Estimating Ammonia Fluxes from Cattle Feedlots using Time-averaged Concentration Data Measured with Conditionally Deployed Passive Samplers

Livestock production is the largest source of ammonia emissions and represents a major off-farm transport pathway for reactive nitrogen. Fugitive ammonia can convert to an aerosol and be transported great distances before being deposited back to the surface where it negatively impacts the environment. Improved estimates of emissions from livestock are needed to reduce uncertainty in ammonia inventories at regional, national and global scales. Furthermore, more information is needed on how management factors might reduce emissions. Unfortunately, measuring ammonia fluxes remains relative challenging and costly, thus field data is sparse. The objective of this project was to estimate year-round ammonia fluxes from a commercial beef feedlots using robotically controlled passive samplers in combination with inverse modeling. To achieve this goal, a new type of conditional sampler network was perfected that used Radiello passive ammonia samplers. Deployment of the cartridges was conditionally controlled so the samplers were only exposed to air under a user-defined set of weather conditions – namely when winds are blowing directly from the feedlot source with sufficient velocity for turbulent flow. A wireless network is used to control all the samplers. Concentrations measured with the passive samplers were compared to data from a long path IR laser and found to be in good agreement. A network of the robotic Radiello samplers was deployed on the upwind and downwind edges of a 25,000-head feedlot near Fort Morgan, CO, USA. The samplers were replaced at two-week intervals to give a seasonal depiction of concentrations and reveal ammonia hot spots at the operation. Concentration data were then used in an inverse dispersion model (FIDES) to estimate the areal emissions from the feedlot. Results were compared the livestock feeding data to determine what fraction of N inputs lost to the atmosphere. Data will also be very useful for development and verification of models that predict ammonia losses.