Literature DB >> 28363173

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

J J González Costa1, M J Reigosa2, J M Matías3, E F Covelo1.   

Abstract

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Adsorption; Heavy metals; Retention; SVM multiple regression

Year:  2017        PMID: 28363173     DOI: 10.1016/j.scitotenv.2017.03.195

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  4 in total

1.  Analysis of the adsorption and retention models for Cd, Cr, Cu, Ni, Pb, and Zn through neural networks: selection of variables and competitive model.

Authors:  Juan J González-Costa; Manuel J Reigosa-Roger; José M Matías; Emma Fernández-Covelo
Journal:  Environ Sci Pollut Res Int       Date:  2018-06-29       Impact factor: 4.223

2.  Using Artificial Intelligent to Model Predict the Biological Resilience With an Emphasis on Population of cyanobacteria in Jajrood River in The Eastern Tehran, Iran.

Authors:  Naghmeh Jafarzadeh; S Ahmad Mirbagheri; Taher Rajaee; Afshin Danehkar; Maryam Robati
Journal:  J Environ Health Sci Eng       Date:  2022-01-13

3.  The performance of emerging materials derived from waste organism blood and saponified modified orange peel for immobilization of available Cd in soil.

Authors:  Zhuoxi HuangFu; Zongxin Ran; Yinpeng Mo; Zichen Xu; Wei Wei; Jiang Yu; Bo Lai; Xingrun Wang
Journal:  RSC Adv       Date:  2020-10-09       Impact factor: 4.036

4.  Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy.

Authors:  Jianli Ding; Aixia Yang; Jingzhe Wang; Vasit Sagan; Danlin Yu
Journal:  PeerJ       Date:  2018-10-17       Impact factor: 2.984

  4 in total

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