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

TL;DR

This paper introduces OmniAir, a novel semantic topology learning framework for global air quality forecasting that effectively captures complex spatial correlations and generalizes well to unseen regions, outperforming existing models.

Contribution

The paper presents OmniAir, a new approach that encodes physical environmental attributes into station identities and constructs adaptive topologies for improved worldwide air quality prediction.

Findings

OmniAir outperforms 18 baseline models in accuracy.

It is nearly 10 times faster than existing methods.

Effective in data-sparse and diverse regions.

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.