The EPA press release dated February 23, 2004 states that the three Buckeye Egg Farm facilities had the potential to emit more than a combined total of 1850 tons per year of particulate matter (PM). This number was based on flowrate calculations that were three times higher than those measured as well as a failure to include particle size distributions in the emissions calculations. The annual PM emission for each facility was approximately 35 tons per year. The EPA was unjustified in requiring Buckeye Egg Farm to obtain Title V and PSD permits as the facilities could not have met the thresholds for these permits. Engineers need to be concerned with correctly measuring and calculating emission rates in order to enforce the current regulations. Consistency among regulators and regulations includes using the correct emission factors for regulatory permitting purposes. EPA has adopted AERMOD as the preferred dispersion model for regulatory use on the premise that it more accurately models the dispersion of pollutants near the surface of the Earth than ISCST3; therefore, it is inappropriate to use the same emission factor in both ISCST3 and AERMOD in an effort to equitably regulate PM sources. For cattle feedlots in Texas, the ISCST3 emission factor is 7 kg/1000 hd-day (16 lb/1000 hd-day) while the AERMOD emission factor is 5 kg/1000 hd-day (11 lb/1000 he-day). The EPA is considering implementing a crustal exclusion for the PM emitted by agricultural sources. Over the next five years, it will be critical to determine a definition of crustal particulate matter that researchers and regulators can agree upon. It will also be necessary to develop a standard procedure to determine the crustal mass fraction of particulate matter downwind from a source to use in the regulatory process. It is important to develop a procedure to determine the particulate matter mass fraction of crustal downwind from a source before the crustal exclusion can be implemented to ensure that the exclusion is being used correctly and consistently among all regulators. According to my findings, the mass fraction of crustal from cattle feedlot PM emissions in the Texas High Plains region is 52%.

The following thesis titled, Atlanta Automotive Particulate Matter Exposure and Evaluation, presents data obtained as a part of a joint project with Emory University, Rollin's School of Public Health. The Atlanta Commuters Exposure (ACE) Study uses both real-time and time-integrated sampling techniques for ambient aerosol concentrations. The ACE study is unique in that it will correlate the ambient aerosol concentrations with the concurrent health measurements. The primary objective of this thesis is to measure the concentration, size distribution and the chemical composition of PM2.5 inside the vehicle cabin for several commuters. The vehicles followed a scripted route along roadways in the Atlanta metropolitan region during periods of peak traffic volume, while the compact air sampling package of both real-time and time-integrated instruments recorded data. Real-time measurements for Particulate Matter (PM) were made using compact Optical Particle Counters (OPC), a Condensation Particle Counter, and a MicroAethalometer. The time-integrated measurements for Elemental Carbon (EC), Organic Carbon (OC), Water Soluble Organic Carbon (WSOC), particulate elemental concentrations, and speciated organics required filter collection methods. Thus a compact air-sampling package was created to combine both sets of real-time and time-integrated instruments. The following results are presented for the first four commutes. The framework for analyzing and presenting results is developed, and will be used for future commutes.

Four receptor models and a chemical transport model were used to quantify the sources of PM2.5 impacting the St. Louis Supersite (STL-SS) between June 2001 and May 2003. The receptor models utilized two independent datasets, one that included ions and trace elements and a second that incorporated 1-in-6 day organic molecular marker data. Since each source apportionment (SA) technique has its own limitations, this work compared the results of five different SA approaches to better understand the biases and limitations of each. The source impacts predicted by these five models were then integrated into an ensemble-trained SA methodology. The ensemble method offered several improvements over the five individual SA techniques. Primarily, the ensemble method calculated source impacts on days when individual models either did not converge to a solution or did not have adequate input data to develop source impact estimates. Additionally, the ensemble method resulted in fewer days on which major emissions sources (e.g., secondary organic carbon and diesel vehicles) were estimated to have either a zero or negative impact on PM2.5 concentrations at the STL-SS. When compared with a traditional chemical mass balance (CMB) approach using measurement-based source profiles (MBSPs), the ensemble method was associated with better fit statistics, including reduced chi-squared values and improved PM2.5 mass reconstruction. A comparison of the different modeling techniques also revealed some of the subjectivities associated with applying specific SA models to the STL-SS dataset. For instance, positive matrix factorization (PMF) results were very sensitive to both the fitting species and number of factors selected for the analysis, whereas source impacts predicted in CMB were sensitive to the selection of source profiles to represent local metals processing emissions. Additionally, the different SA approaches predicted different impacts for the same source on a given day, with correlation coefficients ranging from 0.03 to 0.66 for gasoline vehicle, -0.51 to 0.85 for diesel vehicles, -0.29 to 0.86 for dust, -0.34 to 0.76 for biomass burning, 0.22 to 0.72 for metals processing, and -0.70 to 0.68 for secondary organic carbon. These issues emphasized the value of using several different SA techniques at a given receptor site, either by comparing source impacts predicted by different models or by utilizing an ensemble-trained SA technique.

<p>In recent decades much effort has gone into reducing particle emissions in the exhaust gases of heavy-duty diesel engines. Engine development has now reached the stage where it is worth to put heavy focus on the contribution of lubricating oil to particulate emissions in order to further reduce these emissions.</p><p> </p><p>A literature study demonstrates that the cylinder system is usually the largest source of oil-related particles. Oil consumption in the cylinder can be divided into <em>throw-off</em> effects when inertia forces act on the piston, piston rings and oil; <em>evaporation</em> from hot surfaces; <em>reverse blow-by</em> when gas pressure drives the oil consumption; and <em>top land scraping </em>when oil is scraped off the cylinder liner.</p><p> </p><p>The pressure between the compression rings strongly affects the stability and position of the upper compression ring as well as the oil consumption caused by the reverse blow-by. A method to measure the inter-ring pressure was developed and evaluated. The measurements showed that cycle-to-cycle variations were small, but that the inter-ring pressure varied over time. Calculations with AVL Excite Piston and Rings confirmed that ring gap positions can have a major influence on the inter-ring pressure.</p><p> </p><p>The measured particle size and number distributions at motoring conditions show interesting and unexpected results. The high number of particles with a diameter of around 100 nm was greatly reduced when the temperature in the diluter was increased. The mean number particle diameter decreased until 10 nm and then became stable independent of further temperature increase. Other authors have found that the small particles (nucleation mode) are reduced and the larger particles (accumulation mode) are more or less unaffected when exhaust gases are heated up and diluted.  </p><p> </p>

Diss. (sammanfattning) Umeå : Umeå universitet, 2009. / Härtill 5 uppsatser. Även tryckt utgåva.

Pulmonary fibrosis is a potentially deadly pathology characterized by excessive deposition of extracellular matrix (ECM), increased tissue stiffness, and loss of tissue structure and function. Recent evidence has suggested epithelial to mesenchymal transition (EMT), the transdifferentiation of an epithelial cell into a mesenchymal fibroblast, is one mechanism that results in the accumulation of myofibroblasts and excessive deposition of ECM. EMT is a highly orchestrated process involving the integration of biochemical signals from specific integrin mediated interactions with ECM proteins and soluble growth factors including TGFβ. TGFβ, a potent inducer of EMT, can be activated by cell contraction mediated mechanical release of the growth factor from a macromolecular latent complex. Therefore, TGFβ activity and subsequent EMT may be influenced by both the biochemical composition and biophysical state of the surrounding ECM. Based on these knowns it was first investigated how changes in the biochemical composition of the matrix and changes in tissue rigidity together modulate EMT due to changes in epithelial cell contraction and TGFβ activation. Here we show that integrin specific interactions with fibronectin (Fn) variants displaying both the RGD and PHSRN binding sites facilitate cell binding through α3β1 and α5β1 integrins, and that these interactions maintain an epithelial phenotype despite engagement of increased tissue rigidities. Conversely, Fn fragments that facilitate cell binding through αv integrins drive TGFβ activation and subsequent EMT even while engaging soft underlying substrates. Adding to the complexity of studying mechanisms that contribute to pulmonary fibrosis, is exposure of the lung to injuries from environmental particulates. Therefore, we investigated how EMT is altered in response to particulate matter (PM). Here we show that PM exposure further drives TGFβ activation, EMT, and increases intracellular levels of reactive oxygen species (ROS). Additionally, cells binding the ECM through α5β1 and α3β1 integrins only partially recover an epithelial phenotype, suggesting ROS may be a secondary driver of TGFβ and EMT. Taken together these results suggest dynamic changes to the ECM microenvironment are major contributors to the control of EMT responses and provide insights into the design of biomaterial-based microenvironments for control of epithelial cell phenotype.

This project resulted from an alleged dust problem affecting the residents in a Florida community. The residents claimed that there were elevated dust levels caused by a rock quarry adjacent to their homes. The purpose of this work was to assess total particulate, respirable particulate, and the coarse content of the sampled particles through traditional NIOSH methods, and using a new, real-time instrument known as the EPAM 5000. Data from the EPAM and NIOSH methods were compared to the EPA's particulate matter standards and the OSHA permissible exposure limits for total and respirable dust. Dust levels using the NIOSH methods were below the limit of detection. There were measurable dust levels in all three size fractions (PM10, PM[subscript 2.5], PM1) for the EPAM. Due to the undetectable levels of the NIOSH method sampling, further sampling in a laboratory environment was conducted in order to compare NIOSH methods with the EPAM 5000 method. The project continued into an aerosol chamber in the USF College of Public Health Breath Lab for further data collection in order to compare results using traditional NIOSH methods with the results obtained from the EPAM 5000. The chamber was associated with a dust generator that released a steady flow of fly ash particulate at a specific revolution per minute (rpm). Each run of data collection sampled approximately 1 m³ of air and persisted for six to seven hours. Four separate runs were conducted, each at a different generation rate of fly ash. There were measurable dust levels using the NIOSH total dust and NIOSH respirable dust methods. There were also measurable dust levels in all three size fractions (PM10, PM[subscript 2.5], PM1) for the EPAM. The results of all methods were compared. The PM[subscript 2.5] and PM1 sampling heads of the EPAM 5000 were compared to the NIOSH respirable dust sampling results. The PM10 sampling head of the EPAM was compared to the NIOSH total dust sampling results. NIOSH 0500 concentration results were within 10% of the EPAM PM10 concentration.

This dissertation investigates the impact of 2000-2050 climate change on fine particulate matter \((PM_{2.5})\) air quality. We first applied a multiple linear regression model to study the correlations of total \(PM_{2.5}\) and its components with meteorological variables using the past decadal \(PM_{2.5}\) observations over the contiguous US. We find that daily variation in meteorology can explain up to 50% of \(PM_{2.5}\) variability. Temperature is positively correlated with sulfate and organic carbon (OC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. Relative humidity (RH) is positively correlated with sulfate and nitrate, but negatively with OC. Precipitation is strongly negatively correlated with all \(PM_{2.5}\) components. We then compared the observed correlations of \(PM_{2.5}\) with meteorological variables with results from the GEOS-Chem chemical transport model. The results indicate that most of the correlations of \(PM_{2.5}\) with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling \(PM_{2.5}\) variability, and showed that 20-40% of the observed \(PM_{2.5}\) daily variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. From 1999-2010 observations we further showed that interannual variability of annual mean \(PM_{2.5}\) in most of the US is strongly correlated with the synoptic period T of the dominant meteorological mode as diagnosed from a spectral-autoregressive analysis. We then used the observed local \(PM_{2.5}\)-to-period sensitivity to project \(PM_{2.5}\) changes from the 2000-2050 changes in T simulated by fifteen IPCC AR4 GCMs following the SRES A1B scenario. We project a likely increase of \(\sim 0.1 \mu g m^{-3}\) in annual mean \(PM_{2.5}\) in the eastern US arising from less frequent frontal ventilation, and a likely decrease of \(\sim 0.3 \mu g m^{-3}\) in the northwestern US due to more frequent maritime inflows. These circulation-driven changes are relatively small, representing only a minor climate penalty or benefit for \(PM_{2.5}\) regulatory purpose. / Engineering and Applied Sciences

Background: Most countries of sub-Saharan Africa (SSA) are experiencing a high rate of ur­banization accompanied with unplanned development resulting into sprawl of slums. The weath­er patterns and air pollution sources in most urban areas are changing with significant effects on health. Studies have established a link between environmental exposures, such as weather variation and air pollution, and adverse health outcomes. However, little is known about this relationship in urban populations of SSA where more than half the population reside in slums, or slum like conditions. A major reason for this is the lack of systematic collection of data on exposure and health outcomes. High quality prospective data collection and census registers still remain a great challenge. However, within small and spatially defined areas, dynamic cohorts have been established with continuous monitoring of health outcomes. Collection of environmental exposure data can complement cohort studies to investigate health effects in relation to environmental exposures. The objective of this research was to study the health effects of selected environmental exposure among the urban poor population in Nairobi, Kenya. Methods: We used the platform of the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), including two nested research studies, to provide data on mortality and mor­bidity. The NUHDSS was established in two areas of Nairobi, Korogocho and Viwandani, in 2003 and provides a unique opportunity for access to longitudinal population data. In addition, we conducted real-time measurements of particulate matter (PM2.5) in the areas from February to October in 2013. We obtained meteorological measurements from the Moi Air Base and Nairobi airport weather stations for the study period. We also conducted a cross-sectional survey to estab­lish the communities’ perceptions about air pollution and its related health risks. Time series re­gression models with a distributed lag approach were used to model the relationship between weather and mortality. A semi-ecological study with group level exposure assignment to individuals was used to assess the relationship between child health (morbidity and mortality) and the extent of PM2.5 exposure. Results: There was a significant association between daily mean temperature and all-cause mor­tality with minimum mortality temperature (MMT) in the range of 18 to 20 °C. Both mortality risk and years of life lost analysis showed risk increases in relation to cold temperatures, with pronounced effect among children under-five. Overall, mortality risks were found to be high during cold periods of the year, rising with lower temperature from MMT to about 40% in the 0–4 age group, and by about v 20% among all ages. The results from air pollution assessment showed high levels of PM2.5 concentration exceeding World Health Organization (WHO) guideline limits in the two study ar­eas. The air pollution concentration showed similar seasonal and diurnal variation in the two slums. The majority of community residents reported to be exposed to air pollution at work, with 66% reporting to be exposed to different sources of air pollution. Despite the observed high level of exposure, residents had poor perception of air pollution levels and associated health risks. Children in the high-pollution areas (PM2.5≥ 25 μg⁄m3) were at significantly higher risk for morbidity (OR = 1.30, 95% CI: 1.13-1.48) and cough as the only form of morbidity (OR = 1.33, 95% CI: 1.15-1.53) compared to those in low-pollution areas. In addition, exposure to high levels of pollution was associated with high child mortality from all-causes (IRR=1.15, 95% CI: 1.03-1.28), and indicated a positive association to respiratory related mortality (IRR=1.10, 95% CI: 0.91-1.33). Conclusion: The study findings extend our knowledge on health impacts related to environmental exposure by providing novel evidence on the risks in disadvantaged urban populations in Af­rica. More specifically, the study illustrates the invisible health burden that the urban poor population are facing in relation to weather and air pollution exposures. The effect of cold on population is preventable. This is manifested by the effective adaptation to cold conditions in high-latitude Nordic countries by housing standards and clothing, as well as a well-functioning health system. Further, awareness and knowledge of consequences, and reductions in exposure to air pollution, are necessary to improve public health in the slum areas. In conclusion, adverse health impacts caused by environmental stressors are critical to assess further in disadvantaged populations, and should be followed by development of mitigation measures leading to improved health and well being in SSA.

Through biomass gasification, biomass can be converted at high temperature to a product gas rich in carbon monoxide, hydrogen, and methane. After cleaning and upgrading, the product gas can be converted to biofuels such as hydrogen; methanol; dimethyl ether; and synthetic diesel, gasoline, and natural gas. Particulate matter (PM) is formed as a contaminant in the gasification process, and the aim of this work was to develop and apply a method for sampling and characterization of PM in the hot product gas.   A particle measurement system consisting of a dilution probe combined in series with a bed of granular activated carbon for tar adsorption was developed, with the aim of extracting a sample of the hot product gas without changing the size distribution and composition of the PM. The mass size distribution and concentration, as well as the morphology and elementary composition, of PM in the size range 10 nm to 10 µm in the product gas from a bubbling fluidized bed (BFB) gasifier, a circulating fluidized bed (CFB) gasifier and an indirect BFB gasifier using various types of biomass as fuel were determined.   All gasifiers and fuels displayed a bimodal particle mass size distribution with a fine mode in the &lt;0.5 µm size range and a coarse mode in the &gt;0.5 µm size range. Compared with the mass concentration of the coarse mode the mass concentration of the fine mode was low from all gasifiers. The evaluation of the results for the fine-mode PM was complicated by condensing potassium chloride for the CFB gasifier when using miscanthus as fuel and by condensing tars for the indirect BFB gasifier when using wood C as fuel. The mass concentration of the coarse-mode PM was higher from the CFB gasifier than from the two BFB gasifiers. The coarse-mode PM from the BFB gasifier when using wood A as fuel was dominated by char. In the CFB gasifier the coarse-mode PM was mainly ash and bed material when using all fuels. The coarse-mode PM from the indirect BFB gasifier when using wood C as fuel was mainly ash.