The demand for meat and other animal products over the past couple decades has led to a changeover from small family operated farms, into large commercial facilities. The increase in animal density and population has created new issues related to waste management and pollution. Aerial pollutants, such as carbon dioxide, methane, ammonia, nitrous oxide and particulate matter, are all byproducts of agricultural processes. This study examines the concentrations, emission rates, and emission factors of ammonia, methane, and particulate matter that are emitted from a commercial dairy barn. The commercial dairy facility was located near New Hamburg in Ontario, Canada. It has the capacity to house 501 animals total, split between lactating cows, dry cows, heifers, bulls and calves. Lactating cows are confined in tie stalls while the rest of the herd used free stall pens. The barn is mechanically ventilated and uses a set of 14 fans with diameters of 1.22 m to ventilate the barn. Concentration data were measured over two sampling periods; the first took place in January – March 2013 and the second from May – July 2013. The pollutant concentrations, ventilation rates, and animal weights were used to generate emission factors based on an animal unit (AU – equivalent to 500 kg live mass) basis. The emission factors for ammonia, methane, PM10 and PM2.5 averaged over the two campaigns were 1.12 g hr-1 AU-1, 25.08 g hr-1 AU-1, 9.33 mg hr-1 AU-1 and 4.96 mg hr-1 AU-1, respectively and agree well with reported values in the literature. The time of year had an impact on the emission levels as all of the pollutants, except methane, were higher in the second sampling campaign compared to the first. A large increase was seen in the ammonia and particulate matter, while a more moderate change was seen in the methane. Peaks in ammonia emissions correlated well with feed times, and are highly influenced by animal activity. Methane emissions were dominated by the digestive process in the rumen, and have a lag period after feeding before the emissions spike. Although particulate matter increased, it was not correlated to a specific event in the barn. / Agriculture and Agri-Food Canada, Ontario Ministry of Agriculture Food, Ontario Ministry of Rural Affairs, Dairy Farmers of Canada, Dairy Farmers of Ontario

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Accurate information on the spatial-temporal distributions of air pollution at a regional scale is crucial for effective air quality control, as well as to impact studies on local climate and public health. The current practice of mapping air quality relies heavily on data from monitoring stations, which are often quite sparse and irregularly spaced. The research presented in this dissertation seeks to advance the methodologies involved in spatiotemporal analysis of air quality that integrates remotely-sensed data and in situ measurement. Aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is analyzed to estimate fine particulate matter (PM2.5) concentrations as the target air pollutant. The spatial-temporal distribution of columnar aerosol loading is investigated through mapping MODIS AOD in southern Ontario, Canada throughout 2004. Clear distribution patterns and strong seasonality are found for the study area. There is a detectable relationship between an AOD level and underlying land use structure and topography on the ground. MODIS AOD was correlated with the ground-level PM2.5 concentration (GL-[PM2.5]) at various wavelengths. The AOD-PM2.5 correlation is found to be sensitive to spatial-temporal scale changes. Further, a semi-empirical model has been developed for a more accurate prediction of GL-[PM2.5]. The model employs MODIS AOD data, assimilated meteorological fields, and ground-based meteorological measurements and is able to explain 65% of the variability in GL-[PM2.5]. To achieve a more accurate and informative spatiotemporal modelling of GL-[PM2.5], a method is proposed that integrates the model-predictions and in situ measurements in the framework of Bayesian Maximum Entropy (BME) analysis. A case study of southern Ontario demonstrates the procedures of the method and support for its advantages by comparison with conventional geostatistical approaches. The BME estimation, coupled with BME posterior variance, can be used to depict GL-[PM2.5] distribution in a stochastic context. The methodologies covered in this work are expected to be applicable to the modelling or analysis of other types of air pollutant concentrations. / Thesis (Ph.D, Geography) -- Queen's University, 2009-01-08 14:43:49.333

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