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Domain adaptation based deep calibration of low-cost PM2.5 sensors

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posted on 20.08.2021, 03:24 by Sonu Kumar Jha, Mohit Kumar, Vipul AroraVipul Arora, Sachchida Nand Tripathi, Vidyanand Motiram Motghare, A.A. Shingare, Karan Singh Rajput, Sneha Kamble
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.

Funding

MPCB

Bloomberg philanthropies

History

Email Address of Submitting Author

vipular@iitk.ac.in

ORCID of Submitting Author

https://orcid.org/0000-0002-1207-1258

Submitting Author's Institution

IIT Kanpur

Submitting Author's Country

India

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