全氟和多氟烷基化合物来源分析中的传统模型与机器学习方法

王新宇; 史亚利; 蔡亚岐

doi:10.7536/PC20260413

化学进展 ›› :0- DOI: 10.7536/PC20260413

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全氟和多氟烷基化合物来源分析中的传统模型与机器学习方法

王新宇 ¹^,² ,
史亚利 ¹^,²^,³ ,
蔡亚岐 ¹^,²^,³

作者信息 +

Xinyu Wang ¹^,² ,
Yali Shi ¹^,²^,³ ,
Yaqi Cai ¹^,²^,³

Author information +

文章历史 +

摘要

全氟和多氟烷基化合物（Per- and polyfluoroalkyl substances，PFAS）因其环境持久性、生物累积性及潜在毒性而受到广泛关注。随着对一些典型PFAS环境管控的持续推进，其环境来源已由传统长链PFAS的单一排放逐渐演变为长链残留、短链或超短链替代品及前体物和转化产物并存的复合污染格局，显著增加了来源解析的复杂性。准确识别PFAS污染来源对于区域污染防控、环境管理及健康风险评估具有重要意义。目前，PFAS来源分析主要包括比值分析、统计分析及受体模型等传统

方法，以及近年来逐渐兴起的机器学习（Machine

Learning，ML）方法。传统方法基于明确的统计或质量守恒原理，在污染源定性识别及贡献率定量解析方面具有较高的可解释性，但在复杂非线性关系解析、多源混合污染识别及新型PFAS表征方面存在一定局限。相比之下，ML方法在高维数据挖掘、非线性关联识别及复杂模式提取方面展现出较强优势，能够有效提升复杂环境体系中PFAS来源识别的精度与稳定性。然而，现有ML模型仍面临训练数据依赖性强、模型可解释性有限以及与PFAS环境行为和来源机制耦合不充分等问题。

本文系统综述了近十年PFAS来源分析领域传统方法与ML方法的研究进展，比较了不同方法在适用范围、优势与局限性方面的差异，探讨了未来发展方向，以期为PFAS污染识别、管控和治理提供方法参考。

Abstract

Per- and polyfluoroalkyl substances (PFAS) have attracted widespread attention owing to their environmental persistence, bioaccumulation potential, and adverse toxicological effects. With the continued expansion of industrial PFAS applications, environmental contamination patterns have evolved from the direct release of conventional long-chain PFAS to a more complex scenario characterized by the coexistence of residual long-chain compounds, short- and ultra-short-chain alternatives, and transformation products of precursor substances. This transition has substantially increased the complexity of PFAS source apportionment. Accurate identification of PFAS contamination sources is of critical importance for regional pollution prevention and control, environmental management, and human health risk assessment. At present, PFAS source analysis primarily relies on traditional approaches, including ratio analysis, statistical analysis, and receptor models, as well as machine learning (ML) techniques that have gained increasing attention in recent years. Traditional methods, which are grounded in established statistical theories or mass conservation principles, provide a highly interpretable framework for qualitative source identification and quantitative source contribution estimation. However, their applicability is often limited when addressing complex nonlinear relationships, mixed-source contamination, and the characterization of emerging PFAS compounds. In contrast, ML approaches demonstrate considerable advantages in high-dimensional data mining, nonlinear relationship recognition, and complex pattern extraction, thereby enhancing the accuracy and robustness of PFAS source identification in complex environmental systems. Nevertheless, current ML models remain challenged by their strong dependence on training datasets, limited interpretability, and insufficient coupling with the environmental behavior and source-related mechanisms of PFAS. This review systematically summarizes the research progress of both traditional and ML-based approaches for PFAS source analysis over the past decade. The applicability, strengths, and limitations of different methodologies are comparatively evaluated, and future research directions are discussed. The findings are intended to provide methodological guidance for PFAS source identification, pollution control, and environmental management.

Contents

1 Introduction

2 Classification of PFAS pollution sources

2.1 Direct sources

2.2 Indirect sources

3 Application of Traditional Models in PFAS Source Analysis

3.1 Ratio analysis

3.2 Statistical analysis

3.3 Receptor models

3.4 Other models

4 Application of ML in PFAS Source Analysis

4.1 Overview of machine learning

4.2 Supervised learning

4.3 Unsupervised learning

4.4 Applicability Boundaries of ML Methods

5 Conclusions and Prospects

关键词

全氟和多氟烷基化合物 / 来源分析 / 受体模型 / 人工智能 / 机器学习

Key words

per- / and / polyfluoroalkyl / substances / source / analysis / receptor / model / artificial / intelligence / machine / learning

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王新宇, 史亚利, 蔡亚岐. 全氟和多氟烷基化合物来源分析中的传统模型与机器学习方法[J]. 化学进展, , (): 0 DOI:10.7536/PC20260413

Xinyu Wang, Yali Shi, Yaqi Cai. Progress in Chemistry 0 DOI:10.7536/PC20260413

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参考文献

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[1]	Dai S, Zhang G, Dong C, Yang R, Pei Z, Li Y, Li A, Zhang Q, Jiang G. Water Res., 2025, 271: 122979.
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[3]	Neuwald I J, Hübner D, Wiegand H L, Valkov V, Borchers U, Nödler K, Scheurer M, Hale S E, Arp H P H, Zahn D. Environ. Sci. Technol., 2022, 56( 10): 6380- 6390.
[4]	Wang Y, Munir U, Huang Q. Soil Environ. Health, 2023, 1( 1): 100004.
[5]	Li X, Hou M, Zhang F, Ji Z, Cai Y, Shi Y. Environ. Sci. Technol., 2025: acs.est.4c08701.
[6]	Meng L, Song B, Zhong H, Ma X, Wang Y, Ma D, Lu Y, Gao W, Wang Y, Jiang G. Environ. Int., 2021, 156: 106735.
[7]	Ahrens L, Rakovic J, Ekdahl S, Kallenborn R. Chem., 2023, 345: 140463.
[8]	Wen W, Gao L, Cheng H, Xiao L, Zhang S, Li S, Jiang X, Xia X. Water Res., 2024, 262: 122095.
[9]	Lu Y, Liang Y, Zhou Z, Wang Y, Jiang G. Environ. Sci. Technol., 2019, 53( 24): 14091- 14092.
[10]	Hamid N, Junaid M, Sultan M, Yoganandham S T, Chuan O M. Water Res., 2024, 250: 121044.
[11]	Ateia M, Maroli A, Tharayil N, Karanfil T. Chem., 2019, 220: 866- 882.
[12]	Zarębska M, Bajkacz S, Hordyjewicz-Baran Z. Environ. Res., 2024, 251: 118608.
[13]	Cousins I T, Johansson J H, Salter M E, Sha B, Scheringer M. Environ. Sci. Technol., 2022, 56( 16): 11172- 11179.
[14]	Steffen W, Richardson K, Rockström J, Cornell S E, Fetzer I, Bennett E M, Biggs R, Carpenter S R, De Vries W, De Wit C A, Folke C, Gerten D, Heinke J, Mace G M, Persson L M, Ramanathan V, Reyers B, Sörlin S. Science, 2015, 347( 6223): 1259855.
[15]	Hua Z lin, Gao C, Zhang J yun, Li X qing. Sci. Total Environ., 2023, 857: 159452.
[16]	Xu C, Song X, Liu Z, Ding X, Chen H, Ding D. Sci. Total Environ., 2021. 145731.
[17]	Nilsen E, Muensterman D, Carini L, Waite I, Payne S, Field J A, Peterson J, Hafley D, Farrer D, Jones G D. Sci. Total Environ., 2024. 167798.
[18]	Zhang Y, Zhou Y, Dong R, Song N, Hong M, Li J, Yu J, Kong D. J. Hazard. Mater., 2024, 465: 133270.
[19]	Céline C, Catherine B, Romane C, Laurence C. Food Chem. Toxicol., 2024, 187: 114625.
[20]	Li J, Duan W, An Z, Jiang Z, Li L, Guo M, Tan Z, Zeng X, Liu X, Liu Y, Li A, Guo H. J. Hazard. Mater., 2024, 480: 135795.
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[23]	Feng X, Ye M, Li Y, Zhou J, Sun B, Zhu Y, Zhu L. J. Hazard. Mater., 2020. 122124.
[24]	Xing Z, Wang G, Liu S, Chen H, Dong X, Wang H, Liu Y. J. Hazard. Mater., 2024, 474: 134770.
[25]	Zhao W, Lu J, Wei Q, Cao J, Cui J, Hou Y, Zhang K, Chen H, Zhao W. Sci. Total Environ., 2024, 953: 176166.
[26]	Shang M, Dong J, Xie H, Wang Y, Du Y. Water Res., 2023, 243: 120384.
[27]	Langberg H A, Hale S E, Breedveld G D, Jenssen B M, Jartun M. Environ. Sci. Process. Impacts., 2022. 330- 342.
[28]	Zhang W, Pang S, Lin Z, Mishra S, Bhatt P, Chen S. Environ. Pollut., 2021, 272: 115908.
[29]	Lohmann R, Letcher R J. Curr. Opin. Green Sustain. Chem., 2023, 41: 100795.
[30]	Young C J, Furdui V I, Franklin J, Koerner R M, Muir D C G, Mabury S A. Environ. Sci. Technol., 2007, 41( 10): 3455- 3461.
[31]	Evich M G, Davis M J B, McCord J P, Acrey B, Awkerman J A, Knappe D R U, Lindstrom A B, Speth T F, Tebes-Stevens C, Strynar M J, Wang Z, Weber E J, Henderson W M, Washington J W. Science, 2022, 375( 6580): eabg9065.
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[33]	Guo S, Zhang S, Lv X, Tang Y, Zhang T, Hua Z. Sci. Total Environ., 2024, 954: 176260.
[34]	Guo C, Zhang Y, Zhao X, Du P, Liu S, Lv J, Xu F, Meng W, Xu J. Chem., 2015, 127: 201- 207.
[35]	Armitage J M, MacLeod M, Cousins I T. Environ. Sci. Technol., 2009, 43( 15): 5830- 5836.
[36]	Simcik M F, Dorweiler K J. Environ. Sci. Technol., 2005, 39( 22): 8678- 8683.
[37]	Xiao S K, Wu Q, Pan C G, Yin C, Wang Y H, Yu K F. Environ Res., 2021. 111485.
[38]	Shan G, Xiang Q, Feng X, Wu W, Yang L, Zhu L. Sci. Total Environ., 2021. 146747.
[39]	Khan K, Younas M, Ali J, Shah N S, Kavil Y N, Assiri M A, Cao X, Sher H, Maryam A, Zhou Y, Yaseen M, Xu L. Mar. Pollut. Bull., 2024, 203: 116415.
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[42]	Chen C E, Yang Y Y, Zhao J L, Liu Y S, Hu L X, Li B B, Li C L, Ying G G. Chem., 2021, 283: 131301.
[43]	Ikotun A M, Ezugwu A E, Abualigah L, Abuhaija B, Heming J. Inf. Sci., 2023, 622: 178- 210.
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Received	Published	Revised
2026-04-09	2026-07-24	2026-06-06
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