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

Viewed

Total article views: 4,108 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,597 1,451 60 4,108 141 65 67
  • HTML: 2,597
  • PDF: 1,451
  • XML: 60
  • Total: 4,108
  • Supplement: 141
  • BibTeX: 65
  • EndNote: 67
Views and downloads (calculated since 13 Sep 2018)
Cumulative views and downloads (calculated since 13 Sep 2018)

Viewed (geographical distribution)

Total article views: 4,108 (including HTML, PDF, and XML) Thereof 3,938 with geography defined and 170 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 27 Mar 2024
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.
Altmetrics
Final-revised paper
Preprint