Quantitative assessment of poly- and perfluoroalkyl substances (PFASs) in aqueous film forming foam (AFFF)-impacted soils: a comparison of analytical protocols.

IF 3.8 2区 化学 Q1 BIOCHEMICAL RESEARCH METHODS
Analytical and Bioanalytical Chemistry Pub Date : 2024-12-01 Epub Date: 2024-10-16 DOI:10.1007/s00216-024-05585-2
Nicholas Gonda, Chuhui Zhang, Dylan Tepedelen, Adam Smith, Charles Schaefer, Christopher P Higgins
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引用次数: 0

Abstract

Quantitatively assessing all per- and poly fluoroalkyl substances (PFASs) in an environmental sample, particularly soils impacted by aqueous film forming foams (AFFFs), has proven to be a challenge. To make such an assessment, a comprehensive sample processing procedure and analytical tool must be used. However, doubts remain whether current analytical tools such as high-resolution mass spectrometry (HRMS) with targeted quantitation and semi-quantitative analysis of suspects (Semi-Q HRMS) or total organic fluorine (TOF) are capable of accurately quantifying all non-polymeric PFASs in a sample. Further, current comprehensive soil PFAS HRMS methods are incompatible with TOF, preventing direct comparisons of the approaches. To enable direct comparisons, a soil sample processing procedure that is comprehensive as well as compatible with multiple analytical tools is needed. In this study, we assessed the performance of a previously developed soil PFAS method, EPA Method 1633, and a hybrid solid phase extraction (SPE)-based method for characterizing AFFF-impacted soil composites while maintaining compatibility with multiple analytical tools (i.e., Semi-Q HRMS and TOF). Comparative results for AFFF-impacted soil composites indicate analysis via EPA Method 1633 (as compared to the novel hybrid method) results in maybe up to 75% of the PFAS mass being missed by only analyzing for compounds listed in EPA Method 1633. Simply expanding the EPA Method 1633 analyte list was insufficient to account for the missing mass: up to 69% of the PFAS mass was still missed because of EPA Method 1633's extraction and cleanup bias. Additionally, the novel method developed offers a more comprehensive analysis with minimal reductions to sensitivity when compared to those reported in EPA Method 1633, with limits of quantification ranging from 0.12 to 2.4 ng/g as compared to 0.16-4.0 ng/g, respectively. For these reasons, an alternative hybrid SPE-based method is proposed for comprehensive evaluation of PFASs in AFFF-impacted soils.

受水成膜泡沫 (AFFF) 影响的土壤中多氟和全氟烷基物质 (PFAS) 的定量评估:分析方案比较。
事实证明,定量评估环境样本中的所有全氟和多氟烷基物质 (PFAS) 是一项挑战,尤其是受到水成膜泡沫 (AFFF) 影响的土壤。要进行此类评估,必须使用全面的样本处理程序和分析工具。然而,目前的分析工具(如高分辨质谱法 (HRMS))是否能够准确量化样品中的所有非聚合全氟辛烷磺酸(PFAS),尚存在疑问,这些工具包括高分辨质谱法 (HRMS)、目标定量分析和可疑物半定量分析 (Semi-Q HRMS) 或总有机氟 (TOF)。此外,目前全面的土壤 PFAS HRMS 方法与 TOF 不兼容,因此无法对这两种方法进行直接比较。为了进行直接比较,需要一种既全面又能与多种分析工具兼容的土壤样品处理程序。在本研究中,我们评估了之前开发的土壤 PFAS 方法(EPA 方法 1633)和基于混合固相萃取 (SPE) 的方法的性能,前者用于表征受 AFFF 影响的土壤复合材料,后者则与多种分析工具(即 Semi-Q HRMS 和 TOF)保持兼容。受 AFFF 影响的土壤复合材料的比较结果表明,通过 EPA 方法 1633(与新型混合方法相比)进行分析,仅分析 EPA 方法 1633 中列出的化合物,可能会漏掉高达 75% 的 PFAS 质量。仅仅扩大 EPA 方法 1633 分析物清单还不足以解释遗漏的质量:由于 EPA 方法 1633 的提取和净化偏差,仍有高达 69% 的 PFAS 质量被遗漏。此外,与 EPA 方法 1633 中报告的灵敏度相比,所开发的新方法提供了更全面的分析,灵敏度降低幅度最小,定量限分别为 0.12 至 2.4 纳克/克,而 EPA 方法 1633 的定量限为 0.16 至 4.0 纳克/克。因此,建议采用一种基于混合 SPE 的替代方法来全面评估受 AFFF 影响的土壤中的 PFAS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
4.70%
发文量
638
审稿时长
2.1 months
期刊介绍: Analytical and Bioanalytical Chemistry’s mission is the rapid publication of excellent and high-impact research articles on fundamental and applied topics of analytical and bioanalytical measurement science. Its scope is broad, and ranges from novel measurement platforms and their characterization to multidisciplinary approaches that effectively address important scientific problems. The Editors encourage submissions presenting innovative analytical research in concept, instrumentation, methods, and/or applications, including: mass spectrometry, spectroscopy, and electroanalysis; advanced separations; analytical strategies in “-omics” and imaging, bioanalysis, and sampling; miniaturized devices, medical diagnostics, sensors; analytical characterization of nano- and biomaterials; chemometrics and advanced data analysis.
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