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

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.