Abstract
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 PM2.5 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 PM2.5 levels of LCSDs. The dataset used for the experimentation
consists of PM2.5 values and other parameters (PM10, temperature, and
humidity) at hourly duration over a period of three months data. We
propose new features, by combining PM2.5, PM10, 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 (R2) 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.