Recalibration of low-cost air pollution sensors: Is it worth it?

Abstract. The appropriate period of collocation of a low-cost air sensor (LCS) with reference measurements is often unknown. Previous low-cost air sensor studies have shown that due to sensor ageing and seasonality of environmental interferences periodical sensor calibration needs to be performed to guarantee sufficient data quality. While the limitations are well-established it is still unclear how often a recalibration of a sensor needs to be carried out. In this study, we aim to demonstrate how frequently widely used air sensors for the relevant air pollutants O₃ and PM_2.5 by two manufacturers (Alphasense and Sensirion) should be recalibrated. Sensor calibration functions were built using Multiple Linear Regression, Ridge Regression, Random Forest and Extreme Gradient Boosting. We use state-of-the-art test protocols for air sensors provided by the United States Environmental Protection Agency (EPA) and the European Committee for Standardization (CEN) for evaluative guidance. We conducted a yearlong collocation campaign at an urban background air and climate monitoring station next to the University Hospital Augsburg, Germany. LCS were exposed to a wide range of environmental conditions, with air temperatures between -10 and 36 °C, relative air humidity between 19 and 96 % and air pressure between 937 and 983 hPa. The ambient concentration ranges for O₃ and PM_2.5 were up to 83 ppb and 153 µg m^-3, respectively. For the baseline single training of 5 months, the calibrated O₃ and PM_2.5 sensors were able to reflect the hourly reference data well during the training (R²: O₃ = 0.92–1.00; PM_2.5 = 0.93–0.98) and the following test period (R²: O₃ = 0.93–0.97; PM_2.5 = 0.84–0.93). Additionally, the sensor errors were generally acceptable during the training (RMSE: O₃ = 0.80–4.35 ppb; PM_2.5 = 1.45–2.51 µg m^-3) and the following test period (RMSE: O₃ = 3.62–5.84 ppb; PM_2.5 = 2.04–3.02 µg m^-3). By investigating different recalibration cycles using a pairwise calibration strategy, our results indicate that a regular in-season recalibration is required to obtain the highest quantitative validity for the analysed low-cost air sensors, with monthly recalibrations appearing to be the most suitable approach. In contrast, an extension of the training period for the calibration models had only a minor overall impact on improving the low-cost air sensors’ ability to capture temporal variations in observed O₃ concentrations and PM_2.5 concentrations. The measurement uncertainty of the calibrated O₃ LCS and PM_2.5 LCS were able to meet the data quality objective (DQO) for indicative measurements for different calibration models. Compared to one-time pre-deployment sensor calibration, in-season recalibration can broaden the scope of application for a LCS (indicative measurements, objective estimation, non-regulatory supplemental and informational monitoring).