Ammoniated aerosols are important for urban air quality, but emissions of the key precursor NH are not well quantified. Mobile laboratory observations are used to characterize fleet-integrated NH emissions in six cities in the U.S. and China. Vehicle NH:CO emission ratios in the U.S. are similar between cities (0.33-0.40 ppbv/ppmv, 15% uncertainty) despite differences in fleet composition, climate, and fuel composition. While Beijing, China has a comparable emission ratio (0.36 ppbv/ppmv) to the U.S. cities, less developed Chinese cities show higher emission ratios (0.44 and 0.55 ppbv/ppmv). If the vehicle CO inventories are accurate, NH emissions from U.S. vehicles (0.26 ± 0.07 Tg/yr) are more than twice those of the National Emission Inventory (0.12 Tg/yr), while Chinese NH vehicle emissions (0.09 ± 0.02 Tg/yr) are similar to a bottom-up inventory. Vehicle NH emissions are greater than agricultural emissions in counties containing near half of the U.S. population and require reconsideration in urban air quality models due to their colocation with other aerosol precursors and the uncertainties regarding NH losses from upwind agricultural sources. Ammonia emissions in developing cities are especially important because of their high emission ratios and rapid motorizations.

Atmospheric emissions from animal husbandry are important to both air quality and climate, but are hard to characterize and quantify as they differ significantly due to management practices and livestock type, and they can vary substantially throughout diurnal and seasonal cycles. Using a new mobile laboratory, ammonia (NH), methane (CH), nitrous oxide (NO), and other trace gas emissions were measured from four concentrated animal feeding operations (CAFOs) in northeastern Colorado. Two dairies, a beef cattle feedlot, and a sheep feedlot were chosen for repeated diurnal and seasonal measurements. A consistent diurnal pattern in the NH to CH enhancement ratio is clearly observed, with midday enhancement ratios approximately four times greater than nighttime values. This diurnal pattern is similar, with slight variations in magnitude, at the four CAFOs and across seasons. The average NH to CH enhancement ratio from all seasons and CAFOs studied is 0.17 (+0.13/-0.08) mol/mol, in agreement with statewide inventory averages and previous literature. Enhancement ratios for NH to NO and NO to CH are also reported. The enhancement ratios can be used as a source signature to distinguish feedlot emissions from other NH and CH sources, such as fertilizer application and fossil fuel development, and the large diurnal variability is important for refining inventories, models, and emission estimates.

A model aircraft equipped with a custom laser-based, open-path methane sensor was deployed around a natural gas compressor station to quantify the methane leak rate and its variability at a compressor station in the Barnett Shale. The open-path, laser-based sensor provides fast (10 Hz) and precise (0.1 ppmv) measurements of methane in a compact package while the remote control aircraft provides nimble and safe operation around a local source. Emission rates were measured from 22 flights over a one-week period. Mean emission rates of 14 ± 8 g CH4 s(-1) (7.4 ± 4.2 g CH4 s(-1) median) from the station were observed or approximately 0.02% of the station throughput. Significant variability in emission rates (0.3-73 g CH4 s(-1) range) was observed on time scales of hours to days, and plumes showed high spatial variability in the horizontal and vertical dimensions. Given the high spatiotemporal variability of emissions, individual measurements taken over short durations and from ground-based platforms should be used with caution when examining compressor station emissions. More generally, our results demonstrate the unique advantages and challenges of platforms like small unmanned aerial vehicles for quantifying local emission sources to the atmosphere.

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.