Calibration of low-cost no2 sensors through environmental factor correction

Abstract

Low-cost air quality sensors (LCSs) have become more widespread due to their low cost and increased capabilities; however, to supplement more traditional air quality networks, the performance of these LCSs needs to be validated. This study focused on NO2 measurements from eight Clarity Node-S sensors and used various environmental factors to calibrate the LCSs. To validate the calibration performance, we calculated the root-mean-square error (RMSE), mean absolute error (MAE), R2, and slope compared to reference measurements. Raw results from six of these sensors were comparable to those reported for other NO2 LCSs; however, two of the evaluated LCSs had RMSE values ~20 ppb higher than the other six LCSs. By applying a sensor-specific calibration that corrects for relative humidity, temperature, and ozone, this discrepancy was mitigated. In addition, this calibration improved the RMSE, MAE, R2, and slope of all eight LCS compared to the raw data. It should be noted that relatively stable environmental conditions over the course of the LCS deployment period benefited calibration performance over time. These results demonstrate the importance of developing LCS calibration models for individual sensors that consider pertinent environmental factors.