GEMAS: Prediction of solid-solution phase partitioning coefficients (Kd) for oxoanions and boric acid in soils using mid-infrared diffuse reflectance spectroscopy

Abstract

The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total or surface-bound metal/metalloid concentration of the solid phase to the total concentration in the solution phase. Values of Kd were measured for boric acid (B[OH]3(0)) and selected added soluble oxoanions: molybdate (MoO4(2-)), antimonate (Sb[OH](6-)), selenate (SeO4(2-)), tellurate (TeO4(2-)) and vanadate (VO4(3-)). Models were developed using approximately 500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) program. These calibration soils represented the major properties of the entire 4813 soils of the GEMAS project. Multiple linear regression (MLR) from soil properties, partial least-squares regression (PLSR) using mid-infrared diffuse reflectance Fourier-transformed (DRIFT) spectra, and models using DRIFT spectra plus analytical pH values (DRIFT + pH), were compared with predicted log K(d + 1) values. Apart from selenate (R(2) = 0.43), the DRIFT + pH calibrations resulted in marginally better models to predict log K(d + 1) values (R(2) = 0.62-0.79), compared with those from PSLR-DRIFT (R(2) = 0.61-0.72) and MLR (R(2) = 0.54-0.79). The DRIFT + pH calibrations were applied to the prediction of log K(d + 1) values in the remaining 4313 soils. An example map of predicted log K(d + 1) values for added soluble MoO4(2-) in soils across Europe is presented. The DRIFT + pH PLSR models provided a rapid and inexpensive tool to assess the risk of mobility and potential availability of boric acid and selected oxoanions in European soils. For these models to be used in the prediction of log K(d + 1) values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration.

Antimonate sorption in soils increases with ageing

“…Calculated Sb(V) K D values (Eq.1) are presented in Table 1 and are within the range of previously reported values, i.e. 4 -5500 l kg -1 (Janik et al, 2015). The ageing factors (7 -190 days) increased with increasing oxalate-extractable Fe and Al concentration in soil and with decreasing soil pH (Figure 2).…”

“…Second, the pH effect on ageing of Sb is in line with its effect on sorption, i.e. sorption of the antimonate anion increases when pH decreases due to increased positive charge (Janik et al, 2015;Leuz et al, 2006;Tighe et al, 2005;Verbeeck et al, 2019). Similar to what was found for antimonate in this study, arsenate ageing factors are also higher in acid soils (Tye et al, 2002;Wang et al, 2017).…”

Field-Scale Predictions of Soil Contaminant Sorption Using Visible–Near Infrared Spectroscopy

“…Including pH as a predictor in sorption models from soil spectra improved the predictive ability at large scales where soil components control soil pH variability. 22,23 Since pH is an easy and inexpensive measurement and it is included in most soil databases, the effort to include this extra predictor is actually low.…”

“…21 More recently, the K d values of cationic metals, oxoanions and boric acid were predicted in European soils from mid-IR diffuse reflectance infrared Fourier transform (DRIFT) spectra. 22,23 Adding soil pH as an independent variable input improved the accuracy of the DRIFT models for most of the chemicals studied. 22,23 Even though an overall better performance of mid-IR over vis-NIR has been reported, mid-IR spectroscopy requires more complicated sample preparation than vis-NIR.…”

“…22,23 Adding soil pH as an independent variable input improved the accuracy of the DRIFT models for most of the chemicals studied. 22,23 Even though an overall better performance of mid-IR over vis-NIR has been reported, mid-IR spectroscopy requires more complicated sample preparation than vis-NIR. Recent technological advancements have resulted in cheaper instruments and there are systems dedicated to on-the-go vis-NIR analysis 24,25 and handheld mid-IR sensors for in situ measurements.…”