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  Graph machine learning for improved imputation of missing tropospheric ozone data

Betancourt, C., Li, C. W. Y., Kleinert, F., & Schultz, M. G. (2023). Graph machine learning for improved imputation of missing tropospheric ozone data. Environmental Science & Technology, 57, 18246-18258. doi:10.1021/acs.est.3c05104.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-C036-D Version Permalink: https://hdl.handle.net/21.11116/0000-000E-0AC3-B
Genre: Journal Article

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betancourt-et-al-2023-graph-machine-learning-for-improved-imputation-of-missing-tropospheric-ozone-data.pdf (Publisher version), 5MB
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betancourt-et-al-2023-graph-machine-learning-for-improved-imputation-of-missing-tropospheric-ozone-data.pdf
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Copyright Date:
2023
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http://creativecommons.org/licenses/by/4.0/

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 Creators:
Betancourt, Clara1, Author
Li, Cathy Wing Yi1, 2, Author                 
Kleinert, Felix1, Author
Schultz, Martin G.1, Author
Affiliations:
1external, ou_persistent22              
2Environmental Modelling, MPI for Meteorology, Max Planck Society, ou_2149681              

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Language(s): eng - English
 Dates: 20232023-092023-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acs.est.3c05104
 Degree: -

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Title: Environmental Science & Technology
  Abbreviation : Environ. Sci. Technol.
Source Genre: Journal
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Publ. Info: Easton, PA : American Chemical Society
Pages: - Volume / Issue: 57 Sequence Number: - Start / End Page: 18246 - 18258 Identifier: ISSN: 0013-936X
CoNE: https://pure.mpg.de/cone/journals/resource/954921342157