The accurate reporting of methane (CH₄) emissions from point sources, such as fugitive leaks from oil and gas infrastructure, is important for evaluating climate change impacts, assessing CH₄ fees for regulatory programs, and validating methane intensity in differentiated gas programs. Currently, there are disagreements between emissions reported by different quantification techniques for the same sources. It has been suggested that downwind CH₄ quantification methods using CH₄ measurements on the fence-line of production facilities could be used to generate emission estimates from oil and gas operations at the site level, but it is currently unclear how accurate the quantified emissions are. To investigate model accuracy, this study uses fence-line simulated data collected during controlled release experiments as input for eddy covariance, aerodynamic flux gradient, backward Lagrangian stochastic model, and the Gaussian plume inverse methods in a range of atmospheric conditions. Eddy covariance’s data failed the quality test based on Mauder and Foken (2004) (0-1-2 system) quality test and could not be used for quantification. The aerodynamic flux gradient method quantified within a relative factor (estimated emission/actual emission) of 0.4 to 0.85 for a single release single emission, and at between 2.51 and 4.21 for multiple releases single emissions. The backward Lagrangian stochastic model for point sources using WindTrax performed well for single release single emissions, relative factor of between 0.82 to 1.07, but largely overestimated emissions for multiple releases single emissions, relative factor of 418.8, 2156.7, and 3.91 at 5, 10, and 15-minute averaging. Similar to the backward Lagrangian stochastic model, the Gaussian plume inverse model performed well for single point sources, average relative factor of 3. However, the model largely overestimated emissions when multiple releases were happening, relative factor between 20 and 30. As continuous monitoring of oil and gas sites involves complex emissions where plumes are not defined due to multiple sources, this study shows that the common downwind point source dispersion models could largely overestimate emissions. Aerodynamic flux gradient provided promising results for multiple releases quantification, and this study recommends more testing of flux quantification models for oil and gas continuous monitoring quantification.

Methane concentration data for eddy covariance, Gaussian plume inverse method, and the backward Lagrangian stochastic model were collected through an inlet tubing (3.275 inner diameter) at 3 m height, connected to the ABB (Zurich, Switzerland) GLA131 Series Microportable Greenhouse Gas Analyzer (MGGA) set to sample at 10 Hz. The MGGA is a closed-path greenhouse gas analyzer with a ~3.2 lpm pump flowrate, 10 cm cell length, 1 inch cell diameter (~0.23 standard cubic centimeters per minute (sccm) effective volume), and 0.4 s gas flow response time. The inlet tubing was collocated with an R. M. Young (Traverse City, MI, USA) 81000 sonic anemometer (R.M. Young Company, 2023), which measured micrometeorology at 10 Hz. The northward, eastward, and vertical separation of the inlet tubing from the sonic anemometer was 0, 0, -10 cm, respectively. For aerodynamic flux gradient, CH₄ concentration data were collected at 2 and 4 m using two Aeris (Hayward, CA, USA) MIRA Ultra Series analyzers connected to tubing with a 3.275 inner diameter. As we had only one sonic anemometer, data from the sonic anemometer collocated with the MGGA were used for the aerodynamic flux gradient quantification.