Single-blind validation of space-based point-source detection and quantification of onshore methane emissions

High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China

Coal mines are significant anthropogenic sources of methane emissions, detectable and traceable from high spatial resolution satellites. Nevertheless, estimating local or regional-scale coal mine methane emission intensities based on high-resolution satellite observations remains challenging. In this study, we devise a novel interpolation algorithm based on high-resolution satellite observations (including Gaofen5-01A/02, Ziyuan-1 02D, PRISMA, GHGSat-C1 to C5, EnMAP, and EMIT) and conduct assessments of annual mean coal mine methane emissions in Shanxi Province, China, one of the world's largest coal-producing regions, spanning the period 2019 to 2023 across various scales: point-source, local, and regional. We use high-resolution satellite observations to perform interpolation-based estimations of methane emissions from three typical coal-mining areas. This approach, known as IPLTSO (Interpolation based on Satellite Observations), provides spatially explicit maps of methane emission intensities in these areas, thereby providing a novel local-scale coal mine methane emission inventory derived from high-resolution top-down observations. For regional-scale estimation and mapping, we utilize high-resolution satellite data to complement and substitute facility-level emission inventories for interpolation (IPLTSO+GCMT, Interpolation based on Satellite Observations and Global Coal Mine Tracker). We evaluate our IPLTSO and IPLTSO+GCMT estimation with emission inventories, top-down methane emission estimates from TROPOMI observations, and TROPOMI's methane concentration enhancements. The results suggest a notable right-skewed distribution of methane emission flux rates from coal mine point sources. Our IPLTSO+GCMT estimates the annual average coal mine methane emission in Shanxi Province from 2019 to 2023 at 8.9 ± 0.5 Tg/yr, marginally surpassing top-down inversion results from TROPOMI (8.5 ± 0.6 Tg/yr in 2019 and 8.6 ± 0.6 Tg/yr in 2020). Furthermore, the spatial patterns of methane emission intensity delineated by IPLTSO+GCMT and IPLTSO closely mirror those observed in TROPOMI's methane enhancements. Our comparative assessment underscores the superior performance and substantial potential of the developed interpolation algorithm based on high-resolution satellite observations for multi-scale estimation of coal mine methane emissions.

Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

US oil and gas system emissions from nearly one million aerial site measurements

As airborne methane surveys of oil and gas systems continue to discover large emissions that are missing from official estimates1-4, the true scope of methane emissions from energy production has yet to be quantified. We integrate approximately one million aerial site measurements into regional emissions inventories for six regions in the USA, comprising 52% of onshore oil and 29% of gas production over 15 aerial campaigns. We construct complete emissions distributions for each, employing empirically grounded simulations to estimate small emissions. Total estimated emissions range from 0.75% (95% confidence interval (CI) 0.65%, 0.84%) of covered natural gas production in a high-productivity, gas-rich region to 9.63% (95% CI 9.04%, 10.39%) in a rapidly expanding, oil-focused region. The six-region weighted average is 2.95% (95% CI 2.79%, 3.14%), or roughly three times the national government inventory estimate5. Only 0.05-1.66% of well sites contribute the majority (50-79%) of well site emissions in 11 out of 15 surveys. Ancillary midstream facilities, including pipelines, contribute 18-57% of estimated regional emissions, similarly concentrated in a small number of point sources. Together, the emissions quantified here represent an annual loss of roughly US$1 billion in commercial gas value and a US$9.3 billion annual social cost6. Repeated, comprehensive, regional remote-sensing surveys offer a path to detect these low-frequency, high-consequence emissions for rapid mitigation, incorporation into official emissions inventories and a clear-eyed assessment of the most effective emission-finding technologies for a given region.

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

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

Semantic segmentation of methane plumes with hyperspectral machine learning models

Methane is the second most important greenhouse gas contributor to climate change; at the same time its reduction has been denoted as one of the fastest pathways to preventing temperature growth due to its short atmospheric lifetime. In particular, the mitigation of active point-sources associated with the fossil fuel industry has a strong and cost-effective mitigation potential. Detection of methane plumes in remote sensing data is possible, but the existing approaches exhibit high false positive rates and need manual intervention. Machine learning research in this area is limited due to the lack of large real-world annotated datasets. In this work, we are publicly releasing a machine learning ready dataset with manually refined annotation of methane plumes. We present labelled hyperspectral data from the AVIRIS-NG sensor and provide simulated multispectral WorldView-3 views of the same data to allow for model benchmarking across hyperspectral and multispectral sensors. We propose sensor agnostic machine learning architectures, using classical methane enhancement products as input features. Our HyperSTARCOP model outperforms strong matched filter baseline by over 25% in F1 score, while reducing its false positive rate per classified tile by over 41.83%. Additionally, we demonstrate zero-shot generalisation of our trained model on data from the EMIT hyperspectral instrument, despite the differences in the spectral and spatial resolution between the two sensors: in an annotated subset of EMIT images HyperSTARCOP achieves a 40% gain in F1 score over the baseline.

Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (<1 km), making attribution challenging. We present the first observations from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer showing quantification and attribution of fine-scale methane (0.3 to 73 tonnes CH4 hour-1) and carbon dioxide sources (1571 to 3511 tonnes CO2 hour-1) spanning the oil and gas, waste, and energy sectors. For selected countries observed during the first 30 days of EMIT operations, methane emissions varied at a regional scale, with the largest total emissions observed for Turkmenistan (731 ± 148 tonnes CH4 hour-1). These results highlight the contributions of current and planned point source imagers in closing global carbon budgets.

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed, site-level emission inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that operator-level top-down methane measurements are lower during the end-of-project phase than during the baseline phase. However, gaps persist between end-of-project top-down measurements and bottom-up site-level inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) validate specific snapshot measurements and determine how they relate to the temporal emission profile of a given site and (ii) create a measurement-informed, site-level inventory that can be validated with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how to reconcile CMS rate estimates and top-down snapshot measurements jointly with bottom-up inventories at the site level. More broadly, it demonstrates the importance of multiscale measurements when creating measurement-informed, site-level emission inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as the Oil and Gas Methane Partnership 2.0, and corporate strategies.