Variability of Ammonia and Methane Emissions from Animal Feeding Operations in Northeastern Colorado

Abstract

Concentrated animal feeding operations (CAFOs) are major emitters of both ammonia (NH3) and methane (CH4). However, current emission inventories have limited temporal resolution and use data derived from a small subset of farms. To this end, we deployed three mobile laboratories during the DISCOVER-AQ campaign in summer 2014 with a focus on northeastern Colorado. Observations of NH3 and CH4 plumes downwind of 43 CAFOs were used to investigate the diurnal and site-to-site variability of emissions with an inverse area source plume modeling approach. Ammonia emissions scaled to all permitted animals in Weld, Morgan, and Larimer counties were estimated at 1.9 Gg month–1, 50% greater than the U.S. NEI 2014 and 360% greater than EDGAR for the month of August. Methane emissions were likewise estimated at 10.6 Gg month–1, consistent with the U.S. GHGI but 99% greater than EDGAR. Significant differences between individual CAFOs with repeat observations were also observed for both CH4 and NH3 emissions. The large subfarm, site-to-site, and diurnal variabilities observed show the importance of measurements taken across these scales in order to derive representative emission factors.

Monthly Patterns of Ammonia Over the Contiguous United States at 2‐km Resolution

“…While IASI identifies Cache site is only ∼100 m away from feedlots, a distance in which concentrations are strongly enhanced relative County as a hotspot, the AMoN site has the highest annual average in the CONUS. However, this AMoN to background levels ( Golston et al, 2020 ; Miller et al, 2015 ). There are also intrinsic differences between a surface concentration and column amount that complicate comparisons between IASI and AMoN (e.g., boundary layer height).…”

“…While IASI identifies Cache County as a hotspot, the AMoN site has the highest annual average in the CONUS. However, this AMoN site is only ∼100 m away from feedlots, a distance in which concentrations are strongly enhanced relative to background levels (Golston et al., 2020; Miller et al., 2015). There are also intrinsic differences between a surface concentration and column amount that complicate comparisons between IASI and AMoN (e.g., boundary layer height).…”

Validation of IASI Satellite Ammonia Observations at the Pixel Scale Using In Situ Vertical Profiles

“…The mobile laboratories included in situ measurements from an open‐path, quantum cascade laser‐based instrument onboard the Princeton Atmospheric Chemistry Experiment, a cavity ring down instrument (Picarro G2103) onboard the NOAA Chemical Sciences Division (CSD) van, and a closed‐path, quantum cascade laser‐based instrument (QC‐TILDAS) on the Aerodyne Mobile Laboratory (Eilerman et al., 2016; Herndon et al., 2005; Tao et al., 2015). These three mobile laboratories conducted stationary and moving intercomparisons and showed agreement to within 10% on average (Golston et al., 2020). Column NH 3 abundances were measured by the University of Colorado Solar Occultation Flux (CU SOF) instrument onboard the CU/NCAR mobile laboratory (Kille et al., 2017).…”

“…The NSF/NCAR C-130 aircraft measured NH 3 with a closed-path, quantum cascade laser-based instrument using a fast-response inertial inlet to minimize sampling artifacts (QC-TILDAS, Aerodyne Research, Inc.) (Schiferl et al, 2016 (Eilerman et al, 2016;Herndon et al, 2005;Tao et al, 2015). These three mobile laboratories conducted stationary and moving intercomparisons and showed agreement to within 10% on average (Golston et al, 2020). Column NH 3 abundances were measured by the University of Colorado Solar Occultation Flux (CU SOF) instrument onboard the CU/NCAR mobile laboratory (Kille et al, 2017).…”

“…NH 3 is unregulated in many countries, but active efforts are being made to quantify NH 3 emissions and understand their trends on regional to global scales (Paulot et al., 2014; Van Damme et al., 2018). There remain significant uncertainties in bottom‐up NH 3 emission inventories as they require representative measurements to scale from a small subset of sources to the entire global budget (Beusen et al., 2008; Golston et al., 2020; Zhang et al., 2018). On the other hand, top‐down approaches that depend on inverse modeling of NH 4 + wet deposition data require widespread observations, accurate vertical profiles, and estimates of chemical and deposition lifetimes (Paulot et al., 2014; Zhu et al., 2013).…”