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Design hochfester TWIP Stähle für die Wasserstoffinfrastruktur der Zukunft

Integriertes und an Raum-Zeit-Messungsskalen angepasstes Global Random Walk - Modell für reaktiven Transport im Grundwasser

Zur Lösung von Fluss- und reaktiven Transportgleichungen in heterogenen Grundwassersystemen werden neue Global Random Walk (GRW) Algorithmen entwickelt und implementiert, die stabil und frei von numerischer Diffusion sind. Um das Auftreten von Interpolationsfehlern zu vermeiden, wird ein integriertes GRW-Lösungsverfahren entwickelt, das Geschwindigkeiten und Konzentrationen auf dem selben regulären Gitter berechnet. Wir nutzen grobkörnige (engl. Coarse grained) (CG) Mittelwerte in Raum und Zeit über die Trajektorien der berechneten Partikel, die die Konzentrationen der reaktiven chemischen Spezies in den GRWSimulationen beschreiben. Diese werden genutzt, um eine kontinuierliche Beschreibung der Transportprozesse zu erhalten. Nachdem die Mittelungsprozedur die Variation der simulierten Konzentrationen reduziert, genügt eine relativ kleine Anzahl von Monte Carlo - Simulationen, um die statistischen Kennzahlen zu gewinnen, und gleichzeitig der Auswirkung der Raum-Zeit-Skalen der hydrologischen Beobachtungen Rechnung zu tragen. Des weiteren können lokale Bilanzgleichungen für die CG Raum-Zeit-Mittel genutzt werden, um die hochskalierten Diffusionskoeffizienten und Reaktionsterme zu berechnen.

The iron-snow regime in Fe-FeS cores: a numerical and experimental approach

In the Earth, the dynamo action is strongly linked to core freezing. There is a solid inner core, the growth of which provides a buoyancy flux that drives the dynamo. The buoyancy in this case derives from a difference in composition between the solid inner core and the fluid outer core. In planetary bodies smaller than the Earth, however, this core differentiation process may differ - Fe may precipitate at the core-mantle boundary (CMB) rather than in the center and may fall as iron snow and initially remelt with greater depth. A chemical stable sedimentation zone develops that comprises with time the entire core - at that time a solid inner core starts to grow. The dynamics of this system is not well understood and also whether it can generate a magnetic field or not. The Jovian moon Ganymede, which shows a present-day magnetic dipole field, is a candidate for which such a scenario has been suggested. We plan to study this Fe-snow regime with both a numerical and experimental approach. In the numerical study, we use a 2D/3D thermo-chemical convection model that considers crystallization and sinking of iron crystals together with the dynamics of the liquid core phase (for the 3D case the influence of the rotation of the Fe snow process is further studied).The numerical calculations will be complemented by two series of experiments: (1) investigations in metal alloys by means of X-ray radioscopy, and (2) measurements in transparent analogues by optical techniques. The experiments will examine typical features of the iron snow regime. On the one hand they will serve as a tool to validate the numerical approach and on the other hand they will yield important insight into sub-processes of the iron snow regime, which cannot be accessed within the numerical approach due to their complexity.

Vertical partitioning and sources of CO2 production and effects of temperature, oxygen and root location within the soil profile on C turnover

For surface soils, the mechanisms controlling soil organic C turnover have been thoroughly investigated. The database on subsoil C dynamics, however, is scarce, although greater than 50 percent of SOC stocks are stored in deeper soil horizons. The transfer of results obtained from surface soil studies to deeper soil horizons is limited, because soil organic matter (SOM) in deeper soil layers is exposed to contrasting environmental conditions (e.g. more constant temperature and moisture regime, higher CO2 and lower O2 concentrations, increasing N and P limitation to C mineralization with soil depth) and differs in composition compared to SOM of the surface layer, which in turn entails differences in its decomposition. For a quantitative analysis of subsoil SOC dynamics, it is necessary to trace the origins of the soil organic compounds and the pathways of their transformations. Since SOM is composed of various C pools which turn over on different time scales, from hours to millennia, bulk measurements do not reflect the response of specific pools to both transient and long-term change and may significantly underestimate CO2 fluxes. More detailed information can be gained from the fractionation of subsoil SOM into different functional pools in combination with the use of stable and radioactive isotopes. Additionally, soil-respired CO2 isotopic signatures can be used to understand the role of environmental factors on the rate of SOM decomposition and the magnitude and source of CO2 fluxes. The aims of this study are to (i) determine CO2 production and subsoil C mineralization in situ, (ii) investigate the vertical distribution and origin of CO2 in the soil profile using 14CO2 and 13CO2 analyses in the Grinderwald, and to (iii) determine the effect of environmental controls (temperature, oxygen) on subsoil C turnover. We hypothesize that in-situ CO2 production in subsoils is mainly controlled by root distribution and activity and that CO2 produced in deeper soil depth derives to a large part from the mineralization of fresh root derived C inputs. Further, we hypothesize that a large part of the subsoil C is potentially degradable, but is mineralized slower compared with the surface soil due to possible temperature or oxygen limitation.

Community-mediated mechanisms to stabilize pollination of agricultural production highly dependent on shrinking honey bee populations under global change

Almond in California represents an agroecosystem pollinated solely by a single species, the European honey bee, a species that is becoming increasingly difficult and expensive to manage due to substantial, unpredictable mortality. Therefore, sustainable and high output production require a more integrated approach that diversifies sources of pollination. For this purpose, detailed data of our understanding how diversity can stabilize pollination are required. The project will identify alternative wild pollinator species and collect high quality data contributing to our understanding of how diversity (pollen and insects) can bolster honey bee pollination during stable and unstable climatic conditions. The research will be carried out on almond orchards in Northern California known to be either pollinator species rich (up to 30 species) or depauperate (honey bees only). The replicated extremes in pollinator diversity represent a unique opportunity to study the effects of diversity on pollination in real agroecosystems combined with laboratory and glasshouse experiments. The overall goal is to provide basic research that is essential for our general understanding of how insect diversity can affect high-quality pollination under land use and climate change.

SuSteelAG, SuSteelAG: Nachhaltige Stahlproduktion aus Australien und Deutschland

Anlagen nach Bundesimmissionsschutzgesetz in Brandenburg - Download-Service (WFS-LFU-BIMSCHG)

Der Download Service ermöglicht das Herunterladen von Geodaten zu Anlagen nach Bundesimmissionsschutzgesetz (BImSchG) im Land Brandenburg. Datenquelle ist das Anlageninformationssystem LIS-A. Die Anlagen werden zum einen gruppiert nach Anlagenarten 1. Ordnung (ohne Anlagenteile), zum anderen nach Tierhaltungs- und Aufzuchtanlagen, nach Blockheizkraftwerken und nach großen Feuerungsanlagen. Die BImSchG-Anlagen 1. Ordnung werden unterschieden nach: - Wärmeerzeugung, Bergbau und Energie (Nr. 1) - Steine und Erden, Glas, Keramik, Baustoffe (Nr. 2) - Stahl, Eisen und sonstige Metalle einschließlich Verarbeitung (Nr. 3) - Chemische Erzeugnisse, Arzneimittel, Mineralölraffination und Weiterverarbeitung (Nr. 4) - Oberflächenbehandlung mit organischen Stoffen, Herstellung von bahnenförmigen Materialien aus - Kunststoffen, sonstige Verarbeitung von Harzen und Kunststoffen (Nr. 5) - Holz, Zellstoff (Nr. 6) - Nahrungs-, Genuss- und Futtermittel, landwirtschaftliche Erzeugnisse (Nr. 7) - Verwertung und Beseitigung von Abfällen und sonstigen Stoffen (Nr. 8) - Lagerung, Be- und Entladen von Stoffen und Gemischen (Nr. 9) - Sonstige Anlagen (Nr. 10) Die Tierhaltungs- und Aufzuchtanlagen werden gemäß 4. BImSchV unterteilt in: - Geflügel (Nr. 7.1.1 bis 7.1.4) - Rinder und Kälber (Nr. 7.1.5 und 7.1.6) - Schweine (Nr. 7.1.7 bis 7.1.9) - gemischte Bestände (Nr. 7.1.11) Die großen Feuerungsanlagen werden gemäß 4. BImSchV unterteilt in: - Wärmeerzeugung, Energie (Nr. 1.1, 1.4.1.1, 1.4.2.1) - Zementherstellung (Nr. 2.3.1) - Raffinerien (Nr. 4.1.12, 4.4.1) - Abfallverbrennung (Nr. 8.1.1.1, 8.1.1.3) Es werden nur Anlagen gemäß 13. und 17. BImSchV berücksichtigt. Die Blockheizkraftwerke werden hinsichtlich ihrer elektrischen Leistung unterschieden.

Soil physical data of agricultural soils in Saxony

The continuous agricultural soil monitoring program (BDF) by the Saxon State Office for Environment, Agriculture, and Geology (LfULG) is operational since 1995, collecting and analysing samples periodically from 60 monitoring sites across Saxony, Germany. This dataset contains additional soil physical data for 441 samples collected during a sampling campaign in September 2023. Samples were collected from four sites across Saxony using different sampling devices (split spoon push core, steel syringe, sampling spade, soil rings) to evaluate their suitability for true-to-volume sampling. Total bulk density, fine soil bulk density and the fine soil stock were calculated using both air-dry and oven-dry weights. Particle size distribution was determined by wet sieving and the integral suspension pressure method (ISP+) using the Meter Pario+ system. This dataset is part of a mid-infrared soil spectral library for agricultural soils in Saxony, Germany.

Anlagen nach Bundesimmissionsschutzgesetz in Brandenburg - View-Service (WMS-LFU-BIMSCHG)

Der View Service stellt Anlagen nach Bundesimmissionsschutzgesetz (BImSchG) im Land Brandenburg dar. Datenquelle ist das Anlageninformationssystem LIS-A. Die Anlagen werden zum einen gruppiert nach Anlagenarten 1. Ordnung (ohne Anlagenteile), zum anderen nach Tierhaltungs- und Aufzuchtanlagen, nach Blockheizkraftwerken und nach großen Feuerungsanlagen. Die BImSchG-Anlagen 1. Ordnung werden unterschieden nach: - Wärmeerzeugung, Bergbau und Energie (Nr. 1) - Steine und Erden, Glas, Keramik, Baustoffe (Nr. 2) - Stahl, Eisen und sonstige Metalle einschließlich Verarbeitung (Nr. 3) - Chemische Erzeugnisse, Arzneimittel, Mineralölraffination und Weiterverarbeitung (Nr. 4) - Oberflächenbehandlung mit organischen Stoffen, Herstellung von bahnenförmigen Materialien aus - Kunststoffen, sonstige Verarbeitung von Harzen und Kunststoffen (Nr. 5) - Holz, Zellstoff (Nr. 6) - Nahrungs-, Genuss- und Futtermittel, landwirtschaftliche Erzeugnisse (Nr. 7) - Verwertung und Beseitigung von Abfällen und sonstigen Stoffen (Nr. 8) - Lagerung, Be- und Entladen von Stoffen und Gemischen (Nr. 9) - Sonstige Anlagen (Nr. 10) Die Tierhaltungs- und Aufzuchtanlagen werden gemäß 4. BImSchV unterteilt in: - Geflügel (Nr. 7.1.1 bis 7.1.4) - Rinder und Kälber (Nr. 7.1.5 und 7.1.6) - Schweine (Nr. 7.1.7 bis 7.1.9) - gemischte Bestände (Nr. 7.1.11) Die großen Feuerungsanlagen werden gemäß 4. BImSchV unterteilt in: - Wärmeerzeugung, Energie (Nr. 1.1, 1.4.1.1, 1.4.2.1) - Zementherstellung (Nr. 2.3.1) - Raffinerien (Nr. 4.1.12, 4.4.1) - Abfallverbrennung (Nr. 8.1.1.1, 8.1.1.3). Es werden nur Anlagen gemäß 13. und 17. BImSchV berücksichtigt. Die Blockheizkraftwerke werden hinsichtlich ihrer elektrischen Leistung unterschieden. Windkraftanlagen werden nicht dargestellt! Maßstab: 1:500000; Bodenauflösung: nullm; Scanauflösung (DPI): null

Continuous turbidity observations near DynaCom experimental in the back-barrier tidal flat, Spiekeroog, Germany, 2018-09 to 2023-09

Data presented here were collected between September 2018 to September 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). To measure local turbidity, a turbidity recorder equipped with a Seapoint® turbidity meter (RBRsolo Tu, RBR Ltd., Ontario/Canada) was installed in the back-barrier tidal flat near the experimental islands in a shallow tidal creek (0.9 m NHN). Another one was installed at the saltmarsh edge (1.2 m NHN). Both loggers were bottom mounted through a steel girder (buried 0.3 m deep in the sediment) and were positioned 15 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. The turbidity recorders were pre-calibrated by the manufacturer (Seapoint Sensors, Inc., NH/USA). Recorded data were internally logged and exported using Ruskin software V2.24.3.x (RBR Ltd., Ontario/Canada). Subsequent data processing was done using MATLAB (R2024b). Post-processing and quality control included the removal of (a) low tide data (sensors exposed to air), (b) data covering maintenance activities, (c) data affected by biofouling, and (d) implausible values, i.e. negative values and values exceeding the linear response range of the sensor (1250 NTU). According to manufacturer specifications, the linear measurement range extends up to 1250 NTU, while 750 NTU represent a more conservative estimate of linearity. Therefore, 1250 NTU was adopted as the upper threshold for valid measurements in this dataset.

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