- 1Department of Applied Physics, Aalto University, Espoo, Finland (hilda.sandstrom@aalto.fi, patrick.rinke@aalto.fi)
- 2Physics Department, TUM School of Natural Sciences, Technical University of Munich, Garching, Germany (patrick.rinke@aalto.fi)
- 3Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, Garching, Germany (patrick.rinke@aalto.fi)
Atmospheric particles impact our climate and adversely affect air quality and human health (IPCC, 2022; Pozzer et al., 2023). Molecular emissions in the atmosphere can react with ozone and radicals, forming a diverse array of organic compounds that can drive particle formation (Bianchi et al., 2019). However, due to the vast number of potential reactions and precursors, the identities of many of these particle-forming products remain largely unknown. Electron ionization mass spectrometry (EI-MS) is a widely used tool for identifying organic compounds in aerosol particle samples (Franklin et al., 2022; Worton et al., 2017; Hamilton et al., 2004). High-confidence identification relies on matching recorded EI-MS spectra to reference spectra in mass spectral libraries, which contain reference data for known compounds (Laskin et al., 2018). However, the identification of many atmospheric compounds is limited by a lack of reference data for these species (Nozière et al., 2015; Sandström et al., 2024).
In this presentation, I will introduce a simulated reference mass spectrometry dataset for atmospheric organic compounds. Using quantum chemistry and machine learning-based EI-MS simulation tools (Wei et al., 2019; Koopman & Grimme, 2021), we have simulated mass spectra for organic atmospheric compounds from the Master Chemical Mechanism (MCM v3.2, http://mcm.leeds.ac.uk/MCM, Wang et al., 2017). This simulated mass spectral dataset will be made publicly available to support future efforts to identify atmospheric organic compounds and advance our understanding of organic particle formation processes.
This work was supported by the VILMA (Virtual laboratory for molecular level atmospheric transformations) centre of excellence funded by the Research Council of Finland under grant 346377.
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How to cite: Sandström, H. and Rinke, P.: Towards Atmospheric Compound Identification: A Reference Library of Simulated Electron Ionization Mass Spectra, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10129, https://doi.org/10.5194/egusphere-egu25-10129, 2025.
