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

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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)30) and selected added soluble oxoanions: molybdate (MoO42‐), antimonate (Sb(OH)6‐), selenate (SeO42‐), tellurate (TeO42‐) and vanadate (VO43). Models were developed using ~500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS). 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 to predict log Kd+1 values. Apart from selenate (R2 = 0.43), the DRIFT+pH calibrations resulted in marginally better models to predict log Kd+1 values (R2 = 0.62 to 0.79), compared to R2 = 0.61‐0.72 for PSLR‐DRIFT and R2 = 0.54‐0.79 for MLR. The DRIFT+pH calibrations were applied to the prediction of log Kd+1 values in the remaining 4313 soils. An example map of predicted log Kd+1 values for added soluble MoO42‐ 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 (B) and selected oxoanions in European soils. For these models to be used in the prediction of log Kd+1 values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration. This article is protected by copyright. All rights reserved

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