Identification of Dust-Dominated Periods and a PM_2.5 Correction Based Solely on Plantower PMS Sensor Observations

Abstract. The Plantower PMS5003/6003 sensor is widely used for low-cost monitoring of particulate matter (PM), but it substantially underestimates PM_2.5 and PM₁₀ during dust events. This limitation is especially critical in the arid regions, such as the western United States, where windblown dust frequently degrades air quality, visibility, and public health. Identification of dust episodes typically relies on federal reference or equivalent method (FRM/FEM) monitors, often supplemented with satellite or meteorological data, but these resources have limited spatial resolution. This study examines whether dust episodes can be directly identified from PMS5003 sensor outputs. We analyzed measurements from 109 PMS sensors collocated or nearby 75 U.S. EPA monitoring sites with hourly FEM PM_2.5 and/or PM₁₀ between January 2017 to May 2025. Two dust-event cutoff thresholds (threshold1 and threshold2) were developed using the sensor-reported ratio of coarse (2.5–10 µm) to ultrafine (0.3–1 µm) mass concentration, and relative humidity, to identify potential dust events. The thresholds can be used in real time, relying on the preceding 336 hourly measurements (consistent with PurpleAir’s public archive display). To improve PM_2.5 estimates from the PMS sensor (pm2.5_alt, a common correction for Plantower PMS measurements reported by PurpleAir), this study used pm2.5_alt measurements identified as potential dust events to develop a correction factor for dust events through non-linear regression. This correction reduced the mean bias error between pm2.5_alt and FEM PM_2.5 for 103 of the 109 sensors by 59.8 ± 24.3 %, while ensuring that the mean bias error did not fall below -1 µg/m³. This framework enhances the utility of PMS5003/6003 sensors for detecting and quantifying dust-related pollution episodes, extending monitoring capabilities in regions where regulatory coverage is limited.