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

Park, Seohui; Shin, Minso; Im, Jungho; Song, Chang-Keun; Choi, Myungje; Kim, Jhoon; Lee, Seungun; Park, Rokjin; Kim, Jiyoung; Lee, Dong-Won; Kim, Sang-Kyun

doi:https://doi.org/10.5194/acp-19-1097-2019

Articles | Volume 19, issue 2

https://doi.org/10.5194/acp-19-1097-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-19-1097-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

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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Final revised paper (published on 28 Jan 2019)
Supplement to the final revised paper
Preprint (discussion started on 13 Sep 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Referee Comment', Anonymous Referee #1, 24 Sep 2018
- AC1: 'Responses to Reviewer#1', Jungho Im, 02 Dec 2018
RC2: 'review comments', Anonymous Referee #2, 10 Oct 2018
- AC2: 'Responses to Reviewer#2', Jungho Im, 02 Dec 2018
RC3: 'comments', Anonymous Referee #3, 07 Nov 2018
- AC3: 'Responses to Reviewer#3', Jungho Im, 02 Dec 2018

Peer-review completion

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

AR by Jungho Im on behalf of the Authors (18 Jan 2019)

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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.