Smart multi-sensor calibration of low-cost particulate matter monitors

Edwin Villanueva, Soledad Espezua, George Castelar, Kyara Diaz, Erick Ingaroca

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

Resumen

A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.

Idioma originalInglés
Número de artículo3776
Número de páginas18
PublicaciónSensors (Basel, Switzerland)
Volumen23
N.º7
DOI
EstadoPublicada - 6 abr. 2023

Nota bibliográfica

Funding Information:
The authors gratefully acknowledge financial support by Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (Fondecyt)—Mundial Bank (Grant: 50-2018-FONDECYT-BM-IADT-MU) and from “Horizonte Ciudadano” Foundation (PUCP project PI0731).

Publisher Copyright:
© 2023 by the authors.

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