Concentration-Temporal Multilevel Calibration of Low-Cost PM2.5 Sensors

Ambient aerosols have a significant impact on plant species mortality, air pollution, and climate change. It is critical to monitor the concentrations of aerosols, especially particulate matter with an aerodynamic diameter <= 2.5 mu m (PM2.5), which has a direct relationship with human respiratory diseases. Recently, low-cost PM2.5 sensors have been deployed to provide a denser monitoring coverage than that of government-built monitoring supersites, which only give a macro perspective of air quality. To increase the measurement accuracy, low-cost sensors need to be calibrated. In current practice, regression techniques are used to calibrate sensors. This paper proposes a concentration-temporal multilevel calibration method to cope with the varying regression relation in different concentration and temporal domains. The performance of our method is evaluated with real field data from a supersite sensor and a low-cost sensor deployed in Puli, Taiwan. The experimental results show that our calibration method significantly outperforms linear regression in terms of R-2, Root Mean Square Error, and Normalized Mean Error. Moreover, our method compares favorably with a machine learning calibration method based on gradient regression tree boosting.