Microplastic is a widespread pollutant in marine and fresh water systems. A major pathway by which microplastics end up in these systems is via storm water. Storm water is generated as precipitation drain off of impenetrable surfaces like paving. Microplastic analysis of storm water make up a good foundation for better understanding what sources and factors contribute to microplastic pollution in marine and fresh water systems. This study puts emphasis on characterization and quantification of microplastics through visual characterization. As visual characterization is a subjective form of analysis, the characterization was performed based on guidelines in order to minimize the risk of identifying false positives. The concentration of microplastic was found to be higher in the current study than in comparison to larger water bodies and storm water streams in less urban areas. Fragments, i.e. irregular shaped particles with the appearance of being broken from a larger piece of litter, were found to be the most abundant type of microplastics, pointing at littering as a major source of microplastics in storm water.

In this thesis, a new aerosol module is developed for the STEM model (the Sulfur Transport and dEposition Model) to better understand the chemical aging of dust during long range transport and assess the impact of heterogeneous reactions on tropospheric chemistry. The new aerosol module is verified and first applied in a box model, and then coupled into the 3-Dimentional STEM model. In the new aerosol model, a non-equilibrium (dynamic or kinetic) approach to treat gas-to-particular conversion is employed to replace the equilibrium method in STEM model. Meanwhile, a new numerical method solving the aerosol dynamics equation is introduced into the dynamic aerosol model for its improved computational efficiency and high accuracy. Compared with the equilibrium method, the new dynamic approach is found to provide better results on predicating the different hygroscopicity and chemical aging patterns as a function of size. The current modeling study also takes advantage of new findings from laboratory experiments about heterogeneous reactions on mineral oxides and dust particles, in order to consider the complexity of surface chemistry (such as surface saturation, coating and relative humidity). Modeling results show that the impacts of mineralogy and relative humidity on heterogeneous reactions are significant and should be considered in atmospheric chemistry modeling with first priority. Finally, the upgraded 3-D STEM model is utilized to explore the observations from the Intercontinental Chemical Transport Experiment - Phase B (INTEX-B). The new dynamic approach for gas-to-particular conversion and RH-dependent heterogeneous uptake of HNO3 improve the model performance in term of aerosol predictions under different conditions. It is shown that these improvements change the modeled nitrate and sulfate concentrations, but also modify their size distributions significantly.

Nanoscopic spatial reorganization of selectin ligands, CD44 and PSGL-1, during the initial step of hematopoietic stem/progenitor cell (HSPC) homing, tethering and rolling of migrating cells over E-selectins, has been recently reported. However, the exact spatial distribution of these ligands and their spatial reorganization during the cell rolling on E-selectins are still an open question. The spatiotemporal characterization at the nanoscale level requires high resolution imaging methods. In this study, I quantitatively characterize nanoscopic spatiotemporal behavior of the selectin ligands on the migrating cells to understanding the molecular mechanism of the cell rolling at the nanoscale level by means of a microfluidics-based 3D super-resolution fluorescence microscopy technique. The obtained results suggest that PSGL-1 on the cell shows significant change in the axial distribution on the cell during the cell rolling on E-selectin whereas the spatial distribution of CD44 along the axial direction is not affected significantly by the cell rolling. These findings indicate that each selectin ligand has a distinct contribution to the initial step of the HSPC homing because of their distinct spatial localizations on the cells that regulate at least partly the accessibility of these ligands to the surface E-selectin.

Conventional coastal ocean models solve the shallow water equations, which describe the conservation of mass and momentum when the horizontal length scale is much greater than the vertical length scale. In this case vertical pressure gradients in the momentum equations are nearly hydrostatic. The outputs of coastal ocean models are thus sensitive to the bottom stress terms defined through the formulation of Manning’s n coefficients. This thesis considers the Bayesian inference problem of the Manning’s n coefficient in the context of storm surge based on the coastal ocean ADCIRC model. In the first part if the thesis, we apply an ensemble-based Kalman filter, the singular evolutive interpolated Kalman (SEIK) filter to estimate both a constant Manning’s n coefficient and a 2-D parameterized Manning’s coefficient on one ideal and one of more realistic domain using observation system simulation experiments (OSSEs). We study the sensitivity of the system to the ensemble size. we also access the benefits from using an inflation factor on the filter performance. To study the limitation of the Guassian restricted assumption on the SEIK filter, we also implemented in the second part of this thesis a Markov Chain Monte Carlo (MCMC) method based on a Generalized Polynomial chaos (gPc) approach for the estimation of the 1-D and 2-D Mannning’s n coefficient. The gPc is used to build a surrogate model that imitate the ADCIRC model in order to make the computational cost of implementing the MCMC with the ADCIRC model reasonable. We evaluate the performance of the MCMC-gPc approach and study its robustness to different OSSEs scenario. we also compare its estimates with those resulting from SEIK in term of parameter estimates and full distributions. we present a full analysis of the solution of these two methods, of the contexts of their algorithms, and make recommendation for fully realistic application.

The colossal amounts of data generated daily are increasing exponentially at a never-before-seen pace. A variety of applications—including stock trading, banking systems, health-care, Internet of Things (IoT), and social media networks, among others—have created an unprecedented volume of real-time stream data estimated to reach billions of terabytes in the near future. As a result, we are currently living in the so-called Big Data era and witnessing a transition to the so-called IoT era. Enterprises and organizations are tackling the challenge of interpreting the enormous amount of raw data streams to achieve an improved understanding of data, and thus make efficient and well-informed decisions (i.e., data-driven decisions). Researchers have designed distributed data stream processing systems that can directly process data in near real-time. To extract valuable information from raw data streams, analysts need to create and implement data stream processing applications structured as a directed acyclic graphs (DAG). The infrastructure of distributed data stream processing systems, as well as the various requirements of stream applications, impose new challenges. Cluster heterogeneity in a distributed environment results in different cluster resources for task execution and data transmission, which make the optimal scheduling algorithms an NP-complete problem. Scheduling streaming applications plays a key role in optimizing system performance, particularly in maximizing the frame-rate, or how many instances of data sets can be processed per unit of time. The scheduling algorithm must consider data locality, resource heterogeneity, and communicational and computational latencies. The latencies associated with the bottleneck from computation or transmission need to be minimized when mapped to the heterogeneous and distributed cluster resources. Recent work on task scheduling for distributed data stream processing systems has a number of limitations. Most of the current schedulers are not designed to manage heterogeneous clusters. They also lack the ability to consider both task and machine characteristics in scheduling decisions. Furthermore, current default schedulers do not allow the user to control data locality aspects in application deployment.In this thesis, we investigate the problem of scheduling streaming applications on a heterogeneous cluster environment and develop the maximum throughput scheduler algorithm (MT-Scheduler) for streaming applications. The proposed algorithm uses a dynamic programming technique to efficiently map the application topology onto a heterogeneous distributed system based on computing and data transfer requirements, while also taking into account the capacity of underlying cluster resources. The proposed approach maximizes the system throughput by identifying and minimizing the time incurred at the computing/transfer bottleneck. The MT-Scheduler supports scheduling applications that are structured as a DAG, such as Amazon Timestream, Google Millwheel, and Twitter Heron. We conducted experiments using three Storm microbenchmark topologies in both simulated and real Apache Storm environments. To evaluate performance, we compared the proposed MT-Scheduler with the simulated round-robin and the default Storm scheduler algorithms. The results indicated that the MT-Scheduler outperforms the default round-robin approach in terms of both average system latency and throughput.

An increase in the frequency and intensity of storm events is predicted by numerous climate researchers for the north of Europe. Not only this but also landuse change in form of clear-cutting can have an impact on the discharge of rivers and with that on road drainage structures. Extensive societal costs can be the consequences of blockage and underdimensioned structures. Hydrological models are powerful instruments that can be used to assess the future dimension requirements for road drainage structures especially in specifically vulnerable areas. In this thesis the hydrological model MIKE SHE was set up to study the discharge and water level at two pipe bridges and one culvert within the catchment of the river Hakerud in Västra Götaland, Sweden. Three scenarios were considered including a changing climate until 2050 and 2100 and a clear-cut scenario aiming to find out if the current design is sufficient for the future. This model can be used as an example model set-up for similar studies taking the recommendations of the experience gained in this thesis into consideration. For the Swedish Transport Administration further studies on this basis can contribute to decision making on the dimensioning of road drainage structures in the future to ensure a safe and robust infrastructural system.

Title: Ionospheric drift measurements Author: Daniel Kouba Department: Department of Surface and Plasma Science Supervisor: RNDr. Petra Koucká Knížová, Ph.D. Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic Abstract: This thesis deals with ionospheric drift measurements using Digisonde DPS-4. The results are divided into theoretical and practical part. It is shown that for drift data processing it is necessary to choose correct reflection points corresponding to drift movements first. The selection is made in three steps: restriction of Doppler frequency shift, height range selection, choice of the maximum zenith angle. After- wards it is possible to credibly estimate the vector of drift velocity. The experimental results of drift measurements are based on data from the Pru- honice station. To study the common behavior of ionospheric drifts during quiet conditions, the statistical characteristics of drift velocity components are investiga- ted in layers E and F during geomagnetic quiet year 2006. The maximal daily amplitude of horizontal velocity demonstrates the influence of geomagnetic activity. It is proved to be considerable in the F layer meanwhile it was not observed in the E layer. The effect of strong geomagnetic disturbances is shown at two examples of extreme storms. The...

Sinking Creek (HUC 06010103046), in the Watauga watershed of northeast Tennessee, is impaired due to Escherichia coli. To assess how E. coli and other water quality parameters fluctuated during storm events, water samples were collected with automated samplers during eight storms at two locations: Sinking Creek and a feeder spring. Turbidity and electrical conductivity data loggers were deployed in the creek, and dissolved oxygen (DO) was measured in situ. The presence of optical brighteners, used in detergents and an indicator of residential wastewater, was assessed using cotton fabric deployed at both sites and analyzed by an external laboratory. The Colilert Quanti-Tray method was used to process water samples for E. coli. Relationships between water quality parameters and lagged precipitation were assessed using cross-correlation. At the creek, E. coli and turbidity increased within 2 h of precipitation, exceeding the single sample water quality standard of 941 cfu 100 ml−1 during the storm. At the spring, E. coli became elevated more quickly than at the stream, within 30 min of precipitation, and decreased below the standard during the event. Electrical conductivity decreased within 1.5 h of the storm at the creek, and DO levels were higher at the creek than at the spring. Optical brightener analysis indicated possible presence of residential wastewater during one of two sampled storms. Targeted sampling and dye tracing are recommended to validate this hypothesis. These results may be used to inform field methods in similar storm sampling studies and will be useful in watershed restoration efforts in Sinking Creek.

The thermosphere-ionosphere regions are mainly controlled by the solar, but also by geomagnetic activity. In this case study, the Earth’s ionospheric response to the 25-26 August 2018 intense geomagnetic storm is investigated using the International GNSS System (IGS) Total Electron Content (TEC) observations. During this major storm, the minimum disturbance storm time (Dst) index reached -174 nT. We use observations and model simulations to analyse the ionospheric response during the initial phase and the main phase of the magnetic storm. A significant difference between storm day and quiet day TEC is observed. The O/N2 ratio observed from the GUVI instrument onboard the TIMED satellite is used to analyse the storm effect. The result shows a clear depletion of the O/N2 ratio in the high latitude region, and an enhancement in the low latitude region during the main phase of the storm. Furthermore, the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model simulations were used. The results suggest that the CTIPe model can capture the ionospheric variations during storms. / Die Regionen der Ionosphären und Thermosphäre werden hauptsächlich von der Sonne sowie auch von geomagnetische Aktivität beeinflusst. In dieser Fallstudie wurde die ionosphärische Reaktion der Erde auf den starken geomagnetischen Sturm vom 25./26. August 2018 unter Verwendung der Gesamtelektronengehaltsdaten (Total Electron Content, TEC) vom Internationalen GNSS Service untersucht. Während dieses großen Sturms wurde ein ”Disturbance Storm Time Index” Dst von -174 nT erreicht. Beobachtungen und Modellsimulationen wurden verwendet, um die ionosphärische Reaktion während der Anfangsphase und der Hauptphase des magnetischen Sturms zu untersuchen. Ein signifikanter Unterschied zwischen TEC während eines Sturmtages und eines ruhigen Tages wurde beobachtet. Das vom GUVI-Instrument an Bord des TIMED-Satelliten beobachtete O/N2 -Verhältnis wurde verwendet, um den Sturmeffekt weiter zu untersuchen. Das Ergebnis zeigt eine deutliche Abnahme/Zunahme des O/N2 Verhältnis in hohen/niedrigen Breiten während der Hauptphase des Sturms. Darüber hinaus wurde das Coupled Thermosphere Ionosphere Plasmasphere ectrodynamics (CTIPe) Modell verwendet. Die Ergebnisse legen nahe, dass das CTIPe-Modell die ionosphärischen Schwankungen während eines Sturms erfassen kann.

The thermosphere-ionosphere regions are mainly controlled by the solar, but also by geomagnetic activity. In this case study, the Earth’s ionospheric response to the 25-26 August 2018 intense geomagnetic storm is investigated using the International GNSS System (IGS) Total Electron Content (TEC) observations. During this major storm, the minimum disturbance storm time (Dst) index reached -174 nT. We use observations and model simulations to analyse the ionospheric response during the initial phase and the main phase of the magnetic storm. A significant difference between storm day and quiet day TEC is observed. The O/N2 ratio observed from the GUVI instrument onboard the TIMED satellite is used to analyse the storm effect. The result shows a clear depletion of the O/N2 ratio in the high latitude region, and an enhancement in the low latitude region during the main phase of the storm. Furthermore, the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model simulations were used. The results suggest that the CTIPe model can capture the ionospheric variations during storms. / Die Regionen der Ionosphären und Thermosphäre werden hauptsächlich von der Sonne sowie auch von geomagnetische Aktivität beeinflusst. In dieser Fallstudie wurde die ionosphärische Reaktion der Erde auf den starken geomagnetischen Sturm vom 25./26. August 2018 unter Verwendung der Gesamtelektronengehaltsdaten (Total Electron Content, TEC) vom Internationalen GNSS Service untersucht. Während dieses großen Sturms wurde ein ”Disturbance Storm Time Index” Dst von -174 nT erreicht. Beobachtungen und Modellsimulationen wurden verwendet, um die ionosphärische Reaktion während der Anfangsphase und der Hauptphase des magnetischen Sturms zu untersuchen. Ein signifikanter Unterschied zwischen TEC während eines Sturmtages und eines ruhigen Tages wurde beobachtet. Das vom GUVI-Instrument an Bord des TIMED-Satelliten beobachtete O/N2 -Verhältnis wurde verwendet, um den Sturmeffekt weiter zu untersuchen. Das Ergebnis zeigt eine deutliche Abnahme/Zunahme des O/N2 Verhältnis in hohen/niedrigen Breiten während der Hauptphase des Sturms. Darüber hinaus wurde das Coupled Thermosphere Ionosphere Plasmasphere ectrodynamics (CTIPe) Modell verwendet. Die Ergebnisse legen nahe, dass das CTIPe-Modell die ionosphärischen Schwankungen während eines Sturms erfassen kann.