I investigate the links between mercury use, release, deposition, and population exposure in Tampa Bay, with the focus of identifying levers for reducing population mercury exposures. To achieve this, I investigated the trends in mercury use and release by products and processes in the Tampa Bay area using a Material Flow Analysis. Analysis of USEPA National Emissions Inventory data over time (1999 - 2008) identified relevant air source emission categories, and explored and compared state and regional trends in mercury emissions. To understand source contributions to wet deposited mercury in the Tampa Bay area, I analyzed trends in mercury deposition data from the National Atmospheric Deposition Program, Mercury Deposition Network, and the 2001 Bay Regional Atmospheric Chemistry Experiment. I also collected wet deposition samples for mercury and trace metals in the Tampa Bay area during a 6-month campaign at a site at the University of South Florida (USF) campus. Samples were analyzed using Cold Vapor Atomic Fluorescence Spectrometry (CVAFS) for mercury, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for trace metals analysis. Concentration data were analyzed for source contributions using HYSPLIT back-trajectory meteorology-based modeling to assess source locations, and the Positive Matrix Factorization (PMF) statistical receptor model to apportion the deposition data by source type. To explore the factors influencing fish consumption behaviors of the local angler population, I analyzed population surveys collected previously from fisher-folks along the Hillsborough River, in Hillsborough County, Florida. Results from the mercury inventory indicate that mercury releases from industrial sources and dental facilities were the most important sources of mercury to the Tampa Bay area. Furthermore, the solid-waste pool was the most important direct sink in the domain, with air emissions an important indirect sink. Emissions inventory data indicated that coal-fired power plants were the largest contributors of mercury emissions in the Tampa Bay area. Medical and municipal waste incineration also accounted for significant fractions of total mercury releases to the domain. Emissions from sources in Hillsborough County accounted for a significant portion of mercury emissions in the region and state. Measurement data indicated that event mercury concentration was only very weakly correlated with event precipitation depth, with both studies showing agreement with this phenomenon. Back-trajectory simulations reveal that high mercury concentration events were often from air masses with recent trajectories over Florida land (6 and 24 hr), and with previous high precipitation depth events over the trajectory in the long term (72 hr). The statistical PMF results indicate the importance of coal burning power plant emissions, medical and municipal waste incineration, and agrochemicals on mercury in wet deposition in the Tampa Bay area. Changes were observed between the 2001 and 2012 data, including greater mercury concentrations in 2012, and the removal of medical waste incineration as a mercury source in the 2012 model results. Together with local emissions inventory data, these results suggest that sources local to the Tampa Bay area and in Florida likely contribute substantially to mercury deposition in the region. Finally, population survey data suggests that mercury exposure risks are poorly understood by the fishing population in Hillsborough County. Taken together, these results suggest that policies targeting mercury emissions control, particurlarly for coal-fired power plants and municipal waste processing, and fish consumption education may be instrumental to the protection of susceptible populations.

The Particulate Matter Source Apportionment Technology (PSAT) is used together with PMCAMx, a regional chemical transport model, to estimate how local emissions and pollutant transport affect primary and secondary particulate matter concentration levels in European megacities such as Paris, London and Po Valley. The case of Paris megacity was investigated in detail. During the summer and the winter period examined, only 13% of the PM2.5 is due to local Paris emissions, with 36% due to mid range (within 500 km from the center of the Paris) sources and 51% resulting from long range transport (more than 500 km from the center of the Paris). The local emissions contribution to elemental carbon (EC) is significant, with almost 60% of the EC originating from local sources during both summer and winter. Approximately 50% of the fresh primary organic aerosol (POA) originated from local sources and another 45% from areas 100-500 km from the receptor region during summer. Regional sources dominated the secondary PM components. More than 70% of the sulfate originated from SO2 emitted more than 500 km away from the center of the Paris. Also more than 45% of secondary organic aerosol (SOA) was due to the oxidation of VOC precursors that were emitted 100-500 km from the center of the Paris. Long range sources are more important during winter because the photochemical activity is lower. PSAT results for contributions of local and regional sources were also compared with observation-based estimates from field campaigns that took place during the MEGAPOLI project. PSAT predictions are in general consistent with these estimates OA and sulfate but PSAT predicts lower transported EC for both seasons. / Ο καταμεριστικός αλγόριθμος ατμοσφαιρικών σωματιδίων (PSAT, Particulate Matter Source Apportionment Technology) χρησιμοποιείται σε συνδυασμό με το τρισδιάστατο μοντέλο χημικής μεταφοράς PMCAMx με σκοπό να εκτιμήσει κατά πόσο οι τοπικές εκπομπές και η μεταφορά της ρύπανσης επηρεάζουν τα πρωτογενή και τα δευτερογενή επίπεδα σωματιδιακών συγκεντρώσεων σε Ευρωπαϊκές μεγαλουπόλεις όπως το Παρίσι, το Λονδίνο και η κοιλάδα του ποταμού Πάδου στη βόρεια Ιταλία (Po Valley). Η περίπτωση του Παρισιού μελετήθηκε λεπτομερώς. Κατά τη διάρκεια του καλοκαιριού και του χειμώνα που εξετάστηκε, μόνο το 13% των PΜ2.5 σωματιδίων προέρχονται από τοπικές πηγές, 36% προέρχεται από ενδιάμεσες πηγές (μεταξύ 500 km από το κέντρο του Παρισιού) και 51% από απομακρυσμένες περιοχές (σε αποστάσεις μεγαλύτερες των 500 km από το κέντρο του Παρισιού). Η συνεισφορά των τοπικών πηγών στο στοιχειακό άνθρακα είναι σημαντική, 60% περίπου του στοιχειακού άνθρακα προέρχεται από τοπικές πηγές κατά τη διάρκεια τόσο του καλοκαιριού όσο και του χειμώνα. Σχεδόν 50% των φρέσκων πρωτογενών οργανικών σωματιδίων (POA) προέρχονται από τοπικές πηγές και 45% από περιοχές 100-500 km από των αποδέκτη κατά τη διάρκεια του καλοκαιριού. Οι συνεισφορά από απομακρυσμένες περιοχές κυριαρχεί στα δευτερογενή σωματίδια. Περισσότερο από 70% των θεϊκών σωματιδίων προέρχεται από διοξείδιο του θείου το οποίο εκπέμπεται από αποστάσεις μεγαλύτερες των 500 km από το κέντρο του Παρισιού. Επίσης περισσότερο από το 45% των δευτερογενών οργανικών σωματιδίων οφείλεται στην οξείδωση των πτητικών οργανικών ενώσεων (VOCs) που εκπέμπονται από 100 έως 500 km μακριά από το κέντρο του Παρισιού. Οι απομακρυσμένες περιοχές είναι πιο σημαντικές κατά τη διάρκεια του χειμώνα λόγω της ελάχιστης φωτοχημείας. Τα αποτελέσματα που προέκυψαν από τον αλγόριθμο PSAT για τις συνεισφορές των τοπικών όσο και των απομακρυσμένων περιοχών επίσης συγκρίνονται με μετρήσεις πεδίου από πειραματικές διατάξεις στο πλαίσιο του διεθνούς προγράμματος MEGAPOLI. Ο αλγόριθμος PSAT προβλέπει γενικά ικανοποιητικά τις συνεισφορές σε σχέση με αυτές που υπολογίστηκαν από τις μετρήσεις πεδίου.

Understanding the effect of human-induced pollution on the environment is an important precursor to promoting public health and environmental stability. One aspect of understanding pollution is understanding pollution sources. Various methods have been used and developed to understand pollution sources and the amount of pollution those sources emit. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air. Previous approaches to multivariate receptor modeling make the following two key assumptions: (1) PM measurements are independent and (2) source profiles are constant through time. Notwithstanding these assumptions, the existence of temporal correlation among PM measurements and time-varying source profiles is commonly accepted. In this thesis an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source contributions than other multivariate receptor model approaches. Additionally, when source profiles are constant through time, the DP model outperforms other pollution receptor models by more accurately estimating source profiles and source contributions.

During the latter part of the 20th century, a transition away from coal as a major energy source in developed countries was accompanied by a notable decrease in air pollution-related deaths in those countries. Currently the same phenomenon is being observed in developing nations like China and India. However, many areas that do still rely on coal for their energy production or industrial needs also reflect a gap in research on the effects of those specific processes on local populations. Located in Nepal at the foot of the Himalayan Plateau, Kathmandu represents one such location. The local economy of Kathmandu and the surrounding area relies heavily on the production of bricks using coal-fired kilns, which produce large amounts of particulate matter. This particulate matter contains a characteristic mix of metals. This unique fingerprint can be used to identify and track kiln emissions in ambient samples. We collected hourly samples of ambient metal concentrations over a period of three months at the start of 2019. We then used these data to perform positive matrix factorization (PMF) to identify several factors contributing to the ambient air pollution of the sampled area, each representing a source type. The PMF output included the chemical ‘fingerprint’ of each factor as well as hourly variation of each factor. We were able to isolate the fraction of PM2.5 contributed by coal and estimate the health effects attributable to this fraction using a modified risk ratio of 1.05 to reflect the higher toxicity of coal emissions. We found that the current estimates of health impacts in Nepal underestimate the true impact of coal by 416 deaths per year.

Ambient and indoor air pollution is a major cause of premature mortality, and has been associated with more than three million preventative deaths per year worldwide. Most of these health impacts are from the effects from fine particulate matter. It is suspected that PM2.5 health effects vary by composition, which depends on the mixture of pollutants emitted by sources. This has led to efforts to estimate relationships between sources of PM2.5 and health effects. The health effects of PM2.5 may be preferentially dependent on specific species; however, recent work has suggested that health impacts may actually be caused by the net effect of the mixture of pollutants which make up PM2.5. Recently, there have been efforts to use source impacts from source apportionment (SA) studies as a proxy for these multipollutant effects. Source impacts can be quantified using both receptor and chemical transport models (RMs and CTMs), and have both advantages and limitations for their use in health studies. In this work, a technique is developed that reconciles differences between source apportionment (SA) models by ensemble-averaging source impacts results from several SA models. This method uses a two-step process to calculate the ensemble average. An initial ensemble average is used calculate new estimates of uncertainties for the individual SA methods that are used in the ensemble. Next, an updated ensemble average is calculated using the SA method uncertainties as weights. Finally, uncertainties of the ensemble average are calculated using propagation of errors that includes covariance terms. The ensemble technique is extended to include a Bayesian formulation of weights used in ensemble-averaging source impacts. In a Bayesian approach, probabilistic distributions of the parameters of interest are estimated using prior distributions, along with information from observed data. Ensemble averaging results in updated estimates of source impacts with lower uncertainties than individual SA methods. Overall uncertainties for ensemble-averaged source impacts were ~45 - 74%. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R2=0.66) and water soluble potassium (R2=0.63) than source impacts estimated using more traditional methods, and more closely agreed with observed total mass. Power spectra of the time series of biomass burning source impacts suggest that profiles/factors associated with this source have the greatest variability across methods and locations. A secondary focus of this work is to examine the impacts of biomass burning. First a field campaign was undertaken to measure emissions from prescribed fires. An emissions factor of 14±17 g PM2.5/kg fuel burned was determined. Water soluble organic carbon (WSOC) was highly correlated with potassium (K) (R2=.93) and levoglucosan (R2=0.98). Results using a biomass burning source profile derived from this work further indicate that source apportionment is sensitive to levels of potassium in biomass burning source profiles, underscoring the importance of quantifying local biomass burning source profiles. Second, the sensitivity of ambient PM2.5 to various fire and meteorological parameters in was examined using the method of principle components regression (PCR) to estimate sensitivity of PM2.5 to fire data and, observed and forecast meteorological parameters. PM2.5 showed significant sensitivity to PB, with a unit-based sensitivity of 3.2±1 µg m-3 PM2.5 per 1000 acres burned. PM2.5 had a negative sensitivity to dispersive parameters such as wind speed.

Atmospheric haze, which builds up over South Africa including our study areas, Cape Town and the Mpumalanga Highveld under calm weather conditions, causes public concern. The scope of this study was to determine the concentration and composition of atmospheric aerosol at Khayelitsha (an urban site in the Western Cape) and Ferrobank (an industrial site in Witbank, Mpumalanga). Particulate matter was collected in Khayelitsha from 18 May 2007 to 20 July 2007 (i.e. 20 samples) using a Partisol-plus sampler and a Tapered Element Oscillating Microbalance (TEOM) sampler. Sampling took place at Ferrobank from 07 February 2008 to 11 March 2008 (6 samples) using a Partisol-plus sampler and an E-sampler. The gravimetric mass of each exposed sample was determined from pre- and post-sampling weighing. The elemental composition of the particulate matter was determined for 16 elements at Khayelitsha using Proton Induced X-ray Emission (PIXE). The concentration of the elements Al, Si, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Cu, Zn, As, Br, Sn, and Pb was determined by analysing the PIXE spectra obtained. In similar manner, the elemental composition of the particulate matter was determined for 15 elements at Ferrobank (Al, Si, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Cu, Zn, As, Br and Pb). The average aerosol mass concentrations for different days at the Khayelitsha site were found to vary between 8.5 μg/m3 and 124.38 μg/m3. At the Khayelitsha site on three occasions during the sampling campaign the average aerosol mass concentrations exceeded the current South African air quality standard of 75 μg/m3 over 24 h. At the Ferrobank site, there are no single days that exceeded the limit of the South African air quality standard during the sampling campaign. Enrichment factors for each element of the particles sampled with an aerodynamic diameter of less than 10 μm (PM10) samples have been calculated in order to identify their possible sources. The analysis yielded five potential sources of PM10 : soil dust, sea salt, gasoline emissions, domestic wood and coal combustion. Interestingly, enrichment factor values for the Khayelitsha samples show that sea salt constitutes a major source of emissions, while Ferrobank samples, the source apportionment by unique ratios (SPUR) indicate soil dust and coal emission are the major sources of pollution. The source apportionment at Khayelitsha shows that sea salt and biomass burning are major source of air pollution.

<p>Wood combustion is mainly used in cold regions as a primary or supplemental space heating source in residential areas. In several industrialized countries, there is a renewed interest in residential wood combustion (RWC) as an alternative to fossil fuel and nuclear power consumption. The main objective of this thesis was to investigate the impact of RWC on the air quality in urban areas. To this end, a field campaign was conducted in Northern Sweden during wintertime to characterize atmospheric aerosol particles and polycyclic aromatic hydrocarbons (PAH) and to determine their source apportionment.</p><p>A large day-to-day and hour-to-hour variability in aerosol concentrations was observed during the intensive field campaign. On average, total carbon contributed a substantial fraction of PM10 mass concentrations (46%) and aerosol particles were mostly in the fine fraction (PM1 accounted for 76% of PM10). Evening aerosol concentrations were significantly higher on weekends than on weekdays which could be associated to the use of wood burning for recreational purposes or higher space heat demand when inhabitants spend longer time at home. It has been shown that continuous aerosol particle number size distribution measurements successfully provided source apportionment of atmospheric aerosol with high temporal resolution. The first compound-specific radiocarbon analysis (CSRA) of atmospheric PAH demonstrated its potential to provide quantitative information on the RWC contribution to individual PAH. RWC accounted for a large fraction of particle number concentrations in the size range 25-606 nm (44-57%), PM10 (36-82%), PM1 (31-83%), light-absorbing carbon (40-76%) and individual PAH (71-87%) mass concentrations.</p><p>These studies have demonstrated that the impact of RWC on air quality in an urban location can be very important and largely exceed the contribution of vehicle emissions during winter, particularly under very stable atmospheric conditions.</p>

Wood combustion is mainly used in cold regions as a primary or supplemental space heating source in residential areas. In several industrialized countries, there is a renewed interest in residential wood combustion (RWC) as an alternative to fossil fuel and nuclear power consumption. The main objective of this thesis was to investigate the impact of RWC on the air quality in urban areas. To this end, a field campaign was conducted in Northern Sweden during wintertime to characterize atmospheric aerosol particles and polycyclic aromatic hydrocarbons (PAH) and to determine their source apportionment. A large day-to-day and hour-to-hour variability in aerosol concentrations was observed during the intensive field campaign. On average, total carbon contributed a substantial fraction of PM10 mass concentrations (46%) and aerosol particles were mostly in the fine fraction (PM1 accounted for 76% of PM10). Evening aerosol concentrations were significantly higher on weekends than on weekdays which could be associated to the use of wood burning for recreational purposes or higher space heat demand when inhabitants spend longer time at home. It has been shown that continuous aerosol particle number size distribution measurements successfully provided source apportionment of atmospheric aerosol with high temporal resolution. The first compound-specific radiocarbon analysis (CSRA) of atmospheric PAH demonstrated its potential to provide quantitative information on the RWC contribution to individual PAH. RWC accounted for a large fraction of particle number concentrations in the size range 25-606 nm (44-57%), PM10 (36-82%), PM1 (31-83%), light-absorbing carbon (40-76%) and individual PAH (71-87%) mass concentrations. These studies have demonstrated that the impact of RWC on air quality in an urban location can be very important and largely exceed the contribution of vehicle emissions during winter, particularly under very stable atmospheric conditions.

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.

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.