Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Cross, Eben S.; Williams, Leah R.; Lewis, David K.; Magoon, Gregory R.; Onasch, Timothy B.; Kaminsky, Michael L.; Worsnop, Douglas R.; Jayne, John T.

doi:https://doi.org/10.5194/amt-10-3575-2017

Articles | Volume 10, issue 9

https://doi.org/10.5194/amt-10-3575-2017

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

https://doi.org/10.5194/amt-10-3575-2017

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

Articles | Volume 10, issue 9

Research article

|

29 Sep 2017

Research article |

| 29 Sep 2017

Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Eben S. Cross, Leah R. Williams, David K. Lewis, Gregory R. Magoon, Timothy B. Onasch, Michael L. Kaminsky, Douglas R. Worsnop, and John T. Jayne

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Final revised paper (published on 29 Sep 2017)
Supplement to the final revised paper
Preprint (discussion started on 05 May 2017)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'ARISense Review', Anonymous Referee #1, 30 May 2017
RC2: 'Review', Anonymous Referee #2, 07 Jul 2017
SC1: 'Review of Cross et al. from CMU', Naomi Zimmerman, 07 Jul 2017
AC1: 'Response_to_Reviewers', Eben Cross, 22 Aug 2017

Peer-review completion

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

AR by Eben Cross on behalf of the Authors (22 Aug 2017) Manuscript

ED: Referee Nomination & Report Request started (23 Aug 2017) by Gavin Phillips

RR by Anonymous Referee #3 (01 Sep 2017)

ED: Publish as is (01 Sep 2017) by Gavin Phillips

AR by Eben Cross on behalf of the Authors (07 Sep 2017)

Short summary

Low-cost air quality sensor technologies offer new opportunities for fast and distributed measurements of air pollution, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. We present results from a newly developed integrated AQ-sensor system (ARISense) and demonstrate the utility of using high-dimensional model representation to improve the conversion of raw sensor signal to ambient concentration.