Understanding the effect of temperature and relative humidity on sensor sensitivities in field environments and improving the calibration models of multiple electrochemical carbon monoxide (CO) sensors in a tropical environment

The data quality of low-cost air quality monitoring sensor systems in field environments needs to be improved through calibrations to compare with regulatory data. This research study focused on improving linear regression calibration models of multiple Alphasense CO-B4 sensors compared to the factory calibration, evaluating the sensor sensitivity and calibration model variations in different temperature and relative humidity (RH) ranges, evaluating the unit-to-unit variability of sensor performances and the use of averaged calibration models instead of individual calibration models. In this study, changes were observed in field sensor sensitivities and manufacturer-given sensor sensitivities, and the inclusion of field sensor sensitivities improved the calibration model performances significantly. Sensor calibration models performed best in the temperature range of 20–24 0C and the RH range of 75–90%, with averaged r and RMSE values around 0.98 and 50 ppb. When compared with raw sensor data (RMSE = 172.1 ppb), the averaged RMSE values of the calibrated concentration values were 97.4 ppb or below for different calibration models. Additionally, results clearly illustrated that the use of averaged calibration model was a reliable alternative for individual calibrations. Furthermore, it was observed that the field calibrations significantly reduce the effect of temperature and RH on sensor outputs and improve the factory calibrations.