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.<p> 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.<p> 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).

Impact of odour emissions from livestock operation sites on the air quality of neighboring areas has raised public concerns. A practical means to solve this problem is to set adequate setback distance. Air dispersion modeling was proved to be a promising method in predicting proper odour setback distance. Although a lot of air dispersion models have been used to predict odour concentrations downwind agricultural odour sources, not so much information regarding the capability of these models in odour dispersion modeling simulation could be found because very limited field odour data are available to be applied to evaluate the modeling result. A main purpose of this project was evaluating AERMOD and CALPUFF air dispersion models for odour dispersion simulation using field odour data.<p> Before evaluating and calibrating AERMOD and CALPUFF, sensitivity analysis of these two models to five major climatic parameters, i.e., mixing height, ambient temperature, stability class, wind speed, and wind direction, was conducted under both steady-state and variable meteorological conditions. It was found under steady-state weather condition, stability class and wind speed had great impact on the odour dispersion; while, ambient temperature and wind direction had limited impact on it; and mixing height had no impact on the odour dispersion at all. Under variable weather condition, maximum odour travel distance with odour concentrations of 1, 2, 5 and 10 OU/m3 were examined using annual hourly meteorological data of year 2003 of the simulated area and the simulation result showed odour traveled longer distance under the prevailing wind direction.<p> Evaluation outcomes of these two models using field odour data from University of Minnesota and University of Alberta showed capability of these two models in odour dispersion simulation was close in terms of agreement of modeled and field measured odour occurrences. Using Minnesota odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 3.6% applying conversion equation from University of Minnesota and 3.1% applying conversion equation from University of Alberta between two models. However, if field odour intensity 0 was not considered in Minnesota measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 3.1% applying conversion equation from University of Minnesota and 1.6% applying conversion equation from University of Alberta between two models. Using Alberta odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 0.7% applying conversion equation from University of Alberta and 1.2% applying conversion equation from University of Minnesota between two models. However, if field odour intensity 0 was not considered in Alberta measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 0.4% applying conversion equation from University of Alberta and 0.7% applying conversion equation from University of Minnesota between two models. Application of scaling factors can improve agreement of modeled and measured odour intensities (including all field odour measurements and field odour measurements without intensity 0) when conversion equation from University of Minnesota was used.<p> Both models were used in determining odour setback distance based on their close performance in odour dispersion simulation. Application of two models in predicting odour setback distance using warm season (from May to October) historical annul hourly meteorological data (from 1999 to 2002) for a swine farm in Saskatchewan showed some differences existed between models predicted and Prairie Provinces odour control guidelines recommended setbacks. Accurately measured field odour data and development of an air dispersion model for agricultural odour dispersion simulation purpose as well as acceptable odour criteria could be considered in the future studies.

Impact of odour emissions from livestock operation sites on the air quality of neighboring areas has raised public concerns. A practical means to solve this problem is to set adequate setback distance. Air dispersion modeling was proved to be a promising method in predicting proper odour setback distance. Although a lot of air dispersion models have been used to predict odour concentrations downwind agricultural odour sources, not so much information regarding the capability of these models in odour dispersion modeling simulation could be found because very limited field odour data are available to be applied to evaluate the modeling result. A main purpose of this project was evaluating AERMOD and CALPUFF air dispersion models for odour dispersion simulation using field odour data.<p> Before evaluating and calibrating AERMOD and CALPUFF, sensitivity analysis of these two models to five major climatic parameters, i.e., mixing height, ambient temperature, stability class, wind speed, and wind direction, was conducted under both steady-state and variable meteorological conditions. It was found under steady-state weather condition, stability class and wind speed had great impact on the odour dispersion; while, ambient temperature and wind direction had limited impact on it; and mixing height had no impact on the odour dispersion at all. Under variable weather condition, maximum odour travel distance with odour concentrations of 1, 2, 5 and 10 OU/m3 were examined using annual hourly meteorological data of year 2003 of the simulated area and the simulation result showed odour traveled longer distance under the prevailing wind direction.<p> Evaluation outcomes of these two models using field odour data from University of Minnesota and University of Alberta showed capability of these two models in odour dispersion simulation was close in terms of agreement of modeled and field measured odour occurrences. Using Minnesota odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 3.6% applying conversion equation from University of Minnesota and 3.1% applying conversion equation from University of Alberta between two models. However, if field odour intensity 0 was not considered in Minnesota measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 3.1% applying conversion equation from University of Minnesota and 1.6% applying conversion equation from University of Alberta between two models. Using Alberta odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 0.7% applying conversion equation from University of Alberta and 1.2% applying conversion equation from University of Minnesota between two models. However, if field odour intensity 0 was not considered in Alberta measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 0.4% applying conversion equation from University of Alberta and 0.7% applying conversion equation from University of Minnesota between two models. Application of scaling factors can improve agreement of modeled and measured odour intensities (including all field odour measurements and field odour measurements without intensity 0) when conversion equation from University of Minnesota was used.<p> Both models were used in determining odour setback distance based on their close performance in odour dispersion simulation. Application of two models in predicting odour setback distance using warm season (from May to October) historical annul hourly meteorological data (from 1999 to 2002) for a swine farm in Saskatchewan showed some differences existed between models predicted and Prairie Provinces odour control guidelines recommended setbacks. Accurately measured field odour data and development of an air dispersion model for agricultural odour dispersion simulation purpose as well as acceptable odour criteria could be considered in the future studies.

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.<p> 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.<p> 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).