Dispersion Modeling to Compare Alternative Technologies for Odor Remediation at Swine Facilities

Abstract

The effectiveness of 18 alternative technologies for reducing odor dispersion at and beyond the boundary of swine facilities was assessed in conjunction with an initiative sponsored through agreements between the Attorney General of North Carolina and Smithfield Foods, Premium Standard Farms, and Frontline Farmers. The trajectory and spatial distribution of odor emitted at each facility were modeled at 200 and 400 m downwind from each site under two meteorological conditions (daytime and nighttime) using a Eulerian–Lagrangian model. To predict the dispersion of odor downwind, the geographical area containing the odorant sources at each facility was partitioned into 10-m² grids on the basis of satellite photographs and architectural drawings. Relative odorant concentrations were assigned to each grid point on the basis of intensity measurements made by the trained odor panel at each facility using a 9-point rating scale. The results of the modeling indicated that odor did not extend significantly beyond 400 m downwind of any of the test sites during the daytime when the layer of air above the earth’s surface is usually turbulent. However, modeling indicated that odor from all full-scale farms extended beyond 400 m onto neighboring property in the evenings when deep surface cooling through long-wave radiation to space produces a stable (nocturnal) boundary layer. The results also indicated that swine housing, independent of waste management type, plays a significant role in odor downwind, as do odor sources of moderate to moderately high intensity that emanate from a large surface area such as a lagoon. Human odor assessments were utilized for modeling rather than instrument measurements of volatile organic compounds (VOCs), hydrogen sulfide, ammonia, or particulates less than 10 μm in diameter (PM₁₀) because these physical measurements obtained simultaneously with human panel ratings were not found to accurately predict human odor intensity in the field.