Charbonnet Joseph A, Rodowa Alix E, Joseph Nayantara T, Guelfo Jennifer L, Field Jennifer A, Jones Gerrad D, Higgins Christopher P, Helbling Damian E, Houtz Erika F
Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado 80401, United States.
Biochemical and Exposure Science Group, National Institute of Standards & Technology, Charleston, South Carolina 29412, United States.
Environ Sci Technol. 2021 Jun 1;55(11):7237-7245. doi: 10.1021/acs.est.0c08506. Epub 2021 May 13.
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.
全氟和多氟烷基物质(PFASs)的来源追踪是环境法医鉴定领域中一个新兴且日益必要的子领域。我们将PFAS来源追踪定义为对导致环境中PFAS污染的多个来源进行准确表征和区分。PFAS来源追踪应在概念性场地模型的框架内,采用分析测量、多变量分析以及对PFAS归宿和迁移的理解。用于区分PFAS来源的汇聚证据线包括:识别与独特来源密切相关的PFAS;污染场地中PFAS同系物、类别和异构体的比例;以及场地的水文地球化学条件。随着PFAS来源追踪领域的发展,新的PFAS分析标准的制定以及高分辨率质谱数据的更广泛可得性将提升当前可用的分析能力。此外,多变量计算工具,包括无监督(即探索性)和有监督(即预测性)机器学习技术,可能会带来新的见解,从而确定一份对环境PFAS来源追踪有用的目标PFAS清单。在本观点文章中,我们确定了当前可用的工具以及实现对环境来源追踪更有信心以识别和分配PFAS来源所需的主要进展。