Articles | Volume 18, issue 15
https://doi.org/10.5194/amt-18-3635-2025
© Author(s) 2025. 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-18-3635-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seasonal effects in the application of the MOment MAtching (MOMA) remote calibration tool to outdoor PM2.5 air sensors
Lena Francesca Weissert
CORRESPONDING AUTHOR
Aeroqual Ltd, 460 Rosebank Road, Te Whau / Avondale, Tāmaki Makaurau / Auckland, 1026, Aotearoa / New Zealand
Geoff Stephen Henshaw
Aeroqual Ltd, 460 Rosebank Road, Te Whau / Avondale, Tāmaki Makaurau / Auckland, 1026, Aotearoa / New Zealand
Andrea Lee Clements
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modelling, 109 T.W. Alexander Drive, P.O. Box 12055, Research Triangle Park, NC 27711, USA
Rachelle Monique Duvall
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modelling, 109 T.W. Alexander Drive, P.O. Box 12055, Research Triangle Park, NC 27711, USA
Carry Croghan
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modelling, 109 T.W. Alexander Drive, P.O. Box 12055, Research Triangle Park, NC 27711, USA
retired
Related authors
Lena Francesca Weissert, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori
Atmos. Meas. Tech., 16, 4709–4722, https://doi.org/10.5194/amt-16-4709-2023, https://doi.org/10.5194/amt-16-4709-2023, 2023
Short summary
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.
Lena Francesca Weissert, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori
Atmos. Meas. Tech., 16, 4709–4722, https://doi.org/10.5194/amt-16-4709-2023, https://doi.org/10.5194/amt-16-4709-2023, 2023
Short summary
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.
Karoline K. Barkjohn, Brett Gantt, and Andrea L. Clements
Atmos. Meas. Tech., 14, 4617–4637, https://doi.org/10.5194/amt-14-4617-2021, https://doi.org/10.5194/amt-14-4617-2021, 2021
Short summary
Short summary
Although widely used, air sensor measurements are often biased. In this work we develop a correction with a relative humidity term that reduces the bias and improves consistency between different United States regions. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the US on the AirNow Fire and Smoke Map and has the potential to be successfully used in other air quality and public health applications.
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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.
This study evaluates a remote calibration tool, referred to as MOment MAtching (MOMA), for...