Partial least squares regression (PLSR) models, using mid-infrared (MIR) diffuse reflectance Fourier-transformed (DRIFT) spectra, were used to predict distribution coefficient (Kd) values for selected added soluble metal cations (Ag+, Co2+, Cu2+, Mn2+, Ni2+, Pb2+, Sn4+, and Zn2+) 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 Kd 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 Kd+1 for Co, Mn, Ni, Pb, and Zn (R2 ≥ 0.83) but poor predictions for Ag, Cu, and Sn (R2<0.50). These models were applied to the prediction of log Kd+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 Kd+1 values in soils worldwide with different soil types and properties not covered in the existing model.
GEMAS: Prediction of solid-solution partitioning coefficients (Kd) for cationic metals in soils using mid-infrared diffuse reflectance spectroscopy / Janik, Leslie J; Forrester, Sean T; Soriano-Disla, José M; Kirby, Jason K; Mclaughlin, Michael J; Reimann, Clemens; The GEMAS Project Team [..; S. Albanese; M. Andersson; R. Baritz; M.J. Batista; A. Bel-lan; M. Birke; D. Cicchella; A. Demetriades; B. De Vivo;W. De Vos; E. Dinelli; M. Ďuriš; A. Dusza-Dobek; O.A. Eggen; M. Eklund; V. Ernstsen; P. Filzmoser; D.M.A. Flight; M. Fuchs; U. Fügedi; A. Gilucis; M. Gosar; V. Gregorauskiene; W. De Groot; A. Gulan; J. Halamić; E. Haslinger; P. Hayoz; R. Hoffmann; J. Hoogewerff; H. Hrvatovic; S. Husnjak; G. Jordan; M. Kaminari; V. Klos; F. Krone; P. Kwećko; L. Kuti; A. Ladenberger; A. Lima; J. Locutura; P. Lucivjansky; A. Mann; D. Mackovych; B.I. Malyuk; R. Maquil; R.G. Meuli; G. Mol; P. Négrel; P. O'Connor; K. Oorts; R.T. Ottesen; A. Pasieczna; W. Petersell; S. Pfleiderer; M. Poňavič; C. Prazeres; U. Rauch; S. Radusinović; M. Sadeghi; I. Salpeteur; R. Scanlon; A. Schedl; A.J. Scheib; I. Schoeters; E Sellersjö; I. Slaninka; A. Šorša; R. Srvkota; T. Stafilov; T. Tarvainen; V. Trendavilov; P. Valera; V. Verougstraete; D. Vidojević; Z. Zomeni; ..]. - In: ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. - ISSN 0730-7268. - STAMPA. - 34:2(2015), pp. 224-234. [10.1002/etc.2736]
GEMAS: Prediction of solid-solution partitioning coefficients (Kd) for cationic metals in soils using mid-infrared diffuse reflectance spectroscopy
DINELLI, ENRICO;
2015
Abstract
Partial least squares regression (PLSR) models, using mid-infrared (MIR) diffuse reflectance Fourier-transformed (DRIFT) spectra, were used to predict distribution coefficient (Kd) values for selected added soluble metal cations (Ag+, Co2+, Cu2+, Mn2+, Ni2+, Pb2+, Sn4+, and Zn2+) 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 Kd 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 Kd+1 for Co, Mn, Ni, Pb, and Zn (R2 ≥ 0.83) but poor predictions for Ag, Cu, and Sn (R2<0.50). These models were applied to the prediction of log Kd+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 Kd+1 values in soils worldwide with different soil types and properties not covered in the existing model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
