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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.

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

The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. We present a method for measuring LNG terminal CO2 emissions using remote sensing imaging spectroscopy. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron. The power plant data set shows a robust correlation between our estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, eight point sources are identified with emission rates from 219.69 ± 54.95 to 1,083.22 ± 308.06 t/hr. At Cameron, three point sources are identified with emission rates from 91.64 ± 25.81 to 265.61 ± 67.80 t/hr. The liquefaction carbon intensity estimates also align with past study ranges. This illustrates that remote sensing can validate environmental reporting and CO2 inventories for industrial facilities.

Satellites are increasingly seen as a tool for identifying large greenhouse gas point sources for mitigation, but independent verification of satellite performance is needed for acceptance and use by policy makers and stakeholders. We conduct to our knowledge the first single-blind controlled methane release testing of satellite-based methane emissions detection and quantification, with five independent teams analyzing data from one to five satellites each for this desert-based test. Teams correctly identified 71% of all emissions, ranging from 0.20 [0.19, 0.21] metric tons per hour (t/h) to 7.2 [6.8, 7.6] t/h. Three-quarters (75%) of quantified estimates fell within ± 50% of the metered value, comparable to airplane-based remote sensing technologies. The relatively wide-area Sentinel-2 and Landsat 8 satellites detected emissions as low as 1.4 [1.3, 1.5, 95% confidence interval] t/h, while GHGSat's targeted system quantified a 0.20 [0.19, 0.21] t/h emission to within 13%. While the fraction of global methane emissions detectable by satellite remains unknown, we estimate that satellite networks could see 19-89% of total oil and natural gas system emissions detected in a recent survey of a high-emitting region.

Natural gas distribution systems within municipalities supply a substantial fraction of energy consumed in the United States. As decarbonization of the natural gas system necessitates new modes of operation outside original design purposes, for example, increased hydrogen or biogas blending, it becomes increasingly important to understand in advance how existing infrastructure will respond to these changes. Such an analysis will require detailed information about the existing asset base, such as local soil composition, plastic type, and other characteristics that are not systematically tracked at present or have substantial missing data. Opportunistic sampling, for example, collecting measurements at assets that are already undergoing maintenance, has the potential to substantially reduce the cost of gathering such data but only if the results are representative of the full asset base. To assess prospects for such an approach, we employ a dataset including the entire service line and leak database from a large natural gas distribution utility (∼66,700 km of service pipelines and over 530,000 leaks over decades of observations). This dataset shows that service lines affected by excavation damage produce an approximately random sample of plastic and steel service lines, with similar distributions of component age, operating pressure, and pipeline diameter, as well as a relatively uniform spatial distribution. This means that opportunistic measurements at these locations will produce a first-order estimate of the relative prevalence of key characteristics across the utility's full asset base of service lines. We employ this approach to estimate the plastic type, which is unknown for roughly 80% of plastic service lines in the database. We also find that while 32% of leaks across all components occur in threaded steel junctions, excavation damage accounts for 75% of hazardous grade 1 leaks in plastic service lines and corrosion accounts for 47% in steel service lines. Insights from this sampling approach can thus help natural gas utilities collect the data they need to ensure a safe and reliable transition to a lower-emission system.

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

Abstract. Sentinel-2 satellite imagery has been shown by studies to becapable of detecting and quantifying methane emissions from oil and gasproduction. However, current methods lack performance calibration withground-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 calibratedusing data from a large-scale controlled-release test in Ehrenberg, Arizona,in fall 2021, with three algorithm parameters tuned based on the trueemission rates. Tuned parameters are the pixel-level concentration upper-bound threshold during extreme value removal, the number of comparisondates, and the pixel-level methane concentration percentage threshold whendetermining the spatial extent of a plume. We found that a low value of theupper-bound threshold during extreme value removal can result in falsenegatives. A high number of comparison dates helps enhance the algorithmsensitivity to the plumes in the target date, but values in excess of12 d are neither necessary nor computationally efficient. A high percentagethreshold when determining the spatial extent of a plume helps enhance thequantification accuracy, but it may harm the yes/no detection accuracy. Wefound that there is a trade-off between quantification accuracy anddetection accuracy. In a scenario with the highest quantification accuracy,we achieved the lowest quantification error and had zero false-positivedetections; however, the algorithm missed three true plumes, which reduced theyes/no detection accuracy. In contrast, all of the true plumes weredetected in the highest detection accuracy scenario, but the emission ratequantification had higher errors. We illustrated a two-step method thatupdates the emission rate estimates in an interim step, which improvesquantification accuracy while keeping high yes/no detection accuracy. Wealso validated the algorithm's ability to detect true positives and truenegatives in two application studies.

Associated gas flaring during crude oil production is an important contributor to global warming. Satellite technology has made global flaring monitoring possible with high spatial resolution. In this study, we construct a granular database to geographically match global oil and gas fields with remote sensing flaring data from the Visible Infrared Imaging Radiometer Suite from 2012 to 2019. The geographic information system database contains over 50 000 oil and gas fields and around 4700 infrastructure sites (e.g. refineries, terminals) in 51 countries and regions, representing 96% of global oil production and 89% of natural gas production. Over 2900 fields and 140 infrastructure sites in 47 countries contain matching flares. The annual matched flare volume covers 89%-92% of the satellite-estimated flaring volume of these countries and 85%-87% of total worldwide volume detected by the satellite. In 2019, a set of 263 'high-flare' fields (which flare more than 0.1 billion cubic meters per year) account for 67% of the total matched satellite-estimated volume. These fields are mainly concentrated in the Persian Gulf, West and East Siberia, Eastern Venezuela Basin, Permian and Williston Basins in the United States, the Gulf of Mexico, and West and North Africa. Accounting for asymmetric instrument uncertainty suggests that country-level flaring rates are accurate to within -8% to +29%, the global average within 1%.

Methane leakage from point sources in the oil and gas industry is a major contributor to global greenhouse gas emissions. The majority of such emissions come from a small fraction of “super-emitting” sources. We evaluate the emission detection and quantification capabilities of Kairos Aerospace’s airplane-based hyperspectral imaging methane emission detection system for methane fluxes of 18–1,025 kg per hour of methane (kgh(CH4 )). In blinded controlled releases of methane conducted over 4 days in San Joaquin County, CA, Kairos detected 182 of 200 valid nonzero releases, including all 173 over 15 kgh(CH4 ) per meter per second (mps) of wind and none of the 12 nonzero releases below 8.3 kgh(CH4 )/mps. Nine of the 26 releases in the partial detection range of 5–15 kgh(CH4 )/mps were detected. There were no false positives: Kairos did not detect methane during any of the 21 negative controls. Plume quantification accuracy depends on the wind measurement technique, with a parity slope of 1.15 (s ¼ 0.037, R2 ¼ 0.84, N ¼ 185) using a cup-based wind meter and 1.45 (s ¼ 0.059, R2 ¼ 0.80, N ¼ 157) using an ultrasonic anemometer. Performance is comparable even with only modeled wind data. For emissions above 15 kgh/mps, quantification error scales as roughly 30%–40% of emission size, even when using wind reanalysis data instead of ground-based measurements. This reflects both uncertainty in wind measurements and in Kairos’ estimates. These findings suggest that at 2 mps winds under favorable environmental conditions in the United States, Kairos could detect and quantify over 50% of total emissions by identifying super-emitting sources.

Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH4 emissions at different spatial scales are ~1.5-2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH4 mitigation policy priorities.

A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060-15065] argue that it is feasible to provide "low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055", with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.

The Canadian oil sands underlie 142,000 km2 of the boreal forest in northeastern Alberta. Oil sands production greenhouse gas (GHG) emissions increased from 15 million tonnes (Mt) to 55 Mt between 1990 and 2011. Their production represents the fastest-growing source of GHG emissions in Canada. A large body of studies show that oil sands industries have large environmental impacts, including effects on climate, land, water, and air quality but GHG emissions from oil sands land use disturbance and future land use impacts have yet to be examined in detail and the associated literature is scarce and incomplete. Our paper examines the historical and potential land use change and GHG emissions associated with oil sands development in Canada. Disturbance occurred between 1985 and 2009 from oil sands development were identified using remote sensing technique and mapped onto spatially explicit soil, biomass and peatlands carbon maps. We found that land use and GHG disturbance of oil sands production, especially in-situ technology that will be the dominant technology of choice for future oil sands development, are greater than previously reported. We estimate additional 500 km2 and 2,400 km2 of boreal forest including carbon-rich peatlands would be disturbed from surface mining and in-situ production, respectively, between 2012 and 2030; releasing additional 107–182 million tonnes of GHG from land use alone. Future efforts to monitor land use impacts of in-situ production are needed to reduce landscape impacts and associated GHG emissions. In addition, land reclamation after oil sands projects needs to be enforced for broad ecological benefits together with GHG benefits.

The global petroleum system is undergoing an “oil transition,” shifting from conventionally produced petroleum to a suite of substitutes for conventional petroleum (SCPs). This paper describes the Regional Optimization Model for Emissions from Oil Substitutes, or ROMEO, which models this oil transition. ROMEO models the dynamics of the transition to substitutes for oil and the environmental impacts (greenhouse gas (GHG) intensity) of such a transition. It models the global liquid fuel market in an optimization framework. The ROMEO market mechanism operates differently than “perfect foresight” models: it solves each year sequentially, with each year optimized under uncertainty about future prevailing prices or resource quantities.ROMEO includes more fuel types than models designed for integrated assessments of climate change. ROMEO also includes the differing carbon intensities and costs of production of these fuel types. We use ROMEO to calculate the uncertainty of future costs, emissions, and total fuel production under a number scenarios. We first explore the effects of altering three key input parameters. We then use this flexibility to more formally explore two uncertainties simultaneously: the endowment of conventional petroleum, and future carbon taxes. Results indicate that emissions penalties from production of oil substitutes are on the order of 5-20 GtC over the next 50 years, and that these results are highly sensitive to the endowment of conventional oil and less sensitive to the values of a carbon tax.