Validating the performance of low-cost IAQ sensors through co-location

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Abstract

Low-cost indoor air quality (IAQ) sensors offer new opportunities for real-time monitoring in the built environment by occupants and researchers. However, their performance can vary substantially depending on the environmental conditions. This study presents a comprehensive evaluation of carbon dioxide (CO2) and fine particulate matter (PM2.5) measurements from two consumer-grade low-cost sensors (the Airthings View Plus for CO2 only and Air Gradient Pro for CO2 and PM2.5) through co-location tests with two reference instruments, Graywolf DSII-8 for CO2 and Lighthouse Handheld 3016 for PM2.5. Using time-series analysis, linear regression, Pearson correlation, Root-Mean Squared Error (RMSE), Bland-Altman test, and paired t-tests, we assess the precision and accuracy of these sensors. At a 5-minute sampling interval, the Air Gradient sensor had a higher coefficient of determination (R2), stronger Pearson correlation, and narrower range of limits of agreement (LoAs), but higher bias (i.e. the mean difference) and RMSE, suggesting higher precision but lower accuracy when compared to Airthings. As a result, it can perform well for tracking the relative changes in CO2, though less ideal for absolute concentrations without calibration. For PM2.5, the Air Gradient also had relatively high R2 (0.79), moderately strong Pearson correlation (ρ = 0.69, p < 0.05), and a narrow range of LOAs (30.1 μg/m3) and low RMSE (5.8 μg/m3). Averaging the 5-minute measurements over 30-minute intervals generally improved the accuracy and precision of both sensors. However, statistically significant differences from the reference instruments remained for both sensors. Overall, this study offers a multi-metric assessment of consumer-grade sensors and highlights the need for in-situ calibration prior to long-term deployment.

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APA

Zaky, N., Li, T., & Stopps, H. (2026). Validating the performance of low-cost IAQ sensors through co-location. Journal of Building Physics, 49(6 Special Issue: eSim2024, part two), 829–851. https://doi.org/10.1177/17442591251367436

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