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Aerosol mass spectrometry produces large amounts of complex data, the analysis of which necessitates chemometrics – the application of advanced statistical and mathematical tools to chemical data. Here, we perform a data-driven analysis of multiple aerosol mass spectrometric data sets, to show that the traditional separation of organics and inorganics is not necessary. The resulting 7-component aerosol speciation explains 83 % to 96 % of observed variability at our boreal forest experiment site.
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ACP | Articles | Volume 19, issue 6
ACP | Articles | Volume 19, issue 6
Atmos. Chem. Phys., 19, 3645–3672, 2019
https://doi.org/10.5194/acp-19-3645-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 19, 3645–3672, 2019
https://doi.org/10.5194/acp-19-3645-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ACP | Articles | Volume 19, issue 6

Research article 21 Mar 2019

Research article | 21 Mar 2019

Constructing a data-driven receptor model for organic and inorganic aerosol – a synthesis analysis of eight mass spectrometric data sets from a boreal forest site

Mikko Äijälä et al.

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Short summary
Aerosol mass spectrometry produces large amounts of complex data, the analysis of which necessitates chemometrics – the application of advanced statistical and mathematical tools to chemical data. Here, we perform a data-driven analysis of multiple aerosol mass spectrometric data sets, to show that the traditional separation of organics and inorganics is not necessary. The resulting 7-component aerosol speciation explains 83 % to 96 % of observed variability at our boreal forest experiment site.
Read more
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Final-revised paper
Preprint
Atmospheric Chemistry and Physics

An interactive open-access journal of the European Geosciences Union

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