Sean T. Forrester

Influence of the nature of soil organic matter on the sorption behaviour of pentadecane as determined by PLS analysis of mid-infrared DRIFT and solid-state 13C NMR spectra

The nature of soil organic matter (SOM) functional groups associated with sorption processes was determined by correlating partitioning coefficients with solid-state (13)C nuclear magnetic resonance (NMR) and diffuse reflectance mid-infrared (DRIFT) spectral features using partial least squares (PLS) regression analysis. Partitioning sorption coefficients for n-pentadecane (n-C(15)) were determined for three alternative models: the Langmuir model, the dual distributed reactive domain model (DRDM) and the Freundlich model, where the latter was found to be the most appropriate. NMR-derived constitutional descriptors did not correlate with Freundlich model parameters. By contrast, PLS analysis revealed the most likely nature of the functional groups in SOM associated with n-C(15) sorption coefficients (K(F)) to be aromatic, possibly porous soil char, rather than aliphatic organic components for the presently investigated soils. High PLS cross-validation correlation suggested that the model was robust for the purpose of characterising the functional group chemistry important for n-C(15) sorption.

Prediction of Atrazine Sorption Coefficients in Soils Using Mid-Infrared Spectroscopy and Partial Least-Squares Analysis

This study explored the potential of mid-infrared spectroscopy (MIR) with partial least-squares (PLS) analysis to predict sorption coefficients (Kd) of pesticides in soil. The MIR technique has the advantage of being sensitive to both the content and the chemistry of soil organic matter and mineralogy, the important factors in the sorption of nonionic pesticides. MIR spectra and batch Kd values of atrazine were determined on a set of 31 soil samples as reference data for PLS calibration. The samples, with high variability in soil organic carbon content (SOC), were chosen from 10 southern Australian soil profiles (A1, A2, B, and C in one case). PLS calibrations, developed for the prediction of Kd from the MIR spectra and reference Kd data, were compared with predictions from Koc-based indirect estimation using SOC content. The reference Kd data for the 31 samples ranged from 0.31 to 5.48 L/kg, whereas Koc ranged from 30 to 680 L/kg. Both coefficients generally increased with total SOC content but showed a relatively poor coefficient of determination (R2 = 0.53; P > 0.0001) and a high standard error of prediction (SEP =1.22) for the prediction of Kd from Koc. This poor prediction suggested that total SOC content alone could explain only half of the variation in Kd. In contrast, the regression plot of PLS predicted versus measured Kd resulted in an improved correlation, with R2 = 0.72 ( P > 0.0001) and standard error of cross-validation (SECV) = 0.63 for three PLS factors. With the advantages of MIR-PLS in mind, (i) more accurate prediction of Kd, (ii) an ability to reflect the nature and content of SOC as well as mineralogy, and (iii) high repeatability and throughput, it is proposed that MIR-PLS has the potential for an improved and rapid assessment of pesticide sorption in soils.

Rapid prediction of total petroleum hydrocarbons in soil using a hand-held mid-infrared field instrument.

This manuscript reports on the performance of a hand-held diffuse reflectance (mid)-infrared Fourier transform (DRIFT) spectrometer for the prediction of total petroleum hydrocarbons (TPH) in three different diesel-contaminated soils. These soils include: a carbonate dominated clay, a kaolinite dominated clay and a loam from Padova Italy, north Western Australia and southern Nigeria, respectively. Soils were analysed for TPH concentration using a standard laboratory methods and scanned in DRIFT mode with the hand-held spectrometer to determine TPH calibration models. Successful partial least square regression (PLSR) predictions, with coefficient of determination (R(2)) ~0.99 and root mean square error (RMSE) <200mg/kg, were obtained for the low range TPH concentrations of 0 to ~3,000mg/kg. These predictions were carried out using a set of independent samples for each soil type. Prediction models were also tested for the full concentration range (0-60,000mg/kg) for each soil type model with R(2) and RMSE values of ~0.99 and <1,255mg/kg, respectively. Furthermore, a number of intermediate concentration range models were also generated for each soil type with similar R(2) values of ~0.99 and RMSE values <800mg/kg. This study shows the capability of using a portable mid-infrared (MIR) DRIFT spectrometer for predicting TPH in a variety of soil types and the potential for being a rapid in-field screening method for TPH concentration levels at common regulatory thresholds. A novel hand-held mid-infrared instrument can accurately detect TPH across different soil types and concentrations, which paves the way for a variety of applications in the field.

GEMAS: prediction of solid-solution partitioning coefficients (Kd ) for cationic metals in soils using mid-infrared diffuse reflectance spectroscopy.

Partial least squares regression (PLSR) models, using mid-infrared (MIR) diffuse reflectance Fourier-transformed (DRIFT) spectra, were used to predict distribution coefficient (Kd) values for selected added soluble metal cations (Ag(+), Co(2+), Cu(2+), Mn(2+), Ni(2+), Pb(2+), Sn(4+), and Zn(2+)) in 4813 soils of the Geochemical Mapping of Agricultural Soils (GEMAS) program. For the development of the PLSR models, approximately 500 representative soils were selected based on the spectra, and Kd values were determined using a single-point soluble metal or radioactive isotope spike. The optimum models, using a combination of MIR-DRIFT spectra and soil pH, resulted in good predictions for log Kd+1 for Co, Mn, Ni, Pb, and Zn (R(2) ≥ 0.83) but poor predictions for Ag, Cu, and Sn (R(2)  < 0.50). These models were applied to the prediction of log Kd+1 values in the remaining 4313 unknown soils. The PLSR models provide a rapid and inexpensive tool to assess the mobility and potential availability of selected metallic cations in European soils. Further model development and validation will be needed to enable the prediction of log K(d+1) values in soils worldwide with different soil types and properties not covered in the existing model.

Use of handheld mid-infrared spectroscopy and partial least-squares regression for the prediction of the phosphorus buffering index in Australian soils

The development of techniques for the rapid, inexpensive and accurate determination of the phosphorus (P) buffer index (PBI) in soils is important in terms of increasing the efficiency of P application for optimum crop requirements and preventing environmental pollution due to excessive use of P fertilisers. This paper describes the successful implementation of partial least-squares regression (PLSR) from spectra obtained with bench-top and handheld mid-infrared (MIR) spectrometers for the prediction of PBI on 601 representative Australian agricultural soils. By contrast, poor predictions were obtained for available (Colwell) P. Regression models were successfully derived for PBI ranges of 0–800 and 0–150, the latter range resulting in the optimum model considering the dominance of low PBI soils in the sample set. Concentrations of some major soil minerals (mainly kaolinite and gibbsite content for high PBI, and smectites or illites for low PBI), quartz (representative of low surface area of soils) and, to a lesser extent, carbonate and soil organic matter were identified as the main drivers of the PBI models. Models developed with soils sieved to <2 mm presented an accuracy similar to those developed using fine-ground material. The accuracy of the PLSR for the prediction of PBI by using bench-top and handheld instruments was also similar. Our results confirm the possibility of using MIR spectroscopy for the onsite prediction of PBI.

The use of mid-infrared diffuse reflectance spectroscopy for acid sulfate soil analysis

Good management of sulfide minerals and sulfuric acid in Acid Sulfate Soils (ASS) requires cost-effective rapid analytical data for their characterisation. However, the determination of properties in ASS samples using traditional laboratory techniques is expensive and time consuming. Excessive delays in analysis risks sample changes from oxidation. Mid-infrared (MIR) spectroscopy with multivariate regression offers a quicker and cheaper surrogate. This manuscript reports the prediction of some of the following key soil parameters in ASS characterisation using benchtop (Perkin Elmer) and handheld (ExoScan) diffuse reflectance MIR Fourier transform (DRIFT) spectrometers: Total Organic Carbon (TOC), Titratable Actual Acidity (TAA), Extractable Sulfate Sulfur (ESS), Reduced Inorganic Sulfur (RIS), Retained Acidity (RA), Acid Neutralising Capacity (ANC), and Lime Calculation (LC). Three sets of representative ASS soil profiles, comprising 132 samples from hyposulfidic, hypersulfidic and sulfuric materials, and covering a wide range of environments in South Australia were scanned under laboratory conditions. These were combined with reference laboratory data in partial least squares regression (PLSR) calibration models. The calibrations were validated by leave-one-out cross validation, with a further test set available for validation. Predictions with coefficient of determination (R2) > 0.75, were obtained for TOC (0.95), TAA (0.88), RIS (0.86), LC (0.76) and ANC (0.76), but models for ESS (0.66) and RA (0.41) were less satisfactory. The handheld spectrometer performed similarly to the benchtop spectrometer in terms of PLSR prediction accuracies with the potential for in-field sampling. Results thus confirmed the possibility of using MIR spectroscopy for the rapid and cost-effective characterisation of ASS.

U-Th signatures of agricultural soil at the European continental scale (GEMAS): Distribution, weathering patterns and processes controlling their concentrations

Agricultural soil (Ap-horizon, 0-20cm) samples were collected in Europe (33 countries, 5.6millionkm2) as part of the GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) soil-mapping project. The GEMAS survey area includes diverse groups of soil parent materials with varying geological history, a wide range of climate zones, and landscapes. The soil data have been used to provide a general view of U and Th mobility at the continental scale, using aqua regia and MMI® extractions. The U-Th distribution pattern is closely related to the compositional variation of the geological bedrock on which the soil is developed and human impact on the environment has not concealed these genuine geochemical features. Results from both extraction methods (aqua regia and MMI®) used in this study support this general picture. Ternary plots of several soil parameters have been used to evaluate chemical weathering trends. In the aqua regia extraction, some relative Th enrichment-U loss is related to the influence of alkaline and schist bedrocks, due to weathering processes. Whereas U enrichment-Th loss characterizes soils developed on alkaline and mafic bedrock end-members on one hand and calcareous rock, with a concomitant Sc depletion (used as proxy for mafic lithologies), on the other hand. This reflects weathering processes sensu latu, and their role in U retention in related soils. Contrary to that, the large U enrichment relative to Th in the MMI® extraction and the absence of end-member parent material influence explaining the enrichment indicates that lithology is not the cause of such enrichment. Comparison of U and Th to the soil geological parent material evidenced i) higher capability of U to be weathered in soils and higher resistance of Th to weathering processes and its enrichment in soils; and, ii) the MMI® extraction results show a greater affinity of U than Th for the bearing phases like clays and organic matter. The comparison of geological units with U anomalies in agricultural soil at the country scale (France) enables better understanding of U sources in the surficial environment and can be a useful tool in risk assessments.

GEMAS: CNS concentrations and C/N ratios in European agricultural soil

A reliable overview of measured concentrations of TC, TN and TS, TOC/TN ratios, and their regional distribution patterns in agricultural soil at the continental scale and based on measured data has been missing - despite much previous work on local and the European scales. Detection and mapping of natural (ambient) background element concentrations and variability in Europe was the focus of this work. While total C and S data had been presented in the GEMAS atlas already, this work delivers more precise (lower limit of determination) and fully quantitative data, and for the first time high-quality TN data. Samples were collected from the uppermost 20cm of ploughed soil (Ap horizon) at 2108 sites with an even sampling density of one site per 2500km2 for one individual land-use class (agricultural) across Europe (33 countries). Laboratory-independent quality control from sampling to analysis guaranteed very good data reliability and accuracy. Total carbon concentrations ranged from 0.37 to 46.3wt% (median: 2.20wt%) and TOC from 0.40 to 46.0wt% (median: 1.80wt%). Total nitrogen ranged from 0.018 to 2.64wt% (median: 0.169wt%) and TS from 0.008 to 9.74wt% (median: 0.034wt%), all with large variations in most countries. The TOC/TN ratios ranged from 1.8 to 252 (median: 10.1), with the largest variation in Spain and the smallest in some eastern European countries. Distinct and repetitive patterns emerge at the European scale, reflecting mostly geogenic and longer-term climatic influence responsible for the spatial distribution of TC, TN and TS. Different processes become visible at the continental scale when examining TC, TN and TS concentrations in agricultural soil Europe-wide. This facilitates large-scale land-use management and allows specific areas (subregional to local) to be identified that may require more detailed research.