Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.414 IF 5.414
  • IF 5-year value: 5.958 IF 5-year
    5.958
  • CiteScore value: 9.7 CiteScore
    9.7
  • SNIP value: 1.517 SNIP 1.517
  • IPP value: 5.61 IPP 5.61
  • SJR value: 2.601 SJR 2.601
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 191 Scimago H
    index 191
  • h5-index value: 89 h5-index 89
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.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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
Publications Copernicus
Download
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.
This study proposed machine-learning-based models to estimate ground-level particulate matter...
Citation
Final-revised paper
Preprint