Accurate detection and high throughput profiling of unknown PFAS transformation products for elucidating degradation pathways

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-04-15 DOI:10.1016/j.watres.2025.123645

Bei Zhang , Jibao Liu , Shanshan Qing , Thilini Maheshika Herath , Huan Zhao , Supaporn Klabklaydee , Qing-Long Fu , Eunsang Kwon , Nozomi Takeuchi , Douyan Wang , Takao Namihira , Toshihiro Isobe , Yanrong Zhang , Xiaoying Zhu , Baoliang Chen , Mohamed Ateia , Manabu Fujii

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Abstract

The accurate detection of unknown per- and polyfluoroalkyl substances (PFAS) transformation products (TPs) is essential for elucidating degradation pathways and advancing remediation strategies. Herein, we developed a workflow that combined Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with a paired mass distance (PMD) network. This study achieved high throughput profiling of PFAS TPs with mDa resolving power and sub-ppm mass error. UV treatment revealed chain-shortening pathways, while plasma treatment uncovered competing mechanisms of chain shortening and lengthening, generating oxygen-rich TPs with increased hydrophilicity. Specifically, UV treatment of a 15-PFAS mixture and contaminated natural water showed disappearance of 7 unknown PFAS homologues and the emergence of 12 unknown PFAS homologues. Despite PFAS persistence under UV exposure, previously undetected low-abundance PFAS species were identified, indicating non-negligible photochemical transformation. Under plasma treatment of isolated PFOS, 39 unknown PFAS homologues including 142 suspect and 34 unknown PFAS TPs were identified, highlighting the extensive transformation of emerging and persistent PFAS. Overall, our approach enabled accurate and high-throughput profiling of unknown PFAS TPs and their degradation pathways, providing new insights into persistent unknown PFAS.

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未知PFAS转化产物的精确检测和高通量分析用于阐明降解途径

准确检测未知的全氟烷基和多氟烷基物质（PFAS）转化产物（TPs）对于阐明降解途径和推进修复策略至关重要。在此，我们开发了一个将傅里叶变换离子回旋共振质谱（FT-ICR MS）与配对质量距离（PMD）网络相结合的工作流程。本研究实现了具有mDa分辨能力和亚ppm质量误差的PFAS TPs的高通量分析。紫外线处理揭示了链缩短途径，而等离子体处理揭示了链缩短和链延长的竞争机制，产生了亲水性增强的富氧TPs。具体来说，对15-PFAS混合物和受污染的天然水进行紫外处理后，7种未知PFAS同源物消失，12种未知PFAS同源物出现。尽管PFAS在紫外线照射下持续存在，但以前未检测到的低丰度PFAS物种被鉴定出来，表明不可忽略的光化学转化。在分离的PFOS血浆处理下，鉴定出39种未知的PFAS同源物，其中包括142种疑似PFAS和34种未知PFAS tp，突出了新发和持久性PFAS的广泛转化。总的来说，我们的方法能够准确和高通量地分析未知的PFAS tp及其降解途径，为持久性未知PFAS提供新的见解。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.