Quantifying Methane Emissions Research Articles

Comparison of Sub-Ppm Instrument Response Suggests Higher Detection Limits Could Be Used to Quantify Methane Emissions from Oil and Gas Infrastructure

Comparison of Sub-Ppm Instrument Response Suggests Higher Detection Limits Could Be Used to Quantify Methane Emissions from Oil and Gas Infrastructure

A satellite constellation dedicated to frequently monitor methane emissions from oil and gas facilities around the world

This paper discusses GHGSat’s pioneering operating satellites dedicated to high-resolution measurements of methane emissions. We demonstrate their role as an integral part of an emissions intensity improvement strategy. The instrument on board each satellite uses spectroscopy principles to detect and quantify methane emissions, exhibiting a spatial resolution of less than 30 m. A discussion on how this constellation can be used as a key system in the mitigation of emissions is presented. Results reflect observations taken with GHGSat’s satellites. Recent examples of oil and gas emitting facilities around the world are shown followed by how this constellation can serve three objectives: (1) emissions mitigation; (2) regulatory compliance; and (3) a system to achieve voluntary efforts such as OGMP 2.0. The intention is to show that there isn’t a one-size-fits-all solution, but that the use of a satellite constellation is a complimentary and efficient way of monitoring for large leaks with increased frequency. This paper also reviews GHGSat’s top-down approach through its aircraft sensor technology as another solution for monitoring smaller leaks. Mitigation success stories are discussed providing opportunities for achievement of regulatory certifications in regions like the United States, Australia and Iraq. The resulting datasets are integrated into GHGSat’s ESRI ArcGIS-based portal, SPECTRA, to facilitate the interpretation of its high-resolution measurements, giving further insights through the available analytics layers. This platform provides situational awareness of methane emissions to operators, creating opportunities for mitigation.

Spatiotemporal variations and influencing factors of methane emissions from livestock in China: A spatial econometric analysis

In recent years, China has been implementing policies to improve the livestock industry in response to the global trend toward green and low-carbon development. These policies include the establishment of demonstration zones for high-standard agriculture, the relocation of farms to the north, etc. This study aims to investigate the impact of changes in the spatial structure of the livestock industry on methane emissions. It used panel data from 31 provinces in China from 2001 to 2021 and applied the IPCC methodology to quantify methane emissions at both the national and provincial levels. In addition, a spatial econometric model was used to analyze the impact of changes in the spatial structure of the livestock industry on methane emissions. The results show that methane from livestock in China decreased from 13.85 million tons in 2001 to 11.82 million tons in 2021. In addition, methane emissions from livestock in China show a significant spatial gradient and correlation. The Southwest has the highest methane emissions, accounting for 24 % of the total emissions. After controlling for spatial correlation and other factors in the model, it was found that the spatial structure of the livestock industry has a different influence on methane emissions both in the province and in neighboring provinces. To improve methane emission efficiency in the future, policies such as establishing functional zones for livestock farming, strengthening technological innovation and sharing for green development in agriculture, and promoting the optimization of agricultural and rural management structures should be implemented.

Multi-open-path laser dispersion spectroscopy combined with Bayesian state estimation for localizing and quantifying methane emissions

A global effort towards improved quantitative understanding of greenhouse gas emissions is taking pace. This includes developing source identification, quantification, and apportionment in an attempt to understand global budget and trends, but also developing monitoring systems making emission reduction commitment verifiable. In this context, we demonstrate a novel approach to continuous methane emission monitoring at the spatial scale of an industrial facility. By combining multi-directional measurements of path-integrated methane concentrations with Bayesian state estimation, we show a realistic tomographic gas plume reconstruction, its evolution in time, and the associated estimation of the source map. The method is validated using measurements from controlled methane releases over a domain of area 120×40 m2. For the first demonstration, a two dimensional geometry has been used in the gas flow model; nevertheless, sources are located within 3 to 12 meters, and mass emission rates are estimated within <30% for 80% of the cases.

Quantifying methane emissions from United States landfills.

Methane emissions from solid waste may represent a substantial fraction of the global anthropogenic budget, but few comprehensive studies exist to assess inventory assumptions. We quantified emissions at hundreds of large landfills across 18 states in the United States between 2016 and 2022 using airborne imaging spectrometers. Spanning 20% of open United States landfills, this represents the most systematic measurement-based study of methane point sources of the waste sector. We detected significant point source emissions at a majority (52%) of these sites, many with emissions persisting over multiple revisits (weeks to years). We compared these against independent contemporaneous in situ airborne observations at 15 landfills and established good agreement. Our findings indicate a need for long-term, synoptic-scale monitoring of landfill emissions in the context of climate change mitigation policy.

Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation

Abstract. ​​​​​​​The ability to detect and quantify methane emissions from offshore platforms is of considerable interest in providing actionable feedback to industrial operators. While satellites offer a distinctive advantage for remote sensing of offshore platforms which may otherwise be difficult to reach, offshore measurements of methane from satellite instruments in the shortwave infrared are challenging due to the low levels of diffuse sunlight reflected from water surfaces. Here, we use the GHGSat satellite constellation in a sun glint configuration to detect and quantify methane emissions from offshore targets around the world. We present a variety of examples of offshore methane plumes, including the largest single emission at (84 000 ± 24 000) kg h−1 observed by GHGSat from the Nord Stream 2 pipeline leak in 2022 and the smallest offshore emission measured from space at (180 ± 130) kg h−1 in the Gulf of Mexico. In addition, we provide an overview of the constellation's offshore measurement capabilities. We measure a median column precision of 2.1 % of the background methane column density and estimate a detection limit, from analytical modelling and orbital simulations, that varies between 160 and 600 kg h−1 depending on the latitude and season.

Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful case to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C8–C10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C8–C10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.

Measuring methane emissions during the installation of residential and commercial natural gas meters in China

Methane is a potent greenhouse gas that accounts for one-quarter of the world's radiative forcing. Methane emissions from the natural gas sector are prevalent throughout the natural gas (NG) chain. Studies have shown that methane emissions from post-meter uses of natural gas are vastly understated. A surge in the number of natural gas users, for example, would amplify the climate impact of methane emissions during the installation of natural gas meters. Thus, quantifying methane emissions during the installation of natural gas meters is critical in light of severe global climate change and urgent reduction targets. In this study, we used a mass balance approach to calculate methane emissions during the separate installation of 1444 residential natural gas meters and 51 commercial natural gas meters. Our results revealed the methane emission had a fat tail distribution. Specifically, the estimated mean methane emissions for household users were 0.008 (0.001–0.022) kg per household and 0.192 (0.013–0.816) kg per commercial user. Extrapolating these statistics to the whole of China, total emissions from 2007 to 2021 were 3.80 million metric tons (MMt) CH4, with an annual average of 0.25 MMt. Notably, in terms of economic development and population size, the provinces with the highest methane emissions were concentrated in the southeast. Our findings close a gap in measuring CH4 emissions in China across the natural gas chain and provide data to support the reduction targets set and the development of reduction technologies.

A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases

Abstract. Satellite observations of dry-column methane mixing ratios (XCH4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250 times sparser than TROPOMI. Here, we present a blended TROPOMI+GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25∘ × 0.3125∘ resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument.

S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery

Methane, as a crucial greenhouse gas, plays a significant role in global warming, contributing to approximately one-quarter of the observed climate change since pre-industrial times. Consequently, the detection and quantification of major methane emitters are vital in addressing this issue effectively. Satellite sensors with shortwave infrared (SWIR) spectral bands provide valuable information for monitoring methane emissions. For example, Sentinel-2 multispectral data have the capability to detect methane plumes of large point sources. As such, a wide range of quantification approaches have been developed to quantify methane source rates based on this dataset. Most of the existing methods, however, require auxiliary data, such as wind speed, and have large uncertainties. In this study, we introduce a novel approach based on deep learning models to enhance the precision of methane quantification using Sentinel-2 data without the reliance on external data sources. To train the proposed deep learning model, a comprehensive benchmark dataset has been generated, using Sentinel-2 data. This dataset is created by integrating simulated plumes and background noise extracted from real Sentinel-2 images. This approach ensures the integration of realistic environmental conditions within the simulated data, enhancing the robustness and reliability of our proposed model. The generated benchmark dataset is utilized in different deep learning architectures, namely VGG-19, ResNet-50, Inception-v3, DenseNet-121, Swin-T, and EfficientNet-V2L, to estimate methane source rate. The performance of deep models has been evaluated in three learning strategies, namely from scratch, transfer-learning, and fine-tuning. The fine-tuned EfficientNet-V2L achieves the highest accuracy with root-mean-square error (RMSE), mean absolute percentage error (MAPE), and Pearson R of 2101 kg h−1, 10.05%, and 95.70%, respectively. More importantly, the proposed model demonstrates superior performance compared to conventional physical-based quantification methods (e.g., integrated mass enhancement) and recently developed deep learning model techniques (e.g., MethaNet). In particular, the proposed model exhibits an improvement of approximately 1287 kg h−1 in terms of RMSE, a 3.92% reduction in MAPE, and a 5.01% enhancement in R compared to the IME method. These results highlight the advancements achieved by the proposed approach in accurately quantifying methane emissions using Sentinel-2 imagery. The generated benchmark dataset and the developed deep learning model presented in this study serve as a fundamental resource and constructive framework for future research, promoting extensive implementation across various methane monitoring scenarios on different satellites and in distinct geographic regions, which delivering greater effectiveness to global methane emission monitoring initiatives.

Methane emissions from shale gas production sites in southern Sichuan, China: A field trial of methods

Shale gas is an important alternative natural gas source that can meet China's growing energy demand. However, methane emissions during shale gas production could largely determine whether it could be considered as a bridging clean energy source. In this study, we performed methane emission measurements using three methods (on-site optical gas imaging, on-site methane patrolling, and downwind measurement) at shale gas production sites in Sichuan province, China, covering 18 well sites with 81 wells. The results showed that flowback water tanks were the major methane sources, with valves, separators, chemical injection pumps, and pneumatic controllers as other sources. The methane concentrations inside the 18 sites ranged from 1.42 to 459 μmol mol−1, with over 59% of the sites having the highest concentration less than 50 μmol mol−1. The optical gas imaging method, with its low detection limit over 5000 μmol mol−1, could not capture the emission effectively. The three successful downwind measurements resulted in site level emission rates between 0.02 and 0.17 kg h−1. When divided by the number of wells at each site, the emission intensity was 4%–28% of the wellhead emission factor for natural gas production in China. More measurements are required to better quantify methane emissions from shale gas sites in China. For sites in mountainous regions or long-term low-wind regions, detection methods other than downwind measurements should be suitable for quantifying site-level emissions.

Improving quantification of methane point source emissions from imaging spectroscopy

The matched filter (MF) method is widely used for hyperspectral imaging spectrometers to detect and quantify methane point sources due to its high computational efficiency. However, it would result in an unavoidable underestimation, especially for large concentration enhancements. The lognormal matched filter (LMF) has provided a unique and not limited to weak methane plumes theory through lognormal background radiances modeling, but validations on simulated experiments and applications to real data remain to be explored. Moreover, the covariance contamination caused by the enhanced pixels and the surface heterogeneity have detrimental effects on the detection methods for real data application. In this study, we propose the iterative lognormal matched filter (ILMF) method to address these challenges. We evaluate the performance of the ILMF with two ideal simulations and the end-to-end simulation. The results of random simulated enhancement retrievals show that the retrieved enhancement by the ILMF method agrees well with the true enhancement, with an R2 of 0.984 and a small root-mean-square error (RMSE) of 55.856 ppb. The ILMF method reduce the RMSE of retrieved enhancement by 80% compared with the MF method. The results of end-to-end simulations show the underestimation of MF method at different sites regarding the emission rate, as well as the improvement of the ILMF method. Further, we apply the ILMF method to detect and quantify point sources in some methane hotspot regions. Our study reports the underestimation in traditional MF method and provides an unbiased and robust method for quantifying methane emissions.

Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action

Abstract. We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ∼25 km × 25 km resolution, using spatially allocated national United Nations Framework Convention on Climate Change (UNFCCC) reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up inventories (+103 % for gas, +53 % for waste, +49 % for livestock, −14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0–5.5) Tg a−1; best estimate and uncertainty range), Turkmenistan (4.4 (2.8–5.1) Tg a−1), Saudi Arabia (4.3 (2.4–6.0) Tg a−1), Algeria (3.5 (2.4–4.4) Tg a−1), Egypt (3.4 (2.5–4.0) Tg a−1), and Turkey (3.0 (2.0–4.1) Tg a−1). Most oil–gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil–gas emission hotspots in Turkmenistan, Algeria, and Oman and offshore in the Persian Gulf. We show that oil–gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil–gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil–gas emission inventories. We examined the methane intensity, defined as the upstream oil–gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil–gas sector and using as reference the 0.2 % target set by the industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through the capture of associated gas, modern infrastructure, and the concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil–gas upstream emissions and a 26 % decrease in total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.

Quantifying methane emissions under field conditions under 2 different dairy production scenarios: Low-input versus high-input milk production

Livestock production systems with ruminants play a relevant role in the emission of the greenhouse gas CH4, which is known to significantly contribute to global warming. Consequently, it is a major societal concern to develop strategies in mitigating such emissions. In addition to breeding toward low-emitting cows, management strategies could also help in reducing greenhouse gas emissions from dairy farms. However, information is required for appropriate decision making. To the best of our knowledge, this is the first study that considers different, already available equations to estimate CH4 emissions of small-scale dairy farms in the mountain region, which largely differ from large dairy farms in the lowlands concerning management and production. For this study, 2 different production systems, both typical for small-scale dairy farming in mountain regions, were simultaneously run over 3 yr at an experimental farm as follows: (1) a high-input production system, characterized by intensive feeding with high amounts of external concentrates and maize silage, year-round housing, and high yielding Simmental cattle breed, and (2) a low-input production system, characterized by prevailing hay and pasture feeding and silage ban, thus covering most of the energy requirements by forage harvested on-farm and the use of the local Tyrolean Grey cattle breed. Results reveal that feeding management has a significant effect on the amount of CH4 emissions. The low-input production system produced less CH4 per cow and per day compared with the high-input production system. However, if calculated per kilogram of milk, the high-input scenario produced proportionally less CH4 than the low-input one. Findings of this study highlight the potential to assess in a fast and cost-effective way the CH4 emission in different dairy production systems. This information contributes to the debate about the future of sustainable milk production in mountain regions, where the production of feed resources is climatically constrained, and could be useful for breeding purposes toward lower CH4-emissions.

Review Spotlights Methane Detection, Quantification Technologies

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205166, “Deployment of Methane Detection and Quantification Technologies,” by Noor A.A. Talip, Mohd H.M. Pikri, and Shahrul A.Z. Abidin, Petronas, et al. The paper has not been peer reviewed. _ In supporting the energy transition, a need exists to improve accurate quantification and reduction of methane emissions and determine long-term reduction targets to drive the competitive advantages of natural gas as a low-carbon fuel. The complete paper reviews an initiative taken to measure baseline data for methane emissions for gas-processing facilities and gas-transmission and regasification units through the use of accurate measurement tools and methodologies. Introduction A methane quantification tool has been developed in-house by Petronas, with two of the operator’s business units as the first users. Methane emission is quantified based on methane sources listed in the Oil and Gas Climate Initiative Guidelines. The quantification covers the following methane-emission sources based on applicability to sample plants: - Unintended release (fugitive leaks, loss of primary containment) - Intended release (venting and flaring of hydrocarbon, stationary combustion, compressor seals, and pneumatic controls and pumps) Methane-emission volume from fugitive leaks, compressor seals, and pneumatic devices had not been quantified accurately by the operator until before the development of its methane-quantification tool. Much of the complete paper is dedicated to describing methodologies used to detect and quantify methane emissions; this synopsis identifies those methodologies but directs the reader to the complete paper for fuller discussions thereof, as well as equations used in those methodologies. Methodology Fugitive Leaks. Global fugitive-emission standards for quantification are nonbinding, regardless of regulations or policies. Nevertheless, the oil and gas industry is still working to develop regulations for methane-emission measurements for effective monitoring and, ultimately, setting the targets for emission reduction. The choice of methods to estimate emission depends on how the estimates will be used and the degree of accuracy required. Methods using site-specific data from direct measurement are preferred. Table 1 of the complete paper shows the fugitive-emission-quantification methodologies sorted into inclining order with respect to accuracy. The methodology-complexity level can be depicted per the terminology in Fig. 1. The five methods are described by the authors, and detailed in the complete paper, as the following: - Method 1: Facility-/plant-level average emission factors - Method 2: Equipment-level average emission factors - Method 3: Device-component-level average emission factors - Method 4: Alternative-leak/no-leak emission factors for optical-gas-imaging technologies - Method 5: Screened value correlation approach

Analysis of a tiered top-down approach using satellite and aircraft platforms to monitor oil and gas facilities in the Permian basin

As energy systems transition towards decarbonization, natural gas is expected to play an important role as a cleaner hydrocarbon fuel source to bridge the gap between renewable energy production and increasing energy demand. Regulations aim to reduce methane emissions from natural gas systems to ensure the total environmental impacts of natural gas production and consumption are indeed lesser than for other fossil fuels. GHGSat Inc., has developed a tiered top-down remote sensing approach utilizing satellite and airborne instruments to monitor methane emissions from industrial sites globally. This satellite-aircraft hybrid system was demonstrated in the Permian basin for detecting and quantifying methane emissions from oil and gas (O&G) facilities. A heavy-tail distribution of methane emissions was observed, with 12% of emitting targets (total sample size of 106) accounting for over 50% of total detected methane emissions. Traditional leak detection and repair (LDAR) programs often incorporate multiple annual screening campaigns with ground-based optical gas imaging (OGI) technologies to detect methane leaks from O&G infrastructure. Three alternative LDAR programs incorporating GHGSat's satellite and airborne monitoring technologies were modelled using the Arolytics AroFEMP methane model. Model parameters were derived from GHGSat's 2021 Permian basin data to evaluate their equivalency and costs against traditional LDAR monitoring. When compared to the required practice of performing OGI surveys four times per year, the proposed alternative LDAR programs were modelled to have at least equivalent methane reduction performance while being substantially more cost-effective.

Huge CH4, NO2 and CO Emissions from Coal Mines in the Kuznetsk Basin (Russia) Detected by Sentinel-5P

The coal industry is the largest global emitter of carbon dioxide (CO2). However, recent data suggests that coal mine methane (CH4) emissions worldwide are higher than those of the oil and gas industry. Furthermore, the coal industry is less active in reducing methane emissions than the oil and gas sectors due to lower profitability. Although uncertainties remain in quantifying methane emissions from mines, the use of satellite observations is revolutionizing the process of monitoring and improving the accuracy of emission accounting. The methodology presented here allows us to determine background CH4 concentrations and improve our ability to detect emission events using Sentinel-5P data. Knowing the background concentrations for the area of interest provides us the opportunity to track seasonal and annual variations and trends, as well as quickly detect periodic or accidental emissions from unregulated sources, etc. The methodology and systematic research applied in this paper for the period of May 2018 to the end of 2022 enables us to detect hundreds of large-scale emissions of CH4, NO2, and CO from the coal mines in the Kuznetsk Basin (the Kemerovo region), the largest coal mining area in Russia. We estimated that the amount of these emissions is significantly higher than the emissions reported by various authors for other coal mining regions such as Poland and Australia. We found that in cases of high methane quantity there is a positive correlation between NO2 and CO emissions in time and location. The source of emissions in the study area is homogeneous, which allows it to be used as a benchmark for building models to estimate and track emissions in heterogeneous areas.

Understanding the potential of Sentinel-2 for monitoring methane point emissions

Abstract. The use of satellite instruments to detect and quantify methane emissions from fossil fuel production activities is highly beneficial to support climate change mitigation. Different hyperspectral and multispectral satellite sensors have recently shown potential to detect and quantify point-source emissions from space. The Sentinel-2 (S2) mission, despite its limited spectral design, supports the detection of large emissions with global coverage and high revisit frequency thanks to coarse spectral coverage of methane absorption lines in the shortwave infrared. Validation of S2 methane retrieval algorithms is instrumental in accelerating the development of a systematic and global monitoring system for methane point sources. Here, we develop a benchmarking framework for such validation. We first develop a methodology to generate simulated S2 datasets including methane point-source plumes. These benchmark datasets have been created for scenes in three oil and gas basins (Hassi Messaoud, Algeria; Korpeje, Turkmenistan; Permian Basin, USA) under different scene heterogeneity conditions and for simulated methane plumes with different spatial distributions. We use the simulated methane plumes to validate the retrieval for different flux rate levels and define a minimum detection threshold for each case study. The results suggest that for homogeneous and temporally invariant surfaces, the detection limit of the proposed S2 methane retrieval ranges from 1000 to 2000 kg h−1, whereas for areas with large surface heterogeneity and temporal variations, the retrieval can only detect plumes in excess of 500 kg h−1. The different sources of uncertainty in the flux rate estimates have also been examined. Dominant quantification errors are either wind-related or plume mask-related, depending on the surface type. Uncertainty in wind speed, both in the 10 m wind (U10) and in mapping U10 to the effective wind (Ueff) driving plume transport, is the dominant source of error for quantifying individual plumes in homogeneous scenes. For heterogeneous and temporally variant scenes, the surface structure underlying the methane plume affects the plume masking and can become a dominant source of uncertainty.

Quantifying Methane Emissions from Aquaculture Ponds in China.

Small ponds are important methane (CH4) sources. However, current estimates of CH4 emissions from aquaculture ponds are largely uncertain due to data paucity, especially in China─the largest aquaculture producer in the world. Here, we present a nationwide metadata analysis with a database of 55 field observations to examine total CH4 emissions from aquaculture ponds in China. We found that the annual CH4 fluxes from aquaculture ponds are much larger than those from reservoirs and lakes. The total CH4 emission from aquaculture ponds is 1.60 ± 0.62 Tg CH4 yr-1, with an average growth rate of ∼0.03 Tg CH4 yr-2 during the period 2008-2019. Compared with global major protein-producing livestocks, aquaculture species have a lower (63%) emission intensity, defined by the amount of CH4 emitted per unit of animal proteins. Our study highlights the essential contribution of China's aquaculture ponds to national CH4 emissions and the lower environmental cost of the aquaculture sector for future animal protein production. More field measurements with multi-scale observations are urgently needed to reduce the uncertainty of CH4 emissions from aquaculture ponds.

Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery

Abstract. Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emissions from oil and gas production. However, current methods lack performance calibration with ground-truth testing. This study developed a multi-band–multi-pass–multi-comparison-date methane retrieval algorithm that enhances Sentinel-2 sensitivity to methane plumes. The method was calibrated using data from a large-scale controlled-release test in Ehrenberg, Arizona, in fall 2021, with three algorithm parameters tuned based on the true emission rates. Tuned parameters are the pixel-level concentration upper-bound threshold during extreme value removal, the number of comparison dates, and the pixel-level methane concentration percentage threshold when determining the spatial extent of a plume. We found that a low value of the upper-bound threshold during extreme value removal can result in false negatives. A high number of comparison dates helps enhance the algorithm sensitivity to the plumes in the target date, but values in excess of 12 d are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quantification accuracy, but it may harm the yes/no detection accuracy. We found that there is a trade-off between quantification accuracy and detection accuracy. In a scenario with the highest quantification accuracy, we achieved the lowest quantification error and had zero false-positive detections; however, the algorithm missed three true plumes, which reduced the yes/no detection accuracy. In contrast, all of the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We illustrated a two-step method that updates the emission rate estimates in an interim step, which improves quantification accuracy while keeping high yes/no detection accuracy. We also validated the algorithm's ability to detect true positives and true negatives in two application studies.