Modeling and simulations of large-scale systems are used extensively to not only better understand a natural phenomenon, but also to predict future events. Accurate model results are critical for design optimization and policy making. They can be used effectively to reduce the impact of a natural disaster or even prevent it from happening. In reality, model predictions are often affected by uncertainties in input data and model parameters, and by incomplete knowledge of the underlying physics. A deterministic simulation assumes one set of input conditions, and generates one result without considering uncertainties. It is of great interest to include uncertainty information in the simulation. By ``Uncertainty Quantification,'' we denote the ensemble of techniques used to model probabilistically the uncertainty in model inputs, to propagate it through the system, and to represent the resulting uncertainty in the model result. This added information provides a confidence level about the model forecast. For example, in environmental modeling, the model forecast, together with the quantified uncertainty information, can assist the policy makers in interpreting the simulation results and in making decisions accordingly. Another important goal in modeling and simulation is to improve the model accuracy and to increase the model prediction power. By merging real observation data into the dynamic system through the data assimilation (DA) technique, the overall uncertainty in the model is reduced. With the expansion of human knowledge and the development of modeling tools, simulation size and complexity are growing rapidly. This poses great challenges to uncertainty analysis techniques. Many conventional uncertainty quantification algorithms, such as the straightforward Monte Carlo method, become impractical for large-scale simulations. New algorithms need to be developed in order to quantify and reduce uncertainties in large-scale simulations. This research explores novel uncertainty quantification and reduction techniques that are suitable for large-scale simulations. In the uncertainty quantification part, the non-sampling polynomial chaos (PC) method is investigated. An efficient implementation is proposed to reduce the high computational cost for the linear algebra involved in the PC Galerkin approach applied to stiff systems. A collocation least-squares method is proposed to compute the PC coefficients more efficiently. A novel uncertainty apportionment strategy is proposed to attribute the uncertainty in model results to different uncertainty sources. The apportionment results provide guidance for uncertainty reduction efforts. The uncertainty quantification and source apportionment techniques are implemented in the 3-D Sulfur Transport Eulerian Model (STEM-III) predicting pollute concentrations in the northeast region of the United States. Numerical results confirm the efficacy of the proposed techniques for large-scale systems and the potential impact for environmental protection policy making. ``Uncertainty Reduction'' describes the range of systematic techniques used to fuse information from multiple sources in order to increase the confidence one has in model results. Two DA techniques are widely used in current practice: the ensemble Kalman filter (EnKF) and the four-dimensional variational (4D-Var) approach. Each method has its advantages and disadvantages. By exploring the error reduction directions generated in the 4D-Var optimization process, we propose a hybrid approach to construct the error covariance matrix and to improve the static background error covariance matrix used in current 4D-Var practice. The updated covariance matrix between assimilation windows effectively reduces the root mean square error (RMSE) in the solution. The success of the hybrid covariance updates motivates the hybridization of EnKF and 4D-Var to further reduce uncertainties in the simulation results. Numerical tests show that the hybrid method improves the model accuracy and increases the model prediction quality. / Ph. D.

The Mexico City Metropolitan Area (MCMA) is one of the largest cities in the world, and as with many megacities worldwide, it experiences serious air quality and pollution problems, especially with ozone and particulate matter. Ozone levels exceed the health-based standard, which is equivalent to the U.S. standard, on approximately 80% of all days, and concentrations of particulate matter 10 μm and smaller (PM10) exceed the standard on more than 40% of all days in most years. Particulate polycyclic aromatic hydrocarbons (PAHs) are a class of semi-volatile compounds that are formed during combustion and many of these compounds are known or suspected carcinogens. Recent studies on PAHs in Mexico City indicate that very high concentrations have been observed there and may pose a serious health hazard. The first part of this thesis describes results from the Megacities Initiative: Local and Regional Observations (MILAGRO) study in Mexico City in March 2006. During this field campaign, we measured PAH and aerosol active surface area (AS) concentrations at six different locations throughout the city using the Aerodyne Mobile Laboratory (AML). The different sites encompassed a mix of residential, commercial, industrial, and undeveloped land use. The goals of this research were to describe spatial and temporal patterns in PAH and AS concentrations, to gain insight into sources of PAHs, and to quantify the relationships between PAHs and other pollutants. We observed that the highest measurements were generally found at sites with dense traffic networks. Also, PAH concentrations varied considerably in space. An important implication of this result is that for risk assessment studies, a single monitoring site will not adequately represent an individual's exposure. Source identification and apportionment are essential for developing effective control strategies to improve air quality and therefore reduce the health impacts associated with fine particulate matter and PAHs. However, very few studies have separated gasoline- versus diesel-powered vehicle emissions under a variety of on-road driving conditions. The second part of this thesis focuses on distinguishing between the two types of engine emissions within the MCMA using positive matrix factorization (PMF) receptor modeling. The Aerodyne Mobile Laboratory drove throughout the MCMA in March 2006 and measured on-road concentrations of a large suite of gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitric oxide (NO), benzene (C6H6), formaldehyde (HCHO), ammonia (NH3), fine particulate matter (PM2.5), PAHs, and black carbon (BC). These pollutant species served as the input data for the receptor model. Fuel-based emission factors and annual emissions within Mexico City were then calculated from the source profiles of the PMF model and fuel sales data. We found that gasoline-powered vehicles were responsible for 90% of mobile source CO emissions and 85% of VOCs, while diesel-powered vehicles accounted for almost all of NO emissions (99.98%). Furthermore, the annual emissions estimates for CO and VOC were lower than estimated during the MCMA-2003 field campaign. The number of megacities is expected to grow dramatically in the coming decades. As one of the world's largest megacities, Mexico City serves as a model for studying air quality problems in highly populated, extremely polluted environments. The results of this work can be used by policy makers to improve air quality and reduce related health risks in Mexico City and other megacities. / Master of Science

Diese Dissertation präsentiert Modelle zur Lösung von Verhandlungsproblemen mit diskreten Strukturen. Hauptgrundlage der Betrachtung ist dabei die Erweiterung und Anwendung von Theorien der kooperativen Spieltheorie. Insbesondere der Shapley-Wert spielt eine wichtige Rolle. Als erste Problemklasse werden kooperative Spiele präsentiert, bei denen einige Spiele feste Auszahlungen erhalten. Als zweite Problemklasse werden kooperative Spiele untersucht, deren Lösungen ausschließlich ganzzahlig sein dürfen.

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.

Formulary apportionment is an intensively debated mechanism for allocating tax base within multinational groups. Systems under which the formula is identical in all jurisdictions and systems under which jurisdictions can determine the weights on the formula factors individually can be observed. The latter systems produce uncertainty about the overall tax-liable share of the future group tax base. Counter-intuitively, I identify scenarios under which increased uncertainty leads to higher expected future group income. My results provide helpful insights for firms and policy makers debating the specific design of a formulary apportionment system. (author's abstract) / Series: WU International Taxation Research Paper Series

The subject matter of the thesis is a comparative analysis of the possibilities of tax competition in the USA and in the European Union. It aims to compare the extent and form of the tax competition within these systems. The thesis consists of five parts. At the beginning, the term tax competition is examined and the possibility of a distinction between fair and unfair tax practices is explored. In the next part the analysis of the US tax system with a focus on competition in the area of direct and indirect taxation is carried out. The third part provides similarly structured analysis of the tax competition in the EU. The fourth part presents the evaluation and the comparison of the tax competition in both systems. The final part concludes with certain normative recommendations for the future of the EU tax system which are made with respect to their applicability for current deliberation of the reform of the taxation in the European Union. The performed analysis yielded interesting results. Contrary to the expectations, it was determined that the tax competition in the US was more intense than in the EU and the tax practices used by some US states included at least the same level of unfairness as those used by some member states in the EU.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively). Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.

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.