A method with the potential for comprehensive microplastic monitoring in river sediments is presented in this study. We introduce a novel combination of electrostatic separation, density separation, and differential scanning calorimetry (DSC). Currently, microplastic analysis in sediments is limited in terms of sample masses, processing time, and analytical robustness. This work evaluated a method to process large sample masses efficiently and still obtain robust results. Four particulate matrices, including commercial sands and river sediments, were spiked with PCL, LD-PE, and PET microplastic particles (63–200 µm). Samples with a mass of 100 g and 1,000 g (sand only) contained 75 mg of each microplastic. After electrostatic separation, the mass of sand samples was reduced by 98%. Sediment samples showed a mass reduction of 70–78%. After density separation, the total mass reduction of sediment samples was above 99%. The increased concentration of total organic carbon seems to have the highest impact on mass reduction by electrostatic separation. Nevertheless, the recovery of microplastic was independent of the particulate matrix and was polymer-specific. In 100 g samples, the average recovery rates for PCL, LD-PE, and PET were 74 ± 9%, 93 ± 9%, and 120 ± 18%, respectively. The recoveries of microplastic from 1,000 g samples were 50 ± 8%, 114 ± 9%, and 82 ± 11%, respectively. In scale up experiments, high recoveries of all microplastics were observed with a decrease in standard deviation. Moreover, the biodegradable polymer PCL could be used as an internal standard to provide quality assurance of the process. This method can overcome the current limitations of routine microplastic analysis in particulate matrices. We conclude that this method can be applied for comprehensive microplastic monitoring in highly polluted sediments. More studies on electrostatic separation and polymer-specific recovery rates in complex matrices are proposed.

No description available.

On a global scale, the deployment of wind power plants is soaring. However, the availability of their construction materials could be a potential bottleneck, a problem rarely discussed in literature so far. Rare earth elements represent the most critical materials in terms of high economic importance, supply risk and environmental risk. This study therefore provides a market analysis of rare earths, ascertaining that geological scarcity is not the main problem. Instead, four kinds of market failure are identified: market power, co-production, by-production and negative externalities. Altogether, the market for rare earth metals is in a state of severe disequilibrium. Subsequently, an estimate of future rare earth demand patterns based on the wind power industry by 2050 reveals that the current level of supply is unlikely to be sufficient in the long run. To allow for a more elaborate analysis, two options of including a rare earth side condition in an integrated assessment model are finally suggested.

The risk of carbon emissions from permafrost is linked to an increase in ground temperature and thus in particular to thermal insulation by vegetation, soil layers and snow cover. Ground insulation can be influenced by the presence of large herbivores browsing for food in both winter and summer. In this study, we examine the potential impact of large herbivore presence on the soil carbon storage in a thermokarst landscape in northeastern Siberia. Our aim in this pilot study is to conduct a first analysis on whether intensive large herbivore grazing may slow or even reverse permafrost thaw by affecting thermal insulation through modifying ground cover properties. As permafrost soil temperatures are important for organic matter decomposition, we hypothesize that herbivory disturbances lead to differences in ground-stored carbon. Therefore, we analyzed five sites with a total of three different herbivore grazing intensities on two landscape forms (drained thermokarst basin, Yedoma upland) in Pleistocene Park near Chersky. We measured maximum thaw depth, total organic carbon content, δ13C isotopes, carbon-nitrogen ratios, and sediment grain-size composition as well as ice and water content for each site. We found the thaw depth to be shallower and carbon storage to be higher in intensively grazed areas compared to extensively and non-grazed sites in the same thermokarst basin. First data show that intensive grazing leads to a more stable thermal ground regime and thus to increased carbon storage in the thermokarst deposits and active layer. However, the high carbon content found within the upper 20 cm on intensively grazed sites could also indicate higher carbon input rather than reduced decomposition, which requires further studies including investigations of the hydrology and general ground conditions existing prior to grazing introduction. We explain our findings by intensive animal trampling in winter and vegetation changes, which overcompensate summer ground warming. We conclude that grazing intensity—along with soil substrate and hydrologic conditions—might have a measurable influence on the carbon storage in permafrost soils. Hence the grazing effect should be further investigated for its potential as an actively manageable instrument to reduce net carbon emission from permafrost.

Markttransparenz in Deutschland wird durch die Gutachterausschüsse und auch durch verschiedene private Akteure am Immobilienmarkt realisiert. Insbesondere in Teilmärkten mit geringen Transaktionszahlen stellt die Markttransparenz eine Herausforderung dar, da nicht ausreichend Daten zur Analyse der jeweiligen Märkte zur Verfügung stehen. Aus diesem Grund bedürfen diese Märkte einer tiefergehenden Untersuchung, um auch hier eine ausreichende Markttransparenz zu erreichen. Die Vielfältigkeit der Teilmärkte mit geringen Transaktionszahlen muss dafür differenziert betrachtet werden. Im Rahmen der Arbeit werden zunächst Unterschiede in den Eigenschaften der Märkte mit geringen Transaktionszahlen untersucht. Hierzu wird mittels einer qualitativen Untersuchung von Leitfadeninterviews sowie der Literatur zum Thema eine Theorie zur Systematisierung der Märkte gebildet. Differenziert für einzelne Märkte kann mit dieser Strukturierung eine passende Auswertestrategie entwickelt werden. Anschließend erfolgt die Untersuchung von verschiedenen Daten, die bereits in den Märkten mit geringer Transaktionszahl genutzt werden. Kauffälle, die unvollständig erfasst sind, werden derzeit bei Auswertungen vollständig ausgeschlossen (Fallweiser Ausschluss). Teilweise fehlt jedoch nur eine Information für eine multivariate Analyse. Im Rahmen der Arbeit wird untersucht, ob und mit welchen Methoden diese Datenlücken geeignet gefüllt werden können, um eine höhere Genauigkeit in den Analysen auch mit wenigen Daten zu erhalten. Als Methoden werden neben dem Fallweisen Ausschluss eine Mittelwertimputation sowie die Auffüllung der Datenlücken mittels Expectation-Maximization und Random-Forest-Regression untersucht. Darüber hinaus wird das Expertenwissen, das in verschiedenen Formen von Expertisen (Befragungen, Angebotspreise, Gutachten) geäußert werden kann, untersucht. Zur Erlangung eines Überblicks, wird zunächst das Expertenwissen im Rahmen einer quantitativen Befragung näher betrachtet, um Handlungsweisen und Unterschiede von Experten aus verschiedenen Gruppen aufzudecken. Anschließend werden intersubjektive Experten- und Laienbefragungen im Kontext der Immobilienbewertung ausgewertet sowie Angebotspreise, die von Maklern und ohne Makler vermarktet werden, im Verhältnis zu den realisierten Kaufpreisen untersucht. Da die untersuchten zusätzlichen Daten wie Angebotsdaten oder Expertenbefragungen in einigen Teilmärkten nicht zur Verfügung stehen oder nur mit hohem Aufwand erzeugt werden können, sind alternative Nutzungsansätze notwendig. Hierzu werden zwei Methoden auf ihre Eignung hinsichtlich räumlich zusammengefasster Auswertungen geprüft. Der Vergleich erfolgt zur in der Praxis etablierten multiplen linearen Regressionsanalyse. Zum einen werden die geographisch gewichtete Regressionsanalyse, die lokale Märkte besser abbilden kann, zum anderen die künstlichen neuronalen Netze, die Nichtlinearitäten besser abbilden können, angewendet. Im Ergebnis zeigt sich, dass eine Strukturierung der Märkte mit geringer Transaktionszahl möglich ist. Eine sinnvolle Strukturierung erfolgt anhand der Grundgesamtheit des jeweiligen sachlichen/-räumlichen Marktes. Ebenso kann eine Differenzierung nach ländlichen und urbanen Räumen erfolgen. Mit Imputationsmethoden können die Ergebnisse von Regressionsanalysen deutlich verbessert werden. Selbst bei einem großen Vorkommen von Datenlücken in unterschiedlichen Parametern kann eine Auswertung noch gute Ergebnisse in der Größenordnung der vollständigen Kauffälle liefern. Auch mit der simplen Methode der Mittelwertimputation kann ein gutes Ergebnis erzielt werden. Experten im Bereich der Immobilienbewertung haben die unterschiedlichsten beruflichen Herkünfte. In ihrer Arbeitsweise lassen sich jedoch keine wesentlichen Systematiken feststellen. Lediglich bei der Nutzung von Daten können Systematiken aufgedeckt werden. Expertenbefragungen weisen grundsätzlich hohe Streuungsmaße auf. Die Streuungsmaße werden dann reduziert, wenn bei den Befragungen Einschränkungen beispielsweise durch eine vorgegebene Skala oder durch vorgeschlagene Werte erfolgen. Weitere Untersuchungen sind dahingehend notwendig. Auch die Abschläge zwischen Angebotspreisen und Kaufpreisen, aber auch die Anpassung von Angebotspreisen im Vermarktungszeitraum, weisen hohe Streuungsbreiten auf. Einen signifikanten Unterschied zwischen der Vermarktung mit oder ohne Makler kann in der untersuchten Stichprobe nicht nachgewiesen werden. Sowohl die Nutzung der geographisch gewichteten Regressionsanalyse (GWR) als auch die Nutzung von künstlichen neuronalen Netzen (KNN) bieten bei der Auswertung von räumlich zusammengefassten Daten in einer Kreuzvalidierung einen Vorteil. Dies lässt darauf schließen, dass die Märkte sowohl räumlich inhomogen als auch nichtlinear sind. Zielführend erscheint eine Kombination der geographischen Komponente mit nichtparametrischen Ansätzen wie dem Lernverfahren der KNN. / In Germany market transparency is realised by expert’s committees and due to the publication of market reports and market values and by various private players in the real estate market. In sub-markets with low transaction numbers, market transparency is a challenge because not enough data is available to analyse the respective markets. These markets require a more in-depth investigation to achieve sufficient market transparency. The diversity of sub-markets with low transaction numbers must be considered in a differentiated way. In the context of this work, differences in the characteristics of markets with a small number of transactions are examined. A theory for the systematisation of these markets is formed, using a qualitative investigation of guideline interviews and literature on the topic. Differentiated for individual markets, a suitable evaluation strategy can be developed using the proposed structuring. Subsequently, the analysis of different data, which is already used in real estate valuation, is carried out to investigate its usability for regions with few transactions. Purchase cases which are recorded incompletely, are today excluded from evaluations (case-wise exclusion). However, most of the time only one or two pieces of information for multivariate analysis are missing per case. It is examined whether and with which methods these data gaps can be filled suitably. Besides the case-by-case rejection (default method today), a mean-value-imputation, as well as the filling of data gaps using Expectation-Maximization and Random-Forest-Regression are investigated. Furthermore, the expert’s knowledge, which can be expressed in different forms of expert’s opinions (surveys, offer prices, expert reports), is examined. First of all, the expert knowledge, in general, is examined more closely within the framework of a quantitative survey to uncover patterns of action and differences between experts from different groups. Subsequently, intersubjective expert and layman surveys are evaluated in the context of real estate valuation. Additional offer prices, marketed with or without real estate agents, are compared to the realised purchase prices. Since the additional data examined, such as the supply data or the expert surveys, is not available in some sub-markets or can only be generated at great expense, alternative approaches to utilisation are necessary. For this purpose, two methods are tested for their suitability with regard to spatially summarised data. A comparison to the classically used linear regression analysis is made. On one hand, the geographically weighted regression analysis, which represents local markets more accurately, and the artificial neural networks, which are more suited to represent non-linearities, are applied. The result shows that a systematisation of markets with a low number of transactions is possible. A structuring based on the population of the respective functional/spatial sub-market takes place. It is also possible to differentiate between rural and urban areas. With imputation methods, the results of regression analyses can be improved significantly. Even if there are large numbers of data gaps in different parameters, an evaluation can still provide adequate results in comparison to an analysis with complete purchase cases if the overall sample is big enough. Already the simple method of mean-value-imputation leads to good results. Experts in the field of real estate valuation have a wide variety of professional backgrounds. However, significant systematics cannot be identified in their working methods. Different behaviour can only be identified by the usage of different data sources. Expert surveys generally show a high degree of dispersion. This degree of dispersion is reduced if the surveys are restricted, e.g. by a given scale or suggested values. Further investigations on these topics are necessary. The discounts between offer prices and purchase prices as well as the adjustment of offer prices within the marketing period are showing a high degree of dispersion. A significant difference between marketing with or without an agent cannot be proven in the examined sample. Both, the use of geographically weighted regression analysis and the use of artificial neural networks (ANN) offer an advantage when evaluating spatially summarised data in cross-validation. This leads to the conclusion that the markets are both geographically inhomogeneous and non-linear. A combination of the geographic component with non-parametric approaches such as the learning procedure of the ANN is appropriate.

Die Reduzierung der Treibhausgasemissionen zur Begrenzung des globalen Klimawandels stellt eine der wesentlichen gesellschaftlichen Herausforderungen des 21. Jahrhunderts dar. Im Zuge der damit verbundenen Energiewende in Deutschland sind die Verteilnetze der elektrischen Energieversorgung durch die Verteilnetzbetreiber an die veränderten Last- und Einspeiseverhältnisse anzupassen. Ungeachtet dieser Veränderungen sind auch weiterhin die Versorgungszuverlässigkeit und Preisgünstigkeit der Energieversorgung sicherzustellen. Einen bedeutenden Einflussfaktor für die Verteilnetzbetreiber stellt die staatliche Regulierung dar, die durch die natürliche Monopolstellung der Verteilnetzbetreiber begründet ist. Neben den energiepolitischen Vorgaben an den Betrieb der Verteilnetze sind durch die Verteilnetzbetreiber außerdem die Ansprüche der Eigen- und Fremdkapitalgeber zu berücksichtigen. Aus diesen unterschiedlichen Anforderungen der internen und externen Anspruchsgruppen leiten sich verschiedene Ziele ab, deren Erreichung durch den Verteilnetzbetreiber angestrebt wird. Zwischen diesen Zielen können sowohl komplementäre als auch konkurrierende Zielbeziehungen vorliegen. Eine wesentliche Einflussmöglichkeit zur Erreichung dieser Ziele stellen die im Zuge der Netzerneuerung auszuwählenden Investitions- und Instandhaltungsmaßnahmen dar. Durch den Verteilnetzbetreiber ist im Rahmen des Planungsprozesses diejenige Investitions- und Instandhaltungsstrategie festzulegen, deren Umsetzung einen möglichst hohen Beitrag zur Erreichung der Unternehmensziele leistet. Zur Unterstützung der Entscheidung über die auszuwählende Investitions- und Instandhaltungsstrategie wird im Rahmen der vorliegenden Arbeit ein Modell zur Erneuerung von Verteilnetzen konzipiert. Durch Anwendung dieses Modells soll es Verteilnetzbetreibern ermöglicht werden, unter Berücksichtigung der Interessen der internen und externen Anspruchsgruppen die Investitions- und Instandhaltungsstrategie zu wählen, mit der die vorliegenden Unternehmensziele bestmöglich erreicht werden können.

Numerical solutions of the Navier-Stokes Equations became more popular in recent decades with increasingly accessible and powerful computational resources. Simulations in reconstructed or artificial pore geometries are often performed to gain insight into microscopic fluid flow structures or are used for upscaling quantities of interest, like hydraulic conductivity. A physically adequate representation of pore-scale flow fields requires analysis of large domains. We solve the incompressible NSE in artificial ordered and random pore-space structures. A simple cubic and face-centred packings of spheres placed in a square duct are analysed. For the fluid flow simulations of random media, packings of spheres, icosahedra, and cubes forming unconsolidated sediments are generated using a rigid body simulation software. The Direct Numerical Simulation method is used for the solution of the NSE implemented in the open-source computational fluid dynamics software OpenFOAM. The influence of the number of spheres in ordered packings, the mesh type, and the mesh resolution is investigated for fluid flow up to Reynolds numbers of 100 based on the spheres' diameter. The random media mesh generation method relies on approximate surface reconstruction. The resulting tetrahedral meshes are then used for steady-state simulations and refined based on an a-posteriori error estimator. The fluid flow simulation results can further be used twofold: 1) They provide homogenized hydro-mechanical properties of the analysed medium for the larger meso and macro groundwater flow simulations. A concept of one-way binding for large-scale simulations is presented. 2) Visualisation: A post-processing image rendering technique was employed in interactive and still image visualisation environments allowing better overview over local fluid flow structures. The ogs FEM code for the solution of large-scale groundwater processes was inspected for computational efficiency. The conclusions drawn from this analysis formed the~basis for the implementation of the~new version of the code---ogs6. The improvements include comparison of linear algebra software realisations and an implementation of optimized memory access patterns in FEM-local assembler part.

Like other countries of the world, Costa Rica faced the challenge of dealing with a variety of trade-offs when implementing sustainability goals in agriculture. Very often, economic promotion is in conflict with goals regarding human and environmental health protection. Organic farming practices could provide strategies to overcome some of these trade-offs. However, in Costa Rica, the majority of farmers still relies on conventional farm practices. In this paper, I investigate the potential for a sustainable transformation in Costa Rica’s agriculture by focusing on organic farming policies. I shed light on the role local actors and organizations play in this process compared to other actor types. I argue that local actors are “the agents of change” in these processes, as these are the target groups of organic farming policies and are the ones who are asked to change their farm practices. Based on survey data and network analysis, I was able to illustrate how differently integrated local actors are compared to other actor types in Costa Rica’s implementation of organic farming policies. Local actors show interest and willingness to further participate in land-use implementation processes when institutional barriers are alleviated, and further promotion instruments are available.

No description available.

Growth rates in e-commerce, changing consumer behaviors, and COVID-19 have all put pressure on local retailers worldwide, threatening the resilience of city centers. Local online platforms (LOPs) have been considered as a solution to help local retailers increase their visibility and survive on the market. However, most platforms fail to attract a significant number of stakeholders. Simultaneously, digital platform solutions with more holistic urban perspectives, such as urban data platforms (UDPs), have emerged. However, a question remains: how can the integration of retail data (e.g., product availabilities) into a UDP succeed? Therefore, in this paper, we explore stakeholder-oriented networking processes to integrate local retail data into a UDP in Leipzig, Germany. Leipzig has increased its population by 26% since 2000, but presents the highest retail vacancy rate, compared to other major German cities. To investigate the networking process in Leipzig, we conduct a social network analysis which combines qualitative interviews, mapping, and ethnographic research. We interview ten stakeholders and uncover conflicts within the networking process: First, all stakeholders have different understandings of UDPs and how to integrate local retail data; second, the interviewees acknowledge the importance of, but none of them feel responsible for, initiating or managing the process; and third, the city administration has shown diverging interest, in terms of taking on more responsibility.