Livestock odour has been an obstacle for the development of livestock industry. Air dispersion models have been applied to predict odour concentrations downwind from the livestock operations. However, most of the air dispersion models were designed for industry pollutants and can only predict hourly average concentrations of pollutants. Currently, a livestock odour dispersion model that can consider the difference between livestock odour and traditional air pollutants and can account for the short time fluctuations is not available. Therefore, the objective of this research was to develop a dispersion model that is designed specifically for livestock odour and is able to consider the short time odour concentration fluctuations.
A livestock odour dispersion model (LODM) was developed based on Gaussian fluctuating plume theory to account for odour instantaneous fluctuations. The model has the capability to predict mean odour concentration, instantaneous odour concentration, peak odour concentration and the frequency of odour concentration that is equal to or above a certain level with the input of hourly routine meteorological data.
LODM predicts odour frequency by a weighted odour exceeding half width method. A simple and effective method is created to estimate the odour frequency from multiple sources. Both Pasquill-Gifford and Hogstr¨¯m dispersion coefficients are applied in this model. The atmospheric condition is characterized by some derived parameters including friction velocity, sensible heat flux, M-O length, and mixing height. An advanced method adapted from AERMOD model is applied to derive these parameters. An easy to use procedure is generated and utilized to deal with the typical meteorological data input as ISC met file.
LODM accepts and only requires routine meteorological data. It has the ability to process individual or multiple sources which could be elevated point sources, ground level sources, livestock buildings, manure storages, and manure land applications. It can also deal with constant and varied emission rates. Moreover, the model considers the relationships between odour intensity and odour concentrations in the model. Finally, the model is very easy to use with a friendly interface.
Model evaluations and validations against field plume measurement data and ISCST3 and CALPUFF models indicate that LODM can achieve fairly good odour concentration and odour frequency predictions. The sensitivity analyses demonstrate a medium sensitivity of LODM to the controllable odour source parameters, such as stack height, diameter, exit velocity, exit temperature, and emission rate. This shows that the model has a great potential for application on resolving odour issues from livestock operations. From that perspective, the most effective way to reduce odour problems from livestock buildings is to lessen the odour emission rate (e.g. biofiltration of exhaust air, diet changes)
