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Recent interest in measuring methane (CH4) emissions from abandoned oil and gas infrastructure has resulted in several methods being continually used to quantify point source emissions less than 200 g CH4 h−1. The choice of measurement approach depends on how close observers can come to the source, the instruments available, and the meteorological/micrometeorological conditions. As such, static chambers, dynamic chambers, Bacharach Hi Flow Sampler (BHFS) measurements, Gaussian plume (GP) modeling, and backward Lagrangian stochastic (bLs) models have all been used, but there is no clear understanding of the accuracy or precision of each method. To address this, we copy the experimental design for each of the measurement methods to make single field measurements of a known source, to simulate single measurement field protocol, and then make repeat measurements to generate an understanding of the accuracy and precision of each method. Here, we present estimates for the average percentage difference between the measured emission and the known emission for three repeat measurements, Ar, for emissions of 40 to 200 g CH4 h−1. The static chamber data were not presented because of safety concerns during the experiments. Both the dynamic chamber (Ar = −10 %, −8 %, and −10 % at emission rates of 40, 100, and 200 g CH4 h−1, respectively) and BHFS (Ar = −18 %, −16 %, and −18 %) repeatedly underestimate the emissions, but the dynamic chamber had better accuracy. The standard deviation of emissions from these direct measurement methods remained relatively constant for emissions between 40 and 200 g CH4 h−1. For the far-field methods, the bLs method generally underestimated emissions (Ar = +6 %, −6 %, and −7 %) while the GP method significantly overestimated the emissions (Ar = +86 %, +57 %, and +29 %) despite using the same meteorological and concentration data as input. Variability in wind speed, wind direction, and atmospheric stability over the 20 min averaging period are likely to propagate through to large variability in the emission estimate, making these methods less precise than the direct measurement methods. To our knowledge, this is the first time that methods for measuring CH4 emissions from point sources between 40 and 200 g CH4 h−1 have been quantitatively assessed against a known reference source and against each other.

Continuous emission monitoring (CM) solutions promise to accelerate fugitive methane emission mitigation in natural gas infrastructure by detecting large sources sooner than they would be detected in traditional leak surveys. Quantification by CM solutions has also been proposed as the foundation of measurement-based inventories. Herein we present the first published results of CM solutions tested using a consensus protocol. Single-blind testing was performed with controlled releases at a dedicated facility, using test conditions that were challenging but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicate detection limits (emission rate with 90% probability of detection) of 3-30 kg CH4/h, with 6 of 11 solutions in the range of 3-6 kg CH4/h, with high uncertainty. Of the 11 solutions tested, 6 provided emission rates estimates and for emission rate of 0.1-1 kg/h, the solutions' mean relative errors ranged from -44% to +586% with single estimates between -90% and +300% (all uncertainties stated as empirical 95% confidence intervals) with most solutions' upper uncertainty exceeding +900%. Solutions' mean relative error for emission rates >1 kg/h is -40% to +93% with two solutions achieving mean relative errors below 20%. In this range, all solutions exhibited single-estimate relative error from below -80% to values above +240%. When test results were simulated using emissions data from a portfolio of facilities, two solutions estimated emissions to within ±20%, while 4 estimated emissions at least double the true emission rate. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicate that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting. Additionally, given the rapid development of these solutions, additional implementation standards and regular testing will also be necessary.

Recent interest in quantifying trace gas emissions from point sources, such as measuring methane (CH4) emissions from oil and gas wells, has resulted in several methods being used to estimate emissions from sources with emission rates below 200g CH4 hour−1. The choice of measurement approach depends on how close observers can get to the source, the instruments available and the meteorological/micrometeorological conditions. As such, static chambers, dynamic chambers, HiFlow measurements, Gaussian plume modelling and backward Lagrangian stochastic (bLs) models have all been used, but there is no clear understanding of the accuracy or precision of each method. To address this, we copy the experimental design for each of the measurement methods to make single field measurements of a known source, to simulate single measurement field protocol, and then make repeat measurements to generate an understanding of the accuracy and precision of each method. Here, for comparison, we present estimates for the percentage difference between the measured emission and the known emission, A, and the average percentage difference for three repeat measurements, Ar , for emissions of 200 g CH4 h−1. Our results show that, even though the dynamic chamber repeatedly underestimates the emission, it is the most accurate for a single measurement and the accuracy improves with subsequent measurements (A = −11 %, Ar = −10 %). The single HiFlow emission estimate was also an underestimate, however, poor instrument precision resulted in reduced accuracy of emission estimate to becomes less accurate after repeat measurements (A = −16 %, Ar = −18 %). Of the far field methods, the bLs method underestimated emissions both for single and repeat measurements (A = −11 %, Ar = −7 %) while the GP method significantly overestimated the emissions (A = 33 %, Ar = 29 %) despite using the same meteorological and concentration data as input. Additionally, our results show that the accuracy and precision of the emission estimate increases as the flow rate of the source is increased for all methods. To our knowledge this is the first time that methods for measuring CH4 emissions from point sources less than 200 g CH4 h−1 have been quantitively assessed against a known reference source and each other.

Methane (CH4) leakage from natural gas (NG) pipelines poses an environmental, safety, and economic threat to the public. While previous leak detection and quantification studies focus on the aboveground infrastructure, the analysis of underground NG pipeline leak scenarios is scarce. Furthermore, no data from controlled release experiments have been published on the accuracy of methods used to (1) quantify emissions from an area source and (2) use these emissions to quantify the size of a subsurface leak. This proof-of-concept work uses CH4 mole fraction, as measured by a single gas sensor, as an input to a simple dispersion-based model (WindTrax) under ideal conditions (i.e., in a field) and compares the calculated emissions to the known controlled NG release rates. The aboveground and surface CH4 mole fractions were measured for 5 days at a field testbed using controlled underground release rates ranging from 0.08 to 0.52 kg hr–1 (3.83–24.94 ft3 hr–1). Results confirmed that the mean normalized CH4 mole fraction increases as the atmosphere transitions from the Pasquill–Gifford (PG) stability class A (extremely unstable) to G (extremely stable). The estimated surface CH4 emissions showed large temporal variability, and for the emission rates tested, at least 6 h of data are needed to have a representative estimate from subsurface pipeline leaks (±27% of the controlled release rate on average). The probability that the emission estimate is within ±50% of the controlled release rate (P±50%) is approximately 50% when 1 h of data is collected; the probability approaches 100% with 3–4 h of data. Findings demonstrate the importance of providing enough data over time for accurate estimation of belowground leak scenarios. By adopting the estimation method described in this study, operators can better estimate leakage rates and identify and repair the largest leaks, thereby optimizing annual greenhouse gas emissions reductions and improving public safety.

Methane (CH4) is emitted during extraction, processing, and transport processes in the natural gas industry. As a powerful greenhouse gas, CH4 releases are harmful to the environment. Operators aim to minimize methane loss, and continuous monitoring using low-cost fence-line sensors are now being developed to observe methane enhancements downwind of operations. However, it is not clear how useful these systems are and whether they can be used to quantify emissions or simply identify the presence of a leak. To investigate this, we deployed 4 calibrated low-cost sensors 30 m from emissions of known rates over a 48-h period. The aims were to determine: (1) how much of the time a fence-line system would detect a leakage event from a single, point source of the size typically seen at oil and gas production well pads and (2) how accurately a fence-line system can estimate CH4 emissions using a relatively simple downwind dispersion method. Our results show that during a 48-h measurement period, the fence-line sensor network could detect CH4 releases of 84 g h−1 40% of the time and 100% of the time for emissions greater than 167 g h−1 using an enhancement threshold of 2 ppm. A Gaussian plume inversion based on binned centerline, maximum measured concentrations and the WindTrax Lagrangian particle model were each tested. With these models, average estimated emissions were within ±50% of a known emission rate in 24 h and ±25% in 48 h; however, estimated individual 20-min average emissions vary by more than a factor of 10. A simple Gaussian plume inversion using all of the measured concentrations produced unreasonable average emission estimates because of the inability of the equation to parameterize lateral dispersion at distances less than 100 m when the sensor was on the edge of the plume. This study provides evidence to support the use of low-cost sensors as autonomous fence-line monitoring systems to detect and potentially quantify emissions. If the sensors are properly calibrated and sensor deployment location is optimized for prevailing wind directions at each site, fence-line systems could be used routinely to quantify emissions from oil and gas infrastructure.

Leaks in natural gas distribution networks are often initially detected by odor and then localized during operators' walking surveys. The size of the enhancement detected and the leak's proximity to infrastructure are used to determine leak severity of the leak and how quickly it is repaired. Methane (CH4) enhancements on the surface above the leak change with atmospheric conditions, but currently the leak severity assessment is made without considering the environmental conditions and could result in misdiagnosis of the leak. This study has developed the “ESCAPE” model, a tool that can be used to estimate CH4 enhancements on the surface above a leak using meteorological and leak data as input. Surface CH4 concentrations calculated using the ESCAPE model agrees well with measurements made during controlled experiments (m = 0.95 and R2 = 0.95). This study highlights the effect of micrometeorology on gas transport from the surface to the atmosphere, where the surface and the atmospheric conditions have the largest effect on the flux. Here, recommendations are made to improve industry best practices, including recording the meteorological conditions at the time of the leak detection, avoiding walking surveys on days where there are strong winds or strong solar irradiance and that known leak locations should be revisited and measured in different conditions. Mitigating CH4 emissions from the gas distribution network is a cost-effective and economically realistic target. Distribution operators can be directed by the output of ESCAPE model to correctly identify and repair the largest distribution leaks and could reduce annual CH4 emissions by 0.69 Tg, this may go some way to reducing energy sector emissions in accordance with the Paris Agreement.