Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 7
Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4641-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4641-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 7
Model description paper
 | 
28 Jul 2021
Model description paper |  | 28 Jul 2021

Exploring deep learning for air pollutant emission estimation

Lin Huang, Song Liu, Zeyuan Yang, Jia Xing, Jia Zhang, Jiang Bian, Siwei Li, Shovan Kumar Sahu, Shuxiao Wang, and Tie-Yan Liu

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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.
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