Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 19
Articles | Volume 17, issue 19
https://doi.org/10.5194/amt-17-5747-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-5747-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 19
Research article
 | 
30 Sep 2024
Research article |  | 30 Sep 2024

Post-process correction improves the accuracy of satellite PM2.5 retrievals

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

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Short summary
This study focuses on improving the accuracy of satellite-based PM2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM2.5 conversion ratio using various data sources. The approach produces high-resolution PM2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.
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