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Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach.
Environ Int. 2021 Apr;149:106392. doi: 10.1016/j.envint.2021.106392. Epub 2021 Jan 29.
Environ Int. 2021.
PMID: 33516989
Free article.
A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5.
Yan X, Zang Z, Jiang Y, Shi W, Guo Y, Li D, Zhao C, Husi L.
Yan X, et al. Among authors: husi l.
Environ Pollut. 2021 Jan 11;273:116459. doi: 10.1016/j.envpol.2021.116459. Online ahead of print.
Environ Pollut. 2021.
PMID: 33465651
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