空气污染如何损害我们的健康 https://www.who.int/zh/air-pollution/news-and-events/how-air-pollution-is-destroying-our-health Shi, X. et al. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems (2015), 802–810. Song, C. et al. 2019. Modeling Air Pollution Transmission Behavior as Complex Network and Mining Key Monitoring Station. IEEE Access. 7, (2019), 121245–121254. DOI:https://doi.org/10.1109/access.2019.2936613.  Hu, J. et al. 2014. Spatial and temporal variability of PM 2.5 and PM 10 over the North China Plain and the Yangtze River Delta, China. Atmospheric Environment. 95, (2014), 598–609. DOI:https://doi.org/10.1016/j.atmosenv.2014.07.019.  Chen, Z. et al. 2020. Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism. Environment International. 139, (Jun. 2020), 105558. DOI:https://doi.org/10.1016/j.envint.2020.105558 https://www.epa.gov/cmaq Battaglia, P.W. et al. 2018. Relational inductive biases, deep learning, and graph networks. (2018). Qi, Y. et al. 2019. A hybrid model for spatiotemporal forecasting of PM 2.5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment. 664, February (2019), 1–10. DOI:https://doi.org/10.1016/j.scitotenv.2019.01.333.  Kipf, T.N. and Welling, M. 2017. Semi-supervised classification with graph convolutional networks. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings (Sep. 2017). I. Uno, N. Sugimoto, A. Shimizu, K. Yumimoto, Y. Hara, and Z. Wang, “Record heavy PM2.5 air pollution over China in January 2013: Vertical and horizontal dimensions,” Sci. Online Lett. Atmos., vol. 10, no. 1, pp. 136–140, 2014.