Spatial calibration and PM2.5 mapping of low-cost air quality sensors

This study proposes an efficient calibration and mapping approach based on spatial regression and interpolation model. The proposed model is further applied to a low-cost PM2.5 sensor in high relative humidity. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.