Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Chakraborty, Somsubhra; Weindorf, David C.; Li, Bin; Aldabaa, Abdalsamad Abdalsatar Ali; Ghosh, Rakesh Kumar; Paul, Subhadip; Ali, M.-D. Nasim

doi:10.1016/j.scitotenv.2015.01.087

Cited by 69 publications

(38 citation statements)

References 59 publications

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“…The wavelength of 2207 nm is near to those reported by Chakraborty et al . () and Forrester et al . () (2220 nm), whereas the wavelength of 2302 nm is close to 2298, reported for hydrocarbon contamination in soils by Mullins et al .…”

Section: Resultsmentioning

confidence: 88%

“…Over the last two decades, laboratory techniques have been developed for analysing soil contamination in the laboratory, which are time consuming and expensive (Okparanma & Mouazen, ; Chakraborty et al ., ). Furthermore, laboratory techniques require prior sample analysis, extraction and sometimes removal of the non‐volatile compounds of contaminants from the GC injection ports and columns (Forrester et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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Rapid detection of alkanes and polycyclic aromatic hydrocarbons in oil‐contaminated soil with visible near‐infrared spectroscopy

Douglas

Nawar

Alamar

et al. 2018

European J Soil Science

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Summary Recent developments and applications of rapid measurement tools (RMTs) such as visible near‐infrared (vis–NIR) spectroscopy confirmed that these technologies can provide ‘fit for purpose’ and cost‐effective data for risk assessment and management of oil‐contaminated sites. Although vis–NIR spectroscopy has been used frequently to predict total petroleum hydrocarbons (TPHs), it has had limited use for polycyclic aromatic hydrocarbons (PAHs) and there has been none for alkanes. In the present study, the potential of vis–NIR spectroscopy (350–2500 nm) to measure PAHs and alkanes in 85 fresh (wet, unprocessed) oil‐contaminated soil samples collected from three sites in the Niger Delta, Nigeria, was evaluated. The vis–NIR signal and alkanes and PAHs measured in the laboratory by sequential ultrasonic solvent extraction followed by gas chromatography‐mass spectrometry (GC‐MS) were then used to develop calibration models using partial least squares regression (PLSR) and random forest (RF) modelling tools. Prior to model development, the pre‐processed spectra were divided into calibration (75%) and prediction (25%) sets. Results showed that the prediction performance of RF calibration models for both alkanes (a coefficient of determination (R2) of 0.58, a root mean square error of prediction (RMSEP) of 53.95 mg kg−1 and a residual prediction deviation (RPD) of 1.59) and PAHs (R2 = 0.71, RMSEP = 0.99 mg kg−1 and RPD = 1.99) outperformed PLSR (R2 = 0.36, RMSEP = 66.66 mg kg−1 and RPD = 1.29, and R2 = 0.56, RMSEP = 1.21 mg kg−1 and RPD = 1.55, respectively). The RF modelling approach accounted for nonlinearity of the soil spectral responses and therefore resulted in considerably greater prediction accuracy than the linear PLSR. Adoption of vis–NIR spectroscopy coupled with RF is recommended for rapid and cost‐effective assessment of PAHs and alkanes in contaminated soil. Highlights We evaluated the potential of vis–NIR to estimate alkanes and PAHs in oil‐contaminated soil. The prediction performance of RF models was better than PLSR models for both alkanes and PAHs. The spectral response to alkanes and PAHs in soil embodies considerable non‐linearity. Results suggest that RF‐vis–NIR is a promising tool for rapid in situ assessment of soil alkanes and PAHs.

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Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 97%

Rapid detection of alkanes and polycyclic aromatic hydrocarbons in oil‐contaminated soil with visible near‐infrared spectroscopy

Douglas

Nawar

Alamar

et al. 2018

European J Soil Science

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“…The data fusion approach, employed by Wang et al (2015), Aldabaa et al (2015), and Chakraborty et al (2015), was tested on a range of elements in the preliminary stages of this study. This involved concatenation of vis-NIR, MIR, and pXRF spectra, followed by Cubist modeling of the combined data.…”

Section: Potential Of Synergistic Use Of Spectral Methods In Soil Anamentioning

confidence: 99%

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

O’Rourke

Minasny

Holden

et al. 2016

Soil Science Soc of Amer J

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Proposed legislation to secure and maintain soil quality in Europe has generated interest surrounding how best to characterize soil geochemistry, and how to assess and monitor soil contamination. Visible-near infrared (vis-NIr), mid-infrared (MIr), and portable X-ray fluorescence (pXrF) spectroscopy can reduce time and cost associated with new soil monitoring programs. Before becoming deployable, accuracy of these techniques needs to be quantified. This study investigated potential of these techniques to characterize a full suite of soil geochemistry (40 elements), pH, and soil organic carbon (SOC) in a diverse set of agricultural soils from the Irish National Soil Database (NSDB) archive. Cubist models were employed to assess goodness of fit from spectrally derived estimates of each soil property. Then a model ensemble, or model averaging, approach was tested to examine if combining model outcomes of either vis-NIr or MIr with pXrF could improve accuracy of soil property prediction. In total, 15 of the 42 soil properties could be predicted to a good accuracy status using individual spectral methods, mostly achieved by MIr and pXrF. Combining model outcomes of vis-NIr or MIr with pXrF resulted in a positive improvement, increasing the number of soil properties that could be predicted from 15 to 25. Most notably is the large number of trace elements (As, Cd, Co, Cu, Hg, Mn, Ni, and Zn) predicted to good or reasonable accuracy. It was concluded that the synergistic use of vis-NIr, MIr, and pXrF spectral methods is well placed as a tool to permit large scale routine soil monitoring.Abbreviations: ICP, inductively coupled plasma; IQ, interquartile range; MIR, midinfrared; NSDB, Irish National Soil Database; pXRF, portable X-ray fluorescence; RPIQ, ratio of performance to inter quartile distance; SOC, soil organic carbon; vis-Nir, visiblenear infrared.T he strategy for protection of soils in Europe (European Commission, 2006a) recognizes a number of degradation processes or 'threats' to important soil functions. Among these is soil contamination arising from the introduction of substances on/in the soil that hamper soil functions, or present risks to human health or the environment. Proposed legislation for the protection of soils in Europe outlines a plan for member states to identify contaminated sites and introduce a program of remediation (European Commission, 2006b). A lack of legislation for soil protection in Europe (aside from the Directive on the management of waste from extractive industries [European Commission, 2006c]) makes regulation of contaminated sites difficult ( Jones et al., 2012). In the 39 countries of the European Environment Agency, the total number of contaminated soil sites in 2011 was estimated to be 2.5 million. The key sources of soil contamination arose from waste disposal and treatment, industrial, and commercial activities (e.g., mining, oil extraction, and power plants), storage (e.g., oil, oil extraction, and obsolete chemicals), transport spills on land (e.g., oil and other hazardou...

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“…Moreover, a nonlinear RF might exploit its capacity to control the interactions among these elements implicitly, whereas in traditional linear regression models the researcher has to select wisely and explicitly the interaction terms in the model. The coefficient of determination ( R 2 ), root mean square error (RMSE), residual prediction deviation (RPD) (Equation ), ratio of performance to interquartile distance (RPIQ) and bias (Equation ) were used to judge the generalizing capability of the model (Chang et al ., ; Chakraborty et al ., ):

\begin{matrix} R P D = {([], \frac{1 / (n - 1) \sum_{i = 1}^{n} {((), Y_{normalo normalb normals} - Y_{mean})}^{2}}{1 / n \sum_{i = 1}^{n} {((), Y_{normalo normalb normals} - Y_{pred})}^{2}})}_{_{validation}}^{^{0.5}} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Simultaneous assessment of key properties of arid soil by combined PXRF and Vis–NIR data

Weindorf

Chakraborty

Herrero

et al. 2016

European J Soil Science

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n d o r f a , S . C h a k r a b o r t y b , J . H e r r e r o c , SummaryArid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory-based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X-ray fluorescence (PXRF) and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS)) to scan a diverse set (n = 116) of samples from arid soil in Spain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression (PSR), support vector regression (SVR) and random forest (RF) analysis, which were applied to Vis-NIR DRS data, PXRF data and PXRF and Vis-NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR + RF predicted gypsum with a ratio of performance to interquartile distance (RPIQ) of 5.90 and residual prediction deviation (RPD) of 4.60, electrical conductivity (1:5 soil : water) with RPIQ of 3.14 and RPD of 2.10, Ca content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and Vis-NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non-destructive scans. Highlights• Two combined proximal sensors were used to improve assessment of arid soil properties.• Combining sensors improved quantitative accuracy relative to either sensor alone. • Prediction accuracy: gypsum > electrical conductivity and Ca content > calcium carbonate equivalent.• Combined proximal sensor platforms outperform single sensors for quantifying arid soil properties.

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Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Cited by 69 publications

References 59 publications

Rapid detection of alkanes and polycyclic aromatic hydrocarbons in oil‐contaminated soil with visible near‐infrared spectroscopy

Rapid detection of alkanes and polycyclic aromatic hydrocarbons in oil‐contaminated soil with visible near‐infrared spectroscopy

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

Simultaneous assessment of key properties of arid soil by combined PXRF and Vis–NIR data

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