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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.
New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals.
Yan X, Zang Z, Liang C, Luo N, Ren R, Cribb M, Li Z.
Yan X, et al. Among authors: zang z.
Environ Pollut. 2021 May 1;276:116707. doi: 10.1016/j.envpol.2021.116707. Epub 2021 Feb 8.
Environ Pollut. 2021.
PMID: 33609902
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New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data.
Yan X, Zang Z, Luo N, Jiang Y, Li Z.
Yan X, et al. Among authors: zang z.
Environ Int. 2020 Nov;144:106060. doi: 10.1016/j.envint.2020.106060. Epub 2020 Sep 10.
Environ Int. 2020.
PMID: 32920497
Free article.
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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: zang z.
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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Fugitive Road Dust PM2.5 Emissions and Their Potential Health Impacts.
Chen S, Zhang X, Lin J, Huang J, Zhao D, Yuan T, Huang K, Luo Y, Jia Z, Zang Z, Qiu Y, Xie L.
Chen S, et al. Among authors: zang z.
Environ Sci Technol. 2019 Jul 16;53(14):8455-8465. doi: 10.1021/acs.est.9b00666. Epub 2019 May 28.
Environ Sci Technol. 2019.
PMID: 31117536
Free article.
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Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.
Zang Z, Zhang Y, Zuo C, Chen J, He B, Luo N, Zou J, Zhao W, Shi W, Yan X.
Zang Z, et al.
Environ Sci Technol. 2023 Dec 5;57(48):19881-19890. doi: 10.1021/acs.est.3c07982. Epub 2023 Nov 14.
Environ Sci Technol. 2023.
PMID: 37962866
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