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Distribution and concentration of nutrients, carbon compounds and methane in water samples in the southern German Bight (North Sea) in September 2024 during the MOSES Sternfahrt 12

The 12th Sternfahrt of the ElbeXtreme and MOSES projects took place in 2024 from September 02 to 13, within the area of the German Bight (North Sea). Its objective was to get a more systematic grid of sampling data by spatially integrated onboard sensors. Therefore, the MOSES-laboratory container was installed again. Water samples were taken from the surface with a rosette or via Niskin bottles. The first part of the cruise was conducted by the research vessel (RV) Ludwig Prandtl, starting on the 2nd of September on Heligoland. From there, the crew navigated towards Cuxhaven covering some stations from previous MOSES cruises. For the next days, the ship followed a rectangular track, shifting northward each day, heading towards Heligoland again. Due to strong winds, the sampling stations were reduced to three on the last day. On Heligoland the RV Mya II took over the laboratory container and other sampling equipment for the second part of the cruise. Persistent strong winds delayed the start of the cruise until September 11. Since most of the planned stations were already covered from the RV Ludwig Prandtl, the crew decided to expand the sampling area using a more systematic zig-zag line. With the return of Mya II in the afternoon of the 13th September 2024, the campaign was successfully finished.

Hydrographical time series data of Helgoland, Southern North Sea, since 2013

This dataset contains annual oceanographic observations collected at the Helgoland underwater observatory (54°11.594′ N, 07°52.762′ E; WGS84) from 2013 onwards. Measurements include water temperature, salinity, oxygen saturation, chlorophyll-a concentration, and turbidity. The observatory is located at a water depth of approximately 10 m (± tidal variation) and is equipped with one or more sensors for each measured parameter. Observations are recorded at an hourly temporal resolution. All data have undergone quality control using the four-step plausibility and quality assurance procedure described by Waldmann et al. (2022). The cabled observatory infrastructure and sensor configuration are described in detail on the AWI COSYNA underwater observatory webpage. For comprehensive information on instrumentation, sensor deployment, calibration procedures, and data processing, please refer to the annual metadata documents entitled metadata_heluwobs_yyyy_hydrography.pdf associated with each yearly dataset.

Continuous current observations near DynaCom experimental islands in the back-barrier tidal flat, Spiekeroog, Germany, 2019-11 to 2023-09

Data presented here were collected between November 2019 to September 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) 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). A recording current meter (RCM; SEAGUARD® Recording Current Meter, Aanderaa Data Instruments AS, Bergen/Norway) was installed in the back-barrier tidal flat near the experimental islands. The sensor was bottom-mounted in a shallow tidal creek (0.59 m NHN) using a steel girder buried in the sediment, which caused the sensor to be exposed during low tide. All low-tide data have been removed from the dataset. The system was equipped with a ZPulse Doppler Current Sensor (DCS), a conductivity sensor, an oxygen optode, and two analogue sensors for chlorophyll-a and turbidity (16445). All sensors were pre-calibrated by the manufacturer. Recorded data were internally logged until readout with the SeaGuard Studio software (V1.5.23). Salinity was derived in the SeaGuard Studio software using temperature-dependent, nonlinear seawater conductivity compensation following the Practical Salinity Scale (PSS-78). Subsequent data processing was done using MATLAB (R2024b). Turbidity and chlorophyll-a data were excluded from the final dataset, as the recorded signals show implausible values and did not pass quality-control criteria. Post-processing and quality control included (a) the removal of low tide data, data covering maintenance activities, and data affected by biofouling, (b) the removal of implausible values, c) an outlier detection using the Hampel filter method, and (d) visual checks. Identified outlier were removed and synchronously removed across all associated parameters of the respective sensor.

Sensor.Community Sensor 85269

Die Messwerte wurden mittels des Sensor-Modells "Sensirion AG SPS30" erfasst.

Sensor.Community Sensor 22582

Die Messwerte wurden mittels des Sensor-Modells "Bosch BME280" erfasst.

Continuous wave and tide observations at DynaCom artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2019-01 to 2019-12

Data presented here were collected between January 2019 to December 2019 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

Messung des spektralen Extinktionskoeffizienten atmosphärischer Aerosole bei Umgebungsbedingungen und Charakterisierung der hygroskopischen Eigenschaften in Quellregionen (beeinflusst durch: MARINE - DUST - SMOKE) mit Hilfe von SÆMS, LIDAR und in-situ Methoden.

Im Rahmen des Projektes soll der spektrale Extinktionskoeffizient des unbeeinflussten atmosphärischen Aerosols bei 3 Wellenlängen (405, 532, 850 nm) mit einem neu entwickelten portablen SAEMS (Spectral Aerosol Extinction Monitoring System) in Kombination mit einem Ramanlidar untersucht werden. Dieses Fernmessverfahren soll weltweit in ausgewählten Quellregionen mit charakteristischen, unterschiedlichen Aerosoltypen (marines Aerosol, Mineralstaubaerosol, Biomasseverbrennungsaerosol) und natürlichen Aerosolmischungen eingesetzt werden. Das Messsystem wird mit Feuchte- und Temperatursensoren ausgestattet sein damit speziell auch die Änderung der optischen Eigenschaften der atmosphärischen Partikel bei realistischen Umgebungsfeuchtebedingungen erfasst wird. Die Wegstreckenauswahl dieses Aufbaus wird für die jeweiligen Aerosolbedingungen optimiert (horizontale Messstreckenlänge 1-3 km, 10-20 m über dem Boden). Die hygroskopischen Eigenschaften des Aerosols im Umgebungsfeuchtebereich sollen spezifisch für den jeweiligen Einsatzort bestimmt werden und eine aerosoltypabhängige Parametrisierung erarbeitet werden. Ziel ist langfristig die Charakterisierung der Aerosolsäule durch Kombination von bodengebundenen in-situ Methoden und Fernmessverfahren und sowie eine systematische Untersuchung des Feuchteeinflusses auf die aerosoloptischen Eigenschaften. Dazu soll die automatisierte Ermittlung des Extinktionskoeffizienten im bodennahen Bereich in den kontinuierlichen Messbetrieb der mobilen Landstation LACROS (Leipzig Aerosol and Cloud Remote Observations System) des TROPOS integriert werden. Derartige Untersuchungen sind von großer Wichtigkeit für die Klimamodellierung (Parametrisierung der Strahlungswechselwirkung von Aerosolen) sowie der Synthese der aktiven Fernerkundung (Bestimmung der optischen Eigenschaften der Aerosolsäule) und der mikrophysikalischen Eigenschaften durch in-situ Bodenmessungen.

REIN-project: Air quality data from gas and particle sensors at the river Rhine in Koblenz, Germany

This dataset provides data from various air quality sensors at a floating platform of the Federal Institute of Hydrology at the river bank of the Rhine in Koblenz, Germany. The data was collected as part of the mFUND project "REIN" which investigated the suitability of low-cost sensors for the determination of ship emissions with high spatial and temporal resolution. Measurement data involves various pollutants, including nitrogen oxides (NO, NO2), carbon dioxde (CO2) and particulate matter (particle number concentration, mass concentration, size distribution, ultrafine particles, black carbon concentration). Averaged data is provided at 1 minute and 1 hour temporal resolution and covers both low-cost and standard instruments. The dataset is related to the publication "Applicability of compact low- and mid-cost sensors to monitor air pollutant emissions from in-land ships".

Digital GreenTech 2 - DIWA: Digitale, vernetzte und interaktive Wasserqualitätsüberwachung, ein Konzept für autonome Frühwarnsysteme zum Gewässerschutz

Sensor.Community Sensor 77389

Die Messwerte wurden mittels des Sensor-Modells "Nova Fitness SDS011" erfasst.

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