A Machine Learning-Based Approach to Calibrate Low-Cost Particulate Matter Sensors

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 calibratio...

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Bibliographic Details
Published inBrazilian Symposium on Computing System Engineering pp. 1 - 8
Main Authors Pastorio, Andre F., Spanhol, Fabio A., Martins, Leila D., de Camargo, Edson T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.11.2022
Subjects
Air quality
Atmospheric modeling
calibration
Humidity
low-cost sensors
Optical filters
Particle measurements
particulate matter
Sensors
Temperature sensors
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Summary: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.
ISSN:2324-7894
DOI:10.1109/SBESC56799.2022.9964983