Abstract
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.
Supported by the National Key R&D Program of China under Grant No. 2020YFB1710200, the National Natural Science Foundation of China under Grant No. 61872105 and No. 62072136, the Fundamental Research Funds for the Central Universities under Grant No. 3072020CFT2402 and No. 3072020CFT0603, and the Opening Fund of Acoustics Science and Technology Laboratory under Grant No. SSKF2020003.
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Abdel-Rahman, A.A.: On the dispersion models and atmospheric dispersion. Int. J. Glob. Warming 3(4), 257–273 (2011)
Arystanbekova, N.K.: Application of gaussian plume models for air pollution simulation at instantaneous emissions. Math. Comput. Simul. 67(4), 451–458 (2004)
Bergin, M.S., Noblet, G.S., Petrini, K., Dhieux, J.R., Milford, J.B., Harley, R.A.: Formal uncertainty analysis of a Lagrangian photochemical air pollution model. Environ. Sci. Technol. 33(7), 1116–1126 (1999)
Chen, L., Cai, Y., Ding, Y., Lv, M., Yuan, C., Chen, G.: Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 1076–1087 (2016)
Cheng, W., Shen, Y., Zhu, Y., Huang, L.: A neural attention model for urban air quality inference: learning the weights of monitoring stations. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI), pp. 2151–2158 (2018)
Guizilini, V., Ramos, F.: A nonparametric online model for air quality prediction. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 651–657 (2015)
Hsieh, H., Lin, S., Zheng, Y.: Inferring air quality for station location recommendation based on urban big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 437–446 (2015)
Jiang, Y., Sun, X., Wang, W., Young, S.D.: Enhancing air quality prediction with social media and natural language processing. In: Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL), pp. 2627–2632 (2019)
Jin, B.J., Bu, P.S., Jin, K.J.: Urban flow and dispersion simulation using a CFD model coupled to a mesoscale model. J. Appl. Meteorol. Climatol. 48(8), 1667–1681 (2009)
Jutzeler, A., Li, J.J., Faltings, B.: A region-based model for estimating urban air pollution. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp. 424–430 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (ICLR) (2017)
Li, X., et al.: Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ. Pollut. 231, 997–1004 (2017)
Liang, Y., Ke, S., Zhang, J., Yi, X., Zheng, Y.: GeoMAN: multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3428–3434 (2018)
Luo, Z., Huang, J., Hu, K., Li, X., Zhang, P.: AccuAir: winning solution to air quality prediction for KDD cup 2018. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1842–1850 (2019)
Paolo, Z.: Gaussian models. In: Air Pollution Modeling, pp. 141–183. Springer, Boston (1990). https://doi.org/10.1007/978-1-4757-4465-1_7
Pramanik, P., Mondal, T., Nandi, S., Saha, M.: AirCalypse: can Twitter help in urban air quality measurement and who are the influential users? In: Proceedings of the 29th International World Wide Web Conferences (WWW), pp. 540–545 (2020)
Rakowska, A., et al.: Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon. Atmos. Environ. 98, 260–270 (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the 28th Conference on Neural Information Processing Systems (NIPS), pp. 3104–3112 (2014)
Wilson, T., Tan, P., Luo, L.: A low rank weighted graph convolutional approach to weather prediction. In: Proceeding of the 18th IEEE International Conference on Data Mining (ICDM), pp. 627–636 (2018)
Yi, X., Zhang, J., Wang, Z., Li, T., Zheng, Y.: Deep distributed fusion network for air quality prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 965–973 (2018)
Zhang, Y., et al.: Multi-group encoder-decoder networks to fuse heterogeneous data for next-day air quality prediction. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pp. 4341–4347 (2019)
Zhao, X., Xu, T., Fu, Y., Chen, E., Guo, H.: Incorporating spatio-temporal smoothness for air quality inference. In: Proceeding of the 17th IEEE International Conference on Data Mining (ICDM), pp. 1177–1182 (2017)
Zheng, Y., Liu, F., Hsieh, H.: U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1436–1444 (2013)
Zheng, Y., et al.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 2267–2276 (2015)
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Lu, D., Wu, L., Chen, R., Han, Q., Wang, Y., Ge, Y. (2021). Modeling Dynamic Spatial Influence for Air Quality Prediction with Atmospheric Prior. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_28
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