Improving Mobile Platform Gaussian-Derived Emission Estimates Using Hierarchical Sampling and Large Eddy Simulation

Abstract

Abstract. Mobile laboratory measurements provide information on the distribution of CH4 emissions from point sources such as oil and gas wells, but uncertainties are poorly constrained or justified. Sources of uncertainty and bias in ground-based Gaussian derived emissions estimates from a mobile platform were analyzed in a combined field and modeling study. In a field campaign where 1009 natural gas sites in Pennsylvania were sampled, a hierarchical measurement strategy was implemented with increasing complexity. Of these sites, ~93% were sampled with an average of 2 transects (standard 15 sampling), ~5% were sampled with an average of 10 transects (replicate sampling) and ~2% were sampled with an average of 20 transects while simultaneously deploying a tower to measure high-frequency meteorological data (intensive sampling). Five of the intensive sampling sites were modeled using large eddy simulation (LES) to reproduce CH4 concentrations in a turbulent environment. The LES output and derived emission estimates were used to compare with the results of a standard Gaussian approach. The LES and Gaussian derived emission rates agreed within a factor of 2, in most cases with average differences of 20 25%. A controlled release was also used to investigate sources of bias in either technique. The Gaussian agreed with the release rate more closely than the LES underlying the importance of inputs as sources of uncertainty for the LES. The LES was also used as a virtual experiment to investigate optimum number of repeat transects and spacing needed to produce representative statistics. Approximately 10 repeat transects spaced at least 1 min apart are required to produce statistics similar to the observed variability over the entire LES simulation period of 30 min. In addition, other sources of uncertainty including source location, 25 wind speed and stability were analyzed. In total, atmospheric variability, observed by repeat measurements at individual sites under relatively constant conditions, was found to be the most significant contributor to total uncertainty. Accurate measurements of this condition provide a reasonable estimate of the lower bound for emission uncertainty. It is recommended that future mobile monitoring schemes quantify this metric under representative conditions to accurately estimate emission uncertainty. 30Atmos. Chem. Phys. Discuss., https://doi

Methane emissions from conventional and unconventional oil and gas production sites in southeastern Saskatchewan, Canada

“…We used a point-source Gaussian Dispersion Model (GDM) (Bosanquet and Pearson 1936, Sutton 1953, Pasquill 1974, Stern 1976, De Visscher 2013 to estimate CH 4 emission rates. Other transect-based mobile dispersion studies (Rella et al 2015, Yacovitch et al 2015, Caulton et al 2017 often integrate observed concentration enhancements across the entire width of the plume. The integration method is suitable so long as certain conditions can be satisfied, such as the emission source height being within range of the sampling inlet height, signal to noise from the background is sufficient to fully resolve the plume edges, and the transect is perpendicular downwind of the plume.…”

Methane emissions from contrasting production regions within Alberta, Canada: Implications under incoming federal methane regulations

“…These are discussed in detail in SM-S3. Previous ground based mobile dispersion studies have recorded uncertainty estimates ranging from 50-350% (Caulton et al, 2017). To quantify methodological uncertainty in this study, we conducted a series of controlled release experiments at the Carbon Management Canada Research Institutes Field Research Station near Brooks, AB, described further in SM-S3.2.…”

Using computational fluid dynamics and field experiments to improve vehicle-based wind measurements for environmental monitoring

“…Plume dispersion applications feed wind measurements paired with gaseous concentrations from mobile measurement platforms into gaussian dispersion models to locate emitting infrastructure, estimate source emission rates, and quantify emitted volumes of methane in oil and gas developments (Atherton et al, 2017;Caulton et al, 2017). In the gaussian dispersion model, emission rate scales linearly with wind speed.…”

“…Mobile surveying studies using trucks or sport utility vehicles (Atherton et al, 2017;Jackson et al, 2014;Phillips et al, 2013;Rella et al, 2015;Zazzeri et al, 2015) with anemometer placements above the vehicle are vulnerable to flow bias, and should use flow compensations to account for wind bias from the shape of the vehicle. Transect-based studies with anemometers placed atop sport utility vehicles (Caulton et al, 2017), should also apply flow compensations, although some transect-based studies using mobile laboratories (Roscioli et al, 2015;Yacovitch et al, 2015) with anemometers placed on a boom ahead of and above the vehicle are much more resilient to bias from the flow of the vehicle. Similarly, studies quantifying emissions in which the vehicle stops to obtain wind measurements (Brantley et al, 2014) and the anemometer is placed ahead of and above the vehicle, are unlikely to require compensations for the vehicle's flow field.…”