[A-AS08] Atmospheric Chemistry

[AAS08-12] Artificial intelligence for the exploration of traffic enhanced new particle formation events

*Federica Bortolussi¹, Hilda Sandström^2,7,8, James Brean³, Avinash Kumar⁴, Shawon Barua⁴, Siddharth Iyer⁴, Alex Rowell³, David Beddows³, Kay Weinhold⁵, Peter Mettke⁵, Maik Merkel⁵, Alexandra Karppinen⁴, Alfred Wiedensohler⁵, Miikka Dal Maso⁴, Zongbo Shi³, Roy Harrison^3,6, Patrick Rinke^9,8,7,2, Matti Rissanen^4,1 (1. Department of Chemistry, University of Helsinki, 00560 Helsinki, Finland, 2. Department of Applied Physics, Aalto University, Espoo, 11000, Finland, 3. Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom, 4. Aerosol Physics laboratory, Tampere University, Tampere, 33720, Finland, 5. Leibniz Institute for Tropospheric Research, Leipzig, 04318, Germany, 6. Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, 7. Physics Department, TUM School of Natural Sciences, Technical University of Munich, Garching, Germany, 8. Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, Garching, Germany, 9. Munich Center for Machine Learning (MCML))

Keywords:new particle formation, Traffic, machine learning, artificial intelligence, chemical ionization mass spectrometry, highly oxygenated organic molecules