Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors

Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM
_2.5
using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM
_2.5
levels of LCSDs. The dataset used for the experimentation consists of PM
_2.5
values and other parameters (PM
₁₀
, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM
_2.5
, PM
₁₀
, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R
²
) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.