Browsing by Author "Douglas, Reward K."

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    Evaluation of vis-NIR reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils
    (Elsevier, 2018-02-19) Douglas, Reward K.; Newar, S; Cipullo, Sabrina; Alamar, M. Carmen; Coulon, Frederic; Mouazen, A. M.
    This study investigated the sensitivity of visible near-infrared spectroscopy (vis-NIR) to discriminate between fresh and weathered oil contaminated soils. The performance of random forest (RF) and partial least squares regression (PLSR) for the estimation of total petroleum hydrocarbon (TPH) throughout the time was also explored. Soil samples (n = 13) with 5 different textures of sandy loam, sandy clay loam, clay loam, sandy clay and clay were collected from 10 different locations across the Cranfield University's Research Farm (UK). A series of soil mesocosms was then set up where each soil sample was spiked with 10 ml of Alaskan crude oil (equivalent to 8450 mg/kg), allowed to equilibrate for 48 h (T2 d) and further kept at room temperature (21 °C). Soils scanning was carried out before spiking (control TC) and then after 2 days (T2 d) and months 4 (T4 m), 8 (T8 m), 12 (T12 m), 16 (T16 m), 20 (T20 m), 24 (T24 m), whereas gas chromatography mass spectroscopy (GC–MS) analysis was performed on T2 d, T4 m, T12 m, T16 m, T20 m, and T24 m. Soil scanning was done simultaneously using an AgroSpec spectrometer (305 to 2200 nm) (tec5 Technology for Spectroscopy, Germany) and Analytical Spectral Device (ASD) spectrometer (350 to 2500 nm) (ASDI, USA) to assess and compare their sensitivity and response against GC–MS data. Principle component analysis (PCA) showed that ASD performed better than tec5 for discriminating weathered versus fresh oil contaminated soil samples. The prediction results proved that RF models outperformed PLSR and resulted in coefficient of determination (R2) of 0.92, ratio of prediction deviation (RPD) of 3.79, and root mean square error of prediction (RMSEP) of 108.56 mg/kg. Overall, the results demonstrate that vis–NIR is a promising tool for rapid site investigation of weathered oil contamination in soils and for TPH monitoring without the need of collecting soil samples and lengthy hydrocarbon extraction for further quantification analysis.
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    Rapid detection of alkanes and polycyclic aromatic hydrocarbons in oil-contaminated soil with visible near-infrared spectroscopy
    (Wiley, 2018-05-16) Douglas, Reward K.; Nawar, S.; Alamar, M Carmen; Coulon, Frederic; Mouazen, A. M.
    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.
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    Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques
    (Elsevier, 2017-11-09) Douglas, Reward K.; Nawar, Said; Alamar, M Carmen; Mouazen, Abdul; Coulon, Frederic
    Petroleum hydrocarbons contamination in soil is a worldwide significant environmental issue which has raised serious concerns for the environment and human health (Brevik and Burgess, 2013). Petroleum hydrocarbons encompass a mixture of short and long-chain hydrocarbon compounds. However the difference between the term petroleum hydrocarbons (PHC) as such and the term total petroleum hydrocarbons (TPH) should be noted. PHC typically refer to the hydrogen and carbon containing compounds that originate from crude oil, while TPH refer to the measurable amount of petroleum-based hydrocarbons in an environmental matrix and thus to the actual results obtained by sampling and chemical analysis (Coulon and Wu, 2017). TPH is thus a method-defined term. Among a range of techniques, gas chromatography is preferred for the measurement of hydrocarbon contamination in environmental samples, since it allows to detect a broad range of hydrocarbons and can provide both sensitivity and selectivity depending on the detector and hyphenated configuration used (Brassington et al., 2010; Drozdova et al., 2013). However, GC-based techniques can be time consuming and expensive and do not allowed rapid and broad scale analysis of petroleum contamination on-site (Okparanma and Mouazen, 2013; Okparanma et al., 2014).