Decoding PM oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Journal: Journal of hazardous materials
Published Date: Jun 9, 2025

Abstract

PM oxidative potential (OP), a key driver of health risks, was investigated in Ningbo, China, using dual dithiothreitol (DTT) and ascorbic acid (AA) assays combined with machine learning (ML). This approach accounts for the complexity of interactions among key chemical drivers and accurately identifies chemical species and PM sources associated with OP - a critical gap in prior studies relying solely on correlation analysis and linear regression. Year-long PM samples revealed higher nighttime and summer OP (volume-based OP-DTTv and OP-AAv), linked to aerosol acidity and photochemical aging. Among six ML models, Extremely Randomized Trees (ERT) outperformed others by 9.5-30.7 %, identifying Cu, Fe, V, As, Co, Cd, NO, Ni, and quinones as primary OP drivers, with synergistic effects for most constituents except antagonistic Fe. Source apportionment attributed OP mainly to vehicular emissions (40 %), marine/sea salt (20 %), and secondary aerosols (16 %). Biomass burning, industry, and road dust contributed minimally. Results emphasize targeting quinones, traffic-related metals (Cu, V), and synergistic metal interactions to mitigate PM toxicity in coastal cities. The dual-assay ML framework provides actionable insights for prioritizing OP-driven regulation, particularly in regions blending anthropogenic and marine influences, to reduce oxidative stress-related health burdens.

Authors

  • Lord Famiyeh
    Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
  • Ke Chen
    Department of Signal Processing, Tampere University of Technology, Finland.
  • Jingsha Xu
    Zhongfa Aviation Institute of Beihang University, Hangzhou, PR China.
  • Fiseha Berhanu Tesema
    School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China.
  • Mosses Solomon
    Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, PR China.
  • Dongsheng Ji
    School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730000, China.
  • Honghui Xu
    School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
  • Chengjun Wang
    School of Chemical and Environmental Engineering, Hunan Institute of Technology, Hengyang, PR China.
  • Qingjun Guo
    Center for Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China.
  • Conghua Wen
    School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, PR China. Electronic address: conghua.wen@xjtlu.edu.cn.
  • John L Zhou
    Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia. Electronic address: Junliang.zhou@uts.edu.au.
  • Jun He
    Institute of Animal Nutrition, Sichuan Agricultural University, Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Key Laboratory of Animal Disease-resistant Nutrition and Feed of China Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Disease-resistant Nutrition of Sichuan Province, Ya'an, 625014, China.

Keywords

External Resources

Popular Topics
Recent Journals