Fine-Grained Air Pollution Inference at Large-Scale Region Level via 3D Spatiotemporal Attention Super-Resolution Model

Air pollution presents a serious hazard to human health and the environment for the global rise in industrialization and urbanization. While fine-grained monitoring is crucial for understanding the formation and control of air pollution and their effects on human health, existing macro-regional level or ground-level methods make air pollution inference in the same spatial scale and fail to address the spatiotemporal correlations between cross-grained air pollution distribution. In this paper, we propose a 3D spatiotemporal attention super-resolution model (AirSTFM) for fine-grained air pollution inference at a large-scale region level. Firstly, we design a 3D-patch-wise self-attention convolutional module to extract the spatiotemporal features of air pollution, which aggregates both spatial and temporal information of coarse-grained air pollution and employs a sliding window to add spatial local features. Then, we propose a bidirectional optical flow feed-forward layer to extract the short-term air pollution diffusion characteristics, which can learn the temporal correlation contaminant diffusion between closeness time intervals. Finally, we construct a spatiotemporal super-resolution upsampling pretext task to model the higher-level dispersion features mapping between the coarse-grained and fined-grained air pollution distribution. The proposed method is tested on the PM${}_{2.5}$ pollution datatset of the Yangtze River Delta region. Our model outperforms the second best model in RMSE, MAE, and MAPE by 2.6%, 3.05%, and 6.36% in the 100% division, and our model also outperforms the second best model in RMSE, MAE, and MAPE by 3.86%, 3.76%, and 12.18% in the 40% division, which demonstrates the applicability of our model for different data sizes. Furthermore, the comprehensive experiment results show that our proposed AirSTFM outperforms the state-of-the-art models.