<p>Istraživanje u okviru doktorske disertacije obuhvata uzorkovanja inhalativne frakcije praškastih materija generisane u zubotehničkoj laboratoriji i procesom brušenja čelika EN 90MnCrV8, primenu savremenih metodologija uzorkovanja, analizu uzorka laserskom difrakcijom i analizom slike, zasnovanom na mikrografijama skenirajućeg elektronskog mikroskopa i statističku obradu podataka. Osnovni cilj istraživanja je razvoj funkcionalnog modela za evaluaciju rezultata merenja karakteristika praškastih materija, koji je obuhvatio identifikaciju, procenu i vrednovanje karakteristika prahova, u cilju unapređenja tačnosti rezultata u vezi sa karakterizacijom čestica. Složena geometrija čestica je ispitana analizom 14 parametara, 6 koji definišu veličinu i 8 parametara koji opisuju morfološke karakteristike, upotrebom dva softvera za analizu slike. Dobijeni rezultati obrađeni su primenom metode analize glavnih komponenti i hijerarhijske klaster analize, čime se prevazišao problem subjektivnog prilaza i izvršila redukcija i selekcije reprezantativnih parametara na osnovu kojih se može dovoljno dobro definisati i okarakterisati uzorak.</p> / <p>Тhis PhD thesis presented the development of the functional model for evaluation оf measuring results of powder materials&rsquo; characteristics, which involved the identification, assessment and evaluation of particles characteristics, in order to improve the accuracy of the results. Sampling of inhalation fraction of the powder materials&rsquo; was done in the dental laboratory and grinding process of the EN 90MnCrV8 steel. Particle analysis was performed whit laser diffraction and image analysis based on micrographics from scanning electron microscopy and data were statistically analyzed. The complex particle geometry was examined by analyzing 14 parameters, 6 which define the size and 8 parameters that describe the morphological characteristics, using two image analysis software. To deal with a large number of the numerical parameters used to characterise the particle geometry and the nonlinear relationships among these parameters, principal component analysis (PCA) and hierarchical cluster analysis (CA) algorithm was applied to identify representative numerical descriptors within each group.</p>

No mercado nacional de irrigação localizada, a indústria de gotejamento disponibiliza uma ampla gama de emissores, cujas tecnologias são provenientes de diferentes países: Israel, Espanha, EUA, Grécia, Índia, etc. Aos projetistas, técnicos e agricultores irrigantes que atuam no Brasil, cabem à árdua tarefa de selecionar os tipos de emissores mais adequados na implantação dos projetos. Este trabalho teve por objetivo apresentar uma proposta de Norma Técnica para ensaios de tubogotejadores, em função da presença de partículas sólidas em suspensão na água de irrigação. Avaliar o desempenho dos tubos emissores é algo bastante desejado, pois torna a escolha dos emissores mais técnica e segura. Propostas de normas técnicas devem focar a princípio em ensaios de grande porte com a análise de vários emissores simultaneamente, durante a fase inicial de criação do banco de dados. Estudou-se neste trabalho o posicionamento dos emissores tipo pastilha, com os orifícios direcionados para cima e para baixo durante os ensaios, assim como a distribuição do fluxo de água por meio do mainfold de alimentação dos tubogotejadores na bancada e sua influência no caminhamento seletivo de partículas sólidas nos ensaios de grande porte. O experimento foi conduzido em três fases distintas, utilizando-se duas faixas granulométricas de dióxido de silício (SiO2) na água bombeada (areia muito fina e areia média), com as seguintes características: a) partículas com diâmetros de 0,053 a 0,103 mm nas primeiras 80 horas; b) Partículas com diâmetros de 0,103 a 0,250 mm entre 80 a 160 horas e c) Partículas com diâmetros de 0,250 a 0,500 mm entre 160 a 240 horas de ensaio. As fases foram baseadas em duas concentrações de partículas em suspensão, com 125 ppm e 500 ppm para um tempo total de ensaio de 480 horas. Para testar a hipótese de que um mesmo tubogotejador poderia apresentar comportamento diferenciado de resistência ao entupimento em função da posição de entrada do tubogotejador no mainfold, utilizou-se um delineamento em blocos não casualisados e para a comparação das médias de vazões relativas o teste Scott Knott com significância de 5% na análise de variância. Constataram-se diferenças significativas de vazões relativas nos tratamentos de posição dos emissores pastilhas, de tipos de emissores, e posições no mainfold confirmando as hipóteses iniciais do trabalho. Em função dos resultados obtidos sugere-se uma proposta de norma técnica para este tipo de ensaio de resistência ao entupimento de tubogotejadores. / In the market for drip irrigation, drip provides a wide range of issuers, whose technologies are coming from different countries: Israel, Spain, USA, Greece, India, etc.. To designers, technicians and farmers irrigators operating in Brazil, fit the arduous task of selecting the appropriate type of transmitter in deployment projects. The study aimed to propose a Technical Standard for testing large, due to the presence of suspended solids in the irrigation water conducted from August 2010 to June 2012. Evaluate performance of the tubes emitters is quite desired, as it makes the choice of emitters safer and should focus on the large test - analyzing large amounts of both issuers / initial phase of creating the database. We studied also the positioning of the emitters type wafer with holes directed upwards and downwards. The main detail analysis was performed for the distribution of water flow through \"mainfold\" Feed the dripline on the bench and their influence on selective traversal of solid particles in the test large. The experiment was conducted in three distinct stages, using two particle sizes of silicon dioxide (SiO2) in water pumped from very fine sand and medium sand, described below: a) particles with diameters of 0.053 to 0.103 mm in the first 80 hours, b) particles with diameters from .103 to .250 mm 80 to 160 hours and c) Particle diameter 0.250 to 0.500 mm between 160 to 240 hours of testing. The phases are performed in two concentrations of particles in suspension with 125 ppm, and then with 500 ppm of the total time test at 480 hours. To test the hypothesis that the same behavior tubogotejador could present different resistance to clogging as a function of input position with the tubogotejador \"mainfold\", used a non-randomized block design, and the Scott test, with significance of Knott 5% in the analysis of variance to compare the means. Differences were found in the treatments of the emitters position lozenges, flow variations between types of emitters, between blocks (positions of the mainfold) and particle sizes and concentrations of silicon dioxide, confirming the hypothesis initial work. Depending on the results obtained, some models have produced significant effects, and also the different installation positions pad (up, down) and suggest a proposed technical standard for this type of test for resistance to clogging of emitters.

A preocupação com a qualidade do ar é crescente. Nos últimos anos, as emissões industriais e veiculares acarretam em muitos casos, especialmente em áreas urbanas, condições inadequadas para a saúde da população. Diversos estudos relacionam aumento em internações hospitalares, especialmente por problemas respiratórios, durante episódios de altas concentrações de material particulado. O objetivo deste estudo foi coletar dados diários de concentração de MP10 e compará-los com uma série de dados de quase 10 anos de coleta disponíveis para a cidade de São Carlos-SP. Além disso, ambas as séries de dados foram utilizadas para criar modelos de previsão da concentração de material particulado utilizando como ferramenta as redes neurais artificiais. O local escolhido para aquisição dos dados foi a Praça Voluntários da Pátria, no centro de São Carlos, em função da grande circulação de pessoas e veículos. As duas séries de dados foram coletadas com um equipamento amostrador de grande volume &#40;AGV-MP10&#41;. A série 1 &#40;1997-2006&#41; foi obtida para caracterizar o MP de São Carlos. As coletas eram realizadas alternando-se os dias na semana. Para a série 2 &#40;2014-2015&#41; foram realizadas coletas diárias, com o mesmo equipamento e no mesmo local de amostragem da série 1. Para a criação dos modelos de previsão foram utilizados dados do Banco de Dados Meteorológicos para Ensino e Pesquisa &#40;BDMEP&#41; do Instituto Nacional de Meteorologia &#40;INMET&#41;, de temperatura &#40;&#176;C&#41;, umidade relativa &#40;&#37;&#41;, velocidade do vento &#40;m&#47;s&#41; e o acumulado de precipitação &#40;mm&#41;. Os modelos foram desenvolvidos no softwareMatLab, utilizando duas arquiteturas de redes neurais, uma do tipo MLP &#40;Multilayer Percetron&#41; e outra do tipo NARX &#40;nonlinear autoregressive network with exogenous inputs&#41;. Comparando os dados de MP10 da série 1 e da série 2, foi observado que houve uma redução nos índices de concentração de MP10 ao longo do tempo. Isto se deve à implantação de leis mais rigorosas e o desenvolvimento de tecnologias menos poluentes. Em relação aos modelos de previsão, para os dados da série 1, a utilização do modelo para previsão de médias mensais de concentração de MP10 foi mais eficiente do que o modelo para previsão de médias diárias, ambos tiveram como neurônios de entrada apenas variáveis climáticas, compostas de médias diárias e médias mensais, respectivamente. Para a previsão da série 2, o modelo utilizando a rede neural NARX, que utilizou como neurônios de entrada as variáveis climáticas e o MP10 do dia anterior, apresentou o maior erro absoluto médio &#40;7,13&#41;, no entanto, o modelo NARX apresentou a convergência mais rápida. O menor erro absoluto &#40;6,00&#41; foi obtido pelo modelo em que foi utilizada a rede do tipo MLP, que apresentava como neurônios de entrada as médias diárias das variáveis climáticas e da concentração de MP10 do dia anterior. A rede MLP também foi utilizada para criação de um modelo em que apenas as variáveis climáticas eram utilizadas, para este modelo foi encontrado o valor de 6,52 como erro absoluto. A apresentação do dado de concentração de MP10 do dia anterior torna o desempenho dos modelos de previsão mais eficiente. / Concern about air quality is growing. In recent years, industrial and vehicular emissions have in many cases, especially in urban areas, resulted in inadequate conditions for the health of the population. Several studies have reported an increase in hospital admissions, especially due to respiratory problems, during episodes of high concentrations of particulate matter. The objective of this study was to collect daily PM10 concentration data and compare them with a series of data of almost 10 years of collection available for the city of São Carlos-SP. In addition, both sets of data were used to create forecasting models of the concentration of particulate material using artificial neural networks as a tool. The place chosen for data collect was Praça Voluntários da Pátria, in the center of São Carlos, due to the great circulation of people and vehicles. The two data series were collected with a high volume air sampler. The series 1 &#40;1997-2006&#41; was obtained to characterize the PM of São Carlos. The collections were carried out alternating the days in the week. For the series 2 &#40;2014-2015&#41; daily collections were carried out with the same equipment and in the same sampling site of the series 1. For the creation of the forecast models we used data from the Banco de Dados Meteorológicos para Ensino e Pesquisa &#40;BDMEP&#41; do do Instituto Nacional de Meteorologia &#40;INMET&#41;, de temperature &#40;&#176;C&#41;, relative humidity &#40;&#37;&#41;, wind speed &#40;m&#47;s&#41; and precipitation &#40;mm&#41;. The models were developed in the MatLab software, using two neural network architectures, one of the MLP &#40;Multilayer Percetron&#41; type and another of the NARX &#40;nonlinear autoregressive network with exogenous inputs&#41; type. Comparing PM10 data from series 1 and series 2, it was observed that there was a reduction in PM10 concentration indices over time. This is due to the implementation of stricter laws and the development of cleaner technologies. In relation to the prediction models, for the data of the series 1, the use of the model to predict monthly means of concentration of PM10 was more efficient than the model for prediction of daily means, both had as input neurons only climatic variables, daily averages and monthly averages, respectively. For the prediction of series 2, the model using the NARX neural network, which used as input neurons the climatic variables and PM10 of the previous day, presented the highest mean absolute error &#40;7,13&#41;, however, the NARX model presented the better convergence. The lowest absolute error &#40;6.00&#41; was obtained by the model in which the MLP type network was used, which presented as input neurons the daily averages of the climatic variables and PM10 concentration of the previous day. The MLP network was also used to create a model in which only the climatic variables were used, for this model was found the value of 6.52 as absolute error. Presenting an PM10 concentration data from the previous day improves the performance of forecast models.

Spatial distribution of diurnal variations of aerosol properties in South Korea, both long term and short term, is studied by using 9 AERONET sites from 1999 to 2017 for long-term averages and from an additional 10 sites during the KORUS-AQ field campaign. The extent to which WRF-Chem model and the GOCI satellite retrieval can describe these variations is also analyzed. In daily average, Aerosol Optical Depth (AOD) at 550 nm is 0.386 and shows a diurnal variation of +20 to -30% in inland sites, respectively larger than the counterparts of 0.308 and ± 20% in coastal sites. Both the inland and coastal sites have their diurnal variation peaks in the early morning and in the evening with noontime and early afternoon valleys. In contrast, Angstrom exponent values in all sites are between 1.2 and 1.4 with the exception of the inland rural sites having smaller values near 1.0 during the early morning hours. All inland sites experience a pronounced increase of Angström Exponent from morning to evening, reflecting overall decrease of particle size in daytime. To statistically obtain the climatology of diurnal variation of AOD, a minimum of requirement of ~2 years of observation is needed in coastal rural sites, twice more than the urban sites, which suggests that diurnal variation of AOD in urban setting is distinct and persistent. AERONET, GOCI, WRF-Chem, and observed PM2.5 data consistently show dual peaks for both AOD and PM2.5, one at ~ 10 KST and another ~14 KST. While Korean GOCI satellite is able to consistently capture the diurnal variation of AOD, WRF-Chem clearly has the deficiency to describe the relatively change of peaks and variations between the morning and afternoon, suggesting further studies for the diurnal profile of emissions. Overall, the relative small diurnal variation of PM2.5 is in high contrast with large AOD diurnal variation, which suggests the need to use AOD from geostationary satellites for constrain either modeling or analysis of surface PM2.5 for air quality application.

Atmospheric aerosols deteriorate visibility and pose a significant risk to human health. The global fluxes of secondary organic aerosols (SOA) that form in the atmosphere from aromatic hydrocarbons are poorly constrained and highly uncertain. The lack of molecular tracers to quantify anthropogenic SOA (ASOA) in part limits the understanding of its abundance and variability, and results in a systematic underestimation of the role of ASOA in the atmosphere. The research presented in this thesis advances the knowledge about ASOA through the i) development of new and advanced methods to quantify potential ASOA tracers, ii) evaluation of their suitability as tracers for ASOA, and iii) application of the validated tracers to assess the spatial, diurnal and seasonal variation of ASOA in three urban environments. In this research, a greater understanding of the role of ASOA is gained through the expansion of tracers for SOA from aromatic hydrocarbons. An analytical method to quantify furandiones, which are produced in high yields from the photooxidation of aromatic hydrocarbons, was developed and enabled the first ambient measurements of furandiones. The optimized method allows for the simultaneous extraction of primary source tracers (e.g., polycyclic aromatic hydrocarbons, hopanes, levoglucosan) and other potential ASOA tracers (e.g., 2,3-dihydroxy-4-oxopentanoic acid [DHOPA], benzene dicarboxylic acids, and nitromonoaromatics). The systematic evaluation of potential ASOA tracers by their detectability, gas-particle partitioning, and specificity revealed that DHOPA, phthalic acid, 4-methylphthalic acids, and some nitromonoaromatics are good ASOA tracers because they are specific to aromatic hydrocarbon photooxidation, readily detected in ambient air, and substantially partition to the particle phase under ambient conditions. These tracers are thus recommended for use in field studies to estimate ASOA contributions to atmospheric aerosol relative to other sources. ASOA was determined to be a significant contributor to PM2.5 organic carbon (OC) in three urban environments. In the industrial Houston Ship Channel area in Houston, TX, ASOA contributed 28% of OC, while biogenic SOA (BSOA) contributed 11%. Diurnally, ASOA peaked during daytime and was largely associated with motor vehicle emissions. In Shenzhen, a megacity in China, 13-23% of OC mass was attributed to ASOA, three folds higher than BSOA. When China controlled the emissions from fossil fuel-related sources, the ASOA contribution to OC reduced by 42-75% and visibility remarkably improved. In downtown Atlanta, GA, ASOA contributed 29% and 16% of OC during summer and winter, respectively. ASOA dominates over BSOA during winter, while high biogenic VOC fluxes made BSOA the major SOA source in Atlanta, GA during summertime. These results indicate the high abundance of ASOA in urban air that has potential to be reduced by modification of anthropogenic activities. Overall, the work presented in this dissertation advances the knowledge about the abundance and variation of ASOA in urban atmospheres through the development of quantification methods and expansion of ASOA tracers. These tracers improve source apportionment of ASOA in receptor based models, which can ultimately aid in developing and implementing effective strategies for air quality management.

This thesis presents the use of data assimilation with optimal interpolation (OI) to develop atmospheric aerosol concentration estimates for the United States at high spatial and temporal resolutions. Concentration estimates are highly desirable for a wide range of applications, including visibility, climate, and human health. OI is a viable data assimilation method that can be used to improve Community Multiscale Air Quality (CMAQ) model fine particulate matter (PM2.5) estimates. PM2.5 is the mass of solid and liquid particles with diameters less than or equal to 2.5 μm suspended in the gas phase. OI was employed by combining model estimates with satellite and surface measurements. The satellite data assimilation combined 36 x 36 km aerosol concentrations from CMAQ with aerosol optical depth (AOD) measured by MODIS and AERONET over the continental United States for 2002. Posterior model concentrations generated by the OI algorithm were compared with surface PM2.5 measurements to evaluate a number of possible data assimilation parameters, including model error, observation error, and temporal averaging assumptions. Evaluation was conducted separately for six geographic U.S. regions in 2002. Variability in model error and MODIS biases limited the effectiveness of a single data assimilation system for the entire continental domain. The best combinations of four settings and three averaging schemes led to a domain-averaged improvement in fractional error from 1.2 to 0.97 and from 0.99 to 0.89 at respective IMPROVE and STN monitoring sites. For 38% of OI results, MODIS OI degraded the forward model skill due to biases and outliers in MODIS AOD. Surface data assimilation combined 36 × 36 km aerosol concentrations from the CMAQ model with surface PM2.5 measurements over the continental United States for 2002. The model error covariance matrix was constructed by using the observational method. The observation error covariance matrix included site representation that scaled the observation error by land use (i.e. urban or rural locations). In theory, urban locations should have less effect on surrounding areas than rural sites, which can be controlled using site representation error. The annual evaluations showed substantial improvements in model performance with increases in the correlation coefficient from 0.36 (prior) to 0.76 (posterior), and decreases in the fractional error from 0.43 (prior) to 0.15 (posterior). In addition, the normalized mean error decreased from 0.36 (prior) to 0.13 (posterior), and the RMSE decreased from 5.39 μg m-3 (prior) to 2.32 μg m-3 (posterior). OI decreased model bias for both large spatial areas and point locations, and could be extended to more advanced data assimilation methods. The current work will be applied to a five year (2000-2004) CMAQ simulation aimed at improving aerosol model estimates. The posterior model concentrations will be used to inform exposure studies over the U.S. that relate aerosol exposure to mortality and morbidity rates. Future improvements for the OI techniques used in the current study will include combining both surface and satellite data to improve posterior model estimates. Satellite data have high spatial and temporal resolutions in comparison to surface measurements, which are scarce but more accurate than model estimates. The satellite data are subject to noise affected by location and season of retrieval. The implementation of OI to combine satellite and surface data sets has the potential to improve posterior model estimates for locations that have no direct measurements.

Understanding the composition of atmospheric organic particulate matter (OPM) is essential for predicting its effects on climate, air quality, and health. However, the polar oxygenated fraction (PO-OPM), which includes a significant mass contribution from carboxylic acids, is difficult to speciate and quantitatively determine by current analytical methods such as gas chromatography-mass spectrometry (GC-MS). The method of chemical derivatization and two-dimensional GC with time of flight MS (GC×GC/TOF-MS) was examined in this study for its efficacy in: 1) quantifying a high percentage of the total organic carbon (TOC) mass of a sample containing PO-OPM; 2) quantitatively determining PO-OPM components including carboxylic acids at atmospherically relevant concentrations; and 3) tentatively identifying PO-OPM components. Two derivatization reagent systems were used in this study: BF₃/butanol for the butylation of carboxylic acids, aldehydes, and acidic ketones, and BSTFA for the trimethylsilylation (TMS) of carboxylic acids and alcohols. Three α-pinene ozonolysis OPM filter samples and a set of background filter samples were collected by collaborators in a University of California, Riverside environmental chamber. Derivatization/GC×GC TOF-MS was used to tentatively identify some previously unidentified α-pinene ozonolysis products, and also to show the characteristics of all oxidation products determined. Derivatization efficiencies as measured were 40-70% for most butyl derivatives, and 50-58% for most trimethylsilyl derivatives. A thermal optical method was used to measure the TOC on each filter, and a value of the quantifiable TOC mass using a gas chromatograph was calculated for each sample using GC×GC separation and the mass-sensitive response of a flame ionization detector (FID). The TOC quantified using TMS and GC×GC-FID (TMS/TOCGC×GC FID) accounted for 15-23% of the TOC measured by the thermal-optical method. Using TMS and GC×GC/TOF-MS, 8.85% of the thermal optical TOC was measured and 48.2% of the TMS/TOCGC×GC-FID was semi-quantified using a surrogate standard. The carboxylic acids tentatively identified using TMS and GC×GC/TOF-MS accounted for 8.28% of the TOC measured by thermal optical means. GC×GC TOF-MS chromatograms of derivatized analytes showed reduced peak tailing due in part to the lesser interactions of the derivatized analytes with the stationary phase of the chromatography column as compared to the chromatograms of underivatized samples. The improved peak shape made possible the greater separation, quantification, and identification of high polarity analytes. Limits of detection using derivatization and GC×GC/TOF-MS were μL injected for a series of C2-C6 di-acids, cis-pinonic acid, and dodecanoic acid using both butylation and TMS. Derivatization with GC×GC/TOF-MS was therefore effective for determining polar oxygenated compounds at low concentrations, for determining specific oxidation products not previously identified in OPM, and also for characterizing the probable functional groups and structures of α-pinene ozonolysis products.

Living in counties where manufacturers release environmental toxins, such as those tracked by the Environmental Protection Agency's (EPA) toxic release inventory (TRI), may elevate infants' health risks. Because infant mortality (IM) is a strong indicator of a population's health status, it is an important topic in public health research. The purpose of this research was to examine the potential relationships between IM, community size, and factors related to mothers' SES in counties where more than 25,000 pounds of annual toxic air releases occur. The dependent variable was IM per 1,000 live births in a given community for each of the 3 years included in this analysis (1987, 1995, and 2004). The independent variables included county size and factors related to mother's SES (education, age, ethnicity, and marital status). The theoretical framework consisted of Mosley and Chen's framework for exploring child survival. Archival, publicly available data were pulled from (a) the EPAs TRI data, and (b) linked birth and infant death files from the National Center for Health Statistics. The researcher followed a quantitative, retrospective cross-sectional design and conducted 3 linear regression models to test the research questions. Results indicated that an increase in community size was significantly associated with an increase in IM. Regarding the relationships between IM and the 4 different maternal characteristics (education, age, ethnicity, and marital status) included in the analysis, findings were mixed for the 3 years examined. Despite these unexpected findings, the overall results from this investigation, when considered alongside findings from previous research on IM, indicate that policy changes and interventions are needed to reduce socioeconomic disparities in IM, and to save the lives of more infants.

Excessive PM2.5 emissions in China threaten peoples’ health and cause massive economic burdens to society. Under pressure from the public, and the international community, China published PM2.5 standards for the first time in March 2012. Following the introduction of standards, several pilot cities began to build PM2.5 monitoring networks. This paper is designed to explore whether PM2.5 monitoring can be effectively undertaken and implemented in China and whether monitoring results can offer a technical basis to facilitate a significant reduction in actual PM2.5 emissions and protect public health. PM2.5 monitoring is essential in helping the government and public monitor pollution levels and supervise local compliance with PM2.5 standards. Key aspects to facilitate an effective monitoring process are discussed in the analysis. In addition, a case study – Lanzhou’s PM2.5 monitoring network – is provided to analyze and improve current PM2.5 monitoring practices at local levels, as well as suggest credible technical support to local authorities so as to cut PM2.5 emissions levels. Based on detailed analysis, the results suggest that PM2.5 monitoring can be successfully implemented in China by following several key principles – designing a representative PM2.5 monitoring network, applying QA/QC to ensure data quality, interpreting the data scientifically to understand real pollution levels, etc. In addition, this paper recommends three measures critical to realizing PM2.5 reduction goals: (1) emissions source control, (2) public participation to add input to the decision-making process and supervise local compliance with PM2.5 standards, and (3) non-governmental organization/international cooperation to improve local government and environmental agencies’ capacity with regards to environmental protection. Lessons derived from the case study can help improve PM2.5 monitoring performance not just in Lanzhou, but in cities that share similar monitoring issues across China. Scientific monitoring, together with the application of the above three measures, can more effectively curb PM2.5 emissions, improve air quality, and mitigate negative health effects associated with air pollution. / acase@tulane.edu

The overall goals of this dissertation are: 1) to better quantify the spatial heterogeneity of coarse particulate matter (PM10-2.5) and its chemical composition; and 2) to evaluate the performance (accuracy and precision) of passive samplers analyzed by computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy (CCSEM-EDS) for PM10-2.5. For these goals, field studies were conducted over multiple seasons in Cleveland, OH and were the source of data for this dissertation. To achieve the first goal, we characterized spatial variability in the mass and composition of PM10-2.5 in Cleveland, OH with the aid of inexpensive passive samplers. Passive samplers were deployed at 25 optimized sites for three week-long intervals in summer 2008 to characterize spatial variability in components of PM10-2.5. The size and composition of individual particles were determined using CCSEM-EDS. For each sample, this information was used to estimate PM10-2.5 mass and aerosol composition by particle class. The highest PM10-2.5 means were observed at three central industrial urban sites (35.4 Μg m-3, 43.4 Μg m-3, and 47.6 Μg m-3), whereas lower means were observed to the west and east of this area with the lowest means observed at outskirt suburban background sites (12.9 Μg m-3 and 14.7 Μg m-3). Concentration maps for PM10-2.5 and some compositional components of PM10-2.5 (Fe oxide and Ca rich) show an elongated shape of high values stretching from Lake Erie south through the central industrial area, whereas those for other compositional components (e.g., Si/Al rich) are considerably less heterogeneous. The findings from the spatial variability of coarse particles by compositional class analysis, presented in Chapter II of this dissertation, show that the concentrations of some particle classes were substantially more spatially heterogeneous than others. The data suggest that industrial sources located in The Flats district in particular may contribute to the observed concentration variability and heterogeneity. Lastly, percent relative spatial heterogeneity (SH%) is more consistent with spatial heterogeneity as visualized in the concentration surface maps compared to the coefficient of divergence (COD). The second goal was achieved by assessing the performance of passive samplers analyzed by CCSEM-EDS to measure PM10-2.5 (Chapter III) and investigating potential sources of variability in the measurement of PM10-2.5 with passive samplers analyzed by CCSEM-EDS (Chapter IV). Data for these analyses were obtained in studies conducted in summer 2009 and winter 2010. The precision of PM10-2.5 measured with the passive samplers was highly variable and ranged from a low coefficient of variation (CV) of 2.1% to a high CV of 90.8%. Eighty percent of the CVs were less than 40%. This assessment showed the CV for passive samplers was greater than that recommended by the United States Environmental Protection Agency (EPA) guidelines for the Federal Reference Method (FRM). Several CV values were high, exceeding 40% indicating substantially dissimilar results between co-located passive samplers. The overall CV for the passive samplers was 41.2% in 2009 and 33.8% in 2010. The precision when high CVs > 40% (n = 5 of 25) were excluded from the analysis was 24.1% in 2009 and 18.2% for 2010. Despite issues with precision, PM10-2.5 measured with passive samplers agreed well with that measured with FRM samplers with accuracy approaching EPA Federal Equivalent Method (FEM) criteria. The intercept was 1.21 and not statistically significant (p = 3.88). The passive to FRM sampler comparison (1:1) line fell within the 95% confidence interval (CI) for the best-fit linear regression and was statistically significant (p < 0.05). However, several data points had large standard deviations resulting in high variability between co-located passive samplers (n = 3), which extend outside of the 95% CI's. The passive sampler limit of detection (LOD) for the CCSEM method was 2.8 Μg m-3. This study also showed certain samples had higher CVs and that further investigation was needed to better understand the sources of variability in the measurement of PM10-2.5 with passive samplers. Sources of variability observed in the measurement of PM10-2.5 with passive samplers analyzed by CCSEM were explored in Chapter IV of this dissertation. This research suggests mass concentrations greater than 20 Μg m-3 for week long samples are needed on the passive sampler substrate to obtain overall CVs by mass less than 15%. It also suggests that greater than 55 particle counts within a compositional class are needed to reduce analytical CVs to less than 15%. Another finding from this study was increasing the concentration from 6.2 to 10.6 Μg m-3 increases the CCSEM analytical precision by mass 38% and by number 75% for random orientation. Also certain compositional classes appeared problematical for precision of passive sampler measurements. For example, the presence of salt plus moisture introduces challenges for CCSEM analysis through the wetting of salt crystalline particles which dissolve creating a displaced dry deposition pattern of particles upon subsequent evaporation. This process can falsely elevate or reduce the particle count and alter its distribution on the sampling media.