10:15am - 10:30amWE1-3: 1
A coupled Lagrangian-Equilibrium approach for the simulation of volcanic aerosol plumes
Georgios Efstathiou1, George Papadakis2, Benjamin Devenish3, William Jones1, Stelios Rigopoulos1
1Department of Mechanical Engineering, Imperial College London, London, UK; 2Department of Aeronautics, Imperial College London, London, UK; 3Met Office, Exeter, UK
In this work, we propose a formulation for two-way coupled large eddy simulations of particle laden dilute flows, with the aim of simulating volcanic aerosol plumes.The method comprises a stochastic Lagrangian approach coupled with the equilibrium model for low-inertia particles (Stokes number less than 1). This allows for capturing events in regions where the applicability of the equilibrium model is ambiguous and retain the benefits of using a Eulerian approach, which include facilitating the modelling of microphysical kinetic processes of aerosol interaction such as aggregation, while keeping the computational cost and memory requirements low.
10:30am - 10:45amWE1-3: 2
Towards atmospheric compound identification in chemical ionization mass spectrometry with machine learning
Federica Bortolussi1, Hilda Sandström2, Fariba Partovi3,4, Joona Mikkilä4, Patrick Rinke2,5,6,7, Matti Rissanen1,3
1Department of Chemistry, University of Helsinki, 00560 Helsinki, Finland; 2Department of Applied Physics, Aalto University, Espoo, Finland; 3Aerosol Physics Laboratory, Physics Unit, Tampere University, 33720 Tampere, Finland; 4Karsa Ltd., A. I. Virtasen aukio 1, 00560 Helsinki, Finland; 5Physics Department, TUM School of Natural Sciences, Technical University of Munich, Garching, Germany; 6Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, Garching, Germany; 7Munich Center for Machine Learning (MCML)
Chemical ionization mass spectrometry (CIMS) is essential in atmospheric chemistry research but faces challenges in compound identification due to complex reagent ion-target compound interactions. Quantum chemical calculations can model these interactions, yet the vast configuration space and high costs hinder database creation, preventing a definitive compound identification workflow. This project explores a machine learning (ML) cost-efficient approach for CIMS compound identification. As a first step, the ML workflow developed can predict the detection and signal intensity of known compounds, and map the functional groups likely interacting with the reagent ion.
10:45am - 11:00amWE1-3: 3
The Role of Hygroscopic Properties in Nitrate Formation and Its Impact on Haze in Seoul
Qihua Hu1, Hwajin Kim1,2
1Graduate School of Public Health, Seoul National University; 2Institute of Health and Environment, Seoul National University
Severe PM pollution episodes frequently impact the Seoul Metropolitan Area, with nitrate playing a dominant role. Nitrate formation occurs through complex atmospheric pathways, including gas-phase oxidation, heterogeneous uptake, and aqueous-phase processes. This study employs explainable machine learning to analyze drivers of nitrate formation in Seoul, using high-resolution aerosol mass spectrometry data. Results reveal that nitrate formation sharply increases at RH >65%, persisting even as RH declines, suggesting prolonged aqueous-phase processing. Additionally, temperature exhibits a strong nonlinear effect, with nitrate formation suppressed below freezing due to phase-state constraints. These findings highlight the role of hygroscopic properties in nitrate-driven haze.
11:00am - 11:15amWE1-3: 4
Increasing Impact of Transported Dust to Europe in a Changing Climate
Petros Nikolaou Vasilakos, Abhishek Upadhyay, Jenk Theo Manuel, Anja Eichler, Imad El Haddad, Kaspar Dällenbach
Paul Scherrer Institute, Switzerland
In this work, we present the most complete database of transported metals, along with a machine learning model designed to predict dust concentrations over Europe. The model is validated thoroughly with a combination of long measurement timeseries and ice core records. We find that dust concentrations increased over the period of 2012-2021, driven by more severe episodes, as a consequence of further desertification, which has significant implications both for regulatory purposes but also for public health.
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