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Accurate air quality forecasting is crucial for public health, environmentalmonitoring and protection, and urban planning. However, existing methods failto effectively utilize multi-scale information, both spatially and temporally.Spatially, there is a lack of integration between individual monitoringstations and city-wide scales. Temporally, the periodic nature of air qualityvariations is often overlooked or inadequately considered. To address theselimitations, we present a novel Multi-spatial Multi-temporal air qualityforecasting method based on Graph Convolutional Networks and Gated RecurrentUnits (M2G2), bridging the gap in air quality forecasting across spatial andtemporal scales. The proposed framework consists of two modules: Multi-scaleSpatial GCN (MS-GCN) for spatial information fusion and Multi-scale TemporalGRU(MT-GRU) for temporal information integration. In the spatial dimension, theMS-GCN module employs a bidirectional learnable structure and a residualstructure, enabling comprehensive information exchange between individualmonitoring stations and the city-scale graph. Regarding the temporal dimension,the MT-GRU module adaptively combines information from different temporalscales through parallel hidden states. Leveraging meteorological indicators andfour air quality indicators, we present comprehensive comparative analyses andablation experiments, showcasing the higher accuracy of M2G2 in comparison tonine currently available advanced approaches across all aspects. Theimprovements of M2G2 over the second-best method on RMSE of the 24h/48h/72h areas follows: PM2.5: (7.72%, 6.67%, 10.45%); PM10: (6.43%, 5.68%, 7.73%); NO2:(5.07%, 7.76%, 16.60%); O3: (6.46%, 6.86%, 9.79%). Furthermore, we demonstratethe effectiveness of each module of M2G2 by ablation study.

Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data