Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils

Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. In this study, we (i) explored reflectance spectroscopy as an alternative method for assessing heavy metals, and (ii) further explored the physicochemical mechanism that allows estimation of he...

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Bibliographic Details
Published in:Soil Science Society of America journal 2007-05, Vol.71 (3), p.918-926
Main Authors: Wu, Y, Chen, J, Ji, J, Gong, P, Liao, Q, Tian, Q, Ma, H
Format: Article
Language:English
Subjects:
absorption
agricultural soils
Agronomy. Soil science and plant productions
Biological and medical sciences
chemical concentration
Chemical, physicochemical, biochemical and biological properties
Chemicals
Correlation analysis
Developing countries
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
English language
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Geochemistry
Heavy metals
iron
iron oxides
LDCs
methodology
Mineral components. Ionic and exchange properties
Mining accidents & safety
near-infrared reflectance spectroscopy
Neural networks
new methods
physicochemical properties
Physics, chemistry, biochemistry and biology of agricultural and forest soils
Pollution, environment geology
Remote sensing
sequential extraction
soil analysis
Soil and rock geochemistry
Soil and water pollution
soil pollution
soil properties
Soil science
Soils
spectral analysis
Spectrum analysis
statistical analysis
Statistical methods
Studies
Trace elements
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Summary:Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. In this study, we (i) explored reflectance spectroscopy as an alternative method for assessing heavy metals, and (ii) further explored the physicochemical mechanism that allows estimation of heavy metals with the reflectance spectroscopy method. We first investigated the spectral response of changing concentrations of heavy metals in soils. The results indicated that only at very high concentration can transition elements exhibit their inherent absorption features. In spite of this observation, we successfully predicted low levels of heavy metals in agricultural soils. The best prediction accuracies were obtained for the siderophile elements Ni, Cr, and Co. The poorest prediction was for Cd. The order of prediction accuracy for metals was approximately the same as the order of their correlation coefficients with Fe. Complementary to some previous studies that found that the intercorrelation between heavy metals and active soil components (such as Fe oxides, organic matter, and clay) is the major predictive mechanism, in the present study we concluded that the correlation with total Fe (including active and residual Fe) is the major mechanism. This conclusion was further supported by both correlation analysis and chemical sequential extraction. Correlation analysis showed that all metals are negatively correlated with reflectance while positively correlated with the absorption depth at about 500 nm, a feature resulting from goethite. The chemical forms of heavy metals, which showed that besides the crystalline Fe oxide and organic matter fractions, heavy metals have significant amounts in the residual fraction, also strengthened the conclusion.
ISSN:0361-5995
1435-0661
DOI:10.2136/sssaj2006.0285