Measuring complex structural emissions with inverse dispersion method and correcting for deposition in the case of ammonia

Measuring trace gas exchanges from structural sources and surfaces with complex topography remains a challenge. The inverse dispersion method provides a suitable option that allows long-term monitoring without interfering with the system. In the case of ammonia emissions such as from animal housings and slurry storage tanks the inverse dispersion method is beneficial compared to other methods which can interfere with daily operations over longer time periods. Ammonia emissions from agriculture can constitute up to 80-90% of reactive N inputs in nearby ecosystems, especially in areas with high livestock densities such as Switzerland. Almost half of these originate from animal housings, which can be difficult to quantify in order to investigate and test mitigation options. However, when measuring emissions from housing and slurry storage facilities, it is necessary to install the instruments at some distance downwind of the structures to avoid turbulence interferences. Since ammonia strongly adsorbs to surfaces immediately following emission, this can lead to a considerable loss before the point of measurement.

Deposition models can be used to correct for this loss. Most models consist of a series of resistances to deposition that must be overcome to estimate the total loss. However, the calculation of the bulk canopy resistance requires significant additional site information, which even with its incorporation results in a relatively high uncertainty. Here we present measurements of ammonia emissions from animal housings using the inverse dispersion method which includes a simplified deposition correction. Based on multiple measurement campaigns we show this method to provide a reasonable estimate without the need for additional data while remaining within the same uncertainty range. We further discuss the importance of small-scale deposition from large point sources using controlled release experiments and highlight future development opportunities.