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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472
EISSN: 1735-2630
Vol. 12, No. 6, 2015, pp. 1965-1974
Bioline Code: st15183
Full paper language: English
Document type: Research Article
Document available free of charge

International Journal of Environment Science and Technology, Vol. 12, No. 6, 2015, pp. 1965-1974

 en Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site
Niazi, N. K.; Singh, B. & Minasny, B.

Abstract

The potential of mid-infrared spectroscopy in combination with partial least-squares regression was investigated to estimate total and phosphate-extractable arsenic contents in soil samples collected from a highly variable arsenic-contaminated disused cattle-dip site. Principal component analysis was performed prior to mid-infrared partial least-squares analysis to identify spectral outliers in the absorbance spectra of soil samples. The mid-infrared partial least-squares calibration model (n = 149) excluding spectral outliers showed an acceptable reliability (coefficient of determination, Rc2 = 0.75 (P> 0.01); ratio of performance to interquartile distance, RPIQc = 2.20) to estimate total soil arsenic. For total soil arsenic, the validation of final calibration model using 149 unknown samples also resulted in a good acceptability with Rv2 = 0.67 (P> 0.05) and RPIQv = 2.01. However, the mid-infrared partial leastsquares calibration model based on phosphate-extractable arsenic was not acceptable to estimate the extractable (bioavailable) arsenic content in soil (Rc2 = 0.13 (P> 0.05); RPIQc = 1.37; n = 149). The results show that the midinfrared partial least-squares prediction model based on total arsenic can provide a rapid estimate of soil arsenic content by taking into account the integrated effects of adsorbed arsenic, arsenic-bearing minerals and arsenic associated with organic components in the soils. This approach can be useful to estimate total soil arsenic in situations, where analysis of a large number of samples is required for a single soil type and/ or to monitor changes in soil arsenic content following (phyto)remediation at a particular site.

Keywords
Mid-infrared; Partial least-squares; Principal component; Cattle-dip sites; (Phyto)remediation; Prediction model; Contamination

 
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