Articles | Volume 14, issue 7
Geosci. Model Dev., 14, 4641–4654, 2021
https://doi.org/10.5194/gmd-14-4641-2021
Geosci. Model Dev., 14, 4641–4654, 2021
https://doi.org/10.5194/gmd-14-4641-2021

Model description paper 28 Jul 2021

Model description paper | 28 Jul 2021

Exploring deep learning for air pollutant emission estimation

Lin Huang et al.

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Cited articles

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Appel, K. W., Napelenok, S., Hogrefe, C., Pouliot, G., Foley, K. M., Roselle, S. J., Pleim, J. E., Bash, J., Pye, H. O. T., and Heath, N.: Overview and Evaluation of the Community Multiscale Air Quality (CMAQ) Modeling System Version 5.2, in: Air Pollution Modeling and its Application XXV, edited by: Mensink, C. and Kallos, G., ITM 2016, Springer Proceedings in Complexity, Springer, Cham, 69–73, https://doi.org/10.1007/978-3-319-57645-9_11, 2018. 
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Short summary
Accurate estimation of emissions is a prerequisite for effectively controlling air pollution, but current methods lack either sufficient data or a representation of nonlinearity. Here, we proposed a novel deep learning method to model the dual relationship between emissions and pollutant concentrations. Emissions can be updated by back-propagating the gradient of the loss function measuring the deviation between simulations and observations, resulting in better model performance.