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 Hg_total 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. Hg_total 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. Hg_total 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 Hg_total 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.

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.

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).

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.

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.

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.

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.

Multivariate calibration with robust signal regression

Motivated by a multivariate calibration problem from a soil characterization study, we proposed tractable and robust variants of penalized signal regression (PSR) using a class of non-convex Huber-like criteria as the loss function. Standard methods may fail to produce a reliable estimator, especially when there are heavy-tailed errors. We present a computationally efficient algorithm to solve this non-convex problem. Simulation and empirical examples are extremely promising and show that the proposed algorithm substantially improves the PSR performance under heavy-tailed errors.

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, R²_val 0.86; residual prediction deviation, RPD 2.72) and Cu (R²_val 0.77; RPD 2.12), while dried and powdered samples allowed for stable validation prediction of Pb (R²_val 0.90; RPD 3.29), Fe (R²_val 0.80; RPD 2.29), Cd (R²_val 0.75; RPD 2.07) and Cu (R²_val 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.

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.

Analysis of petroleum contaminated soils by spectral modeling and pure response profile recovery of n-hexane

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r(2) = 0.87, RMSE = 0.580 log10 mg kg(-1), and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r(2) = 0.65 and RMSE 0.261 log10 mg kg(-1)) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane.

Metals analysis of agricultural soils via portable X-ray fluorescence spectrometry

To assess the applicability of portable X-ray fluorescence (PXRF) spectrometry for metals analysis, total concentrations of As, Pb, Cu, and Zn in 47 agricultural soils were determined using in situ PXRF analysis, ex situ PXRF analysis, and conventional laboratory analysis. The correlation regression parameters of PXRF data with the data of conventional analysis were significantly improved upon going from in situ to ex situ, indicating that improvement of the ex situ PXRF data quality was achieved thorough sample preparation. Use of PXRF in situ was inferior to other analyses, especially when attempting to quantify relatively low levels of metals in agricultural soils. A high degree of linearity and similar spatial distribution existed between ex situ PXRF and laboratory analysis, suggesting that PXRF can be used in rapid detection or screening of agricultural soils, but is best followed with additional sample preparation ex situ and laboratory confirmation.

Assessing the risks of trace elements in environmental materials under selected greenhouse vegetable production systems of China

The risk assessment of trace elements of different environmental media in conventional and organic greenhouse vegetable production systems (CGVPS and OGVPS) can reveal the influence of different farming philosophy on the trace element accumulations and their effects on human health. These provide important basic data for the environmental protection and human health. This paper presents trace element accumulation characteristics of different land uses; reveals the difference of soil trace element accumulation both with and without consideration of background levels; compares the trace element uptake by main vegetables; and assesses the trace element risks of soils, vegetables, waters and agricultural inputs, using two selected greenhouse vegetable systems in Nanjing, China as examples. Results showed that greenhouse vegetable fields contained significant accumulations of Zn in CGVPS relative to rice-wheat rotation fields, open vegetable fields, and geochemical background levels, and this was the case for organic matter in OGVPS. The comparative analysis of the soil medium in two systems with consideration of geochemical background levels and evaluation of the geo-accumulation pollution index achieved a more reasonable comparison and accurate assessment relative to the direct comparison analysis and the evaluation of the Nemerow pollution index, respectively. According to the Chinese food safety standards and the value of the target hazard quotient or hazard index, trace element contents of vegetables were safe for local residents in both systems. However, the spatial distribution of the estimated hazard index for producers still presented certain specific hotspots which may cause potential risk for human health in CGVPS. The water was mainly influenced by nitrogen, especially for CGVPS, while the potential risk of Cd and Cu pollution came from sediments in OGVPS. The main inputs for trace elements were fertilizers which were relatively safe based on relevant standards; but excess application caused trace element accumulations in the environmental media.

Accumulation and health risk of heavy metals in vegetables from harmless and organic vegetable production systems of China

Heavy metal accumulation in vegetables is a growing concern for public health. Limited studies have elucidated the heavy metal accumulation characteristics and health risk of different vegetables produced in different facilities such as greenhouses and open-air fields and under different management modes such as harmless and organic. Given the concern over the aforementioned factors related to heavy metal accumulation, this study selected four typical greenhouse vegetable production bases, short-term harmless greenhouse vegetable base (SHGVB), middle-term harmless greenhouse vegetable base (MHGVB), long-term harmless greenhouse vegetable base (LHGVB), and organic greenhouse vegetable base (OGVB), in Nanjing City, China to study heavy metal accumulation in different vegetables and their associated health risks. Results showed that soils and vegetables from SHGVB and OGVB apparently accumulated fewer certain heavy metals than those from other bases, probably due to fewer planting years and special management, respectively. Greenhouse conditions significantly increased certain soil heavy metal concentrations relative to open-air conditions. However, greenhouse conditions did not significantly increase concentrations of As, Cd, Cu, Hg, and Zn in leaf vegetables. In fact, under greenhouse conditions, Pb accumulation was effectively reduced. The main source of soil heavy metals was the application of large amounts of low-grade fertilizer. There was larger health risk for producers' children to consume vegetables from the three harmless vegetable bases than those of residents' children. The hazard index (HI) over a large area exceeded 1 for these two kinds of children in the MHGVB and LHGVB. There was also a slight risk in the SHGVB for producers' children solely. However, the HI of the whole area of the OGVB for two kinds of children was below 1, suggesting low risk of heavy metal exposure through the food chain. Notably, the contribution rate of Cu and Zn to the HI were high in the four bases, yet current Chinese standards provide no limit for the concentrations of Cu and Zn; thus a potential health risk concerning these metals exists.

In-situ assessment of metal contamination via portable X-ray fluorescence spectroscopy: Zlatna, Romania

Zlatna, Romania is the site of longtime mining/smelting operations which have resulted in widespread metal pollution of the entire area. Previous studies have documented the contamination using traditional methods involving soil sample collection, digestion, and quantification via inductively coupled plasma atomic emission spectroscopy or atomic absorption. However, field portable X-ray fluorescence spectroscopy (PXRF) can accurately quantify contamination in-situ, in seconds. A PXRF spectrometer was used to scan 69 soil samples in Zlatna across multiple land use types. Each site was georeferenced with data inputted into a geographic information system for high resolution spatial interpolations. These models were laid over contemporary aerial imagery to evaluate the extent of pollution on an individual elemental basis. Pb, As, Co, Cu, and Cd exceeded governmental action limits in >50% of the sites scanned. The use of georeferenced PXRF data offers a powerful new tool for in-situ assessment of contaminated soils.

Spectral data mining for rapid measurement of organic matter in unsieved moist compost

Fifty-five compost samples were collected and scanned as received by visible and near-IR (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy. The raw reflectance and first-derivative spectra were used to predict log(10)-transformed organic matter (OM) using partial least squares (PLS) regression, penalized spline regression (PSR), and boosted regression trees (BRTs). Incorporating compost pH, moisture percentage, and electrical conductivity as auxiliary predictors along with reflectance, both PLS and PSR models showed comparable cross-validation r(2) and validation root-mean-square deviation (RMSD). The BRT-reflectance model exhibited best predictability (residual prediction deviation=1.61, cross-validation r(2)=0.65, and RMSD=0.09 log(10)%). These results proved that the VisNIR-BRT model, along with easy-to-measure auxiliary variables, has the potential to quantify compost OM with reasonable accuracy.

Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) for rapid measurement of organic matter in compost

Commercial compost is the inherently variable organic product of a controlled decomposition process. In the USA, assessment of compost's physicochemical parameters presently relies on standard laboratory analyses set forth in Test Methods for the Examination of Composting and Compost (TMECC). A rapid, field-portable means of assessing the organic matter (OM) content of compost products would be useful to help producers ensure optimal uniformity in their compost products. Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is a rapid, proximal-sensing technology proven effective at quantifying organic matter levels in soils. As such, VisNIR DRS was evaluated to assess its applicability to compost. Thirty-six compost samples representing a wide variety of source materials and moisture content were collected and scanned with VisNIR DRS under moist and oven-dry conditions. Partial least squares (PLS) regression and principal component regression (PCR) were used to relate the VisNIR DRS spectra with laboratory-measured OM to build compost OM prediction models. Raw reflectance, and first- and second-derivatives of the reflectance spectra were considered. In general, PLS regression outperformed PCR and the oven-dried first-derivative PLS model produced an r(2) value of 0.82 along with a residual prediction deviation value of 1.72. As such, VisNIR DRS shows promise as a suitable technique for the analysis of compost OM content for dried samples.

Use of portable X-ray fluorescence spectrometry for environmental quality assessment of peri-urban agriculture

Urban expansion into traditional agricultural lands has augmented the potential for heavy metal contamination of soils. This study examined the utility of field portable X-ray fluorescence (PXRF) spectrometry for evaluating the environmental quality of sugarcane fields near two industrial complexes in Louisiana, USA. Results indicated that PXRF provided quality results of heavy metal levels comparable to traditional laboratory analysis. When coupled with global positioning system technology, the use of PXRF allows for on-site interpolation of heavy metal levels in a matter of minutes. Field portable XRF was shown to be an effective tool for rapid assessment of heavy metals in soils of peri-urban agricultural areas.

Rapid identification of oil-contaminated soils using visible near-infrared diffuse reflectance spectroscopy

In the United States, petroleum extraction, refinement, and transportation present countless opportunities for spillage mishaps. A method for rapid field appraisal and mapping of petroleum hydrocarbon-contaminated soils for environmental cleanup purposes would be useful. Visible near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, nondestructive, proximal-sensing technique that has proven adept at quantifying soil properties in situ. The objective of this study was to determine the prediction accuracy of VisNIR DRS in quantifying petroleum hydrocarbons in contaminated soils. Forty-six soil samples (including both contaminated and reference samples) were collected from six different parishes in Louisiana. Each soil sample was scanned using VisNIR DRS at three combinations of moisture content and pretreatment: (i) field-moist intact aggregates, (ii) air-dried intact aggregates, (iii) and air-dried ground soil (sieved through a 2-mm sieve). The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content in the soil using partial least squares (PLS) regression and boosted regression tree (BRT) models. Each model was validated with 30% of the samples that were randomly selected and not used in the calibration model. The field-moist intact scan proved best for predicting TPH content with a validation r2 of 0.64 and relative percent difference (RPD) of 1.70. Because VisNIR DRS was promising for rapidly predicting soil petroleum hydrocarbon content, future research is warranted to evaluate the methodology for identifying petroleum contaminated soils.