Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-573-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-573-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Model description paper
 | 
26 Jan 2023
Model description paper |  | 26 Jan 2023

The AirGAM 2022r1 air quality trend and prediction model

Sam-Erik Walker, Sverre Solberg, Philipp Schneider, and Cristina Guerreiro

Viewed

Total article views: 2,199 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,632 519 48 2,199 139 42 40
  • HTML: 1,632
  • PDF: 519
  • XML: 48
  • Total: 2,199
  • Supplement: 139
  • BibTeX: 42
  • EndNote: 40
Views and downloads (calculated since 05 May 2022)
Cumulative views and downloads (calculated since 05 May 2022)

Viewed (geographical distribution)

Total article views: 2,199 (including HTML, PDF, and XML) Thereof 2,130 with geography defined and 69 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 12 Nov 2024
Download
Short summary
We have developed a statistical model for estimating trends in the daily air quality observations of NO2, O3, PM10 and PM2.5, adjusting for trends and short-term variations in meteorology. The model is general and may also be used for prediction purposes, including forecasting. It has been applied in a recent comprehensive study in Europe. Significant declines are shown for the pollutants from 2005 to 2019, mainly due to reductions in emissions not attributable to changes in meteorology.
Read more