The research project focuses on the development of a cloud application for identification and tracking of micropollutants in surface waters. The cloud application bases on analytical data from multiple laboratories equipped with high-resolution mass spectrometry devices (HRMS) and thus able to detect even unknown substances using non-target screening (NTS). The primary NTS-result data files delivered from the lab devices will uploaded in the cloud. Within the cloud data will pre-processed, linked and evaluated across laboratories by algorithms and artificial intelligence.
Surface waters serve directly or indirectly via groundwater as an important resource for drinking water production. However, surface waters are usually contaminated with a variety of micropollutants and microorganisms. The vast majority of all micropollutants is not detectable by conventional analytical methods. In most cases the origin of these substances cannot be assigned to an issuer. In addition surface waters are polluted by micro-organisms.
In this joint research project, a demonstrator of a cloud-based application will be designed, implemented and tested with real water samples. With the interaction of multiple HRMS laboratories, i.e. laboratories with high-resolution mass spectrometry, on the one hand as well as an intelligent NTS-data evaluation approach on the other, sources of known and unknown trace substances in surface waters can be quickly localized and tracked. Analytical data and metadata provided by different laboratories are evaluated in the cloud with assistance of collective intelligence from the water supply laboratories and artificial intelligence. The extent to which advanced microbiological identification methods for bacteria and viruses can also be included in an analogous manner will done by a feasibility study.
