Transferability of machine-learning-based global calibration models for NO₂ and NO low-cost sensors

Abstract

It is essential to accurately assess and verify the effects of air pollution on human health and the environment in order to develop effective mitigation strategies. More accurate analysis of air pollution can be achieved by utilizing a higher-density sensor network. In recent studies, the implementation of low-cost sensors has demonstrated their capability to quantify air pollution at a high spatial resolution, alleviating the problem of coarse spatial measurements associated with conventional monitoring stations. However, the reliability of such sensors is in question due to concerns about the quality and accuracy of their data. In response to these concerns, active research efforts have focused on leveraging machine learning (ML) techniques in the calibration process of low-cost sensors. These efforts demonstrate promising results for automatic calibration, which would significantly reduce the efforts and costs of traditional calibration methods and boost the low-cost sensors' performance. As a contribution to this promising research field, this study aims to investigate the calibration transferability between identical low-cost sensor units (SUs) for NO2 and NO using ML-based global models. Global models would further reduce calibration efforts and costs by eliminating the need for individual calibrations, especially when utilizing networks of tens or hundreds of low-cost sensors. This study employed a dataset acquired from four SUs that were located across three distinct locations within Switzerland. We also propose utilizing O3 measurements obtained from available nearby reference stations to address the cross-sensitivity effect. This strategy aims to enhance model accuracy as most electrochemical NO2 and NO sensors are extremely cross-sensitive to O3. The results of this study show excellent calibration transferability between SUs located at the same site (Case A), with the average model performance being R2 = 0.90 ± 0.05 and root mean square error (RMSE) = 3.4 ± 0.9 ppb for NO2 and R2 = 0.97 ± 0.02 and RMSE = 3.1 ± 0.8 ppb for NO. There is also relatively good transferability between SUs deployed at different sites (Case B), with the average performance being R2 = 0.65 ± 0.08 and RMSE = 5.5 ± 0.4 ppb for NO2 and R2 = 0.82 ± 0.05 and RMSE = 5.8 ± 0.8 ppb for NO. Interestingly, the results illustrate a substantial improvement in the calibration models when integrating O3 measurements, which is more pronounced when SUs are situated in regions characterized by elevated O3 concentrations. Although the findings of this study are based on a specific type of sensor and sensor model, the methodology is flexible and can be applied to other low-cost sensors with different target pollutants and sensing technologies. Furthermore, this study highlights the significance of leveraging publicly available data sources to promote the reliability of low-cost air quality sensors.