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

O, Sungmin; Yoon, Ji Won; Park, Seon Ki

doi:https://doi.org/10.5194/amt-18-1471-2025

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

Supplement

https://doi.org/10.5194/amt-18-1471-2025-supplement

Data sets

Use of GEMS AOD data to estimate ground-level particulate matter concentrations in South Korea Sungmin O https://github.com/osungmin/gems_aod

ERA5 hourly data on single levels from 1940 to present Copernicus Climate Change Service (C3S) https://doi.org/10.24381/cds.adbb2d47

ERA5-Land hourly data from 1950 to present Copernicus Climate Change Service (C3S) https://doi.org/10.24381/cds.e2161bac

Short summary

Air pollutants such as particulate matter with diameters of 10 µm and 2.5 µm or less (PM₁₀and PM_2.5) 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.