Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 6
Articles | Volume 18, issue 6
https://doi.org/10.5194/amt-18-1471-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1471-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 6
Research article
 | 
28 Mar 2025
Research article |  | 28 Mar 2025

Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach

Sungmin O, Ji Won Yoon, and Seon Ki Park

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Short summary
Air pollutants such as particulate matter with diameters of 10 µm and 2.5 µm or less (PM10 and PM2.5) can cause adverse public health and environment effects; therefore their regular monitoring is crucial to keep pollutant concentrations under control. Our study demonstrates the potential of high-resolution aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) satellite to estimate ground-level PM concentrations using machine learning models. 
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