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Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site

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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, \(R_{\text{c}}^{2}\) = 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 \(R_{\text{v}}^{2}\) = 0.67 (P < 0.05) and RPIQv = 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 (\(R_{\text{c}}^{2}\) = 0.13 (P > 0.05); RPIQc = 1.37; n = 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.

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Acknowledgments

The first author gratefully acknowledges the Higher Education Commission of Pakistan for the award of a PhD scholarship. The project has been assisted by the NSW Government through its Environmental Trust. Thanks to Dr Lukas van Zwieten, Stephen Kimber, George Nastase, Joshua Rust, Scott Petty, Desmond Cook and Victor Warren at the Wollongbar Agricultural Research Institute, Wollongbar for field assistance.

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  1. N. K. Niazi

    Present address: Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan

Authors and Affiliations

  1. Department of Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, Sydney, NSW, 2006, Australia

    N. K. Niazi, B. Singh & B. Minasny

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  1. N. K. Niazi
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Correspondence to N. K. Niazi.

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Niazi, N.K., Singh, B. & Minasny, B. Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site. Int. J. Environ. Sci. Technol. 12, 1965–1974 (2015). https://doi.org/10.1007/s13762-014-0580-5

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  • DOI: https://doi.org/10.1007/s13762-014-0580-5

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