Estimating the relative magnitudes of adsorption to solid-water and air/oil-water interfaces for per- and poly-fluoroalkyl substances
Estimating the relative magnitudes of adsorption to solid-water and air/oil-water interfaces for per- and poly-fluoroalkyl substances
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DOI：
10.1016/j.envpol.2019.113102
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发表时间：
2019-11-01
影响因子：
8.9
通讯作者：
Brusseau, Mark L.
中科院分区：
文献类型：
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作者：
Brusseau, Mark L.
全氟烷基和多氟烷基物质（PFAS）由于其在环境中的广泛存在和潜在的人类健康风险而引起了相当大的关注。考虑到PFAS在多相系统中保留的复杂性，能够容易地确定给定PFAS和一组地下条件的各个保留过程的相对重要性对于表征和建模目的将是有用的。定量结构/性质相关性（QSPR）进行了分析，PFAS的吸附土壤，沉积物，颗粒活性炭（GAC），并与先前的分析进行吸附空气-水和油-水界面。结果表明，采用摩尔体积的模型提供了合理的预测有机碳归一化土壤/沉积物吸附系数（log K-oc），GAC吸附系数（log K-d），和空气/油-水界面吸附系数（log K-i）PFAS。固体-水和空气/油-水界面吸附的相对大小作为控制变量的函数进行了比较。一个诺模图的开发，提供了一个一阶确定这些界面吸附过程中的多相多孔介质系统的相对意义。(C)2019爱思唯尔有限公司版权所有。
Per- and poly-fluoroalkyl substances (PFAS) have attracted considerable concern due to their widespread occurrence in the environment and potential human health risks. Given the complexity of PFAS retention in multi-phase systems, it would be useful for characterization and modeling purposes to be able to readily determine the relative significance of the individual retention processes for a given PFAS and set of subsurface conditions. A quantitative-structure/property-relationship (QSPR) analysis was conducted for adsorption of PFAS by soils, sediments, and granular activated carbon (GAC), and integrated with a prior analysis conducted for adsorption to air-water and oil-water interfaces. The results demonstrated that a model employing molar volume provided reasonable predictions of organic-carbon normalized soil/sediment adsorption coefficients (log K-oc), GAC-adsorption coefficients (log K-d), and air/oil-water interfacial adsorption coefficients (log K-i) for PFAS. The relative magnitudes of solid-water and air/oil-water interfacial adsorption were compared as a function of controlling variables. A nomograph was developed that provides a first-order determination of the relative significance of these interfacial adsorption processes in multi-phase porous-media systems. (C) 2019 Elsevier Ltd. All rights reserved.