Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets

Wyche, K. P.; Monks, P. S.; Smallbone, K. L.; Hamilton, J. F.; Alfarra, M. R.; Rickard, A. R.; McFiggans, G. B.; Jenkin, M. E.; Bloss, W. J.; Ryan, A. C.; Hewitt, C. N.; MacKenzie, A. R.

doi:https://doi.org/10.5194/acp-15-8077-2015

Articles | Volume 15, issue 14

Atmos. Chem. Phys., 15, 8077–8100, 2015

https://doi.org/10.5194/acp-15-8077-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Atmos. Chem. Phys., 15, 8077–8100, 2015

https://doi.org/10.5194/acp-15-8077-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 15, issue 14

Research article 22 Jul 2015

Research article | 22 Jul 2015

Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets

K. P. Wyche et al.

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Final revised paper (published on 22 Jul 2015)
Supplement to the final revised paper
Preprint (discussion started on 20 Jan 2015)

Interactive discussion

Status: closed

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

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RC C391: 'Referee Comment', Anonymous Referee #1, 26 Feb 2015
- AC C2541: 'Final authour response to referee 1 and 2 comments', K. P. Wyche, 13 May 2015
RC C611: 'Referee Comment', Anonymous Referee #2, 10 Mar 2015
- AC C2543: 'Final authour response to referee 1 and 2 comments', K. P. Wyche, 13 May 2015

Peer-review completion

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

AR by K. P. Wyche on behalf of the Authors (14 May 2015) Author's response Manuscript

ED: Publish as is (18 May 2015) by V. Faye McNeill

Short summary

This paper describes a new ensemble methodology for the statistical analysis of atmospheric gas- & particle-phase composition data sets. The methodology reduces the huge amount of data derived from many chamber experiments to show that organic reactivity & resultant particle formation can be mapped into unique clusters in statistical space. The model generated is used to map more realistic plant mesocosm oxidation data, the projection of which gives insight into reactive pathways & precursors.