Browsing by Author "Mouazen, A. M."
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Item Open Access 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, S.; Cipullo, Sabrina; Douglas, Reward; Coulon, Frederic; Mouazen, A. 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.Item Open Access The application of a handheld mid-infrared spectrometry for rapid measurement of oil contamination in agricultural sites(Elsevier, 2019-02-07) Douglas, R. K.; Nawar, S.; Alamar, M. Carmen; Coulon, Frederic; Mouazen, A. 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.Item Open Access Combined sensor of dielectric constant and visible and near infrared spectroscopy to measure soil compaction using artificial neural networks(Cranfield University, 2014-05) Al-Asadi, Raed; Mouazen, A. M.; Brewer, Timothy R.Soil compaction is a widely spread problem in agricultural soils that has negative agronomic and environmental impacts. The former may lead to poor crop growth and yield, whereas the latter may lead to poor hydraulic properties of soils, and high risk to flooding, soil erosion and degradation. Therefore, the elimination of soil compaction must be done on regular bases. One of the main parameters to quantify soil compaction is soil bulk density (BD). Mapping of within field variation in soil BD will be a main requirement for within field management of soil compaction. The aim of this research was to develop a new approach for the measurement of soil BD as an indicator of soil compaction. The research relies on the fusion of data from visible and near infrared spectroscopy (vis-NIRS), to measure soil gravimetric moisture content (ω), with frequency domain reflectometry (FDR) data to measure soil volumetric moisture content (θv). The values of the estimated ω and θv, for the same undisturbed soil samples were collected from selected locations, textures, soil moisture contents and land use systems to derive soil BD. A total of 1013 samples were collected from 32 sites in the England and Wales. Two calibration techniques for vis-NIRS were evaluated, namely, partial least squares regression (PLSR) and artificial neural networks (ANN). ThetaProbe calibration was performed using the general formula (GF), soil specific calibration (SSC), the output voltage (OV) and artificial neural networks (ANN). ANN analyses for both ω and θv properties were based either on a single input variable or multiple input variables (data fusion). Effects of texture, moisture content, and land use on the prediction accuracy on ω, θv and BD were evaluated to arrive at the best experimental conditions for the measurement of BD with the proposed new system. A prototype was developed and tested under laboratory conditions and implemented in-situ for mapping of ω, θv and BD. When using the entire dataset (general data set), results proved that high measurement accuracy can be obtained for ω and θv with PLSR and the best performing traditional calibration method of the ThetaProbe with R2 values of 0.91 and 0.97, and root mean square error of prediction (RMSEp) of 0.027 g g-1 and 0.019 cm3 cm-3, respectively. However, the ANN – data fusion method resulted in improved accuracy (R2 = 0.98 and RMSEp = 0.014 g g-1 and 0.015 cm3 cm-3, respectively). This data fusion approach gave the best accuracy for BD assessment when only vis-NIRS spectra and ThetaProbe V were used as an input data (R2 = 0.81 and RMSEp = 0.095 g cm-3). The moisture level (L) impact on BD prediction revealed that the accuracy improved with soil moisture increasing, with RMSEp values of 0.081, 0.068 and 0.061 g cm-3, for average ω of 0.11, 0.20 and 0.28 g g-1, respectively. The influence of soil texture was discussed in relation with the clay content in %. It was found that clay positively affected vis-NIRS accuracy for ω measurement and no obvious impact on the dielectric sensor readings was observed, hence, no clear influence of the soil textures on the accuracy of BD prediction. But, RMSEp values of BD assessment ranged from 0.046 to 0.115 g cm-3. The land use effect of BD prediction showed measurement of grassland soils are more accurate compared to arable land soils, with RMSEp values of 0.083 and 0.097 g cm-3, respectively. The prototype measuring system showed moderate accuracy during the laboratory test and encouraging precision of measuring soil BD in the field test, with RMSEp of 0.077 and 0.104 g cm-3 of measurement for arable land and grassland soils, respectively. Further development of the prototype measuring system expected to improve prediction accuracy of soil BD. It can be concluded that BD can be measured accurately by combining the vis-NIRS and FDR techniques based on an ANN-data fusion approach.Item Open Access The effects of low and controlled traffic systems on soil physical properties, yields and the profitability of cereal crops on a range of soil types(Cranfield University, 2011-04) Chamen, W. C. T.; Mouazen, A. M.; Godwin, R. J.Soil compaction is an inevitable consequence of mechanised farming systems whose machines are degrading soils to the extent that some are considered uneconomic to repair. A number of mitigating actions have been proposed but their ability to reduce or avoid damage has not been well tested. The aim of this research was to determine whether actions to reduce damage have been, or are likely to be effective and to assess whether the practice of controlled traffic farming (confining all field vehicles to the least possible area of permanent traffic lanes) has the potential to be a practical and cost effective means of avoidance. The literature confirmed that soil compaction from field vehicles had negative consequences for practically every aspect of crop production. It increases the energy needed to establish crops, compromises seedbed quality and crop yield, and leads to accelerated water run-off, erosion and soil loss. It is also implicated in enhanced emissions of nitrous oxide and reduced water and nutrient use efficiency. Replicated field trials showed that compaction is created by a combination of loading and contact pressure. Trafficking increased soil penetration resistance by 47% and bulk density by 15% while reducing wheat yield by up to 16%, soil porosity by 10% and infiltration by a factor of four. Low ground pressure systems were a reasonable means of compaction mitigation but were constrained due to their negative impact on topsoils and gradual degradation of subsoils whose repair by deep soil loosening is expensive and short lived. Controlled traffic farming (CTF) was found to be practical and had fundamental advantages in maintaining all aspects of good soil structure with lowered inputs of energy and time. On a farm in central England, machinery investment with CTF fell by over 20% and farm gross margin increased in the range 8-17%.Item Open Access 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.Item Open Access Finite element modelling of the interaction between flexible tines and soil for mechanical weeding(Cranfield University, 2011-10) Theuer, Jan; Mouazen, A. M.This research was carried out to obtained information about the interaction of flexible tines and soil for mechanical weeding. Due to the fact that chemical weeding has negative effects on the environment, mechanical weeding is widely used today as an alternative and more sustainable solution. The complexity of soil-tine interaction, with flexible tines, makes it highly difficult to extract the needed information from experimental works only. Therefore, the aim of this study was to obtain extended knowledge about optimal design parameters (rake angle and tine geometry) and operational conditions (working depth and bulk density) for optimal soil disturbance and least energy consumption, by the use of numerical finite element computer simulation. To achieve this, a test bench was designed to study different tine designs and operational conditions of selected flexible tines, provided by Einböck, an Austrian manufacturer of weed harrows. The results of the test bench were mainly used to validate the established finite element model, which enabled a more informed analysis of the weeding process. Furthermore, soil parameters and soil-metal properties needed as input for the FEM simulation were determined by standard laboratory tests. Results showed that FEM is an acceptable and cost effective alternative to experiments. The simulation errors for draught and upward tine tip movement were generally smaller than 15 % and 10 %, respectively. Software associated limitations were experienced to model the entire working process satisfactorily, for instance no crack propagation in soils could be taken into account so far. Nevertheless, it was possible to simulate the first soil-tine interaction contact. This was sufficient to optimise tine design parameters and operational conditions, which was considered a cost effective method for the manufacturing of prototypes. From the FEM simulation and soil bin test, it could be concluded that a stiffer tine with a higher torsion spring constant and a small rake angle should be used when less variation in working depth and more aggressive weeding is required. Otherwise, trailing positions should be used, if a shallow working depth is desired, to achieve higher soil disturbance in the surface for smaller draught requirements.Item Open Access Fusion of multi soil data for the delineation of management zones for variable rate irrigation(Cranfield University, 2013-02) Alhwaimel, Saad Abdulaziz; Mouazen, A. M.; Waine, Toby W.Up until now, there have been no multi-sensor approaches used to estimate available water content (AWC) in order to determine variable rate irrigation. This has been a major problem for growers adopting precision farming technologies. The aim of this project is to implement an on-line multi-sensor platform and data fusion approach for the delineation of management zones for site specific irrigation in vegetable crop production systems. This is performed by simultaneous measurement of soil moisture content (MC), organic carbon (OC), clay content (CC), plasticity index (PI) and bulk density (BD) with an on-line visible and near infrared (vis-NIR) spectroscopy sensor and a load cell attached to a subsoiler and frame, which was linked to a three-point linkage of a tractor. The soil apparent Electrical Conductivity (ECa) was separately measured with an Electromagnetic Induction (EMI) device. Measurements were carried out in three fields in Lincolnshire and one in Cambridgeshire. Vis-NIR calibration models of soil properties were developed using partial least squares (PLS) regression. A multiple linear regression analysis (MLR) and an Artificial Neural Network (ANN) was used to derive zones of water holding capacity (WHC), based on correlation between on-line measured OC, CC, PI, BD and ECa with MC. The AWC was calculated with empirical equations, as a function of clay and sand fractions. Result showed that the on-line measurement accuracy for OC and MC were good to excellent (R2=0.71-0.83 and R2=0.75-0.85, RPD=2.00-2.57 and RPD=1.94-2.10 for OC and MC, respectively). For CC and PI, the measurement accuracy (R2=0.64-0.69 and RPD=0.55-0.66 for clay content and PI) was evaluated as moderate. It was observed in the study fields, that the ECa results had a minor response to MC distribution. Furthermore, the fusion of multi-soil data to derive a WHC index with MLR and ANN resulted in successful delineation of homogeneous zones. These were divided into four different normalisation categories of low (0 – 0.25), medium (0.25 – 0.5), high (0.5 – 0.75) and very high (0.75 – 1) of WHC. Spatial similarity between WHC maps with those of CC, IP and MC was documented, and found to be in line with the literature. AWC maps calculated as a function of soil texture classes, showed spatial similarity with WHC maps. Low values of AWC were observed at zones with low WHC index and vice versa. This supports the final conclusion of this work that multi-sensor and data fusion is a useful approach to guide positions of moisture sensor and optimise the amount of water used for irrigation.Item Open Access A multi sensor data fusion approach for creating variable depth tillage zones(Cranfield University, 2015-06) Whattoff, David; Mouazen, A. M.; Waine, Toby W.Efficiency of tillage depends largely on the nature of the field, soil type, spatial distribution of soil properties, and the correct setting of the tillage implement. However, current tillage practice is often implemented without full understanding of machine design and capability leading to lowered efficiency and further potential damage to the soil structure. By modifying the physical properties of soil only where the tillage is needed for optimum crop growth, variable depth tillage (VDT) has been shown to reduce costs, labour, fuel consumption and energy requirements. To implement VDT it is necessary to determine and map soil physical properties, spatially and with depth through the soil profile. Up until now the measurement of soil compaction for VDT has been soil penetration resistance, expressed as Cone Index (CI). In this research a multi-sensor and data fusion approach was developed that allowed augmenting data collected with an electromagnetic sensor, a standard penetrometer, and conventional methods for the measurement of bulk density (BD) and moisture content (MC). Packing density values were recorded for eight soil layers of 0-5, 5-10, 10-15, 15-20, 20-25, 25-30 30-35 and 35-40 cm. From the results only 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful tool to guide VDT operations. The results provided in this study indicate that the new multi0sensor and data fusion approach introduced is a useful approach to map layered soil compaction to guide VDT operations. The economic benefit analysis demonstrated fuel savings of 48% by implementing the proposed system. Further work is needed to implement the packing density map for VDT in larger numbers of field in order to generalise the approach.Item Open Access Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals(2016-12) Whetton, Rebecca L.; Mouazen, A. M.; Waine, Toby W.The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare.Item Open Access On-line measurement of selected soil properties towards the refinement of Nitrogen fertilisation management in vegetable crops(Cranfield University, 2015-02) Jimenez-Donaire, Virginia; Mouazen, A. M.; Waine, Toby W.Fertiliser applications in vegetable crops are one of the main input costs of production. Thematic soil maps have been widely used for decades to characterise soil nutrients and, therefore, apply variable rate fertilisers. However, traditional variable rate methods used in soil sampling are time- consuming, costly and not accurate. Thus, they fail in providing a true estimate of the nutrients soil needs. To obtain better crop response to inputs, a rapid, non-destructive, timely and cost-effective soil analysis are needed to enable site-specific fertiliser applications. Proximal soil sensing with visible and near infrared (vis-NIR) spectroscopy is a promising tool to assist in variable rate applications. This thesis aims to develop reliable calibration models for a previously developed on-line visible (vis) and near infrared (NIR) spectroscopy sensor (Mouazen, 2006), for the prediction of soil properties in vegetable crop fields for a better N fertiliser management. Experiments were established in crops of cauliflower (Brassica oleracea) during 2013 season (two fields) and 2014 season (three fields), in UK. A mobile, fibre-type, vis–NIR spectrophotometer (AgroSpec, Tec5 Technology for Spectroscopy, Germany) with a measurement range of 305-2200 nm was used to measure soil spectra in diffuse reflectance mode, measuring up to ~1500 points per ha. Four different calibration sets were tested to establish the most accurate calibration model for moisture content (MC), soil organic carbon (OC), pH and total nitrogen (TN), using partial least squares (PLS) regression analysis selected according to different spectral library size and geographical scale: Scenario 1 (SC1 (local)), Scenario 2 (SC2 (regional)), Scenario 3 (SC3 (national)), Scenario 4 (SC4 (continental)). The best results in cross-validation were obtained for MC with SC2 (R2[R squared] = 0.89; RPD > 2.5), followed by SC4 (R2[R squared] = 0.88; RPD = 2.91-3.31, in 2013 and 2014, respectively); and SC1 and SC4 worked very well for MC on- line prediction (R2[R squared] > 0.90 and RPD > 2.5). SC3 and SC4 both provided the best performance for OC and TN in cross-validation, whereas no clear trend was observed for on-line prediction. Poor model performance was obtained for pH in on-line predictions (R2[R squared] < 0.30 and RPD < 0.9). Although the calibration models using the on-line vis-NIR sensor provided good and detailed information of the soil nutrients analysed, future research will be needed to estimate these properties more accurately, with the aim to develop reliable vis-NIR calibration models for the on-line measurement in vegetable crop fields.Item Open Access On-line measurement of some selected soil properties for controlled input crop management systems(Cranfield University, 2012-02) Kuang, Boyan Y.; Mouazen, A. M.The evaluation of the soil spatial variability using a fast, robust and cheap tool is one of the key steps towards the implementation of Precision Agriculture (PA) successfully. Soil organic carbon (OC), soil total nitrogen (TN) and soil moisture content (MC) are needed to be monitored for both agriculture and environmental applications. The literature has proven that visible and near infrared (vis-NIR) spectroscopy to be a quick, cheap and robust tool to acquire information about key soil properties simultaneously with relatively high accuracy. The on-line vis-NIR measurement accuracy depends largely on the quality of calibration models. In order to establish robust calibration models for OC, TN and MC valid for few selected European farms, several factors affecting model accuracy have been studied. Nonlinear calibration techniques, e.g. artificial neural network (ANN) combined with partial least squares regression (PLSR) has provided better calibration accuracy than the linear PLSR or principal component regression analysis (PCR) alone. It was also found that effects of sample concentration statistics, including the range or standard derivation and the number of samples used for model calibration are substantial, which should be taking into account carefully. Soil MC, texture and their interaction effects are other principle factors affecting the in situ and on-line vis-NIR measurement accuracy. This study confirmed that MC is the main negative effect, whereas soil clay content plays a positive role. The general calibration models developed for soil OC, TN and MC for farms in European were validated using a previously developed vis-NIR on-line measurement system equipped with a wider vis-NIR spectrophotometer (305 – 2200 nm) than the previous version. The validation results showed this wider range on-line vis-NIR system can acquire larger than 1500 data point per ha with a very good measurement accuracy for TN and OC and excellent accuracy for MC. The validation also showed that spiking few target field samples into the general calibration models is an effective and efficient approach for upgrading the implementation of the on-line vis-NIR sensor for measurement in new fields in the selected European farms.Item Open Access Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression(Nature Publishing Group, 2019-03-14) Cipullo, S.; Nawar, S.; Mouazen, A. M.; Campo Moreno, Pablo; Coulon, FredericA number of studies have shown that visible and near infrared spectroscopy (VIS-NIRS) offers a rapid on-site measurement tool for the determination of total contaminant concentration of petroleum hydrocarbons compounds (PHC), heavy metals and metalloids (HM) in soil. However none of them have yet assessed the feasibility of using VIS-NIRS coupled to random forest (RF) regression for determining both the total and bioavailable concentrations of complex chemical mixtures. Results showed that the predictions of the total concentrations of polycyclic aromatic hydrocarbons (PAH), PHC, and alkanes (ALK) were very good, good and fair, and in contrast, the predictions of the bioavailable concentrations of the PAH and PHC were only fair, and poor for ALK. A large number of trace elements, mainly lead (Pb), aluminium (Al), nickel (Ni), chromium (Cr), cadmium (Cd), iron (Fe) and zinc (Zn) were predicted with very good or good accuracy. The prediction results of the total HMs were also better than those of the bioavailable concentrations. Overall, the results demonstrate that VIS-NIR DRS coupled to RF is a promising rapid measurement tool to inform both the distribution and bioavailability of complex chemical mixtures without the need of collecting soil samples and lengthy extraction for further analysis.Item Open Access 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.Item Open Access Rapid measurement of polycyclic aromatic hydrocarbon contamination in soils by visible and near-infrared spectroscopy(Cranfield University, 2013-10) Okparanma, R. N.; Mouazen, A. M.; Mayr, T.Polycyclic aromatic hydrocarbons (PAHs) are widely distributed organic pollutants. At petroleum contaminated sites, PAHs are often the key risk drivers because of their carcinogenicity. Assessing the risk of PAH at contaminated sites by conventional soil sampling, solvent extraction and gas chromatography–mass spectrometry (GC–MS) analysis is expensive and time-consuming. Employing a rapid and cheap measurement technique for PAH would be beneficial to risk assessment by eliminating costs and time associated with the conventional method. The literature has shown that visible and near infrared (vis-NIR) spectroscopy is a rapid and cheap technique for acquiring information about key soil properties. In this study, models based on vis-NIR spectroscopy (350–2500 nm) were developed to predict and map PAH in contaminated soils for the ultimate aim of informing risk assessment and/or remediation. The reference chemical analytical method used was GC–MS while the multivariate analytical technique used for model development was partial least squares (PLS) regression analysis with full cross-validation. A total of 150 soil samples from the UK were used for the laboratory-scale study while 137 samples were used for the near-onsite adaptive trials at three oil spill sites in Ogoniland, Niger Delta province of Nigeria. Both laboratory- and field-scale results showed that soil diffuse reflectance decreased with increasing PAH concentration. Hydrocarbon absorption features observed around 1647 nm in the first overtone region of the NIR spectrum showed a positive link to PAH. Laboratory-scale study showed that both individual and combined effects of oil concentration, and moisture and clay contents on soil spectral characteristics and calibration models were significant (p<0.05). For the field-scale study, inverse distance weighting soil maps of PAH developed with chemically-measured and vis-NIR-predicted data were comparable with a fair to good agreement between them (Kappa coefficient = 0.19–0.56). Hazard assessment of the oil spill sites using both measurement methods showed that the impact of the contamination varied distinctly across the management zones. The type of action required for site-specific risk assessment and/or remediation also varied among the different zones. This result shows promise that vis-NIR can be a good screening tool for petroleum release sites.Item Open Access Visible and near-infrared spectroscopy analysis of a polycyclic aromatic hydrocarbon in soils(Hindawi Publishing Corporation, 2013-11-29) Okparanma, R. N.; Mouazen, A. M.Visible and near-infrared (VisNIR) spectroscopy is becoming recognised by soil scientists as a rapid and cost-effective measurement method for hydrocarbons in petroleum-contaminated soils. This study investigated the potential application of VisNIR spectroscopy (350–2500 nm) for the prediction of phenanthrene, a polycyclic aromatic hydrocarbon (PAH), in soils. A total of 150 diesel-contaminated soil samples were used in the investigation. Partial least-squares (PLS) regression analysis with full cross-validation was used to develop models to predict the PAH compound. Results showed that the PAH compound was predicted well with residual prediction deviation of 2.0–2.32, root-mean-square error of prediction of 0.21–0.25 mg kg−1, and coefficient of determination () of 0.75–0.83. The mechanism of prediction was attributed to covariation of the PAH with clay and soil organic carbon. Overall, the results demonstrated that the methodology may be used for predicting phenanthrene in soils utilizing the interrelationship between clay and soil organic carbon.