Articles | Volume 17, issue 23
https://doi.org/10.5194/amt-17-6945-2024
https://doi.org/10.5194/amt-17-6945-2024
Research article
 | 
11 Dec 2024
Research article |  | 11 Dec 2024

Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics

Nicholas D. Beres, Julia Burkart, Elias Graf, Yanick Zeder, Lea Ann Dailey, and Bernadett Weinzierl

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2853', Anonymous Referee #1, 17 Feb 2024
    • RC2: 'Reply on RC1', Anonymous Referee #2, 21 Mar 2024
      • AC2: 'Reply on RC2', Nicholas D. Beres, 08 Jun 2024
    • AC1: 'Reply on RC1', Nicholas D. Beres, 08 Jun 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Nicholas D. Beres on behalf of the Authors (09 Aug 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (12 Aug 2024) by Francis Pope
RR by Anonymous Referee #2 (20 Aug 2024)
ED: Publish as is (27 Sep 2024) by Francis Pope
AR by Nicholas D. Beres on behalf of the Authors (12 Oct 2024)  Manuscript 
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Short summary
We tested a method to identify airborne microplastics (MPs), merging imaging, fluorescence, and machine learning of single particles. We examined whether combining imaging and fluorescence data enhances classification accuracy compared to using each method separately and tested these methods with other particle types. The tested MPs have distinct fluorescence, and a combined imaging and fluorescence method improves their detection, making meaningful progress in monitoring MPs in the atmosphere.