Articles | Volume 21, issue 13
https://doi.org/10.5194/acp-21-10081-2021
https://doi.org/10.5194/acp-21-10081-2021
Research article
 | 
06 Jul 2021
Research article |  | 06 Jul 2021

Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

Liine Heikkinen, Mikko Äijälä, Kaspar R. Daellenbach, Gang Chen, Olga Garmash, Diego Aliaga, Frans Graeffe, Meri Räty, Krista Luoma, Pasi Aalto, Markku Kulmala, Tuukka Petäjä, Douglas Worsnop, and Mikael Ehn

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Liine Heikkinen on behalf of the Authors (17 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 May 2021) by Hang Su
RR by Anonymous Referee #2 (26 May 2021)
ED: Publish subject to technical corrections (08 Jun 2021) by Hang Su
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Short summary
In many locations worldwide aerosol particles have been shown to be made up of organic aerosol (OA). The boreal forest is a region where aerosol particles possess a high OA mass fraction. Here, we studied OA composition using the longest time series of OA composition ever obtained from a boreal environment. For this purpose, we tested a new analysis framework and discovered that most of the OA was highly oxidized, with strong seasonal behaviour reflecting different sources in summer and winter.
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