Improving air quality assessment using physics-inspired deep graph learning

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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.
Funding Information
  • CAS | Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science (XDA19040501)
  • National Natural Science Foundation of China (42071369)

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