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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 28 Nov 2019

Submitted as: research article | 28 Nov 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Insights on Atmospheric Oxidation Processes by Performing Factor Analyses on Sub-ranges of Mass Spectra

Yanjun Zhang1, Otso Peräkylä1, Chao Yan1, Liine Heikkinen1, Mikko Äijälä1, Kaspar R. Daellenbach1, Qiaozhi Zha1, Matthieu Riva1,2, Olga Garmash1, Heikki Junninen1,3, Pentti Paatero1, Douglas Worsnop1,4, and Mikael Ehn1 Yanjun Zhang et al.
  • 1Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00014, Finland
  • 2Univ Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, F-69626, Villeurbanne, France
  • 3Institute of Physics, University of Tartu, Tartu, 50090, Estonia
  • 4Aerodyne Research, Inc., Billerica, MA 01821, USA

Abstract. With the recent developments in mass spectrometry, combined with the strengths of factor analysis techniques, our understanding of atmospheric oxidation chemistry has improved significantly. The typical approach for using techniques like positive matrix factorization (PMF) is to input all measured data for the factorization in order to separate contributions from different sources and/or processes to the total measured signal. However, while this is a valid approach for assigning the total signal to factors, we have identified several cases where useful information can be lost if solely using this approach. For example, gaseous molecules emitted from the same source can show different temporal behaviors due to differing loss terms, like condensation at different rates due to different molecular masses. This conflicts with one of PMF's basic assumptions of constant factor profiles. In addition, some ranges of a mass spectrum may contain useful information, despite contributing only minimal fraction to the total signal, in which case they are unlikely to have a significant impact on the factorization result. Finally, certain mass ranges may contain molecules formed via pathways not available to molecules in other mass ranges, e.g. dimeric species versus monomeric species. In this study, we attempted to address these challenges by dividing mass spectra into sub-ranges and applying the newly developed binPMF method to these ranges separately. We utilized a dataset from a chemical ionization atmospheric pressure interface time-of-flight (CI-APi-TOF) mass spectrometer as an example. We compare the results from these three different ranges, each corresponding to molecules of different volatilities, with binPMF results from the combined range. Separate analysis showed clear benefits in dividing factors for molecules of different volatilities more accurately, in resolving different chemical processes from different ranges, and in giving a chance for high-molecular-weight molecules with low signal intensities to be used to distinguish dimeric species with different formation pathways. In addition, daytime dimer formation (diurnal peak around noon) was identified, which may contribute to NPF in Hyytiälä. Also, dimers from NO3 oxidation were separated by the sub-range binPMF, which would not be identified otherwise. We recommend PMF users to try running their analyses on selected sub-ranges in order to further explore their datasets.

Yanjun Zhang et al.
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Yanjun Zhang et al.
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Short summary
Typical approach for factor analysis with mass spectrometric data is to input all data in factorization, with which we recently identified that useful information can be lost. In this study, we investigated the performance of sub-range analysis of mass spectra. Clear benefits in separating molecules with different volatilities, formation chemistry and signal intensities were found, as well as new insights on atmospheric oxidation processes.
Typical approach for factor analysis with mass spectrometric data is to input all data in...