Low-cost air quality sensors are increasingly used in environmental monitoring networks, but their readings change when temperature and humidity change, even if the actual pollution level stays the same. This paper investigates how much temperature and relative humidity affect the readings of three common sensor types — an optical particle counter for PM2.5, a metal-oxide gas sensor for NO₂, and an electrochemical sensor for CO₂. Sensors were placed in a controlled chamber and exposed to six combinations of temperature (15–35 °C) and relative humidity (30–90%). Results show that measurement error grows significantly at high humidity levels, with PM2.5 error reaching nearly 9 µg/m³ at 85% relative humidity compared to 1.2 µg/m³ at 30%. A simple linear correction formula is proposed that reduces this error by up to 74%. The findings confirm that environmental compensation is necessary for reliable low-cost sensor deployments.
Effect Of Temperature And Humidity On Low-Cost Air Quality Sensor Readings
DOI:
Keywords:
Abstract
References
Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., & Norford, L. (2015). The rise of low-cost sensing for managing air quality in cities. Environment International, 75, 199–205. https://doi.org/10.1016/j.envint.2014.11.019
Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., & Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environment International, 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018
Crilley, L. R., Shaw, M., Pound, R., Kramer, L. J., Price, R., Young, S., Lewis, A. C., & Pope, F. D. (2018). Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmospheric Measurement Techniques, 11(2), 709–720. https://doi.org/10.5194/amt-11-709-2018
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., & Bonavitacola, F. (2017). Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Sensors and Actuators B: Chemical, 238, 706–715. https://doi.org/10.1016/j.snb.2016.07.095
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., & Allen, R. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11(1), 291–313. https://doi.org/10.5194/amt-11-291-2018
Maag, B., Zhou, Z., & Thiele, L. (2018). A survey on sensor calibration in air quality monitoring deployments. IEEE Internet of Things Journal, 5(6), 4857–4870. https://doi.org/10.1109/JIOT.2018.2853660
Rai, A. C., Kumar, P., Pilla, F., Skouloudis, A. N., Di Sabatino, S., Ratti, C., Yasar, A., & Rickerby, D. (2017). End-user perspective of low-cost sensors for outdoor air pollution monitoring. Science of the Total Environment, 607–608, 691–705. https://doi.org/10.1016/j.scitotenv.2017.06.266
Badura, M., Batog, P., Drzeniecka-Osiadacz, A., & Modzel, P. (2018). Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements. SN Applied Sciences, 1(6), 622. https://doi.org/10.1007/s42452-019-0630-1
Jayaratne, R., Liu, X., Thai, P., Dunbabin, M., & Morawska, L. (2018). The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog. Atmospheric Measurement Techniques, 11(8), 4883–4890. https://doi.org/10.5194/amt-11-4883-2018
Vaisala. (2022). Humidity and temperature effects on electrochemical gas sensors: Application note AN-142. Vaisala Oyj.