Winter brown carbon over six of China's megacities: light absorption, molecular characterization, and improved source apportionment revealed by multilayer perceptron neural network

Wang, Diwei; Shen, Zhenxing; Zhang, Qian; Lei, Yali; Zhang, Tian; Huang, Shasha; Sun, Jian; Xu, Hongmei; Cao, Junji

doi:https://doi.org/10.5194/acp-22-14893-2022

Articles | Volume 22, issue 22

https://doi.org/10.5194/acp-22-14893-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-22-14893-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 22

Research article

|

23 Nov 2022

Research article |

| 23 Nov 2022

Winter brown carbon over six of China's megacities: light absorption, molecular characterization, and improved source apportionment revealed by multilayer perceptron neural network

Diwei Wang, Zhenxing Shen, Qian Zhang, Yali Lei, Tian Zhang, Shasha Huang, Jian Sun, Hongmei Xu, and Junji Cao

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https://doi.org/10.5194/acp-22-14893-2022-supplement

Data sets

Winter brown carbon over six China's megacities: Light absorption, molecular characterization, and improved source apportionment revealed by multilayer perceptron neural network Diwei Wang https://doi.org/10.5281/zenodo.6790321

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Short summary

The optical properties and molecular structure of atmospheric brown carbon (BrC) in winter of several megacities in China were analyzed, and the source contribution of brown carbon was improved by using positive matrix factorization coupled with a multilayer perceptron neural network. These results can provide a basis for the more effective control of BrC to reduce its impacts on regional climates and human health.