Poor regional air quality in some states across the US cotton belt has resulted in increased pressure on agricultural sources of particulate matter (PM) from air pollution regulators. Moreover, inaccurate emission factors used in the calculation of annual emissions inventories led to the identification of cotton harvesting as a significant source of PM10 in California and Arizona. As a result, cotton growers in these states are now required to obtain air quality permits and submit management practice plans detailing the actions taken by the producer to reduce fugitive PM emissions from field operations. The objective of this work was to develop accurate PM emission factors for cotton harvesting in terms of total suspended particulate (TSP), PM10, and PM2.5. Two protocols were developed and used to develop PM emission factors from cotton harvesting operations on three farms in Texas during 2006 and 2007. Protocol one utilized TSP concentrations measured downwind of harvesting operations with meteorological data measured onsite in a dispersion model to back-calculate TSP emission flux values. Flux values, determined with the regulatory dispersion models ISCST3 and AERMOD, were converted to emission factors and corrected with results from particle size distribution (PSD) analyses to report emission factors in terms of PM10 and PM2.5. Emission factors were developed for two-row (John Deere 9910) and sixrow (John Deere 9996) cotton pickers with protocol one. The uncertainty associated with the emission factors developed through protocol one resulted in no significant difference between the emission factors for the two machines. Under the second protocol, emission concentrations were measured onboard the six-row cotton picker as the machine harvested cotton. PM10 and PM2.5 emission factors were developed from TSP emission concentration measurements converted to emission rates using the results of PSD analysis. The total TSP, PM10, and PM2.5 emission factors resulting from the source measurement protocol are 1.64 ± 0.37, 0.55 ± 0.12, and 1.58E- 03 ± 4.5E-04 kg/ha, respectively. These emission factors contain the lowest uncertainty and highest level of precision of any cotton harvesting PM emission factors ever developed. Thus, the emission factors developed through the source sampling protocol are recommended for regulatory use.

Previous studies have demonstrated that EPA approved federal reference method (FRM) samplers can substantially misrepresent the fractions of particles being emitted from agricultural operations due to the relationship between the performance characteristics of these samplers and existing ambient conditions. Controlled testing in a wind tunnel is needed to obtain a clearer understanding and quantification of the performance shifts of these samplers under varying aerosol concentrations, wind speeds and dust types. In this study, sampler performance was tested in a controlled environment wind tunnel meeting EPA requirements for particulate matter (PM) sampler evaluation. The samplers evaluated included two low-volume PM10 and Total Suspended Particulate (TSP) pre-separators. The masses and particle size distributions (PSDs) obtained from the filters of tested samplers were compared to those of a collocated isokinetic sampler. Sampler performance was documented using two parameters: cut-point (d_50) and slope. The cut-point is the particle diameter corresponding to 50% collection efficiency of the pre-separator while the slope is the ratio of particle sizes corresponding to cumulative collection efficiencies of 84.1% and 50% (d_84.1/d_50) or 50% and 15.9% (d_50/d_15.9) or the square root of 84.1% and 15.9% (d_84.1/d_ 15.9). The test variables included three levels of wind speeds (2-, 8-, and 24-km/h), five aerosol concentrations varying from 150 to 1,500 mu g/m3 and three aerosols with different PSDs (ultrafine Arizona Road Dust (ARD), fine ARD and cornstarch). No differences were detected between the performance of the flat and louvered FRM PM10 samplers (a = 0.05). The mean cut-point of both the PM10 samplers was 12.23 mu m while the mean slope was 2.46. The mean cut-point and slope values were statistically different from the upper limit of EPA-specified performance criteria of 10.5 mu m for the cut-point and 1.6 for the slope. The PM10 samplers over-sampled cornstarch but under-sampled ultrafine and fine ARD. The performance of the dome-top TSP sampler was close to the isokinetic sampler, and thus it can be used as a reference sampler in field sampling campaigns to determine true PM concentrations. There were large variations in the performance of the cone-top TSP samplers as compared to the isokinetic sampler. Dust type and wind speed along with their interaction had an impact on sampler performance. Cut-points of PM10 samplers were found to increase with increasing wind speeds. Aerosol concentration did not impact the cut-points and slopes of the tested samplers even though their interaction with dust types and wind speeds had an impact on sampler performance.

Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated. In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated. In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for absence of solar radiation. Using AERMOD predictions of pollutant concentrations off property for regulatory purposes will likely affect a source?s ability to comply with limits set forth by State Air Pollution Regulatory Agencies (SAPRAs) and could lead to inappropriate regulation of the source.

Mit einer mobilen Probenahme- bzw. Messeinrichtung untersuchte das Institut für Luft- und Kältetechnik Dresden (ILK) im Auftrag des LfULG diffuse Staubemissionen in einer Bauschuttrecyclinganlage. Neben der mobilen Messung mit dem Partikelmessgerät APS 3321 kamen stationäre Messeinrichtungen (Impaktoren u. a.) am Messort zum Einsatz. Im Ergebnis wurden repräsentative Feinstaub- sowie Quarzstaubimmissionen für eine Bauschuttrecyclinganlage ermittelt. In einem weiteren Schritt wurden die Messergebnisse nachgerechnet. Geprüft wurde, ob mit einer Immissionsprognose nach TA Luft auf Basis von Emissionsfaktoren nach VDI 3790 Blatt 3, ähnliche Ergebnisse ermittelt werden. Anhand der durchgeführten Rechnungen wird eingeschätzt, dass die berechneten Werte in der Regel über den Messergebnissen, aber mit einer akzeptablen Toleranz, liegen. Bei Anpassung der Rechenparameter an die Betriebsbedingungen vor Ort wird eine relativ gute Übereinstimmung erzielt.

Luftverschmutzung ist ein zentrales Thema der Umweltmedizin. Dabei beeinflusst partikuläre Luftverschmutzung entscheidend die Luft, besonders auch in Leipzig. Die Problematik „Feinstaub“ steht aktuell im Fokus von Politik, Rechtssprechung, Presse und Wissenschaft. Zahlreiche Studien belegen die Zusammenhänge zwischen aerosolbelasteter Luft und vielgestaltigen Gesundheitsgefährdungen. Insbesondere das respiratorische System kann durch luftgetragene Partikel gravierende Schädigung erfahren. Lange Zeit wurden ausschließlich die Massekonzentrationen der Partikel untersucht. Jüngere Forschungsbemühungen auf diesem Gebiet konzentrieren sich jedoch verstärkt auf chemische und physikalische Eigenschaften der Partikel. Die vorliegende Arbeit untersucht die Einflüsse von Partikelkonzentrationen auf die respiratorische Gesundheit im prähospitalen Bereich der notfallmedizinischen Versorgung in Leipzig. Es galt hierbei vorrangig die Einflüsse von Anzahl, Oberfläche und Volumen der luftgetragenen Partikel auf die respiratorischen Notfallrisiken zu untersuchen.

Emissions from residential combustion appliances vary significantly depending on the firing behaviours and combustion conditions, in addition to combustion technologies and fuel quality. Although wood pellet combustion in residential heating boilers is efficient, the combustion conditions during start-up and stop phases are not optimal and produce significantly high emissions such as carbon monoxide and hydrocarbon from incomplete combustion. The emissions from the start-up and stop phases of the pellet boilers are not fully taken into account in test methods for ecolabels which primarily focus on emissions during operation on full load and part load.  The objective of the thesis is to investigate the emission characteristics during realistic operation of residential wood pellet boilers in order to identify when the major part of the annual emissions occur. Emissions from four residential wood pellet boilers were measured and characterized for three operating phases (start-up, steady and stop). Emissions from realistic operation of combined solar and wood pellet heating systems was continuously measured to investigate the influence of start-up and stop phases on total annual emissions. Measured emission data from the pellet devices were used to build an emission model to predict the annual emission factors from the dynamic operation of the heating system using the simulation software TRNSYS.  Start-up emissions are found to vary with ignition type, supply of air and fuel, and time to complete the phase. Stop emissions are influenced by fan operation characteristics and the cleaning routine. Start-up and stop phases under realistic operation conditions contribute 80 – 95% of annual carbon monoxide (CO) emission, 60 – 90% total hydrocarbon (TOC), 10 – 20% of nitrogen oxides (NO), and 30 – 40% particles emissions. Annual emission factors from realistic operation of tested residential heating system with a top fed wood pelt boiler can be between 190 and 400 mg/MJ for the CO emissions, between 60 and 95 mg/MJ for the NO, between 6 and 25 mg/MJ for the TOC, between 30 and 116 mg/MJ for the particulate matter and between 2x1013 and 4x1013 /MJ for the number of particles. If the boiler has the cleaning sequence with compressed air such as in boiler B2, annual CO emission factor can be up to 550 mg/MJ. Average CO, TOC and particles emissions under realistic annual condition were greater than the limits values of two eco labels. These results highlight the importance of start-up and stop phases in annual emission factors (especially CO and TOC). Since a large or dominating part of the annual emissions in real operation arise from the start-up and stop sequences, test methods required by the ecolabels should take these emissions into account. In this way it will encourage the boiler manufacturers to minimize annual emissions.  The annual emissions of residential pellet heating system can be reduced by optimizing the number of start-ups of the pellet boiler. It is possible to reduce up to 85% of the number of start-ups by optimizing the system design and its controller such as switching of the boiler pump after it stops, using two temperature sensors for boiler ON/OFF control, optimizing of the positions of the connections to the storage tank, increasing the mixing valve temperature in the boiler circuit and decreasing the pump flow rate. For 85 % reduction of start-ups, 75 % of CO and TOC emission factors were reduced while 13% increase in NO and 15 % increase in particle emissions was observed.

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