Calibration Of Low-Cost Pm2.5 Sensors Using Data Continuously Generated At A Reference Station In El Paso, Texas
Year: 2025
Type: article
Abstract: 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.
Author: Hernandez, Brenda Lizbeth
Institution: The University of Texas at El Paso
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7134496225
FWCI:
Cites:
Cited by:
Related to: