Seasonal effects in the application of the MOMA remote calibration tool to outdoor PM2.5 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 PM_2.5 sensors monitoring near temporally varying pollution sources of PM_2.5. 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. Results showed that the MOMA calibration tool made the PurpleAir PM_2.5 data more comparable to the co-located reference data (calibrated Mean Absolute Error (MAE): $2.8-3.7\mu \text{g}{\text{m}}^{-3}$ , Mean Bias Error (MBE): $-1.8-0.1\mu \text{g}{\text{m}}^{-3}$ ). The improvements were comparable to the Environmental Protection Agency (EPA) correction factor (MAE: $2.8-3.7\mu \text{g}{\text{m}}^{-3}$ , MBE: $-0.9-0.4\mu \text{g}{\text{m}}^{-3}$ ). However, MOMA provided a better estimate of daily average concentrations than the EPA correction factor when compared to the reference data under smoke conditions. Using the MOMA gain, representative of the sensor-proxy relationship, MOMA was able to distinguish between PM sources such as winter wood burning, wildfires, and dust events in the summer.