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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

Assessing spatial variability of soil petroleum contamination using visible near-infrared diffuse reflectance spectroscopy.

Visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) is a rapid, non-destructive method for sensing the presence and amount of total petroleum hydrocarbon (TPH) contamination in soil. This study demonstrates the feasibility of VisNIR DRS to be used in the field to proximally sense and then map the areal extent of TPH contamination in soil. More specifically, we evaluated whether a combination of two methods, penalized spline regression and geostatistics could provide an efficient approach to assess spatial variability of soil TPH using VisNIR DRS data from soils collected from an 80 ha crude oil spill in central Louisiana, USA. Initially, a penalized spline model was calibrated to predict TPH contamination in soil by combining lab TPH values of 46 contaminated and uncontaminated soil samples and the first-derivative of VisNIR reflectance spectra of these samples. The r(2), RMSE, and bias of the calibrated penalized spline model were 0.81, 0.289 log(10) mg kg(-1), and 0.010 log(10) mg kg(-1), respectively. Subsequently, the penalized spline model was used to predict soil TPH content for 128 soil samples collected over the 80 ha study site. When assessed with a randomly chosen validation subset (n = 10) from the 128 samples, the penalized spline model performed satisfactorily (r(2) = 0.70; residual prediction deviation = 2.0). The same validation subset was used to assess point kriging interpolation after the remaining 118 predictions were used to produce an experimental semivariogram and map. The experimental semivariogram was fitted with an exponential model which revealed strong spatial dependence among soil TPH [r(2) = 0.76, nugget = 0.001 (log(10) mg kg(-1))(2), and sill 1.044 (log(10) mg kg(-1))(2)]. Kriging interpolation adequately interpolated TPH with r(2) and RMSE values of 0.88 and 0.312 log(10) mg kg(-1), respectively. Furthermore, in the kriged map, TPH distribution matched with the expected TPH variability of the study site. Since the combined use of VisNIR prediction and geostatistics was promising to identify the spatial patterns of TPH contamination in soils, future research is warranted to evaluate the approach for mapping spatial variability of petroleum contaminated soils.

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.

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.

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.

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.

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.

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.