Articles | Volume 15, issue 8
https://doi.org/10.5194/amt-15-2591-2022
https://doi.org/10.5194/amt-15-2591-2022
Research article
 | 
29 Apr 2022
Research article |  | 29 Apr 2022

Development and application of a supervised pattern recognition algorithm for identification of fuel-specific emissions profiles

Christos Stamatis and Kelley Claire Barsanti

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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 amt-2021-304', Santtu Mikkonen, 03 Dec 2021
    • AC1: 'Reply on RC1', Christos Stamatis, 16 Feb 2022
  • RC2: 'Comment on amt-2021-304', Anonymous Referee #2, 06 Dec 2021
    • AC2: 'Reply on RC2', Christos Stamatis, 16 Feb 2022
  • RC3: 'Comment on amt-2021-304', Anonymous Referee #3, 09 Dec 2021
    • AC3: 'Reply on RC3', Christos Stamatis, 16 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Christos Stamatis on behalf of the Authors (25 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (25 Feb 2022) by Glenn Wolfe
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Short summary
Building on the identification of hundreds of gas-phase chemicals in smoke samples from laboratory and field studies, an algorithm was developed that successfully identified chemical patterns that were consistent among types of trees and unique between types of trees that are common fuels in western coniferous forests. The algorithm is a promising approach for selecting chemical speciation profiles for air quality modeling using a highly reduced suite of measured compounds.