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Statistical modelling for long-term trends of pollutants - Use of a GAM model for the assessment of measurements of O3, NO2 and PM

Solberg, Sverre; Walker, Sam-Erik; Guerreiro, Cristina de Brito Beirao; Colette, Augustin


Publikasjon: Statistical modelling for long-term trends of pollutants - Use of a GAM model for the assessment of measurements of O3, NO2 and PM
Serie: Eionet Report - ETC/ATNI, 14/2019
År: 2020
Utgiver: ETC/ATNI, Kjeller
Lenke: Fulltekst

The current report provides a short overview of previous years’ studies on long-term trends in O3, NO2 and PM and the role of meteorological variability for the concentration of these pollutants. The previous studies on the link between trends and meteorology has shown that these links could be estimated by a careful design of model setups using CTMs (chemical transport models). The conclusions from this work is that CTMs are certainly useful tools for explaining pollutant trends in terms of the separate impact of individual physio-chemical drivers such as emissions and meteorology although computationally demanding. The statistical GAM model that have been developed as part of the recent ETC/ACM and ETC/ATNI tasks could be considered as complementary to the use of CTMs for separating the influence of meteorological variability from other processes. The main limitation of the statistical model is that it contains no parameterisation of the real physio-chemical processes and secondly, that it relies on a local assumption, i.e. that the observed daily concentrations could be estimated based on the local meteorological data. We found clear differences in model performance both with respect to geographical area and atmospheric species. In general, the best performance was found for O3 (although not for peak levels) with gradually lower performance for NO2, PM10 and PM2.5 in that order. With respect to area, the model produced the best predictions for Central Europe (Germany, Netherlands, Belgium, France, Austria, Czech Republic) and poorer agreement with observations in southern Europe. Although the GAM model did not detect many meteorology induced long-term trends in the data, the model is well suited for separating the influence of meteorology from the other driving forces, such as emissions and boundary conditions. The GAM model thus provides robust and smooth long-term trend functions corrected for meteorology as well as the perturbations from year to year, reflecting the variability in weather conditions. One could consider to define a set of performance criteria to decide if the GAM model is applicable for a specific station and parameter.