Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT

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Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM

Published:2024-08-01 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Wang Xiaohu,Zhang Suo,Chen Yi,He Longying,Ren Yongmei,Zhang Zhen,Li Juan,Zhang Shiqing

Funder

Natural Science Foundation of Hunan Province

National College Students Innovation and Entrepreneurship Training Program

Hunan Provincial Department of Education Science Research Fund Project

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-68874-x.pdf

Reference43 articles.

1. Kampa, M. & Castanas, E. Human health effects of air pollution. Environ. Pollut. 151, 362–367 (2008).

2. Pollution, W. A. A. A Global Assessment of Exposure and Burden of Disease 360 (World Health Organization, 2016).

3. Liu, H., Chen, Y. & Ma, L. Effect of time-varying exposure to air pollution on subjective well-being. J. Clean. Prod. 281, 125364 (2021).

4. Tesche, T. et al. CMAQ/CAMx annual 2002 performance evaluation over the eastern US. Atmos. Environ. 40, 4906–4919 (2006).

5. Wang, Z. et al. Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control. Sci. China Earth Sci. 57, 3–13 (2014).

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