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 monit...

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Bibliographic Details
Published in:IEEE sensors journal 2021-11, Vol.21 (22), p.25941-25949
Main Authors: Jha, Sonu Kumar, Kumar, Mohit, Arora, Vipul, Tripathi, Sachchida Nand, Motghare, Vidyanand Motiram, Shingare, A. A., Rajput, Karansingh A., Kamble, Sneha
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
Air monitoring
Air pollution
Air quality
Calibration
Collocation
Computational modeling
Correlation
Correlation coefficients
deep neural network
domain adaptation
Domains
Environmental monitoring
Experimentation
Humidity
Low cost
Monitoring
PM₂.
Pollution measurement
Pollution monitoring
Sensor phenomena and characterization
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Summary: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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3118454