Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8439–8452, 2022
Geosci. Model Dev., 15, 8439–8452, 2022
Model description paper
21 Nov 2022
Model description paper | 21 Nov 2022

Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement

Haochen Sun et al.

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Cited articles

Ayturan, Y. A., Ayturan, Z. C., and Altun, H. O.: Air pollution modelling with deep learning: a review, International Journal of Environmental Pollution and Environmental Modelling, 1, 58–62, 2018. 
Bi, J., Knowland, K. E., Keller, C. A., and Liu, Y.: Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast, Environ. Sci. Technol., 56, 1544–1556, 2022. 
Bui, T.-C., Le, V.-D., and Cha, S.-K.: A deep learning approach for forecasting air pollution in South Korea using LSTM, arXiv [preprint],, 2018. 
Fan, C., Li, Y., Guang, J., Li, Z., Elnashar, A., Allam, M., and de Leeuw, G.: The impact of the control measures during the COVID-19 outbreak on air pollution in China, Remote Sensing, 12, 1613,, 2020. 
Gilliam, R. C., Hogrefe, C., Godowitch, J. M., Napelenok, S., Mathur, R., and Rao, S. T.: Impact of inherent meteorology uncertainty on air quality model predictions, J. Geophys. Res.-Atmos., 120, 12259–12280, 2015. 
Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.