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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Latest update: 22 Oct 2021
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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.