Intelligent Air Pollution Sensors Calibration for Extreme Events and Drifts Monitoring

Air quality low-cost sensors (LCSs) are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular, when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting LCSs measurements accurately and detecting the calibrators' drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators' strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of LCSs suffered from sensing accuracy.

Automated summary

Air quality low-cost sensors (LCSs) are affordable and scalable for high-resolution air pollution monitoring, but face challenges in accuracy, especially for extreme events. We propose a Bayesian calibration method that effectively corrects LCSs measurements and detects calibration drift. Experimental results on smoking events show accurate estimation of aerosol mass concentration. Black-box calibrators outperform white-box ones, but may drift during new events, while white-box calibrators remain robust. Implementing both calibrators enables strength extraction and drifting monitoring for calibration models. The method can be applied to other LCSs with accuracy issues.