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

Journal: Environmental science & technology
PMID: 39636119

Abstract

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 (Δ) of SA-amines dimer clusters, a key parameter for assessing IEP, was calculated for 58 amines. Based on the calculated Δ values, seven ML models were constructed and the best one was further utilized to predict the Δ 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.

Authors

  • Fangfang Ma
    College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
  • 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.
  • Weihao Tang
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
  • Rongjie Zhang
    Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China.
  • 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.
  • Jingwen Chen
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China. Electronic address: jwchen@dlut.edu.cn.
  • Hong-Bin Xie
    Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.

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