Articles | Volume 18, issue 13
Atmos. Chem. Phys., 18, 9597–9615, 2018
Atmos. Chem. Phys., 18, 9597–9615, 2018

Research article 09 Jul 2018

Research article | 09 Jul 2018

Identification of new particle formation events with deep learning

Jorma Joutsensaari et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jorma Joutsensaari on behalf of the Authors (18 Jun 2018)  Author's response    Manuscript
ED: Publish as is (26 Jun 2018) by Fangqun Yu
Short summary
New particle formation (NPF) in the atmosphere is globally an important source of aerosol particles. NPF events are typically identified and analyzed manually by researchers from particle size distribution data day by day, which is time consuming and might be inconsistent. We have developed an automatic analysis method based on deep learning for NPF event identification. The developed method can be easily utilized to analyze any long-term datasets more accurately and consistently.
Final-revised paper