An Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) was used to obtain mass spectra of individual aerosol particles in the 0.5 – 2 µm size range in downtown Toronto, Canada for one to two month periods during each season of 2007. A modified version of the Adaptive Resonance Theory (ART-2a) clustering algorithm, which clusters particles based on the similarity of their mass spectra, was shown to be more accurate than the existing algorithm and was used to cluster the ambient data. A total of 21 unique particle types were identified and were characterized based on their chemical composition, their size, and their temporal trends and seasonal variations. Potential sources are also discussed. Particles containing trimethylamine (TMA) were also observed and a more detailed investigation of ambient trends in conjunction with a laboratory experiment was performed in order to elucidate conditions for which TMA will be observed in the particle phase in Southern Ontario.

Particles reduce visibility, change climate, and affect human health. In 1997, the National Ambient Air Quality Standard (NAAQS) for PM2.5 (particles less than 2.5 mm) was promulgated. The annual mean PM2.5 mass concentrations in Atlanta, Georgia exceed the standard, and control is needed. The first goal of this study is to develop the control strategies of PM2.5 in Atlanta, Georgia. Based on the statistical analysis of measured data, from 22% to 40% of emission reductions are required to meet the NAAQS at 95% CI. The estimated control levels can be tested using the Community Multiscale Air Quality (CMAQ) model to better assess if the proposed levels will achieve sufficient reduction in PM2.5. The second goal of this study is to analyze various uncertainties residing in CMAQ. For the model to be used in such applications with confidence, it needs to be evaluated. The model performance is calculated by the relative agreement between volume-averaged predictions and point measurements. Up to 14% of the model error for PM2.5 mass is due to the different spatial scales of the two values. CMAQ predicts PM2.5 mass concentrations reasonably well, but CMAQ significantly underestimates PM2.5 number concentrations. Causes of the underestimation include that assumed inaccurate particle density and particle size of the primary emissions in CMAQ, in addition to the expression of the particle size with three lognormal distributions. Also, the strength and limitations of CMAQ in performing PM2.5 source apportionment are compared with those of the Chemical Mass Balance with Molecular Markers. Finally, the accuracy of emissions, one of the important inputs of CMAQ, is evaluated by the inverse modeling. Results show that base level emissions for CO and SO2 sources are relatively accurate, whereas NH3, NOx, PEC and PMFINE emissions are overestimated. The emission adjustment for POA and VOC emissions is significantly different among regions.

Neighboring northern Kaohsiung with a dense population of petrochemical and petroleum industrial complexes included China Petroleum Company (CPC) refinery plant, Renwu and Dazher petrochemical industrial plants. In recent years, although many scholars have conducted regional studies, but are still limited by the lack of relevant information evidences (such as odorous matters identification and VOCs fingerprint database), while unable to clearly identify the causes of poor ambient air quality. By sampling and analyzing VOCs, we will be able to understand the major sources of VOCs in northern Kaohsiung and their contribution, and to provide the air quality management and control countermeasures for local environmental protection administration. In this study, we sampled and analyzed the speciation of VOCs and sulfur-containing odorous matters (SOMs) in the CPC refinery plants, Renwu and Dazher petrochemical complexes simultaneously with stack sampling. The sampling of VOCs and SOMs were conducted on January 7th, 14th, and 19th, 2011 (dry season) and May 6th, 13rd, and 23rd, 2011 (wet season). We established the emission source database, investigated the characteristics of VOC fingerprints, and estimate the emission factor of each stack. It helps us understand the temporal and spatial distribution of VOCs and ascertain major sources and their contribution of VOCs. Major VOCs emitted from the stacks of the CPC refinery plant were toluene and acetone. It showed that petroleum refinery processes had similar VOCs characteristics and fingerprints. The fingerprints of stack emissions at Renwu and Dashe industrial complexes varied with their processes. Hydrogen sulfide was the major sulfur-containing odorous matter in all petrochemical plants. Compared to other petrochemical complexes, Renwu industrial complex emitted a variety of SOMs species as well as relatively high concentrations of sulfur-containing odorous matters. The petrochemical industrial complexes in the industrial ambient of VOCs analysis results showed that isobutane, butane, isopentane, pentane, propane of alkanes, propene of alkenes, toluene, ethylbenzene, xylene, styrene of aromatics, 2-Butanone (MEK), acetone, of carbonyls are major species of VOCs. In addition, ethene+acetylene+ethane (C2), 1,2-dichloroethane, chloromethane, dichloromethane, MTBE were also occasionally found. Sulfur-containing odorous matter (SOMs) analytical results showed that major odorous matters included hydrogen sulfide, methanethiol, dimethyl sulfide, and carbon disulfide. The highest hydrogen sulfide concentration went up to 5.5 ppbv. In this study, the species of VOCs were divided into alkanes, alkenes, aromatics, carbonyls, and others. The temporal and spatial distribution of various types of VOCs strongly correlated with near-surface wind direction. The most obvious contaminants were alkanes, aromatics, and carbonyls of the dispersion to the downwind. Generally, the ambient air surrounding the petrochemical industrial complexes was influenced by various pollutants in the case of high wind speeds. It showed that stack emission and fugitive sources had an important contribution to ambient air quality. TSOMs and hydrogen sulfide emitting mainly from local sources resulted in high concentration of TSOMs and hydrogen sulfide surrounding the petrochemical industrial complex. Principal component analysis (PCA) results showed that the surrounding areas of petrochemical industrial complexes, regardless of dry or wet seasons, were mainly influenced by the process emissions and solvent evaporation. The impact of traffic emission sources ranked the second. Chemical mass balance receptor modeling showed that stack emissions from the CPC refinery plants contributed about 48 %, while fugitive emission sources and mobile sources contributed about 30 % and 11%, respectively. The stack emissions from Renwu industrial complex contributed about 75 %, while fugitive emission sources and mobile sources contributed about 17 % and 5 %, respectively. The stack emissions from Dazher industrial complex contributed about 68 %, while fugitive emission sources and mobile sources contributed about 21 % and 2 %, respectively.

Urban wet weather discharge impairs the receiving water and sediment quality. Among other factors, particulate matter plays a role. It increases the suspended sediment load of the receiving water and may thus enhance the clogging of the bed sediment which serves as an important river habitat. This thesis investigates how much urban areas may contribute to the fine sediment and associated element load which is retarded by the bed sediment. It is based on an extensive field study. The study area was the Bode River, a mid-sized stream in Central Germany. About 10 km upstream of the river mouth, the sampling campaign took place close to Staßfurt, a town of 20’000. During the sampling campaign, the intrusion of fine sediment into the bed sediment was captured by sediment traps. Furthermore three possible sources of this fine sediment were sampled. Within the Town of Staßfurt, we sampled urban wet weather discharge at three sites to capture urban areas. As second source naturally occurring fine sediment was considered. Therefore we took sediment cores upstream of the Town of Staßfurt. As third source, the impact of the upstream catchment was captured by taking suspended sediment samples. For all sample types, particle-bound element contents were determined to establish element patterns of the receptor and the source sites. The rationale thereby is that the element pattern at the receptor sites results from the element patterns of the sources. Consequently the contribution of the sources can be calculated by mixing models. In the study area, particulate matter from urban areas is distinct from river borne fine sediment due to elevated copper, zinc, nitrogen and phosphorus contents. We conducted an in-depth analysis of this element pattern by a cluster analysis. It revealed that the particle-bound element pattern is source specific whereby nitrogen, phosphorus and carbon are related to sewage and behave differently than most metals such as copper which mainly originate from surface runoff. The degree to which element patterns agree from site to site is limited by the variability encountered within sample sets from individual sites. Thereby the variability of the element pattern depends on the complexity of the catchment. The contribution of urban areas to fine sediment and associated elements which were captured by sediment traps was calculated by a mixing model. Based on this mixing model, about 10% of the fine sediment originate from urban areas. Thereby the impact of the Town of Staßfurt could not be detected leading to the conclusion that upstream urban areas contribute most. Because of the elevated content of e.g. copper and zinc, urban areas contribute up to 40% and thus disproportionally high to particle-associated copper and zinc load. The source apportionment of the fine sediment is little influenced by the elements considered in the mixing model. Different element patterns showed that the median contribution of urban areas ranges from 0 – 20%. This lies within the interquartile range of the initial mixing model. Another result of the measurement campaign ist that sediment traps over-estimated the anthropogenic impact because they did not resemble the surrounding bed sediment. When they were exposed, they were completely free from fine sediment and hence served as sink of suspended sediment. During the sampling campaign, one source was not directly taken into account. It was possible, though, to delineate this source by nonnegative matrix factorization. Within the Town of Staßfurt, a soda ash production site discharges into the Bode River. The nonnegative matrix factorization uncovered that the soda ash production site is a major source of particulate matter and contributes up to 30% of the fine sediment captured by the traps downstream of the Town of Staßfurt. This source dilutes most element contents as it mainly consists of carbonates. This was revealed by studying the element binding according to the BCR extraction scheme. This thesis shows that urban areas may be a major source of particulate matter and especially associated elements retarded by the bed sediment. It shows that the element contents form a viable pattern to calculate how much urban areas contribute to fine sediment by mixing models. The thesis further shows that nonnegative matrix factorization is a viable tool to delineate such a distinct source as soda ash production site. / Misch- und Regenwasserentlastungen beeinträchtigen die Qualität von Vorflutgewässern. Unter anderem gelangt Feinsediment während Entlastungsereignissen in Vorflutgewässer. Dieses erhöht die Fracht an suspendiertem Sediment und verstärkt die Kolmatierung der Gewässersohle. Damit ist das hyporheische Interstitial, das ein wichtiges Fließgewässerhabitat ist, vom Eintrag von Feinsediment betroffen. Diese Arbeit untersucht, wie sehr urbane Flächen zur Feinsedimentfracht und zur Fracht von partikulär gebundenen Elementen beitragen können, die im Bettsediment zurückgehalten werden. Sie beruht auf einer umfangreichen Messkampagne. Das Untersuchungsgebiet dafür war die Bode, ein mittelgroßer Fluss in Mitteldeutschland. Etwa 10 km flussaufwärts der Mündung fand die Messkampagne nahe der Kleinstadt Staßfurt statt. Im Rahmen dieser Messkampagne haben wir den Eintrag von Feinsediment in das Bettsediment durch Sedimentkörbe erfasst. Drei Quellen dieses Feinsediments haben wir berücksichtigt. In Staßfurt wurden eine Regen- und zwei Mischwassereinleitungen beprobt, um urbane Flächen zu erfassen. Als zweite Quelle wurde natürlich vorkommendes Feinsediment berücksichtigt. Dafür haben wir Sedimentkerne flussaufwärts von Staßfurt genommen. Als dritte Quelle haben wir das stromaufwärts liegende Einzugsgebiet erfasst, indem wir das suspendierte Sediment beprobt haben. Für alle Proben wurde der Elementgehalt bestimmt, um das Elementmuster des Feinsediments, das ins Bettsediment eingetragen wurde, und der Quellen zu ermitteln. Der Grund für diese Messstrategie war, dass das Elementmuster des Feinsediments in den Körben aus den Elementmustern der Quellen, Regen- bzw. Mischwassereinleitungen, natürlich vorkommendes Feinsediment und suspendiertes Sediment aus dem Einzugsgebiet, resultieren sollte. Damit ist es möglich, den Beitrag über Mischungsmodelle zu berechnen. Im Untersuchungsgebiet unterscheidet sich das Feinsediment, das von urbanen Flächen stammt, von dem flussbürtigen Feinsediment aufgrund erhöhter Kupfer-, Zink-, Stickstoff- und Phosphorgehalte. Wir haben das Elementmuster der urbanen Flächen mit einer Clusteranalyse genauer untersucht. Dies ergab, dass das partikulär gebundene Elementmuster quellenspezifisch ist, wobei sich Stickstoff, Phosphor und Kohlenstoff Abwasser zuordnen lassen, während die meisten Metalle wie Kupfer und Zink hauptsächlich aus dem Oberflächenabfluss stammen. Das Maß, zu dem die Muster von Messpunkt zu Messpunkt übereinstimmen, wird durch die Variabilität beschränkt, die die Proben eines Messpunktes aufweisen. Diese Variabilität hängt dabei von der Komplexität des Einzugsgebiets ab. Über eine Mischungsrechnung konnten wir berechnen, wie viel urbane Flächen zur Fracht von Feinsediment und daran gebundenen Elementen in den Sedimentkörben beitrugen. Im Untersuchungsgebiet stammen etwa 10 % des Feinsediments, das durch die Sedimentkörbe aufgefangen wurde, von urbanen Flächen. Der Beitrag der Stadt Staßfurt konnte dabei aber nicht von dem Beitrag weiter flussaufwärts gelegener urbaner Gebiete getrennt werden. Daraus folgt, dass weiter stromaufwärts liegende Gebiete mehr beitragen als Staßfurt. Wegen des erhöhten Gehalts an z.B. Kupfer und Zink tragen urbane Flächen ca. 40 % und damit überproportional hoch zur partikulär gebundenen Kupfer- und Zinkfracht bei. Für die Berechung des Quellenbeitrags zum Feinsediment spielt es keine große Rolle, welche Elemente in der Mischungsrechnung berücksichtigt werden. Verschiedene Elementmuster ergeben, dass der Medianbeitrag urbaner Flächen zwischen 0 und 20 % liegt. Dies entspricht dem Interquartilsabstand der ursprünglichen Mischungsrechnung. Ein weiteres Resultat der Untersuchungen ist, dass die Sedimentkörbe den anthropogenen Einfluss überschätzten, weil sie das umgebende Bettsediment nicht exakt abbildeten und als Falle funktionierten. Innerhalb Staßfurts gibt es ein Sodawerk, das seine Produktionsabwässer in die Bode einleitet. Während der Messkampagne wurde diese Quelle nicht direkt erfasst. Es war trotzdem möglich, diese Quelle durch nicht-negative Matrix-Faktorisierung zu identifizieren. Die nicht-negative Matrix-Faktorisierung ergab, dass das Abwasser des Sodawerks eine Hauptquelle des Feinsediments der Bode ist. Bis zu 30 % des Feinsediments in den Sedimentkörben flussabwärts von Staßfurt lassen sich dem Sodawerk zuordnen. Dieses Feinsediment besteht hauptsächlich aus Karbonaten und verdünnt die meisten Elementgehalte. Dies wurde deutlich, indem die Elementbindungen nach dem BCR Extraktionsschema untersucht wurden. Diese Arbeit zeigt die Relevanz, die urbane Flächen als Quelle von Feinsediment und daran gebundener Elementfracht haben, die ins Interstitial eingetragen werden. Sie zeigt, dass die Elementgehalte ein Muster bilden, mit dem es möglich ist, über eine Mischungsrechnung zu klären, wie viel urbane Flächen zum Feinsediment beitragen. Die Arbeit zeigt ferner, dass nicht-negative Matrix-Faktorisierung ermöglicht, eine so charakteristische Quelle wie ein Sodawerk zu identifizieren.

La mise en place de politiques efficaces visant à la réduction des niveaux de concentrations en PM exige la connaissance préalable des sources primaires et secondaires de l’aérosol organique, une fraction majoritaire des PM demeurant encore mal appréhendée. Les travaux réalisés au cours de cette thèse s’inscrivent dans le cadre du projet FORMES qui avait pour principal objectif d’évaluer et de contraindre les principales méthodes de quantification de l’influence des différentes sources de la fraction organique de l’aérosol en milieu récepteur et d’en optimiser les procédures. La caractérisation physico-chimique de l’aérosol s’est articulée autour de deux campagnes de mesures intensives de 15 jours chacune dans deux environnements urbains très contrastés : Marseille en été, et Grenoble en hiver. Ce travail de thèse s’est concentré sur le cas de Marseille, un environnement très complexe, combinant une activité photo oxydante très intense à un ensemble d’émissions primaires qui incluent les sources industrielles et les émissions par les bateaux.L’analyse CMB appliquée sur le cas de Marseille a montré que les sources primaires sont dominées par les émissions véhiculaires contribuant à 17 % du carbone organique (OC). Bien que les sources industrielles contribuent faiblement à la masse de l’aérosol (2.5 % de l’OC), ces émissions contrôlent les concentrations des HAP et de certains métaux lourds. Ces sources contribuent également, en moyenne sur la période, à environ 30% du nombre des particules ultrafines (Dp<50 nm), ce qui augmente probablement leurs effets sanitaires. Contrairement à Grenoble, où la combustion de bois est une source prépondérante (environ 70 % de l’OC), à Marseille ces émissions ne constituent qu’une source minoritaire, contribuant à 0.8 % de l’OC. Toutefois, la principale information révélée par la déconvolution de sources par CMB est que les sources primaires considérées ne permettent d’expliquer, que 22 % de l’OC mesuré ; 78 % du carbone restant non expliqué. Cette fraction est associée majoritairement à l’aérosol organique secondaire (SOA). En combinant les résultats CMB aux mesures 14C, il a été montré que plus que 70% de cet aérosol est très vraisemblablement d’origine biogénique. En conséquence, les contributions du SOA formé à partir de l’isoprène, l’&#945;-pinène et le &#946;-caryophyllène ont été examinées moyennant une approche basée sur les marqueurs de cet aérosol. Le SOA issu de ces précurseurs a été estimé comme contribuant uniquement à 4.3 % de l’OC, laissant une grande fraction de ce carbone non-attribuée. Cette sous-estimation est la conséquence de trois causes : (i) les incertitudes associées à l’approche utilisée, (ii) des précurseurs biogéniques non-considérés et (iii) le vieillissement de l’aérosol secondaire au cours de son transport dans l’atmosphère, comme suggéré par les mesures d’organosulfates et de la fraction polycarboxylique de type HULIS. Cette dernière fraction peut contribuer à près de 40% de l’OC non-attribué. / Tougher particulate matter regulations around the world and especially in Europe point out the need of source apportionment studies in order to better understand the different primary and secondary sources of organic aerosol, a major fraction of particulate matter that remains not well constrained. The work carried out in this thesis takes part of the FORMES project whose main objective is the source apportionment of the organic aerosol using different approaches, including mainly CMB modelling, AMS/PMF and radiocarbon (14C) measurements. The aerosol characterisation was performed within two intensive field campaigns conducted in two contrasted urban environments: Grenoble during winter and Marseille during summer. The present work focuses on the Marseille case study that presents a particularly complex environment, combining an intense photochemistry to a mixture of primary emissions including shipping and industrial emissions. Primary organic carbon (POC) apportioned using CMB modelling contributed on average for only 22% and was dominated by vehicular emissions accounting on average for 17% of OC. Even though, industrial emissions contribute for only 2.3% of the total OC, they are associated with ultrafine particles and high concentrations of Polycyclic Aromatic Hydrocarbons and heavy metals such as Pb, Ni and V, which most likely relate them with acute health outcomes. Whereas in Grenoble the organic aerosol was dominated by wood burning smoke (70% of OC), this source was negligible in Marseille contributing for less than 1% of OC. The main result from this source apportionment exercise is that 78% of OC mass cannot be attributed to the major primary sources and remains un-apportioned; this fraction is mostly associated with secondary organic aerosol. Radiocarbon measurements suggest that more than 70% of this fraction is of modern origins, assigned predominantly to biogenic secondary organic carbon (BSOC). Therefore, contributions from three traditional BSOC precursors, isoprene, &#945;-pinene and &#946;-caryophellene, were considered using a marker based approach. The aggregate contribution from BSOC derived from these precursors was estimated at only 4.2% of total OC. As a result, these estimates underpredict the high loading of OC. This underestimation can be associated with (i) uncertainties underlying the marker-based approach, (ii) presence of other SOC precursors and (iii) further processing of fresh SOC, as indicated by organosulfates (RSO4) and HUmic LIke Substances (HULIS) measurements. This HULIS can contribute up to 40% of the unattributed OC.

La connaissance des principales sources de l’aérosol permet d’améliorer, d’adapter et de cibler les mesures prises pour réduire les concentrations de particules fines. Ainsi, l’identification et la hiérarchisation des sources de particules fines sont des étapes essentielles à la mise en place d'une politique efficace d'amélioration de la qualité de l'air. Le travail mené durant cette thèse s’inscrit dans cette démarche puisqu'il avait pour objectif de quantifier les sources de PM2.5 en milieu industriel. Afin de répondre à cet objectif, deux campagnes de prélèvements ont été réalisés dont une sous les vents des principales activités industrielles afin de caractériser leurs émissions (profils) et une en zones urbaines caractéristiques de l’exposition de la population aux particules fines. Les résultats ont permis d'obtenir des empreintes représentatives des principales activités industrielles de la zone d'étude. L’analyse ME-2 menée a permis, avec la combinaison d’analyses radiocarbones, de déterminer que la source de combustion de biomasse est la source majoritaire pendant l’automne et l’hiver où les épisodes de PM2.5 ont été observés. La source industrielle est la source majoritaire des PM2.5 au printemps et en été mais ne constitue pas un driver fort de la concentration des PM2.5. Toutefois, cette étude a montré que les sources industrielles impactent significativement la population de particules (taille, composition, etc.) dans la zone d’étude. / In order to limit the impact of air quality on human health, public authorities need reliable and accurate information on the sources contribution. So, the identification of the main sources of PM2.5 is the first step to adopt efficient mitigation policies. This work carry out in this thesis take place in this issue and was to determine the main sources of PM2.5 inside an industrial area. To determinate the main sources of PM2.5, two campaigns were lead to collect daily PM2.5 to: 1/ determine the enrichment of atmospheric pollutants downwind from the main industrial activities and 2/ collect PM2.5 in urban areas characteristic of the population exposition. Results allowed to obtain very representative profiles for the main industrial activities implanted inside the studied area. ME-2 analysis, combined to radiocarbon measurements, allowed to highlight the very high impact of Biomass Burning sources for all the PM2.5 pollution events recorded from early autumn to March. This study showed that industrial sources, even if they are the major sources during spring and summer, are not the major PM2.5 driver. However, this study highlights that industrial sources impact significantly the aerosol population (size, composition, etc.) in the studied area.

The promulgation of revised standards for atmospheric fine particles (PM2.5) by the US EPA has sparked renewed interest in the ability to accurately measure and characterize suspended atmospheric particulate matter. Semi-volatile material (SVM), consisting of ammonium nitrate and semi-volatile organic material (SVOM), is not accurately measured by EPA accepted methods such as the Federal reference method (FRM) or Tapered Element Oscillating Microbalance (TEOM). However, SVM is often a major fraction of urban aerosols. Recent advances in atmospheric sampling instrumentation allowed for the semi-continuous characterization of urban PM2.5, including SVM. The Filter Dynamic Measurement System (FDMS) was shown to measure total PM2.5 mass including semi-volatile species. Validation of the FDMS was performed by comparison with the particle concentrator-Brigham Young University organic sampling system (PC-BOSS) and the real-time total ambient mass sampler (RAMS). Semi-continuous ambient particulate concentrations of sulfate, nitrate and ammonium ion were measured by a newly developed Dionex instrument which was field tested and validated for the first time in Fresno, CA. Either a modified Sunset Laboratory carbon monitor, collocated with a conventional Sunset carbon monitor employing a common inlet, or the newly developed dual-oven Sunset monitor allowed for the semi-continuous determination of both nonvolatile and semi-volatile organic material. This was the first attempt to characterize both nonvolatile and semi-volatile fractions of an urban aerosol in a semi-continuous manner using all semi-continuous instruments. A suite of instruments for semi-continuous PM2.5 monitoring was recommended including, an R&P FDMS for the measurement of PM2.5 mass, a dual-oven Sunset monitor for the measurement of nonvolatile and semi-volatile carbonaceous species, and a Dionex GP-IC for the measurement of inorganic species. A TEOM monitor is also recommended to measure nonvolatile PM2.5 mass. Using these instruments, semi-continuous mass closure was obtained for the first time during a study conducted in Riverside, CA. The advantage of using semi-continuous sampler data in the application of source apportionment was elucidated. Local aerosols are often impacted by short-term pollution episodes that cannot be temporally resolved using integrated samplers. One-h averaged data applied to source apportionment models was shown to increase the power of the model to predict sources, both primary and secondary, that exhibit diurnal short-term episodes.

Les aérosols organiques (AO), issus de nombreuses sources et de différents processus atmosphériques, ont un impact significatif sur la qualité de l’air et le changement climatique. L’objectif de ce travail de thèse était d’acquérir une meilleure connaissance de l’origine des AO par l’utilisation de marqueurs organiques moléculaires au sein de modèles source-récepteur de type positive matrix factorization (PMF). Ce travail expérimental était basé sur deux campagnes de prélèvements réalisées à Grenoble (site urbain) au cours de l’année 2013 et dans la région parisienne (site péri-urbain du SIRTA, 25 km au sud-ouest de Paris) lors d’un intense épisode de pollution aux particules (PM) en Mars 2015. Une caractérisation chimique étendue (de 139 à 216 espèces quantifiées) a été réalisée et l’utilisation de marqueurs moléculaires primaires et secondaires clés dans la PMF a permis de déconvoluer de 9 à 11 sources différentes de PM10 (Grenoble et SIRTA, de façon respective) incluant aussi bien des sources classiques (combustion de biomasse, trafic, poussières, sels de mer, nitrate et espèces inorganiques secondaires) que des sources non communément résolues telles que AO biogéniques primaires (spores fongiques et débris de plantes), AO secondaires (AOS) biogéniques (marin, oxydation de l’isoprène) et AOS anthropiques (oxydation des hydrocarbures aromatiques polycycliques (HAP) et/ou des composés phénoliques). En outre, le jeu de données obtenu pour la région parisienne à partir de prélèvements sur des pas de temps courts (4h) a permis d’obtenir une meilleure compréhension des profils diurnes et des processus chimiques impliquées. Ces résultats ont été comparés à ceux issus d’autres techniques de mesures (en temps réel, ACSM (aerosol chemical speciation monitor) et analyse AMS (aerosol mass spectrometer) en différée) et/ou d’autres méthodes de traitement de données (méthodes traceur EC (elemental carbon) et traceur AOS). Un bon accord a été obtenu entre toutes les méthodes en termes de séparation des fractions primaires et secondaires. Cependant, et quelle que soit l’approche utilisée, la moitié de la masse d’AOS n’était toujours pas complètement décrite. Ainsi, une nouvelle approche d’étude des sources de l’AO a été développée en combinant les mesures en temps réel (ACSM) et celles sur filtres (marqueurs moléculaires organiques) et en utilisant un script de synchronisation des données. L’analyse PMF combinée a été réalisée sur la matrice de données unifiée. 10 facteurs AO, incluant 4 profils chimiques différents en lien avec la combustion de biomasse, ont été mis en évidence. Par rapport aux approches conventionnelles, cette nouvelle méthodologie a permis d’obtenir une meilleure compréhension des processus atmosphériques liés aux différentes sources d’AO. / Organic aerosols (OAs), originating from a wide variety of sources and atmospheric processes, have strong impacts on air quality and climate change. The present PhD thesis aimed to get a better understanding of OA origins using specific organic molecular markers together with their input into source-receptor model such as positive matrix factorization (PMF). This experimental work was based on two field campaigns, conducted in Grenoble (urban site) over the 2013 year and in the Paris region (suburban site of SIRTA, 25 km southwest of Paris) during an intense PM pollution event in March 2015. Following an extended chemical characterization (from 139 to 216 species quantified), the use of key primary and secondary organic molecular markers within the standard filter-based PMF model allowed to deconvolve 9 and 11 PM10 sources (Grenoble and SIRTA, respectively). These included common ones (biomass burning, traffic, dust, sea salt, secondary inorganics and nitrate), as well as uncommon resolved sources such as primary biogenic OA (fungal spores and plant debris), biogenic secondary AO (SOA) (marine, isoprene oxidation) and anthropogenic SOA (polycyclic aromatic hydrocarbons (PAHs) and/or phenolic compounds oxidation). In addition, high time-resolution filter dataset (4h-timebase) available for the Paris region also illustrated a better understanding of the diurnal profiles and the involved chemical processes. These results could be compared to outputs from other measurement techniques (online ACSM (aerosol chemical speciation monitor), offline AMS (aerosol mass spectrometer) analyses), and/or to other data treatment methodologies (EC (elemental carbon) tracer method and SOA tracer method). A good agreement was obtained between all the methods in terms of separation between primary and secondary OA fractions. Nevertheless, and whatever the method used, still about half of the SOA mass was not fully described. Therefore, a novel OA source apportionment approach has finally been developed by combining online (ACSM) and offline (organic molecular markers) measurements and using a time synchronization script. This combined PMF analysis was performed on the unified matrix. It revealed 10 OA factors, including 4 different biomass burning-related chemical profiles. Compared to conventional approaches, this new methodology provided a more comprehensive description of the atmospheric processes related to the different OA sources.

Made available in DSpace on 2016-12-23T14:04:31Z (GMT). No. of bitstreams: 1 dissertacao Trindade.pdf: 2131237 bytes, checksum: 514907f9bd367cc5bd486dcd27fa2d9d (MD5) Previous issue date: 2009-03-13 / A identificação de fontes para material particulado tem sido um tema de crescente interesse em todo o mundo para auxiliar a gestão da qualidade do ar. Esta classe de estudos é convencionalmente baseada no uso de modelos receptores, que identificam e quantificam as fontes responsáveis a partir da concentração do contaminante no receptor. Existe uma variedade de modelos receptores disponíveis na literatura, este trabalho compara os resultados dos modelos receptores balanço químico de massa (BQM) e fatoração de matriz positiva (FMP) para o banco de dados de PM2,5, da região de Brighton, Colorado, com o intuito de investigar as dificuldades na utilização de cada modelo, bem como suas vantagens e desvantagens. Inicialmente, já é conhecido que o modelo BQM tem a desvantagem de necessitar dos perfis das fontes, determinados experimentalmente, para ser aplicado e também tem limitações quando as fontes envolvidas são similares. Já o modelo FMP não requer os perfis de fontes, mas tem a desvantagem de precisar de elevada quantidade amostral da concentração do contaminante no receptor. Os resultados mostraram, baseados nas medidas de performance que os dois modelos foram aptos para reproduzir os dados do receptor com ajustes aceitáveis. Todavia, resultados diferentes se ajustaram a medidas de performance. O modelo BQM, utilizou 9 tipos de fontes e o modelo FMP encontrou apenas 6 tipos de fontes. Constatou-se com isso que o modelo FMP tem dificuldades em modelar fontes que aparecem ocasionalmente. As fontes sulfato de amônio, solos, veículos a diesel e nitrato de amônio tiverem boas correlações nos resultados dos dois modelos de contribuições de fontes. Os perfis de fontes utilizados no modelo BQM e resultados do modelo FMP que mais se assimilaram foram das fontes nitrato de amônio, solos, sulfato de amônio e combustão de madeira e ou/ veículos desregulados. Verificou-se no modelo FMP que as espécies não características de determinadas fontes aparecem nos resultados dos perfis das fontes, o que torna-se ainda mais complexo a identificação das fontes, requerendo elevado conhecimento sobre a composição de inúmeras fontes. / The identification of sources of particulate matter has been a topic of growing interest throughout the world to assist the air quality management. This class of studies is conventionally based on the use of receptor models, which identify and quantify the sources responsible from the concentration of the contaminant in the receptor. There are a variety of receptor models, this study compares the results of chemical mass balance (CMB) and positive matrix factorization (PMF) models for a database of PM2.5, for the region of Brighton, Colorado, with a view to investigate the difficulties in the use of each model, as well as its advantages and disadvantages. It is known that the CMB model has the disadvantage of requiring source profiles, determined experimentally, to be applied and also has limitations when the sources involved are similar. On the other hand, the PMF model does not require source profiles, it has the disadvantage to require a large amount sample, in receptor. The results showed, based on performance measures that both models were able to reproduce the data of the receptor with reasonable fit. However, different results were adjusted for performance measurements. The CMB model, used 9 types of sources and PMF model found only 6 types of sources, it was noted by that what the PMF model has difficulty in modeling sources that appear occasionally. The sources ammonium sulfate, soil, diesel vehicles and ammonium nitrate have good correlation in the results of the two model of sources apportionment. The source profiles used in the CMB model and results of the PMF model that present more similarities were of the sources ammonium nitrate, soil, ammonium sulfate and combustion of wood and/or smoker vehicles. It was verified what the PMF model does not separate well species in the source profiles, therefore becomes even more complex to identify the sources in the FMP model, requiring considerable knowledge about the composition of many sources. For the database used with similar sources, the lack of confidence in the results based only on receptors models for a final decision on the source apportionment.

Characterization and Source Apportionment of Ambient PM2.5 in Atlanta, Georgia: On-Road Emission, Biomass Burning and SOA Impact Bo Yan 260 Pages Directed by Drs. Armistead G. Russell and Mei Zheng Various airborne PM2.5 samples were collected in the metropolitan Atlanta and surrounding areas, which are directly impacted or dominated by on-road mobile and other typical urban emissions, regional transport sources, prescribed burning plumes, wildfire plumes, as well as secondary sources with anthropogenic and biogenic nature in origin. Detailed PM2.5 chemical speciation was conducted including over one hundred of GC/MS-quantified organic compounds, organic carbon (OC), water-soluble organic carbon (WSOC), elemental carbon (EC), ionic species, and tens of trace metals. Day-night, seasonal and spatial variations of PM2.5 characterization were also studied. Contributions of PM2.5 major sources were identified quantitatively through the receptor source apportionment models. These modeling results, especially on-road mobile source contributions and secondary organic carbon (SOC) were assessed by multiple approaches. Furthermore, new season- and location-specific source profiles were developed in this research to reflect real-world and representative local emission characterizations of on-road mobile sources, aged prescribed burning plumes, and wildfire plumes. Secondary organic aerosol (SOA), a major component of PM2.5 in the summer, was also explored for sources and contributions.