Development of a multiple-source odor setback model for livestock production systems.

摘要

Odors emitted from livestock production systems have been the source of many nuisance complaints and lawsuits towards the agricultural community. These circumstances have resulted in a number of local guidelines for the siting of livestock facilities amongst neighboring residences and community centers, also known as setback guidelines. There exist no country-wide standards for these setback distances, and in some cases local economic growth is hindered due to larger-than-necessary setbacks being enforced. Hence, a significant need for scientific models to determine setback distances that are fair to both livestock producers and neighbors is presented.;The purpose of this project is to expand upon the Generation-1 Purdue Odor Setback Model (Lim, et al., 2000) and recommend parametric factor improvement. Odor samples were collected and analyzed via the internationally-standardized forced choice olfactometry method to determine the odor emission rates for dairy lactation barns. Reconstruction of the Purdue Model to an Excel platform allowed for parameterization of up to 10 individual odor sources within a farmstead, each with their own odor emission and abatement characteristics. The resulting individual-source and overall-site setback distances are generated considering the position of each source within the farmstead. Additional odor concentration data were taken downwind of an Indiana-representative dairy and swine facility to test the Model's setback recommendations. It was determined that while the Model predicts favorably compared to raw data, further updates are needed for the factors representing directional wind frequency, effects of topography, and effects of the odor sensitivity of different receptor groups (aka exposures). A new method for determining the applied factor for the directional wind frequency is presented, along with recommendations for topography and exposure factor improvement. Finally, an investigation was made into future odor application of the Gaussian dispersion theory commonly applied to atmospheric pollutants. While the Gaussian method has been historically disregarded for odor-uses, it is presented here that new plume-spread coefficients can be derived from downwind odor concentration data, the Gaussian method can be applied, and adequate estimation of odor setback distances is possible.