- 1University of Helsinki, Department of Chemistry, Helsinki, Finland (federica.bortolussi@helsinki.fi)
- 2Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
- 3Aerosol Physics Laboratory, Physics Unit, Tampere University, 33720 Tampere, Finland
- 4Department of Applied Physics, Aalto University, Espoo, Finland
- 5Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- 6Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, Garching, Germany
- 7Munich Center for Machine Learning (MCML)
New particle formation (NPF) is a significant source of atmospheric aerosols. The formation rate (J) quantifies the rate at which new particles form, and can be empirically determined from the particle number size distribution (PNSD). However, identifying the specific causes of NPF in urban areas remains challenging.
It is believed that in urban environments, most NPF occurs through the clustering of sulfuric acid (H2SO4) with bases. However, in a chamber study, it was observed that also the clustering of H2SO4 with highly oxygenated organic molecules (HOMs) may greatly promote NPF (Riccobono, 2014, Science, 344(6185), 717-721).
This research project employs AI data-driven approaches to predict J and examine the role of HOMs in NPF events.
The data were collected in August 2022 in two close sites in Leipzig, Germany: an urban background (Leibniz Institute for Tropospheric Research) and a roadside (Eisenbahnstraße). The data include HOMs, H2SO4, and bases from the nitrate CIMS, PNSD measurements, pollutants such as BC, and meteorological variables. Previous research indicated a higher concentration of OOMs at the roadside (Brean, 2024, Environ. Sci. Technol. 58, 10664−10674), suggesting its potential impact on J.
Preliminary results show that our data-driven model successfully predicted J values on a logarithmic scale with a mean absolute error of 0.33 at the urban background site and 0.63 at the roadside. Further analysis reveals the most significant contributors to predicting J, indicating that alongside H2SO4, various HOMs play a crucial role.
How to cite: Bortolussi, F., Brean, J., Barua, S., Kumar, A., Karppinen, A., Iyer, S., Sandström, H., Shi, Z., Harrison, R., Rinke, P., and Rissanen, M.: Exploring the Role of Highly Oxygenated Organic Molecules in New Particle Formation Events with Explainable Artificial Intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6788, https://doi.org/10.5194/egusphere-egu25-6788, 2025.
