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  Separating daily 1 km PM2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data

Wei, J., Li, Z., Chen, X., Li, C., Sun, Y., Wang, J., et al. (2023). Separating daily 1 km PM2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data. Environmental Science & Technology, early access. doi:10.1021/acs.est.3c00272.

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 Creators:
Wei, Jing1, Author
Li, Zhanqing1, Author
Chen, Xi1, Author
Li, Chi1, Author
Sun, Yele1, Author
Wang, Jun1, Author
Lyapustin, Alexei1, Author
Brasseur, Guy P.2, Author                 
Jiang, Mengjiao1, Author
Sun, Lin1, Author
Wang, Tao1, Author
Jung, Chang Hoon1, Author
Qiu, Bing1, Author
Fang, Cuilan1, Author
Liu, Xuhui1, Author
Hao, Jinrui1, Author
Wang, Yan1, Author
Zhan, Ming1, Author
Song, Xiaohong1, Author
Liu, Yuewei1, Author
Affiliations:
1external, ou_persistent22              
2Environmental Modelling, MPI for Meteorology, Max Planck Society, ou_2149681              

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Language(s): eng - English
 Dates: 2023-04-28
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: BibTex Citekey: WeiLiEtAl2023
DOI: 10.1021/acs.est.3c00272
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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: - Sequence Number: early access Start / End Page: - Identifier: ISSN: 0013-936X
CoNE: https://pure.mpg.de/cone/journals/resource/954921342157