Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 20
Articles | Volume 16, issue 20
https://doi.org/10.5194/amt-16-4709-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-4709-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 20
Research article
 | 
18 Oct 2023
Research article |  | 18 Oct 2023

Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors

Lena Francesca Weissert, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Lena Weissert on behalf of the Authors (31 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Aug 2023) by Albert Presto
RR by Anonymous Referee #1 (09 Aug 2023)
RR by Anonymous Referee #2 (22 Aug 2023)
ED: Publish as is (05 Sep 2023) by Albert Presto
AR by Lena Weissert on behalf of the Authors (06 Sep 2023)
Download
Short summary
We apply a previously developed remote calibration framework to a network of particulate matter (PM) sensors deployed in Southern California. Our results show that a remote calibration can improve the accuracy of PM data, which was particularly visible for PM10. We highlight that sensor drift was mostly due to differences in particle composition than monitor operational factors. Thus, PM sensors may require frequent calibration if PM sources vary with different wind conditions or seasons.
Read more