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Monitoring of cyanobacteria and cyanotoxines in surface-waters in rural regions (Cyanoscreen)

Project

Risks

This project contributes to the research aim 'Risks'. Which funding institutions are active for this aim? What are the sub-aims? Take a look:
Risks


Project code: MLUK Brandenburg, 68789/18
Contract period: 01.01.2018 - 31.12.2020
Purpose of research: Applied research
Keywords: surface water, inventory recording

Cyanotoxins occur in practically all surface waters of the Federal Republic of Germany. They are formed by various cyanobacteria and are increasingly observed in waters rich in plant nutrients (especially total phosphorus). In addition, they can also occur in animal watering places during the summer season. If they come into contact with or are absorbed into the human body, they can cause irritation of the skin and mucous membranes, earache, allergic reactions, nausea and vomiting. Acute liver damage caused by cyanotoxins has been described many times for domestic and farm animals as well as wildlife, including fish and birds. In the research project, long-term sampling of model surface waters will be carried out, the environmental conditions, the cyanobacteria species as well as the toxins occurring in the water will be determined and the distribution and stability of the toxins within the food chain will be investigated. Strategies for avoiding and displacing the dangerous cyanobacteria with harmless green algae are being developed, which will contribute to the remediation of water bodies. Within the running research project, continuous sampling from model surface-waters within the Federal State of Brandenburg and systematic analysis of the complex interplay between different environmental parameters are done. Focus is on a description of an inventory of species in the different model waters, including a list of the possible toxins and the current climatic conditions. Furthermore, improvement of cyanobacterial detection will be proven with novel analytical methods. The described sub-populations of the different organisms will be linked to environmental conditions to create a prediction model.

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