Calibration Of Low-Cost Pm2.5 Sensors Using Data Continuously Generated At A Reference Station In El Paso, Texas

Low-cost sensors (LCS) offer a cost-effective way to expand air-quality monitoring networks, but they require strong calibration methods to ensure reliable performance under changing environmental conditions. In this study, Clarity LCSs were co-located with a state reference monitor for 6 months to evaluate fixed-time (2-, 3-, 4-, and 6-week) and rolling (2- and 3-week) calibration approaches. A multivariate regression model using PM1, PM2.5, PM10, and relative humidity was used to generate calibration coefficients for a “base sensor.” Data from two additional sensors were normalized to the base unit using linear regression, and their calibration coefficients were applied across the network. The fixed-time calibration using 6 weeks provides the best performance, based on its correlation to the reference station and the error metrics, indicating that its predictions are consistently close to observed values with minimal variability, followed by rolling calibration (3 weeks), which showed slightly weaker correlation but higher mean absolute error (MAE), which suggests less consistent performance.