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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-59
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-59
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Mar 2019

Research article | 20 Mar 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.

A Novel Approach for Simple Statistical Analysis of High-Resolution 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, 00140, 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. Recent advancements in atmospheric mass spectrometry provide huge amounts of new information, but at the same time present considerable challenges for the data analysts. High-resolution (HR) peak identification and separation can be effort- and time-consuming, yet still tricky and inaccurate due to the complexity of overlapping peaks, especially at larger mass-to-charge ratios. This study presents a simple and novel method, mass spectral binning combined with positive matrix factorization (binPMF) to address these problems. Different from unit mass resolution (UMR) analysis or HR peak fitting, which represent the routine data analysis approaches for mass spectrometry datasets, binPMF divides the mass spectra into small bins and takes advantage of PMF’s strength in separating different sources or processes based on different temporal patterns. In this study, we applied the novel approach to both ambient and synthetic datasets to evaluate its performance. It not only succeeded in separating overlapping ions, but was found to be sensitive to subtle variations as well. Being fast and reliable, binPMF has no requirement for a priori peak information and can save much time and effort from conventional HR peak fitting, while still utilizing nearly the full potential of HR mass spectra. In addition, we identify several future improvements and applications for binPMF, and believe it will become a powerful approach in the data analysis of mass spectra.

Yanjun Zhang et al.
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Status: closed
Status: closed
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Yanjun Zhang et al.
Yanjun Zhang et al.
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Short summary
Recent advancements in atmospheric mass spectrometry provide huge amounts of new information, but at the same time present considerable challenges for the data analysis, for example, in high-resolution peak identification and separation. To address these problems, this study presents a simple and novel method, which succeeds in analyzing both synthetic and ambient datasets. We believe it will become a powerful approach in the data analysis of mass spectra.
Recent advancements in atmospheric mass spectrometry provide huge amounts of new information,...
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