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ACP | Articles | Volume 19, issue 2
Atmos. Chem. Phys., 19, 1097–1113, 2019
https://doi.org/10.5194/acp-19-1097-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 19, 1097–1113, 2019
https://doi.org/10.5194/acp-19-1097-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Jan 2019

Research article | 28 Jan 2019

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Seohui Park et al.

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This study proposed machine-learning-based models to estimate ground-level particulate matter concentrations using satellite observations and numerical model-derived data. Aerosol optical depth was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed and solar radiation. The results show that the proposed models produced better performance than the existing approaches, particularly in improving on the biases of the process-based models.
This study proposed machine-learning-based models to estimate ground-level particulate matter...
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