This dataset provides carbonate chemistry and hydrological measurements supporting the analysis of the stability of alkalinity and carbon transport potential in the Elbe Estuary, northern Germany. It includes (1) results from laboratory incubation experiments using water samples from the Elbe conducted in 2023, (2) historical water chemistry monitoring records from multiple stations, and (3) monthly flow discharge measurements from the Neu Darchau gauging station. Experimental data were collected from the experiments varying salinity and seasonal conditions, and parameters measured include pH, temperature, and total alkalinity. Major ion concentrations (Na+, K+, Ca2+, Mg2+, Cl-, SO42-) were reconstructed from stoichiometry. The saturation states of calcite and aragonite, as well as pCO2, were calculated using the phreeqpython geochemical package. Historical data, covering carbonate chemistry and major ions at several stations and over multiple years, were collected from digitized sources and FGG Elbe. Together, this dataset facilitates the investigation of long-term trends in the carbonate system and carbon transport in the land ocean transition zone of the Elbe River.
Here, we examine the ecosystem ramifications of changes in sediment-dwelling invertebrate bioturbation behaviour—a key process mediating nutrient cycling—associated with nearfuture environmental conditions (+ 1.5 °C, 550 ppm [pCO2]) for species from polar regions experiencing rapid rates of climate change. This dataset is included in the OA-ICC data compilation maintained in the framework of the IAEA Ocean Acidification International Coordination Centre (see https://oa-icc.ipsl.fr). Original data were downloaded from Polar Data Centre (see Source) by the OA-ICC data curator. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2024) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2024-07-11.
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
Der Klimawandel während der mittelalterlichen Klimaanomalie (MCA) und der kleinen Eiszeit (LIA) führte zur Ausdehnung bzw. Verringerung der hypoxischen Bodenbedeckung in der Ostsee. Hier schlagen wir eine Modellierungsstudie vor, um Mechanismen, durch die der Klimawandel zu den beobachteten Trends geführt hat, systematisch zu analysieren und Modellergebnisse anhand von geochemischen Sedimentkerndaten zu validieren. Das Zusammenspiel zwischen physikalischen und biogeochemischen Prozessen führt zu einer komplexen Dynamik, die den Sauerstoffgehalt in der Ostsee steuert. Die Sedimente spielen eine wichtige Rolle, indem sie sowohl als Quelle als auch als Senke für Phosphat fungieren, das den wichtigsten biolimitierenden Nährstoff bildet. Es ist jedoch kaum bekannt, wie der Klimawandel während der MCA zur Ausbreitung von Hypoxie führte. Es wurden bereits verschiedene Auslöser vorgeschlagen, um die Ausbreitung der Hypoxie während der MCA zu erklären, wie z.B. eine erhöhte Produktion von Cyanobakterien unter wärmeren Bedingungen, eine erhöhte / verringerte Stratifikation aufgrund sich ändernder Niederschlagsmuster und eine sedimentäre Freisetzung von Phosphaten. Im ersten Teil des Projekts (Arbeitspaket AP1) werden wir ein modernes Ökosystemmodell verwenden, um Szenarien zu identifizieren, die den Zusammenhang zwischen Klimawandel und Hypoxie im Mittelalter erklären können. Das Modell wird durch die Implementierung eines frühen diagenetischen Moduls verbessert, das chemische Profile im Sediment vertikal auflösen kann (AP2). Für biogeochemische Reaktionen werden temperaturabhängige Ratenausdrücke implementiert. Das Sedimentmodul wird zunächst auf den aktuellen Zustand der Sedimente kalibriert (AP3). Szenarien aus AP1, die die Sauerstofftrends erfolgreich erklären können, werden anschließend in Modellläufen vom Mittelalter bis zur Gegenwart getestet (AP4). Die Simulation des Mittelalters kann durch verschiedene Sedimentproxies validiert werden, die Trends in den Redoxbedingungen des Tiefenwassers, in der Zufuhr von Metallen aus Schelfe in tiefere Becken, welche die Sequestrierung von Phosphat beeinflusst, und in der Menge an in Sedimenten erhaltenem Phosphor und organischer Substanz rekonstruieren können. Die erwarteten Ergebnisse des Projekts sind die Zuordnung der Ausbreitung von Hypoxie während der MCA zu einem Mechanismus und ein verbessertes Verständnis der Rolle der benthischen Dynamik, die die Eutrophierung als Reaktion auf den Klimawandel beeinflusst.
Das internationale Pool-System fuer Mehrwegfischtransportverpackungen ist aufgebaut und etabliert sich zunehmend im Markt. 1996 konnten ueber 1,6 Mio. Vermietungen von Mehrwegboxen erzielt werden. Zur Zeit wird noch an der Entwicklung einer massgeschneiderten EDV-Loesung fuer unser internationales Mehrwegsystem gearbeitet.
The accurate estimation of nitrous oxide (N2O) emissions and monitoring of nitrate (NO3) leaching in agricultural catchments are critical for contemporary environmental science and policymaking. These issues contribute to climate change and groundwater pollution, necessitating a thorough understanding of underlying processes to develop effective mitigation strategies. Our research aims to develop a robust upscaling procedure for N2O emissions and NO3 leaching at the catchment scale, where mitigation actions are finally applied. This involves an integrated approach spanning three scientific disciplines: 1. Field and laboratory measurements: Utilizing local chamber-based and laboratory-based measurements to assess microbial N cycling fluxes and process rates, providing essential data for process understanding. 2. Remote sensing: Leveraging satellite data with unprecedented spatiotemporal resolution to gather catchment-scale information on geomorphology, topography, land use, standing biomass, and soil water status, enhancing our understanding of the catchment environment. 3. Modelling: Employing a fusion of machine learning techniques and mechanistic modeling, we aim to integrate all information from the collected datasets, facilitating the upscaling of N2O emissions and NO3 leaching to the entire catchment scale. Our work program comprises two interrelated work packages focusing on data collection and modeling. WP 1 Data Collection: Creation of a comprehensive dataset, including N2O and NH3 emissions, NO3 leaching, soil d15N isotopic composition, site preference and d15N-N2O, and lab-based measurements of N process rates such as gross nitrification. This dataset will provide a deeper understanding of microbial N-cycling processes such as nitrification and denitrification and their roles in N2O production and NO3 leaching. Hot spot monitoring: Continuous measurements at model-guided identified N2O emission hot spots, covering potential hot moments such as freeze-thaw periods and fertilization events. WP 2 Modeling: Machine Learning: Extracting knowledge from all collected data to create models predicting N2O emissions and NO3 leaching. Mechanistic modelling: Improving a state-of-the-art biogeochemical model that includes a spatially explicit hydrology model for the lateral flow of water and nutrients. Improving will be particularly based on incorporating isotopic data and an isotopic tracing model. Combining machine learning and mechanistic models to benefit from each other, with mechanistic models enhancing machine learning through providing additional data and machine learning to identify and improve structural deficiencies of the mechanistic model. This interdisciplinary proposal seeks to advance our understanding of N2O emissions and NO3 leaching at the catchment scale, ultimately providing valuable insights for environmental assessment and mitigation strategies in agricultural landscapes.
Raw physical oceanography data was acquired by a ship-based Seabird SBE911+ CTD-Rosette system onboard RV MARIA S. MERIAN during research cruise MSM97/2. The CTD system is comprised of a Seabird SBE911Plus including dual respectively redundant sensor and pump packages. The SBE11plus Deck Unit remains on board in a laboratory and supplies on one hand power to the SBE9plus underwater unit, on the other hand data telemetry between the SBE9plus and a measurement PC. The SBE9plus underwater unit itself holds a pressure sensor and is interfacing with dual SEB3 temperature, SBE4 conductivity and SBE43 oxygen sensors and two SBE5 pumps to provide a pumped water supply past each sensor. The system also carries an optical FLNTU sensor to measure a combinations of back-scattering, turbidity, and chlorophyll-a. To quantify the photo-synthetically active radiation a PAR sensor is installed as well. Water sampling is supported via 24 Niskin water sample bottles holding 10L each, fired via a SBE32 carousel water sampler.
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