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

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

Research output: Contribution to journalArticle in a journalpeer-review

2 Scopus citations


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.

Original languageEnglish
Article number3776
Number of pages18
JournalSensors (Basel, Switzerland)
Issue number7
StatePublished - 6 Apr 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.


  • Air quality
  • Low-cost sensor
  • Machine learning
  • Multi-sensor calibration
  • Particulate matter
  • Sensor calibration


Dive into the research topics of 'Smart multi-sensor calibration of low-cost particulate matter monitors'. Together they form a unique fingerprint.

Cite this