Browsing by Author "Douglas, Reward K."

Now showing 1 - 8 of 8
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    Almost 25 years of chromatographic and spectroscopic analytical method development for petroleum hydrocarbons analysis in soil and sediment: State-of-the-art, progress and trends
    (Taylor & Francis, 2017-10-11) Douglas, Reward K.; Nawar, Said; Alamar, M. Carmen; Coulon, Frederic; Mouazen, Abdul M.
    This review provides a critical insight into the selection of chromatographic and spectroscopic techniques for semi-quantitative and quantitative detection of petroleum hydrocarbons in soil and sediment matrices. Advantages and limitations of both field screening and laboratory-based techniques are discussed and recent advances in chemometrics to extract maximum information from a sample by using the optimal pre-processing and data mining techniques are presented. An integrated analytical framework based on spectroscopic techniques integration and data fusion for the rapid measurement and detection of on-site petroleum hydrocarbons is proposed. Furthermore, factors influencing petroleum hydrocarbons analysis in contaminated samples are discussed and recommendations on how to reduce their influence provided.
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    The applicability of spectroscopy methods for estimating potentially toxic elements in soils: state-of-the art and future trends
    (Taylor and Francis, 2019-05-08) Nawar, Said; Cipullo, Sabrina; Douglas, Reward K.; Coulon, Frederic; Mouazen, Abdul M.
    Potentially toxic elements (PTEs) in soils pose severe threats to the environment and human health. It is therefore imperative to have access to simple, rapid, portable, and accurate methods for their detection in soils. In this regard, the review introduces recent progresses made in the development and applications of spectroscopic methods for in situ semi-quantitative and quantitative detection of PTEs in soil and critically compares them to standard analytical methods. The advantages and limitations of these methods are discussed together with recent advances in chemometrics and data mining techniques allowing to extract useful information based on spectral data. Furthermore, the factors influencing soil spectra and data analysis are discussed and recommendations on how to reduce or eliminate their influences are provided. Future research and development needs for spectroscopy techniques are emphasized, and an analytical framework based on technology integration and data fusion is proposed to improve the measurement accuracy of PTEs in soil.
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    The application of a handheld mid-infrared spectrometry for rapid measurement of oil contamination in agricultural sites
    (Elsevier, 2019-02-07) Douglas, Reward K.; Nawar, Said; Alamar, M. Carmen; Coulon, Frederic; Mouazen, Abdul M.
    Rapid analysis of oil-contaminated soils is important to facilitate risk assessment and remediation decision-making process. This study reports on the potential of a handheld mid-infrared (MIR) spectrometer for the prediction of total petroleum hydrocarbons (TPH), including aliphatic (alkanes) and polycyclic aromatic hydrocarbons (PAH) in limited number of fresh soil samples. Partial least squares regression (PLSR) and random forest (RF) modelling techniques were compared for the prediction of alkanes, PAH, and TPH concentrations in soil samples (n = 85) collected from three contaminated sites located in the Niger Delta, Southern Nigeria. Results revealed that prediction of RF models outperformed the PLSR with coefficient of determination (R2) values of 0.80, 0.79 and 0.72, residual prediction deviation (RPD) values of 2.35, 1.96, and 2.72, and root mean square error of prediction (RMSEP) values of 63.80, 83.0 and 65.88 mg kg−1 for TPH, alkanes, and PAH, respectively. Considering the limited dataset used in the independent validation (18 samples), accurate predictions were achieved with RF for PAH and TPH, while the prediction for alkanes was less accurate. Therefore, results suggest that RF calibration models can be used successfully to predict TPH and PAH using handheld MIR spectrophotometer under field measurement conditions.
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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, Said; Cipullo, Sabrina; Alamar, M. Carmen; Coulon, Frederic; Mouazen, Abdul 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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    Evaluation of vis-NIR reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils
    (Cranfield University, 2018-01-18 08:49) Coulon, Frederic; Douglas, Reward K.; Alamar Gavidia, Maria del carman; Mouazen, Abdul Mounem ; Nawar, Said
    Underpinning data on1. hydrocarbons data analysis by GCMS - quantification and 2. vis-NIR spectra analysis and chemometrics
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    Rapid detection of alkanes and polycyclic aromatic hydrocarbons
    (Cranfield University, 2018-01-18T08:53:28Z) Coulon, Frederic; Mouazen, Abdul; Nawar, Said; del carmen Alamar Gavidia, Maria; Douglas, Reward K.
    Vis-NIR data spectra analysis and chemometric modelling
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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, Said; Alamar, M. Carmen; Coulon, Frederic; Mouazen, Abdul 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).