Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting

• URL: http://arxiv.org/abs/2601.21899v1
• Date: Thu, 29 Jan 2026 15:58:07 GMT
• Title: Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
• Authors: Zhiqing Cui, Siru Zhong, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang,
• Abstract summary: We propose OmniAir, a semantic topology learning framework tailored for global station-level prediction.<n>Our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks.<n>Experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models.
• Score: 99.4484686548807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Global air quality forecasting grapples with extreme spatial heterogeneity and the poor generalization of existing transductive models to unseen regions. To tackle this, we propose OmniAir, a semantic topology learning framework tailored for global station-level prediction. By encoding invariant physical environmental attributes into generalizable station identities and dynamically constructing adaptive sparse topologies, our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks. We further curate WorldAir, a massive dataset covering over 7,800 stations worldwide. Extensive experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models, while effectively bridging the monitoring gap in data-sparse regions.

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