Recalibration of low-cost O3 and PM2.5 sensors: linking practices to recent air sensor test protocols

Abstract

The appropriate period of collocation of a low-cost air sensor (LCS) with reference measurements is often unknown. Previous LCS 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 demonstrate how widely used air sensors (OX-B431 and SPS30) for the relevant air pollutants ozone (O3) and fine particulate matter (PM2.5) by two manufacturers (Alphasense and Sensirion) should be recalibrated for real-world monitoring applications. Sensor calibration functions were built using Multiple Linear Regression, Ridge Regression, Random Forest and Extreme Gradient Boosting. We use multiple novel test protocols for air sensors provided by the United States Environmental Protection Agency and the European Committee for Standardization for evaluative guidance and to identify possible applications for OX-B431 and SPS30 sensors. We conducted a yearlong collocation campaign at an urban background air and climate monitoring station next to the University Hospital Augsburg, Germany. LCSs 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 PM2.5 were up to 82 ppb and 153 µg m−3, respectively. For the baseline single training of 5 months, the calibrated O3 and PM2.5 sensors were able to reflect the hourly reference data well during the training (R2: O3 = 0.92–1.00; PM2.5 = 0.93–0.97) and the following test period (R2: O3 = 0.93–0.98; PM2.5 = 0.84–0.93). Additionally, the sensor errors were generally acceptable during the training (RMSE: O3 = 0.80–4.35 ppb; PM2.5 = 1.45–2.51 µg m−3) and the following test period (RMSE: O3 = 3.62–5.84 ppb; PM2.5 = 2.04–3.02 µg m−3). We investigated different recalibration cycles using a pairwise calibration strategy, which is an uncommon method for recurrent LCS calibration. Our results indicate that a regular in-season recalibration is required to obtain the highest quantitative validity and broadest range of applications (indicative and non-regulatory supplemental measurements) for the analysed LCSs. Monthly recalibrations are observed to be the most suitable approach. The measurement uncertainties of the calibrated O3 LCSs and PM2.5 LCSs were able to meet the data quality objective for indicative measurements for different calibration models. In-season recalibration, rather than reliance on a single pre-deployment calibration, should be adopted by end-user communities. This approach is required for certain real-world applications to be performed reliably by LCSs and to achieve sufficient information content.