Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6717-2025
https://doi.org/10.5194/gmd-18-6717-2025
Methods for assessment of models
 | 
02 Oct 2025
Methods for assessment of models |  | 02 Oct 2025

A close look at using national ground stations for the statistical modeling of NO2

Foeke Boersma and Meng Lu

Data sets

A close look at using national ground stations for the statistical mapping of NO2 Foeke Boersma https://doi.org/10.5281/zenodo.15193954

Model code and software

A close look at using national ground stations for the statistical mapping of NO2 Foeke Boersma https://github.com/FoekeBoersma/A-close-look-at-using-national-ground-stations-for-the-statistical-mapping-of-NO2

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Short summary
Air pollution harms health and society. Understanding and predicting it is crucial. Various models have been developed to model air pollution. However, the consistency exhibited by a model in different areas is commonly neglected. Our study accounts for this and shows lower accuracy near busy roads but higher accuracy in less populated areas. Considering location characteristics in air pollution predictions is important in comparing statistical models and understanding the health–society–space relationship.
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