Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Feng, Jin; Li, Yanjie; Qiu, Yulu; Zhu, Fuxin

doi:https://doi.org/10.5194/acp-23-375-2023

Articles | Volume 23, issue 1

https://doi.org/10.5194/acp-23-375-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-375-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 1

Research article

|

10 Jan 2023

Research article |

| 10 Jan 2023

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Jin Feng, Yanjie Li, Yulu Qiu, and Fuxin Zhu

Supplement

https://doi.org/10.5194/acp-23-375-2023-supplement

Data sets

Data for "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data" J. Feng https://doi.org/10.5281/zenodo.6982879

Video supplement

Animation for "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data" J. Feng https://doi.org/10.5281/zenodo.6982971

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Short summary

It is important to use weather data to estimate aerosol concentrations. Here, a weather index for aerosol concentration based on deep learning was developed, linking weather and short-term variations in aerosol concentrations over China. The index provides better performance than chemical transport model simulation and other data-based estimation approaches. It can be used as a robust tool for estimating daily variations in aerosol concentrations.