Towards Atmospheric Compound Identification Using Simulated Electron Ionization Mass Spectra

H. Sandström, P. Rinke

Atmospheric particles impact our climate and adversely affect air quality and human health [1,2]. Molecular emissions in the atmosphere can react with ozone and radicals, forming a diverse array of organic compounds that can drive particle formation [3]. 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 [4,5,6]. High-confidence identification relies on matching recorded EI-MS spectra to reference spectra in mass spectral libraries, which contain reference data for known compounds [7]. However, the identification of many atmospheric compounds is limited by a lack of reference data for these species [8, 9]. Here, I will introduce a simulated reference mass spectrometry dataset for atmospheric organic compounds. Using quantum chemistry, molecular dynamics and machine learning-based EI-MS simulation tools [10, 11], we have simulated mass spectra for organic atmospheric compounds. 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.

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