Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning
文献类型：期刊论文
| 作者 | Famiyeh, Lord1; Chen, Ke1,3; Xu, Jingsha2; Tesema, Fiseha Berhanu4; Solomon, Mosses1; Ji, Dongsheng5; Xu, Honghui6; Wang, Chengjun7; Guo, Qingjun8; Wen, Conghua9 |
| 刊名 | JOURNAL OF HAZARDOUS MATERIALS
 |
| 出版日期 | 2025-09-05 |
| 卷号 | 495页码:138877 |
| 关键词 | Oxidative potential Machine learning Source apportionment PM2.5 Oxidative potential Machine learning Chemical constituents Source apportionment |
| ISSN号 | 0304-3894 |
| DOI | 10.1016/j.jhazmat.2025.138877 |
| 产权排序 | 8 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| WOS关键词 | SOURCE APPORTIONMENT ; DITHIOTHREITOL DTT ; AMBIENT PARTICLES ; SITE ; COMPONENTS ; URBAN ; PAHS |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001511686400002 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214511]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Wen, Conghua; He, Jun |
| 作者单位 | 1.Univ Nottingham Ningbo China, Dept Chem & Environm Engn, Ningbo, Peoples R China; 2.Beihang Univ, Zhongfa Aviat Inst, Hangzhou, Peoples R China; 3.Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; 4.Univ Nottingham Ningbo China, Sch Comp Sci, Ningbo, Peoples R China; 5.Chinese Acad Sci, Inst Atmospher Phys, State Kay Lab Atmospher Boundary Layer Phys & Atmo, Beijing, Peoples R China; 6.Zhejiang Inst Meteorol Sci, Hangzhou, Peoples R China; 7.Hunan Inst Technol, Sch Chem & Environm Engn, Hengyang, Peoples R China; 8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Ctr Environm Remediat, Beijing, Peoples R China; 9.Xian Jiaotong Liverpool Univ, Sch Math & Phys, Suzhou, Peoples R China; 10.Nottingham Ningbo China Beacon Excellence Res & In, Ningbo, Peoples R China |
| 推荐引用方式 GB/T 7714 | Famiyeh, Lord,Chen, Ke,Xu, Jingsha,et al. Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning[J]. JOURNAL OF HAZARDOUS MATERIALS,2025,495:138877. |
| APA | Famiyeh, Lord.,Chen, Ke.,Xu, Jingsha.,Tesema, Fiseha Berhanu.,Solomon, Mosses.,...&He, Jun.(2025).Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.JOURNAL OF HAZARDOUS MATERIALS,495,138877. |
| MLA | Famiyeh, Lord,et al."Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning".JOURNAL OF HAZARDOUS MATERIALS 495(2025):138877. |
入库方式： OAI收割
来源：地理科学与资源研究所
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