Epidemiological studies have linked exposure to elevated levels of airborne particulate matter with increased incidences of several types of respiratory disease, hospital admissions and morbidity. Millions of tons of airborne particulate matter are generated and released into the atmosphere each year. However, particulate matter resulting from the combustion of fuel oil and coal are of particular concern, because they are generally composed of small particles that can easily penetrate deep into the lungs, and can contain significant concentrations of toxic transition metals, such as zinc, iron and vanadium. Pulmonary toxicity (i.e. damage caused to lung tissues) of particulate matter is currently evaluated via time-consuming in-vivo testing, or via in-vitro testing. Compared to in-vivo testing, in-vitro testing offers significant advantages in terms of time savings and sample throughput. Unfortunately, the number of in-vitro testing methods are currently very limited, and do not allow a thorough investigation of the mechanisms of particulate matter toxicity. In light of these issues, the goals of the study described here were three-fold: *To adapt several in-vitro toxicity assays currently used in other applications to use in measuring particulate matter toxicity on lung cell layers; *To use these adapted assays to quantify the toxicity of numerous types of oil and coal ashes with varying particle sizes and transition metal concentrations, and; *To use the same assays to quantify the toxicities of several transition metals found in coal and oil ashes to better understand their relative contributions to overall particulate matter toxicity. Three colorimetric in-vitro assays were chosen for adaptation, and proved effective in measuring adverse cellular response to particulate matter exposure. Particle size was shown to have a large effect on the overall cytotoxicity of particulate matter; fine (less than 2.5 μm aerodynamic diameter) particles proved substantially more toxic than coarse (larger than 2.5 μm aerodynamic diameter) particles. Dose-response experiments measuring the toxic effects of the transition metals zinc, vanadium and iron revealed that zinc was the most toxic; a concentration of 0.6 mM caused a 50% drop in cellular metabolism, compared to 3 mM and 4 mM for vanadium and iron respectively.

Engine exhaust characteristics from two different biodiesel blends, formulated from soy and animal fat biodiesel blended with ultra-low sulphur diesel, were tested during two different test programs with similar operating conditions. Engine exhaust was measured in real-time for nitrogen oxides, total hydrocarbons, particle-bound polyaromatic hydrocarbons, and particle size distribution. Diesel particulate matter was collected on filters and subsequently analyzed for organic carbon, elemental carbon, soluble organic fraction, cations, and anions. The use of biodiesel was found to increase nitrogen oxide emissions, but decrease total hydrocarbons and particulate matter emissions. The most significant impact on emissions was the difference between the engine operating conditions rather than the fuel type. Minor differences were found between the soy and animal fat biodiesel blends through speciation of the diesel particulate matter.

Engine exhaust characteristics from two different biodiesel blends, formulated from soy and animal fat biodiesel blended with ultra-low sulphur diesel, were tested during two different test programs with similar operating conditions. Engine exhaust was measured in real-time for nitrogen oxides, total hydrocarbons, particle-bound polyaromatic hydrocarbons, and particle size distribution. Diesel particulate matter was collected on filters and subsequently analyzed for organic carbon, elemental carbon, soluble organic fraction, cations, and anions. The use of biodiesel was found to increase nitrogen oxide emissions, but decrease total hydrocarbons and particulate matter emissions. The most significant impact on emissions was the difference between the engine operating conditions rather than the fuel type. Minor differences were found between the soy and animal fat biodiesel blends through speciation of the diesel particulate matter.

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

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

There are several reasons for reducing particulate formation as a result of combustion processes and to date, a number of approaches have been proposed to numerically predict soot. There is a trade-off between accuracy and computational cost and processing time. Two equation semi-empirical models have been used, with some success, to reconcile the need for fast solution turn around and accuracy. However, these models do not account for the mass balance between the gas phase and soot. In this study, the effects of mass conservation of the soot precursors in the gas phase were investigated in an ethylene-air laminar flame simulation at atmospheric pressure. Soot formation was predicted with a two-equation model. Then the model was modified for predicting soot in a turbulent ethylene-air flame operating at 1 atm. The new model is a [2+1]-equation model which accounts for the mass conservation of soot precursors.

The research presented in this thesis provides a quantitative analysis of atmospheric influences on particulate matter pollution in Christchurch across a wide range of spatial and temporal scales. A complex interaction of low level flow characteristics that form in response to local and regional features of complex terrain, together with an urban setting that is characterised by low density housing, mostly comprised of single storey dwellings that are poorly insulated, regularly leads to nocturnal smog events during winter in Christchurch. Provided synoptic flow is weak, the above mentioned flow interaction promotes flow stagnation over the city, when nocturnal katabatic drainage flows and day-time north-easterly on-shore winds converge over the city. Additionally, undercutting of the density currents promotes highly stable atmospheric stratification close to the surface, so that, in combination, both horizontal and vertical air movement is suppressed. As particulate emission release from solid fuel burning for home heating coincides with this poor atmospheric dispersion potential, particle concentrations can increase substantially so that national air quality guidelines are regularly exceeded during winter in Christchurch. At the core of this thesis is a classification based approach that examines the day-to-day probabilities of breaches of the national air quality guideline for PM over the last decade at a single location in Christchurch as a result of variations in meteorological conditions alone. It is shown that, based on variations in temperature and wind speed, up to 85% of exceedence occurrence can be explained. From this, concentration trends over time, when meteorological variability is kept to a minimum, are assessed and evidence is found that recent regulatory measures to enhance air quality are beginning to show positive effects. Atmospheric processes that control pollution dispersion on the mesoscale are investigated through means of atmospheric numerical modelling in a novel approach that assimilates observational climatic wind field averages to drive low level flow for two idealised case studies. It is shown that this approach is able to reproduce the observed diurnal concentration patterns very well and that much of these patterns can be attributed to mesoscale circulation characteristics and associated atmospheric dispersion potential, namely flow stagnation and recirculation of contaminants. When timing of stagnation and subsequent recirculation is such that it occurs within a few hours after peak emission release, concentration increase is enhanced and dilution is delayed, thus severely exacerbating the problem. Links between exceedence probabilities and synoptic situations that favour the degradation of air quality are established and various synoptic transition scenarios are examined with regard to local air quality. The progression of anticyclones across the country is identified to be the dominant synoptic control mechanism and it is shown that latitudinal variation in the progression path determines the extent of expected exceedence probability. On interdecadal hemispheric scales, it is found that a particular combination of local and synoptic atmospheric conditions that favours air quality degradation, shows a re-occurring pattern of frequency maxima (and minima) with a periodicity of approximately 14 - 16 years. For the synoptic part of this interdecadal variability, a close relationship to Southern Hemispheric pressure anomalies in high latitudes is revealed. Finally, for verification of the combined findings and to assess their prediction capability, a validation case study is given which shows that the applied methodology is able to capture day-to-day variations in pollution levels with acceptable (statistically significant) accuracy.

Abstract Objective: In Christchurch there is concern that winter air pollution, dominated by particulate matter (PM₁₀) from domestic heating, causes a local increase in cases of cardiorespiratory disease. Our aim was to investigate whether the particulate levels did influence emergency hospital admissions, and if so to what extent. Method: Air pollution and meteorological data was obtained from a Canterbury Regional Council monitoring station. Two local hospitals provided data on emergency admissions for both adults and children with cardiac and respiratory disorders. All data was obtained for the period from June 1988 to December 1998. Missing PM₁₀ data was interpolated from other known pollution values when necessary. The PM₁₀ data was compared to the admissions data using a time series analysis approach, with weather variables controlled for using a generalised additive model. Results: There was a significant association between PM₁₀ levels and cardiorespiratory admissions. For children and adults combined there was a 3.4% increase in respiratory admissions for every interquartile (14.8 µg/m³) increase in PM₁₀. In adults there was a 1.3 % increase in cardiac admissions for each interquartile increase in PM₁₀. There was no relationship between PM₁₀ levels and appendicitis, the condition that we selected to be our control. Conclusion: In Christchurch there is a significant relationship between particulate levels and the admissions for cardiac and respiratory illnesses. The size of the effect is comparable to other international studies, and the greatest impact is seen on the respiratory system.

There are several reasons for reducing particulate formation as a result of combustion processes and to date, a number of approaches have been proposed to numerically predict soot. There is a trade-off between accuracy and computational cost and processing time. Two equation semi-empirical models have been used, with some success, to reconcile the need for fast solution turn around and accuracy. However, these models do not account for the mass balance between the gas phase and soot. In this study, the effects of mass conservation of the soot precursors in the gas phase were investigated in an ethylene-air laminar flame simulation at atmospheric pressure. Soot formation was predicted with a two-equation model. Then the model was modified for predicting soot in a turbulent ethylene-air flame operating at 1 atm. The new model is a [2+1]-equation model which accounts for the mass conservation of soot precursors.

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