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

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Cited articles

Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,, 2001. 
Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone in urban areas and their use in assessing ozone trends, Atmos. Environ., 41, 7127–7137,, 2007. 
Carslaw, D. C.: The openair manual – open-source tools for analysing air pollution data, Manual for version 2.6–6, University of York, (last access: 21 January 2022), 2019. 
Carslaw, D. C.: deweather: Remove the influence of weather on air quality data, R package version 0.7, (last access: 21 January 2022), 2021. 
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.