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GEMAS: Prediction of solid‐solution partitioning coefficients ( K d ) for cationic metals in soils using mid‐infrared diffuse reflectance spectroscopy
Author(s) -
Janik Leslie J.,
Forrester Sean T.,
SorianoDisla José M.,
Kirby Jason K.,
McLaughlin Michael J.,
Reimann Clemens
Publication year - 2015
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.2736
Subject(s) - soil water , partial least squares regression , chemistry , diffuse reflectance infrared fourier transform , metal , analytical chemistry (journal) , diffuse reflection , mineralogy , environmental chemistry , soil science , environmental science , mathematics , physics , organic chemistry , biochemistry , statistics , photocatalysis , optics , catalysis
Partial least squares regression (PLSR) models, using mid‐infrared (MIR) diffuse reflectance Fourier‐transformed (DRIFT) spectra, were used to predict distribution coefficient ( K d ) values for selected added soluble metal cations (Ag + , Co 2+ , Cu 2+ , Mn 2+ , Ni 2+ , Pb 2+ , Sn 4+ , and Zn 2+ ) in 4813 soils of the Geochemical Mapping of Agricultural Soils (GEMAS) program. For the development of the PLSR models, approximately 500 representative soils were selected based on the spectra, and K d values were determined using a single‐point soluble metal or radioactive isotope spike. The optimum models, using a combination of MIR–DRIFT spectra and soil pH, resulted in good predictions for log K d+1 for Co, Mn, Ni, Pb, and Zn ( R 2 ≥ 0.83) but poor predictions for Ag, Cu, and Sn ( R 2 < 0.50). These models were applied to the prediction of log K d+1 values in the remaining 4313 unknown soils. The PLSR models provide a rapid and inexpensive tool to assess the mobility and potential availability of selected metallic cations in European soils. Further model development and validation will be needed to enable the prediction of log K d+1 values in soils worldwide with different soil types and properties not covered in the existing model. Environ Toxicol Chem 2014;9999:1–11. © 2014 SETAC