Journal article
Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site
nternational Journal of Environmental Science and Technology, Vol.112(6), pp.1965-1974
2015
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Source: InCites
Abstract
<p>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 (<em>n</em> = 149) excluding spectral outliers showed an acceptable reliability (coefficient of determination, R2c = 0.75 (<em>P</em> < 0.01); ratio of performance to interquartile distance, RPIQ<sub>c</sub> = 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 R2v = 0.67 (<em>P</em> < 0.05) and RPIQ<sub>v</sub> = 2.01. However, the mid-infrared partial least-squares calibration model based on phosphate-extractable arsenic was not acceptable to estimate the extractable (bioavailable) arsenic content in soil ( R2c = 0.13 (<em>P</em> > 0.05); RPIQ<sub>c</sub> = 1.37; <em>n</em> = 149). The results show that the mid-infrared 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.</p>
Details
- Title
- Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site
- Creators
- Nabeel Khan Niazi - University of SydneyBalwant Singh - University of SydneyB Minasny - University of Sydney
- Publication Details
- nternational Journal of Environmental Science and Technology, Vol.112(6), pp.1965-1974
- Identifiers
- 1335; 991012820888202368
- Academic Unit
- Southern Cross GeoScience
- Resource Type
- Journal article