Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 18
Articles | Volume 18, issue 18
https://doi.org/10.5194/amt-18-4771-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-4771-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 18
Research article
 | 
25 Sep 2025
Research article |  | 25 Sep 2025

Machine learning data fusion for high spatio-temporal resolution PM2.5

Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen

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Short summary
This study proposes a novel machine learning method to estimate pollution levels (PM2.5) on urban areas at fine scale. Our model generates hourly PM2.5 maps with high spatial resolution, by combining satellite data, ground measurements, geophysical model data, and different geographical indicators. The model properly accounts for spatial and temporal variability of the urban pollution levels, and can be highly beneficial for air quality monitoring and health protection.
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