Improving air quality assessment using physics-inspired deep graph learning

Autor: Lianfa Li, Jinfeng Wang, Meredith Franklin, Qian Yin, Jiajie Wu, Gustau Camps-Valls, Zhiping Zhu, Chengyi Wang, Yong Ge, Markus Reichstein
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: npj Climate and Atmospheric Science, Vol 6, Iss 1, Pp 1-13 (2023)
Druh dokumentu: article
ISSN: 2397-3722
64518906
DOI: 10.1038/s41612-023-00475-3
Popis: 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.
Databáze: Directory of Open Access Journals
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