Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 19
Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6717-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6717-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 19
Methods for assessment of models
 | 
02 Oct 2025
Methods for assessment of models |  | 02 Oct 2025

A close look at using national ground stations for the statistical modeling of NO2

Foeke Boersma and Meng Lu

Viewed

Total article views: 1,672 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,267 317 88 1,672 84 97 148
  • HTML: 1,267
  • PDF: 317
  • XML: 88
  • Total: 1,672
  • Supplement: 84
  • BibTeX: 97
  • EndNote: 148
Views and downloads (calculated since 23 Oct 2023)
Cumulative views and downloads (calculated since 23 Oct 2023)

Viewed (geographical distribution)

Total article views: 1,672 (including HTML, PDF, and XML) Thereof 1,649 with geography defined and 23 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jan 2026
Download
Short summary
Air pollution harms health and society. Understanding and predicting it is crucial. Various models have been developed to model air pollution. However, the consistency exhibited by a model in different areas is commonly neglected. Our study accounts for this and shows lower accuracy near busy roads but higher accuracy in less populated areas. Considering location characteristics in air pollution predictions is important in comparing statistical models and understanding the health–society–space relationship.
Read more
Share