Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Famiyeh, L; Chen, K; Xu, J; Tesema, FB; Solomon, M; Ji, D; Xu, H; Wang, C; Guo, Q; Wen, C; Zhou, JL; He, J

Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Famiyeh, L Chen, K Xu, J Tesema, FB Solomon, M Ji, D Xu, H Wang, C Guo, Q Wen, C Zhou, JL He, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: J Hazard Mater, 2025, 495, pp. 138877
Issue Date:: 2025-09-05

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Field	Value	Language
dc.contributor.author	Famiyeh, L
dc.contributor.author	Chen, K
dc.contributor.author	Xu, J
dc.contributor.author	Tesema, FB
dc.contributor.author	Solomon, M
dc.contributor.author	Ji, D
dc.contributor.author	Xu, H
dc.contributor.author	Wang, C
dc.contributor.author	Guo, Q
dc.contributor.author	Wen, C
dc.contributor.author	Zhou, JL
dc.contributor.author	He, J
dc.date.accessioned	2026-01-12T03:00:42Z
dc.date.available	2025-06-08
dc.date.available	2026-01-12T03:00:42Z
dc.date.issued	2025-09-05
dc.identifier.citation	J Hazard Mater, 2025, 495, pp. 138877
dc.identifier.issn	0304-3894
dc.identifier.issn	1873-3336
dc.identifier.uri	http://hdl.handle.net/10453/191629
dc.description.abstract	PM2.5 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 PM2.5 sources associated with OP - a critical gap in prior studies relying solely on correlation analysis and linear regression. Year-long PM2.5 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, NO3-, 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 PM2.5 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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER
dc.relation.ispartof	J Hazard Mater
dc.relation.isbasedon	10.1016/j.jhazmat.2025.138877
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	03 Chemical Sciences, 05 Environmental Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.subject.classification	34 Chemical sciences
dc.subject.classification	40 Engineering
dc.subject.classification	41 Environmental sciences
dc.subject.mesh	China
dc.subject.mesh	Particulate Matter
dc.subject.mesh	Machine Learning
dc.subject.mesh	Air Pollutants
dc.subject.mesh	Oxidation-Reduction
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Aerosols
dc.subject.mesh	Ascorbic Acid
dc.subject.mesh	Humans
dc.subject.mesh	Humans
dc.subject.mesh	Ascorbic Acid
dc.subject.mesh	Aerosols
dc.subject.mesh	Air Pollutants
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Oxidation-Reduction
dc.subject.mesh	China
dc.subject.mesh	Particulate Matter
dc.subject.mesh	Machine Learning
dc.subject.mesh	China
dc.subject.mesh	Particulate Matter
dc.subject.mesh	Machine Learning
dc.subject.mesh	Air Pollutants
dc.subject.mesh	Oxidation-Reduction
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Aerosols
dc.subject.mesh	Ascorbic Acid
dc.subject.mesh	Humans
dc.title	Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.
dc.type	Journal Article
utslib.citation.volume	495
utslib.location.activity	Netherlands
utslib.for	03 Chemical Sciences
utslib.for	05 Environmental Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2026-01-12T03:00:35Z
pubs.publication-status	Published
pubs.volume	495

Abstract:

PM2.5 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 PM2.5 sources associated with OP - a critical gap in prior studies relying solely on correlation analysis and linear regression. Year-long PM2.5 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, NO3-, 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 PM2.5 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.

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http://hdl.handle.net/10453/191629