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PreprintCC BY-NC-NDChemRxiv

Molecular Fingerprinting Enables Mass Quantification and Semi-Quantitative Source Apportionment of Black Carbon in Biological Samples and Beyond

  • Yike Zhang1,2
  • Di Liu1,2
  • Yu Zou1,2
  • Cunkun Deng1,2
  • Xia-Yuhui Wang3,4
  • Hong Pan5
  • Yan Wu6,7,8
  • Liangpo Liu8,9
  • Jiwei Shu10
  • Xiaobo Li11,12,13,14
  • Rui Chen11,12,13,14
  • Pan Yang3,4
3 more authors
  • Yi Lin1,2
  • Heng Wang15
  • Heqing Shen1,2,16
16 affiliations
  1. 1State Key Laboratory of Vaccines for Infectious Diseases, School of Public Health
  2. 2Xiamen University
  3. 3Department of Public Health and Preventive Medicine, School of Medicine
  4. 4Jinan University
  5. 5Zigong Fourth People's Hospital, Ziliujing District, Zigong, Sichuan, China
  6. 6NHC Key Laboratory of Etiological Epidemiology of Chronic Diseases with High Incidence in Fujian-Taiwan Area
  7. 7Fujian Medical University
  8. 8School of Public Health
  9. 9Shanxi Medical University
  10. 10Daishan County Center for Disease Control and Prevention
  11. 11Beijing Key Laboratory of Environmental Toxicology, School of Public Health, School of Public Health
  12. 12Capital Medical University
  13. 13Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health
  14. 14Southeast University
  15. 15Zhoushan Municipal Center for Disease Control and Prevention (Zhoushan Municipal Institute of Public Health Supervision
  16. 16Department of Obstetrics, Women and Children's Hospital, School of Medicine
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Abstract

Black carbon (BC) particles are a health-relevant component of combustion-derived air pollution, but internal exposure remains difficult to assess in biological samples. Existing biomonitoring approaches detect or count BC particles but provide limited information on particle mass or source-resolved chemical fingerprints. We reconstructed benzene polycarboxylic acid (BPCA) molecular-marker analysis into a small-volume workflow combining ambient nitric-acid digestion, nanoparticle enrichment, organic-interference removal, single-step solid-phase extraction, and gas chromatography–mass spectrometry (GC–MS). Orthogonal partial least-squares regression quantified BC mass, and a hierarchical OPLS discriminant model classified its sources. We expanded the detectable BPCA panel to 24 congeners; ten quantitative congeners supported the mass model (R 2 Y=0.955; Q 2 =0.954). Benzene hexacarboxylic acid (B6CA)-based quantification showed linear ranges matched both serum (R 2 =0.996; detection/quantification limits: 0.25/1 ng/μL) and tissue (R 2 =0.979; 0.2/4 ng/mg). Recoveries were 87.1–111.5%, with intra-day and inter-day precision below 17.5% across serum, blood, and tissue. Hierarchical modelling separated biomass-derived from fossil-derived BC, resolved selected subclasses, and correctly classified 91.0% of controlled mixtures, supporting semi-quantitative source apportionment. Method applications to biological and environmental samples showed broad matrix compatibility and source-dependent molecular fingerprints. The novel analytical workflow could support individual based human exposure and epidemiology study, and BC source-oriented climate change issue.

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Cite

BibTeXRIS
BibTeX
@misc{zhang2026molecular,
  title = {{Molecular Fingerprinting Enables Mass Quantification and Semi-Quantitative Source Apportionment of Black Carbon in Biological Samples and Beyond}},
  author = {Yike Zhang and Di Liu and Yu Zou and Cunkun Deng and Xia-Yuhui Wang and Hong Pan and Yan Wu and Liangpo Liu and Jiwei Shu and Xiaobo Li and Rui Chen and Pan Yang and Yi Lin and Heng Wang and Heqing Shen},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010371/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010371/v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - Molecular Fingerprinting Enables Mass Quantification and Semi-Quantitative Source Apportionment of Black Carbon in Biological Samples and Beyond
AU  - Yike Zhang
AU  - Di Liu
AU  - Yu Zou
AU  - Cunkun Deng
AU  - Xia-Yuhui Wang
AU  - Hong Pan
AU  - Yan Wu
AU  - Liangpo Liu
AU  - Jiwei Shu
AU  - Xiaobo Li
AU  - Rui Chen
AU  - Pan Yang
AU  - Yi Lin
AU  - Heng Wang
AU  - Heqing Shen
PY  - 2026
DA  - 2026/10/13
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010371/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010371/v1
AB  - Black carbon (BC) particles are a health-relevant component of combustion-derived air pollution, but internal exposure remains difficult to assess in biological samples. Existing biomonitoring approaches detect or count BC particles but provide limited information on particle mass or source-resolved chemical fingerprints. We reconstructed benzene polycarboxylic acid (BPCA) molecular-marker analysis into a small-volume workflow combining ambient nitric-acid digestion, nanoparticle enrichment, organic-interference removal, single-step solid-phase extraction, and gas chromatography–mass spectrometry (GC–MS). Orthogonal partial least-squares regression quantified BC mass, and a hierarchical OPLS discriminant model classified its sources. We expanded the detectable BPCA panel to 24 congeners; ten quantitative congeners supported the mass model (R 2 Y=0.955; Q 2 =0.954). Benzene hexacarboxylic acid (B6CA)-based quantification showed linear ranges matched both serum (R 2 =0.996; detection/quantification limits: 0.25/1 ng/μL) and tissue (R 2 =0.979; 0.2/4 ng/mg). Recoveries were 87.1–111.5%, with intra-day and inter-day precision below 17.5% across serum, blood, and tissue. Hierarchical modelling separated biomass-derived from fossil-derived BC, resolved selected subclasses, and correctly classified 91.0% of controlled mixtures, supporting semi-quantitative source apportionment. Method applications to biological and environmental samples showed broad matrix compatibility and source-dependent molecular fingerprints. The novel analytical workflow could support individual based human exposure and epidemiology study, and BC source-oriented climate change issue.
ER  -