An investigation into the long-term binding and uptake of PFOS, PFOA and PFHxS in soil - plant systems.

This study investigated the potential aging and plant bioaccumulation of three perfluoroalkyl acids (PFAAs), perfluorosulphonic acid (PFOS), perfluorooctanoic acid (PFOA) and perfluorohexanesulphonic acid (PFHxS) in 20 soils over a six-month period. Sorption coefficients (Log Kd) ranged from 0.13-1.28 for PFHxS, 0.17-1.06 for PFOA and 0.98-2.03 for PFOS, respectively, and bioaccumulation factors (Log BAFs) ranged from 0.29-1.24, 0.22-1.46 and 0.05-0.65 for PFHxS, PFOA and PFOS, respectively. Over the six-month period, Kd values significantly increased for PFHxS and PFOA but the magnitude of the increase was very small and did not translate into differences in plant PFAA-concentrations between aged and freshly spiked treatments. The Kd and BAF values were modelled by multiple linear regression (MLR) to soil physico-chemical properties and by partial least squares regression to soil spectra acquired by mid-infrared spectroscopy (DRIFT-PLSR). Modelling of each PFAA was influenced by different soil properties, including organic carbon, pH, CEC, exchangeable cations (Ca2+, Mg2+, Na+ and K+) and oxalate extractable Al. BAF values were not strongly correlated to any soil property but were inversely correlated to Kd values. Our results indicate that limited aging occurred in these soils over the six-month period.

Addressing challenges in providing a reliable ecotoxicology data for graphene-oxide (GO) using an algae (Raphidocelis subcapitata), and the trophic transfer consequence of GO-algae aggregates.

The graphene oxide (GO) due to its exceptional structure, physicochemical and mechanical properties is a very attractive material for industry application. Even though, the unique properties of GO (e.g. structure, size, shape, etc.) make the risk assessment of this nanomaterial very challenging in comparison with conventional ecotoxicology studies required by regulators. Thus, there is a need for standardized characterization techniques and methodology to secure a high quality/reliable data on the ecotoxicology of GO, and to establish environmentally acceptable levels. Herein, authors address the crucial quality criteria when evaluating the ecotoxicology of GO using an algae (Raphidocelis subcapitata) and a shrimp (Paratya australiensis). This study provides a detail characterization and modification of the used GO, robust quantification and a suspension stability in different media for ecotoxicology studies. It was observed that under the same exposure conditions the behavior of GO and the estimated outcomes (IC50 values) in modified algae media differed in comparison to the referent media. Further to that, the adverse effects of GO on the algae cell structure and the potential uptake of GO by the algae cells were examined using the TEM with different staining techniques to avoid artefacts. Shrimps which were exposed to GO-algae aggregates via the food intake did not indicate stress or accumulation of GO. Our work presents an important insight to necessity of establishing a benchmark ecotoxicology assays for GO (e.g. characterization techniques, choice of media, etc.) and providing a reliable data to be used by regulators in risk assessment of two-dimensional (2D) nanomaterials.

Predicting partitioning of radiolabelled 14C-PFOA in a range of soils using diffuse reflectance infrared spectroscopy.

The aim of this study was to establish partitioning coefficients (Kd) of perfluorooctanoic acid (PFOA) in a wide range of soils and determine if those values can be predicted from soil properties using multiple linear regression (MLR) and from infrared spectra of soils using partial least squares regression (PLSR). For 100 different soils, the Kd values of spiked radiolabelled 14C-PFOA ranged from 0.6 to 14.8 L/kg and significantly decreased with soil depth (p < 0.05) due to soil properties that change with depth. The MLR modelling revealed that PFOA sorption was significantly (p < 0.05) influenced, in decreasing order, by organic carbon (OC) content, silt-plus-clay content and soil pH. Soils were partitioned into all soils and surface soils alone. The MLR models using OC, silt-plus-clay content and pH together explained most of the variation in sorption in all soils as well as surface soils alone (0-15 cm). However, correlations between soil properties and Kd values in some soils could not be explained by the MLR model. Modelling of Kd prediction in soils with PLSR and diffuse reflectance (mid) infrared Fourier transform spectroscopy (DRIFT) showed comparable success in explaining the predictions of Kd values, including some of the outliers identified in the MLR model. The PLSR loading weights suggested that quartz, and possibly pyrophyllite minerals, were inversely correlated with the Kd values. Given that MLR requires a-priori characterisation of a range of soil properties and PLSR-DRIFT is a method based on the direct relationship between spectra and soil components, mid-infrared spectroscopy may be a more economical and rapid technique to predict the solid-liquid partitioning of PFOA in soils.

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.

Assessment of cyanide contamination in soils with a handheld mid-infrared spectrometer.

We examined the feasibility of using handheld mid-infrared (MIR) Fourier-Transform infrared (FT-IR) instrumentation for detecting and analysing cyanide (CN) contamination in field contaminated soils. Cyanide spiking experiments were first carried out, in the laboratory, to test the sensitivity of infrared Fourier transform (DRIFT) spectrometry to ferro- and ferricyanide compounds across a range of reference soils and minerals. Both benchtop and handheld diffuse reflectance infrared spectrometers were tested. Excellent results were obtained for the reference soils and minerals, with the MIR outperforming the near-infrared (NIR) range. Spectral peaks characteristic of the -C≡N group were observed near 2062 and 2118cm-1 in the MIR region for the ferro- and ferricyanide compounds spiked into soils/minerals, respectively. In the NIR region such peaks were observed near 4134 and 4220cm-1. Cyanide-contaminated samples were then collected in the field and analyzed with the two spectrometers to further test the applicability of the DRIFT technique for soils containing aged CN residues. The prediction of total CN in dry and ground contaminated soils using the handheld MIR instrument resulted in a coefficient of determination (R2) of 0.88-0.98 and root mean square error of the cross-validation (RMSE) of 21-49mgkg-1 for a CN range of 0-611mgkg-1. A major peak was observed in the MIR at about 2092cm-1 which was attributed to "Prussian Blue" (Fe4[Fe(CN)6]3·xH2O). These results demonstrate the potential of handheld DRIFT instrumentation as a promising alternative to the standard laboratory method to predict CN concentrations in contaminated field 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 phase partitioning coefficients (Kd) for oxoanions and boric acid in soils using mid-infrared diffuse reflectance spectroscopy.

The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total or surface-bound metal/metalloid concentration of the solid phase to the total concentration in the solution phase. Values of Kd were measured for boric acid (B[OH]3(0)) and selected added soluble oxoanions: molybdate (MoO4(2-)), antimonate (Sb[OH](6-)), selenate (SeO4(2-)), tellurate (TeO4(2-)) and vanadate (VO4(3-)). Models were developed using approximately 500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) program. These calibration soils represented the major properties of the entire 4813 soils of the GEMAS project. Multiple linear regression (MLR) from soil properties, partial least-squares regression (PLSR) using mid-infrared diffuse reflectance Fourier-transformed (DRIFT) spectra, and models using DRIFT spectra plus analytical pH values (DRIFT + pH), were compared with predicted log K(d + 1) values. Apart from selenate (R(2) = 0.43), the DRIFT + pH calibrations resulted in marginally better models to predict log K(d + 1) values (R(2) = 0.62-0.79), compared with those from PSLR-DRIFT (R(2) = 0.61-0.72) and MLR (R(2) = 0.54-0.79). The DRIFT + pH calibrations were applied to the prediction of log K(d + 1) values in the remaining 4313 soils. An example map of predicted log K(d + 1) values for added soluble MoO4(2-) in soils across Europe is presented. The DRIFT + pH PLSR models provided a rapid and inexpensive tool to assess the risk of mobility and potential availability of boric acid and selected oxoanions in European soils. For these models to be used in the prediction of log K(d + 1) values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration.

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.

Development of a rapid "fingerprinting" system for wine authenticity by mid-infrared spectroscopy.

This paper reports on the development of a rapid and simple method for red wine authenticity confirmation during transport and processing; namely, a wine "fingerprinting" system. When wine is transported between two sites, a sample is taken and a mid-infrared (MIR) spectrum is obtained. One hundred sixty-one (n = 161) samples of three main red wine varieties grown in Australia, Shiraz, Cabernet Sauvignon, and Merlot, were collected from six commercial wineries across Australia and scanned in transmission on two MIR spectrophotometers located at The Hardy Wine Company's main site at Reynella, South Australia (Foss WineScan FT 120) (926-5012 cm-1). A similarity index (SI) method was used as a tool to classify wine samples on the basis of their spectral data. The results showed that high rates of classification were obtained when wine samples scanned in different instruments were analyzed. The SI has been proven to provide an acceptable measurement for authentication of red wine integrity during transportation. In five of the six winery data sets, the SI correctly classified 98% of the wines. It was also observed that less than 1% of wines were misclassified between the different wineries investigated. Further studies are needed in order to test the applicability of the SI in a commercial situation and to evaluate its potential as a rapid quality control tool for routine use to authenticate wine samples during transport.