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Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm

Surface ozone (O[Formula: see text] ) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclu...

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Autores principales: Balamurugan, Vigneshkumar, Balamurugan, Vinothkumar, Chen, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983660/
https://www.ncbi.nlm.nih.gov/pubmed/35383223
http://dx.doi.org/10.1038/s41598-022-09619-6
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author Balamurugan, Vigneshkumar
Balamurugan, Vinothkumar
Chen, Jia
author_facet Balamurugan, Vigneshkumar
Balamurugan, Vinothkumar
Chen, Jia
author_sort Balamurugan, Vigneshkumar
collection PubMed
description Surface ozone (O[Formula: see text] ) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R[Formula: see text] = 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R[Formula: see text] = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R[Formula: see text] ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O[Formula: see text] precursors information has little effect on the ML model’s performance.
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spelling pubmed-89836602022-04-06 Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm Balamurugan, Vigneshkumar Balamurugan, Vinothkumar Chen, Jia Sci Rep Article Surface ozone (O[Formula: see text] ) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R[Formula: see text] = 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R[Formula: see text] = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R[Formula: see text] ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O[Formula: see text] precursors information has little effect on the ML model’s performance. Nature Publishing Group UK 2022-04-05 /pmc/articles/PMC8983660/ /pubmed/35383223 http://dx.doi.org/10.1038/s41598-022-09619-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Balamurugan, Vigneshkumar
Balamurugan, Vinothkumar
Chen, Jia
Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
title Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
title_full Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
title_fullStr Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
title_full_unstemmed Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
title_short Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
title_sort importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983660/
https://www.ncbi.nlm.nih.gov/pubmed/35383223
http://dx.doi.org/10.1038/s41598-022-09619-6
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