Feasibility of estimating heavy metal concentrations in wetland soil using hyperspectral technology

Heavy metals that are present in soil are poisonous to both plants and animals. Measuring the heavy metal concentrations in wetland soil are of great significance for the assessment of wetland ecosystem health. This study was conducted in the Taihu Lake wetland region of China, and is aimed at comparing the partial least squares regression (PLSR) as well as support vector machine regression (SVMR) methods for estimating the zinc (Zn), arsenic (As) and copper (Cu) concentrations present in wetland soil utilizing hyperspectral technology. In total, there were 100 homogeneous wetland soil samples collected, and their Zn, As and Cu concentration models were developed based on laboratory-based hyperspectral data (350–2500 nm). According to independent validation, the SVMR method achieved better accuracies, which had determination coefficients of 0.61, 0.66 and 0.72 for Zn, As and Cu, respectively. It was concluded that the SVMR method combined with laboratory-based hyperspectral data has the cumulative potential to estimate heavy metal concentrations within homogeneous wetland soil.

[1]  Lutgarde M. C. Buydens,et al.  Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains , 2001 .

[2]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  L. Buydens,et al.  Comparing support vector machines to PLS for spectral regression applications , 2004 .

[5]  Raphael A. Viscarra Rossel,et al.  ParLeS: Software for chemometric analysis of spectroscopic data , 2008 .

[6]  Guofeng Wu,et al.  Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals. , 2014, Journal of hazardous materials.

[7]  A. Gholizadeh,et al.  Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues , 2013, Applied spectroscopy.

[8]  Freek D. van der Meer,et al.  Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain , 2008 .

[9]  K. G. Tiller.  Heavy Metals in Soils and Their Environmental Significance , 1989 .

[10]  Zhihao Qin,et al.  Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils , 2005 .

[11]  J. M. Soriano-Disla,et al.  The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soils , 2013 .

[12]  Godwin A. Ayoko,et al.  Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang River Delta, China , 2012 .

[13]  Wouter Saeys,et al.  Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy , 2005 .

[14]  D. Qiu,et al.  Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy: A Case Study , 2009 .

[15]  Guofeng Wu,et al.  Estimating Soil Organic Carbon Content with Visible–Near-Infrared (Vis-NIR) Spectroscopy , 2014, Applied spectroscopy.

[16]  Thomas Kemper,et al.  Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. , 2002, Environmental science & technology.

[17]  Kenneth A. Sudduth,et al.  WAVELENGTH IDENTIFICATION AND DIFFUSE REFLECTANCE ESTIMATION FOR SURFACE AND PROFILE SOIL PROPERTIES , 2009 .

[18]  Michael Vohland,et al.  A spectroscopic approach to assess trace–heavy metal contents in contaminated floodplain soils via spectrally active soil components , 2009 .

[19]  M. McKee,et al.  SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES 1 , 2006 .