Correction of PM 2.5 underestimation in low-cost sensors under elevated dust loading using only sensor measurements
Year: 2026
Type: erratum
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 PM 10 during periods of elevated dust loading, when the particle size distribution is dominated by particles > 1 µm in diameter. 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. Accurate estimation of PM 2.5 and PM 10 concentrations during periods dominated by dust typically relies on federal reference or equivalent methods (FRM/FEM), but these resources have limited spatial resolution. This study investigates whether PMS5003/6003 measurements alone can be used to detect and to bias correct for these dust-dominant PM conditions. We analyzed measurements from 109 PMS sensors collocated or near 75 U.S. EPA monitoring sites with hourly FEM PM 2.5 and/or PM 10 between January 2017 to May 2025. Two cutoff thresholds (threshold1 and threshold2) were developed using relative humidity and the sensor-reported ratio of coarse (2.5–10 µm) to submicron (0.3–1 µm) mass concentration to identify potential periods dominated by dust when the PMS sensor underestimated PM 2.5 concentration. 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-dominated periods to develop a correction factor through non-linear regression. This correction reduced the mean bias error between PMS PM 2.5 estimates (pm2.5_alt) and FEM PM 2.5 by approximately 50 % for 97 sensors, and reduced the root mean square error by approximately 30 % for 84 sensors. This framework enhances the utility of PMS5003/6003 measurements during periods of elevated dust loading, extending monitoring capabilities in regions where regulatory coverage is limited.
Institution: University of Utah
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,585
OpenAlex ID: W7129009555