Modeling Dynamic Spatial Influence for Air Quality Prediction with Atmospheric Prior

Air quality prediction is an important task benefiting both individual outdoor activities and urban emergency response. To account for complex temporal factors that influence long-term air quality, researchers have formulated this problem using an encoder-decoder framework that captures the non-linear temporal evolution. Besides, as air quality presents natural spatial correlation, researchers have proposed to learn the spatial relation with either a graph structure or an attention mechanism. As well supported by atmospheric dispersion theories, air quality correlation among different monitoring stations is dynamic and changes over time due to atmospheric dispersion, leading to the notion of dispersion-driven dynamic spatial correlation. However, most previous works treated spatial correlation as a static process, and nearly all models relied on only data-driven approaches in the modeling process. To this end, we propose to model dynamic spatial influence for air quality prediction with atmospheric prior. The key idea of our work is to build a dynamic spatial graph at each time step with physical atmospheric dispersion modeling. Then, we leverage the learned embeddings from this dynamic spatial graph in an encoder-decoder model to seamlessly fuse the dynamic spatial correlation with the temporal evolution, which is key to air quality prediction. Finally, extensive experiments on real-world benchmark data clearly show the effectiveness of the proposed model.