AI-supported Holographic Environmental Water Monitoring

In this project we want to couple the powerful methods of holographic microscopy for water analysis with state of the art IoT and AI methods. We will build and deploy several holographic microscopes to collect and characterize water samples locally at a few strategically chosen locations in the network. These holographic microscopes take samples fully autonomously and image each and every light scattering object in the sample volume. This is done by shining coherent light through the sample where some of the light is scattered by microscopic objects dispersed in the water. This scattered light interferes with the unscattered fraction of the illuminating light on the detector array of the microscope. By numerical back propagation we can locate each and every object in the sample volume that scatteres enough light to be distinguished. By repeating the procedure we can track the objects’ motion. In practice this method allows to characterize what is in the water in terms of plastic objects, plant fragments, sediments, small animals, algae and bacteria. In this project we want to correlate the wealth of information about dispersed objects in water with time series of currently available quality parameters. We will analyse the supply network and current sample collection sites to strategically place our holographic microscopes in the water supply network objective to make the early detection of environmental issues possible.