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Predicting the relationship between PFAS component signatures in water and non-water phases through mathematical transformation: Application to machine learning classification
Chemosphere ( IF 8.8 ) Pub Date : 2021-06-05 , DOI: 10.1016/j.chemosphere.2021.131097
Tohren C G Kibbey 1 , Rafal Jabrzemski 2 , Denis M O'Carroll 3
Affiliation  

Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, as a result of decades of use across a range of applications. While PFAS contamination often enters the environment in the aqueous phase, PFAS is regularly detected in a range of different phases, including soils, sediments and biota. Although PFAS at a given site may originate from the same sources, the compositions observed in different phases are nearly always different, a fact that can complicate source allocation efforts. This paper presents a quantitative method for prediction of the relative composition of PFAS in different phases for components for which differences in behavior are primarily driven by hydrophobicity. The derived equations suggest that under these conditions, the relative compositions in different phases in contact with water should be independent of overall affinity for the phase, and as such should be the same for all non-water phases. This result is illustrated with data from individual samples, as well as from site-wide evaluations for a range of different phases. The results of the work provide a useful tool to reconcile PFAS composition differences in different phases, and provide a baseline for recognizing cases where hydrophobicity is not the primary driver of differences in distribution between phases. Furthermore, the results may be useful in forensic applications for classification of PFAS across different phases. The use of the resulting equations to transform water data to train a supervised learning algorithm for forensic analysis of PFAS in non-water phases is illustrated.



中文翻译:

通过数学变换预测水相和非水相中 PFAS 组分特征之间的关系:在机器学习分类中的应用

全氟和多氟烷基物质 (PFAS) 在环境中广泛存在,这是在一系列应用中使用数十年的结果。虽然 PFAS 污染通常以水相进入环境,但 PFAS 经常在一系列不同的相中被检测到,包括土壤、沉积物和生物群。尽管给定地点的全氟和多氟烷基物质可能来自相同的来源,但在不同阶段观察到的成分几乎总是不同的,这一事实会使源分配工作复杂化。本文提出了一种定量方法,用于预测不同相中 PFAS 的相对组成,其行为差异主要由疏水性驱动。推导出的方程表明,在这些条件下,与水接触的不同相中的相对组成应该独立于该相的整体亲和力,因此对于所有非水相应该是相同的。这个结果用来自单个样本的数据以及来自不同阶段范围的现场评估的数据来说明。这项工作的结果提供了一个有用的工具来协调不同相中的 PFAS 组成差异,并为识别疏水性不是相间分布差异的主要驱动因素的情况提供基线。此外,结果可能有助于法医应用程序对不同阶段的 PFAS 进行分类。说明了使用所得方程转换水数据以训练监督学习算法,用于对非水相中的全氟和多氟烷基物质进行法医分析。

更新日期:2021-06-11
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