Atmospheric number of AI and CI
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Al Air pollution and climate change are in the heart of research done by the ATMOS group at the University of Oulu. The atmosphere and its processes are studied with computational methods utilizing measurement data and theoretical knowledge of physics, chemistry and meteorology. We use Machine Learning methods to leverage and improve the performance of traditional physical models, to enable more accurate predictions, and to provide us with new insights to the large pool of digital data that is continuously produced with measurements around the world.
Cl Chemical ionization Orbitrap mass spectrometry (CI-Orbitrap) represents a promising new technique for gas-phase analysis in analytical and atmospheric chemistry mainly due to its very high mass resolving power. In this work, we performed the first side-by-side comparison between a CI-Orbitrap and the widely used atmospheric pressure interface time-of-flight mass spectrometry (CI-APi-TOF) using two different chemical ionization methods, i.e., acetate-ion-based (CH3COO–) and aminium-ion-based (n-C3H7NH3+) schemes. The capability of the CI-Orbitrap at accurately measuring low concentrations of gaseous species formed from the oxidation of α-pinene was explored. Although this study reveals a lack of linearity of the CI-Orbitrap when measuring product ions at very low concentrations (<1 × 106 molecules cm–3), very good agreement between both techniques can be achieved by applying a newly developed linearity correction. It is experimentally shown that the correction function is independent of the reagent ion used. Thus, accurate quantification of organic compounds at concentrations as low as 1 × 105 molecules cm–3 by the CI-Orbitrap can be achieved. Finally, by means of tandem mass spectrometry, the unique capability of the Orbitrap allows the direct determination of the binding energy of cluster ions between analyte and reagent ions, that is needed for the assessment of a chosen ionization scheme.
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