Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India

Somsubhra Chakraborty; David C. Weindorf; Sathi Paul; Bhaswati Ghosh; Bin Li; Md Nasim Ali; Rakesh Kumar Ghosh; D. P. Ray; K. Majumdar

doi:10.1016/j.geodrs.2015.04.004

Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India

Somsubhra Chakraborty, David C. Weindorf, Sathi Paul, Bhaswati Ghosh, Bin Li, Md Nasim Ali, Rakesh Kumar Ghosh, D. P. Ray, K. Majumdar

Earth, Environment, & Sustainability

Research output: Contribution to journal › Article › peer-review

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Abstract

Soil lead (Pb) contamination by anthropogenic and industrial activities is a problem of global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIR-DRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky-Golay (SG) first derivative spectra (RPD = 3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost.

Original language	English
Pages (from-to)	77-85
Number of pages	9
Journal	Geoderma Regional
Volume	5
DOIs	https://doi.org/10.1016/j.geodrs.2015.04.004
State	Published - Aug 1 2015

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title = "Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India",

abstract = "Soil lead (Pb) contamination by anthropogenic and industrial activities is a problem of global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIR-DRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky-Golay (SG) first derivative spectra (RPD = 3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost.",

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T1 - Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India

AU - Chakraborty, Somsubhra

AU - Weindorf, David C.

AU - Paul, Sathi

AU - Ghosh, Bhaswati

AU - Li, Bin

AU - Ali, Md Nasim

AU - Ghosh, Rakesh Kumar

AU - Ray, D. P.

AU - Majumdar, K.

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Soil lead (Pb) contamination by anthropogenic and industrial activities is a problem of global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIR-DRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky-Golay (SG) first derivative spectra (RPD = 3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost.

AB - Soil lead (Pb) contamination by anthropogenic and industrial activities is a problem of global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIR-DRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky-Golay (SG) first derivative spectra (RPD = 3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost.

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VL - 5

SP - 77

EP - 85

JO - Geoderma Regional

JF - Geoderma Regional

ER -

Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India

Abstract

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