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
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
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Total article views: 3,578 (including HTML, PDF, and XML)
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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...
