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Improving air quality assessment using physics-inspired deep graph learning.
- Source :
- NPJ Climate & Atmospheric Science; 9/27/2023, Vol. 6 Issue 1, p1-13, 13p
- Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23973722
- Volume :
- 6
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Climate & Atmospheric Science
- Publication Type :
- Academic Journal
- Accession number :
- 172361084
- Full Text :
- https://doi.org/10.1038/s41612-023-00475-3