Articles | Volume 17, issue 19
https://doi.org/10.5194/amt-17-5747-2024
https://doi.org/10.5194/amt-17-5747-2024
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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Cited articles

Belward, A. S., Estes, J. E., and Kline, K. D.: The IGBP-DIS global 1-km land-cover data set DISCover: A project overview, Photogramm. Eng. Rem. S., 65, 1013–1020, 1999. a
Buchard, V., Da Silva, A., Randles, C., Colarco, P., Ferrare, R., Hair, J., Hostetler, C., Tackett, J., and Winker, D.: Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States, Atmos. Environ., 125, 100–111, 2016. a
Fujisada, H., Urai, M., and Iwasaki, A.: Advanced methodology for ASTER DEM generation, IEEE T. Geosci. Remote, 49, 5080–5091, 2011. a
Fujisada, H., Urai, M., and Iwasaki, A.: Technical methodology for ASTER global DEM, IEEE T. Geosci. Remote, 50, 3725–3736, 2012. a
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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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