Mobile laboratory measurements provide information on the distribution of CH 4 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 in < 5 min (standard sampling), ∼ 5 % were sampled with an average of 10 transects in < 15 min (replicate sampling) and ∼ 2 % were sampled with an average of 20 transects in 15-60 min. For sites sampled with 20 transects, a tower was simultaneously deployed to measure highfrequency meteorological data (intensive sampling). Five of the intensive sampling sites were modeled using large eddy simulation (LES) to reproduce CH 4 concentrations in a turbulent environment. The LES output and LES-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 all except one case; the average difference was 25 %. A controlled release was also used to investigate sources of bias in either technique. The Gaussian method agreed with the release rate more closely than the LES, underlining the importance of inputs as sources of uncertainty for the LES. The LES was also used as a virtual experiment to determine an optimum number of repeat transects and spacing needed to produce repre-sentative 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. Sources of uncertainty from source location, wind speed, background concentration and atmospheric stability were also analyzed. The largest contribution to the total uncertainty was from atmospheric variability; this is caused by insufficient averaging of turbulent variables in the atmosphere (also known as random errors). Atmospheric variability was quantified by repeat measurements at individual sites under relatively constant conditions. Accurate quantification of atmospheric variability provides a reasonable estimate of the lower bound for emission uncertainty. The uncertainty bounds calculated for this work for sites with >50 ppb enhancements were 0.05-6.5q (where q is the emission rate) for single-transect sites and 0.5-2.7q for sites with 10+ transects. More transects allow a mean emission rate to be calculated with better precision. It is recommended that future mobile monitoring schemes quantify atmospheric variability, and attempt to minimize it, under representative conditions to accurately estimate emission uncertainty. These recommendations are general to mobile-laboratory-derived emissions from other sources that can be treated as point sources.

A large-scale study of methane emissions from well pads was conducted in the Marcellus shale (Pennsylvania), the largest producing natural gas shale play in the United States, to better identify the prevalence and characteristics of superemitters. Roughly 2100 measurements were taken from 673 unique unconventional well pads corresponding to ∼18% of the total population of active sites and ∼32% of the total statewide unconventional natural gas production. A log-normal distribution with a geometric mean of 2.0 kg h −1 and arithmetic mean of 5.5 kg h −1 was observed, which agrees with other independent observations in this region. The geometric standard deviation (4.4 kg h −1 ) compared well to other studies in the region, but the top 10% of emitters observed in this study contributed 77% of the total emissions, indicating an extremely skewed distribution. The integrated proportional loss of this representative sample was equal to 0.53% with a 95% confidence interval of 0.45−0.64% of the total production of the sites, which is greater than the U.S. Environmental Protection Agency inventory estimate (0.29%), but in the lower range of other mobile observations (0.09−3.3%). These results emphasize the need for a sufficiently large sample size when characterizing emissions distributions that contain superemitters.

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