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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Oct 2023
Research article |
| 18 Oct 2023
| 18 Oct 2023
Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors
Lena Francesca Weissert
CORRESPONDING AUTHOR
Aeroqual Ltd, 460 Rosebank Road, Avondale, Tāmaki Makaurau/Auckland, 1026, Aotearoa/New Zealand
Geoff Steven Henshaw
Aeroqual Ltd, 460 Rosebank Road, Avondale, Tāmaki Makaurau/Auckland, 1026, Aotearoa/New Zealand
David Edward Williams
School of Chemical Sciences and MacDiramid Institute for Advanced Materials and Nanotechnology, University of Auckland, Private Bag 92019, Tāmaki Makaurau/Auckland, 1142, Aotearoa/New Zealand
Brandon Feenstra
South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765, USA
Randy Lam
South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765, USA
Ashley Collier-Oxandale
South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765, USA
Vasileios Papapostolou
South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765, USA
Andrea Polidori
South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765, USA
Related authors
Lena Francesca Weissert, Geoff Stephen Henshaw, Andrea Lee Clements, Rachelle Monique Duvall, and Carry Croghan
Atmos. Meas. Tech., 18, 3635–3645, https://doi.org/10.5194/amt-18-3635-2025, https://doi.org/10.5194/amt-18-3635-2025, 2025
Short summary
Short summary
This study evaluates a remote calibration tool, referred to as MOment MAtching (MOMA), for calibrating PurpleAir PM2.5 sensors, especially for varying PM sources, and compares it with the Environmental Protection Agency (EPA) correction. MOMA improved the accuracy of PurpleAir sensor data comparable to the EPA correction. Although reliant on nearby reference sites, MOMA offers valuable insights into potential PM sources, thereby increasing the overall value of PurpleAir sensor networks.
Lena Francesca Weissert, Geoff Stephen Henshaw, Andrea Lee Clements, Rachelle Monique Duvall, and Carry Croghan
Atmos. Meas. Tech., 18, 3635–3645, https://doi.org/10.5194/amt-18-3635-2025, https://doi.org/10.5194/amt-18-3635-2025, 2025
Short summary
Short summary
This study evaluates a remote calibration tool, referred to as MOment MAtching (MOMA), for calibrating PurpleAir PM2.5 sensors, especially for varying PM sources, and compares it with the Environmental Protection Agency (EPA) correction. MOMA improved the accuracy of PurpleAir sensor data comparable to the EPA correction. Although reliant on nearby reference sites, MOMA offers valuable insights into potential PM sources, thereby increasing the overall value of PurpleAir sensor networks.
Pawan Gupta, Prakash Doraiswamy, Jashwanth Reddy, Palak Balyan, Sagnik Dey, Ryan Chartier, Adeel Khan, Karmann Riter, Brandon Feenstra, Robert C. Levy, Nhu Nguyen Minh Tran, Olga Pikelnaya, Kurinji Selvaraj, Tanushree Ganguly, and Karthik Ganesan
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-140, https://doi.org/10.5194/amt-2022-140, 2022
Revised manuscript not accepted
Short summary
Short summary
The use of low-cost sensors in air quality monitoring has been gaining interest across all walks of society. We present the results of evaluations of the PurpleAir against regulatory-grade PM2.5. The results indicate that with proper calibration, we can achieve bias-corrected PM2.5 data using PA sensors. Our study also suggests that pre-deployment calibrations developed at local or regional scales are required for the PA sensors to correct data from the field for scientific data analysis.
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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.
We apply a previously developed remote calibration framework to a network of particulate matter...
