Machine Learning Calibration Transfer for Low-Cost Air Quality Sensors: Distance-Based Uncertainty Quantification in a Hybrid Urban Monitoring Network

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the oper...

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Autori principali:	Petar Zhivkov, Stefka Fidanova
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2026-03-01
Serie:	Atmosphere
Soggetti:	air quality monitoring low-cost sensors machine learning calibration calibration transfer Random Forest uncertainty quantification
Accesso online:	https://www.mdpi.com/2073-4433/17/4/335
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