Prediction of compost organic matter via color sensor.

Composted materials serve as an effective soil nutrient amendment. Organic matter in compost plays an important role in quantifying composted materials overall quality and nutrient content. Measuring organic matter content traditionally takes considerable time, resources, and various laboratory equipment (e.g., oven, muffle furnace, crucibles, precision balance). Much like the quantitative color indices (e.g., sRGB R, sRGB G, sRGB B, CIEL*a* b*) derived from the low-cost NixPro2 color sensor have proven adept at predicting soil organic matter in-situ, the NixPro2 color sensor has the potential to be effective for predicting organic matter in composted materials without the need for traditional laboratory methods. In this study, a total of 200 compost samples (13 different compost types) were measured for organic matter content via traditional loss-on-ignition (LOI) and via the NixPro2 color sensor. The NixPro2 color sensor showed promising results with an LOI-prediction model utilizing the CIEL*a* b* color model through the application of the Generalized Additive Model (GAM) algorithm yielding an excellent prediction accuracy (validation R2 = 0.87, validation RMSE = 4.66 %). Moreover, the PCA scoreplot differentiated the three lowest organic matter compost types from the remaining 10 compost types. These results have valuable practical significance for the compost industry by predicting compost organic matter in real time without the need for laborious, time-consuming methods.

A Smartphone-Enabled Imaging Device for Chromotropic Acid-Based Measurement of Nitrate in Soil Samples.

In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3-). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample's image, the SMART NP provides instant predictions of soil NO3- levels. The limit of detection was calculated as 0.1 mg L-1 with a sensitivity of 0.26 mg L-1. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO3- data, combined with kriging interpolation, showcased spatial variability in soil NO3- levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO3- content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO3-content.

Detailed characterization of iron-rich tailings after the Fundão dam failure, Brazil, with inclusion of proximal sensors data, as a secure basis for environmental and agricultural restoration.

Following the Fundão dam failure in Brazil, 60 million m3 of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R2 and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fundão dam failure can be in detail characterized via pXRF ex situ, providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes.

Mobility and bioaccessibility of arsenic (As) bound to titanium dioxide (TiO2) water treatment residuals (WTRs).

This work systematically describes arsenic mobility and potential bioaccessibility of arsenic-enriched titanium dioxide water treatment residuals (TiO2 WTRs) by employing a suite of wet chemical experiments and spectroscopic measurements. Specifically, Environmental Protection Agency (EPA) digestion method 3051a indicated <3% of total arsenic in the solid phase was released, and arsenic assessed by EPA method 1340 for bioaccessibility was below detection limits. A novel finding is while the arsenic appeared to be stable under highly acidic digestion conditions, it is in fact highly mobile when exposed to simple phosphate solutions. On average, 55% of arsenic was extracted from all samples during a 50-day replenishment study. This was equivalent to 169 mg kg-1 arsenic released from the solid phase. Macroscopic desorption experiments indicated arsenic likely formed inner-sphere bonds with the TiO2 particles present in the samples. This was confirmed with X-ray absorption spectroscopy (XAS), where an interatomic distance of 3.32 Å and a coordination number (CN) of 1.79 titanium atoms were determined. This translates to a configuration of arsenic on TiO2 surfaces as a bidentate binuclear inner-sphere complex. Thus, both macroscopic and spectroscopic data are in agreement. During incubation experiments, arsenic(V) was actively reduced to arsenic(III); the amount of arsenic(III) in solution varied from 8 to 38% of total dissolved arsenic. Lastly, elevated concentrations and mobility of vanadium in these systems merit further investigation. The high mobility of arsenic and its potential for reduction when reintroduced into the environment, particularly in agriculturally important areas, presents an important risk when waste products are not properly managed.

Holm oak (Quercus ilex L.) cover: A key soil-forming force in controlling C and nutrient stocks in long-time coppice-managed forests.

In forest ecosystems, soil-plant interactions drive the physical, chemical, and biological soil properties and, through soil organic matter cycling, control the dynamics of nutrient cycles. Parent material also plays a fundamental role in determining soil's chemical properties and nutrient availability. In this study, eight long-time coppice-managed Holm oak forests under conversion to high forest, located under similar climatic conditions in Tuscany and Sardinia Regions (Italy), and grown on soils developed from three different lithologies (limestone, biotite granite, and granite with quartz veins) were evaluated. The research aimed to a) estimate the amount of C and nutrients (total N and potentially available P, Ca, Mg, and K) stored both in the organic, organo-mineral, and mineral horizons and at fixed depth intervals (0-0.3 and 0.3-0.5 m), and b) assess the dominant pedological variables driving elemental accumulation. The soils were described and sampled by genetic horizons and each sample was analyzed for its C and nutrient concentration in both the fine earth and skeleton fractions. Despite the different parent materials from which the soils had evolved, the physicochemical properties and the C and nutrient stocks for the 0-0.3 and 0.3-0.5 m layers did not show substantial differences among the eight soils. Conversely, some differences were observed in the stocks of potentially available P and Ca per 0.01 m of mineral horizons. The findings show that over time, plant-induced pedogenic processes (acidification, mineral weathering, organic matter addition, and nutrient cycling) almost obliterated the influence of parent materials on soil properties. This resulted in the upper soil horizons that showed similar characteristics, even though derived from different lithologies. However, among the study sites, some differences occurred due to lithology, as in the case of the soils derived from calcareous parent materials that had high concentrations of exchangeable Ca in the mineral horizons and, likely, to environmental variables (e.g., exposure), which possibly influenced litter degradation and the release of nutrients such as N and available P.

Geochemistry signatures of mercury in soils of the Amazon rainforest biome.

Mercury (Hg) toxicity in soils depends on Hg species and other physical and chemical attributes, as selenium (Se) hotspots in soils, particularly relevant in Amazonian soils. The study of Hg species and their relations in representative locations of the Amazon rainforest biome is critical for assessing the potential risks of Hg in this environment. This work aimed to determine the concentration of total Hg and its species (Hg0, Hg22+ and Hg2+), and to correlate Hgtotal concentration with total elemental composition, magnetic susceptibility, and physicochemical attributes of Amazon soils. Nine sites in the Amazon rainforest biome, Brazil, were selected and analyzed for their chemical, physical, and mineralogical attributes. The clay fraction of the studied Amazon soils is dominated by kaolinite, goethite, hematite, gibbsite, and quartz. Mica was also found in soils from the States of Acre and Amazonas. Hgtotal ranged from 21.5 to 208 µg kg-1 (median = 104 µg kg-1), and the concentrations did not exceed the threshold value established for Brazilian soils (500 µg kg-1). The Hg2+ was notably the predominant species. Its occurrence and concentration were correlated with the landscape position and soil attributes. Hgtotal was moderately and positively correlated with TiO2, clay, and Se. The findings showed that geographic location, geological formation, and pedological differences influence the heterogeneity and distribution of Hgtotal in the studied soil classes. Thus, a detailed characterization and knowledgment of the soil classes is very important to clarify the complex behavior of this metal in the Amazon rainforest biome.

Pocket-sized sensor for controlled, quantitative and instantaneous color acquisition of plant leaves.

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R2, root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing.

Heavy Metal-Resistant Filamentous Fungi as Potential Mercury Bioremediators.

Filamentous fungi native to heavy metals (HMs) contaminated sites have great potential for bioremediation, yet are still often underexploited. This research aimed to assess the HMs resistance and Hg remediation capacity of fungi isolated from the rhizosphere of plants resident on highly Hg-contaminated substrate. Analysis of Hg, Pb, Cu, Zn, and Cd concentrations by X-ray spectrometry generated the ecological risk of the rhizosphere soil. A total of 32 HM-resistant fungal isolates were molecularly identified. Their resistance spectrum for the investigated elements was characterized by tolerance indices (TIs) and minimum inhibitory concentrations (MICs). Clustering analysis of TIs was coupled with isolates' phylogeny to evaluate HMs resistance patterns. The bioremediation potential of five isolates' live biomasses, in 100 mg/L Hg2+ aqueous solution over 48 h at 120 r/min, was quantified by atomic absorption spectrometry. New species or genera that were previously unrelated to Hg-contaminated substrates were identified. Ascomycota representatives were common, diverse, and exhibited varied HMs resistance spectra, especially towards the elements with ecological risk, in contrast to Mucoromycota-recovered isolates. HMs resistance patterns were similar within phylogenetically related clades, although isolate specific resistance occurred. Cladosporium sp., Didymella glomerata, Fusarium oxysporum, Phoma costaricensis, and Sarocladium kiliense isolates displayed very high MIC (mg/L) for Hg (140-200), in addition to Pb (1568), Cu (381), Zn (2092-2353), or Cd (337). The Hg biosorption capacity of these highly Hg-resistant species ranged from 33.8 to 54.9 mg/g dry weight, with a removal capacity from 47% to 97%. Thus, the fungi identified herein showed great potential as bioremediators for highly Hg-contaminated aqueous substrates.

Assessment of iron-rich tailings via portable X-ray fluorescence spectrometry: the Mariana dam disaster, southeast Brazil.

On November 5, 2015, the Fundão dam collapsed and released > 60 million m3 of iron-rich mining sediments into the Doce river basin, covering >1000 ha of floodplain soils across ~80 km from the rupture. The characterization of alluvial mud covering and/or mixed with native soil is a priority for successful environmental rehabilitation. Portable X-ray fluorescence (pXRF) spectrometry was used to (1) assess the elemental composition of native soils and alluvial mud across impacted riparian areas; and 2) predict fertility properties of the mud and soils that are crucial for environmental rehabilitation and vegetation establishment (e.g., pH, available macro and micronutrients, cation exchange capacity, organic matter). Native soils and alluvial mud were sampled across impacted areas and analyzed via pXRF and conventional laboratory methods. Random forest (RF) regression was used to predict fertility properties using pXRF data for pooled soil and alluvial mud samples. Mud and native surrounding soils were clearly differentiated based on chemical properties determined via pXRF (mainly SiO2, Al2O3, Fe2O3, TiO2, and MnO). The pXRF data and RF models successfully predicted pH for pooled samples (R2 = 0.80). Moderate predictions were obtained for soil organic matter (R2 = 0.53) and cation exchange capacity (R2 = 0.54). Considering the extent of impacted area and efforts required for successful environmental rehabilitation, the pXRF spectrometer showed great potential for screening impacted areas. It can assess total elemental composition, differentiate alluvial mud from native soils, and reasonably predict related fertility properties in pooled heterogeneous substrates (native soil + mud + river sediments).

Agricultural soils of the Animas River watershed after the Gold King Mine spill: An elemental spatiotemporal analysis via portable X-ray fluorescence spectroscopy.

In August 2015, 11.3 million L of heavy metal-contaminated water spilled into the Animas River from the Gold King Mine (Colorado, USA). National attention focused on water quality and agricultural production in areas affected by the spill. In response to local concerns, surface soil elemental concentrations were analyzed in three New Mexico agricultural fields to determine potential threats to agronomic production. Irrigated fields in the Animas watershed were scanned using portable X-ray fluorescence (PXRF) spectrometry to monitor the spatiotemporal variability of Pb, As, Cu, and Cr. A total of 175 locations were scanned using PXRF before and after the growing season for 3 yr. The geostatistical model with the lowest RMSE was chosen as the optimal model. The lowest RMSE for the elements ranged from to 0.10 to 0.44 m for As, from 0.50 to 0.98 m for Cr, from 0.15 to 0.91 m for Cu, and from 0.14 to 0.44 m for Pb across the models selected. The spatial dependence between the measured values exhibited strong to moderate autocorrelation for all metals except for As, for which spatial dependence was strong to weak. Some areas in each field exceeded the New Mexico Environment Department soil screening limit of 7.07 mg As kg-1 . All sampling locations were below the screening limit at last sampling time in 2019. Mixed models used for temporal analysis showed a significant decrease only in As below the screening value at the end of the study. Results indicate that the agricultural soils were below the soil screening guideline values.

Predicting total petroleum hydrocarbons in field soils with Vis-NIR models developed on laboratory-constructed samples.

Accurate quantification of petroleum hydrocarbons (PHCs) is required for optimizing remedial efforts at oil spill sites. While evaluating total petroleum hydrocarbons (TPH) in soils is often conducted using costly and time-consuming laboratory methods, visible and near-infrared reflectance spectroscopy (Vis-NIR) has been proven to be a rapid and cost-effective field-based method for soil TPH quantification. This study investigated whether Vis-NIR models calibrated from laboratory-constructed PHC soil samples could be used to accurately estimate TPH concentration of field samples. To evaluate this, a laboratory sample set was constructed by mixing crude oil with uncontaminated soil samples, and two field sample sets (F1 and F2) were collected from three PHC-impacted sites. The Vis-NIR TPH models were calibrated with four different techniques (partial least squares regression, random forest, artificial neural network, and support vector regression), and two model improvement methods (spiking and spiking with extra weight) were compared. Results showed that laboratory-based Vis-NIR models could predict TPH in field sample set F1 with moderate accuracy (R2  > .53) but failed to predict TPH in field sample set F2 (R2  < .13). Both spiking and spiking with extra weight improved the prediction of TPH in both field sample sets (R2 ranged from .63 to .88, respectively); the improvement was most pronounced for F2. This study suggests that Vis-NIR models developed from laboratory-constructed PHC soil samples, spiked by a small number of field sample analyses, can be used to estimate TPH concentrations more efficiently and cost effectively compared with generating site-specific calibrations.

Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry.

Foliar analysis is very important for the nutritional management of crops and as a supplemental parameter for soil fertilizer recommendation. The elemental composition of plants is traditionally obtained by laboratory-based methods after acid digestion of ground and sieved leaf samples. This analysis is time-consuming and generates toxic waste. By comparison, portable X-ray fluorescence (pXRF) spectrometry is a promising technology for rapid characterization of plants, eliminating such constraints. This worked aimed to assess the pXRF performance for elemental quantification of leaf samples from important Brazilian crops. For that, 614 samples from 28 plant species were collected across different regions of Brazil. Ground and sieved samples were analyzed after acid digestion (AD), followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES) to determine the concentration of macronutrients (P, K, Ca, Mg, and S) and micronutrients (Fe, Zn, Mn, and Cu). The same plant nutrients were directly analyzed on ground leaf samples via pXRF. Four certified reference materials (CRMs) for plants were used for quality assurance control. Except for Mg, a very strong correlation was observed between pXRF and AD for all plant-nutrients and crops. The relationship between methods was nutrient- and crop-dependent. In particular, eucalyptus displayed optimal correlations for all elements, except for Mg. Opposite to eucalyptus, sugarcane showed the worst correlations for all the evaluated elements, except for S, which had a very strong correlation coefficient. Results demonstrate that for many crops, pXRF can reasonably quantify the concentration of macro- and micronutrients on ground and sieved leaf samples. Undoubtedly, this will contribute to enhance crop management strategies concomitant with increasing food quality and food security.

The use of portable X-ray fluorescence spectrometry to measure apparent total tract digestibility in beef cattle and sheep.

The use of portable X-ray fluorescence (PXRF) spectrometry to detect external markers on processed or unprocessed cattle and sheep fecal specimens to estimate apparent total tract digestibility (ATTD) was evaluated. Exp. 1: ruminally cannulated Angus-crossbred steers (n = 7; BW = 520 ± 30 kg) were individually fed ad libitum for 21 d in a completely randomized design (CRD). Markers (Cr2O3 and TiO2) were placed inside the rumen twice daily (7.5 g of each marker). Fecal samples were collected twice daily from day 14 to 21. Exp. 2: crossbred wethers (n = 8; BW = 68 ± 3 kg) were individually fed ad libitum for 21 d in a CRD. During this period, 2 g of Cr2O3 and TiO2 were top-dressed onto the feed twice daily. Sheep were housed in metabolism crates for 5 d for total fecal collection. Concentration of markers was determined on diets, refusals, and fecal specimens (fresh, dry-only, and dried/ground) using atomic absorption to detect Cr and spectrophotometry for Ti. Concentration of both markers was also determined via the PXRF spectrometer. Delta between ATTD estimated by wet chemistry and PXRF was not different from zero (P ≥ 0.14) when using cattle fresh fecal specimens for both markers, whereas ATTD estimated by PXRF with dry-only and dried/ground fecal specimens were 3.6 and 1.1 percent units lower (P ≤ 0.04), respectively, than ATTD estimated by wet chemistry for Cr and Ti, respectively. Regardless of the fecal sample preparation method on cattle specimens, Ti concentration was similar (P = 0.39) among methodologies, while Cr was underestimated (P < 0.01) by 13% when PXRF was used in dry-only or dried/ground samples. The ATTD of sheep was underestimated (P < 0.01) by 2.4 percent units compared with control when Cr was measured by PXRF in dry-only samples. The Cr concentration in dry-only fecal specimens of sheep tended (P = 0.09) to be lower compared with wet chemistry analysis. Fresh and dry/ground sheep fecal samples assessed for Cr, and dry-only assessed for Ti were not (P ≥ 0.49) affected by detection method. The Cr fecal recovery tended (P = 0.10) to be the lowest for dry-only, the greatest for wet chemistry, intermediate for fresh and dry/ground sheep-fecal specimens; while not affected (P = 0.40) for Ti. The PXRF is an accurate technology to detect Cr and Ti in fresh cattle fecal samples to estimate ATTD. For fresh and dry/ground, the technology was effective for determining the concentration of Cr, or dry-only fecal specimens when detecting Ti in sheep specimens.

Quantitative Analysis of Iron and Silicon Concentrations in Iron Ore Concentrate Using Portable X-ray Fluorescence (XRF).

As a technique capable of rapid, nondestructive, and multi-elemental analysis, portable X-ray fluorescence (pXRF) has applications to mineral exploration, environmental evaluation, and archaeological analysis. However, few applications have been conducted in the smelting industry especially when analyzing the metal concentration in ore concentrate samples. This research analyzed the effectiveness of using pXRF in determining the metal concentration in Fe concentrate. For this proof of concept study, Fe ore samples dominated by Fe and Si were collected from the Northeastern University Mineral Processing Laboratory (Shenyang, China) and directly analyzed using pXRF, laboratory-based XRF, and titration methods. The compactness (density) of the ore concentrate was found to have very little effect on pXRF readings. The pXRF readings for Fe and Si were comparative to laboratory-based XRF results. Based on the strong correlations between the pXRF and XRF results (Fe: R2 > 0.99, Si: R2 > 0.96), linear calibrations were adopted to improve the accuracy of pXRF readings. Linear regression equations derived from the relations between XRF results and pXRF results of 21 Fe ore concentrate samples were used to calibrate the pXRF, and then validation was performed on five additional samples. Results from this preliminary study suggest that ordinary least squares (OLS) regression improves the accuracy dramatically, especially for Fe with relative errors (REs) decreasing to 0.03%-3.27% from 4.26%-8.32%. Consequently, pXRF shows strong promise for rapid, quantitative analysis of Fe concentration in Fe ore concentrate. Based on the results obtained in this study, a larger, more comprehensive study is warranted to confirm the results obtained.

Morphological, physiological and biochemical aspects of salt tolerance of halophyte Petrosimonia triandra grown in natural habitat.

Salt tolerance mechanisms of halophyte Petrosimonia triandra, growing in its natural habitat in Cluj County, Romania, were investigated via biomass, growth parameters, water status, ion content, photosynthetic and antioxidative system efficiency, proline accumulation and lipid degradation. Two sampling sites with different soil electrical conductivities were selected: site 1: 3.14 dS m-1 and site 2: 4.45 dS m-1. Higher salinity proved to have a positive effect on growth. The relative water content did not decline severely, Na+ and K+ content of the roots, stem and leaves was more, and the functions of the photosynthetic apparatus and photosynthetic pigment contents were not altered. The efficiency of the antioxidative defence system was found to be assured by coordination of several reactive oxygen species scavengers. The presence of higher salinity led to accumulation of the osmolyte proline, while degradation of membrane lipids was reduced. As a whole, P. triandra evolved different adaptational strategies to counteract soil salinity, including morphological and physiological adaptations, preservation of photosynthetic activity, development of an efficient antioxidative system and accumulation of the osmotic compound, proline.

Elemental assessment of vegetation via portable X-ray fluorescence (PXRF) spectrometry.

Elemental concentrations in vegetation are of critical importance, whether establishing plant essential element concentrations (toxicity vs. deficiency) or investigating deleterious elements (e.g., heavy metals) differentially extracted from the soil by plants. Traditionally, elemental analysis of vegetation has been facilitated by acid digestion followed by quantification via inductively coupled plasma (ICP) or atomic absorption (AA) spectroscopy. Previous studies have utilized portable X-ray fluorescence (PXRF) spectroscopy to quantify elements in soils, but few have evaluated the vegetation. In this study, a PXRF spectrometer was employed to scan 228 organic material samples (thatch, deciduous leaves, grasses, tree bark, and herbaceous plants) from smelter-impacted areas of Romania, as well as National Institute of Standards and Technology (NIST) certified reference materials, to demonstrate the application of PXRF for elemental determination in vegetation. Samples were scanned in three conditions: as received from the field (moist), oven dry (70 °C), and dried and powdered to pass a 2 mm sieve. Performance metrics of PXRF models relative to ICP atomic emission spectroscopy were developed to asses optimal scanning conditions. Thatch and bark samples showed the highest mean PXRF and ICP concentrations (e.g., Zn, Pb, Cd, Fe), with the exceptions of K and Cl. Validation statistics indicate that the stable validation predictive capacity of PXRF increased in the following order: oven dry intact < field moist < oven dried and powdered. Even under field moist conditions, PXRF could reasonably be used for the determination of Zn (coefficient of determination, R2val 0.86; residual prediction deviation, RPD 2.72) and Cu (R2val 0.77; RPD 2.12), while dried and powdered samples allowed for stable validation prediction of Pb (R2val 0.90; RPD 3.29), Fe (R2val 0.80; RPD 2.29), Cd (R2val 0.75; RPD 2.07) and Cu (R2val 0.98; RPD of 8.53). Summarily, PXRF was shown to be a useful approach for quickly assessing the elemental concentration in vegetation. Future PXRF/vegetation research should explore additional elements and investigate its usefulness in evaluating phytoremediation effectiveness.

Compost salinity assessment via portable X-ray fluorescence (PXRF) spectrometry.

Compost salinity is an ongoing concern for compost producers, especially with certain feedstocks and in arid or semiarid regions. Current testing protocols call for sampling and testing ex-situ via 1:5 (w/v) slurries via electrical conductance. For this research an alternate approach has been proposed, the use of portable X-ray fluorescence (PXRF) spectrometry. Adapting methods developed for soil and water salinity analysis via PXRF, elemental data was used as a proxy for the prediction of compost salinity. In total, 74 compost samples were scanned with PXRF followed by traditional laboratory analysis. Results indicated a strong correlation between the datasets (R2 0.80; RMSE 1.04 dS m-1), similar to findings for soil and water salinity. Furthermore, using the same elemental dataset, compost pH was reasonably predicted (R2 0.63; RMSE 0.35). PXRF has the benefit of being able to be conducted in-situ or in the laboratory. And, multiple chemical parameters of interest can potentially be predicted from the same dataset. In conclusion, PXRF shows promise for rapid, in-situ salinity determination of composted products.

Forensic identification of pharmaceuticals via portable X-ray fluorescence and diffuse reflectance spectroscopy.

The importance of unknown substance identification in forensic science is vital to implementation or exclusion of criminal charges against an offender. While traditional laboratory measures include the use of gas chromatography/mass spectroscopy, an alternate method has been proposed to efficiently perform presumptive analyses of unknown substances at a crime scene or at airport security points. The use of portable X-ray fluorescence (PXRF) and visible near infrared diffuse reflectance spectroscopy (DRS) to determine elemental composition was applied to pharmaceutical medications (n=83), which were then categorized into 21 classifications based on their active ingredients. Each pharmaceutical was processed by standard laboratory procedures and scanned with both PXRF and DRS. Lastly, the datasets obtained were compared using multivariate statistical analyses. The aforementioned devices indicate that differentiation of unknown substances is clearly demonstrated among the samples with 73.49% DRS classification accuracy. Thus, the approach shows promise for future development as a rapid analytical technique for unknown pharmaceutical substances and/or illicit narcotics.

Two fixed ratio dilutions for soil salinity monitoring in hypersaline wetlands.

Highly soluble salts are undesirable in agriculture because they reduce yields or the quality of most cash crops and can leak to surface or sub-surface waters. In some cases salinity can be associated with unique history, rarity, or special habitats protected by environmental laws. Yet in considering the measurement of soil salinity for long-term monitoring purposes, adequate methods are required. Both saturated paste extracts, intended for agriculture, and direct surface and/or porewater salinity measurement, used in inundated wetlands, are unsuited for hypersaline wetlands that often are only occasionally inundated. For these cases, we propose the use of 1:5 soil/water (weight/weight) extracts as the standard for expressing the electrical conductivity (EC) of such soils and for further salt determinations. We also propose checking for ion-pairing with a 1:10 or more diluted extract in hypersaline soils. As an illustration, we apply the two-dilutions approach to a set of 359 soil samples from saline wetlands ranging in ECe from 2.3 dS m(-1) to 183.0 dS m(-1). This easy procedure will be useful in survey campaigns and in the monitoring of soil salt content.

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils.

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R(2)=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. CAPSULE: Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils.