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CO2 storage potential of the Middle Buntsandstein Subgroup - EEZ of the German North Sea

The CO2 storage potential of the Middle Buntsandstein Subgroup within the Exclusive Economic Zone (EEZ) of the German North Sea was analysed within the framework of the GEOSTOR-Project. A total of 71 potential storage sites were mapped based on existing 3D models, seismic and well data. Static CO2 capacities were calculated for each structure using Monte Carlo simulations with 10,000 iterations to account for uncertainties. All potential reservoirs were evaluated based on their static capacity, burial depth, top seal integrity and trap type. Analysis identified 38 potential storage sites with burial depths between 800 m and 4500 m, reservoir capacities (P50) above 5 Mt CO2 and suitable sealing units. The best storage conditions are expected on the West Schleswig Block where salt-controlled anticlines with moderate burial depths, large reservoir capacities and limited lateral flow barriers are the dominant trap types. Relatively poor storage conditions can be anticipated for small (P50 <5 Mt CO2), deeply buried (> 4500 m) and structurally complex potential storage sites in the Horn and Central Graben. For more detailed information on the methodology and findings, please refer to the full publication: Fuhrmann, A., Knopf, S., Thöle, H., Kästner, F., Ahlrichs, N., Stück, H. L., Schlieder-Kowitz, A. und Kuhlmann, G. (2024) CO2 storage potential of the Middle Buntsandstein Subgroup - German sector of the North Sea. Open Access International Journal of Greenhouse Gas Control, 136 . Art.Nr. 104175. DOI 10.1016/j.ijggc.2024.104175

Zeitliche und räumliche Dynamik der Mutationslast des Antarktischen Prädatoren Arctocephalus gazella

Die Resilienz natürlicher Populationen gegen Umweltveränderungen wird von der Menge schädlicher Mutationen in der Population, d.h. ihrer Mutationslast bestimmt. Deren Fitnesseffekt hängt vom Selektionsdruck und der Populationsgröße ab, welche beide in Raum und Zeit veränderlich sind. Auswirkungen dieser Dynamik auf die Mutationslast sind wenig erforscht, was unser Verständnis der Gefährdung von Arten durch Umweltveränderungen behindert. Drastische Reduzierungen der Populationsgröße führen zu Inzucht, was die Mutationslast stärker exponiert und selektiv wirksam macht. Dies verursacht eine Fitness-Reduktion betroffener Individuen, ermöglicht aber auch eine Entfernung schädlicher Mutationen durch Purging, was die Mutationslast langfristig verringern kann. Folglich hat Übernutzung in der Vergangenheit die Mutationslast vieler Arten beeinflusst, vor allem in der Antarktis, wo Robben- und Walfang große ökologische Auswirkungen hatten. In meinem Projekt plane ich durch Genomsequenzierung räumliche und zeitliche Dynamiken der Mutationslast in Antarktischen Pelzrobben zu erforschen. Dabei verfolge ich drei komplementäre Ziele. Erstens werde ich räumliche Dynamiken der Mutationslast durch den Vergleich von sechs Populationen mit unterschiedlicher effektiver Populationsgröße und Geschichte untersuchen. Erkenntnisse zur Mutationslast dieser Populationen liefern Aufschluss über deren Gefährdung durch Umweltveränderungen. Zweitens werde ich Langzeit-Dynamiken der Mutationslast durch eine Quantifizierung von Purging zu verschiedenen Zeitpunkten der Populationsgeschichte analysieren. Ein Vergleich von Regionen innerhalb des Genoms welche vor, während und nach dem durch Robbenjagd verursachten Flaschenhals von Inzucht betroffen waren, wird über das genetische Erbe dieses Eingriffes aufklären. Schließlich werde ich kurzfristige Dynamiken der Mutationslast untersuchen, indem ich den Umwelteinfluss auf die Mutationslast einer rückläufigen Population in Südgeorgien analysieren werde. Dort hat sich der Selektionsdruck auf die Robben durch den von Erwärmung getriebenen Rückgang des Antarktischen Krills erhöht. Eine einzigartige, vier Jahrzehnte umfassende Langzeitstudie erlaubt hier die Erforschung des Zusammenhangs der Mutationslast und dem Fitnessmerkmal „Rekrutierungs-Erfolg“. Dies kann zeigen, ob aktuelle Umweltveränderungen die Mutationslast durch Purging verringern, was für die Beständigkeit der Populationen relevant ist. Mein Projekt kombiniert hochauflösende genomische Verfahren mit einem herausragenden Untersuchungssystem und verspricht neue Erkenntnisse zur Beständigkeit eines antarktischen Prädatoren. Diese sind essentiell für das Verständnis der Resilienz des Ökosystems des Südpolarmeeres. Durch die Einführung moderner genomischer Methoden in ein polares Modellsystem werde ich zum SPP beitragen können, zudem werde ich mich durch Kollaborationen und das Ausrichten eines Workshops zu reproduzierbarem coding in die breitere SPP-Gemeinschaft integrieren.

Reservoir-modelling and parametrization of a potential reservoir structure (Pilot area B) in the German North Sea

As part of the CDRmare joint project GEOSTOR (https://geostor.cdrmare.de/), the BGR created detailed static geological 3D models for two potential CO2 storage structures in the Middle Buntsandstein in the Exclusive Economic Zone (EEZ) of the German North Sea and supplemented them with petrophysical parameters (e.g. porosities, permeabilities). The 3D geological model (Pilot area B; ~560 km2) is located in the north-western part of the German North Sea sector, the so-called “Entenschnabel”, an approximately 150 kilometer long and 30 kilometer wide area between the offshore sectors of the Netherlands, Denmark and Great Britain (pilot region B). The model in the Ducks Beak is based on several high-resolution 3D seismic data and geophysical/geological information from four exploration wells. It includes 20 generalized faults and the following 16 horizon surfaces: 1) Sea Floor, 2) Mid Miocene Unconformity, 3) Base Tertiary, 4) Base Upper Cretaceous, 5) Base Lower Cretaceous, 6) Base Upper Jurassic, 7) Base Lower Jurassic, 8) Base Muschelkalk, 9) Base Röt, 10) Base Solling Formation, 11) Base Detfurth Formation, 12) Base Volpriehausen Wechselfolge, 13) Base Volpriehausen Formation, 14) Base Triassic, 15) Base Zechstein, 16) Top Basement. The reservoir formed by sandstones of the Middle Buntsandstein is located within the Mads Graben, which is bounded to the west by the extensive Mads Fault (normal fault). Marine mudstones of the Upper Jurassic and Lower Cretaceous serve as the main seal formations. Petrophysical analyses of all considered well data were conducted and reservoir properties (including porosity and permeability) were calculated to determine the static reservoir capacity for these potential CO2 storage structures. The model parameterized and can be used for further dynamic simulations of storage capacity, geo-risk, and infrastructure analyses, in order to develop a comprehensive feasibility study for potential CO2 storage within the project framework. The 3D models were created by the BGR between 2021 and 2024. SKUA-GOCAD was used as the modeling software. We would like to thank AspenTech for providing licenses for their SSE software package as part of the Academic Program (https://www.aspentech.com/en/academic-program).

CO2 storage potential of the Jurassic within the German Central Graben

Within the framework of the GEOSTOR Project, the CO2 storage potential of the Jurassic succession in the German Central Graben was analysed. Twelve potential trap structures were initially mapped along the base of the Kimmeridge Clay Formation, which serves as the primary seal for potential reservoir sandstones within the Central Graben Subgroup. The Kimmeridge Clay Formation is generally continuously distributed across the German Central Graben, with only localized penetrations by rising salt diapirs. In contrast, the Central Graben Subgroup, serving as a potential reservoir unit, exhibits an uneven distribution across the area, limiting the presence and continuity of reservoir rocks within each trap structure. To further delineate the spatial extent of the mapped reservoir structures, the base of the Central Graben Subgroup was used as an additional reference layer. Due to the intermittent nature of Jurassic sandstones within the Central Graben Subgroup, a subsequent analysis classified each structure based on borehole data to confirm the presence of reservoir sands. Structures were categorized as ‘proven,’ ‘not present,’ or ‘uncertain’ depending on sandstone availability and continuity within the trap. All mapped reservoir structures are buried at depths ranging from 2225 to 3043 meters (apex depth) and are considered closed systems, situated within a complex structural network of salt diapirs, faults, and pinch-outs. Capacity calculations were conducted following the method outlined by Fuhrmann et al. (2024), and the horizons used for mapping are based on the work of Müller et al. (2023) and Thöle et al. (2021). Fuhrmann, A., Knopf, S., Thöle, H., Kästner, F., Ahlrichs, N., Stück, H.L., Schlieder-Kowitz, A., Kuhlmann, G., (2024). CO2 storage potential of the Middle Buntsandstein Subgroup-German sector of the North Sea. International Journal of Greenhouse Gas Control 136. Müller, S.M., Jähne-Klingberg, F., Thöle, H., Jakobsen, F.C., Bense, F., Winsemann, J. & Gaedicke, C. (2023). Jurassic to Lower Cretaceous tectonostratigraphy of the German Central Graben, southern North Sea. – Netherlands Journal of Geosciences, 102: e4. DOI:10.1017/njg.2023.4 Thöle, H., Jähne-Klingberg, F., Doornenbal, H., den Dulk, M., Britze, P. & Jakobsen F. (2021). Deliverable 3.8 – Harmonized depth models and structural framework of the NL-GER-DK North Sea. GEOERA 3DGEO-EU; 3D Geomodeling for Europe; project number GeoE.171.005. Report.

PANGAEA

PANGAEA - Data Publisher for Earth & Environmental Sciences has an almost 30-year history as an open-access library for archiving, publishing, and disseminating georeferenced data from the Earth, environmental, and biodiversity sciences. Originally evolving from a database for sediment cores, it is operated as a joint facility of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) and the Center for Marine Environmental Sciences (MARUM) at the University of Bremen. The the commitment of the hosting institutions ensures FAIRness of published data. Furthermore, PANGAEA guarantees TRUSTed long-term availability (greater than 10 years) of its content. PANGAEA holds a mandate from the World Meteorological Organization (WMO) and is accredited as a World Radiation Monitoring Center (WRMC). It was further accredited as a World Data Center by the International Council for Science (ICS) in 2001 and has been certified with the Core Trust Seal since 2019. The cooperation between PANGAEA and the publishing industry along with the correspondent technical implementation enables the cross-referencing of scientific publications and datasets archived as supplements to these publications. PANGAEA is the recommended data repository of numerous international scientific journals.

Repository der KI-Ideenwerkstatt: robbenblick

# robbenblick A Computer Vision project for object detection and annotation management using YOLOv8, SAHI, and FiftyOne, with the primary aim of counting objects (Robben) in large aerial images. ## Overview This repository provides a complete MLOps pipeline for: * **Data Preparation:** Converting raw CVAT annotations (XML) and large images into a tiled, YOLO-compatible dataset. * **Automated Experiments:** Systematically training and tuning YOLOv8 models. * **Tiled Inference:** Running optimized inference (SAHI) on large, high-resolution images for object counting. * **Evaluation:** Assessing model performance for both detection (mAP) and counting (MAE, RMSE, R²). * **Visualization:** Analyzing datasets and model predictions interactively with FiftyOne. ## Pretrained Model Weights Pretrained model weights are available on Hugging Face: https://huggingface.co/ki-ideenwerkstatt-23/robbenblick/ ## Project Workflow The project is designed to follow a clear, sequential workflow: 1. **Prepare Data (`create_dataset.py`):** Organize your raw images and CVAT `annotations.xml` in `data/raw/` as shown below. ```text data/raw/ ├── dataset_01/ │ ├── annotations.xml │ └── images/ └── dataset_02/ ... ``` Run the script to generate a tiled, YOLO-formatted dataset in `data/processed/` and ground truth count CSVs. 2. **Tune Model (`run_experiments.py`):** Define a set of hyperparameters (e.g., models, freeze layers, augmentation) in `configs/base_iter_config.yaml`. Run the script to train a model for every combination and find the best performer. 3. **Validate Model (`yolo.py`):** Take the `run_id` of your best experiment and run validation on the hold-out `test` set to get **detection metrics (mAP)**. 4. **Infer & Count (`predict_tiled.py`):** Use the best `run_id` to run sliced inference on new, large images. This script generates final counts and visual outputs. 5. **Evaluate Counts (`evaluate_counts.py`):** Compare the `detection_counts.csv` from inference against the `ground_truth_counts.csv` to get **counting metrics (MAE, RMSE)**. 6. **Visualize (`run_fiftyone.py`):** Visually inspect your ground truth dataset or your model's predictions at any stage. ## Configuration This project uses two separate configuration files, managed by `robbenblick.utils.load_config`. * **`configs/base_config.yaml`** * **Purpose:** The single source of truth for **single runs**. * **Used By:** `create_dataset.py`, `predict_tiled.py`, `run_fiftyone.py`, and `yolo.py` (for validation/single-predict). * **Content:** Defines static parameters like data paths (`dataset_output_dir`), model (`model`), and inference settings (`confidence_thresh`). * **`configs/base_iter_config.yaml`** * **Purpose:** The configuration file for **experiments and tuning**. * **Used By:** `run_experiments.py`. * **Content:** Any parameter defined as a **YAML list** (e.g., `model: [yolov8n.pt, yolov8s.pt]`) will be iterated over. `run_experiments.py` will test every possible combination of all lists. ## Environment Setup 1. Clone the repository: ```sh git clone git@github.com:ki-iw/robbenblick.git cd robbenblick ``` 2. Create the Conda environment: ```sh conda env create --file environment.yml conda activate RobbenBlick ``` 3. (Optional) Install pre-commit hooks: ```sh pre-commit install ``` ## Core Scripts & Usage ### `create_dataset.py` * **Purpose:** Converts raw CVAT-annotated images and XML files into a YOLO-compatible dataset, including tiling and label conversion. * **How it works:** * Loads configuration from a config file. * Scans `data/raw/` for dataset subfolders. * Parses CVAT XML annotations and extracts polygons. * Tiles large images into smaller crops based on `imgsz` and `tile_overlap` from the config. * Converts polygon annotations to YOLO bounding box format for each tile. * Splits data into `train`, `val`, and `test` sets and writes them to `data/processed/dataset_yolo`. * Saves a `ground_truth_counts.csv` file in each raw dataset subfolder, providing a baseline for counting evaluation. * **Run:** ```sh # Do a 'dry run' to see statistics without writing files python -m robbenblick.create_dataset --dry-run --config configs/base_config.yaml # Create the dataset, holding out dataset #4 as the test set python -m robbenblick.create_dataset --config configs/base_config.yaml --test-dir-index 4 ``` * **Key Arguments:** * `--config`: Path to the `base_config.yaml` file. * `--dry-run`: Run in statistics-only mode. * `--test-dir-index`: 1-based index of the dataset subfolder to use as a hold-out test set. * `--val-ratio`: Ratio of the remaining data to use for validation. ### `run_experiments.py` * **Purpose:** **This is the main training script.** It automates hyperparameter tuning by iterating over parameters defined in `base_iter_config.yaml`. * **How it works:** * Finds all parameters in the config file that are lists (e.g., `freeze: [None, 10]`). * Generates a "variant" for every possible combination of these parameters. * For each variant, it calls `yolo.py --mode train` as a subprocess with a unique `run_id`. * After all runs are complete, it reads the `results.csv` from each run directory, sorts them by `mAP50`, and prints a final ranking table. * **Run:** ```sh # Start the experiment run defined in the iteration config python -m robbenblick.run_experiments --config configs/base_iter_config.yaml # Run experiments and only show the top 5 results python -m robbenblick.run_experiments --config configs/base_iter_config.yaml --top-n 5 ``` ### `predict_tiled.py` * **Purpose:** **This is the main inference script.** It runs a trained YOLOv8 model on new, full-sized images using Sliced Aided Hyper Inference (SAHI). * **How it works:** * Loads a trained `best.pt` model specified by the `--run_id` argument. * Loads inference parameters (like `confidence_thresh`, `tile_overlap`) from the `base_config.yaml`. * Uses `get_sliced_prediction` from SAHI to perform tiled inference on each image. * Saves outputs, including visualized images (if `--save-visuals`), YOLO `.txt` labels (if `--save-yolo`), and a `detection_counts.csv` file. * **Run:** ```sh # Run inference on a folder of new images and save the visual results python -m robbenblick.predict_tiled \ --config configs/base_config.yaml \ --run_id "best_run_from_experiments" \ --source "data/new_images_to_count/" \ --output-dir "data/inference_results/" \ --save-visuals ``` ### `evaluate_counts.py` * **Purpose:** Evaluates the *counting* performance of a model by comparing its predicted counts against the ground truth counts. * **How it works:** * Loads the `ground_truth_counts.csv` generated by `create_dataset.py`. * Loads the `detection_counts.csv` generated by `predict_tiled.py`. * Merges them by `image_name`. * Calculates and prints key regression metrics (MAE, RMSE, R²) to assess the accuracy of the object counting. * **Run:** ```sh # Evaluate the counts from a specific run python -m robbenblick.evaluate_counts \ --gt-csv "data/raw/dataset_02/ground_truth_counts.csv" \ --pred-csv "data/inference_results/detection_counts.csv" ``` ### `yolo.py` * **Purpose:** The core engine for training, validation, and standard prediction. This script is called by `run_experiments.py` for training. You can use it directly for validation. * **How it works:** * `--mode train`: Loads a base model (`yolov8s.pt`) and trains it on the dataset specified in the config. * `--mode validate`: Loads a *trained* model (`best.pt` from a run directory) and validates it against the `test` split defined in `dataset.yaml`. This provides **detection metrics (mAP)**. * `--mode predict`: Runs standard (non-tiled) YOLO prediction on a folder. * **Run:** ```sh # Validate the 'test' set performance of a completed run python -m robbenblick.yolo \ --config configs/base_config.yaml \ --mode validate \ --run_id "best_run_from_experiments" ``` ### `run_fiftyone.py` * **Purpose:** Visualizes datasets and predictions using FiftyOne. * **How it works:** * `--dataset groundtruth`: Loads the processed YOLO dataset (images and ground truth labels) from `data/processed/`. * `--dataset predictions`: Loads images, runs a specified model (`--run_id`) on them, and displays the model's predictions. * **Run:** ```sh # View the ground truth annotations for the 'val' split python -m robbenblick.run_fiftyone \ --config configs/base_config.yaml \ --dataset groundtruth \ --split val \ --recreate # View the predictions from 'my_best_run' on the 'test' split python -m robbenblick.run_fiftyone \ --config configs/base_config.yaml \ --dataset predictions \ --split test \ --run_id "my_best_run" \ --recreate ``` ### `streamlit_app.py` * **Purpose:** Quick test runs with the trained model of your choice for counting the seals in the image(s) and visualization. * **How it works:** * Loads the selected YOLO model from `runs/detect/`. * Upload images, run model, then displays the counts and model's predictions as image visualization. * **Run:** ```sh # View the ground truth annotations for the 'val' split export PYTHONPATH=$PWD && streamlit run robbenblick/streamlit_app.py ``` ## Recommended Full Workflow 1. **Add Raw Data:** * Place your first set of images and annotations in `data/raw/dataset_01/images/` and `data/raw/dataset_01/annotations.xml`. * Place your second set (e.g., from a different location) in `data/raw/dataset_02/images/` and `data/raw/dataset_02/annotations.xml`. 2. **Create Dataset:** * Run `python -m robbenblick.create_dataset --dry-run` to see your dataset statistics. Note the indices of your datasets. * Let's say `dataset_02` is a good hold-out set. Run: `python -m robbenblick.create_dataset --config configs/base_config.yaml --test-dir-index 2` * This creates `data/raw/dataset_02/ground_truth_counts.csv` for later. 3. **Find Best Model:** * Edit `configs/base_iter_config.yaml`. Define your experiments. ```yaml # Example: Test two models and two freeze strategies model: ['yolov8s.pt', 'yolov8m.pt'] freeze: [None, 10] yolo_hyperparams: scale: [0.3, 0.5] ``` * Run the experiments: `python -m robbenblick.run_experiments`. * Note the `run_id` of the top-ranked model, e.g., `iter_run_model_yolov8m.pt_freeze_10_scale_0.3`. 4. **Validate on Test Set (Detection mAP):** * Check your best model's performance on the unseen test data: `python -m robbenblick.yolo --mode validate --run_id "iter_run_model_yolov8m.pt_freeze_10_scale_0.3" --config configs/base_config.yaml` * This tells you how well it *detects* objects (mAP). 5. **Apply Model for Counting:** * Get a new folder of large, un-annotated images (e.g., `data/to_be_counted/`). * Run `predict_tiled.py`: `python -m robbenblick.predict_tiled --run_id "iter_run_model_yolov8m.pt_freeze_10_scale_0.3" --source "data/to_be_counted/" --output-dir "data/final_counts/" --save-visuals` * This creates `data/final_counts/detection_counts.csv`. 6. **Evaluate Counting Performance (MAE, RMSE):** * Now, compare the predicted counts (Step 5) with the ground truth counts (Step 2). Let's assume your "to_be_counted" folder *was* your `dataset_02`. `python -m robbenblick.evaluate_counts --gt-csv "data/raw/dataset_02/ground_truth_counts.csv" --pred-csv "data/final_counts/detection_counts.csv"` * This gives you the final MAE, RMSE, and R² metrics for your **counting task**. ## Additional Notes This repository contains only the source code of the project. The training data and the fine-tuned model weights are not included or published. The repository is currently not being actively maintained. Future updates are not planned at this time. For transparency, please note that the underlying model used throughout this project is based on **YOLOv8 by Ultralytics**. ## License Copyright (c) 2025 **Birds on Mars**. This project is licensed under the **GNU Affero General Public License v3.0 (AGPL-3.0)**. This aligns with the license of the underlying **YOLOv8** model architecture used in this project. Please note: **Training data and fine-tuned model weights are not part of the licensed materials** and are not included in this repository. For full details, see the LICENSE file. ## Troubleshooting ### FiftyOne: images (partially) not visible Try using `--recreate` flag to force FiftyOne to reload the dataset: ```sh python robbenblick/run_fiftyone.py --dataset groundtruth --split val --recreate ``` ### FiftyOne: failed to bind port If you get: ``` fiftyone.core.service.ServiceListenTimeout: fiftyone.core.service.DatabaseService failed to bind to port ``` Try killing any lingering `fiftyone` or `mongod` processes: ```sh pkill -f fiftyone pkill -f mongod Then rerun your script. ``` # Collaborators The code for this project has been developed through a collaborative effort between [WWF Büro Ostsee](https://www.wwf.de/themen-projekte/projektregionen/ostsee) and [KI-Ideenwerkstatt](https://www.ki-ideenwerkstatt.de), technical implementation by [Birds on Mars](https://birdsonmars.com). <p></p> <a href="https://ki-ideenwerkstatt.de" target="_blank" rel="noopener noreferrer"> <img src="assets/kiiw.jpg" alt="KI Ideenwerkstatt" height="100"> </a> <p></p> Technical realization <br> <a href="https://birdsonmars.com" target="_blank" rel="noopener noreferrer"> <img src="assets/bom.jpg" alt="Birds On Mars" height="100"> </a> <p></p> An AI initiative by <br> <a href="https://www.bundesumweltministerium.de/" target="_blank" rel="noopener noreferrer"> <img src="assets/bmukn.svg" alt="Bundesministerium für Umwelt, Klimaschutz, Naturschutz und nukleare Sicherheit" height="100"> </a> <p></p> In the context of <br> <a href="https://civic-coding.de" target="_blank" rel="noopener noreferrer"> <img src="assets/civic.svg" alt="Civic Coding" height="100"> </a>

Evaluating DBP Formation in Chlorinated Drinking Water: Effects of Contact with System Materials

Climate-driven challenges probably increase disinfection in drinking water systems. Interactions between disinfectants and infrastructure materials remain understudied. This study quantifies the consumption of chlorine, the release of dissolved organic carbon (DOC), and the formation of regulated disinfection byproducts (DBPs) from 20 common materials: polymeric pipes, seals, fittings, epoxy resins, and cement mortar. The materials were pulverized to maximize the surface area and create a worst-case scenario. The results were compared with standardized migration tests. Chlorine depletion (>90%) was observed in the waters exposed to epoxy resins, seals, and cement. The formation of DBP, especially trichloromethane (TCM), exceeded 100 μg/L in samples of epoxy resins, cement, vulcanized fiber, and polyamide; TCM was detected in polyethylene pipe materials at concentrations between 5 and 18 μg/L. Increased temperatures enhanced DOC leaching and THM formation. Relations between DOC and DBP concentrations indicate material leachates as precursors. The results highlight the importance of material selection and testing for disinfected drinking water systems, providing critical insights for risk assessment, material certification, and regulatory development. ©2026 The Authors

Besondere Gebührenverordnung des BMUV für individuell zurechenbare öffentliche Leistungen in dessen Zuständigkeitsbereich (BMUBGebV)

Nichtamtliches Inhaltsverzeichnis § 1 Erhebung von Gebühren und Auslagen (1) Gebühren und Auslagen werden für individuell zurechenbare öffentliche Leistungen (gebührenfähige Leistungen) erhoben, die auf Grund der folgenden Vorschriften erbracht werden: 1. Chemikaliengesetz, auch in Verbindung mit der Verordnung (EU) Nr. 528/2012 des Europäischen Parlaments und des Rates vom 22. Mai 2012 über die Bereitstellung auf dem Markt und die Verwendung von Biozidprodukten (ABl. L 167 vom 27.6.2012, S. 1), die zuletzt durch die Delegierte Verordnung (EU) 2021/407 (ABl. L 81 vom 9.3.2021, S. 15) geändert worden ist, in der jeweils geltenden Fassung und der Verordnung (EU) Nr. 649/2012 des Europäischen Parlaments und des Rates vom 4. Juli 2012 über die Aus- und Einfuhr gefährlicher Chemikalien (ABl. L 201 vom 27.7.2012, S. 60), die zuletzt durch die Delegierte Verordnung (EU) 2020/1068 (ABl. L 234 vom 21.7.2020, S. 1) geändert worden ist, in der jeweils geltenden Fassung, 2. Wasch- und Reinigungsmittelgesetz in Verbindung mit der Verordnung (EG) Nr. 648/2004 des Europäischen Parlaments und des Rates vom 31. März 2004 über Detergenzien (ABl. L 104 vom 8.4.2004, S. 1), die zuletzt durch die Verordnung (EU) Nr. 259/2012 (ABl. L 94 vom 30.3.2012, S. 16) geändert worden ist, in der jeweils geltenden Fassung, 3. Verordnung (EG) Nr. 1013/2006 des Europäischen Parlaments und des Rates vom 14. Juni 2006 über die Verbringung von Abfällen (ABl. L 190 vom 12.7.2006, S. 1), die zuletzt durch die Delegierte Verordnung (EU) 2020/2174 (ABl. L 433 vom 22.12.2020, S. 11) geändert worden ist, in der jeweils geltenden Fassung, 4. Umweltschutzprotokoll-Ausführungsgesetz, 5. Delegierte Verordnung (EU) 2019/1122 der Kommission vom 12. März 2019 zur Ergänzung der Richtlinie 2003/87/EG des Europäischen Parlaments und des Rates im Hinblick auf die Funktionsweise des Unionsregisters (ABl. L 177 vom 2.7.2019, S. 3), die zuletzt durch die Delegierte Verordnung (EU) 2019/1124 (ABl. L 177 vom 2.7.2019, S. 66) geändert worden ist, in der jeweils geltenden Fassung in Verbindung mit dem Treibhausgas-Emissionshandelsgesetz, 6. Trinkwasserverordnung, 7. Upstream-Emissionsminderungs-Verordnung, 8. Verpackungsgesetz, 9. Bundesnaturschutzgesetz, 10. Umweltschadensgesetz, 11. Verordnung (EG) Nr. 338/97 des Rates vom 9. Dezember 1996 über den Schutz von Exemplaren wildlebender Tier- und Pflanzenarten durch Überwachung des Handels (ABl. L 61 vom 3.3.1997, S. 1), die zuletzt durch die Verordnung (EU) 2019/2117 (ABl. L 320 vom 11.12.2019, S. 13) geändert worden ist, in der jeweils geltenden Fassung, 12. Verordnung (EG) Nr. 865/2006 der Kommission vom 4. Mai 2006 mit Durchführungsbestimmungen zur Verordnung (EG) Nr. 338/97 des Rates über den Schutz von Exemplaren wild lebender Tier- und Pflanzenarten durch Überwachung des Handels (ABl. L 166 vom 19.6.2006, S. 1), die zuletzt durch die Verordnung (EU) 2019/220 (ABl. L 35 vom 7.2.2019, S. 3) geändert worden ist, in der jeweils geltenden Fassung, 13. Gesetz zur Umsetzung der Verpflichtungen nach dem Nagoya-Protokoll und zur Durchführung der Verordnung (EU) Nr. 511/2014, 14. Gesetz zu dem Übereinkommen vom 1. Juni 1972 zur Erhaltung der antarktischen Robben, 15. Einwegkunststofffondsgesetz, 16. Verordnung zur Anrechnung von strombasierten Kraftstoffen und mitverarbeiteten biogenen Ölen auf die Treibhausgasquote (37. BImSchV), 17. Verordnung zur Festlegung weiterer Bestimmungen zur Treibhausgasminderung bei Kraftstoffen (38. BImSchV). (2) Für gebührenfähige Leistungen nach Absatz 1 Nummer 9 und 10 in Verbindung mit Abschnitt 9 Nummer 2 und Abschnitt 10 des Gebühren- und Auslagenverzeichnisses in der Anlage gelten die Vorschriften dieser Besonderen Gebührenverordnung nach Maßgabe der Vorgaben des Seerechtsübereinkommens der Vereinten Nationen vom 10. Dezember 1982 (BGBl. 1994 II S. 1798, 1799; 1995 II S. 602) auch im Bereich der deutschen ausschließlichen Wirtschaftszone und des Festlandsockels.

Dissolved inorganic nutrients at station 7, Bülk neu (2021-05 - 2023-01)

In addition to data collection by the logger units, nutrients are measured at 23 locations. Sampling occurs every 14 days at each site. Samples are taken from a water depth of approximately 0.5 meters, filtered (filter size 40 µm), and cooled for further transport. Furthermore, on the sampling day, salinity and temperature are measured at the respective locations using the WTW Cond 3110. After each tour, the water samples are frozen at -20 °C at GEOMAR for later analysis. Since 2020/2021, the analyses have been conducted by Research Area 3: Marine Ecology, FE Experimental Ecology. GEOMAR, Kiel. The samples are analyzed for the concentration of dissolved inorganic nutrients (total oxidized nitrogen, nitrite, ammonium, phosphate and silicate) by UV/VIS spectroscopy using a continuous flow analyzer (type QuAAtro 30; comp. SEAL Analytical, Hamburg, Germany, equipped with a SEAL XY-2 autosampler). Quality control for nutrient measurements is ensured by certified reference material (CRM) by KANSO TECHNOS CO, LTD, Osaka, Japan. Standard analysis methods developed by SEAL Analytical were followed.

Daten-fuer-Dienste (Serie)

Übergeordnete Metadaten für Daten, die speziell für Geodienste bereitgestellt wurden. Die Daten basieren in der Regel auf Monitoring-Daten im schleswig-holsteinischen Wattenmeer. Es werden sowohl aufbereitete Rohdaten, als auch auf ökologische Fragestellungen angepasste Daten bzw. Dienste bereitgestellt, die aus Datenbank-Abfragen generiert wurden. In dieser Serie "Daten-fuer-Dienste" enthalten sind die Metadaten folgender dienstbezogener Datensätze: - Brandgänse: Mauserbestand und Vorkommen ab 2010 - Kegelrobben: Liegeplätze ab 2005 - Kegelrobben: Ruheplätze ab 1989 - Makrophyten-Monitoring: allg. Vorkommen von Seegras und Grünalgen ab 1994 - Menschliche Aktivitäten und Belastungen - MSRL:D5-Eutrophierung: max. Bedeckung von Seegras bzw. Grünalgen pro Jahr - MSRL:D10-Strandmüllmonitoring an der dt. Nordseeküste - Robben: Monitoring: alle Sichtungen während des Robbenmonitorings von Seehunden und Kegelrobben - Seehunde: Liegeplätze ab 1989 - Seehunde: Ruheplätze ab 1989

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