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

• André F. Pastório Universidade Estadual do Oeste do Paraná
• Fabio A. Spanhol Universidade Estadual do Oeste do Paraná / Universidade Tecnológica Federal do Paraná
• Leila D. Martins Universidade Tecnológica Federal do Paraná
• Edson T. de Camargo Universidade Estadual do Oeste do Paraná / Universidade Tecnológica Federal do Paraná

Resumo

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.

Palavras-chave: particulate matter, low-cost sensors, air quality, calibration