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Improving air quality assessment using physics-inspired deep graph learning
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AI
DOI：10.1038/s41612-023-00475-3.png)
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摘要
En 中文
Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inconsistent or implausible results. Here, we report a hybrid multilevel graph neural network that encodes fluid physics to capture spatial and temporal dynamic characteristics of air pollutants. On a multi-air pollutant test in China, our method consistently improved extrapolation accuracy by an average of 11-22% compared to several baseline machine learning methods, and generated physically consistent spatiotemporal trends of air pollutants at fine spatial and temporal scales.
Keyword:
OZONE POLLUTION
CHINA
REGRESSION
CHEMISTRY
NETWORKS
FRAMEWORK
PM2.5
期刊
IF:
8.4
论文数:
1.6K
被引数:
5.4K
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