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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Sep 2023
Research article |
| 14 Sep 2023
| 14 Sep 2023
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning
Vigneshkumar Balamurugan
CORRESPONDING AUTHOR
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Adrian Wenzel
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Frank N. Keutsch
School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
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Gerrit Kuhlmann, Ka Lok Chan, Sebastian Donner, Ying Zhu, Marc Schwaerzel, Steffen Dörner, Jia Chen, Andreas Hueni, Duc Hai Nguyen, Alexander Damm, Annette Schütt, Florian Dietrich, Dominik Brunner, Cheng Liu, Brigitte Buchmann, Thomas Wagner, and Mark Wenig
Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, https://doi.org/10.5194/amt-15-1609-2022, 2022
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Dandan Wei, Hariprasad D. Alwe, Dylan B. Millet, Brandon Bottorff, Michelle Lew, Philip S. Stevens, Joshua D. Shutter, Joshua L. Cox, Frank N. Keutsch, Qianwen Shi, Sarah C. Kavassalis, Jennifer G. Murphy, Krystal T. Vasquez, Hannah M. Allen, Eric Praske, John D. Crounse, Paul O. Wennberg, Paul B. Shepson, Alexander A. T. Bui, Henry W. Wallace, Robert J. Griffin, Nathaniel W. May, Megan Connor, Jonathan H. Slade, Kerri A. Pratt, Ezra C. Wood, Mathew Rollings, Benjamin L. Deming, Daniel C. Anderson, and Allison L. Steiner
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Taylor S. Jones, Jonathan E. Franklin, Jia Chen, Florian Dietrich, Kristian D. Hajny, Johannes C. Paetzold, Adrian Wenzel, Conor Gately, Elaine Gottlieb, Harrison Parker, Manvendra Dubey, Frank Hase, Paul B. Shepson, Levi H. Mielke, and Steven C. Wofsy
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Eleni Dovrou, Kelvin H. Bates, Jean C. Rivera-Rios, Joshua L. Cox, Joshua D. Shutter, and Frank N. Keutsch
Atmos. Chem. Phys., 21, 8999–9008, https://doi.org/10.5194/acp-21-8999-2021, https://doi.org/10.5194/acp-21-8999-2021, 2021
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We examined the mechanism and products of oxidation of dissolved sulfur dioxide with the main isomers of isoprene hydroxyl hydroperoxides, via laboratory and model analysis. Two chemical mechanism pathways are proposed and the results provide an improved understanding of the broader atmospheric chemistry and role of multifunctional organic hydroperoxides, which should be the dominant VOC oxidation products under low-NO conditions, highlighting their significant contribution to sulfate formation.
Jack C. Hensley, Adam W. Birdsall, Gregory Valtierra, Joshua L. Cox, and Frank N. Keutsch
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Atmos. Meas. Tech., 14, 2501–2513, https://doi.org/10.5194/amt-14-2501-2021, https://doi.org/10.5194/amt-14-2501-2021, 2021
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Climate change is one of the defining issues of our time. However, most of the current emission estimates are based on calculations, not on actual measurements as it is difficult to quantify the emissions of large sources such as cities. This study shows how to use the relatively new approach of column measurements to quantify urban greenhouse gas emissions in an exact way using only a few compact measurement systems. The approach can be used to evaluate the effectiveness of mitigation policies.
Cited articles
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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.
In this study, machine learning models are employed to model NO2 and O3 concentrations. We...
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