STRAPS v1.0: evaluating a methodology for predicting electron impact ionisation mass spectra for the aerosol mass spectrometer

Topping, David O.; Allan, James; Alfarra, M. Rami; Aumont, Bernard

doi:https://doi.org/10.5194/gmd-10-2365-2017

Articles | Volume 10, issue 6

https://doi.org/10.5194/gmd-10-2365-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-10-2365-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 6

Methods for assessment of models

| Highlight paper

|

27 Jun 2017

Methods for assessment of models | Highlight paper |

| 27 Jun 2017

STRAPS v1.0: evaluating a methodology for predicting electron impact ionisation mass spectra for the aerosol mass spectrometer

David O. Topping, James Allan, M. Rami Alfarra, and Bernard Aumont

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Final revised paper (published on 27 Jun 2017)
Preprint (discussion started on 17 Jan 2017)

Interactive discussion

Status: closed

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

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RC1: 'Promising concept.', Anonymous Referee #1, 17 Feb 2017
- AC1: 'Author response', David Topping, 11 Apr 2017
RC2: 'Referee Review: Topping et al., 2017', Anonymous Referee #2, 18 Feb 2017
- AC2: 'Author response', David Topping, 11 Apr 2017

Peer-review completion

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

AR by Svenja Lange on behalf of the Authors (18 Apr 2017) Author's response Manuscript

ED: Publish as is (05 May 2017) by Alexander Archibald

Short summary

Our ability to model the chemical and thermodynamic processes that lead to secondary organic aerosol (SOA) formation is thought to be hampered by the complexity of the system. In this proof of concept study, the ability to train supervised methods to predict electron impact ionisation (EI) mass spectra for the AMS is evaluated to facilitate improved model evaluation. The study demonstrates the use of a methodology that would be improved with more training data and data from simple mixed systems.