Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C and C. Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg, and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R=0.63, RMSEP=107.54mgkg and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils.

To achieve a better understanding of the nature of the factors influencing groundwater composition as well as to specify them quantitatively, conventional graphical and multivariate statistical analysis (principal component analysis) were applied on hydrochemical data consisting of 51 groundwater samples collected from domestic boreholes in Yenagoa city, Bayelsa State, Nigeria. The mode of study includes analysis of major ion contents and other chemical parameters such as pH, total dissolved solids and electrical conductivity of the groundwater samples. The PCA yielded three principal components explaining 78.38 % of the total variance of the 11 parameters. The three components are interpreted as controlled by the natural weathering of existing silicate rocks, reverse ion-exchange processes and oxidation reactions which are further supported by the scatter diagrams, ionic signatures and mechanisms controlling the water chemistry diagrams as the common factors influencing the groundwater hydrogeochemical character. Limited anthropogenic influence on the groundwater composition has also been noticed in the study area. The groundwater poses no threat to human health because the concentrations of physico-chemical parameters that can be used to evaluate drinking water quality are within World Health Organisation standard specification. The groundwater in the area is fresh, high salinity and low sodium in nature.

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 preprocessing 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.

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 (R) 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.

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.

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 midinfrared (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 (R 2) 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