Articles | Volume 18, issue 17
https://doi.org/10.5194/amt-18-4357-2025
https://doi.org/10.5194/amt-18-4357-2025
Research article
 | 
10 Sep 2025
Research article |  | 10 Sep 2025

Improving raw readings from low-cost ozone sensors using artificial intelligence for air quality monitoring

Guillem Montalban-Faet, Eric Meneses-Albala, Santiago Felici-Castell, Juan J. Perez-Solano, and Jaume Segura-Garcia

Cited articles

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Antonenko, A., Boretskij, V., and Zagaria, O.: Classification of Indoor Air Pollution Using Low-cost Sensors by Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14856, https://doi.org/10.5194/egusphere-egu23-14856, 2023. a
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Breeze Technologies: Air pollution – How to convert between mg/m3, µg/m3 and ppm, ppb, Breeze Technologies, https://www.breeze-technologies.de/blog/air-pollution-how-to-convert-between-mgm3-%C2%B5gm3-ppm-ppb/ (last access: 27 November 2024), 2024. a
Casey, J. G., Collier-Oxandale, A., and Hannigan, M.: Performance of artificial neural networks and linear models to quantify 4 trace gas species in an oil and gas production region with low-cost sensors, Sensor. Actuat. B-Chem., 283, 504–514, https://doi.org/10.1016/j.snb.2018.12.049 2019. a
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Short summary
The World Health Organization (WHO) warns of ground-level ozone problems in urban areas and recommends increased monitoring. This paper shows that low-cost sensors coupled with artificial intelligence are an excellent alternative.
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