Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations

Bürki, Charlotte; Reggente, Matteo; Dillner, Ann M.; Hand, Jenny L.; Shaw, Stephanie L.; Takahama, Satoshi

doi:https://doi.org/10.5194/amt-13-1517-2020

Articles | Volume 13, issue 3

https://doi.org/10.5194/amt-13-1517-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-1517-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 3

Research article

|

31 Mar 2020

Research article |

| 31 Mar 2020

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations

Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama

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Final revised paper (published on 31 Mar 2020)
Supplement to the final revised paper
Preprint (discussion started on 11 Nov 2019)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'amt-2019-333 review', Anonymous Referee #1, 10 Dec 2019
- AC1: 'Response', Satoshi Takahama, 05 Feb 2020
RC2: 'Review', Anonymous Referee #2, 28 Dec 2019
- AC2: 'Response', Satoshi Takahama, 05 Feb 2020
RC3: 'review', Anonymous Referee #3, 06 Jan 2020
- AC3: 'Response', Satoshi Takahama, 05 Feb 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Satoshi Takahama on behalf of the Authors (05 Feb 2020) Author's response Manuscript

ED: Publish as is (24 Feb 2020) by Hartmut Herrmann

AR by Satoshi Takahama on behalf of the Authors (02 Mar 2020) Author's response Manuscript

Short summary

Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.