The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Xiaoquan Shan - One of the best experts on this subject based on the ideXlab platform.
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evaluation of plant availability of rare earth elements in soils by chemical fractionation and Multiple Regression analysis
Environmental Pollution, 1998Co-Authors: Xiaoquan Shan, Tianhong Zhang, Shuzhen ZhangAbstract:This case field study describes the distribution of rare earth elements (REEs) in different soil fractions obtained by a sequential extraction procedure and plant availability with single correlation and Multiple Regression analysis. Soil and plant samples were collected from a rural region of Beijing, China. Plant samples (corn, rice) were segmented into grain, stem, leaf and root. The results indicated that REE contents in different parts of plants followed the order: root > leaf > stem > grain. REEs in soils were extracted by a three-stage sequential extraction procedure into three fractions: water soluble, exchangeable and carbonate bound (B1), Fe-Mn oxide bound (B2), and organic matter and sulfide bound (B3). Chemical fractionation showed that the concentration of REEs in fraction B1 was close to or lower than 1% of the total REEs in soils, and in fractions B2 and B3 was in the range of 1.42-3.35 and 7.32-30.88% of total REEs in soil collected from corn fields, respectively. In soils from rice fields the concentration of REEs in fractions B1, B2 and B3 ranged from 0.58 to 1.05, 13.47 to 21.60 and 7.53 to 25.37% of total REEs, respectively. Correlation and Multiple Regression analysis indicated that total content of REEs in soils and the sum extracted with the sequential extraction procedure were poor indicators for REE uptake by plants. For La, Ce, Pr, Nd, Sm, Cd, Dy, Er, Yb and Y, consistently significant positive correlations existed between contents in fraction B1 and concentrations in leaves of corn (r = 0.5662-0.7866) and rice (r = 0.5847-0.8262). As for other parts of rice and corn, significant correlations were observed sparsely. Multiple Regression analysis was utilized to obtain the 'best' Regression equations for predicting plant uptake of REEs. The study demonstrated that the sequential extraction procedure applied, though operationally defined, could provide valuable information on plant uptake of REEs in soils. (C) 1998 Elsevier Science Ltd. All rights reserved.
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evaluation of plant availability of soil trace metals by chemical fractionation and Multiple Regression analysis
Environmental Pollution, 1996Co-Authors: Jin Qian, Zijian Wang, Xiaoquan Shan, Bei Wen, Bin ChenAbstract:Soil samples with a range of chemical and physical properties were collected from 10 different rural regions of China. Trace metals (Ni, Co, Cu, and Pb) in the soils were partitioned by a sequential extraction procedure into Mg(NO3)(2) extractable (FI), CH3COONa extractable (F2), NH2OH . HCl extractable (F3), HNO3-H2O2 extractable (F4), and residual (F5) fractions. Chemical fractionation showed that F1 fraction of the metals was less than 1% and residue was the dominant form for Cu and Ni in all samples, and for Co in most of the samples. Significant interrelationships of the fractions varied considerably with the different metals. Winter wheat (Triticum aestivum L.) and alfalfa (Medicago sativa L.) had been grown on the soils in a pot-culture experiment under greenhouse conditions for 40 days. Metal availability to the plants was evaluated by simple and Multiple Regression analysis. The Mg(NO3)(2) extractable Co (F1) was significantly correlated with Co concentrations in different parts of wheat and in the whole of alfalfa. For the other metals, the independent variables of the Multiple Regression models, chosen by stepwise selection, were given as: F1 and F2 + F3 + F4 for Ni; F1, F2 + F3 and F4 for Cu; and F3 + F4 for Pb. The results of this study demonstrate that the sequential extraction procedure, in conjunction with Multiple Regression models using a combination of correlated fractions as an independent variable, may be useful for the prediction of plant absorption of trace metals in soils.
Shuzhen Zhang - One of the best experts on this subject based on the ideXlab platform.
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evaluation of plant availability of rare earth elements in soils by chemical fractionation and Multiple Regression analysis
Environmental Pollution, 1998Co-Authors: Xiaoquan Shan, Tianhong Zhang, Shuzhen ZhangAbstract:This case field study describes the distribution of rare earth elements (REEs) in different soil fractions obtained by a sequential extraction procedure and plant availability with single correlation and Multiple Regression analysis. Soil and plant samples were collected from a rural region of Beijing, China. Plant samples (corn, rice) were segmented into grain, stem, leaf and root. The results indicated that REE contents in different parts of plants followed the order: root > leaf > stem > grain. REEs in soils were extracted by a three-stage sequential extraction procedure into three fractions: water soluble, exchangeable and carbonate bound (B1), Fe-Mn oxide bound (B2), and organic matter and sulfide bound (B3). Chemical fractionation showed that the concentration of REEs in fraction B1 was close to or lower than 1% of the total REEs in soils, and in fractions B2 and B3 was in the range of 1.42-3.35 and 7.32-30.88% of total REEs in soil collected from corn fields, respectively. In soils from rice fields the concentration of REEs in fractions B1, B2 and B3 ranged from 0.58 to 1.05, 13.47 to 21.60 and 7.53 to 25.37% of total REEs, respectively. Correlation and Multiple Regression analysis indicated that total content of REEs in soils and the sum extracted with the sequential extraction procedure were poor indicators for REE uptake by plants. For La, Ce, Pr, Nd, Sm, Cd, Dy, Er, Yb and Y, consistently significant positive correlations existed between contents in fraction B1 and concentrations in leaves of corn (r = 0.5662-0.7866) and rice (r = 0.5847-0.8262). As for other parts of rice and corn, significant correlations were observed sparsely. Multiple Regression analysis was utilized to obtain the 'best' Regression equations for predicting plant uptake of REEs. The study demonstrated that the sequential extraction procedure applied, though operationally defined, could provide valuable information on plant uptake of REEs in soils. (C) 1998 Elsevier Science Ltd. All rights reserved.
Zhao Guiwen - One of the best experts on this subject based on the ideXlab platform.
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Assessment of the bioavailability of rare earth elements in soils by chemical fractionation and Multiple Regression analysis.
Chemosphere, 2000Co-Authors: Cao Xinde, Wang Xiaorong, Zhao GuiwenAbstract:The bioavailability of rare earth elements (REEs) in soils was evaluated, based on the combination of chemical fractionation and Multiple Regression analysis. REEs in soils were partitioned by a sequential extraction procedure into water soluble (F(ws)), exchangeable (F(ec)), bound to carbonates (F(cb)), bound to organic matter (F(om)), bound to Fe–Mn oxides (F(fm)) and residual (F(rd)) fractions. Alfalfa (Medicago Staiva Linn.) had been grown on the soils in a pot-culture experiment under greenhouse conditions for 35 days. The concentrations of REEs in fractions and plant were determined by inductively coupled plasma-mass spectrometry (ICP-MS). Chemical fractionation showed that (F(ws)) fraction of REEs was less than 0.1% and residual (F(rd)) was the dominant form, more than 60% in soils. Bioaccumulation of REEs was observed in Alfalfa. REE availability to the plant was evaluated by Multiple Regression analysis. F(ws), F(ec), F(cb) and F(om) fractions were significantly correlated with REE uptake by alfalfa. But the exchangeable Pr(F(ec)) was significantly correlated with Pr concentration in alfalfa. F(ec), F(cb) and F(om) greatly contributed to La and Nd bioavailability; F(ec) and F(om) to Ce, Gd and Dy; F(ec) and F(cb) to Yb; and F(ws), F(ec) and F(om) to total REEs. This meant that the bioavailability of different species of REEs varied with individual REE. The results of this study indicated that the sequential extraction procedure, in conjunction with Multiple Regression analysis, may be useful for the prediction of plant uptake of REEs from soils.
Al Cripps - One of the best experts on this subject based on the ideXlab platform.
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predicting housing value a comparison of Multiple Regression analysis and artificial neural networks
Journal of Real Estate Research, 2001Co-Authors: Nghiep Nguyen, Al CrippsAbstract:This paper compares the predictive performance of artificial neural networks (ANN) and Multiple Regression analysis (MRA) for single family housing sales. Multiple comparisons are made between the two data models in which the data sample size is varied, the funcional specifications is varied, and the temporal prediction is varied. We conclude that ANN performs better than MRA when a moderate to large data sample size is used. For our application, this "moderate to large data sample size" varied from 13% to 39% of the total data sample (506 to 1506 observations out of 3906 total observations). Our results give a plausible explanation why previous papers have obtained varied results when comparing MRA and ANN predictive performance for housing values.
Nguyen Nghiep - One of the best experts on this subject based on the ideXlab platform.
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predicting housing value a comparison of Multiple Regression analysis and artificial neural networks
Journal of Real Estate Research, 2001Co-Authors: Nguyen NghiepAbstract:This article compares the predictive performance of artificial neural networks (ANN) and Multiple Regression analysis (MRA) for single family housing sales. Multiple comparisons are made between th...
