Janik Leslie J, Forrester Sean T, Soriano-Disla José M, Kirby Jason K, McLaughlin Michael J, Reimann Clemens
Contaminant Chemistry and Ecotoxicology Program, Sustainable Agriculture Flagship Program, Waite Campus, CSIRO Land and Water, South Australia, Australia.
Environ Toxicol Chem. 2015 Feb;34(2):224-34. doi: 10.1002/etc.2736. Epub 2014 Nov 7.
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(+), 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 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 (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 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 K(d+1) values in soils worldwide with different soil types and properties not covered in the existing model.
利用中红外（MIR）漫反射傅里叶变换（DRIFT）光谱的偏最小二乘回归（PLSR）模型，用于预测农业土壤地球化学填图（GEMAS）计划的4813个土壤中选定添加的可溶性金属阳离子（Ag⁺、Co²⁺、Cu²⁺、Mn²⁺、Ni²⁺、Pb²⁺、Sn⁴⁺和Zn²⁺）的分配系数（Kd）值。为了建立PLSR模型，根据光谱选择了约500个代表性土壤，并使用单点可溶性金属或放射性同位素加标法测定Kd值。使用MIR-DRIFT光谱和土壤pH值相结合的最佳模型对Co、Mn、Ni、Pb和Zn的log Kd +1预测效果良好（R²≥0.83），但对Ag、Cu和Sn的预测效果较差（R²<0.50）。这些模型被应用于预测其余4313个未知土壤中的log Kd +1值。PLSR模型提供了一种快速且廉价的工具来评估欧洲土壤中选定金属阳离子的迁移性和潜在有效性。需要进一步开展模型开发和验证工作，以便能够预测现有模型未涵盖的具有不同土壤类型和性质的全球土壤中的log K(d +1)值。
