A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

Canonaco, Francesco; Tobler, Anna; Chen, Gang; Sosedova, Yulia; Slowik, Jay Gates; Bozzetti, Carlo; Daellenbach, Kaspar Rudolf; El Haddad, Imad; Crippa, Monica; Huang, Ru-Jin; Furger, Markus; Baltensperger, Urs; Prévôt, André Stephan Henry

doi:https://doi.org/10.5194/amt-14-923-2021

Articles | Volume 14, issue 2

https://doi.org/10.5194/amt-14-923-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-923-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 2

Research article

|

08 Feb 2021

Research article |

| 08 Feb 2021

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

Francesco Canonaco, Anna Tobler, Gang Chen, Yulia Sosedova, Jay Gates Slowik, Carlo Bozzetti, Kaspar Rudolf Daellenbach, Imad El Haddad, Monica Crippa, Ru-Jin Huang, Markus Furger, Urs Baltensperger, and André Stephan Henry Prévôt

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Final revised paper (published on 08 Feb 2021)
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Preprint (discussion started on 17 Jul 2020)
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Interactive discussion

Status: closed

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

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RC1: 'Review', Anonymous Referee #2, 13 Aug 2020
- AC2: 'answers to comments of referee2', Francesco Canonaco, 03 Nov 2020
RC2: 'review of Canonaco et al.', Anonymous Referee #1, 11 Sep 2020
- AC1: 'answers to comments of referee1', Francesco Canonaco, 03 Nov 2020

Peer-review completion

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

AR by Francesco Canonaco on behalf of the Authors (21 Nov 2020) Author's response Manuscript

ED: Publish as is (21 Nov 2020) by Mingjin Tang

AR by Francesco Canonaco on behalf of the Authors (22 Nov 2020)

Short summary

Long-term ambient aerosol mass spectrometric data were analyzed with a statistical model (PMF) to obtain source contributions and fingerprints. The new aspects of this paper involve time-dependent source fingerprints by a rolling technique and the replacement of the full visual inspection of each run by a user-defined set of criteria to monitor the quality of each of these runs more efficiently. More reliable sources will finally provide better instruments for political mitigation strategies.