Seasonal effects in the application of the MOMA remote calibration tool to outdoor PM_2.5 air sensors

Abstract. Air sensors are being used more frequently to measure hyper-local air quality. The PurpleAir sensor is among one of the most popular air sensors used worldwide to measure fine particulate matter (PM_2.5). However, there is a need to understand PurpleAir data quality especially under different environmental conditions with varying particulate matter (PM) sources and size distributions. Several correction factors have been developed to make the PurpleAir sensor data more comparable to reference monitor data. The goal of this work was to determine the performance of a remote calibration tool called MOment MAtching (MOMA) for temporally varying PM_2.5 sources. MOMA performs calibrations using reference site data within 0–15 km from the sensor. Data from 20 PurpleAir sensors deployed across a network in Phoenix, Arizona from July 2019 to April 2021 were used. The results showed that the MOMA calibration tool improved the accuracy of PurpleAir sensor data across Phoenix and was comparable to the EPA correction factor with a root mean square error (RMSE) of 4.19 – 7.92 µg m^-3 vs. 4.23 – 9.27 µg m^-3. However, MOMA provided a better estimate of daily exceedances compared to the reference data for smoke conditions. Using speciated PM data, MOMA was able to distinguish between different PM sources such as winter wood burning, and wildfires and dust events in the summer.