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.

Elevated reactive nitrogen (Nr) deposition is a concern for alpine ecosystems, and dry NH3 deposition is a key contributor. Understanding how emission hotspots impact downwind ecosystems through dry NH3 deposition provides opportunities for effective mitigation. However, direct NH3 flux measurements with sufficient temporal resolution to quantify such events are rare. Here, we measured NH3 fluxes at Rocky Mountain National Park (RMNP) during two summers and analyzed transport events from upwind agricultural and urban sources in northeastern Colorado. We deployed open-path NH3 sensors on a mobile laboratory and an eddy covariance tower to measure NH3 concentrations and fluxes. Our spatial sampling illustrated an upslope event that transported NH3 emissions from the hotspot to RMNP. Observed NH3 deposition was significantly higher when backtrajectories passed through only the agricultural region (7.9 ng m–2 s–1) versus only the urban area (1.0 ng m–2 s–1) and both urban and agricultural areas (2.7 ng m–2 s–1). Cumulative NH3 fluxes were calculated using observed, bidirectional modeled, and gap-filled fluxes. More than 40% of the total dry NH3 deposition occurred when air masses were traced back to agricultural source regions. More generally, we identified that 10 (25) more national parks in the U.S. are within 100 (200) km of an NH3 hotspot, and more observations are needed to quantify the impacts of these hotspots on dry NH3 deposition in these regions.

Satellite ammonia (NH3) observations provide unprecedented insights into NH3 emissions, spatiotemporal variabilities and trends, but validation with in situ measurements remains lacking. Here, total columns from the Infrared Atmospheric Sounding Interferometer (IASI) were intercompared to boundary layer NH3 profiles derived from aircraft‐ and surface‐based measurements primarily in Colorado, USA, in the summer of 2014. IASI‐NH3 version 3 near real‐time data set compared well to in situ derived columns (windows ±15 km around centroid, ±1 h around overpass time) with a correlation of 0.58, a slope of 0.78 ± 0.14 and an intercept of 2.1 × 1015±1.5 × 1015 molecules cm−2. Agreement degrades at larger spatiotemporal windows, consistent with the short atmospheric lifetime of NH3. We also examined IASI version 3R data, which relies on temperature retrievals from the ERA Reanalysis, and a third product generated using aircraft‐measured temperature profiles. The overall agreement improves slightly for both cases, and neither is biased within their combined measurement errors. Thus, spatiotemporal averaging of IASI over large windows can be used to reduce retrieval noise. Nonetheless, sampling artifacts of airborne NH3 instruments result in significant uncertainties of the in situ‐derived columns. For example, large validation differences exist between ascent and descent profiles, and the assumptions of the free tropospheric NH3 profiles used above the aircraft ceiling significantly impact the validation. Because short‐lived species like NH3 largely reside within the boundary layer with complex vertical structures, more comprehensive validation is needed across a wide range of environments. More accurate and widespread in situ NH3 data sets are therefore required for improved validations of satellite products.

Monthly, high‐resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k‐means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL‐AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial‐resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground‐based network sites.