Modeling Dynamic Spatial Influence for Air Quality Prediction with Atmospheric Prior

Dan Lu, Le Wu, Rui Chen, Qilong Han, Yichen Wang, Yong Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationWeb and Big Data - 5th International Joint Conference, APWeb-WAIM 2021, Proceedings
EditorsLeong Hou U, Marc Spaniol, Yasushi Sakurai, Junying Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030858988
StatePublished - 2021
Event5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021 - Guangzhou, China
Duration: Aug 23 2021Aug 25 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12859 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021


  • Air quality prediction
  • Atmospheric dispersion
  • Dynamic spatial correlation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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