Site-level measurements of methane emissions are used by operators for reconciliation with bottom-up emission inventories with the aim to improve accuracy, thoroughness, and confidence in reported emissions. In that context it is of critical importance to avoid measurement errors and to understand the measurement uncertainty. Remotely piloted aircraft systems (commonly referred to as “drones”) can play a pivotal role in the quantification of site-level methane emissions. Typical implementations use the “mass balance method” to quantify emissions, with a high-precision methane sensor mounted on a quadcopter drone flying in a vertical curtain pattern; the total mass emission rate can then be computed post hoc from the measured methane concentration data and simultaneous wind data. Controlled-release tests have shown that errors with the mass balance method can be considerable. For example, Liu et al. (2024) report absolute errors for more than 100 % for the two drone solutions tested; on the other hand, errors can be much smaller, of the order of 16 % root-mean-square errors in Corbett and Smith (2022), if additional constraints are placed on the data, restricting the analysis to cases where the wind field was steady. In this paper we present a systematic error analysis of physical phenomena affecting the error in the mass balance method for parameters related to the acquisition of methane concentration data and to postprocessing. The sources of error are analyzed individually, and it must be realized that individual errors can accumulate in practice, and they can also be augmented by other sources that are not included in the present work. Examples of these sources include the uncertainty in methane concentration measurements by a sensor with finite precision or the method used to measure the unperturbed wind velocity at the position of the drone. We find that the most important source of error considered is the horizontal and vertical spacings in the data acquisition, as a coarse spacing can result in missing a methane plume. The potential error can be as high as 100 % in situations where the wind speed is steady and the methane plume has a coherent shape, contradicting the intuition of some operators in the industry. The likelihood of the extent of this error can be expressed in terms of a dimensionless number defined by the spatial resolution of the methane concentration measurements and the downwind distance from the main emission sources. What is learned from our theoretical error analysis is then applied to a number of historical measurements in a controlled-release setting. We show how what is learned about the main sources of error can be used to eliminate potential errors during the postprocessing of flight data. Second, we evaluate an aggregated data set of 1001 historical drone flights; our analysis shows that the potential errors in the mass balance method can be of the order of 100 % on occasion, even though the individual errors can be much smaller in the vast majority of the flights. The Discussion section provides some guidelines for industry on how to avoid or minimize potential errors in drone measurements for methane emission quantification.

Production of oil and gas in Romania, one of the largest producers in the European Union (EU), is associated with substantial emissions of methane to the atmosphere and may offer high emission mitigation potential to reach the climate objectives of the EU. However, comprehensive quantification of emissions in this area has been lacking. Here we report top-down emission rate estimates derived from aircraft-based in situ measurements that were carried out with two aircraft during the 2019 ROmanian Methane Emissions from Oil and gas (ROMEO) campaign, supported by simulations with atmospheric models. Estimates from mass balance flights at individual dense production clusters and around larger regions show large variations between the clusters, supporting the important role of individual super-emitters, and possibly show variable operation practices or maintenance states across the production basin. Estimated annual total emissions from the southern Romanian oil and gas (O&G) infrastructure are 227 ± 86 kt CH4 yr−1, consistent with previously published estimates from ground-based site-level measurements during the same period. The comparison of individual plumes between measurements and atmospheric model simulations was complicated by unfavorable low-wind conditions. Similar correlations between measured and simulated CH4 enhancements during large-scale raster flights and mass balance flights suggest that the emission factor determined from a limited number of production clusters is representative of the larger regions. We conclude that ground-based and aerial emission rate estimates derived from the ROMEO campaign agree well, and the aircraft observations support the previously suggested large under-reporting of CH4 emissions from the Romanian O&G industry in 2019 to United Nations Framework Convention on Climate Change (UNFCCC). We also observed large underestimation from O&G emissions in the Emissions Database for Global Atmospheric Research (EDGAR) v7.0 for our domain of study.

The oil and gas (O&G) sector is a significant source of methane (CH4) emissions. Quantifying these emissions remains challenging, with many studies highlighting discrepancies between measurements and inventory-based estimates. In this study, we present CH4 emission fluxes from 21 offshore O&G facilities collected in 10 O&G fields over two regions of the Norwegian continental shelf in 2019. Emissions of CH4 derived from measurements during 13 aircraft surveys were found to range from 2.6 to 1200 t yr−1 (with a mean of 211 t yr−1 across all 21 facilities). Comparing this with aggregated operator-reported facility emissions for 2019, we found excellent agreement (within 1σ uncertainty), with mean aircraft-measured fluxes only 16 % lower than those reported by operators. We also compared aircraft-derived fluxes with facility fluxes extracted from a global gridded fossil fuel CH4 emission inventory compiled for 2016. We found that the measured emissions were 42 % larger than the inventory for the area covered by this study, for the 21 facilities surveyed (in aggregate). We interpret this large discrepancy not to reflect a systematic error in the operator-reported emissions, which agree with measurements, but rather the representativity of the global inventory due to the methodology used to construct it and the fact that the inventory was compiled for 2016 (and thus not representative of emissions in 2019). This highlights the need for timely and up-to-date inventories for use in research and policy. The variable nature of CH4 emissions from individual facilities requires knowledge of facility operational status during measurements for data to be useful in prioritising targeted emission mitigation solutions. Future surveys of individual facilities would benefit from knowledge of facility operational status over time. Field-specific aggregated emissions (and uncertainty statistics), as presented here for the Norwegian Sea, can be meaningfully estimated from intensive aircraft surveys. However, field-specific estimates cannot be reliably extrapolated to other production fields without their own tailored surveys, which would need to capture a range of facility designs, oil and gas production volumes, and facility ages. For year-on-year comparison to annually updated inventories and regulatory emission reporting, analogous annual surveys would be needed for meaningful top-down validation. In summary, this study demonstrates the importance and accuracy of detailed, facility-level emission accounting and reporting by operators and the use of airborne measurement approaches to validate bottom-up accounting.

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA's Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations.

Daytime atmospheric boundary layer (ABL) dynamics—including potential temperature budgets, water vapour budgets, and entrainment rates—are presented from in situ flight data taken on six afternoons near Fresno in the San Joaquin Valley (SJV) of California during July/August 2016. The flights took place as a part of the California Baseline Ozone Transport Study aimed at investigating transport pathways of air entering the Central Valley from offshore and mixing down to the surface. Midday entrainment velocity estimates ranged from 0.8 to 5.4 cm s−1 and were derived from a combination of continuously determined ABL heights during each flight and model-derived subsidence rates, which averaged -2.0 cm s−1 in the flight region. A strong correlation was found between entrainment velocity (normalized by the convective velocity scale) and an inverse bulk ABL Richardson number, suggesting that wind shear at the ABL top plays a significant role in driving entrainment. Similarly, we found a strong correlation between the entrainment efficiency (the ratio of entrainment to surface heat fluxes with an average of 0.23 ± 0.15) and the wind speed at the ABL top. We explore the synoptic conditions that generate higher winds near the ABL top and propose that warm anomalies in the southern Sierra Nevada mountains promote increased entrainment. Additionally, a method is outlined to estimate turbulence kinetic energy, convective velocity scale (w*), and the surface sensible heat flux in the ABL from a slow, airborne wind measurement system using mixed-layer similarity theory.

Robust air pollutant emission reduction strategies rely on accurate source characterizations. Studies have reported that the methane (CH4) emissions from California’s dairy sector are highly variable, and raised questions regarding the representativeness of the statewide CH4 emission estimates. To address these questions, a multi-tiered field study was conducted to evaluate the dairy CH4 emission factors (EFs) used in the California Air Resources Board (CARB) emission inventory. These EFs for dairy cow enteric fermentation and anaerobic lagoons are 144 and 332 kg of CH4 per animal head per year. CH4 emissions from dairies located within the San Joaquin Valley (SJV) were characterized through the analysis of ground-level mobile measurements collected in the summer and the fall of 2019. We developed a novel dispersion modeling-based approach that uses the ground-level mobile measurements of ambient CH4 levels to generate a metric that evaluates the CARB CH4 EFs. The analysis was further expanded using airborne measurements collected between September 2017 and May 2020. The multi-tiered measurements resulted in 126 whole-farm CH4 emission estimates for 107 dairies, making this study the most comprehensive mobile research study focused on CH4 emissions from California dairies to date. CARB CH4 EFs represented dairy CH4 emissions captured in this study fairly well; emissions observed in ground-level mobile measurements in summer and fall, and airborne measurements were greater than the inventory-reported values by only 8% (95% Confidence Interval [CI95%]: -7%–31%), 3% (CI95%: -11 – 21%), and 10% (CI95%: -7 – 45%), respectively. The results underline the effects of meteorology on dairy emissions and suggest that CH4 EFs from dairies with different ranges of herd sizes did not vary significantly. Future evaluation of the CARB CH4 emission inventory must incorporate facility-level data and leverage mechanistic approaches that capture the temporal variation of emissions from California’s dairy farms. Such improvements would enable research studies to better inform the emission inventory and help track the emissions changes from the implementation of new manure management technologies across California.

To combat global warming, Canada has committed to reducing greenhouse gases to be (GHGs) 40 %–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation's (SciAv) Gaussian algorithm and the Environment and Climate Change Canada's top-down emission rate retrieval algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance box-flight methods produce similar estimates that agree (3 %–25 %) within algorithm uncertainties (4 %–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv algorithm may improve emission estimation. Algorithms disagreed when non-ideal conditions occurred (i.e., under non-stationary atmospheric conditions). Overall, the results provide confidence in the box-flight methods and indicate that emissions estimates are not overly sensitive to the choice of algorithm but demonstrate that fundamental algorithm assumptions should be assessed for each flight. Using a different method, the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) independently mapped individual plumes with emissions 5 times larger than the source SciAv sampled three days later. The range in estimates highlights the utility of increased sampling to get a more complete understanding of the temporal variability of emissions and to identify emission sources within facilities. In addition, hourly on-site activity data would provide insight to the observed temporal variability in emissions and make a comparison to reported emissions more straightforward.

Emission rates were estimated for >100 oil and gas production sites with significant liquid-handling equipment (tank battery sites) in the Permian Basin of west Texas. Emission estimates based on equipment counts and emission factors, but not accounting for large uninventoried emission events, led to ensemble average emission rates of 1.8-3.6 kg/h per site. None of the site-specific emission estimates for individual sites, based on equipment counts, exceeded 10 kg/h. On-site drone-based emission measurements led to similar emission estimates for inventoried sources. Multiple aircraft measurement platforms were deployed and reported emissions exceeding 10 kg/h at 14-27% of the sites, and these high-emission rate sites accounted for 80-90% of total emissions for the ensemble of sites. The aircraft measurement systems were deployed asynchronously but within a 5 day period. At least half of the sites with emission rates above 10 kg/h detected by aircraft had emissions that did not persist at a level above 10 kg/h for repeat measurements, suggesting typical high-emission rate durations of a few days or less for many events. The two aircraft systems differed in their estimates of total emissions from the ensembles of sites sampled by more than a factor of 2; however, the normalized distributions of emissions for sites with emission rates of >10 kg/h were comparable for the two aircraft-based methods. The differences between the two aircraft-based platforms are attributed to a combination of factors; however, both aircraft-based emission measurement systems attribute a large fraction of emissions to sites with an emission rate of >10 kg/h.

Daytime onshore lake breezes are a critical factor controlling ozone abundance at coastal sites around Lake Michigan. Coastal counties along the western shore of Lake Michigan have historically observed high ozone episodes dating to the 1970s. We classified ozone episode days based on the extent or absence of the lake breeze (i.e., "inland", "near-shore" or "no" lake breeze) to establish a climatology of these events. This work demonstrated variable gradients in ozone abundances based on these different types of meteorology, with the sharpest ozone concentration gradients on days with a near-shore lake breeze. On 76-82% of days in which ozone reached 70 ppb for at least 1 hour, a lake breeze was present. Evidence of ozone gradients from multiple observation platforms during the 2017 Lake Michigan Ozone Study (LMOS 2017) are shown for two days with different depths of lake breezes.

The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) was conducted in May and June of 2017 to study the transport of ozone (O3) to Clark County, Nevada, a marginal non-attainment area in the southwestern United States (SWUS). This 6-week (20 May–30 June 2017) field campaign used lidar, ozonesonde, aircraft, and in situ measurements in conjunction with a variety of models to characterize the distribution of O3 and related species above southern Nevada and neighboring California and to probe the influence of stratospheric intrusions and wildfires as well as local, regional, and Asian pollution on surface O3 concentrations in the Las Vegas Valley (≈ 900 m above sea level, a.s.l.). In this paper, we describe the FAST-LVOS campaign and present case studies illustrating the influence of different transport processes on background O3 in Clark County and southern Nevada. The companion paper by Zhang et al. (2020) describes the use of the AM4 and GEOS-Chem global models to simulate the measurements and estimate the impacts of transported O3 on surface air quality across the greater southwestern US and Intermountain West. The FAST-LVOS measurements found elevated O3 layers above Las Vegas on more than 75 % (35 of 45) of the sample days and show that entrainment of these layers contributed to mean 8 h average regional background O3 concentrations of 50–55 parts per billion by volume (ppbv), or about 85–95 µg m−3. These high background concentrations constitute 70 %–80 % of the current US National Ambient Air Quality Standard (NAAQS) of 70 ppbv (≈ 120 µg m−3 at 900 m a.s.l.) for the daily maximum 8 h average (MDA8) and will make attainment of the more stringent standards of 60 or 65 ppbv currently being considered extremely difficult in the interior SWUS.

Methane emissions from >100 tank battery sites in the Permian Basin of west Texas were quantified using two different aircraft-based measurement platforms deployed asynchronously, but within a 5-day period. Emissions were detected by aircraft with rates ranging from 0.71 kg/hr to >750 kg/hr. On-site emission measurements were also made using a drone-based technology at a representative sample of 33 sites and were compared to engineering estimates of emission rates. A large fraction of total emissions from the sites sampled by the aircraft systems (90%±6%) were accounted for by sites with emissions greater than 10 kg/hr, when engineering estimates of emissions were assigned to sites with no emissions detected by aircraft. At least half of the sites with emission rates above 10 kg/hr had emissions that did not persist longer than a few days, and consequently, site by site comparisons of asynchronous aircraft measurements showed poor agreement. The two aircraft systems differed in their estimates of total emissions from the ensembles of sites sampled, and in the percentage of sites with emissions greater than 10 kg/hr. The differences in frequencies of detection between the two aircraft based platforms are attributed to a combination of factors, however, both aircraft-based emission measurement systems attribute a large fraction of emissions to sites with emission rates >10 kg/hr.

We use atmospheric observations to quantify methane (CH4) emissions from Mexico’s most important onshore and offshore oil and gas production regions which account for 95% of oil production and 78% of gas production. We use aircraft-based top-down measurements at the regional and facility-levels to determine emissions. Satellite data (TROPOMI CH4 data and VIIRS night-time flare data) provide independent estimates of emissions over 2 years. Our airborne estimate of the offshore region’s emissions is 2800 kg CH4 h−1 (95% confidence interval (CI): 1700–3900 kg CH4 h−1), more than an order of magnitude lower than the Mexican national greenhouse gas inventory estimate. In contrast, emissions from the onshore study region are 29 000 kg CH4 h−1 (95% CI: 19 000–39 000 kg CH4 h−1), more than an order of magnitude higher than the inventory. One single facility—a gas processing complex that receives offshore associated gas—emits 5700 kg CH4 h−1 (CI: 3500–7900 kg CH4 h−1), with the majority of those emissions related to inefficient flaring and representing as much as half of Mexico’s residential gas consumption. This facility was responsible for greater emissions than the entirety of the largest offshore production region, suggesting that offshore-produced associated gas is being transported onshore where it is burned and in the process some released to the atmosphere. The satellite-based data suggest even higher emissions for the onshore region than did the temporally constrained aircraft data (>20 times higher than the inventory). If the onshore production region examined is representative of Mexican production generally, then total CH4 emissions from Mexico’s oil and gas production would be similar to, or higher than, the official inventory, despite the large overestimate of offshore emissions. The main driver of inaccuracies in the inventory is the use of generic, non-Mexican specific emission factors. Our work highlights the need for local empirical characterization of emissions if effective emissions mitigation is to be undertaken.

Methane emissions associated with the production, transport, and use of oil and natural gas increase the climatic impacts of energy use; however, little is known about how emissions vary temporally and with commodity prices. We present airborne and ground-based data, supported by satellite observations, to measure weekly to monthly changes in total methane emissions in the United States' Permian Basin during a period of volatile oil prices associated with the COVID-19 pandemic. As oil prices declined from ∼ USD 60 to USD 20 per barrel, emissions changed concurrently from 3.3 % to 1.9 % of natural gas production; as prices partially recovered, emissions increased back to near initial values. Concurrently, total oil and natural gas production only declined by ∼ 10 % from the peak values seen in the months prior to the crash. Activity data indicate that a rapid decline in well development and subsequent effects on associated gas flaring and midstream infrastructure throughput are the likely drivers of temporary emission reductions. Our results, along with past satellite observations, suggest that under more typical price conditions, the Permian Basin is in a state of overcapacity in which rapidly growing associated gas production exceeds midstream capacity and leads to high methane emissions.

Methane (CH4) emissions from oil and gas activities are large and poorly quantified, with onshore studies showing systematic inventory underestimates. We present aircraft measurements of CH4 emissions from offshore oil and gas platforms collected over the U.S. Gulf of Mexico in January 2018. Flights sampled individual facilities as well as regions of 5-70 facilities. We combine facility-level samples, production data, and inventory estimates to generate an aerial measurement-based inventory of CH4 emissions for the U.S. Gulf of Mexico. We compare our inventory and the Environmental Protection Agency Greenhouse Gas Inventory (GHGI) with regional airborne estimates. The new inventory and regional airborne estimates are consistent with the GHGI in deep water but appear higher for shallow water. For the full U.S. Gulf of Mexico our inventory estimates total emissions of 0.53 Tg CH4/yr [0.40-0.71 Tg CH4/yr, 95% CI] and corresponds to a loss rate of 2.9% [2.2-3.8%] of natural gas production. Our estimate is a factor of 2 higher than the GHGI updated with 2018 platform counts. We attribute this disagreement to incomplete platform counts and emission factors that both underestimate emissions for shallow water platforms and do not account for disproportionately high emissions from large shallow water facilities.

This study derives methane emission rates from 92 airborne observations collected over 23 facilities including 5 refineries, 10 landfills, 4 wastewater treatment plants (POTWs), 2 composting operations, and 2 dairies in the San Francisco Bay Area. Emission rates are measured using an airborne mass-balance technique from a low-flying aircraft. Annual measurement-based sectorwide methane emissions are 19,000 ± 2300 Mg for refineries, 136,700 ± 25,900 Mg for landfills, 11,900 ± 1,500 Mg for POTWs, and 11,100 ± 3,400 Mg for composting. The average of measured emissions for each refinery ranges from 4 to 23 times larger than the corresponding emissions reported to regulatory agencies, while measurement-derived landfill and POTW estimates are approximately twice the current inventory estimates. Significant methane emissions at composting facilities indicate that a California mandate to divert organics from landfills to composting may not be an effective measure for mitigating methane emissions unless best management practices are instituted at composting facilities. Complementary evidence from airborne remote sensing imagery indicates atmospheric venting from refinery hydrogen plants, landfill working surfaces, composting stockpiles, etc., to be among the specific source types responsible for the observed discrepancies. This work highlights the value of multiple measurement approaches to accurately estimate facility-scale methane emissions and perform source attribution at subfacility scales to guide and verify effective mitigation policy and action.

A wide body of research has characterized methane emissions from the oil and gas supply chain in the US, with recent efforts gaining traction in Canada and Europe. In contrast, empirical data is limited for other significant oil and gas producing regions across the global south. Consequently, measuring and characterizing methane emissions across global oil and gas operations is crucial to the design of effective mitigation strategies. Several countries have announced pledges to reduce methane emissions from this sector (e.g., North America, Climate and Clean Air Coalition [CCAC] ministers). In the case of Mexico, the federal government recently published regulations supporting a 40-45% reduction of methane emissions from oil and gas. For these regulations to be effective, it is critical to understand the current methane emission patterns. We present results from multi-scale empirical estimates of methane emissions from Mexico’s major oil and gas production regions (both offshore and onshore), based on a set of airborne-based measurement campaigns, analysis of satellite data (TROPOMI), and development of spatially explicit inventories. Our results provide a revised estimate of total emissions in the sampled regions and highlight the importance of empirically based characterization as a basis for prioritization in terms of emission reduction opportunities. Finally, we highlight how these measurements –as well as similar policy-relevant studies- connect into action, based on the current needs from relevant stakeholders (e.g., inventory builders, regulators and industry).

Ozone is one of the six “criteria” pollutants identified by the U.S. Clean Air Act Amendment of 1970 as particularly harmful to human health. Concentrations have decreased markedly across the United States over the past 50 years in response to regulatory efforts, but continuing research on its deleterious effects have spurred further reductions in the legal threshold. The South Coast and San Joaquin Valley Air Basins of California remain the only two “extreme” ozone nonattainment areas in the United States. Further reductions of ozone in the West are complicated by significant background concentrations whose relative importance increases as domestic anthropogenic contributions decline and the national standards continue to be lowered. These background concentrations derive largely from uncontrollable sources including stratospheric intrusions, wildfires, and intercontinental transport. Taken together the exogenous sources complicate regulatory strategies and necessitate a much more precise understanding of the timing and magnitude of their contributions to regional air pollution. The California Baseline Ozone Transport Study was a field campaign coordinated across Northern and Central California during spring and summer 2016 aimed at observing daily variations in the ozone columns crossing the North American coastline, as well as the modification of the ozone layering downwind across the mountainous topography of California to better understand the impacts of background ozone on surface air quality in complex terrain.