., Safari, Q., Kosumov, K., Karnetzky, C., and East, J.: Three years of UNEP’s Methane Alert and Response System: achievements, lessons learned, and next steps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19379, https://doi.org/10.5194/egusphere-egu26-19379, 2026.
Super-emitting (>100 kg/h) methane sources contribute significantly to total emissions across several oil&gas basins, but the robust quantification and characterization of these sources remains uncertain in the absence of routine, transparent, and robust measurements. Quantification is further complicated by the intermittent nature of many oil&gas emission sources. Solving this quantification gap is particularly important given international regulations and initiatives that require low methane intensities, the ratio of methane emitted to energy produced, across the oil&gas supply chain by country and operator. The Tanager-1 satellite (launched August 2024) has shown capability of detection and quantification of the vast majority of methane super-emitters given adequate observing conditions and spatiotemporal coverage. Here, we show Carbon Mapper’s progress in mapping global super-emitter intensities through intensive tasking of the Tanager-1 satellite of the majority of oil&gas infrastructure across key oil&gas producing basins. We present a hierarchical Bayesian model to address issues of intermittency and detection limit when calculating super-emitter intensities across distinct geographic regions. With 30-m spatial resolution of Tanager-1, we attribute each detection to facility and equipment type, allowing for better understanding of drivers of intensities and how those drivers vary across basins. Building a more complete global picture of super-emitters with attribution to infrastructure will aid in constructing mitigation roadmaps for lower-intensity energy.
How to cite: Cusworth, D., Conrad, B., Ayasse, A., Bon, D., Scarpelli, T., East, J., and Duren, R.: Methane super-emitter intensities across global oil&gas basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5831, https://doi.org/10.5194/egusphere-egu26-5831, 2026.
The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5-Precursor satellite provides daily global observations of atmospheric methane, enabling the detection of super-emitters that are often missing or highly underestimated in bottom-up inventories. The emission rates of these super-emitters are typically quantified using mass balance-based approaches which have large associated uncertainties. Here, we train a convolutional neural network using simulated TROPOMI methane observations and meteorological reanalysis data in order to create ML-SPERE, a machine learning (ML)-based methodology for estimating the emission rates of super-emitter methane plumes observed by TROPOMI. We show that ML-SPERE outperforms the Integrated Mass Enhancement (IME) method on simulated TROPOMI methane plumes and under ideal observation conditions (where the plume head is visible) can achieve a reduction in median absolute percentage error from 42% to 24%. Additionally, our ML-SPERE quantifications for synthetic plumes are unbiased across wind speeds, whereas the IME estimates are systematically biased low at low wind speeds (a regime in which most TROPOMI methane super-emitting plumes are detected). Moving beyond synthetic data to real world application (where ground truth emission rates are not known), we apply ML-SPERE to TROPOMI methane observations of a 200-day well blowout in Kazakhstan and find agreement with TROPOMI-based IME estimates within uncertainties, a smaller offset relative to inverse modeling results than exhibited by TROPOMI IME estimates, and improved consistency with IME estimates derived from high-resolution point-source imagers. We additionally quantify a year's worth of TROPOMI detections of methane super-emitters around the globe, and find generally good agreement with IME quantifications. Global trends in estimated methane emissions via ML-SPERE and the IME method for this dataset are largely consistent, with exceptions in northern Russia, the Congo basin, and southwestern Australia. We also find evidence to suggest that IME emission rate estimates for this dataset are negatively biased at low wind speeds, and that ML-SPERE estimates may be unbiased, as seen in our simulation studies. While our experiments with simulated plumes demonstrate that ML-SPERE more accurately recovers emission rates than the IME method, agreement between the methods for real-world plumes (where no ground truth exists) provides confidence in the robustness of both approaches. Although quantifications remain largely constrained by uncertainties in wind fields (as with the IME method), ML-SPERE provides a valuable addition to the suite of quantification methods available for TROPOMI methane plume observations, owing to its computational efficiency, improved accuracy over the IME method, and reduced sensitivity to wind-related biases.
How to cite: Roberts, C., Maasakkers, J. D., de Jong, T. A., Schuit, B. J., Dogniaux, M., Sharma, S., Huegens, T., Houweling, S., and Aben, I.: Machine learning-based emission rate estimates of global methane super-emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18903, https://doi.org/10.5194/egusphere-egu26-18903, 2026.
Anthropogenic methane emissions, particularly from the oil and gas (O&G) sector, span a broad spectrum of rates and demonstrate significant temporal variability and intermittency. The MethaneSAT satellite addresses a critical limitation in space-based methane monitoring by enabling simultaneous quantification of both discrete point sources and diffuse area sources across regional scales, such as O&G production basins, using snapshot observations. MethaneSAT retrieves the total column dry-air mole fraction of methane (XCH₄) with high spatial resolution (100 m × 400 m) and precision (20–40 ppb) across observation swaths of 220 km × 200 km. Operating from March 2024 to June 2025, MethaneSAT acquired 1,152 scenes over 231 global targets, and to date, EDF has released over 190 emission maps spanning 49 O&G basins.
We present MSAT L4 CORE (MethaneSAT Level 4 Conserved, Optimized Retrieval of Emissions), an inverse modeling framework for quantifying regional-scale methane emissions. CORE employs Hamiltonian Monte Carlo sampling via the Stan software to infer posterior distributions of surface fluxes, conditioned on single-scene MethaneSAT measurements. Emissions are estimated in 4 km × 4 km grid cells, generating ensemble posterior flux distributions that reproduce the observations. A spatially homogeneous prior is imposed on the emissions, and regional-scale boundary inflow is estimated concurrently. MSAT L4 CORE enables regional-scale, snapshot emission estimates with typical uncertainties of 30% on aggregated emissions.
We demonstrate CORE using both simulated and real MethaneSAT data, and discuss its applicability to the airborne MethaneAIR mission as well as other airborne and spaceborne methane observing platforms.
How to cite: Knapp, M., Benmergui, J., Chulakadabba, A., Kyzivat, E., Bushey, J., Sargent, M., Zhang, Z., Roche, S., Miller, C. C., LoFaso, N., Ayvazov, S., Russi, M., Veness, T., Williams, J., Omara, M., MacKay, K., Himmelberger, A., Weatherby, K., Gautam, R., and Wofsy, S.: MethaneSAT - Quantifying Methane Emissions on Basin-Scales From Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13350, https://doi.org/10.5194/egusphere-egu26-13350, 2026.
We present an operational, cloud-based methane emissions monitoring system with near-real-time latency for the Marcellus shale, the largest gas-producing region in the United States. The system uses an analytical inversion via the Integrated Methane Inversion (IMI) framework to infer emissions from TROPOMI satellite instrument at ~12km resolution. We generate and analyze monthly emissions estimates spanning nearly five years (July 2021-present), enabling characterization of both long-term trends and short-term emission variability. Low-latency processing facilitates rapid detection of emission spikes, while high spatial resolution enables attribution among closely collocated source sectors (gas production, coal mining, livestock, and landfills). We validate our estimates using observations from MethaneSAT and summer 2025 aircraft campaigns. The system is fully automated on AWS and delivers results through an interactive web dashboard. We also develop an IMI extension package that enables users to deploy automated emissions monitoring systems for any region worldwide.
How to cite: Estrada, L., Jacob, D., Sulprizio, M., Wang, X., Bruno, J., and Varon, D.: An operational, cloud-based system for near-real-time methane emissions monitoring in the Marcellus shale and beyond, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11743, https://doi.org/10.5194/egusphere-egu26-11743, 2026.
Canada enacted its first federal methane regulations for the oil and gas sector in 2020; however, these federal regulations were ultimately implemented as separate provincial regulations that were each negotiated under federal-provincial regulatory equivalency agreements. This has resulted in significant variation in regulatory stringency, enforcement practices, and approaches to methane mitigation. These differences present a unique opportunity to examine the direct impact of different regulations on methane emissions in adjacent regions, with otherwise similar production characteristics and operators.
In this work we utilize aerial survey data collected using Bridger Photonics’ Gas Mapping LiDAR to compare methane emissions across jurisdictions operating under different regulatory frameworks. First, we examine emissions in the Lloydminster heavy oil production region that straddles the Alberta–Saskatchewan provincial border. Higher allowable venting limits means Saskatchewan’s regulatory framework is substantially weaker than Alberta’s. This directly correlates with a near doubling of methane emissions intensities among comparable production facilities with similar infrastructure. Moreover, for six producers with multiple assets on both sides of the border, five had higher methane intensities in Saskatchewan. These real-world data highlight the critical importance of regulations in driving mitigation, while simultaneously highlighting the limits of voluntary action.
A further case study examines the Peace River region in Alberta, in which a small sub-region was subjected to stricter regulations (Alberta Directive 084), introduced in response to odour complaints, while immediately adjacent regions were not. These regulations effectively prohibit routine venting and, despite not explicitly targeting methane, resulted in substantially lower measured methane emissions among facilities within the Directive 084 zone than among similar facilities outside the zone. Interestingly, these stricter regulations further correlate not only with reductions in occurrence rates of venting tanks but also in a reduction of unlit flares. Overall, these empirical observations demonstrate that producers in both Alberta and Saskatchewan can and do achieve measurably greater methane reductions but are unlikely to do so without a clear regulatory requirement.
How to cite: Wilde, S., Tyner, D., and Johnson, M.: Methane Emissions and Regulatory Stringency: A Case Study Across Canadian Provinces, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15164, https://doi.org/10.5194/egusphere-egu26-15164, 2026.
China, holding the world’s largest shale gas reserves, lacks precise data on methane emissions from its rapidly expanding production. We introduce a two-tiered mobile measurement approach, using a mobile laboratory to measure methane concentrations across 125 well pads (approximately 750 wells) distributed among four major production blocks (Changning, Weiyuan, Fuling, and Luzhou). These blocks contributed 84% of China’s total shale gas production in 2023, providing the first comprehensive ground-level measurements. Stationary downwind monitoring of well pads revealed emission rates from 0.002 to 98.86 kg/h, validated through mobile observations of methane concentrations across the region. Notably, 10% of well pads were responsible for 89% of total emissions. The extrapolation revealed that methane emissions from shale gas production in China for 2023 were estimated at 16,842 t (6,444–29,991 t, 95% CI). The methane leakage rate was 0.10% (0.04%–0.17%, 95% CI), lower than major U.S. fields, and similar to that of U.S. dry gas fields. Our research identifies gas lift venting, incomplete combustion from compressors, and process venting as significant sources of super-emissions in China’s shale gas upstream production chain. The methodology employed, based on comprehensive and targeted field measurements, demonstrates its effectiveness in providing a scientific basis for formulating precise and effective regulatory policies on methane emissions.
How to cite: Guo, M. and Hong, P.: Assessing Methane Emissions from Shale Gas Production in China: A Two-tiered Mobile Measurement Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-120, https://doi.org/10.5194/egusphere-egu26-120, 2026.
As part of the United Nations Environment Programme International Methane Emissions Observatory (UNEP IMEO)’s Science Studies, during 2024 and 2025 the Energy and Emissions Research Lab (EERL) at Carleton University conducted a comprehensive, multi-scale field measurement campaign to quantify methane emissions from upstream oil and gas facilities across Colombia’s major production basins. This pioneering campaign, one of the first of its kind in the Global South, employed a hybrid measurement framework combining top-down and bottom-up measurement techniques. The top-down measurements included aerial gas mapping LiDAR (GML) surveys of approximately 3800 facilities, and site-level scans using uncrewed aerial vehicles (UAVs). Bottom-up measurements involved fugitive emissions screening via optical gas imaging (OGI) and direct on-site measurements of major emission sources, including compressors, flares, and storage tanks. Finally, operator-level bottom-up emissions data emissions were also considered. From analysis of these data, this presentation will share a first measurement-based methane inventory for Colombia’s upstream oil and gas sector. Results provide source-level insights into future mitigation opportunities and demonstrate the role of international collaboration in enabling transparent, science-driven quantification of climate-critical emissions.
How to cite: Calderon-Cangrejo, N., Festa-Bianchet, S. A., Wilde, S. E., Conrad, B. M., Tyner, D. R., Veeramani, S., and Johnson, M. R.: Colombia’s First National Measurement-Based Oil and Gas Inventory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15376, https://doi.org/10.5194/egusphere-egu26-15376, 2026.
Recent measurement studies have found that urban natural gas (NG) emissions are 3.9× larger than bottom-up inventory estimates, on average, across various North American cities.1 Several studies have proposed that post-meter emissions may be a substantial missing source in urban methane (CH4) estimates, but the role of diffuse residential and commercial NG consumption in overall emissions remains uncertain. Long-term, continuous eddy covariance flux measurements can help clarify possible post-meter contributions by providing localized, high-resolution observations of cumulative emissions. Here we present nearly 3 years of CH4 flux measurements collected between July, 2022 and April, 2025 from a 42 m tall stationary tower located in downtown Berkeley, California, USA. Methane source types were characterized using contemporaneous ethane and δ13CH4 measurements, and spatially resolved population, building, and land use datasets were used to determine possible post-meter emissions drivers. Average annual CH4 fluxes in Berkeley were 152 nmol m-2 s-1 [95%: 150,155] and were primarily attributed to natural gas. Fluxes were dominated by a persistent spatial gradient wherein higher fluxes were associated with increased commercial building space and lower population density in the downtown core, with estimated average annual fluxes ranging from 85 nmol m-2 s-1 [95%: 82.8, 88.3] in residential areas to 218 nmol m-2 s-1 [95%: 214, 223] downtown. Flux diurnal trends were distinct between different seasons and dominant land uses, but no significant weekday-weekend differences were observed. Residential areas had lower diurnal variation and higher springtime fluxes—exhibiting no positive correlation with NG consumption. In denser commercial areas, CH4 fluxes were significantly lower during warmer months, and monthly emissions were positively correlated with NG consumption at rates of 0.21% and 0.23%. Overall fluxes were 5× larger than the highest inventory estimates and were elevated relative to urban eddy covariance studies in similarly sized European and Asian cities. Our results emphasize how eddy covariance studies can help identify and track the drivers of larger urban CH4 emissions trends and the importance of evaluating these trends across different spatial scales.
How to cite: Byrne, H. M., Katz, E. F., Cliff, S. J., Weber, R. J., Szutu, D. J., Verfaille, J. G., Baldocchi, D., Goldstein, A. H., and Apte, J.: Urban Natural Gas Seasonality is Associated with Commercial Areas in Berkeley, California, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8094, https://doi.org/10.5194/egusphere-egu26-8094, 2026.
Methane is an important greenhouse gas that contributes to about 12% to Dutch greenhouse gas emissions. In previous studies, top-down modelling and satellite inversions have indicated that methane emissions in the Netherlands as represented in bottom-up inventories might be underestimated. The IMEO-VIME-NL project aims to make use of the abundant data sets of methane measurements in the Netherlands to compile a national measurement-based methane emission baseline.
A compilation of available measurement data shows that not all sectors are covered equally. While sectors like peatlands and wastewater treatment plants were frequently targeted by measurements, other sectors, like biodigesters and domestic combustion, are lacking measurement data. Sometimes data are mainly concentrated on a subsector, e.g. most measurement data for the agriculture sector, the main contributor to Dutch methane emissions, focus on dairy farms. This data set collection is then used for upscaling to national total emissions.
An important outcome of the project is a compilation of upscaling methods that could potentially be transferred to other countries. Sensitivity studies are employed to test different data coverages and different activity data when upscaling to national total emissions. For wastewater treatment plant emissions, two different types of activity data are readily available, inhabitants and water usage. Using these activity data for estimating emissions results in similar emission estimates that are higher than the inventory emission. In a similar way, methane emission measurements and available activity data for other sectors are compiled together and upscaled to a measurement-based national methane emission baseline.
How to cite: Heimerl, K., Velders, G. J. M., Röckmann, T., Witt, H., van Zanten, M., Denier van der Gon, H., Super, I., Houweling, S., Hensen, A., Velzeboer, I., van den Bulk, P., Hutjes, R., Chen, H., and van Heuven, S.: Verifying and improving methane emission inventory data using atmospheric measurements in the Netherlands (IMEO-VIME-NL), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19292, https://doi.org/10.5194/egusphere-egu26-19292, 2026.
Methane (CH4) is a potent greenhouse gas with a global warming potential of about 84 over a 20-year timescale, and an atmospheric lifetime of about 9 years. The increase in CH4 emissions has contributed about 0.6°C to the observed global warming since pre-industrial times. The ongoing increase in atmospheric CH4 undermines efforts to mitigate climate change. To effectively mitigate CH4, it is essential to understand the location, strength and temporal variability of its most important sources, which vary in different regions. A widely used method to distinguish emissions from different source categories is the measurement of CH4 isotopic composition. Such measurements provide additional insight because different CH4 production processes emit CH4 with different isotopic composition.
Traditionally, CH4 isotope measurements have been carried out on atmospheric air samples under controlled laboratory conditions, but since a few years, instruments measuring isotopic composition continuously at monitoring stations have become available. An important application of continuous isotopic CH4 measurements is the evaluation of regional scale emissions with respect to the existing emission inventories. In model simulations using emissions from these inventories, the relative contributions of different source categories to observed enhancements can be calculated. This information can be used to simulate time series of the isotopic composition. By comparing these simulations with observed isotopic data, we can not only assess whether total emissions in a model are over- or underestimated, but also identify which source categories are responsible for any discrepancies.
The mobile isotope ratio mass spectrometry system developed at Utrecht University has been deployed at more than 10 different locations in Europe over the past decade, in most cases for approximately 7 months. The recorded 20-min resolution and high precision isotope data of both d13C and d2H provide empirical constraints to the CH4 source mix at the different locations. The combination with high resolution model simulations has provided many new insights into regional scale emissions. We present an overview of key findings and discuss the value of high resolution isotope measurements for improving our understanding of the regional budgets of this important greenhouse gas.
How to cite: van Es, J., van der Veen, C., Menoud, M., Henne, S., Cover, G., Bettinelli, J., Chen, J., Molnar, M., Áron Baráth, B., Varga, T., Haszpra, L., Cristofanelli, P., Montaguti, S., D’Amico, F., Ammoscato, I., and Röckmann, T.: Characterisation of the regional source mix of methane at different locations in Europe using continuous isotope ratio measurements of d2H and d13C, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17023, https://doi.org/10.5194/egusphere-egu26-17023, 2026.
Methane (CH4) is a key greenhouse gas contributing to global warming, therefore a comprehensive understanding of its sources, sinks, and feedback mechanisms is essential for budgeting of emissions. However accurate methane apportionment, especially for emissions form natural sources (e.g. wetlands) remains a challenge, particularly in tropical regions where emissions from wetlands are highly uncertain. Contributions from human activities are generally better understood, however there are still areas where additional information or methodology improvements would be relevant.
The upcoming CoMet 3.0 Tropics mission (July-August 2026) aims to reduce these uncertainties through intensive airborne measurements of CH4 and CO2 aboard HALO (High Altitude and LOng range research aircraft) over Brazil, targeting tropical wetlands and anthropogenic hotspots.
To support mission planning and interpretation, a regional modeling framework based on ICON-ART is developed, configured in limited-area mode (LAM) over Brazil at ~6.5 km resolution (R03B08 grid). The model is driven by ERA5 meteorology and CAMS inversion-optimized CH4 fields (v22r2), with anthropogenic emissions from EDGAR v8.0 and wetland emissions from WetCharts v1.3.3.
Here, we present a simulation for August 2022 serving as a methodological testbed for the modeling system. This work demonstrates the feasibility of high-resolution CH4 simulations in tropical South America using ICON-ART and provides a foundation for future analysis of the results from CoMet 3.0 Tropics mission.
How to cite: De Santis, N., Ho, D., Gałkowski, M., Botía, S., and Gerbig, C.: Towards high-resolution CH4 simulations for tropical South America: a preparatory study for the CoMet 3.0 mission using ICON-ART, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1937, https://doi.org/10.5194/egusphere-egu26-1937, 2026.
Methane (CH₄) is a potent greenhouse gas, and its emission mitigation plays a crucial role in efforts to combat climate change. The oil and gas (O&G) sector in Romania is a major CH₄ emitter, and the ROMEO campaign in 2019 found O&G emissions to be much higher than previously assumed.
We estimate Romania’s CH₄ emissions for 2019 using three TROPOMI CH₄ products - the operational SRON retrieval, the blended (TROPOMI+GOSAT) product from Harvard University, and the WFM-DOAS retrieval from University of Bremen - combined with the ICON-ART model and an Ensemble Square Root Filter in the Community Inversion Framework (CIF). Additionally, we perform an inversion using the operational TROPOMI retrieval for 2021.
Inversions for 2019 reveal noticeable spatial and temporal inconsistencies across the satellite products, indicating that a posteriori distributions at fine scales should be interpreted with caution. Despite these differences, the country-total emissions agree within the expected range, suggesting robustness at aggregated scales. Applying the system to the operational TROPOMI product for 2021 shows a reduction of 20% in corresponding posterior emissions compared to 2019, with stronger reductions of 30% over a region dominated by oil and gas infrastructure. This decrease is consistent with previous independent findings, namely results from a 2021 aircraft campaign over Romanian O&G infrastructure reporting a 20%-60% reduction in methane emissions.
Our study highlights the limitations of current TROPOMI CH₄ products for estimating regional emission patterns and emphasizes the need for further investigation into the significant discrepancies between them. Nevertheless, trends derived consistently from a single product appear robust and align with independent findings, making them valuable for assessing long-term emission changes in regions with sparse in-situ monitoring.
How to cite: Riess, C., Steiner, M., Thanwerdas, J., Emmenegger, L., and Brunner, D.: Top-Down Estimates of Methane Emissions in Romania Using Multiple TROPOMI CH₄ Products in ICON-ART CIF Inversions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2794, https://doi.org/10.5194/egusphere-egu26-2794, 2026.
Seasonal variability of atmospheric methane (CH4) is governed by the seasonal cycle of surface emissions and the abundance of the tropospheric hydroxyl radical (OH). As substantial uncertainties remain in the variability of both sources and sinks, we constrain monthly emissions and tropospheric OH using the GEOS-Chem chemistry transport model and 14 years of GOSAT XCH4 observations. The seasonal cycle amplitude (SCA) of posterior global methane emissions shows an increasing trend of 4.23 Tg a-1 a-1, substantially larger than the prior estimate of 1.52 Tg a-1 a-1. Boreal wetlands in North America and Siberia dominate this amplification, accounting for 30% and 27% of the global SCA trend, respectively, with additional contributions from tropical wetland regions in central Africa and the Bengal region. Interannual variability (IAV) in tropospheric OH also plays a compensatory role by modulating the methane sink. While OH IAV amplifies the sink SCA trend in the northern midlatitudes, it dampens the trend over the tropics (–0.47 Tg a-1 a-1) through declining posterior tropical OH. Sensitivity tests are performed to attribute the observed XCH4 SCA trends to emissions and tropospheric OH across latitude bands. In the northern hemisphere midlatitudes, the posterior XCH4 SCA trend is 0.41 Tg a-1 a-1 predominantly driven by increasing emission SCA. In contrast, the tropics exhibit a larger XCH4 SCA trend of 0.81 Tg a-1 a-1, where tropical emissions act to suppress the XCH4 SCA trend. This SCA trend analysis improves our understanding of recent methane dynamics and provides information for projecting future atmospheric methane concentrations.
How to cite: Oh, S.-I., Park, R. J., Kim, S.-W., and Parker, R. J.: Trends in Seasonal Variability of Global Methane Budget constrained by GOSAT observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16082, https://doi.org/10.5194/egusphere-egu26-16082, 2026.
Airborne trace gas concentration measurements can be used to infer regional emissions. Foregoing full 3D transport inversion methods, emissions are generally estimated using bulk methods. Measuring concentrations across the upwind and downwind wall of the area of interest, at different altitudes in the boundary layer, allows to specify trace gas inflow and outflow and from the difference to estimate the surface flux.
Here we propose a novel alternative method. We fly parallel tracks over the region of interest at low altitude (200ft), aligned with the wind. The resulting trace gas signal typically can be viewed as a series of concentration peaks superimposed on a linear gradient. We interpret this as that the positive (or negative) gradient is the result of diffuse sources (or sinks) in the landscape. The concentration peaks we interpret as the resulting from point sources, i.e. gaussian plumes intersected by the flight track.
We demonstrate promising results using methane concentrations obtained over a rural landscape in the Netherlands dominated by dairy farms on (drained) organic soils. In this setting we interpret diffuse sources of methane to originate from the very wet parts of the landscape, i.e. ditches and (near) inundated parcels. The concentration peaks we trace back to cattle herds of (small clusters of) individual farms. We will show more details of methodology and results. Finally, we discuss uncertainties and compare obtained emissions with bottom-up estimates in National Inventory Reports and other statistics, as well as recent inversion studies. With the latter we concur that the Netherlands maybe under-reporting its rural methane emissions.
How to cite: Hutjes, R., Franssen, W., Kruijt, B., and Zhao, H.: A novel approach to infer regional emissions from airborne methane measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7912, https://doi.org/10.5194/egusphere-egu26-7912, 2026.
There has been explosive growth in the field of remote sensing of methane plume from aircraft and satellite. The global reach and inherent spatial sampling capabilities of on-orbit instruments make them uniquely suited for consistent, repeatable surveys across regions and borders. These measurements have primarily been applied to the fossil energy and waste sectors (Cusworth et al., 2022; Thorpe et al., 2023), with current satellites typically detecting emissions exceeding ≈100 kg CH₄ per hour, while airborne platforms can observe sources as small as ≈10 kg CH₄ per hour.
Despite the rapid growth in observational capacity, challenges remain. Divergent emissions estimates, opaque methodologies, and inconsistent validation approaches can erode confidence in remote sensing-based emissions data. The emergence of non-public-sector missions using proprietary methods—often without full transparency across the data chain—further highlights the need for community-accepted practices to ensure traceability, comparability, and scientific credibility.
To address this need, the greenhouse gas (GHG) community—through the Committee on Earth Observation Satellites (CEOS) and National Metrology Institutes (NMIs)— developed a document in 2025 to articulate commonly accepted approaches for quantifying methane emissions based on observed plumes (Worden et al., 2025). It provides guidance spanning from Level 0/1 radiance, to Level 2 concentration, to Level 4 emissions, and includes current practices for validation and quality assessment. The focus is on emissions derived from discrete plumes, rather than from spatially diffuse sources.
In this poster, we will discuss some key points of the current practices report as well as plans for next steps to perform intercomparisons and work towards a Best Practices document. This work has shifted from NIST to the Climate Data Collaborative of the Data Foundation in the US, and will be performed in collaboration with researchers and agencies across the US and Europe in 2026 including CEOS, CGMS, NPL, LLBL, UKSA, ESA, and the MEDUSA project.
References:
Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., et al. (2022). Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States. Proceedings of the National Academy of Sciences, 119(38), e2202338119. https://doi.org/10.1073/pnas.2202338119
Thorpe, A. K., Frankenberg, C., Thompson, D. R., Duren, R. M., Aubrey, A. D., Bue, B. D., ... & Dennison, P. E. (2017). Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG. Atmospheric Measurement Techniques, 10(10), 3833-3850.
Worden, J.R., Green, P., Eldering, A., Sherwin, E., 2025, Common Practices for Quantifying Methane Emissions from Plumes Detected by Remote Sensing, https://zenodo.org/records/17047789
How to cite: Eldering, A., Green, P., and Meijer, Y.: Development Plan for Best Practices for Remote Sensing of Methane Plumes from Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4102, https://doi.org/10.5194/egusphere-egu26-4102, 2026.
Methane point sources are spatially sparse, temporally intermittent, and strongly affected by surface heterogeneity, posing significant challenges for large-scale and continuous monitoring. While hyperspectral sensors provide high retrieval accuracy, their limited spatial and temporal coverage motivates the use of global, open-access multispectral satellite data for scalable identification and quantification of methane emissions. In this study, we present a systematic methane point source detection and quantification framework built upon Sentinel-2 imagery and the Google Earth Engine (GEE) platform, enabling scalable and operational analysis across diverse land surface types and emission sources. The framework incorporates adaptive plume detection and segmentation strategies tailored to different land surface conditions by exploiting characteristic methane signatures in the spatial, spectral, and temporal domains. Dedicated data-driven models are employed to segment methane plumes over homogeneous oil and gas regions, spectrally challenging environments such as vegetated and offshore areas, and heterogeneous sources including landfills and coal mining sites. Detected plumes are subsequently quantified using wind-informed emission estimation to derive point-source emission rates directly from Sentinel-2 observations. The proposed framework is evaluated across multiple representative land surfaces, successfully identifying the majority of high-emission sources as well as several previously unreported ones, and demonstrating improved detection consistency and generalization compared to conventional single strategy approaches. By leveraging the global coverage of Sentinel-2 and the computational scalability of GEE, this work provides a practical pathway toward near-global screening and monitoring of methane point sources, supporting climate mitigation and emission inventory improvement efforts.
How to cite: Xu, D., Mason, P., Liu, J., and Wang, Y.: A Surface-Adaptive Framework for Methane Point Source Detection and Quantification Using Sentinel-2 and Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11110, https://doi.org/10.5194/egusphere-egu26-11110, 2026.
Methane is a key target for rapid mitigation because of its large warming potential and short life in the atmosphere. The Methane Alert and Response System (MARS), under the UNEP's International Methane Emissions Observatory (IMEO), supports mitigation efforts through satellite-based methane monitoring. In 2025, MARS notified 3738 methane plumes from the oil and gas sector to governments and companies using public multi-spectral (Sentinel-2 and Landsat) and hyper-spectral (EMIT, PRISMA and EnMAP) detections. An important aspect of MARS is the collection of satellite observations where no methane plumes are detected, which can be used as evidence of effective mitigation efforts reported by companies. However, non-detections can also result from unfavourable observing and environmental conditions, such as strong winds, retrieval artifacts, or differences in the sensitivity of instruments. Therefore, interpreting non-detections properly requires considering the observation-specific probability of detection (PoD), which depends on wind conditions, observation geometry, the on-board satellite instrument, and image noise. Here, we quantify how these factors influence the PoD and develop an operational parametric model to efficiently evaluate MARS observations.
We assess detection performance across a wide range of realistic conditions in oil and gas regions by sampling scenes from the MARS archive covering diverse wind speeds, solar/viewing geometries, and noise regimes that vary with surface albedo and time. Representative synthetic methane plumes simulated with the WRF-LES model are injected into the top-of-atmosphere (TOA) radiance of satellite images. For each scene, multiple plume realizations at different flux rates are processed using the MARS operational detection pipeline to determine the detection frequency. We then fit a logistic PoD curve as a function of flux, with the slope and midpoint related to observation conditions for each instrument. Results show that wind speeds and noise levels are the dominant drivers affecting the slope and shift of the sigmoid PoD curve in most cases. We compare this parametric model on independent testing scenes and provide average probability of detection estimates for different instruments on major oil and gas basins. This parametric model will support the decision-making process in MARS in future potential mitigation actions.
How to cite: Montesino-SanMartin, M., Mateo-García, G., Gorroño, J., Castaneda, C., Johnson, J. E., Raunak, A., and Irakulis-Loitxate, I.: Observation-conditioned probability of detection for satellite methane point sources , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12148, https://doi.org/10.5194/egusphere-egu26-12148, 2026.
Methane (CH₄) ranks as the second most potent anthropogenic greenhouse gas (GHG), driving roughly one-third of contemporary global warming. Beyond its direct role in amplifying climate change, this trace gas acts as a key precursor to tropospheric ozone formation, with cascading indirect impacts on human health, agricultural productivity, and ecosystem integrity. Unlike long-lived GHGs such as CO₂, rapid and sustained curtailment of methane emissions offers the potential to decelerate global warming rates within decades, while delivering co-benefits that span public health protection, food security enhancement, and biodiversity conservation. Pinpointing the spatial distribution of methane emission sources is a cornerstone of effective global mitigation strategies and climate governance. Methane emissions exhibit extreme spatial heterogeneity: a small subset of super-emitters disproportionately contribute to global anthropogenic fluxes. The precise identification and geolocation of these hotspots are therefore pivotal to optimizing the cost-efficiency of mitigation interventions. For regulatory bodies and industrial operators alike, robust source characterization enables the rapid detection of anomalous releases, equipment malfunctions, or operational inefficiencies, facilitating timely remediation and the reduction of chronic unintentional emissions. Remote sensing technologies have revolutionized the detection, spatial mapping, and quantification of near-surface methane plumes, providing unprecedented coverage of global emissions. Yet while elevated methane concentrations can be reliably identified from orbital or airborne sensors, linking these atmospheric anomalies to specific ground-based anthropogenic sources remains a major bottleneck. This task typically relies on labor-intensive manual interpretation of large-scale, multi-temporal imagery datasets—a process that is not only slow and costly but also prone to inter-observer subjectivity. In the absence of accurate source localization, bottom-up emission inventories (compiled from activity data and emission factors) and top-down estimates (derived from atmospheric observations) often diverge by 50% or more, undermining the credibility of climate policies and mitigation targets. As such, the translation of remotely sensed methane hotspots into actionable source locations remains an essential yet elusive goal. Advancing source localization from the regional to the facility scale, and ultimately to individual equipment level, represents a transformative leap in methane monitoring—shifting the paradigm from qualitative detection to quantitative source attribution. To address these interconnected challenges, we introduce a novel Multimodal AI framework designed to integrate and interpret heterogeneous remote sensing datasets. Leveraging the power of multimodal AI for advanced image understanding, this framework enables the automated identification of anthropogenic methane emission sources on a global scale. Using this approach, we have constructed a high-resolution, top-down emission source dataset that catalogs the precise geographic coordinates of key methane-emitting sectors. These include open-pit coal mines and their downstream processing facilities, solid waste landfills, wastewater treatment plants, oil and LNG terminals, and oil and gas extraction areas. Beyond resolving critical discrepancies between top-down and bottom-up emission estimates, our innovative Multimodal AI approach serves as a foundational resource for policymakers, industry stakeholders, and the scientific community to devise targeted, evidence-based methane mitigation strategies.
How to cite: Li, J. and Huang, X.: Global Anthropogenic Methane Emission Source Attribution with Multimodal AI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4259, https://doi.org/10.5194/egusphere-egu26-4259, 2026.
Top-down inversion of satellite observations is a powerful tool used to identify sources of atmospheric methane and to evaluate bottom-up emission estimates, providing essential information for achieving short-term climate mitigation goals. Recent satellite inversions indicate an increasing trend in methane emissions and an underestimate in the bottom-up estimates reported to the United Nations Framework Convention on Climate Change (UNFCCC) over Europe, but these results have been limited by coarse resolution and temporal coverage. Here we use satellite observations from TROPOMI and GOSAT to estimate methane emissions at 25 km resolution over western and central Europe during 2019 and 2024, respectively, using the Copernicus Atmospheric Monitoring Service (CAMS) and Global Fuel Exploitation Inventory (GFEI) anthropogenic emissions as prior estimates. Our high-resolution top-down posterior estimates suggest an upward correction in CAMS livestock (17−29%) and waste (5−29%) emissions, and a downward correction in GFEI coal (63−65%) emissions. The total posterior estimates for 2019 and 2024 are 17.7 Tg and 19.7 Tg, respectively, indicating a 2024 versus 2019 increase, attributed to livestock and waste, especially in Italy and Spain, which is not shown in the CAMS bottom-up emissions.
How to cite: Oak, Y. J., Jacob, D. J., Estrada, L. A., East, J. D., He, M., Wang, X., and Li, X.: High-resolution methane emissions inferred from TROPOMI and GOSAT satellite inversions over western and central Europe , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8515, https://doi.org/10.5194/egusphere-egu26-8515, 2026.
Methane (CH₄) is the second-largest greenhouse gas contributing to climate change, and it is produced by both anthropogenic and biogenic sources. The TROPOspheric Monitoring Instrument (TROPOMI) on board ESA’s Sentinel 5 Precursor (S5-P) satellite provides daily total column-averaged methane mixing ratio values at high spatial resolution, allowing the monitoring and flux estimation of diverse methane sources.
In Denmark, CH₄ emissions are mainly related to the agricultural and waste sectors, mostly attributed to diffuse sources and point sources with low emission rates (below 100 kg/h). Emission estimates compiled in the national GHG emissions inventory are mostly based on emission factors derived from models, with few empirical measurements. The lack of measurements and spatial information of methane sources introduces uncertainty when projecting the inventory emission estimates into a spatial grid. Gridded emission estimates from emissions databases like EDGAR disagree with the inventory on both their spatial distribution and magnitude, raising the question as to how to correctly account for diffuse emissions and which sources to trust.
Understanding the distribution of diffuse anthropogenic methane fluxes and their quantification is crucial for nations to plan mitigation strategies and have an empirical knowledge of their inventories.
In this study, we use TROPOMI data to detect hot spots of diffuse methane sources and estimate fluxes attributed to different sectors and source types over Denmark. Focus is set on analyzing the limitations and challenges of pursuing these tasks, including flux detection thresholds, data availability, background estimation, and methods for flux estimation. A multi-year period ranging from 2019 to 2024 is chosen to both assess seasonal variability and enhance flux estimation through temporal averaging.
How to cite: Fabregat, P., Hvidegaard, S., Stokholm, A., and Scheutz, C.: Limitations and challenges of using satellite remote sensing to estimate diffuse methane emissions at a national level in Denmark, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13220, https://doi.org/10.5194/egusphere-egu26-13220, 2026.
Cities emit methane (CH4) and have a role to play in mitigating the climate impacts of their emissions. Research suggests that CH4 emissions from most cities have large contributions from natural gas distribution and end use. In this work, we examine long-term measurements of CH4, CO, NOx, and VOCs from an urban air monitoring station in Calgary, Canada to resolve key contributors to CH4 enhancements. Using Positive Matrix Factorization (PMF), we identified four primary CH4 emissions source categories: natural gas – fugitives, natural gas – incomplete combustion, waste/biogenic, and petroleum product processing. Results from PMF modeling indicate that the bulk of CH4 emissions in Calgary are from natural gas fugitives and incomplete combustion (81% ± 35%). This is much higher than the proportion derived from available emissions inventories. The CH4 emissions from natural gas sources increase in winter and may be related to increased natural gas use for heating; results further supported by a land-use analysis. Emissions from waste/biogenic sources were the next largest contributor, which doubled in warmer months, consistent with temperature-driven microbial activity. Overall, these findings underscore the need for targeted mitigation strategies focused on the natural gas supply chain, while also highlighting the influence of seasonal dynamics on urban CH4 emissions.
How to cite: Xing, Z., Hugenholtz, C., Barchyn, T., Gough, T., and vollrath, C.: Source category attribution of urban methane emissions using Positive MatrixFactorization (PMF) with long-term trace gas measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5917, https://doi.org/10.5194/egusphere-egu26-5917, 2026.
Urban methane emissions represent a large yet poorly constrained component of national greenhouse gas budgets. Their characterization is challenging because emissions arise from a complex mix of known sources and a large population of small, spatially distributed, and often intermittent emitters embedded within dense and heterogeneous urban infrastructure. These diffuse emissions complicate measurement, source attribution, and scaling from individual components to the city scale. To address this challenge, we initiated the Calgary Urban Methane Emissions Measurement (CURMET) Testbed (www.curmet.ca), a major Canadian research initiative aimed at quantifying urban methane emissions across spatial scales and identifying the dominant contributors in a large Canadian city.
The CURMET Project integrates satellite, drone, vehicle-based, human-portable, and component-level measurements with new analytical and modeling approaches to constrain methane emissions across spatial and temporal scales. Satellite-based analyses using TROPOMI provide independent, top-down estimates of Calgary’s total methane emission rate, placing bounds on city-scale fluxes. Extensive vehicle-based surveys resolve methane enhancements from neighborhood to individual infrastructure scales, enabling source localization, attribution, and the identification of actionable emission hotspots. These surveys directly supported mitigation through the detection and subsequent abatement of several large fugitive sources during the project. Targeted measurements of sewers, natural gas meters, natural gas distribution facilities, landfills, and wastewater treatment plants further provide source-level emission estimates that inform prioritization and evaluation of mitigation efforts.
Key advances from CURMET demonstrate the effectiveness of vehicle-based monitoring for detecting and prioritizing urban methane sources, and the value of geochemical source disambiguation for separating dominant source categories. Early results from Calgary indicate that emissions from natural gas dominate the city’s methane budget, contrasting with research in other Canadian cities where landfills are estimated to be the dominant sources of methane emissions. Methodological developments include a human-portable flux plane technique for quantifying facility-scale emissions, and the deployment of robotic and e-bike–mounted systems to measure emissions in areas inaccessible to conventional vehicles or requiring enhanced maneuverability.
CURMET results create an empirical basis for urban methane mitigation by distinguishing persistent, episodic, and negligible sources across the city. By linking city-scale fluxes with source-resolved measurements, the project supports targeted mitigation actions, improved emissions inventories, and verification of mitigation effectiveness. These outcomes illustrate how integrated urban measurement programs can directly inform cost-effective methane reduction strategies and support municipal, provincial, and national climate policy.
How to cite: Hugenholtz, C., Barchyn, T., Clements, M., Gough, T., Xing, Z., Samuel, J., Butt-Vallieres, S., Vollrath, C., Wearmouth, C., Munn, A., and Bhangoo, E.: The Calgary Urban Methane Emissions Measurement Testbed (CURMET) Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8682, https://doi.org/10.5194/egusphere-egu26-8682, 2026.
Mobile, ground-based methane measurements play a critical role in detecting, locating, and quantifying urban emissions and are increasingly relied upon for mitigation tracking and inventory development. Most existing studies employ automobile-based platforms, which offer broad spatial coverage but constrain sampling speed, proximity to sources, and access to dense or traffic-restricted environments. These limitations introduce persistent uncertainties in source localization and plume interpretation, particularly in urban settings.
To address these challenges, we developed a bicycle-based methane measurement system that prioritizes transport-aware localization as a core measurement capability. The platform integrates an open-path methane sensor with high-accuracy GNSS positioning, and a sonic anemometer mounted directly on the platform. High-frequency (10 Hz) measurements are synchronized and fused in real time, enabling wind-resolved interpretation of methane enhancements within their spatial context. By operating at flexible travel velocities and leveraging the maneuverability of a bicycle, the system enables targeted sampling in narrow corridors, pedestrian zones, and other environments that are often inaccessible or impractical for automobile-based surveys.
Initial deployments in the City of Calgary, Alberta demonstrate the platform’s ability to detect, localize, and attribute methane emissions from a range of anthropogenic sources, including wastewater and other urban infrastructure, that are difficult to resolve using conventional mobile methods. Direct integration of high-fidelity wind measurements on the mobile platform provides critical transport context which can be used in real time to constrain source locations and improve plume-based quantification. Together, these results show that bicycle-based platforms equipped with integrated wind sensing can generate high-resolution methane datasets and represent an effective approach for improving urban methane mapping and emission attribution.
How to cite: Butt-Vallieres, S., Wearmouth, C., Hugenholtz, C., and Barchyn, T.: An e-Bike Measurement System for Urban Methane Emissions Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13466, https://doi.org/10.5194/egusphere-egu26-13466, 2026.
Agricultural methane emissions, from enteric fermentation and manure management, account for approximately 32% of global anthropogenic methane emissions, yet standardized farm- or herd-scale quantification methods remain lacking (Nisbet et al., 2025). Downwind mobile surveys using Gaussian plume modelling for point sources, primarily from oil and gas, and tracer dispersion methods for diffused sources, such as landfills, have been demonstrated to be effective, relatively low-uncertainty approaches for quantifying methane emissions.
Here, we present a computationally efficient framework for quantifying farm-scale methane emissions using Gaussian plume modelling, developed for small- to medium- scale farms in the UK, with ongoing work to extend the approach to grazing cattle. Using a Lagrangian particle model (Oettl & Kuntner, 2024), a virtual upwind point source is assigned to encompass the farm emission footprint (De Visscher, 2014). Gaussian dispersion modelling with Monte Carlo iterations is then applied to downwind vehicle-based methane measurements to derive farm-scale emission estimates.
These estimates are evaluated against results from a simultaneous controlled tracer release conducted by the National Physical Laboratory at the same farm site. We discuss associated uncertainties and highlight future improvements, including process automation, source apportionment, and methods for quantifying emissions from grazing herds.
References:
De Visscher, A. (2014). Air Dispersion Modeling: Foundations and Applications. Wiley. https://doi.org/10.1002/9781118723098
Dietmar Oettl & Markus Kuntner. (2024). GRAL: Graz Lagrangian Model (Version 24.11) [Computer software]. Graz University of Technology. https://gral.tugraz.at/
Nisbet, E. G., Manning, M. R., Lowry, D., Fisher, R. E., Lan, X. (Lindsay), Michel, S. E., France, J. L., Nisbet, R. E. R., Bakkaloglu, S., Leitner, S. M., Brooke, C., Röckmann, T., Allen, G., Denier van der Gon, H. A. C., Merbold, L., Scheutz, C., Woolley Maisch, C., Nisbet-Jones, P. B. R., Alshalan, A., … Dlugokencky, E. J. (2025). Practical paths towards quantifying and mitigating agricultural methane emissions. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 481(2309), 20240390. https://doi.org/10.1098/rspa.2024.0390
How to cite: LeVernois, M., France, J., Rafflin, V., Lowry, D., Yarrow, N., Shaw, J., Innocenti, F., Helmore, J., Lanoisellé, M., Al-Shalan, A., and Fisher, R.: Quantifying Farm-Scale Methane Emissions using Downwind Gaussian Plume Modelling with Tracer Correlation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9756, https://doi.org/10.5194/egusphere-egu26-9756, 2026.
BEEFTWIN is a UK Research and Innovation interdisciplinary project bringing methane emission data together with other parameters to form a digital twin of UK beef farming. Ultimately the project aims to identify ways to reduce greenhouse gas emissions from the beef farming sector whilst improving productivity, beef quality and animal welfare.
We are developing techniques to quantify grazing cattle emissions using mobile measurements of methane concentrations in transects downwind of cattle pastures, together with meteorological measurements and drone imagery to pinpoint locations of the cattle, followed by atmospheric dispersion modelling.
Measurements of methane stable isotopes and methane:carbon dioxide ratios are used to characterise predominant farm emission sources (eructation and manure). We are linking microbial measurements (relative species abundance and gene expression) in manure samples to the stable isotopic and methane:carbon dioxide ratios of manure emissions.
Through these measurements we are gaining a better understanding of the distribution and variability of methane emissions across beef farms and of how variability in methanogenic communities in manure affects emissions. These results will allow us to provide more insightful greenhouse gas emission estimates for farms employing different livestock management practices.
How to cite: Fisher, R., LeVernois, M., Lowry, D., France, J., Rafflin, V., Nisbet, E., Simpson, M., Evans, C., Gao, M., Manning, L., Maull, R., Gillani, F., Rashvand, M., and Ma, X.: Methane emissions from beef farming – in field measurements in the BEEFTWIN project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14423, https://doi.org/10.5194/egusphere-egu26-14423, 2026.
Fugitive methane (CH4) emissions from municipal solid waste landfills represent a significant and often underestimated source of greenhouse gases, particularly in complex sites with multiple cells at different operational stages. In this study, diffuse carbon dioxide (CO2) and CH4 emissions were investigated at the Tenerife municipal solid waste landfill (Canary Islands, Spain), combining ground-based flux measurements with thermal infrared imaging acquired by unmanned aerial vehicles (UAVs).
Diffuse gas fluxes were measured using the accumulation chamber method across more than 1,700 sampling points distributed over active, sealed and closed landfill cells. CH4 emissions were quantified both directly, using an in situ CH4 sensor, and indirectly, by estimating CH4 fluxes from measured CO2 fluxes and CH4/CO2 concentration ratios in the chamber headspace. In parallel, UAV-based thermal surveys were conducted to explore surface temperature patterns and their potential relationship with diffuse gas emissions and landfill cover characteristics.
Results show clear spatial variability in diffuse gas emissions linked to the operational status of the cells. Active and recently used cells exhibit higher and more spatially heterogeneous CH4 fluxes, while sealed and older cells are characterized by lower direct CH4 emissions but relatively higher indirect CH4 estimates. This discrepancy is attributed to CH4 oxidation and limited surface permeability, which reduce effective CH4 transfer to the atmosphere while allowing CO2 to diffuse more efficiently.
The combined use of direct and indirect flux measurements together with thermal imaging provides complementary insights into landfill gas dynamics, allowing differentiation between effective atmospheric emissions and subsurface CH4 presence. This integrated approach improves the characterization of fugitive emissions and supports the assessment of landfill gas management efficiency.
How to cite: Fuentes-Domínguez, P., Asensio-Ramos, M., Vidaña-Glauser, A. E., García-Luis, P., García-Báez, J., González-Torres, S., Ortega-Ramos, V., de los Ríos-Díaz, H., M. Pérez, N., Padrón, Ó., V. Melián, G., and Hernández, P. A.: Thermal imaging and fugitive CH4 emissions at Tenerife’s municipal solid waste landfill , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10767, https://doi.org/10.5194/egusphere-egu26-10767, 2026.
In the UK, the waste sector accounted for approximately 31.7% of national CH4 emissions in 2023 (National Atmospheric Emissions Inventory – NAEI 2023), with landfills contributing for nearly 80% of these sectoral emissions, or ≈ 25.4% of national CH4 emissions. This reality, combined with marked spatial and temporal variability in surface fluxes, requires rigorous measurement protocols and explicit quantification of uncertainties.
As part of the MOMENTUM and DEFRA-funded programmes, mobile measurement campaigns were carried out for multiple active landfill cells. Downwind of each cell, ≥10 road transects were completed, with each group of transects run at a constant vehicle speed (typically 20–60 km h⁻¹), using an instrumented mobile laboratory (Toyota RAV4 hybrid) measuring CH4, CO2, C2H6 and δ13C-CH4 with cavity-enhanced analysers; acquisition protocols were harmonised to maximise comparability.
Flux quantification applies two established methods to the same downwind datasets: (i) Gaussian plume dispersion modelling with Monte-Carlo uncertainty propagation to produce probabilistic emission estimates; and (ii) a tracer-dispersion method using controlled releases of ethane (C2H6) during transects, with CH4 emissions estimated from integrated C2H6/ CH4 plume ratios. Survey results are employed illustratively to explore how external factors, meteorological inputs, atmospheric stability conditions, downwind distance from emission points, tracer placement and measurement routing can influence method outputs and uncertainty characterisation.
The objective of this work was to develop and validate a reproducible Python-based post-processing routine for mobile surveys downwind of landfill cells, implemented as a unified workflow that enables the consistent and traceable application of Gaussian-plume dispersion modelling (with uncertainty propagation) and tracer-release quantification methods to identical downwind datasets. The workflow standardises data ingestion and time synchronisation, quality filtering, baseline treatment, plume and peak selection, and execution of both calculation routes, producing comparable methane flux estimates with associated uncertainty characterisation.
Applied across multiple landfills, this common processing environment enables systematic, like-for-like method evaluation and targeted sensitivity analyses, linking variability in estimated fluxes to meteorological conditions and sampling configuration. It facilitates identification of dominant sources of uncertainty and highlights methodological choices that would benefit from standardisation, providing a robust basis for harmonised mobile CH4 quantification and the development of best-practice guidance for inventories and mitigation planning.
How to cite: Rafflin, V., France, J., Lowry, D., Fisher, R., Alshalan, A., Howes, N., Nguyen, L., and Shaw, J.: A unified Python workflow for mobile downwind quantification of methane emissions from active landfill cells: implementing Gaussian and tracer-release methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22603, https://doi.org/10.5194/egusphere-egu26-22603, 2026.
Methane’s comparatively short atmospheric lifetime and low mitigation costs compared to other greenhouse gases enhance its potential for near-term climate action. Both mitigation accounting and climate science require accurate emission inventories. Most emission factors and model parameters have been derived from measurements in and at the conditions of developed countries. On the contrary, Global South methane emission measurements are scarce and usually unsystematic. As a result, large discrepancies exist among global databases and with national inventories, e.g., -60% to +180% in the case of Colombia. Under the coordination and support of UNEP’s International Methane Emissions Observatory (IMEO), a multi-sector observation-based baseline inventory is currently being developed for Colombia, involving multiple research groups and measurement platforms and methodologies. Methane emissions from solid waste landfills (SWLF) are the fastest growing in Colombia. We will present preliminary results from an ongoing SWLF and wastewater treatment plant (WWTP) emission measurement campaign (MET-CO). To build accurate observational inventories, we applied a “mixed approach”, which involves measuring the larger-emission SWLFs and WWTPs to about half of the total emissions along with a set of smaller emission facilities that properly map the emission controlling variables. MET-CO includes sniffing with a drone for diffuse emission mapping, flux chamber and channeled biogas mass flow measurements. Carleton University’s Energy & Emissions Research Lab (EERL) will conduct high precision drone-borne measurements for facility and sub-facility wide top-down emission estimation. Chamber-measured methane fluxes of SWLF cells closed over 10 years ago have been very small, from -1.3 (very small sink) to +2.3 mg CH4 m-2 day-1, while a recently covered cell showed very high emissions, +153.5 g CH4 m-2 day-1. Methane enhancements have ranged from ~0.1 to ~17 ppmv in SWLFs, and from ~0.1 to ~600 ppmv in WWTPs, with the larger near sources and enclosed operations. Additional results and a synthesis will be present.
How to cite: Jimenez, R., Ardila, A. V., Morales-Rincon, L. A., Vargas-Burbano, A. C., France, J. L., Velandia, N., Baranski, M. R., Calcan, A., and Abichou, T.: Towards a high-granularity methane emissions inventory for Colombia – First in situ measurements of solid waste landfills and wastewater treatment plants emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15950, https://doi.org/10.5194/egusphere-egu26-15950, 2026.
Landfills represent a great opportunity to reduce anthropogenic methane (CH4) emissions in the near future. Although they accounted for 19% of global anthropogenic CH4 emissions in 2020 (Saunois et al., 2024), emissions mitigation strategies, such as landfill gas capture or microbial methane oxidation systems, have been well documented and successfully implemented in the past. However, accurate monitoring methods are required to assess the efficiency of such mitigation efforts. Currently, the IPCC recommends the use of first-order decay (FOD) models to estimate landfill CH4 generation potentials (IPCC, 2006). Such models remain highly uncertain due to large uncertainties in the model inputs (e.g., waste amounts or waste compositions) and/or model parameters (e.g., waste carbon contents, decay constants, biochemical CH4 potential, oxidation potential of landfill cover) (Rasouli et al., 2025, Wang et al., 2024, Mou et al., 2015). Therefore, estimating the CH4 generation potential of a landfill using direct in-situ measurements is preferred. Combining measurements of CH4 emissions, landfill gas collection and CH4 oxidation allows for more accurate estimates of the landfill CH4 generation potential and CH4 recovery efficiency. Although many studies have measured CH4 emissions using a wide range of in-situ methods (e.g., static flux chamber, atmospheric inversion modelling or tracer dispersion), few have also quantified the oxidation capacity of the landfill cover soils (Agdham et al., 2018; Abichou et al., 2006; Chanton et al., 2009). In this study, we performed a CH4 mass balance to estimate the CH4 generation potential at a landfill in Madrid, Spain receiving more than one million tons of household waste yearly. We collected 76 stable isotope samples and over 200 tracer dispersion measurements over the course of two campaigns in May and October 2025. Combining our measurements with landfill gas collection data, we estimated the landfill CH4 generation potential and CH4 recovery efficiency. Additionally, we compared our estimate to the modelled CH4 generation potential using different FOD models (e.g., IPCC FOD, Afvalzorg, LandGEM, GasSim). Leveraging the strength of direct in-situ measurements, this study provides valuable insights into CH4production at landfill sites and how to further enhance CH4 mitigation.
How to cite: Klotz, L. A., Fredenslund, A. M., Menoud, M., Lowry, D., and Scheutz, C.: Methane mass balance of a Spanish landfill, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11472, https://doi.org/10.5194/egusphere-egu26-11472, 2026.
Methane (CH4) emissions from the waste sector represent a substantial and addressable component of global greenhouse gas emissions, accounting for around 20% of total anthropogenic methane and ranking third after agriculture and fossil fuels. In Europe, landfills alone contribute approximately 30% of anthropogenic CH4 emissions, drawing increasing attention due to CH4’s high global warming potential and the relative feasibility and cost-effectiveness of mitigation measures in this sector. Accurate quantification of fugitive landfill methane is therefore critical, both for greenhouse gas mitigation and for evaluating the performance of gas recovery systems and bio-covers.
Recent advances in atmospheric methane measurement techniques have enabled high-resolution, in situ observations using mobile and aerial platforms. In this work, we present an integrated dual-platform approach that combines car-based mobile measurements with unmanned aerial vehicle (UAV) observations to improve the characterization of landfill methane emissions. By merging ground-level and aerial perspectives, this approach enhances spatial coverage and provides three-dimensional insight into plume behaviour, particularly in complex terrains where single-platform methods are often insufficient.
We demonstrate the methodology at the Kotsiatis landfill in Cyprus, a closed municipal waste site currently undergoing environmental rehabilitation. Three measurement campaigns conducted in 2025 captured methane emissions at different stages of post-closure works. During the December 2025 campaign, partner teams from the IM4CA project also participated, deploying two UAV teams alongside three instruments on the mobile platform.
We examine how differences in monitoring platforms, meteorological conditions (including wind, atmospheric pressure, and temperature), stage of post-closure works and flux estimation methodologies influence methane quantification results. This analysis provides critical insight into the strengths and limitations of mobile- and UAV-based approaches for landfill methane emission assessment and supports their effective application in complex real-world settings.
How to cite: Papaconstantinou, R., Quéhé, P.-Y., Geissbühler, D., Jagoda, P., Kanaan, R., Meinen, R., Velandia Salinas, N., Bartyzel, J., Strzelecki, K., Iancu, S., Nęcki, J., Team, U. S. R. L. (., Röckmann, T., Sciare, J., and Paris, J.-D.: Concurrent Mobile–Aerial Monitoring of Landfill Methane Emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13620, https://doi.org/10.5194/egusphere-egu26-13620, 2026.
Ecologically critical regions such as wetlands, coastal beaches, and high-latitude ecosystems play a indispensable role in the global methane (CH₄) budget. However, limited power availability in these environments constrains long-term CH₄ flux observations. As a result, methane eddy covariance (EC) measurements increasingly rely on low-power consumption, highly integrated open-path analyzers. Unlike closed-path systems that measure dry mixing ratios, open-path sensors measure gas density, making EC flux calculations susceptible to spectroscopic effects and density perturbations induced by fluctuations in air temperature and humidity. These effects necessitate complex post-processing corrections and substantially complicate uncertainty quantification.
Here we present a novel open-path CH₄/H₂O analyzer (HT8600P, HealthyPhoton Co., Ltd.) together with a minimally corrective flux calculation framework. Through an innovative instrument design, we establish a pseudo dry mixing ratio formulation that enables point-by-point conversion from density to mixing ratio without relying on spatially separated temperature or water vapor measurements. This allows EC fluxes to be calculated in a manner analogous to closed-path systems, while preserving the logistical advantages of open-path deployment.
Dedicated field experiments, including a zero-flux test, demonstrate that the proposed approach yields near-zero methane fluxes with a random error of 0.057 mg m⁻² h⁻¹. The magnitude of required corrections is an order of magnitude smaller than that of a co-located commercial open-path analyzer. We further identify a “phantom” random error inherent to conventional density-based EC methods, whereby temperature- and humidity-driven fluctuations are misinterpreted as turbulent variance, leading to substantial overestimation of random uncertainty. By removing these artifacts at the signal level, the pseudo dry mixing ratio method reduces apparent random errors by 60–70%, producing uncertainty estimates consistent with the empirically determined noise floor.
Together, the HT8600P analyzer and the optimized pseudo dry mixing ratio EC framework provide a correction-light, noise-resilient solution for expanding long-term CH₄ flux observations in remote regions critical to the global methane budget.
How to cite: Shen, W., Pan, D., Wang, K., Lin, T.-J., Zeng, J., Liu, Z., and Wang, Y.: Towards Correction-Free Open-Path Eddy Covariance Methane Flux Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16573, https://doi.org/10.5194/egusphere-egu26-16573, 2026.
The global concentration of methane (CH4) in the atmosphere has more than doubled since the pre-industrial era and accounts for roughly one-third of current global warming. Because of its short lifetime, reducing anthropogenic CH4 emissions can effectively lower its atmospheric levels and ease its climate impact. Accurately quantifying CH4 emissions remains crucial for informing decision-making and mitigation strategies to lower those emissions.
One of the key objectives of the EU-funded research project IM4CA “Investigating Methane for Climate Actions” is to quantify the anthropogenic CH4 emissions at the site level in Romania as a post-monitoring campaign following ROMEO campaign of MEMO2 Project. To achieve this, a variety of top-down CH4 measurement approaches are to be implemented. These approaches use ambient CH4 mole fraction measurements from sensors in vehicles, drones, aircrafts or tall towers combined with models to estimate total CH4 flux rates at source of different scales. Intercomparing and harmonizing these CH4 measurement methods are essential to accurately quantify CH4 emissions in the context of a large-scale campaign.
Here, we aim to compare car and drone-based measurements prior to their field deployment in IM4CA, using a CH4-controlled release experiment. This experiment was conducted around the Unmanned System Research Laboratory (USRL) airstrip in Orounda, Cyprus, where CH4 was measured simultaneously using infrared spectrometers mounted on drone platforms and cars. To better account for any variability related to the measurement tools, three different drone systems were used, each equipped with one CH4 gas analyzer, and three additional analyzers were mounted in the same car. The quantified CH4 emissions using either mass balance or Gaussian plume model are compared from both measurement platforms and their possible joint use in the field for complete plume characterization is discussed.
The results from this experiment will enhance the accuracy of reported CH4 fluxes and pave the way for harmonized measurement approaches, particularly for intensive large-scale monitoring campaigns within the IM4CA project and other similar third-party measurement initiatives.
How to cite: Kanaan, R., Paris, J.-D., Geissbühler, D., Bartyzel, J., Nęcki, J., Sciare, J., Strzelecki, K., Velandia Salinas, N., Jagoda, P., Quéhé, P.-Y., Papaconstantinou, R., Meinen, R., Iancu, S., and Röckmann, T.: Harmonization of Mobile Methane Measurement Methods for IM4CA Project Based on a Controlled Release Experiment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13302, https://doi.org/10.5194/egusphere-egu26-13302, 2026.
Methane (CH4) is a key driver of near-term climate warming, and rapid mitigation requires robust, independent, and spatially resolved quantification of emissions, including their temporal variability and intermittency. Within the IM4CA (Investigating Methane for Climate Action) project, Unmanned Aerial Vehicles (UAVs) are being developed as flexible platforms to support emission monitoring and verification at the scale of individual sources. However, differences in instrumentation, flight strategies, and emission quantification methodologies can lead to substantial variability in derived flux estimates across teams and campaigns.
To address this challenge, a dedicated UAV intercomparison campaign was conducted in December 2025 at the Unmanned System Research Laboratory (USRL), near Orounda, Cyprus. Over five days, four research teams: Utrecht University (UU), The Cyprus Institute (CyI), AGH University Krakow (AGH), and the National Institute for Aerospace Research Elie Carafoli (INCAS), performed coordinated UAV measurement flights at a common site, targeting a controlled CH4 release with known emission rates.
The teams operated with differing levels of platform independence: UU and CyI flew their instruments on their own UAVs, AGH deployed their sensor both on their own platform and on a CyI UAV, while INCAS operated their CH4 sensor exclusively on a CyI platform. Meteorological data were collected using ground-based stations operated by CyI and AGH around the runway, as well as onboard measurements from the UU UAV. Controlled methane releases were designed to allow each team to sample all release rates under comparable environmental conditions. The emission rates, ranging from 0 to 25 kg h-1, were known by the release operator but disclosed to the teams only after the campaign, ensuring an unbiased intercomparison.
In this contribution, we first describe and compare the experimental setups of the participating teams, including sensor technologies, UAV platforms, and flight strategies. We then present and intercompare the CH4 flux estimates derived from each system for identical release periods, focusing on accuracy relative to the known CH4 flux, their internal consistency, and sensitivity to environmental conditions, such as wind speed and atmospheric stability. Differences arising from flight patterns, data processing choices, background determination and flux estimation methodologies are examined. The results will provide critical insight into the strengths and limitations of the UAV-based methane quantification approaches used in the context of the IM4CA project, and support robust results in future project-wide campaigns.
How to cite: Geissbühler, D., Papaconstantinou, R., Quéhé, P.-Y., Jagoda, P., Kanaan, R., Meinen, R., Velandia Salinas, N., Bartyzel, J., Strzelecki, K., Iancu, S., Nęcki, J., Sciare, J., Paris, J.-D., and Röckmann, T.: UAV-based CH4 flux estimation intercomparison using controlled releases within the IM4CA project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13792, https://doi.org/10.5194/egusphere-egu26-13792, 2026.
Methane (CH4) is a key short-lived climate forcer, making accurate emission quantification essential for climate mitigation strategies. Methods to estimate CH4 emissions from sources such as landfills, wetlands, agriculture, and the oil and gas sector using atmospheric concentration measurements are available and relatively well established, yet their practical implementation remains challenging and they lack standardization. Emission estimates are sensitive to numerous factors, including measurement techniques (e.g. differential absorption lidar, optical gas imaging, diode laser absorption spectroscopy), measurement platforms (e.g. satellite, aircraft, drone, vehicle, ground-based station, handheld system), sites size and complexity, meteorological conditions, signal processing and quantifications approach (e.g. gaussian plume or mass-balance).
To test and validate methods aiming at small and medium leak-like emissions under realistic conditions, the Cyprus Institute (CyI) developed an open-air controlled-release site capable of generating CH4 emissions from 0 to 25 kg h-1, spanning a wide range of real-world emission scenarios. It is located at the Unmanned Systems Research Laboratory (USRL) airfield of CyI, on the Orounda plateau approximately 40 km from Nicosia, Cyprus. The facility includes two runways (200 m × 12 m and 90 m × 6 m) and is surrounded by flat access roads, providing a versatile environment for drone-based and vehicle-mounted measurements. The controlled-release system consists of two units enabling the distribution and precise flow control of high-purity CH4 (99.5%), connected to a dedicated 180 m-long hose (1-inch inner diameter). The gas outlet (open-ended release) is located at a height of 3.2 m above ground level. Multi-level wind measurements are provided by three wind sensors installed at heights of 13 m, 8 m, and 2.5 m.
We present this controlled release system, its operations, and associated uncertainties in flow rates. It has been first used during the IM4CA (Investigating Methane for Climate Action) campaign, intercomparing UAV-based and car-based in-situ methane quantification techniques from 8 to 11 December 2025. As USRL is a National Facility (in the framework of ACTRIS), it can be accessed on a Transnational Access (TNA) basis by a large number of users and can provide access to a large and diverse fleet of fixed and rotary-wing UAS.
How to cite: Quéhé, P.-Y., Papaconstantinou, R., Paris, J.-D., Sciare, J., and Team, U. S. R. L. (.: A methane controlled release system for small and intermediate emission quantification with associated drone airspace, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13485, https://doi.org/10.5194/egusphere-egu26-13485, 2026.
Ground-based sensors are essential for monitoring methane emissions. Among the available technologies, remote sensing systems based on Fourier-transform infrared (FTIR) spectroscopy offer a versatile approach to detect and quantify methane emissions from a distance - without the need for direct sampling at the emission source.
Imaging systems equipped with scanning units or focal plane array (FPA) detectors can even generate real-time chemical images of methane plumes overlaid on video footage of the scene. Such systems provide an intuitive visualization of the source of the methane gas clouds.
When equipped with quantification capabilities, these remote sensing systems deliver column-averaged methane concentrations across the observed plume, whether near the ground or throughout the atmospheric column. Since satellite instruments also measure column-averaged concentrations, ground-based FTIR systems are particularly well-suited for satellite data validation.
This presentation will introduce the working principles of various ground-based FTIR remote sensing systems and highlight application examples, including methane measurements at coal mining sites.
How to cite: Wu, X.: Ground-based FTIR remote sensing systems for identification, visualization, and quantification of methane, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10647, https://doi.org/10.5194/egusphere-egu26-10647, 2026.
This study presents top-down estimates of coal-related methane emissions in Southeast Asia derived from a multi-model ensemble within the Methane Inversion Inter-Comparison for Asia (MICA) project. Using seven atmospheric inversion systems, we applied a standardized protocol featuring consistent prior emission inventories and a comprehensive suite of atmospheric constraints. These observations integrate GOSAT satellite retrievals, the NOAA ObsPack CH₄ dataset, and additional in situ measurements from Asian monitoring sites. Monthly sectoral emission fluxes from both in-situ-based and GOSAT-based inversion simulations were aggregated to characterize regional and national-level contributions and trends.
Total regional coal-related methane emissions are estimated at 7.90 Tg yr⁻¹ (range: 5.12–9.21 Tg yr⁻¹; median, min-max) for 2019-2021, with Indonesia identified as the dominant source, contributing 7.10 Tg yr⁻¹ (4.50–8.29 Tg yr⁻¹). Indonesia accounts for approximately 90 % of coal production in the region and remains a major global exporter, followed by Vietnam as the second-largest producer and consumer. In Indonesia, coal-related emissions exhibit a statistically significant increasing trend based on the Mann–Kendall trend test (p < 0.05), with mean posterior emissions rising nearly fourfold from 2.02 to 8.47 Tg yr⁻¹ between 2010 and 2021. Notably, Indonesia’s most recent National Greenhouse Gas Inventory (NGHGI) reports energy-sector (including coal) methane emissions of 0.784 Tg yr⁻¹ for 2019 , nearly an order of magnitude lower than our estimates. Emissions from Vietnam are estimated at 0.66 Tg yr⁻¹ (0.47–0.74 Tg yr⁻¹) for 2019-2021; while no significant trend was detected over the full study period, a statistically significant increase was observed during 2017–2021.
The rapid growth of coal-related methane emissions poses a critical challenge to Southeast Asia’s climate targets and decarbonization pathways. Our fingdings reveal a substantial discrepancy between top-down estimates and national inventories, identifying a vital opportunity for high-impact mitigation. Prioritizing the recovery of coal mine methane (CMM) is therefore essential; it transforms a significant environmental liability into a valuable energy resource while simultaneously enhancing operational safety. Given the nearly fourfold increase in emissions detected since 2010, aggressive mitigation of the coal sector is imperative if regional climate commitments are to be achieved.
How to cite: Wang, F., Maksyutov, S., Janardanan, R., Belikov, D. A., Patra, P. K., Liang, R., Zhang, Y., Ren, G., Lin, H., Montenegro, N., Berchet, A., Saunois, M., Martinez, A., Hyvärinen, S., Tsuruta, A., Kenea, S. T., Saeki, T., and Matsunaga, T.: Increasing Coal-Related Methane Emissions in Southeast Asia During 2010–2021: A Multi-Model Inverse Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6047, https://doi.org/10.5194/egusphere-egu26-6047, 2026.
Coal mining represents a predominant source of anthropogenic methane emissions, with China’s approximately 4,000 active mines driving a significant portion of global output from this sector. Accurately quantifying these emissions is therefore critical both for global climate mitigation and for informing targeted environmental governance in China’s key coal-producing regions.
In China, underground gas monitoring systems have long been deployed in coal mines for safety purposes, providing valuable baseline data for emission accounting. However, monitoring capabilities vary widely across mines: most track only ventilation systems, while a smaller number also collect data from gas extraction stations. Additionally, while some mines maintain long-term continuous monitoring records, others can supply data for only a few months. As a result, existing underground observations do not fully or consistently reflect the overall methane emissions from China’s coal mining sector.
Satellite observations offer an emerging technological approach for monitoring coal mine methane emissions. Instruments such as S5P/TROPOMI have been preliminarily applied to quantify emissions in coal-intensive regions of China, while point-source satellites like GF5, EMIT, and PRISMA have successfully identified distinct methane plumes from so-called “super-emitter” mines. Nevertheless, limitations in spatial resolution (e.g., from S5P/TROPOMI) and spectral resolution (from point-source satellites) constrain the ability of current satellite technology to support policy-relevant and management-level monitoring. To overcome these constraints, satellite-based retrievals must be integrated with and calibrated by underground and ground-based observational data.
Embracing a Satellite-Surface-Underground synergy research framework, the "Remote Sensing of Carbon Emissions and Air Quality" team at China University of Mining and Technology has conducted field observational experiments at over ten coal mines across China. These efforts have improved existing remote sensing methods for methane emissions. The team has developed China’s most comprehensive, manually verified geographic information database of coal mine emission facilities to date and established a high-resolution methane emission database for the coal mining industry in typical regions. This report will systematically present the team’s latest research progress in the aforementioned areas and outline plans for future work.
How to cite: Qin, K., Wei, H., Bo, Z., Chenjun, D., Xun, T., and Cohen, J.: Integrated Estimation of Coal Mine Methane Emissions in China Using Satellite, Surface, and Underground Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16964, https://doi.org/10.5194/egusphere-egu26-16964, 2026.
Coal mining is a significant human-induced source of atmospheric methane (CH4) on a global scale, contributing notably to national emissions in coal-producing countries such as Australia. Australian mining operators use tiered, bottom-up methods aligned with IPCC guidelines and implemented under the National Greenhouse and Energy Reporting (NGER) scheme to estimate emissions from underground and surface mines. However, underground mine emissions have not been systematically validated by comparison with top-down atmospheric measurements, and surface mine emission factors lack empirical support. Studies have revealed significant discrepancies between top-down and bottom-up estimates at investigated surface mines, prompting concerns about the effectiveness of the current regulatory methods.
In 2023, two independent airborne measurement strategies were used simultaneously by deploying two aircraft to quantify CH4 emission rates from coal mining in the Bowen Basin (Queensland, Australia) as part of the United Nations Environment Programme’s (UNEP) International Methane Emissions Observatory (IMEO) study. A total of 53 emission rate quantifications for 16 coal mines were achieved from the measurements collected within a 31-day campaign. Comparing these estimates with operator-based estimates from underground mines revealed no significant bias at the facility- and aggregated-level, with operator estimates being well within the uncertainties of the airborne estimates. However, a comparison with surface mines showed significant biases at both the facility- and aggregated-level, exceeding the uncertainties of the airborne estimates.
Globally, these results add to growing evidence that Tier 3 approaches based on direct measurements are suitable for estimating fugitive CH₄ emission rates from underground mines. By contrast, the results from surface mines suggests that IPCC Tier 2 and 3 inventory methods (i.e. use of emission factors and potentially coal gas distribution models) for surface mining require careful implementation and independent verification.
This poster will present and discuss the results of the measurements taken in the Bowen Basin.
How to cite: Krautwurst, S., Harris, S. J., Hacker, J., Lunt, M., and Borchardt, J. and the BBCMap23 Team: Evaluation of coal mine methane inventory methods using aircraft-based platforms in the Bowen Basin, Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10540, https://doi.org/10.5194/egusphere-egu26-10540, 2026.
Methane is a potent greenhouse gas and a major contributor to global warming. One of the main anthropogenic contributors to these emissions is the coal sector, which emits most of its methane through ventilation and degasification systems, such as drainage stations in underground mines. In this context, the UNEP’s Methane Alert Response System (MARS) and the Steel Methane Programme (SMP), managed by the International Methane Emissions Observatory (IMEO), have made systematic efforts to identify large emissions associated with coal mines using high-resolution hyperspectral satellites such as EMIT, EnMAP, and PRISMA. These efforts focus particularly on metallurgical coal mines globally, with an emphasis on producing countries such as Kazakhstan, the United States, Poland, Czechia, Russia, Australia, and China, among others.
China is not only one of the world's largest coal producers but also one of the main emitters of methane associated with this activity. This study analyzes emissions and their potential sources from underground mines processing metallurgical, thermal, and mixed coal. The analysis is conducted within the IMEO MARS framework, using spatial information provided by the Global Energy Monitor (GEM) database to identify mine boundaries and potential emission point sources. Based on this information, monitoring areas are defined and integrated into the system, allowing for the acquisition of historical images intersecting these areas and subsequent analysis.
In this study we analyzed the emissions of 94 underground mines distributed across 12 provinces in China. To this end, we processed 600 hyperspectral satellite images acquired between February 2020 and January 2026, applying the wide-window matched filter methodology for the retrieval of the methane concentrations, which is suitable for this study due the heterogeneous environments. Afterwards, through visual inspection, we identified over 700 plumes that were attributed to 150 different emission sources. Among the different sources, we found that 60% of the plumes come from venting shafts, 36% from drainage stations, and 4% from other types of coal facility. Based on this data, we aim to support large scale mine level emission assessment and source specific attribution within the IMEO MARS framework, contributing to improved prioritization of mitigation actions in the coal sector.
How to cite: Castaneda Martinez, C., Irakulis-Loitxate, I., Montesino-SanMartín, M., Raunak, A., Mateo-García, G., Johnson, J. E., Kasprzak, M., and Van Niekerk, L.: Satellite-based detections and source attribution of methane emissions from underground mines in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12195, https://doi.org/10.5194/egusphere-egu26-12195, 2026.
Methane emissions from coalmine shafts contribute significantly to anthropogenic greenhouse gas emissions to the atmosphere. Strategies to quantify and monitor these emissions include remote sensing (using aircraft and satellite imagers) and in-situ measurements (aircraft and UAV measurement campaigns). Each technique offers distinct advantages and limitations. However, quantifying the efficacy and the uncertainties of measurement techniques remains challenging. Here, we use a Large Eddy Simulations (LES) model called Fire Dynamics Simulations (FDS), which can model methane plumes at high-resolutions (<1m). To validate the LES model, we used plumes measured by a HySpex instrument placed approximately 1 km from the Pniowek V coal mine in Poland, next to a Doppler LiDAR instrument able to measure the wind profile.
FDS simulates high-fidelity CH4 plumes compared to the observations, including the angle at the release, the concentration values, and the height of the plume at various distances from the source. Based on our validation, we simulated high-resolution tracks for in-situ instruments (UAV), which measure the near-field of the CH4 plume, and also plume images at a slightly lower resolution (5-30 m) for satellite and aircraft imagers, which measure long-distance plumes. Methane plumes correspond to various velocity values of releases and mine’s air concentrations, under various environmental conditions (mean wind speed, air temperature, relative humidity) to construct an ensemble of simulated experiments. We conclude this study by comparing the effectiveness of each individual method in terms of emissions uncertainties, aiming at monitoring CH4 emissions from the ventilation shafts of deep coal mines.
How to cite: Yuvaraj, R., Knapp, M., Abdallah, C., Butz, A., Gałkowski, M., Fix, A., Swolkien, J., and Lauvaux, T.: Uncertainties in quantifying coal mine shaft CH4 emissions from in-situ and remote sensing instruments using high-resolution plume modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21001, https://doi.org/10.5194/egusphere-egu26-21001, 2026.
Methane (CH4) is an important but often overlooked greenhouse gas contributing to climate change as a short term climate forcer. Coal remains the largest source of CH4 emissions in the EU energy sector. Accurate attribution of methane emissions to responsible coal mining infrastructure is critical under the EU Methane Regulation (EU-MER), which entered into force in 2025. The EU-MER prohibits routine operational venting of CH4 from coal mine drainage systems, requiring capture or flaring with a minimum destruction efficiency of 99%. Despite the existence of methane regulation, robust methodologies for accurately attributing spaceborne detected methane emissions to coal mine facilities remain insufficient.
This study presents a satellite-based approach for attributing methane emissions to coal mine facilities in Poland, with a focus on drainage stations. We utilised high resolution methane plume observations from high resolution point source imagers acquired between January and November 2025 together with coal mine infrastructure data.
The attribution methodology incorporates spatial proximity analysis based on geolocation accuracy with atmospheric transport data (wind speed and direction) at the time of acquisition to determine the most plausible facility responsible for the methane emissions. A qualitative confidence level was assigned to each attribution considering local knowledge on emission patterns, plume morphology and proximity to other mining infrastructure. The results show that 12 methane emission events were captured and attributed to drainage systems in Poland, of which 8 were classified as being attributed with high-confidence. 5 out of 22 investigated drainage systems seemed to vent methane with an average emission flux of approximately 1200 kg/h.
This research demonstrates that reliable compliance monitoring under emerging methane regulations is technically feasible by combining high resolution satellite observations with coal mine facility data. Moreover, the integration of meteorological information and local, expert knowledge substantially improves attribution confidence, demonstrating the value of hybrid quantitative approaches for effective policy enforcement and methane regulation compliance monitoring.
How to cite: Liepa, A., Assan, S., Horner, R., and Necki, J.: Monitoring Compliance of EU Methane Regulation using High-Resolution Satellite Observations: a case study in Poland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6803, https://doi.org/10.5194/egusphere-egu26-6803, 2026.
Abandoned oil and gas wells can be significant sources of methane emissions into the atmosphere. However, the extent of these potential emissions is often unclear, and mitigation requires detailed knowledge of the location of emitters. In Germany, the Federal Institute for Geosciences and Natural Resources has investigated a selection of wells of the in total ~25.000 onshore oil and gas wells for the first time. One challenge in investigating these wells is the mandatory decommissioning process, i.e. plugging, cutting and burying. For such soil buried wells, a measurement strategy was developed in which emissions were measured in an area of 30 x 30 m around the well and, for comparison, in a nearby reference area to record the natural background (Jordan et al., 2025). Between 2022 and 2025, nearly 90 wells of varying ages were investigated in northern Germany, where most of Germany's current and historical oil and gas production has taken place. In addition to investigating potential methane and CO2 emissions, soil gas compositions and stable carbon isotopes (and occasionally also hydrogen isotopes) in the soil gas methane were measured at each study site. In analogy to the reference areas and as is typical for forest, arable, and meadow soils, most well sites acted as methane sinks. Abnormalities compared to the reference areas were only determined at a few wells. For instance, a maximum of ~40 mg h-1 methane emissions were detected at one well, where small amounts of crude oil also appear to be escaping. However, covering the wells with soil offer an advantage here, as the gas and oil composition geochemically indicates a strong and depth-increasing influence of hydrocarbon-degrading microorganisms (Blumenberg et al., 2025). Final evaluations have not yet been completed, but for Germany, our results indicate that methane emissions from old oil and gas wells are relatively low. Open questions that are currently being addressed include the temporal variability of methane emissions, but also the importance of e.g., seasonal factors on the effectiveness of the microbial filter.
How to cite: Blumenberg, M., Jordan, S., Krüger, M., and Schloemer, S.: Methane measurements at 90 abandoned, cut & buried onshore wells in Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2697, https://doi.org/10.5194/egusphere-egu26-2697, 2026.
Non-producing oil and gas wells are a poorly quantified source of anthropogenic methane emissions worldwide, posing a significant risk to the environment and contributing to climate change. In Argentina, a country with a long history of oil and gas production, methane emissions from non-producing wells remain largely uncharacterized. Here, we combine a national well database analysis with ground-based methane measurements to assess emissions from non-producing oil and gas wells across Argentina. By analyzing well databases from the government, we found 85,260 wells in Argentina, of which 53,292 (62%) are non-producing, with the largest number of wells in the provinces of Santa Cruz, Chubut and Neuquén. We analyze key well attributes, including well depth, well type (e.g., oil and gas), location, well age and well abandonment date. These attributes are essential to perform a spatial analysis and identify the regions in Argentina that should be prioritized for field measurements.
We conducted ground-based methane flow rates measurements at 75 non-producing oil and gas wells in Chubut province. Unplugged wells exhibited the highest emissions, with a maximum measured methane flow rate of 41g/h. We further analyzed the influence of categorical well attributes, such as operator, well status, plugging status, well type, and lift system on measured methane emissions, identifying attributes associated with higher or lower emission rates.
Using the field measurement results combined with the national well database, we provide an estimate of methane emissions from non-producing oil and gas wells in Argentina. Overall, our findings contribute to improving the characterization of existing non-producing oil and gas wells, to including methane emission estimates in Argentina’s national greenhouse gas inventory, and to identifying the regions that should be prioritized for continued monitoring of methane emissions.
How to cite: González Sánchez, M., Carreras, F., Calcan, A., France, J. L., and Kang, M.: Methane emissions from non-producing oil and gas wells in Argentina, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12765, https://doi.org/10.5194/egusphere-egu26-12765, 2026.
Non-producing oil and gas wells are a source of fugitive methane emissions. In Canada, methane regulations for the oil and gas sector are pushing the industry to achieve a 72% reduction in methane emissions by 2030 compared to 2012 levels. To reach this goal, effective monitoring and remediation of non-producing wells is important, as there are >400,000 non-producing wells in the country and they account for 13% of methane emissions from the oil and gas sector. Orphan wells are a subset of non-producing wells that have no responsible party, as such the cost of clean-up falls on the public. Thus far, there have been no quantification studies on methane emissions from orphan wells in Canada, leading to knowledge gaps for policy development and contributes to uncertainties in methane emissions from the broader category of non-producing wells.
To fill this gap, we performed an orphan well specific methane measurement campaign in Western Canada during the summer of 2025. We followed a static chamber methodology paired with laser-based methane sensors with detection capabilities in the ppb scale, an approach that was previously deployed at 561 non-orphan wells across Canada. This method allowed for direct component-specific methane flow rate measurements with detection as low as 10-3 mg/hr. We measured methane emissions from 143 individual orphan wells across 16 different counties in two Western Canadian provinces where 75% of non-producing and 65% of orphaned wells in Canada are located (Alberta and British Columbia).
Previous literature showed that a small subset of wells dominates emissions, highlighting the need for a faster, larger-scale detection method. While the static chamber approach provides direct component-specific measurements, it is time- and labor-intensive and limited to wells where site-access is possible. To address this, we deployed a helicopter-mounted light detection and ranging (LiDAR) system enabling rapid identification of high-emitting orphan wells. With this method, we surveyed 180 orphan wells, including those in extremely remote locations that are inaccessible by ground vehicle, or limited to winter-only access.
By integrating both ground- and helicopter-measurements, we aim to improve quantification of methane emissions from orphan wells in Canada and deliver a roadmap for prioritizing remediation efforts to effectively reduce national methane emissions.
How to cite: Mailhot, Z., Ding, G., Prado, P., and Kang, M.: Quantifying methane emissions from orphan oil and gas wells in Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4277, https://doi.org/10.5194/egusphere-egu26-4277, 2026.
Methane is a potent greenhouse gas and has become a global priority to combat global warming. In Oman, fugitive methane emissions from the oil and gas sector account for the majority (77%) of the country’s anthropogenic methane emissions. Oman’s primary oil and gas company has joined the Oil and Gas Methane Partnership 2.0 (OGMP 2.0), committing to monitoring and reducing their methane emissions. However, methane emissions from Oman’s oil and gas sector remain highly uncertain, and there have been no independent, academic-led ground-based measurement studies conducted in Oman until now.
To address this gap, the United Nations Environment Programme’s (UNEP) International Methane Emissions Observatory (IMEO) funded the first vehicle-based methane measurement campaign targeting oil and gas infrastructure in Oman in 2023 to improve data collection in measurement-scarce regions. Methane measurements were collected using vehicle-based Licor-7810 and Los Gatos MGGA-918 analysers, allowing high-resolution methane observations along accessible roads and offroad paths surrounding oil and gas infrastructure. Here, we present initial results across three oil and gas fields, including methane source attribution, detection, and quantification across various oil and gas infrastructure types.
In addition to methane detections, national methane emission estimates also depend on the number of oil and gas well pads and associated infrastructure that exist across the country, but this number remains highly uncertain. To improve estimates of oil and gas well counts, we introduce an initial framework for identifying oil and gas well pads from satellite imagery using machine learning.
By combining mobile measurement data and satellite imagery, we aim to improve methane monitoring in Oman’s largest anthropogenic methane-emitting sector, the oil and gas sector. This approach also demonstrates the value of relatively cost-effective vehicle-based screening methods for assessing emissions across large-scale oil and gas developments, and provides a foundation for similar efforts in regions with limited monitoring data.
How to cite: Boutot, J., France, J. L., Necki, J. M., Jagoda, P., Bartyzel, J., Kang, M., and Lunt, M.: Improving methane emission monitoring in Oman’s oil and gas sector with mobile measurements and well pad identification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14040, https://doi.org/10.5194/egusphere-egu26-14040, 2026.
The oil and gas (O&G) sector is a significant source of global anthropogenic methane (CH4) emissions, prompting increased regulatory oversight and the rapid development of new methane measurement and mitigation technologies. While screening technologies such as optical gas imaging (OGI) are widely used for regulatory compliance due to their ability to visually identify component-level leaks, there is emerging evidence from regulatory effectiveness studies in Canada that OGI surveys do not detect all sources, with remote sensing surveys often identifying significantly higher site-level emissions. Complementary methods with low detection thresholds may be necessary to improve regulatory compliance and fully represent low-level emission distributions in measurement inventories. In this study, we characterize the performance of a truck-based measurement system using controlled release data, and present results from a field case study in which this method was applied alongside aerial LiDAR and quantitative OGI surveys.
Truck-based measurement systems are a relatively inexpensive and efficient option for site-level screening and emission quantification. This method integrates a vehicle-mounted gas analyzer, anemometer, and GPS to collect atmospheric CH4 concentrations and wind characteristics along the driven route. This data is processed via an automated framework in which CH4 plumes are identified, attributed to a source based on wind characteristics and source geometry, and quantified using a Gaussian plume dispersion model. We assess detection, attribution, and quantification performance using data collected by Eotrac Incorporated during controlled release experiments (0.025 - 11 kg/h) at test sites simulating realistic O&G emission scenarios. While release rates and locations were blind to the measurement team during testing, the analysis presented here was conducted after the releases were unblinded.
The truck-based system achieved a true positive detection rate exceeding 95 % with no false positives. We find that increasing the number of downwind measurement transects can significantly reduce the 90 % detection limit, from 0.45 kg/h with one transect to 0.03 kg/h with five transects. During single-source release scenarios, source attribution accuracy was 100 % at the facility level, 99.7 % at the equipment group-level, and 50 % at the individual source-level, indicating strong performance for identifying emitting equipment groups (7-15 m radius) despite challenges in pinpointing exact leak locations.
In the field case study, the site-level emission frequency was 74.3 % for the truck-based method, compared to 8.6 % for QOGI and 31.8 % for aerial LiDAR. This suggests that OGI misses a significant fraction of emitting sites. Truck-based methods therefore offer a reliable complement to existing detection approaches and have the potential to improve both regulatory compliance and the representation of low-level emitters in inventories.
How to cite: Latimer, R., Bourlon, E., Lavoie, M., Khaleghi, A., Perrine, G., Johnson, J., Nwokoye, C., and Risk, D.: Truck-Based Methane Detection, Attribution, and Quantification in Upstream Oil and Gas: Controlled Release Validation and Field Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20580, https://doi.org/10.5194/egusphere-egu26-20580, 2026.
Oil and gas production in Alberta, Canada is dispersed across tens of thousands of sites. Here we present opportunistic results from a major ongoing (2021 - present) methane emissions monitoring program. We used high quality atmospheric data collected with sensor packages mounted on operator trucks that regularly drive on public roads. Data were processed on an ongoing basis into site-resolved emissions rate quantifications (n = 3350), validated non-detects (n = 17076), and localizations. In Alberta, most site-level emissions measured would be unresolvable by satellites but are nevertheless targets for mitigation. We show that vehicle-based surveys from public roads can target these sites at a low cost and provide data that is necessary to monitor emissions. We further demonstrate how site-resolved data can be linked to operator, production data, and produce emissions intensity estimates on a site-by-site basis.
First, we detail results from 190 single-blind emissions quantification tests, detailing model bias and uncertainty modeling through various environmental conditions. We show empirical wind speed and measurement distance dependencies in detection limits and how these can be modeled.
Second, we examine survey data. Measurements covered a wide diversity of production styles in all seasons. Site-resolved emissions rates varied considerably among production styles and operator. Most operators’ emissions met regulatory limits. However, some emissions would exceed anticipated future regulatory standards. Repeat measurements allow for efforts to reduce emissions to be quantified. Most notably, in 2022 a large population of sites showed rate reductions of >20 g/s that may be attributable to emissions mitigation efforts by industry.
Emissions intensity was also calculated at the site-scale, providing a clear ranking of emissions associated with energy production that was resolvable by production style and operator. Granular intensity data highlight how certain sites strongly likely affect larger scale intensity goals.
Non-detect data showed that many sites are low-emitting and demonstrate that operational public-road drive-by data can quickly and inexpensively demonstrate normal, low-emissions operations, with detection limits below most satellites. Non-detect data are helpful for prioritizing mitigation efforts.
Overall, we demonstrate that opportunistic vehicle-based monitoring complements other scales of measurement by providing granular site- and operator-resolved emissions data with an affordable, reliable, and scalable modality.
How to cite: Barchyn, T., Hugenholtz, C., Clements, M., Gough, T., Munn, A., Samuel, J., Wearmouth, C., and Xing, Z.: Large scale opportunistic vehicle-based monitoring of oil and gas emissions across Alberta, Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8190, https://doi.org/10.5194/egusphere-egu26-8190, 2026.
The methane pledge brought about during COP26 requires its signatories to reduce their 2020 methane emissions by 30% by 2030 (European Commission and United States of America, 2021). 65 % of all methane emissions are thought to be anthropogenic in nature (Saunois et al., 2025)
One of the major anthropogenic sectors contributing to methane emissions is from the oil and gas industry. Fugitive emissions of methane are one of the major contributors to emissions from this industry, this may refer to unwanted emission during transportation of product (colloquially referred to as gas leaks), or flaring, where methane undergoes combustion to CO2. As of 2023 the oil and gas industry was responsible for 1.2 % of the UK’s methane emissions, with flaring emissions representing 69 % of this sector's emissions. (North Sea Transition Authority, 2025).
Flaring may occur for one of three reasons; Routine flaring, where an oil producing facility is unable to use the produced gas; Safety flaring, where flaring ensures the safe operation of the facility; Non-routine flaring encompasses all other flaring. The North Sea is one of the most active areas in the world for oil and gas activities. Recent attempts between 2018 and 2022 have reduced flaring activities in the North Sea by 50%, with an aim for zero flaring to take place by 2030. This is an important step to reducing emissions in this region as one fifth of all emissions in the North Sea related to oil and gas production activities are attributed to flaring (North Sea Transition Authority, 2023). While flaring activity continues to be reduced, some facilities require the continued use of flares, in these cases attempts have been made to adapt the flare itself and improve its overall efficiency and ensure that more methane is converted to CO2.
This work features data collected from one sampling flight of a platform with a highly efficient flare that was intentionally flaring while on task. We explore the feasibility of using previous detection methodologies, such that present in (Shaw et al., 2023), while adding additional stages to confirm the detection of a flare, including source identification using NOx : CO2 ratios as well as modelling the dispersion of multiple sources on the platform using ADMS to understand the likelihood of detecting the flare.
How to cite: Moore, T., Lee, J., Hopkins, J., Drysdale, W., Young, S., and Purvis, R.: An updated approach to detect and quantify emissions from highly efficient offshore flares., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10802, https://doi.org/10.5194/egusphere-egu26-10802, 2026.
Accurate methane emissions inventories for oil and gas facilities are increasingly required to support regulatory reporting, voluntary frameworks, and international natural gas trade. Onsite continuous monitoring systems (CMS) provide time-resolved methane concentration measurements, making them a promising avenue for inventory development. To infer emissions from the concentration measurements, however, requires a careful inversion framework considering near-field turbulence and short-term wind conditions. Specifically, it is crucial to be aware of time periods when wind conditions and sensor placement are such that potential emissions are not observable, which we refer to as periods of no information. We present a general framework for constructing measurement-derived methane emissions inventories using CMS data alone, without reliance on bottom-up emission factors or operational estimates. The framework explicitly accounts for no-information periods and provides fully transparent rigorous uncertainty quantification that propagates both inference uncertainty and imputation uncertainty into methane emission inventory estimates. We validate the approach using controlled-release experiments and demonstrate case studies from multiple production sites.
How to cite: Hammerling, D., Sorensen, T., and Daniels, W.: Rigorous Methane Inventories for Oil and Gas facilities based on Continuous Monitoring Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15109, https://doi.org/10.5194/egusphere-egu26-15109, 2026.
Methane (CH₄) emissions from point sources in the oil and gas sector are highly heterogeneous in space and time, with emission patterns ranging from short-lived events to long-lasting, persistent sources. High-resolution satellite observations provide a unique capability to systematically detect, classify, and monitor large emissions at the facility level, and to explore links between emission behaviour, infrastructure characteristics, and potential mitigation outcomes.
In this contribution, we will present the analysis of methane point source emissions in the Algerian oil and gas sector using satellite observations from UNEP’s International Methane Emissions Observatory (IMEO) Methane Alert and Response System (MARS). The study combines hyperspectral and multispectral high spatial resolution satellite data from January 2024 to December 2025 and analyses more than 150 emission point sources with large emissions during this time frame. The source analysis includes a classification by facility type (e.g. flares, gas disposal facilities, pipelines) and facility age based on the historical satellite imagery and visual inspection of high-resolution RGB data. This facility classification aims to assess potential relationships between infrastructure characteristics and emission behaviour.
Given the differences in sensitivity, noise, revisit frequency, etc., of the satellites considered in this study, we investigate detection patterns across satellite types and find systematic differences between hyperspectral-only and multispectral detections. Sources detected exclusively by hyperspectral instruments are associated with lower estimated flux rates, sporadic emissions, or environmental and operational conditions that worsen the detection limits of multispectral sensors. In contrast, single-plume detections captured by multispectral satellites tend to correspond to big, short-duration emission events. Regarding emissions from flares, detections with multispectral instruments are limited to cases where flares are unlit and methane is vented, since active flaring and smoke significantly degrade methane retrievals, preventing the detection of emissions from incomplete combustion.
Another parameter that is specifically analysed is the duration of emissions and monitoring of their status after MARS notification. While all emissions are important, those identified as long-duration (several days of emission) or frequent (e.g., a flare that repeatedly goes out and vents) are targeted for urgent mitigation recommendations by MARS in its engagement process and monitoring of the potential effect of its notifications when the emission cessation happens.
Overall, this work demonstrates the value of an integrated classification framework that combines facility type, emission persistence, and multi-sensor satellite observations. Such an approach improves interpretation of satellite-derived methane detections and supports prioritisation of mitigation efforts in the oil and gas sector.
How to cite: Raunak, A., Irakulis-Loitxate, I., Montesino-San Martín, M., Castaneda Martínez, C., Mateo-García, G., Johnson, J. E., and Mokalled, T.: Characterization of Algerian Oil and Gas Methane Emission Point Sources from Satellites to Drive Mitigation Actions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12054, https://doi.org/10.5194/egusphere-egu26-12054, 2026.
Methane intensity, the emissions relative to production, has been a focus in recent regulations on fossil fuel imports and domestic production globally, given the climate benefits of methane emission reductions. Methodological frameworks to create annual measurement-based emissions inventory estimates and calculate methane intensity using snapshot measurements have been developed. However, there are still multiple decision points within these frameworks, including several affecting methane intensity calculations, whose impact may be underappreciated. These include uncertainty in the underlying facility population and associated production in purview.
In this work, we discuss the development of a comprehensive measurement-based inventory for the dry gas Haynesville Shale Basin, located in northwest Louisiana and northeast Texas in the United States. The inventory was developed using Bridger Photonics LiDAR data. From a measurement dataset covering 7% of all facilities, we estimate annual basin total emissions of 1,030 [710, 1,530] Gg/year and a methane intensity of 1.13% [0.78%, 1.68%] (95% confidence intervals), in agreement with previous studies in the region. We then show that using different facility population data and applying different basin definitions result in a ~15% and ~75% change in the methane intensity, respectively. As such, this work demonstrates the importance of considering all aspects of the methodology to generate comparable methane intensity estimates.
How to cite: Moore, C., Hajny, K., Fosdick, B., Weller, Z., Wong, H. X., and Corbett, A.: Challenges with Developing a Measurement-Based Basin Methane Intensity Estimate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14541, https://doi.org/10.5194/egusphere-egu26-14541, 2026.
The Marcellus and Denver/Juelsberg fields, among the most productive sources of oil and gas in the US, have been frequent targets for measurement campaigns. Here we describe use of mobile platforms (surface and aircraft), remote sensing (lidar), and numerical simulation to quantify the emissions from these fields. Fossil sources are often co-located with other sources such as concentrated animal feeding operations (CAFOs), wetlands, abandoned coal mines, and landfills. To isolate the emissions from oil and gas, we employ tracers including ethane (C2H6), methane 13C isotopes, and acetic acid, H3CCOOH. The use of surface-based mobile lidar greatly enhances measurement uncertainty for corrections due to convergence over the domain. Improvements in engineering have greatly reduced emissions intensity over the past 10 years.
How to cite: Dickerson, R., Allen, D., Canty, T., he, H., Ring, A., Dreessen, J., Ren, X., Fried, A., Daley, H., and Hmiel, B.: Emissions and trends from the Marcellus and Denver/Juelsberg oil and gas fields, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5713, https://doi.org/10.5194/egusphere-egu26-5713, 2026.
Atmospheric CH4 mole fractions have strongly increased since 1750 due to human activity and continue to rise. Reducing CH4 emissions is often easily feasible and also economically interesting, especially from fossil fuel sources (e. g. leakages). For the development of effective reduction strategies and to prioritize actions, CH4 emissions, their spatial distribution and their variability must be well constrained. This study presents airborne top-down emission estimates from Gabonese offshore oil installations as well as emissions from the Libreville urban area. A correlation with installation age and oil production is discussed, and a comparison with reported data and other top-down studies is presented. Further, co-emitted species such as C2H6, CO2, CO and NOy are shown for both offshore fossil fuel sources and the mixture of different urban sources, which include contributions from fossil fuel and biogenic origins (e.g. landfills).
How to cite: Pühl, M., Fiehn, A., Eckl, M., Bräuer, T., Gottschaldt, K.-D., Aufmhoff, H., Eirenschmalz, L., Neumann, G., Sakellariou, F., Sauer, D., Baumann, R., Mengue, L., Ulrich, V. M. A., and Roiger, A.: Offshore and urban methane emissions in Gabon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21364, https://doi.org/10.5194/egusphere-egu26-21364, 2026.
Ethane (C2H6) is co-emitted with methane (CH4) during fossil fuel extraction, processing, and transport, but has few natural sources. Measurements of C2H6 can therefore be used to partition fossil fuel CH4 emission sources. The Total Carbon Column Observing Network (TCCON) site at East Trout Lake, located in Boreal Canada, is equipped with both InGaAs and InSb detectors which enable simultaneous total column remote sensing measurements of CH4 and C2H6 among many other species. East Trout Lake also hosts routine low-altitude NOAA aircraft in situ profile measurements of CH4 and C2H6. We combine these data with in situ CH4 measurements from the Environment and Climate Change Canada tower network in a top-down hierarchical Bayesian inverse model to infer both fossil fuel CH4 fluxes and C2H6:CH4 emission ratios in two of Canada’s major oil producing provinces: Alberta and Saskatchewan.
How to cite: Mastrogiacomo, J.-P., DiMaria, C., Jones, D., and Wunch, D.: Partitioning Canadian Oil and Gas Methane Emissions with TCCON and Aircraft Ethane Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4106, https://doi.org/10.5194/egusphere-egu26-4106, 2026.
Methane (CH4) emissions from the oil and gas (O&G) sector remain highly uncertain on the Arabian Peninsula, despite the region’s major contribution to global hydrocarbon production and increasing mitigation commitments. As the second most important greenhouse gas after carbon dioxide (CO2), CH4 is a key target of global climate initiatives due to its higher short-term global warming potential, enabling faster climate benefits from mitigation. In this context, UNEP’s International Methane Emissions Observatory (IMEO) aims to improve the accuracy of emission data from the oil and gas, waste, and coal sectors through targeted measurement studies to support mitigation activities.
In autumn 2023, airborne observations of CH4 emissions from the O&G and waste sectors were performed for the first time on the Arabian Peninsula, namely in Oman, using the helicopter-towed probe HELiPOD. Equipped with instrumentation measuring the three-dimensional wind vector and in situ CH4 (Picarro G2401-m and LI-7700), repeated upwind and downwind measurements were conducted at varying horizontal distances (~1–5 km) and altitudes (~35–3000 m) to capture inflow conditions and the horizontal and vertical dispersion of CH4 plumes. Co-located mobile ground-based CH4 measurements complemented the airborne probing, with both datasets combined within a mass-balance approach to quantify emissions.
Depending on the surveyed O&G emission source (point or clustered), calculated CH4 emission rates span a wide range from <100 to several thousand kg h⁻¹, which is within the expected range for such installations. These differences reflect variations in production levels as well as stricter safety requirements and newer infrastructure at sour facilities, which generally exhibit lower emissions than sweet installations characterized by partly more aged infrastructure. Importantly, mobile ground-based measurements effectively revealed mitigation-relevant CH4 sources such as leaks and maintenance-related emissions. However, in densely developed production areas with multiple operators, attributing individual leaks to specific companies and isolating sources within complex facility clusters remains challenging. To address this, a dedicated case study demonstrates the combined use of ground-based, airborne, and satellite measurements to disentangle emissions from a complex O&G facility cluster. This integrated approach was also applied to quantify CH4 emissions from Omani landfills: a small landfill, probed by airborne observations, shows emissions up to ~100 kg h⁻¹, whereas the largest landfill, observed by satellite, can emit several tons of CH4 per hour.
Our unique helicopter-borne measurements provide an independent verification tool bridging facility-scale observations, inventories, and satellite products, supporting operators and policymakers in translating CH4 mitigation commitments into measurable and verifiable action. This research was funded within the framework of UNEP’s International Methane Emissions Observatory and forms part of the METHANE-To-Go (MTG) project series. Prior to the MTG-Oman project presented here, CH4 emissions were investigated in Europe (e.g. MTG-Poland) and Central Africa (MTG-Africa).
How to cite: Förster, E., Huntrieser, H., Maier, N., Al Hinaai, H., Pätzold, F., Bretschneider, L., Lampert, A., Nęcki, J., Bartyzel, J., Jagoda, P., Lunt, M., Field, R., (be’ah), O. E. S. H. C., and Roiger, A.: Top-down quantification of methane emissions from the oil, gas and waste sectors on the Arabian Peninsula using helicopter-borne observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8120, https://doi.org/10.5194/egusphere-egu26-8120, 2026.
Methane (CH4) emission mitigation has become the critical and urgent strategy for controlling near-term climate change. Identifying key emitting regions, quantifying their contributions, and elucidating the underlying drivers have become pressing needs. However, regional CH4 emissions remain constrained by diffuse emission sources, limited monitoring capacity, and complex inversion frameworks. To address these challenges, we developed two independent regional inversion systems and applied them to deliver multi-year CH4 emissions estimates for China, the world’s largest anthropogenic CH4 emitter.
Given the difficulty in attributing observations to specific source regions and the scarcity of surface monitoring stations, we presented an innovative regional CH4 inversion system integrating satellite-based carbon monoxide (CO) observations with ground-based CH4-to-CO flux ratios. Our study estimates China’s CH4 fluxes between 2000 and 2021, revealing an average of 48.4 ± 13.8 Tg yr−1 and a significant increasing trend of 1.1 ± 0.2 Tg yr−2. Socioeconomic development drove a 92.1 Tg cumulative increase over this period, partially offset by a 78.1 Tg reduction due to declining emission intensity; however, this mitigating effect weakened after 2015. The approach is validated against independent estimates and supported by comprehensive sensitivity and uncertainty analyses. It demonstrates the feasibility of deriving reliable emission estimates for large-scale regions from single‑site measurements, offering an affordable and practical tool that integrates air-pollution data into regional greenhouse-gas quantification and mitigation.
To obtain higher temporal resolution and enable spatial and sectoral attribution of emissions, we further built a regional atmospheric inversion framework based on the Local Ensemble Transform Kalman Filter (LETKF) algorithm and constrained by TROPOMI satellite data. Built on the global GEOS-Chem CHEmistry and Emissions REanalysis Interface with Observations (CHEEREIO) tool, our study supports high-resolution regional inversion. Applied to East Asia at 0.5° × 0.625° resolution, this system produces weekly CH4 fluxes for China during 2019–2024. We show that China’s CH4 emissions increased from 61.1 (56.2–66.7) Tg in 2019 to 66.8 (61.5–73.0) Tg in 2024. The livestock sector contributed nearly half of the growth, while rising waste and oil-gas emissions and northward expansion of rice cultivation shifted China’s emissions growth to previously low-emitting Northwest and Northeast regions. This framework demonstrates the feasibility of near-real-time, regional-scale emissions monitoring, offering a transferable tool for other high-emitting countries for long-term emission monitoring. In summary, these two inversion systems advance the capability to track and attribute regional CH4 emissions, provide scalable, cost‑effective, and policy‑relevant tools for clarifying emission patterns, tracking mitigation progress, and supporting national and global climate action.
How to cite: Li, Y., Zheng, B., Pendergrass, D., Jacob, D., Tang, Y., and Qiu, J.: Development and application of regional inversion systems for China’s methane emission tracking and mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3435, https://doi.org/10.5194/egusphere-egu26-3435, 2026.
The Oil and Gas Methane Partnership 2.0 (OGMP 2.0) is the United Nations Environment Programme’s (UNEP) comprehensive, measurement-based international framework for reporting and reducing methane emissions from the oil and gas sector under the International Methane Emissions Observatory (IMEO). To date, more than 150 companies with assets in over 90 countries have joined OGMP 2.0, aiming to increase their understanding of methane emissions and improve both measurement and reporting practices, with the ultimate goal of reducing emissions.
The objective of the OGMP 2.0 Independent Data Assessment (IDA) project is to provide an additional measurement-based layer of verification that strengthens the credibility and confidence in the asset-level methane emissions reported by OGMP 2.0 member companies. By integrating the best available empirical data, such as satellite observations and aerial surveys, this project enables the validation of reported emissions and helps identify potential inconsistencies between supplementary regional measurements and OGMP 2.0 reported values based on source- and site-level measurements aggregated to the scale of an oil and gas asset (OGMP 2.0 Level 5 – the highest reporting level).
Here we present the results of the first OGMP 2.0 IDA pilot, which incorporates regional emission quantifications based on aerial remote sensing data collected during a MethaneAIR campaign to reconcile with OGMP 2.0 Level 5 reported emissions at a spatially isolated asset in the Greater Green River Basin (Wyoming, United States) operated by Jonah Energy. The analysis is further supported by independent regional emission estimates from in-situ aircraft mass balance measurements conducted by ChampionX and commissioned by Jonah Energy as part of an internal effort to verify their OGMP 2.0 Level 5 facility-level measurements. The analysis presented here aims to reconcile these two regional quantification approaches with the operator-reported Level 5 emission estimates. It further assesses the potential of the OGMP 2.0 IDA approach to reconcile empirically-based asset-level emissions reporting data with state-of-the-art regional-level measurements for other oil and gas assets and regions in the world.
How to cite: Stavropoulou, F., Lorente, A., Omara, M., Dieter, H. R., Petrova, I. Yu., and Schwietzke, S.: Assessing OGMP 2.0-reported asset-level methane emissions using independent atmospheric measurements: a pilot study in the Greater Green River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18423, https://doi.org/10.5194/egusphere-egu26-18423, 2026.
Accurate quantification of methane emissions from oil and gas operations is essential for guiding mitigation strategies and informing regulatory policy. In the Colorado Ongoing Basin Emissions (COBE) project, we develop a modeling framework to estimate state‑wide annual methane emissions from the upstream oil and gas sector in Colorado by combining instantaneous emission rate measurements from three aerial vendors with emissions distributions from the literature. Aerial surveys conducted throughout 2024 and 2025 by Bridger Photonics, GHGSat, and Insight M captured snapshot measurements of methane emissions across a representative sample of production sites in Colorado. We construct empirical distributions of instantaneous emission rates using these aerial observations, and supplement them with state-of-the art distributions from the literature (Williams et al., 2024 and Sherwin et al., 2025) to capture the small emissions potentially missed by aerial surveys. These distributions are repeatedly sampled from within a Monte Carlo framework to propagate uncertainty, yielding probabilistic estimates of annual emissions at the state level. Aggregation across all production oil and gas sites in Colorado produces a state‑wide annual methane emissions estimate of approximately 90,000 metric tons, varying slightly depending on the literature distribution used, over three times the bottom-up estimate of state-wide emissions. We have also employed continuous monitoring data from point-in-space networks to inform the low emission rate distribution, but do not include those results due to the limited number of sites encompassed. Future work will involve collecting continuous monitoring data at many more sites across Colorado, allowing us to estimate an entirely measurement-derived inventory.
How to cite: Okenberg, C., Brown, J., Moy, M., Daniels, W., Santos, A., Khaliukova, O., Hodshire, A., and Hammerling, D.: Colorado Ongoing Basin Emissions: Combining Aerial Survey Data with Distributions from the Literature to Estimate a State-Wide Methane Emission Inventory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14955, https://doi.org/10.5194/egusphere-egu26-14955, 2026.
With increasing accessibility of methane emission monitoring technology and advancements in emission modeling, numerous approaches have been developed to create measurement-based emission inventories. Yet inventories often leave emissions unaccounted for due to limited detection sensitivity, limited temporal sampling, or unscalable spatial deployment. We present a framework for building measurement-based methane inventories using Bridger Photonics Gas Mapping LiDAR (GML), a high-resolution, source-resolved aerial technology with a 90% probability of detection at 1 kg h⁻¹. We apply this framework in 2024 across major U.S. oil and gas basins including the Permian, Bakken, Appalachia, Haynesville, and Denver-Julesburg. The framework produces basin-scale methane inventories, attributed to the facility- and equipment-levels, along with methane intensity benchmarks derived from basin-representative sampling designs. Multiple survey deployments are used to characterize temporal variability and sub-basin results provide operator and supply-chain relevant benchmarks that support prioritization of LDAR campaigns and emissions reporting. Inventory methods integrate 1) the GML quantification error model that accurately accounts for uncertainty and bias of emissions estimates, and 2) the GML probability of detection model that estimates missed emissions in the partial detection region of the sensor (0.4-3 kg/h). We describe how representative sampling plans are constructed using U.S. energy infrastructure databases, and how emissions are extrapolated across heterogeneous facility populations with varying equipment and operational characteristics. In regions where public energy infrastructure data are sparse, operators can provide facility and equipment datasets to support sampling and inventory development, which often yield the highest quality results. Applying a single workflow across basins provides comparable, policy-relevant benchmarks for U.S. methane emissions and intensities. Ongoing work addresses remaining limitations, including diurnal variability and quantification uncertainty driven by regional wind models. The framework is transferable to international basins where comparable infrastructure data are available, enabling transparent, scalable, and globally comparable measurement-based methane inventories.
How to cite: Donahue, C., Dillon, J., Hengst, V., Bartnik, A., Oberoi, K., Ottsen, P., and Thorpe, M.: Benchmarking Methane Emissions Across Major U.S. Oil and Gas Basins Using Aerial Gas Mapping LiDAR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22414, https://doi.org/10.5194/egusphere-egu26-22414, 2026.
Methane emissions reductions in the oil and gas industry is one of the best readily attainable goals to meeting the global emission reduction targets to combatting climate change. However, real and perceived mitigation costs are often a major barrier to implementing strong regulations. This work analyzes technically and economically achievable mitigation potential on a source- and site-specific basis at upstream oil and gas sites. In contrast to previous analyses, this work considers actual measured sources and sources distributions from recent aerial surveys. Using data for the province of Alberta, Canada as a case study, methane source mitigation via vapour recovery units, vapour combustors, flares, and on-site power generation are considered where applicable to identified sources. Engineering cost models for each source are first created combining available manufacturer data with previous literature estimates. Two main scenarios are considered in line with newly released federal methane regulations from Environment and Climate Change Canada (ECCC). The first scenario estimates costs for eliminating all intentional venting sources, while the second scenario estimates costs to and combined mitigation strategies to reduce the simple site-specific methane intensity to below 0.1%. The ultimate goal of this work is to assess achievable near-term mitigation potential in the upstream oil and sector in Canada.
How to cite: Killeen, E., Tyner, D., and Johnson, M.: A techno-economic analysis of methane mitigation potential in the upstream oil and gas sector: A case study using aerially-measured source data in Alberta, Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15317, https://doi.org/10.5194/egusphere-egu26-15317, 2026.
The Methane Risk Map (MRM) currently quantifies acute health risks from U.S. oil and gas methane emissions events by combining remotely-sensed methane emissions with atmospheric dispersion modeling and gas composition information derived from permit data. Here, we present a pilot study demonstrating the MRM approach can be extended internationally using a super-emitter pipeline leak near Cheltenham, UK and measured benzene-to-methane molar ratios. We modeled the event using satellite-derived emission estimates from GHGSat (236-1,375 kg hr⁻¹ over ~11 weeks) combined with the AERMOD dispersion model driven by 4-km WRF meteorology. We applied the measured benzene-to-methane molar ratios from UK natural gas samples to estimate co-emitted benzene emissions and air concentrations.
Maximum modeled 1-hour benzene concentrations reached 1,277 ppbv near the source and 8-hour averages exceeded 855 ppbv, which is over four times the 200 ppbv EU occupational exposure limit. Critically, modeled benzene enhancements of 1.6 ppbv extended up to 10 km downwind, potentially affecting Cheltenham and several nearby villages. This pilot study validates the technical feasibility of applying MRM methodology internationally and upholds our previous findings (Bisogno et al. 2025) that methane super emitters may pose health risks to surrounding communities. These results also provide actionable information to prioritize mitigation efforts in regions that are subjected to methane super emitter events and motivate expanding MRM internationally. We are currently increasing data collection efforts globally, prioritizing regions with available gas composition data and satellite-detected emissions events to enable worldwide health risk assessment of oil and gas methane emissions.
Reference:
Bisogno, S., Moniruzzaman, C. G., Heath, N., Efstathiou, C., Domen, J. K., Hill, L. A. L., ... & Bilsback, K. R. (2025). Not just a climate problem: the safety and health risks of methane super-emitter events. Environmental Research Letters, 20(9), 094025.
How to cite: Heath, N., Bisogno, S., Domen, J., Sparks, T., Kashtan, Y., Rowland, S., Lebel, E., Huang, G., Lucha, N., Shonkoff, S., Michanowicz, D., and Bilsback, K.: Modeling Health Risks from International Oil and Gas Methane Emissions: A Pilot Study in the United Kingdom, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22582, https://doi.org/10.5194/egusphere-egu26-22582, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 5
EGU26-14127 | Posters virtual | VPS3
From Detection to Mitigation: The California Satellite Methane ProjectTue, 05 May, 14:36–14:39 (CEST) vPoster spot 5
Past aerial hyperspectral mapping campaigns and pilot studies have demonstrated that highly concentrated plumes are a significant portion of California’s total methane emissions, including many unintentional leaks that can be fixed quickly when operators are notified. Satellite plume imagers such as Planet’s Tanager offer the capacity for repeated observations of known methane infrastructure, with enough spatial resolution and sensitivity to address a significant fraction of these leaks by identifying source facilities and contacting operators. Here we present system design and first results from the California Satellite Methane Project (CalSMP), a comprehensive multi-sector effort to notify individual operators of plumes within days of detection and ensure prompt mitigation when possible through a mix of direct regulation and voluntary dialogue with operators.
In May 2025, CARB began retrieving low-latency Tanager plume detections purchased from Carbon Mapper. Using a cloud-based system developed in-house, CARB employees oversee a semi-automated process to assign plumes to a source and facilitate information exchange with operators. The system generates a notification email with instructions and response forms tailored to the specific facility type (e.g. landfill, oil and gas, dairy biogas). These responses allow us to categorize emissions across sectors by emission type (e.g. unintentional, temporary, process) as well as identify sector-specific components or infrastructure (landfill gas collection system, gas well stuffing box) and details of any repairs.
CARB plans to expand its spatiotemporal coverage through additional satellites, with increased automation as we scale up. While the project’s initial focus is direct repair of unintentional leaks, operator responses also effectively survey underlying causes of point-source emissions and can inform future efforts to improve industry operational practices. CARB has dedicated community outreach funds to ensure methane observations are accessible, understandable, and useful to communities, and is committed to sharing technical details, project design, and lessons learned with other jurisdictions to maximize global mitigation efforts.
How to cite: Phillips, D., Yang, E., Schroeder, J., Zelinka, S., Frausto-Vicencio, I., Fibiger, D., and Herner, J.: From Detection to Mitigation: The California Satellite Methane Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14127, https://doi.org/10.5194/egusphere-egu26-14127, 2026.
