Texas, due to its geographical area, population, and economy is home to a variety of industrialized areas that have significant air quality problems. These urban areas are affected by elevated levels of fine particulate matter (PM2.5). The primary objective of this study was to identify and quantify local and regional sources of air pollution affecting the city of Houston, Texas. Positive Matrix Factorization (PMF) techniques were applied to observational datasets from two urban air quality monitoring sites in Houston from 2003 through 2008 in order to apportion sources of pollutants affecting the study region. Data from 68 species for Aldine and 91 for Deer Park were collected, evaluated, and revised to create concentration and uncertainty input files for the PMF2 and EPA PMF (PMF3) source apportionment models. A 11-sources solution for Aldine and 10-sources for Deer Park were identified as the optimal solutions with both models. The dominant contributors of fine particulate matter in these sites were found to be biomass burnings (2%-8.9%), secondary sulfates I (21.3%-7.6%) and II (38.8%-22.2%), crustal dust (8.9%-10.9%), industrial activities (10.9%-4.2%), traffic (23.1%-15.6%), secondary nitrates (4.4%-5.5%), fresh (1%-1.6%) and aged(5.1%-4.6%) sea salt and refineries (1.3%-0.6%), representing a strong case to confirm the high influence of local activities from the industrial area and the ship channel around the Houston channel. Additionally, potential source contribution function (PSCF) and conditional probability function (CPF) analyses were performed to identify local and regional source-rich areas affecting this urban airshed during the study period. Similarly, seasonal variations and patterns of the apportioned sources were also studied in detail.

We interpret satellite, aircraft, and ground-based measurements using the GEOS-Chem Chemical Transport Model (CTM) to better understand the global transport and distribution of fine aerosol (PM2.5). Using satellite retrievals of Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR), we estimate an annual growth in Chinese sulfur emissions of 6.2-9.6% between 2000-2006, in agreement with bottom-up inventories. Using aircraft measurements from the Intercontinental Chemical Transport Experiment (INTEX-B) with a CTM, we calculate that 56% of measured sulfate between 500-900 hPa over British Columbia is due to East Asian sources. We find evidence of a 72-85% increase in the relative contribution of East Asian sulfate to the total burden in spring off the northwest coast of the United States since 1985. We interpret retrievals AOD from MODIS and MISR using GEOS-Chem to estimate global long-term (2001-2006) mean PM2.5 concentrations at a resolution of 0.1° x 0.1°. Evaluation of the satellite-derived estimate with ground-based in-situ measurements indicates significant spatial agreement with North American measurements (r = 0.77, slope = 1.07, n = 1057) and with non-coincident measurements elsewhere (r = 0.83, slope = 0.86, n = 244). The one standard deviation uncertainty in the satellite-derived PM2.5 is 25%, inferred from the AOD retrieval and aerosol vertical profiles errors and sampling. The global population-weighted mean uncertainty is 6.7 µg/m3. We find a global population-weighted geometric mean PM2.5 concentration of 20 ?g/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 µg/m3 annual average) is exceeded over central and eastern Asia for 38% and 50% of the population, respectively. Annual mean PM2.5 concentrations exceed 80 µg/m3 over Eastern China. We test the capability of remotely-sensed PM2.5 to capture extreme short-term events by examining the major biomass burning event around Moscow in summer 2010. We find good agreement (r2=0.85, slope=1.06) between daily estimates of PM2.5 from in-situ and satellite-derived sources in the Moscow region during the fires. Both satellite-derived and in-situ values have peak daily mean concentrations of approximately 600 ?g/m3 on August 7, 2010 in the Moscow region.

Starting in March of 1999, the ASACA study has measured PM2.5 mass and composition using 24-hr integrated and continuous measurement techniques. The ASACA network has one rural (Fort Yargo) and three urban (Fort McPherson, South Dekalb, and Tucker) monitoring sites located in the metropolitan Atlanta area. Supplementary data from the SEARCH and STN monitoring networks is also used where applicable. Yearly-averaged TEOM measurements recorded violations of the annual PM2.5 NAAQS (>15 μg/m3) every year of the study, and the daily NAAQS (>65 μg/m3) was exceeded on five separate occasions. Seven-year PM2.5 averages for the sites ranged from 18.8 – 19.8 μg/m3. PCMs were employed to collect PM2.5 composition data, detect spatial variations of PM species, and compare results with the continuous mass measurements. From 2004 – 2005, approximately 28% of the mass was OC, 24% was sulfate, 10% was ammonium, 6% was nitrate, and 3% was EC. Lesser ions contribute less than 3% to the total PM2.5 mass. Spatial variation of the major species was minimal, especially for species formed from secondary processes. South Dekalb did exhibit elevated levels of EC compared to the other sites, most likely due to its proximity to an interstate heavily used by diesel vehicles. PCM averages were found to be less than the averaged TEOM data due to the presence of unidentified matter (UM). Depending on the season, UM can contribute as little as 5% and as much as 50+% of the total mass. Secondary organic aerosol (SOA) concentrations from 2004 – 2005 were predicted using the EC-tracer method. Peak SOA occurs in mid-summer, and winter concentrations are significant due to biomass burning increasing the estimated OC/EC ratios. PCM, TEOM, and aethalometer data was also subjected to seasonal, day-of-the-week, and diurnal temporal variations. Active photochemistry plays an important role, as most species exhibit higher concentrations during summer months. The lone exception was nitrate, whose peak occurs in winter. Daily-averaged PM2.5 concentrations tend to peak late in the work-week and reach their low point on Sundays. Morning and afternoon rush-hour spikes in one-hour averaged PM2.5 are visible most days.

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Adverse health effects attributable to both short- and long-term exposure to air pollution have turned the focus on different microenvironments. The interior of vehicles is of relevance as road traffic emissions and re-suspension of road dust are major sources of pollutants associated with adverse health effects. Hence, the air quality inside vehicles deserves attention regarding human health. This thesis presents a new virtual methodology, using CFD, to study the distribution of fine particulate matter, PM2.5, inside a car cabin. In the CFD model, unsteady RANS and Lagrangian particle tracking were used to simulate particles entering from the exterior. In this study, a practical measurement of interior particle concentrations was also carried out as a first attempt to validate the CFD model. The objective was to find positions inside the cabin where elevated concentrations of PM2.5 are present. The results from the CFD simulations showed that significantly higher concentrations are present at head height in the front row. Due to a discrepancy in the investigated positions in the CFD model and the practical measurement, the simulation results could not be validated. Nevertheless, the simulation results in this study have provided guidelines for future measurements of interior particle concentrations.

Organic aerosol is a major constituent of atmospheric fine particulates (PM2.5), which adversely affect human health and change the Earth’s radiative energy balance. Primary organic aerosol is directly emitted from sources and secondary organic aerosol (SOA) is formed in the atmosphere following oxidation of volatile organic compounds (VOC) from anthropogenic and biogenic sources. Biogenic SOA is enhanced by anthropogenic pollutants such as sulfate and NOx that mainly come from fossil fuel combustion. However, the extent to which the anthropogenic pollutants enhance biogenic SOA in different environments is unknown. The central hypothesis of this thesis is that organosulfates, organic compounds containing a sulfate ester group, are useful as tracers for anthropogenically-influenced biogenic SOA. This research aims to provide a better understanding of the sources of PM2.5 organic carbon (OC), particularly secondary organic carbon (SOC), through the inclusion of organosulfates in an organic tracer-based source apportionment model. The specific objectives of this research include 1) development of a highly sensitive and accurate method to quantify highly polar organosulfates in atmospheric aerosols, 2) identification and quantification of major organosulfate species in the ambient air, and 3) determination of anthropogenic and biogenic sources and their contributions to PM2.5 OC using an organic tracer-based positive matrix factorization (PMF) model. A highly sensitive and accurate method was developed and validated for the quantification of highly polar organosulfates using hydrophilic interaction liquid chromatography (HILIC) and tandem mass spectrometry (MS/MS). The developed method shows excellent retention of carboxylic acid and hydroxyl containing organosulfates. The HILIC-MS/MS method was applied to PM2.5 samples collected in summer 2013 at a rural site in Centreville, AL. Quantified organosulfates accounted for approximately 0.3% of PM2.5 OC. Other major organosulfates, for which standards are not available, were monitored by their fragmentation to the bisulfate anion and/ or sulfate ion radical. The major organosulfates were determined to be 2-methyltetrol sulfate and other isoprene-derived organosulfates. Eight sources of the PM2.5 OC in Centreville, AL were identified using PMF model through the application of organosulfates and commonly used organic tracers measured in samples collected during the daytime and nighttime: vehicle emissions (8%), prescribed burning (11%), isoprene SOC formed under low-NOx (13%) and high-NOx conditions (11%), SOC formed by photochemical reactions (9%), oxidatively aged biogenic SOC (6%), sulfuric acid-influenced SOC (21%), and monoterpene SOC formed under high-NOx conditions (21%). The organosulfates enabled organic tracer-based PMF to resolve sulfuric acid-influenced SOC, while the daytime and nighttime measurements enabled organic tracer-based PMF to resolve SOC formation pathways with diurnal variations (e.g. SOC formed by photochemical reactions). The PM2.5 OC in Centreville was mainly secondary in origin (81%) and was influenced by NOx, ozone (a product of photochemical reactions of NOx and VOC), and sulfuric acid. Together, primary and secondary OC influenced by the fossil fuel use was 76%. Thus, the majority of the PM2.5 OC in Centreville during summer can be controlled by the reduction of fossil fuel use. The HILIC-MS/MS method was also applied to daily PM2.5 samples collected from an urban site in Atlanta, GA during August 2015. The major organosulfate species identified in Atlanta were dominated by 2-methyltetrol sulfate and other isoprene-derived organosulfates, similar to Centreville. They contributed 16% of PM2.5 OC and accounted for the majority of the isoprene-derived SOA that had not previously been identified at the molecular level. The concentrations of the major isoprene-derived organosulfates in Atlanta were two to six times higher than in Centreville. The greatest enhancement was obtained for 2-methylglyceric acid sulfate, a known isoprene SOA tracer formed under high-NOx conditions, reflecting the 15 times higher average NOx concentration in Atlanta during August 2015 compared to Centreville in summer 2013. These results indicate that NOx had a stronger influence on isoprene-derived organosulfate formation in urban Atlanta compared to rural Centreville. Overall, these results indicate that organosulfates are useful tracers for anthropogenically-influenced biogenic SOA. Thus, it is important to quantify them for use in organic tracer-based PMF modeling to determine the anthropogenically-influenced biogenic SOC in PM2.5 OC.

This thesis characterizes the mass and chemical composition of the fine particle air pollution over several cities in South Asia and quantifies how major sources impact the observed levels by using Chemical Mass Balance modeling with organic compounds as tracers. During February 1999, as part of the INDOEX program, a study was conducted to measure the size distribution and chemical composition of the fine particles in a remote island in Maldives off the coast of India. We found that the fine particle concentrations were comparable to those found in major cities in the United States, and were surprisingly high for a background site. 10-day backwind trajectories pointed the source region towards the Indian subcontinent; other INDOEX studies confirmed the presence of a thick haze layer over the Indian Ocean and the subcontinent during the time of the experiment. Motivated by these findings, a detailed analysis of ambient PM2.5 was carried out in Delhi, Mumbai, Kolkata, and Chandigarhfour cities located upwind of the island in Maldives. Seasonality of the fine particle concentrations was observed in each of these cities with the highest concentrations occurring during the wintertime and the lowest concentrations during the summer. Size distribution and chemical composition of the fine particle emissions from five Bangladeshi biomass (rice straw, coconut leaves, dried cow dung, synthetic biomass log, and jackfruit wood) and three Asian coals (Bangladeshi, Indian, and Chinese) were characterized and important source signatures were identified. Finally, recently developed chemical tracer techniques were applied to the ambient samples from North India to differentiate between the contributions from the many different source types. The emission profiles and source signatures from the source tests conducted previously along with the ones conducted using the Indian Subcontinent fuels were used as inputs to the model. These results serve several purposes. First, they provide a description of the mass and detailed inorganic and organic chemical characteristics of fine particulate matter conducted for the first time ever in this region. Second, the source apportionment study will help to define the relative importance of those sources that should be included within an air quality control program. Chemical tracer techniques are particularly attractive for application in regions that have not been studied previously because they are able to yield rapid insights into the causes of a local air pollution problem before the completion of an accurate emissions inventory. Third, the source tests results will prove useful in constructing and evaluating regional emission inventory and assessing source impacts on air quality. Fourth, this work has been carried out with collaborations from Georgia Tech and several other Indian research institutions where pollution control personnel in India was trained in the operation of air sampling equipments that were left for continued monitoring, thus contributing to technology transfer and knowledge transfer from the US.

Fine particulate matter (PM2.5) has been associated with adverse effects on human health, but whether specific components of PM2.5 are responsible for specific health effects is still under investigation. The complex chemical composition of PM2.5 and issues such as multi-component interactions, spatial variability and sampling/instrument error further complicates this analysis. A complementary approach to examining species-specific associations is to assess associations between health outcomes and sources contributing to PM2.5, which can provide critical information to regulators to tighten controls on sources that contribute most to adverse health effects and allows for better multi-pollutant epidemiologic analyses, as the number of source-categories is typically far less than the number of PM2.5 species. This study develops and evaluates various air quality modeling approaches for determining daily source contributions to ambient PM2.5. Results from long-term air quality simulations using an emissions-based model (Models-3/CMAQ - Community Multiscale Air-Quality model) were evaluated in terms of the model's ability to simulate short-term (e.g., daily) variability in concentrations of PM2.5 components. To examine source-specific health outcomes, an extended PM2.5 source-apportionment model, CMB-LGO (Chemical Mass Balance incorporating the Lipschitz Global Optimizer) was developed and compared with results based on other approaches such as CMB, PMF (Positive Matrix Factorization), and Models-3/CMAQ in terms of simulating the daily variability of source impacts. Based on findings from spatial and temporal analyses of tracer concentrations and source impacts, PM2.5 source-apportionment results from CMB-LGO and PMF were applied in a health-study for the Atlanta area. Despite methodological differences and uncertainties in the apportionment process, good agreement was observed between the CMB-LGO and PMF based risk ratios, indicating to the usefulness of applying apportionment methods in health studies.

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.

This study investigated the source attribution of ship emissions to atmospheric particulate matter with a median aerodynamic diameter less than, or equal to 2.5 micron (PM2.5) in the port city of Halifax, Nova Scotia, Canada. The USEPA PMF model successfully determined the following sources with the average mass (percentage) contribution: Sea salt 0.147 µg m-3 (5.3%), Surface dust 0.23 µg m-3 (8.3%), LRT Secondary (ammonium sulfate) 0.085 µg m-3 (3.1%), LRT Secondary (nitrate and sulfate) 0.107 µg m-3 (3.9%), Ship emissions 0.182 µg m-3 (6.6%), and Vehicles and re-suspended gypsum 2.015 µg m-3 (72.8%). A good correlation was achieved between PM2.5 total mass predicted and observed with R2 = 0.83, bias = -0.23, and RMSE = 0.09 µg m-3. In addition, a 2.5 times (60%) reduction in sulfate was estimated, when compared to 2006-2008 Government data in Halifax.