  Accurate air quality forecasting is crucial for public health, environmental
monitoring and protection, and urban planning. However, existing methods fail
to effectively utilize multi-scale information, both spatially and temporally.
Spatially, there is a lack of integration between individual monitoring
stations and city-wide scales. Temporally, the periodic nature of air quality
variations is often overlooked or inadequately considered. To address these
limitations, we present a novel Multi-spatial Multi-temporal air quality
forecasting method based on Graph Convolutional Networks and Gated Recurrent
Units (M2G2), bridging the gap in air quality forecasting across spatial and
temporal scales. The proposed framework consists of two modules: Multi-scale
Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal
GRU(MT-GRU) for temporal information integration. In the spatial dimension, the
MS-GCN module employs a bidirectional learnable structure and a residual
structure, enabling comprehensive information exchange between individual
monitoring stations and the city-scale graph. Regarding the temporal dimension,
the MT-GRU module adaptively combines information from different temporal
scales through parallel hidden states. Leveraging meteorological indicators and
four air quality indicators, we present comprehensive comparative analyses and
ablation experiments, showcasing the higher accuracy of M2G2 in comparison to
nine currently available advanced approaches across all aspects. The
improvements of M2G2 over the second-best method on RMSE of the 24h/48h/72h are
as 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 demonstrate
the effectiveness of each module of M2G2 by ablation study.
