Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
Junsu Gil
Department of Earth and Environmental Sciences, Korea University,
Seoul, South Korea
Department of Earth and Environmental Sciences, Korea University,
Seoul, South Korea
Jeonghwan Kim
Department of Environmental Science, Hankuk University of Foreign
Studies, Yongin, South Korea
Gangwoong Lee
Department of Environmental Science, Hankuk University of Foreign
Studies, Yongin, South Korea
Joonyoung Ahn
Climate and Air Quality Research
Department, Air Quality Forecasting Center, National Institute of Environmental Research (NIER), Incheon,
South Korea
Cheol-Hee Kim
Department of Atmospheric Sciences, Pusan National University, Busan, South Korea
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Haeyoung Lee, Edward J. Dlugokencky, Jocelyn C. Turnbull, Sepyo Lee, Scott J. Lehman, John B. Miller, Gabrielle Pétron, Jeong-Sik Lim, Gang-Woong Lee, Sang-Sam Lee, and Young-San Park
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Chemical effects on the size-resolved hygroscopicity of urban aerosols were examined based on the KORUS-AQ field campaign data (HTDMA and HR-ToF-AMS). The size-resolved chemical composition data were found to be critical in explaining the size-dependent hygroscopicity, as well as the diurnal variation of κ for small particles. Aerosol mixing state information was associated with the size-resolved chemical composition data to reveal chemical information of different hygroscopicity modes.
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Short summary
In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
In this study, the framework for calculating reactive nitrogen species using a deep neural...