Doctor of Philosophy (PhD) / Abstract Olfactory tasks are used very often with laboratory animals in studies of the neurobiology of learning and memory. Rats and mice are extremely sensitive in their detection and discrimination of odours, learn olfactory tasks rapidly, and can display higher order cognitive functions in olfactory tasks. This cognitive capacity may rival the ability of primates to learn analogous tasks with visual cues and most likely reflects strong anatomical connections between the olfactory bulbs and higher brain regions such as the piriform cortex, orbitofrontal cortex and hippocampus. The current thesis explored olfactory discrimination learning and performance in rats and had two principal aims. The first part of the thesis was oriented around odour masking phenomena in rats: the ability of one odour in a mixture to suppress detection of a second odour in that mixture. A specialized behavioural paradigm was developed to allow the study of odour masking in the rat. The second part of the thesis was pharmacological and determined whether the acquisition, reversal and performance of olfactory discriminations, and analogous auditory discriminations, are affected by two commonly used classes of drugs (benzodiazepines and cannabinoids). Together, these studies attempt to gain a better understanding of the nature of olfactory discrimination learning in rats, by using both psychophysical and pharmacological approaches, and to develop behavioural paradigms which may be used in future psychophysical and pharmacological studies. Following an introduction and review of olfactory and auditory studies in rat (Chapter 1), odour masking phenomena were studied in Chapter 2. The aliphatic aldehydes butanal (C4) and heptanal (C7) were used in the study. Aldehydes were of interest as this class of odorants abound in nature and may be important for rodents’ species-specific communication. Thirsty rats were initially trained to discriminate C4 and C7 in the olfactometer, using a go/no-go olfactory discrimination task. This involved rats learning to nose poke in an odour port and to lick a tube for a water reward on presentation of the rewarded component S+, while withholding licking at the tube when the other, unrewarded, aldehyde (S-) was presented. Odour mixtures (C4C7 or C7C4) were then introduced into the task as an additional non-rewarded condition (mixture S-). The concentration of the non-rewarded aldehyde in the mixture was then systematically decreased, while the concentration of the rewarded aldehyde was kept constant. When the non-rewarded aldehyde reached a critical low level in the mixture, rats started to make responses to the non-rewarded mixture (false alarms) showing that the S+ odour was suppressing the S- odour in the mixture, so the mixture was being responded to in the same manner as the S+ odour presented alone. Results also showed asymmetric suppression in the mixture condition, such that butanal suppressed detection of heptanal at a much lower concentration than vice versa. A second experiment demonstrated that when both butanal and heptanal were present in a binary mixture at the same concentration (10-6 volume %), rats responded to the mixture as if only butanal was present. Our findings are in agreement with human studies showing component interactions in binary mixtures of aldehydes. The molecular feature of carbon chain length appears to be a critical factor in determining the outcome of interactions between aldehydes at peripheral olfactory receptors, with smaller chain aldehydes better able to compete for receptor occupancy. Subsequent chapters explored the effects of two classes of commonly used drugs - benzodiazepines and cannabinoids - on olfactory and auditory discrimination in rats. Animal models such as the radial arm maze, Morris water maze and object recognition test are routinely used to test adverse and facilitatory effects of drugs on cognition in rodents. However, comparatively few pharmacological studies employ olfactory or auditory go/no-go paradigms. Thus, an important part of the present thesis was to assess the viability of using such paradigms in detecting pharmacological effects, and to identify whether such effects may be modality specific (i.e. whether a drug has a greater effect on olfactory or auditory tasks). In Chapter 3, the effects of benzodiazepines on olfactory discrimination tasks were explored. Rats were injected with the benzodiazepine drugs midazolam or diazepam and tested on discrimination tasks involving either the auditory and olfactory modality. Results showed that midazolam (0.5–2 mg/kg sc) did not affect the performance of a well-learned two-odour olfactory discrimination task, and moderately facilitated the performance of a go/no-go auditory discrimination task. On the contrary, midazolam (1 mg/kg) impaired the acquisition of a novel go/no-go olfactory discrimination task, as well as the reversal of a previously well-learned olfactory discrimination. However, midazolam did not affect the acquisition or reversal of an equivalent auditory discrimination task. The olfactory bulb and the piriform cortex are intimately involved in associative learning and behavioural aspects of olfactory performance, and have high concentrations of benzodiazepine receptors. These may therefore be possible neural substrates for the disruptive effects of benzodiazepines on olfactory learning. Findings from Chapter 4 indicated that the prototypical cannabinoid agonist delta-9-tetrahydrocanabinol (Δ9 THC) (0.3, 1 and 3 mg/kg) impairs auditory discrimination performance, but had no effect on equivalent olfactory discriminations. This is in marked contrast to the effects of benzodiazepines. Residual effects were observed, such that auditory discrimination performance was still impaired on the day following Δ9 THC administration. Delta-9-tetrahydrocanabinol effects were prevented by co-administration of the cannabinoid antagonist rimonabant (3 mg/kg). In addition, the anandamide hydrolysis inhibitor URB597 (0.1 and 0.3 mg/kg), which boosts levels of endogenous cannabinoids in the synapse, also impaired auditory discrimination performance, and this effect was also reversed by rimonabant. This study also assessed the effects of Δ9 THC (0.3, 1 and 3 mg/kg) and URB597 (0.1 and 0.3 mg/kg) on acquisition and reversal of novel olfactory discriminations. Results showed that Δ9 THC impairs olfactory reversal learning without affecting acquisition of the original discrimination. It is argued that this reversal deficit may be part of a wider capacity for cannabinoids to impair cognitive flexibility. The final Chapter (General Discussion) discusses the relevance and implications of the combined findings. The results add significantly to our current understanding of perceptual, learning and memory processes involving the olfactory modality in rats. With respect to olfactory perception, this thesis introduced a new behavioural paradigm, which can be used to assess component suppression in mixtures, and may be of use in future psychophysical studies involving rodents or other species. With respect to learning and memory, the thesis provides novel information on the disruptive effects of benzodiazepines and cannabinoids on olfactory and auditory tasks. It is concluded that go/no-go olfactory and auditory discrimination tasks in rats can provide a useful platform for assessing the disruptive and modality-specific effects of drugs on learning, performance and cognitive flexibility. Future studies might expand the range of drugs tested on these paradigms and might consider chronic as well as acute drug effects.

A previous study showed that addition of β-cyclodextrin to goat milk made a difference to its flavour, but in an undescribed way. Cyclodextrins (CDs, comprising α- β- and γ-CD) may be able to bind the free branched chain fatty acids in goat milk responsible for the largely undesirable ‘goaty’ flavour. The primary aim was to test the effect of CDs on this flavour in goat milk and its products with a view to marketing goat milk products with reduced flavour intensity. A secondary aim was to test the effect of β-CD on skatole flavour, a characteristic flavour of milk from pasturefed ruminants. Study design and methods: The present study evaluates addition of mainly β-CD to goat milk, cow milk and their products to reduce undesirable flavours. The methods applied were mainly ranking and hedonic assessment in sensory experiments. The tests done were with CDs added to buffers and milks, some of which were flavour-enhanced with 4-methyloctanoic acid as a representative goaty fatty acid, or with skatole. Goat milk yoghurts were also tested. Free fatty acid concentrations, which may be affected by CD binding, were measured after separating cream and skim milk. The methods applied were standard dairy procedures: titration of free fatty acids in milk fat and the copper-salt method for measuring fatty acids in skim milk. A fungal lipase was added to milks to accelerate fat hydrolysis (lipolysis). This was done to increase the concentration of free fatty acids for several experimental purposes. Some minor experiments studies were also done, for example the comparative effect of lipases on goat milk and cow milk, and the lipolytic activity at different temperatures over different times. Results: The results of skatole experiments were inconclusive. The odour of 4-methyloctanoic acid was reduced in acidic buffers by addition of α- and β-CD, particularly the former. Alpha and β-CD were both effective in goaty flavour reduction in goat milk. γ-CD was not effective. In all this work differences were statistically significant to varying levels. Goaty flavour was reduced by addition of β-CD to goat milk yoghurt, but only when added before fermentation (P < 0.001), not after (P = 0.09). The liking scores for goat milk yoghurts for both plain and flavoured yoghurts increased with β-CD treatment (both P < 0.001 for 59 panellists). The chemistry experiments revealed a reduction of free fatty acid concentration in the fat phase when β-CD treatment was added to full cream cow milk. However, analysis of skim milk did not show a corresponding increase in concentration. Further experiments are required to reveal the fate of the ‘missing’ fatty acids. Conclusion: Overall it was shown that under certain conditions, CDs were effective in reducing goaty flavour in milk and yoghurts. Whereas CDs are approved for addition to foods in many countries – including the bellwether U.S.A. – formal approval by Food Standards Australia New Zealand has not yet been finalized. When it is, the way should be clear to market a range of more consumer-acceptable goat milk products in New Zealand as a primary market. In short, this research has significant commercial relevance.

Calone 1951® (7-methyl-2H-1,5-benzodioxepin-3(4H)-one) is renowned in the fragrance industry for its distinct marine odour and offers an interesting molecular framework for structure-odour-relationship (SOR) research. Limited olfactory reports on 7-membered benzodioxepine analogues with modification influenced by functionality, polarity, and ring size, prompted us to construct a range of aromatic and C-3 substituted structures for olfactory evaluation. Incorporation of a diverse range of functionality contributes valuable information on the molecular aspects that determine the archetypal marine character of Calone 1951®. Here we present the preparation of Calone 1951® analogues including spectroscopic and olfactory details to contribute to the sparse marine fragrance arena. In these studies pertaining to the odour properties of benzodioxepinone systems, we considered the modification of substitution and functionality on the aromatic ring in the context of qua litative olfactory analysis. Application of the patented Williamson and Dieckmann reaction pathways resulted in construction of the benzodioxepinone molecule. Preparation of aryl-substituted benzodioxepinones required introduction of an alternate broadly applicable synthetic pathway due to the diverse nature of the introduced substituents. Limitations of the patented approach led to incorporation of a simple but novel methodology applied to the same range of substituted catechol reagents for synthesis of the benzodioxepinone skeleton and an overview of comparable yields. Single-step mechanisms were also successfully applied to contribute to the repertoire of benzodioxepin(on)e structures prepared for olfactory analysis. Semi-empirical models of the synthesised data set were generated and evaluated in light of previous research undertaken by Archer and Claret and related to the olfactory characteristics of each compound. Evaluation of the models with corresponding olfactory information revealed that functionality and ring size contribute significantly to the conformation adopted by the benzodioxepinone species and therefore the olfactory character of the molecule. It is evident from our observations that aromatic ring substitution and functional alteration of the cyclic ketone modifies the perceived odour of Calone 1951®. Structural modification overall led to a decrease in odour potency. The presence of the aromatic methyl substituent in Calone 1951® reinforces, but is not critical for, the marine tonality. Modification of the ketone of Calone 1951® led to significant deviation in character from the prototypical marine odour.

The issue of odour, greenhouse gas emissions and indoor air quality in swine buildings have become a great concern for the neighbouring communities as well as governments. Air dispersion models have been adopted widely as an approach to address these problems which determine science-based distance between livestock production site and neighbours. However, no existing model considers the diurnal and seasonal variations of odour, gas (ammonia, hydrogen sulphide, greenhouse gas), and dust concentrations and emissions, which may cause great uncertainty. The primary objective of this project is to monitor and model the diurnal and seasonal variations of odour, gases, and dust concentrations and emissions from nursery, farrowing, and gestation rooms. Additionally, this study tried to quantify the greenhouse gas contribution from swine buildings and evaluate the indoor air quality of swine barns. <p>Strip-block experimental design was used to measure the diurnal variation of odour and gas concentrations and emissions in PSC Elstow Research Farm. It was found that: 1) odour and gas concentrations in winter were significantly higher than those in mild and warm weather conditions for all three rooms (P<0.05); 2) the nursery room had higher level of odour and gas concentration and emission than the other two types of rooms, no significant difference existed between the farrowing and gestation rooms (P>0.05); 3) significant diurnal variations occurred in August and April (P<0.05) for odour and some gas concentrations and emissions, while no significant diurnally variations were found in February (P>0.05); 4) apparent diurnal variation patterns were observed in August and April for NH3, H2S and CO2 concentrations, being high in the early morning and low in the late afternoon; 5) positive correlation was found between odour concentrations and NH3, H2S, and CO2 concentrations, respectively. <p>A whole year ( August 2006 to July 2007) monitoring of odour, gas and dust concentrations and emissions revealed that: 1) significant seasonal effect on odour and gas concentrations and emissions, total dust concentrations and dust depositions were observed (P<0.05), but no specific variation pattern was discovered for odour and gas emissions; 2) the total greenhouse gas emission from all the rooms in the gestation, nursery and farrowing area was 2956 CO2 equivalent tons per year, where gestation area, nursery area, and farrowing area accounted for 39.3 %, 37.2% and 23.5%, respectively; the CO2 emission contributed 53.4% to the total greenhouse emission, and CH4 contributed to 43.9%, 2.7% for N2O; N2O could be considered negligible; 3) indoor air quality of the swine barn met the requirements set by the Occupational Health and Safety Regulations (1996) of Saskatchewan for NH3, H2S, and CO2. <p>Statistical models were developed for each type of room to predict the odour and gas concentrations and emissions based on four variables: ventilation rate, room temperature, ambient temperature, and animal unit. The predicted results showed agreeable with measured values for most models (R2 = 0.56-0.96). Generally, gas prediction models performed better (R2=0.61-0.96) than odour prediction models (R2=0.56-0.85).<p>This study was conducted in the province of Saskatchewan throughout one year and the results could be used as representative data for Canada Prairies. Due to the large diurnal and seasonal variabilities of odour emissions, it was recommended to take multiple measurements of odour emission rate under different weather conditions in order to improve the accuracy of air dispersion modeling.

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.

The issue of odour, greenhouse gas emissions and indoor air quality in swine buildings have become a great concern for the neighbouring communities as well as governments. Air dispersion models have been adopted widely as an approach to address these problems which determine science-based distance between livestock production site and neighbours. However, no existing model considers the diurnal and seasonal variations of odour, gas (ammonia, hydrogen sulphide, greenhouse gas), and dust concentrations and emissions, which may cause great uncertainty. The primary objective of this project is to monitor and model the diurnal and seasonal variations of odour, gases, and dust concentrations and emissions from nursery, farrowing, and gestation rooms. Additionally, this study tried to quantify the greenhouse gas contribution from swine buildings and evaluate the indoor air quality of swine barns. <p>Strip-block experimental design was used to measure the diurnal variation of odour and gas concentrations and emissions in PSC Elstow Research Farm. It was found that: 1) odour and gas concentrations in winter were significantly higher than those in mild and warm weather conditions for all three rooms (P<0.05); 2) the nursery room had higher level of odour and gas concentration and emission than the other two types of rooms, no significant difference existed between the farrowing and gestation rooms (P>0.05); 3) significant diurnal variations occurred in August and April (P<0.05) for odour and some gas concentrations and emissions, while no significant diurnally variations were found in February (P>0.05); 4) apparent diurnal variation patterns were observed in August and April for NH3, H2S and CO2 concentrations, being high in the early morning and low in the late afternoon; 5) positive correlation was found between odour concentrations and NH3, H2S, and CO2 concentrations, respectively. <p>A whole year ( August 2006 to July 2007) monitoring of odour, gas and dust concentrations and emissions revealed that: 1) significant seasonal effect on odour and gas concentrations and emissions, total dust concentrations and dust depositions were observed (P<0.05), but no specific variation pattern was discovered for odour and gas emissions; 2) the total greenhouse gas emission from all the rooms in the gestation, nursery and farrowing area was 2956 CO2 equivalent tons per year, where gestation area, nursery area, and farrowing area accounted for 39.3 %, 37.2% and 23.5%, respectively; the CO2 emission contributed 53.4% to the total greenhouse emission, and CH4 contributed to 43.9%, 2.7% for N2O; N2O could be considered negligible; 3) indoor air quality of the swine barn met the requirements set by the Occupational Health and Safety Regulations (1996) of Saskatchewan for NH3, H2S, and CO2. <p>Statistical models were developed for each type of room to predict the odour and gas concentrations and emissions based on four variables: ventilation rate, room temperature, ambient temperature, and animal unit. The predicted results showed agreeable with measured values for most models (R2 = 0.56-0.96). Generally, gas prediction models performed better (R2=0.61-0.96) than odour prediction models (R2=0.56-0.85).<p>This study was conducted in the province of Saskatchewan throughout one year and the results could be used as representative data for Canada Prairies. Due to the large diurnal and seasonal variabilities of odour emissions, it was recommended to take multiple measurements of odour emission rate under different weather conditions in order to improve the accuracy of air dispersion modeling.

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).

Having means to detect and avoid potential predators is a necessity for prey species. Most mammalian prey species are able to detect odours emitted by predators and to adapt their behaviour accordingly. These odour cues are therefore considered to act as semiochemicals. Predator odours consist of several dozen different odourants. In order to assess if single odourants elicit aversive behavioural reactions, predator-naïve CD-1 mice were presented with six odourants which are part of body-borne odours of different mammalian predator species. A two-compartment chamber was used in order to assess place-preference, motor activity and faecal excretions when the animals were simultaneously presented with a predator odourant and a blank control. Further trials were performed to assess whether the odourant concentrations had an influence on the behaviours. The only odourant that elicited a significant aversion was 3-methyl-1-butanethiol, a compound found in the anal gland secretion of skunks, when presented at a factor of 100 above the olfactory detection threshold of mice. Two other concentrations of 3-methyl-1-butanethiol did not elicit significant behavioural changes. Based on the present study, only one out of six selected predator odourants elicited a significant aversive response in CD-1 mice. This suggests that more than one odour component, or perhaps even the full mixture of odourants, may be necessary for CD-1 mice to respond to a predator odour with aversive behaviour.