Articles | Volume 18, issue 6
https://doi.org/10.5194/amt-18-1471-2025
https://doi.org/10.5194/amt-18-1471-2025
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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Cited articles

Baek, K., Kim, J. H., Bak, J., Haffner, D. P., Kang, M., and Hong, H.: Evaluation of total ozone measurements from Geostationary Environmental Monitoring Spectrometer (GEMS), Atmos. Meas. Tech., 16, 5461–5478, https://doi.org/10.5194/amt-16-5461-2023, 2023. a
Chen, J. and Hoek, G.: Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis, Environ. Int., 143, 105974, https://doi.org/10.1016/j.envint.2020.105974, 2020. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 785–794 pp., ACM, https://doi.org/10.1145/2939672.2939785, 2016. a
Cho, Y., Kim, J., Go, S., Kim, M., Lee, S., Kim, M., Chong, H., Lee, W.-J., Lee, D.-W., Torres, O., and Park, S. S.: First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia, Atmos. Meas. Tech., 17, 4369–4390, https://doi.org/10.5194/amt-17-4369-2024, 2024. a, b, c
Choi, M., Lim, H., Kim, J., Lee, S., Eck, T. F., Holben, B. N., Garay, M. J., Hyer, E. J., Saide, P. E., and Liu, H.: Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign, Atmos. Meas. Tech., 12, 4619–4641, https://doi.org/10.5194/amt-12-4619-2019, 2019. a
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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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