Machine learning for improved data analysis of biological aerosol using the WIBS

Ruske, Simon; Topping, David O.; Foot, Virginia E.; Morse, Andrew P.; Gallagher, Martin W.

doi:https://doi.org/10.5194/amt-11-6203-2018

Articles | Volume 11, issue 11

https://doi.org/10.5194/amt-11-6203-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-6203-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 11

Research article

|

19 Nov 2018

Research article |

| 19 Nov 2018

Machine learning for improved data analysis of biological aerosol using the WIBS

Simon Ruske, David O. Topping, Virginia E. Foot, Andrew P. Morse, and Martin W. Gallagher

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Final revised paper (published on 19 Nov 2018)
Preprint (discussion started on 18 Jun 2018)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Reviewer Baumgardner', Darrel Baumgardner, 09 Jul 2018
- AC1: 'Response to Darrell Baumgardner', S. Ruske, 14 Sep 2018
RC2: 'evaluation of analysis techniques for single particle fluorescence measurements made with the WIBS', Anonymous Referee #2, 12 Jul 2018
- AC1: 'Response to Darrell Baumgardner', S. Ruske, 14 Sep 2018

Peer-review completion

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

AR by S. Ruske on behalf of the Authors (15 Oct 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (17 Oct 2018) by Mingjin Tang

RR by Darrel Baumgardner (19 Oct 2018)

ED: Publish as is (26 Oct 2018) by Mingjin Tang

AR by S. Ruske on behalf of the Authors (05 Nov 2018)

Short summary

Pollen, bacteria and fungal spores are common in the environment, can have very important implications for public health and may influence the weather. Biological sensors potentially could be used to monitor quantities of these types of particles. However, it is important to transform the measurements from these instruments into counts of these biological particles. The paper tests a variety of approaches for achieving this aim on data collected in a laboratory.