Abstract

With 35 soil samples gathered from the Bahr El Baqar region, Egypt, the objective of this study was to propose an inference model for prediction of heavy metals (Cd, Cu, Pb, Zn, Ni, Mn, Cr, Co, Fe) with visible near-infrared and short wave infrared (VNIR-SWIR) region (350–2500 nm). For a better understanding of the mechanism that allows the estimation of heavy metals with reflectance spectroscopy, statistical analysis was first made. The fingerprint region of 538, 578, 630, 870, 1900, 2240, and 2376 nm is very useful in recognizing small differences in the heavy metals of Bahr El Baqar region. Partial least squares regression (PLSR) is able to some extent (moderate accuracy) to model heavy metals with laboratory spectra parameters. The best coefficients of determination (R 2) between predicted and chemically analyzed concentrations were for Mn, 0.62; Pb, 0.66; Zn, 0.66, Ni, 0.69, and Cu, 0.60. Iron influences the soil reflectance in the VNIR-SWIR region. This is due to the electronic transition of iron cations (2+, 3+). Mn, Pb, Ni, Cu, and Zn were successfully predicted using PLSR. Only Cr (0.59) and Cd (0.52) content was predicted fairly. The analysis of correlation between heavy metal and soil constitutes of Fe2O3, Al2O3 and OM, which represented the clay minerals, iron oxides and organic matter, respectively, could support the above predicated binding forms of heavy metals. The order of the correlation coefficients from high to low between metal and wavelength is Cu > Zn > Mn > Co > Pb > Cr > Ni > Cd. This is almost the same as the order of their correlation coefficients with Fe2O3, Al2O3 and OM. These findings also validate a mechanism to predict heavy metals that have no absorption features in reflectance spectra. The results concerning the relationships between spectral parameters and heavy metals concentration indicate that iron oxides, clay, and OM play an important role in the prediction of soil heavy metals using reflectance spectra. To identify the specific wavelengths for prediction of the studied heavy metals, the regression coefficient with OM, Al2O3, and Fe2O3 were used. The most significant peaks for Zn prediction are found in the wavelengths of 430, 570, and 1700 nm; for Cu prediction at 538, 1259, 1500, and 2184 nm; for Pb significant peaks are at 440, 578, 915, and 1894 nm; for Cr at 860, 1001, and 2376 nm; for Ni at 870, 909, and 2240 nm; for Cd at 630, and 1270 nm; for Co at 1900, and 2300 nm. As the current results suggested, large concentrations of heavy metals can be predicted using reflectance spectroscopy. However, it is much more difficult to determine small concentrations of heavy metals in soils of Bahr El Baqar region. Organic matter masks spectral signatures, making it difficult to identify metal quantities at these wavelengths. So, a new concept “inference approach” in light of key soil constituents that absorb VNIR-SWIR radiation would became a powerful tool to enhance the accuracy of heavy metal prediction. Due to the interference effects of organic matter and low heavy metal concentration, the selection of the most sensitive original spectral bands still did not result in highly accurate prediction. Therefore, it is important to predict heavy metals through OM, iron oxides, or clays first. These results indicate that it is possible to predict heavy metals in soils using the rapid and economic reflectance spectroscopy.

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