Articles | Volume 17, issue 4
https://doi.org/10.5194/acp-17-3165-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/acp-17-3165-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Mar 2017
Research article |
| 01 Mar 2017
Resolving anthropogenic aerosol pollution types – deconvolution and exploratory classification of pollution events
Mikko Äijälä
Department of Physics, University of Helsinki, Helsinki, Finland
Liine Heikkinen
Department of Physics, University of Helsinki, Helsinki, Finland
Roman Fröhlich
Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen,
Switzerland
Francesco Canonaco
Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen,
Switzerland
André S. H. Prévôt
Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen,
Switzerland
Heikki Junninen
Department of Physics, University of Helsinki, Helsinki, Finland
Tuukka Petäjä
Department of Physics, University of Helsinki, Helsinki, Finland
Markku Kulmala
Department of Physics, University of Helsinki, Helsinki, Finland
Douglas Worsnop
Department of Physics, University of Helsinki, Helsinki, Finland
Aerodyne Research Inc., Billerica, MA, USA
Department of Physics, University of Helsinki, Helsinki, Finland
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Latest update: 17 Apr 2024
Short summary
Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesising this “raw” data into chemical information necessitates the use of advanced, statistics-based data analysis techniques. Here we present an example of combining data dimensionality reduction (factorisation) with exploratory classification (clustering) and show that the results complement and broaden our current perspectives on aerosol chemical classification.
Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of...
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