Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Julian Schade
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
Thomas Adam
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Günther Dollinger
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Ralf Zimmermann
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
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This research aims to develop a novel warning system for the real-time monitoring of pollutants in the atmosphere. The system is capable of sampling and investigating airborne aerosol particles on-site, utilizing artificial intelligence to learn their chemical signatures and to classify them in real time. We applied single-particle mass spectrometry for analyzing the chemical composition of aerosol particles and suggest several supervised algorithms for highly reliable automatic classification.
This research aims to develop a novel warning system for the real-time monitoring of pollutants...
