Predicting partitioning of radiolabelled 14C-PFOA in a range of soils using diffuse reflectance infrared spectroscopy

The aim of this study was to establish partitioning coefficients (K-d) of perfluorooctanoic acid (PFOA) in a wide range of soils and determine if those values can be predicted from soil properties using multiple linear regression (MLR) and from infrared spectra of soils using partial least squares regression (PLSR). For 100 different soils, the K-d values of spiked radiolabelled C-14-PFOA ranged from 0.6 to 14.8 L/kg and significantly decreased with soil depth (p < 0.05) due to soil properties that change with depth. The MLR modelling revealed that PFOA sorption was significantly (p < 0.05) influenced, in decreasing order, by organic carbon (OC) content, silt-plus-clay content and soil pH. Soils were partitioned into all soils and surface soils alone. The MLR models using OC, silt-plus-clay content and pH together explained most of the variation in sorption in all soils as well as surface soils alone (0-15 cm). However, correlations between soil properties and K-d values in some soils could not be explained by the MLR model. Modelling of K-d prediction in soils with PLSR and diffuse reflectance (mid) infrared Fourier transform spectroscopy (DRIFT) showed comparable success in explaining the predictions of K-d values, including some of the outliers identified in the MLR model. The PLSR loading weights suggested that quartz, and possibly pyrophyl-lite minerals, were inversely correlated with the K-d values. Given that MLR requires a-priori characterisation of a range of soil properties and PLSR-DRIFT is a method based on the direct relationship between spectra and soil components, mid-infrared spectroscopy may be a more economical and rapid technique to predict the solid-liquid partitioning of PFOA in soils. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.