RCH: Robust Calibration Based on Historical Data for Low-Cost Air Quality Sensor Deployments

Air pollution has become one of the major threats to human health. Conventional approaches for air pollution monitoring use precise professional devices, but cannot achieve dense deployment due to high cost. Therefore, systems consisting of low-cost sensors are applied as a supplement to obtain fine-grained pollution information. In order to maintain the accuracy of these low-cost sensors, it is essential to calibrate them to minimize the impact from sensor drifts. Existing field calibration methods utilize the real-time data from spatially- adjacent official air quality stations as reference. However, the real-time reference is not always accessible under existing station deployment. In this paper, we propose the Robust Calibration approach using Historical data (RCH) for low-cost air quality sensors. Our method corrects the sensor drift by adapting sensitivity and offset based on pollutant's concentration distribution. Experiments on NO2 data from real-world deployment in Foshan, China show that RCHhas the similar performance compared with existing field-calibration methods using real-time and spatially-adjacent references. It demonstrates that RCH can improve the accuracy and consistency of low-cost air quality sensors without the help of real-time and nearby reference data.