Spectral estimation model construction of heavy metals in mining reclamation areas

Dong, J., Dai, W., Xu, J. and Li, S. (2016) Spectral estimation model construction of heavy metals in mining reclamation areas. International Journal of Environmental Research and Public Health, 13(7), 640. (doi: 10.3390/ijerph13070640) (PMID:27367708) (PMCID:PMC4962181)

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The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R2 of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R2 between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R2 value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R2 and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible.

Item Type:Articles
Additional Information:The research has been founded by the National Natural Science Foundation of China (No. 51374208), National Science and Technology Basic Project (No. 2014FY110800) and the Open Project of State Key Laboratory of Soil Sustainable Agricultural Institute of Soil Science (No. Y412201432).
Glasgow Author(s) Enlighten ID:Xu, Dr Jiren
Authors: Dong, J., Dai, W., Xu, J., and Li, S.
College/School:College of Social Sciences > School of Interdisciplinary Studies
Journal Name:International Journal of Environmental Research and Public Health
Publisher:Molecular Diversity Preservation International
ISSN (Online):1660-4601
Published Online:28 June 2016
Copyright Holders:Copyright © 2016 by the authors
First Published:First published in International Journal of Environmental Research and Public Health 13(7):640
Publisher Policy:Reproduced under a Creative Commons license

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