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dc.contributor.authorPauw, Viktoria
dc.contributor.authorHayek Mohamad
dc.contributor.authorShojaei, Elham
dc.contributor.authorHachinger, Stephan
dc.contributor.authorMüller, Uwe
dc.contributor.authorBader, Tobias
dc.contributor.editorWohlgemuth, Volker
dc.contributor.editorNaumann, Stefan
dc.contributor.editorArndt, Hans-Knud
dc.contributor.editorBehrens, Grit
dc.contributor.editorHöb, Maximilian
dc.date.accessioned2022-09-19T09:20:47Z
dc.date.available2022-09-19T09:20:47Z
dc.date.issued2022
dc.identifier.isbn978-3-88579-722-7
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/39399
dc.description.abstractTens of thousands of chemicals used by consumers, agriculture and industry enter the aquatic environment as micropollutants every day. Using targeted analysis we are so far only able to detect a small subset of the chemicals that are present. Therefore so called non-target screening (NTS) using liquid chromatography in combination with high-resolution mass spectrometry (LCHRMS) is increasingly used by labs to perform more comprehensive monitoring. However, a high degree of variance in measurements and processing workflows results in low comparability of data from separate laboratories. On one hand this is caused by differences in processing techniques which are due to stationary laboratory equipment and on the other hand by differing priorities in the detection strategy and evaluation workflow. The K2I project funded by BMBF aims at fostering collaboration between laboratories by providing a joint platform for uploading and processing LCHRMS data. A cloud based datalake and processing pipeline is being developed. A standardized processing workflow can then be executed which is enhanced by data mining tools including machine learning techniques. An indexing and searching software is employed to create a web based access to the processed data for participants.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofEnviroInfo 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-328
dc.subjectNon-target screening
dc.subjectLC-HRMS
dc.subjectWater Safety
dc.subjectAI
dc.subjectCloud
dc.subjectEnvironmental Chemistry
dc.titleCloud-based Processing of data from Non-Target-Analysis for Tracking Micropollutants in Surface Wateren
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages15
mci.conference.locationHamburg
mci.conference.date26.-30- September 2022


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