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.
@article{arxiv.2601.21899,
title = {Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting},
author = {Zhiqing Cui and Siru Zhong and Ming Jin and Shirui Pan and Qingsong Wen and Yuxuan Liang},
journal= {arXiv preprint arXiv:2601.21899},
year = {2026}
}