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首页> 外文期刊>Environmental Science & Technology >Environmental Source Tracking of Per- and Polyfluoroalkyl Substances within a Forensic Context: Current and Future Techniques
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Environmental Source Tracking of Per- and Polyfluoroalkyl Substances within a Forensic Context: Current and Future Techniques

机译：法医背景下的环境源跟踪（多氟烷基物质：当前和未来的技术

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摘要

The source tracking of per- and polyfluoroalkyl substances (PFASs) is a new and increasingly necessary subfield within environmental forensics. We define PFAS source tracking as the accurate characterization and differentiation of multiple sources contributing to PFAS contamination in the environment. PFAS source tracking should employ analytical measurements, multivariate analyses, and an understanding of PFAS fate and transport within the framework of a conceptual site model. Converging lines of evidence used to differentiate PFAS sources include: identification of PFASs strongly associated with unique sources; the ratios of PFAS homologues, classes, and isomers at a contaminated site; and a site's hydrogeochemical conditions. As the field of PFAS source tracking progresses, the development of new PFAS analytical standards and the wider availability of high-resolution mass spectral data will enhance currently available analytical capabilities. In addition, multivariate computational tools, including unsupervised (i.e., exploratory) and supervised (i.e., predictive) machine learning techniques, may lead to novel insights that define a targeted list of PFASs that will be useful for environmental PFAS source tracking. In this Perspective, we identify the current tools available and principal developments necessary to enable greater confidence in environmental source tracking to identify and apportion PFAS sources.
机译：每氟氟硼酸物质（PFASS）的源跟踪是环境取证中的新且越来越必要的子场。我们将PFAS源跟踪定义为对环境中PFAS污染有贡献的多个来源的准确表征和分化。 PFAS源跟踪应采用分析测量，多变量分析，以及在概念站点模型的框架内进行PFA命运和运输。融合用于区分PFAS源的证据界限包括：识别与独特源强烈相关的PFASS; PFAS同源物，类和异构体在污染部位的比率;和一个网站的水文地球化学条件。由于PFAS源跟踪的领域进行了进展，新的PFAS分析标准的开发和高分辨率质谱数据的更广泛可用性将增强目前可用的分析能力。此外，多变量计算工具，包括无监督（即探索性）和监督（即，预测）机器学习技术，可能导致新颖的见解，这些洞察力定义了对环境PFAS源跟踪有用的目标的PFASS列表。在这种观点中，我们确定了当前的工具和所需的主要开发，为识别和分摊PFAS来源对环境源跟踪提供更大的信心。

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