The AirGAM 2022r1 air quality trend and prediction model

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

doi:https://doi.org/10.5194/gmd-16-573-2023

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

Supplement

https://doi.org/10.5194/gmd-16-573-2023-supplement

Data sets

AirGAM 2022r1 basic data 2005-2019 and scripts Sam-Erik Walker and Sverre Solberg https://doi.org/10.5281/zenodo.6334131

AirGAM 2022r1 input data for all stations 2005-2019 Sam-Erik Walker and Sverre Solberg https://doi.org/10.5281/zenodo.6334171

AirGAM 2022r1 NO2 results for all stations 2005-2019 Sam-Erik Walker and Sverre Solberg https://doi.org/10.5281/zenodo.6334195

AirGAM 2022r1 O3 results for all stations 2005-2019 Sam-Erik Walker and Sverre Solberg https://doi.org/10.5281/zenodo.6334317

AirGAM 2022r1 PM10 results for all stations 2005-2019 Sam-Erik Walker and Sverre Solberg https://doi.org/10.5281/zenodo.6334327

AirGAM 2022r1 PM2.5 results for all stations 2005-2019 Sam-Erik Walker and Sverre Solberg https://doi.org/10.5281/zenodo.6334334

Model code and software

AirGAM 2022r1 model (exact for results) Sam-Erik Walker https://doi.org/10.5281/zenodo.6334104

AirGAM 2022r1 model (latest) Sam-Erik Walker https://doi.org/10.5281/zenodo.6334104

Short summary

We have developed a statistical model for estimating trends in the daily air quality observations of NO₂, O₃, PM₁₀ and PM_2.5, 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.