Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils

Due to rapid economic development, high levels of potentially harmful elements are continuously being released into the suburban soils of the Nanjing area, China. Conventional methods for investigating soil potentially harmful element contamination based on raster sampling and chemical analysis are time consuming and relatively expensive. Reflectance spectroscopy within the visible-near-infrared region has been widely used to predict soil constituents due to its rapidity, convenience and accuracy. The objective of this study was to examine the possibility of using soil reflectance spectra as a rapid method to simultaneously assess contaminant metals (Ni, Cr, Cu, Hg, Pb, Zn) and As in the suburban soils of the Nanjing area. One hundred and twenty soil samples were collected for chemical analyses and spectral measurements. Prediction of contaminant elements was achieved by a partial least-square regression (PLSR) approach. According to their relationships with Fe, the seven contaminant elements could be categorized into two groups. The results showed that the prediction accuracy for Group II (Ni, Cr, Cu and Hg) was higher than that for Group I (Pb, Zn and As). This finding was consistent with the fact that the correlation coefficients between Group II and Fe were higher than that between Group I and Fe. It was concluded that element-sorption by spectrally active Fe oxides was the major mechanism by which to predict spectrally featureless contaminant elements. This conclusion was strengthened by the fact that the PLSR regression coefficients, which revealed the most important wavelengths for prediction, were attributed to absorption features of Fe oxides. Future study with real remote sensing data and field measurements are strongly recommended.