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MADGCN: A Meteorology-Aware Spatio-Temporal Graph Convolution Network for Long-Term Air Pollution Forecasting

Binwu Wang Zhiqing Cui Guangjun Wang Zhen-Qiang Zhou Fan Meng Jingjia Luo Yang Wang
Sep 2026 · IEEE Transactions on Knowledge and Data Engineering · Vol 38, pp. 5947-5962 · 0 citations · 58 references

Abstract

Air quality forecasting has attracted increasing attention as global air pollution worsens. Spatiotemporal graph neural networks have become a leading paradigm, thanks to their ability to capture complex spatial and temporal dynamics in Air Quality Index (AQI) data. However, existing methods remain limited by weak modeling of long-range temporal dependencies and insufficient integration of meteorological factors. Building on a publicly available nationwide air quality dataset spanning eight years, we propose MADGCN, a Meteorology-Aware Decoupled Spatio-Temporal Convolutional Network that jointly addresses long-horizon temporal modeling and meteorological context fusion. MADGCN includes a dynamic causality discovery module grounded in Granger causality, which captures time-varying causal relationships between meteorological conditions and AQI dynamics. The inferred causal structures further guide a causal graph convolution module and a PatchMixer module, enabling effective spatial interaction modeling and multiscale temporal dependency learning. Extensive experiments against 16 strong baselines show that MADGCN achieves competitive performance for long-horizon air pollution forecasting and generalizes well under high-pollution regimes..

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