Articles | Volume 23, issue 17
https://doi.org/10.5194/acp-23-10267-2023
https://doi.org/10.5194/acp-23-10267-2023
Research article
 | 
14 Sep 2023
Research article |  | 14 Sep 2023

Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch

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Latest update: 16 Jun 2024
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Short summary
In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
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