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Title	A Machine Learning-Based Approach To Calibrate Low-Cost Particulate Matter Sensors
ID_Doc	2479
Authors	Pastorio A.F.; Spanhol F.A.; Martins L.D.; De Camargo E.T.
Year	2022
Published	Brazilian Symposium on Computing System Engineering, SBESC, 2022-November
DOI	http://dx.doi.org/10.1109/SBESC56799.2022.9964983
Abstract	Low-cost particulate matter (LC-PM) sensors have been studied around the world as a viable alternative to expensive reference stations for monitoring air quality. However, LC-PM sensors require periodic calibration, since their data are often inaccurate and subject to uncertainty. Sensors calibration can be performed through machine learning methods where the sensor is placed in a real environment subject to the local environmental conditions of the place and its measurement compared to a reference equipment. This work evaluates different machine learning methods in five different models of LC-PM sensors, aiming to select the most appropriate sensor and a calibration method to be used in a low-cost air quality station in the context of smart cities. © 2022 IEEE.
Author Keywords	air quality; calibration; low-cost sensors; particulate matter

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