Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-573-2023
https://doi.org/10.5194/gmd-16-573-2023
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

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

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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.