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  Improving air quality assessment using physics-inspired deep graph learning

Li, L., Wang, J., Franklin, M., Yin, Q., Wu, J., Camps-Valls, G., et al. (2023). Improving air quality assessment using physics-inspired deep graph learning. npj Climate and Atmospheric Science, 6: 152. doi:10.1038/s41612-023-00475-3.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-BEC8-C Version Permalink: https://hdl.handle.net/21.11116/0000-000D-BEC9-B
Genre: Journal Article

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 Creators:
Li, Lianfa, Author
Wang, Jinfeng , Author
Franklin, Meredith, Author
Yin, Qian, Author
Wu, Jiajie, Author
Camps-Valls, Gustau, Author
Zhu, Zhiping, Author
Wang, Chengyi, Author
Ge, Yong, Author
Reichstein, Markus1, Author                 
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1Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1688139              

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 Dates: 2023-09-072023-09-27
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: BGC4350
DOI: 10.1038/s41612-023-00475-3
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Title: npj Climate and Atmospheric Science
  Other : Climate and Atmospheric Science
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 6 Sequence Number: 152 Start / End Page: - Identifier: ISSN: 2397-3722
CoNE: https://pure.mpg.de/cone/journals/resource/2397-3722