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Sulfuric Acid-Driven Nucleation Enhanced by Amines from Ethanol Gasoline Vehicle Emission: Machine Learning Model and Mechanistic Study

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Download Hi-Res ImageDownload to MS-PowerPointCite This:Environ. Sci. Technol. 2024, 58, 50, 22278-22287
    Occurrence, Fate, and Transport of Contaminants in Indoor Air and Atmosphere

    Sulfuric Acid-Driven Nucleation Enhanced by Amines from Ethanol Gasoline Vehicle Emission: Machine Learning Model and Mechanistic Study
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    • Fangfang Ma
      Fangfang Ma
      Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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    • Lihao Su
      Lihao Su
      Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
      More by Lihao Su
      Orcidhttps://orcid.org/0000-0002-0494-8216
    • Weihao Tang
      Weihao Tang
      National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
      More by Weihao Tang
    • Rongjie Zhang
      Rongjie Zhang
      Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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    • Qiaojing Zhao
      Qiaojing Zhao
      Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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    • Jingwen Chen
      Jingwen Chen
      Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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      Orcidhttps://orcid.org/0000-0002-5756-3336
    • Hong-Bin Xie*
      Hong-Bin Xie
      Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
      *Email: [email protected]. Phone/Fax: +86-411-84707251.
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      Orcidhttps://orcid.org/0000-0002-9119-9785
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2024, 58, 50, 22278–22287
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    https://doi.org/10.1021/acs.est.4c06578
    Published December 5, 2024
    Copyright © 2024 American Chemical Society
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    Abstract

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    The sulfuric acid (SA)-amine nucleation mechanism gained increasing attention due to its important role in atmospheric secondary particle formation. However, the intrinsic enhancing potential (IEP) of various amines remains largely unknown, restraining the assessment on the role of the SA-amines mechanism at various locations. Herein, machine learning (ML) models were constructed for high-throughput prediction of IEP of amines, and the nucleation mechanism of specific amines with high IEP was investigated. The formation free energy (ΔG) of SA-amines dimer clusters, a key parameter for assessing IEP, was calculated for 58 amines. Based on the calculated ΔG values, seven ML models were constructed and the best one was further utilized to predict the ΔG values of the remaining 153 amines. Diethylamine (DEA), mainly emitted from ethanol gasoline vehicles, was found to be one of the amines with the highest IEP for SA-driven nucleation. By studying larger SA-DEA clusters, it was found that the nucleation rate of DEA with SA is 3–7 times higher than that of dimethylamine, a well-known key base for SA-driven nucleation. The study provides a powerful tool for evaluating the actual role of amines on SA-driven nucleation and revealed that the mechanism could be particularly important in areas where ethanol gasoline vehicles are widely used.

    Copyright © 2024 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.4c06578.

    • Structures of 58 amine monomers and their dimer clusters; comparison for the ΔG values at low and high levels of theory; performance indicators of seven ML models; hyperparameters of seven ML models; values and description of molecular descriptors; calculated and predicted ΔG values of 58 amines; predicted ΔG values of the remaining 153 amines; Rext2 values for the Lasso model under various applicability domains; predicted ΔG values from the Lasso model versus the calculated values of 58 amines; external validation performance of the Lasso model under various applicability domains; relationship between GB and calculated ΔG values of 58 amines; selection of boundary clusters; calculated ΔG values and evaporation rates of the SA-DMA system at 278.15 K; calculated ΔG values of the SA-DEA system at 298.15 and 268.15 K, and the changes with varying temperatures; effects of kcoag values on the results for the SA-DEA system; and coordinates of SA-DEA clusters (PDF)

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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2024, 58, 50, 22278–22287
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.est.4c06578
    Published December 5, 2024
    Copyright © 2024 American Chemical Society
    Request reuse permissions

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