Agriculture Reference
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and alluminosilicate minerals (e.g. Hanahan et al., 2004; Santona et al., 2006). The chemical
analysis generally reveals the presence of Si, Al, Fe, Ca, Ti as well as an array of minor
constituents such as Na, K, Cr, Ni, Mn, Cu, Zn and Pb ( e.g. Chvedov . et al., 2001; Hanahan
et al., 2004; Palmert et al., 2007).
Red mud varies in physical, chemical and mineralogical properties due to differing ore
sources and refining processes employed and for this reason also this waste material must
be deeply characterized before its use for environmental application.
The red mud waste risk is mainly due to the accumulative contamination of land and the
surrounding dwellings with fine particulate that is highly alkaline and hence needs special
precaution to prevent contamination of surrounding natural or urban environments and to
avoid consequential exposure and health risk to inhabitants (Mymrin &Vazquez-Voamonde,
2001).
For this kind of studies, the total element composition is usually analyzed by X-ray
fluorescence spectroscopy (XRF), whereas the mineral composition is determined by X-ray
diffraction (XRD). The samples are also used for examination of micromorphological
characteristics by SEM and for thermogravimetric analysis. Few spectroscopic studies are
available (Palmer et al, 2007, 2009) including mid-infrared (IR), Raman, near-infrared (NIR),
while there is limited report on the red mud optical characterization.
Recent literature data also show the utilization of imaging spectroscopy and airborne
hyperspectral remote sensing to characterize red mud and mapping the red dust
distribution on soils (Pascucci et al., 2009). Furthermore, different studies have highlighted
the application of field and imaging spectroscopy for identifying minerals and soils
containing pollutants (e.g., heavy metals) as an indicator of contamination in mining areas
(Choe et al., 2008; Mars & Crowley, 2003). Kemper and Sommer (2002) in their study have
been assessed heavy metal concentrations using reflectance spectroscopy and statistical
prediction models recommending the opportunity of applying their technique to remote
sensing. In Swayze et al. (2000) the authors describe a procedure and their results attained
using imaging spectroscopy to map acidic mine waste. Cécillon et al. (2009) in their work
examine critically the suitability of NIR reflectance spectroscopy as a tool for soil quality
assessment concluding that (a) imaging NIR enables the direct mapping of some soil
properties and soil threats, but that further developments to solve several technological
limitations identified are needed before it can be used for soil quality assessment and (b) the
robustness of laboratory NIR spectroscopy for soil quality assessment allows its
implementation in soil monitoring networks, however, its regular employ requires the
development of international soil spectral libraries that should become a priority for soil
quality research.
2.2.1 Hyperspectral remote sensing data for mapping red dust: a case study
Techniques for direct identification of materials through the exploitation of spectral features
from field and laboratory reflectance spectra have been in use for many years being
successfully applied to imaging spectrometer data (Ben-Dor et al., 2009; Clark, 1999; Clark &
Roush, 1984; Viscarra-Rossel et al., 2006).
Within this context, the authors have been optically characterized red dust widespread on
soils by laboratory and field analyses and used hyperspectral remote sensing data to map its
distribution on soils in the surrounding of the impoundment area of an aluminium plant in
Montenegro.
