Malaria is caused by a protozoan parasite, Plasmodium falciparum, which is responsible for over 500,000 deaths per year worldwide. Although malaria medicines are working well in many parts of the world, antimalarial drug resistance has emerged as one of the greatest challenges facing malaria control today. Since the malaria parasites are once again developing widespread resistance to antimalarial drugs, this can cause the spread of malaria to new areas and the re-emergence of malaria in areas where it had already been eradicated. Therefore, the discovery and characterization of novel antimalarials is extremely urgent. A previous drug screen in Dr. Chakrabarti's lab identified several natural products (NPs) with antiplasmodial activities. The focus of this study is to characterize the hit compounds using Principal Component Analysis (PCA) to determine structural uniqueness compared to known antimalarial drugs. This study will compare multiple libraries of different compounds, such as known drugs, kinase inhibitors, macrocycles, and top antimalarial hits discovered in our lab. Prioritizing the hit compounds by their chemical uniqueness will lessen the probability of future drug resistance. This is an important step in drug discovery as this will allow us to increase the interpretability of the datasets by creating new uncorrelated variables that will successively maximize variance. Characterization of the Natural Product inspired compounds will enable us to discover potent, selective, and novel antiplasmodial scaffolds that are unique in the 3-dimensional chemical space and will provide critical information that will serve as advanced starting points for the antimalarial drug discovery pipeline.

No description available.

With the increasing importance of hand-held devices with touch displays, the need for flat panel displays (FPDs) will likely increase in the future. Glass is the most important substrate for FPD manufacturing, where both its bulk and surface properties are critical for its performance. Many properties of the glass used in FPDs are controlled by its surface chemistry. Surface hydroxyls are the most important functional groups on a glass surface, which control processes that occurs on oxide surfaces, including wetting, adhesion, electrostatic charging and discharge, and the rate of contamination. In this dissertation, I present a new approach for determining surface silanol densities on planar surfaces. This methodology consists of tagging surface silanols using atomic layer deposition (ALD) followed by low energy ion scattering (LEIS) analysis of the tags. The LEIS signal is limited to the outermost atomic layer, i.e., LEIS is an extremely surface sensitive technique. Quantification in LEIS is straightforward in the presence of suitable reference materials. An essential part of any LEIS measurement is the preparation and characterization of the sample and appropriate reference materials that best represent the samples. My tag-and-count method was applied to chemically and thermally treated fused silica. In this work, I determined the silanol density of a fully hydroxylated fused silica surface to be 4.67 OH/nm2. This value agrees with the literature value for high surface area silica powder. My methodology should be important in future glass studies. Surface Science Spectra (SSS) is an important, peer-reviewed database of spectra from surfaces. Recently, SSS has been expanding to accept spectra from new surface techniques. I created the first SSS submission form for LEIS spectra (see appendix 5), and used it to create the first SSS LEIS paper (on CaF2 and Au reference materials, see chapter 3). I also show LEIS reference spectra for ZnO, and copper in the appendix 1. The rest of my dissertation focuses on my chemometrics/informatics and data analysis work. For example, I showed the performance and capabilities of a series of summary statistics as new tools for unsupervised exploratory data analysis (EDA) (see chapter 4). Unsupervised EDA is often the first step in understanding complex data sets because it can group, and even classify, samples according to their spectral similarities and differences. Pattern recognition entropy (PRE) and other summary statistics are direct methods for analyzing data - they are not factor-based approaches like principal component analysis (PCA) or multivariate curve resolution (MCR). I show that, in general, PRE outperforms the other summary statistics, especially in image analysis, although I recommend a suite of summary statistics be used in exploring complex data sets. In addition, I introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method and use it to analyze multiple data sets. DS-PRE increases the discrimination power of PRE. I have also prepared a guide that discusses the vital aspects and considerations for chemometrics/informatics analyses of XPS data along with specific EDA tools that can be used to probe XPS data sets, including PRE, PCA, MCR, and cluster analysis (see chapter 5). I emphasize the importance of an initial evaluation/plotting of raw data, data preprocessing, returning to the original data after a chemometrics/informatics analysis, and determining the number of abstract factors to keep in an analysis, including reconstructing the data using PCA. In my thesis, I also show the analysis of commercial automotive lubricant oils (ALOs) with various chemometrics techniques (see chapter 6). Using these methods, the ALO samples were readily differentiated according to their American Petroleum Institute (API) classification and base oil types: mineral, semi-synthetic, and synthetic.

[ES] Esta tesis, desarrollada bajo una beca de formación de personal investigador de la Universitat Politècnica de València, tiene como objetivo proponer y aplicar metodologías de Statistical Machine Learning en contextos de Ingeniería Biomédica. Este concepto pretende aunar el uso de modelos de aprendizaje automático junto con la búsqueda de comprensión e interpretabilidad clásica del razonamiento estadístico, dando lugar a soluciones tecnológicas de problemas biomédicos que no pasen únicamente por el objetivo de optimizar el desempeño predictivo de los modelos. Para ello, se han dibujado dos objetivos principales que vertebran además el documento: proponer metodologías novedosas dentro del paraguas del Statistical Machine Learning, y aplicar soluciones a problemas biomédicos reales manteniendo esta filosofía en mente. Estos objetivos se han materializado en contribuciones metodológicas para la simulación de valores atípicos y la imputación de datos faltantes en presencia de datos atípicos, y en contribuciones aplicadas a casos reales para la mejora de procesos de atención médica, la mejora en el diagnóstico y pronóstico de enfermedades, y la estandarización de procedimientos de medición en entornos biotecnológicos. Dichas contribuciones se han artículado en capítulos correspondientes a las dos partes principales ya mencionadas. Finalmente, las conclusiones y líneas futuras cierran el documento, recalcando los mensajes principales de las contribuciones de la tesis doctoral en general, y sentando además las bases para líneas futuras que se han dibujado a consecuencia del trabajo realizado a lo largo del doctorado. / [CA] Aquesta tesi, desenvolupada sota una beca de formació de personal investigador de la Universitat Politècnica de València, té com a objectiu proposar i aplicar metodologies de Statistical Machine Learning en contextos d'Enginyeria Biomèdica. Aquest concepte pretén unir l'ús de models d'aprenentatge automàtic juntament amb la cerca de comprensió i interpretació clàssica del raonament estadístic, donant lloc a solucions tecnològiques de problemes biomèdics que no passen únicament per l'objectiu d'optimitzar el rendiment predictiu dels models. Per a això, s'han dibuixat dos objectius principals que vertebren a més el document: proposar metodologies noves dins del paraigua del Statistical Machine Learning, i aplicar solucions a problemes biomèdics reals mantenint aquesta filosofia en ment. Aquests objectius s'han materialitzat en contribucions metodològiques per a la simulació de valors atípics i la imputació de dades mancants en presència de valors atípics, i en contribucions aplicades a casos reals per a la millora de processos d'atenció mèdica, la millora en el diagnòstic i pronòstic de malalties, i l'estandardització de procediments de mesurament en entorns biotecnològics. Aquestes contribucions s'han articulat en capítols corresponents a les dues parts principals ja esmentades. Finalment, les conclusions i línies futures tanquen el document, recalant els missatges principals de les contribucions, de la tesi doctoral en general, i assentant a més les bases per a línies futures que s'han dibuixat com a consequència del treball realitzat al llarg del doctorat. / [EN] This thesis, developed under a research personnel formation grant from the Universitat Politècnica de València, aims to propose and apply methodologies of Statistical Machine Learning in Biomedical Engineering contexts. This concept seeks to combine machine learning models with the classic understanding and interpretability of statistical reasoning, resulting in technological solutions for biomedical problems that go beyond solely optimizing the predictive performance of models. To achieve this, two main objectives have been outlined, which also structure the document: proposing novel methodologies within the umbrella of Statistical Machine Learning and applying solutions to real biomedical problems while keeping this philosophy in mind. These objectives have materialized into methodological contributions for simulating outliers and imputing missing data in the presence of outliers and applied contributions to real cases for improving healthcare processes, enhancing disease diagnosis and prognosis, and standardizing measurement procedures in biotechnological environments. These contributions are articulated in chapters corresponding to the aforementioned two main parts. Finally, the conclusions and future lines of research conclude the document, reiterating the main messages of the contributions and the overall doctoral thesis and laying the groundwork for future lines of inquiry stemming from the work conducted throughout the doctorate. / González Cebrián, A. (2024). Statistical Machine Learning in Biomedical Engineering [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203529

In dieser Arbeit werden zwei wesentliche neue Ergebnisse vorgestellt. Das erste bezieht sich auf die olfaktorische Kodierung und das zweite auf das sensorische Gedaechtnis. Beide Phaenomene werden am Beispiel des Gehirns der Honigbiene untersucht. In Bezug auf die olfaktorische Kodierung zeige ich, dass die neuronale Dynamik waehrend der Stimulation im Antennallobus duftspezifische Trajektorien beschreibt, die in duftspezifischen Attraktoren enden. Das Zeitinterval, in dem diese Attraktoren erreicht werden, betraegt unabhaengig von der Identitaet und der Konzentration des Duftes ungefaehr 800 ms. Darueber hinaus zeige ich, dass Support-Vektor Maschinen, und insbesondere Perzeptronen, ein realistisches und biologisches Model der Wechselwirkung zwischen dem Antennallobus (dem kodierenden Netwerk) und dem Pilzkoerper (dem dekodierenden Netzwerk) darstellen. Dieses Model kann sowohl Reaktionszeiten von ca. 300 ms als auch die Invarianz der Duftwahrnehmung gegenueber der Duftkonzentration erklaeren. In Bezug auf das sensorische Gedaechtnis zeige ich, dass eine einzige Stimulation ohne Belohnung dem Hebbschen Postulat folgend Veraenderungen der paarweisen Korrelationen zwischen Glomeruli induziert. Ich zeige, dass diese Veranderungen der Korrelationen bei 2/3 der Bienen ausreichen, um den letzten Stimulus zu bestimmen. In der zweiten Minute nach der Stimulation ist eine erfolgreiche Bestimmung des Stimulus nur bei 1/3 der Bienen moeglich. Eine Hauptkomponentenanalyse der spontanen Aktivitaet laesst erkennen, dass das dominante Muster des Netzwerks waehrend der spontanen Aktivitaet nach, aber nicht vor der Stimulation das duftinduzierte Aktivitaetsmuster bei 2/3 der Bienen nachbildet. Man kann deshalb die duftinduzierten (Veraenderungen der) Korrelationen als Spuren eines Kurzzeitgedaechtnisses bzw. als Hebbsche "Reverberationen" betrachtet werden. / Two major novel results are reported in this work. The first concerns olfactory coding and the second concerns sensory memory. Both phenomena are investigated in the brain of the honeybee as a model system. Considering olfactory coding I demonstrate that the neural dynamics in the antennal lobe describe odor-specific trajectories during stimulation that converge to odor-specific attractors. The time interval to reach these attractors is, regardless of odor identity and concentration, approximately 800 ms. I show that support-vector machines and, in particular perceptrons provide a realistic and biological model of the interaction between the antennal lobe (coding network) and the mushroom body (decoding network). This model can also account for reaction-times of about 300 ms and for concentration invariance of odor perception. Regarding sensory memory I show that a single stimulation without reward induces changes of pairwise correlation between glomeruli in a Hebbian-like manner. I demonstrate that those changes of correlation suffice to retrieve the last stimulus presented in 2/3 of the bees studied. Succesful retrieval decays to 1/3 of the bees within the second minute after stimulation. In addition, a principal-component analysis of the spontaneous activity reveals that the dominant pattern of the network during the spontaneous activity after, but not before stimulation, reproduces the odor-induced activity pattern in 2/3 of the bees studied. One can therefore consider the odor-induced (changes of) correlation as traces of a short-term memory or as Hebbian reverberations.

Der Zweck dieser Arbeit ist es, die Beziehung zwischen die Größe des Landes und das Wachstum auf internationaler Ebene und vergleichsweise in der Wirtschafts-und Währungsunion zu untersuchen und erarbeiten ihre Folgen für das Verhalten der wachstumsorientierte Finanzpolitik. Um ein globales Verständnis des Zusammenhangs zwischen Größe des Landes und das Wachstum in der EWU weiter verfolgen wir einen interdisziplinären Ansatz, einschließlich der makroökonomischen Modellierung (DSGE), Ökonometrie und Analyse der politischen Ökonomie. Die Kombination dieser Untersuchungen schließen wir, dass die Größe des Landes einen Einfluss auf die wirtschaftlichen Strukturen der Nationen, die Auswirkungen ihrer Politik und damit auf ihre Wachstumsdynamik hat. Aus diesem Grund ist es notwendig, die Bedeutung der Größe des Landes und ihre Folgen für die WWU wieder. / The purpose of this dissertation is to investigate the relationship between country size and growth at the international level and comparatively in the Economic and Monetary Union, and to draw up its consequences for the conduct of growth-orientated fiscal policies. To further a global understanding of the link between country size and growth in the EMU, we follow an interdisciplinary approach, including macro-economic modelling (DSGE), econometrics and political economy analysis. Combining these analyses, we conclude that country size has an incidence on the economic structures of nations, the effects of their policies and therefore on their pace of growth. For this reason there is a need to reinstate the importance of country size and its consequences for the EMU.

To refine our knowledge and to adequately test hypotheses concerning theoretical and applied aspects of invasion biology, successful and unsuccessful invaders should be compared. This study investigated insect establishment patterns by comparing the climatic preferences and biological attributes of two groups of polyphagous insect species that are constantly intercepted at New Zealand's border. One group of species is established in New Zealand (n = 15), the other group comprised species that are not established (n = 21). In the present study the two groups were considered to represent successful and unsuccessful invaders. To provide background for interpretation of results of the comparative analysis, global areas that are climatically analogous to sites in New Zealand were identified by an eco-climatic assessment model, CLIMEX, to determine possible sources of insect pest invasion. It was found that south east Australia is one of the regions that are climatically very similar to New Zealand. Furthermore, New Zealand shares 90% of its insect pest species with that region. South east Australia has close trade and tourism links with New Zealand and because of its proximity a new incursion in that analogous climate should alert biosecurity authorities in New Zealand. Other regions in western Europe and the east coast of the United States are also climatically similar and share a high proportion of pest species with New Zealand. Principal component analysis was used to investigate patterns in insect global distributions of the two groups of species in relation to climate. Climate variables were reduced to temperature and moisture based principal components defining four climate regions, that were identified in the present study as, warm/dry, warm/wet, cool/dry and cool/moist. Most of the insect species established in New Zealand had a wide distribution in all four climate regions defined by the principal components and their global distributions overlapped into the cool/moist, temperate climate where all the New Zealand sites belong. The insect species that have not established in New Zealand had narrow distributions within the warm/wet, tropical climates. Discriminant analysis was then used to identify which climate variables best discriminate between species presence/absence at a site in relation to climate. The discriminant analysis classified the presence and absence of most insect species significantly better than chance. Late spring and early summer temperatures correctly classified a high proportion of sites where many insect species were present. Soil moisture and winter rainfall were less effective discriminating the presence of the insect species studied here. Biological attributes were compared between the two groups of species. It was found that the species established in New Zealand had a significantly wider host plant range than species that have not established. The lower developmental threshold temperature was on average, 4°C lower for established species compared with non-established species. These data suggest that species that establish well in New Zealand have a wide host range and can tolerate lower temperatures compared with those that have not established. No firm conclusions could be drawn about the importance of propagule pressure, body size, fecundity or phylogeny for successful establishment because data availability constrained sample sizes and the data were highly variable. The predictive capacity of a new tool that has potential for eco-climatic assessment, the artificial neural network (ANN), was compared with other well used models. Using climate variables as predictors, artificial neural network predictions were compared with binary logistic regression and CLIMEX. Using bootstrapping, artificial neural networks predicted insect presence and absence significantly better than the binary logistic regression model. When model prediction success was assessed by the kappa statistic there were also significant differences in prediction performance between the two groups of study insects. For established species, the models were able to provide predictions that were in moderate agreement with the observed data. For non-established species, model predictions were on average only slightly better than chance. The predictions of CLIMEX and artificial neural networks when given novel data, were difficult to compare because both models have different theoretical bases and different climate databases. However, it is clear that both models have potential to give insights into invasive species distributions. Finally the results of the studies in this thesis were drawn together to provide a framework for a prototype pest risk assessment decision support system. Future research is needed to refine the analyses and models that are the components of this system.

En sjukdom kan många gånger härledas till en kaskadereaktion mellan proteiner, co-faktorer och substrat. Denna kaskadreaktion blir många gånger målet för att behandla sjukdomen med läkemedel. För att designa nya läkemedelsmoleyler används vanligen datorbaserade verktyg. Denna design av läkemedelsmolekyler drar stor nytta av att målproteinet är känt och då framförallt dess tredimensionella (3D) struktur. Är 3D-strukturen känd kan man utföra så kallad struktur- och datorbaserad molekyldesign, 3D-geometrin (f.f.a. för inbindningsplatsen) blir en vägledning för designen av en ny molekyl. Många faktorer avgör interaktionen mellan en molekyl och bindningsplatsen, till exempel fysikalisk-kemiska egenskaper hos molekylen och bindningsplatsen, flexibiliteten i molekylen och målproteinet, och det omgivande lösningsmedlet. För att strukturbaserad molekyldesign ska fungera väl måste två viktiga steg utföras: i) 3D anpassning av molekyler till bindningsplatsen i ett målprotein (s.k. dockning) och ii) prediktion av molekylers affinitet för bindningsplatsen. Huvudsyftena med arbetet i denna avhandling var som följer: i) skapa modeler för att prediktera affiniteten mellan en molekyl och bindningsplatsen i ett målprotein; ii) förfina molekyl-protein-geometrin som skapas vid 3D-anpassning mellan en molekyl och bindningsplatsen i ett målprotein (s.k. dockning); iii) karaktärisera proteiner och framför allt deras sekundärstruktur; iv) bedöma effekten av olika matematiska beskrivningar av lösningsmedlet för förfining av 3D molekyl-protein-geometrin skapad vid dockning och prediktion av molekylers affinitet för proteiners bindningsfickor. Ett övergripande syfte var att använda kemometriska metoder för modellering och dataanalys på de ovan nämnda punkterna. För att sammanfatta så presenterar denna avhandling metoder och resultat som är användbara för strukturbaserad molekyldesign. De rapporterade resultaten visar att det är möjligt att skapa kemometriska modeler för prediktion av molekylers affinitet för bindningsplatsen i ett protein och att dessa presterade lika bra som andra vanliga metoder. Dessutom kunde kemometriska modeller skapas för att beskriva effekten av hur inställningarna för olika parametrar i dockningsprogram påverkade den 3D molekyl-protein-geometrin som dockingsprogram skapade. Vidare kunde kemometriska modeller andvändas för att öka förståelsen för deskriptorer som beskrev sekundärstrukturen i proteiner. Förfining av molekyl-protein-geometrin skapad genom dockning gav liknande och ickesignifikanta resultat oberoende av vilken matematisk modell för lösningsmedlet som användes, förutom för ett fåtal (sex av 30) fall. Däremot visade det sig att användandet av en förfinad geometri var värdefullt för prediktion av molekylers affinitet för bindningsplatsen i ett protein. Förbättringen av prediktion av affintitet var markant då en Poisson-Boltzmann beskrivning av lösningsmedlet användes; jämfört med prediktionerna gjorda med ett dockningsprogram förbättrades korrelationen mellan beräknad affintiet och uppmätt affinitet med 0,7 (R2). / A disease is often associated with a cascade reaction pathway involving proteins, co-factors and substrates. Hence to treat the disease, elements of this pathway are often targeted using a therapeutic agent, a drug. Designing new drug molecules for use as therapeutic agents involves the application of methods collectively known as computer-aided molecular design, CAMD. When the three dimensional (3D) geometry of a macromolecular target (usually a protein) is known, structure-based CAMD is undertaken and structural information of the target guides the design of new molecules and their interactions with the binding sites in targeted proteins. Many factors influence the interactions between the designed molecules and the binding sites of the target proteins, such as the physico-chemical properties of the molecule and the binding site, the flexibility of the protein and the ligand, and the surrounding solvent. In order for structure-based CAMD to be successful, two important aspects must be considered that take the abovementioned factors into account. These are; i) 3D fitting of molecules to the binding site of the target protein (like fitting pieces of a jigsaw puzzle), and ii) predicting the affinity of molecules to the protein binding site. The main objectives of the work underlying this thesis were: to create models for predicting the affinity between a molecule and a protein binding site; to refine the geometry of the molecule-protein complex derived by or in 3D fitting (also known as docking); to characterize the proteins and their secondary structure; and to evaluate the effects of different generalized-Born (GB) and Poisson-Boltzmann (PB) implicit solvent models on the refinement of the molecule-protein complex geometry created in the docking and the prediction of the molecule-to-protein binding site affinity. A further objective was to apply chemometric methodologies for modeling and data analysis to all of the above. To summarize, this thesis presents methodologies and results applicable to structure-based CAMD. Results show that predictive chemometric models for molecule-to-protein binding site affinity could be created that yield comparable results to similar, commonly used methods. In addition, chemometric models could be created to model the effects of software settings on the molecule-protein complex geometry using software for molecule-to-binding site docking. Furthermore, the use of chemometric models provided a more profound understanding of protein secondary structure descriptors. Refining the geometry of molecule-protein complexes created through molecule-to-binding site docking gave similar results for all investigated implicit solvent models, but the geometry was significantly improved in only a few examined cases (six of 30). However, using the geometry-refined molecule-protein complexes was highly valuable for the prediction of molecule-to-binding site affinity. Indeed, using the PB solvent model it yielded improvements of 0.7 in correlation coefficients (R2) for binding affinity parameters of a set of Factor Xa protein drug molecules, relative to those obtained using the fitting software.

Submitted by Biblioteca de Pós-Graduação em Geoquímica BGQ (bgq@ndc.uff.br) on 2016-05-02T17:58:54Z No. of bitstreams: 1 GALLOTTA_30_06_14.pdf: 62695484 bytes, checksum: 33335ee3affc2bcffb11fc10caf9ae3f (MD5) / Made available in DSpace on 2016-05-02T17:58:54Z (GMT). No. of bitstreams: 1 GALLOTTA_30_06_14.pdf: 62695484 bytes, checksum: 33335ee3affc2bcffb11fc10caf9ae3f (MD5) / Universidade Federal Fluminense. Instituto de Química. Programa de Pós-Graduação em Geociências-Geoquímica. Niterói, RJ / Os hidrocarbonetos presentes no ambiente consistem em misturas complexas de compostos derivados de múltiplas fontes. Os combustíveis fósseis representam a principal contribuição, devido à taxa e escala espacial, em que o petróleo tem sido usado como fonte de energia e matéria-prima para a indústria química. O objetivo deste estudo foi avaliar os níveis de concentração e identificar fontes de hidrocarbonetos na Bacia do Alto Iguaçu e, em especial, na área de influência da Refinaria Presidente Getúlio Vargas (REPAR). Além dos fatores de poluição crônica, a área de estudo foi alvo de um derrame acidental de petróleo em julho de 2000. Diversos indicadores em diferentes compartimentos ambientais (água superficial, sedimento, solo e água subterrânea) foram avaliados na fase pós-derrame e no monitoramento ambiental ao longo de várias campanhas por mais de uma década. Os esforços de avaliação foram concentrados nos seguintes indicadores: os nalcanos, os alcanos isoprenoides, os hidrocarbonetos policíclicos aromáticos (HPA), os biomarcadores de petróleo e ainda o total de hidrocarbonetos de petróleo (THP). Os resultados identificaram como principal aporte de matéria orgânica para as águas superficiais dos rios Barigüi e Iguaçu, os fluxos materiais originados em região a montante do acidente, refletindo a contribuição antropogênica crônica da cidade de Curitiba. Em 2007 e 2008, as concentrações de THP e HPA nas águas superficiais e sedimentos dos rios Barigüi e Iguaçu refletem uma expressiva melhoria nas condições desses rios em relação a 2000. Na área interna da refinaria (Ponto Zero), observou-se uma nítida diminuição (atenuação) natural das concentrações de THP no solo em todas as profundidades dos perfis amostrados ao longo do tempo nos Banhados 1 e 4 e, em particular, uma diminuição importante das concentrações entre 2004 e 2007. Para a identificação das fontes de hidrocarbonetos foram utilizadas razões diagnósticas e quimiometria. As razões diagnósticas calculadas a partir de concentrações de HPA sugeriram que, na maioria dos sedimentos dos Rios Barigüi e Iguaçu coletados nas campanhas de 2000 e 2001, a fonte petrogênica é a principal. Somente na estação a montante do acidente no Rio Barigüi, a fonte pirolítica predominou nestas duas campanhas. As razões diagnósticas que apresentaram maior eficiência na identificação de fontes de hidrocarbonetos nos sedimentos dos rios Barigüi e Iguaçu foram: ΣC1-Fenantrenos/Fenantreno; e (ΣHPA parentais de 3-6 anéis)/(Σ5 séries de HPA alquilados). A identificação de fontes através de razões diagnósticas calculadas a partir de áreas e alturas de picos cromatográficos demonstrou sua aplicabilidade verificando a relação entre os compostos encontrados em amostras de solo da área interna da refinaria com a amostra de petróleo derramado no acidente, após quase uma década da ocorrência do vazamento. A identificação de fontes através método de quimiométrico baseado na análise de componentes principais (ACP) de seções pré-processadas e combinadas dos Cromatogramas de Íons Selecionados (CIS) mostrou que as amostras mais contaminadas estão na área interna da refinaria. Essas amostras apresentam um padrão de distribuição petrogênica e diferentes graus de intemperismo. As amostras da área externa à refinaria (Guajuvira, General Lúcio e Balsa Nova) são menos ou não contaminadas e/ou contém uma mistura de contribuições diagenéticas, pirolíticas e petrogênicas onde predominam diferentes proporções. Os locais mais distantes da atividade industrial (Balsa Nova) contem, como esperado, os níveis mais baixos de contaminação por HPA. Os resultados de biomarcadores demonstraram que não há evidências para concluir que as amostras da área externa à refinaria e o óleo Cusiana vazado tenham a mesma origem. Os resultados ao longo dos rios Barigüi e Iguaçu e do Ponto Zero demonstraram que as ações de emergência para a contenção do óleo foram adequadas para os rios, e que a contaminação decorrente do derrame ficou predominantemente contida no Ponto Zero e diminuiu significativamente após uma década. / Hydrocarbons present in the environment consist of complex mixtures of compounds derived from multiple sources. The main contribution lies on fossil fuel inputs due to the rate and spatial scale by which petroleum has been used as an energy source and chemical feedstock. The aim of this study was to assess the concentration levels and identify sources of hydrocarbons in the Upper Iguaçu Watershed and, in particular, in the area of influence of the President Getulio Vargas Refinery (REPAR). In addition to the factors of chronic pollution, the study area was the scenario of an acute accidental oil spill in July 2000. Numerous indicators in different environmental compartments (surface water, sediment, soil and groundwater) were assessed in the post spill phase and during the environmental monitoring programs over the course of several campaigns for more than a decade. Assessment efforts were concentrated on the following indicators: n-alkanes, alkanes isoprenoids, polycyclic aromatic hydrocarbons (PAH), petroleum biomarkers and total petroleum hydrocarbons (TPH). The results identified as the main contribution of organic matter to surface waters of the Barigüi and Iguaçu Rivers the materials flows originated in the region upstream of the accident, reflecting chronic anthropogenic contribution of the city of Curitiba. In 2007 and 2008, the TPH and PAH concentrations in surface waters and sediments of the Barigüi and Iguaçu Rivers revealed a significant improvement in the conditions of these rivers when compared with 2000. Inside the refinery area (Point Zero), it was observed a clear natural decrease (attenuation) of the concentrations of TPH in the soil at all depths sampled over time in Marshes 1 and 4 and, in particular, an important decrease of concentrations between 2004 and 2007. Diagnostic ratios and chemometrics were used to identity hydrocarbon sources. The diagnostic ratios calculated from the concentrations of PAH suggested that, in the majority of sediments from the Barigüi and Iguaçu Rivers collected in 2000 and 2001 campaigns, the main source is petrogenic. Only in the station upstream the accident in the Barigüi River, the pyrolytic source predominated in these two campaigns. The diagnostic ratios that presented higher efficiency in identifying sources of hydrocarbons in sediments of the Barigüi and Iguaçu Rivers were: ΣC1- Phenantrenes/Phenanthrene; and (Σ3-6 rings parental PAH)/(Σ5 alkylated PAH series). The source identification through diagnostic ratios calculated from heights and areas of chromatographic peaks demonstrated its applicability establishing a relationship between the compounds found in soil samples of the internal area of the refinery and the sample of the oil spilled in the accident, after nearly a decade of occurrence of the spillage. The source identification through chemometric method based on principal components analysis (PCA) of preprocessed and combined sections of Selected Ion Chromatograms (SIC) showed that the most contaminated samples are inside the refinery area. These samples present a petrogenic pattern and different weathering degrees. Samples from outside the refinery area (Guajuvira, General Lúcio e Balsa Nova) are either less or not contaminated, and/or contain mixtures of diagenetic, pyrogenic and petrogenic inputs where different proportions predominate. The locations farthest away from industrial activity (Balsa Nova) contain, as expected, the lowest levels of PAH contamination. The biomarkers results do not show any evidences to conclude positive matches between the samples from outside the refinery area and the spilled Cusiana oil. The results along Barigüi and Iguaçu rivers and Point Zero demonstrated that emergency actions to contain the oil were appropriate for the rivers, and that the contamination resulting from the spill was mostly contained in the Point Zero and decreased significantly after one decade

Anthropogenic emissions into the troposphere can impact air quality, leading to poorer health outcomes in the affected areas. Volatile organic compounds (VOCs) are a group of chemical compounds, including some which are toxic, that are precursors in the formation of ground-level ozone and secondary organic aerosols. VOCs have a variety of sources, and the distribution of atmospheric VOCs differs significantly over time and space. Historically, the large number of chemical species present at low concentrations (parts-per-trillion to parts-per-billion by volume) have made VOCs difficult to measure in ambient air. However, with improvements in analytical instrumentation, these measurements are becoming more common place. Direct mass spectrometry (MS), such as membrane introduction mass spectrometry (MIMS) and proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) facilitate real-time, continuous measurements of VOCs in air, with full scan mass spectral data capturing changes in chemical composition with high temporal resolution. Operated on-road, mobilized direct MS has been used for quantitative mapping of VOCs at the neighborhood scale, but identifying VOC sources based on the observed mixture of molecules in the full scan MS dataset has yet to be explored. This dissertation describes the use of chemometric techniques to interrogate full scan MS data, and the progression from discriminating VOC samples of known chemical composition based on full scan MIMS data through to the apportionment of VOC sources measured continuously with a PTR-ToF-MS system operating in a moving vehicle. Lab‐constructed VOC samples of known chemical composition and concentration demonstrated the use of principal component analysis (PCA) to discriminate, and k-nearest neighbours to classify, samples based on normalized full scan MIMS data. Furthermore, multivariate curve resolution-alternating least squares (MCR-ALS) was used to resolve mixtures into molecular component contributions. PCA was also used to discriminate ‘real-world’ VOC mixtures (e.g., woodsmoke VOCs, headspace above aqueous hydrocarbon samples) of unknown chemical composition measured by MIMS. Using vehicle mounted MIMS and PTR-ToF-MS systems, full scan MS data of ambient atmospheric VOCs were collected and PCA was applied to the normalized full scan MS data. A supervised analysis performed PCA on samples collected near known VOC sources, while an unsupervised analysis using PCA followed by cluster analysis was used to identify groups in a continuous, time series PTR-ToF-MS dataset measured between Nanaimo and Crofton, British Columbia (BC). In both the supervised and unsupervised analysis, samples impacted by emissions from different sources (e.g., internal combustion engines, sawmills, composting facilities, pulp mills) were discriminated. With PCA, samples were discriminated based on differences in the observed full scan MS data, however real-world samples are often impacted by multiple VOC sources. MCR-weighted ALS (MCR-WALS) was applied to the continuous, time series PTR-ToF-MS data from three field campaigns on Vancouver Island, BC for source apportionment. Variable selection based on signal-to-noise ratios was used to reduce the mass list while retaining the observed m/z that capture changes in the mixture of VOCs measured, improving model results, and reducing computation time. Both point (e.g., anthropogenic hydrocarbon emissions, pulp mill emissions) and diffuse (e.g., VOCs from forest fire smoke) VOC sources were identified in the data, and were apportioned to determine their contributions to the measured samples. The data analyzed captured fine scale changes in the ambient VOCs present in the air, and geospatial maps of each individual source, and of the source apportionment were used to visualize the distribution of VOC sources across the sampling area. This work represents the first use of MCR-WALS to identify and apportion ambient VOC sources based on continuous PTR-ToF-MS data measured from a moving vehicle. The methods described can be applied to larger scale field campaigns for the source apportionment of VOCs across multiple days to capture diurnal and seasonal variations. Identifying spatial and temporal trends in the sources of VOCs at the regional scale can help to identify pollution ‘hot spots’ and inform evidence-based public policy for improving air quality. / Graduate / 2022-08-17