Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

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

doi:https://doi.org/10.5194/acp-23-10267-2023

Articles | Volume 23, issue 17

https://doi.org/10.5194/acp-23-10267-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-10267-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 17

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

Model code and software

Spatio temporal ML model for NO2 and O3: Initial release V. Balamurgan, J. Chen, A. Wenzel, and F. N. Keutsch https://doi.org/10.5281/zenodo.8330479

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 NO_x and O₃. Despite the fact that metropolitan regions are NO₂ hotspots, rural areas have high O₃ concentrations.