Articles | Volume 19, issue 2
https://doi.org/10.5194/acp-19-1097-2019
https://doi.org/10.5194/acp-19-1097-2019
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, Minso Shin, Jungho Im, Chang-Keun Song, Myungje Choi, Jhoon Kim, Seungun Lee, Rokjin Park, Jiyoung Kim, Dong-Won Lee, and Sang-Kyun Kim

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jungho Im on behalf of the Authors (02 Dec 2018)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (10 Jan 2019) by Michael Schulz
AR by Jungho Im on behalf of the Authors (15 Jan 2019)  Author's response    Manuscript
ED: Publish as is (17 Jan 2019) by Michael Schulz
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Short summary
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.
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