Articles | Volume 25, issue 20
https://doi.org/10.5194/acp-25-13379-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-13379-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Implications of VOC oxidation in atmospheric chemistry: development of a comprehensive AI model for predicting reaction rate constants
Xin Zhang
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
Jiaqi Luo
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
Wenxiao Pan
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
Qiao Xue
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
Jianjie Fu
CORRESPONDING AUTHOR
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, P.R. China
Aiqian Zhang
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, P.R. China
Guibin Jiang
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P.R. China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P.R. China
School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, P.R. China
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Short summary
Volatile organic compounds drive atmospheric chemistry via oxidation, forming PM2.5/ozone precursors. This study introduces Vreact, a graph-based deep learning model predicting reaction rate constants (ki) for multiple oxidants simultaneously. It achieves mean squared error = 0.299 and R² = 0.941 for log10ki , overcoming single-oxidant model limits. Vreact advances pollutant formation insights and supports emission control strategies, aiding global air quality and public health efforts.
Volatile organic compounds drive atmospheric chemistry via oxidation, forming PM2.5/ozone...
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