Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 9, issue 2
Articles | Volume 9, issue 2
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 2
22 Dec 2023
 | 22 Dec 2023

Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information

Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Santiago Hernández, and Mauricio Ramírez

Viewed

Total article views: 1,631 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,341 252 38 1,631 46 101
  • HTML: 1,341
  • PDF: 252
  • XML: 38
  • Total: 1,631
  • BibTeX: 46
  • EndNote: 101
Views and downloads (calculated since 22 Dec 2023)
Cumulative views and downloads (calculated since 22 Dec 2023)

Viewed (geographical distribution)

Total article views: 1,730 (including HTML, PDF, and XML) Thereof 1,730 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 31 Oct 2025
Download
Short summary
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.
Read more
Share