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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2025
Research article |
| 28 Mar 2025
| 28 Mar 2025
Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach
Sungmin O
Department of Electronic & AI System Engineering, Kangwon National University, Samcheok, Republic of Korea
Ji Won Yoon
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, Republic of Korea
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, Republic of Korea
Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Republic of Korea
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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.
Air pollutants such as particulate matter with diameters of 10 µm and 2.5 µm or less (PM10 and...
