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

Initially, 46 petroleum contaminated and non-contaminated soil samples were collected and scanned using visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) at three combinations of moisture content and pretreatment. The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content using partial least squares (PLS) regression and boosted regression tree (BRT) models. 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. Those 46 samples were used to calibrate a penalized spline (PS) model. Subsequently, the PS model was used to predict soil TPH content for 128 soil samples collected over an 80 ha study site. An exponential semivariogram using PS predictions revealed strong spatial dependence among soil TPH [r2 = 0.76, range = 52 m, nugget = 0.001 (log10 mg kg-1)2, and sill 1.044 (log10 mg kg-1)2]. An ordinary block kriging map produced from the data showed that TPH distribution matched the expected TPH variability of the study site. Another study used DRS to measure reflectance patterns of 68 artificially constructed samples with different clay content, organic carbon levels, petroleum types, and different levels of contamination per type. Both first derivative of reflectance and discrete wavelet transformations were used to preprocess the spectra. Principal component analysis (PCA) was applied for qualitative VisNIR discrimination of variable soil types, organic carbon levels, petroleum types, and concentration levels. Soil types were separated with 100% accuracy, and organic carbon levels were separated with 96% accuracy by linear discriminant analysis. The support vector machine produced 82% classification accuracy for organic carbon levels by repeated random splitting of the whole dataset. However, spectral absorptions for each petroleum hydrocarbon overlapped with each other and could not be separated with any classification scheme when contaminations were mixed. Wavelet-based multiple linear regression performed best for predicting petroleum amount with the highest residual prediction deviation (RPD) of 3.97. While using the first derivative of reflectance spectra, PS regression performed better (RPD = 3.3) than the PLS (RPD= 2.5) model. Specific calibrations considering additional soil physicochemical variability are recommended to produce improved predictions

Vesicular (liposomal and nanoparticulated) delivery of curcumin: a comparative study on carbon tetrachloride–mediated oxidative hepatocellular damage in rat model

The liver plays a vital role in biotransforming and extricating xenobiotics and is thus prone to their toxicities. Short-term administration of carbon tetrachloride (CCl4) causes hepatic inflammation by enhancing cellular reactive oxygen species (ROS) level, promoting mitochondrial dysfunction, and inducing cellular apoptosis. Curcumin is well accepted for its antioxidative and anti-inflammatory properties and can be considered as an effective therapeutic agent against hepatotoxicity. However, its therapeutic efficacy is compromised due to its insolubility in water. Vesicular delivery of curcumin can address this limitation and thereby enhance its effectiveness. In this study, it was observed that both liposomal and nanoparticulated formulations of curcumin could increase its efficacy significantly against hepatotoxicity by preventing cellular oxidative stress. However, the best protection could be obtained through the polymeric nanoparticle-mediated delivery of curcumin. Mitochondria have a pivotal role in ROS homeostasis and cell survivability. Along with the maintenance of cellular ROS levels, nanoparticulated curcumin also significantly (P,0.0001) increased cellular antioxidant enzymes, averted excessive mitochondrial destruction, and prevented total liver damage in CCl4-treated rats. The therapy not only prevented cells from oxidative damage but also arrested the intrinsic apoptotic pathway. In addition, it also decreased the fatty changes in hepatocytes, centrizonal necrosis, and portal inflammation evident from the histopathological analysis. To conclude, curcumin-loaded polymeric nanoparticles are more effective in comparison to liposomal curcumin in preventing CCl4-induced oxidative stress–mediated hepatocellular damage and thereby can be considered as an effective therapeutic strategy

Understanding biophysical and socio-economic determinants of maize (Zea mays L.) yield variability in eastern India

AbstractThe aim of this paper was to investigate the key factors limiting maize (Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer's ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be, however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity.A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers

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

Please read abstract in the article.http://www.elsevier.com/locate/jenvman2019-03-15hj2019Plant Production and Soil Scienc

Portable X-ray Fluorescence Analysis of Water: Thin Film and Water Thickness Considerations

Water is requisite for life and essential for many industries. Increasingly, global water supplies include inorganic contaminants leading to millions of deaths annually. High analytical cost and a lack of field portable methods have stymied the evaluation of contaminated water. By comparison, portable X-ray fluorescence (PXRF) spectrometry has emerged as a method suitable for low cost, rapid analysis for many matrices yet few studies have evaluated liquids via PXRF. Herein, a novel means of assessing PXRF analytical performance for liquid matrices was evaluated on 1,440 samples comprised of three different standards (Pb, Cd, Cr) featuring three different film types (Kapton, Mylar, and Prolene) at five different liquid depths (4.29, 8.59, 17.18, 25.77 and 30.06 mm), and with four different concentrations (1,000, 500, 250, 125 μg/g). To adjust the PXRF values for a liquid matrix, regression models were fitted using PXRF reported values as the predictor and the true standard concentration values as the target. Results indicated that prior to statistical adjustment to PXRF reported values, increased liquid depth as well as Mylar or Kapton film provided optimal predictive accuracy. However, after PXRF adjustment (linear for Cr and Cd, quadratic for Pb), a depth of 4.29 mm and any of the three film types provided quality elemental predictions. After PXRF adjustment, the size of the mean of PXRF difference with the known standard concentration vs. the true standard concentration values became much smaller compared to the prior adjustment difference. Additionally, the size of the difference was usually smaller for the larger depth (25.77 and 30.06 mm)

On-farm evaluation of regenerative land-use practices in a semi-arid pasture agroecosystem in West Texas, USA

Continually rising scarcity in water and nutrient resources, especially in semi-arid agricultural systems, combined with increased frequency of extreme weather events such as drought, contribute to a growing need for resilient and regenerative agricultural ecosystems. However, evaluating a myriad of combinations of producer-led sustainable management practices in on-farm research remains challenging. Few studies have elucidated spatial variability in measured soil properties across the study area due to logistical and economic constraints. As such, this study aimed to: 1) establish soil health assessment and landscape variability data immediately after land-use change to a sustainable pasture management system, and 2) delineate relationships and predictive capability between measured soil health parameters. Soil samples were collected on May 23, 2018 in a grid pattern across two adjacent pastures on a farm in the semi-arid Southern High Plains (Texas, USA) that had recently been converted from long-term continuous cotton production to grazed pasture. Significant differences were found in soil chemical and biological properties between pastures (e.g., ~37 % reduction in microbial community size and 36 and 178 % greater electrical conductivity (EC) and Na contents, respectively, in the East pasture) that likely resulted from recent tillage and receiving irrigation compared to similar soil types and management history in the West pasture. Spatial diagrams of measured parameters revealed localization of measured properties, such as higher clay content and soil organic matter in the southeastern portion of the study area, and clear boundaries between pastures in terms of arbuscular mycorrhizal fungi (AMF) distribution. Soil physical and chemical properties were sufficiently correlated with biological measurements to predict soil microbial community size based on routine soil test analyses. The patterns of distributed elements evaluated in this study can provide a basis for management decisions on soil health and potential contaminant monitoring across the study area. These findings provide insight as to how novel, producer-designed soil health management practices in small semi-arid production systems impact soil properties, as well as help develop cost-effective predictive modeling solutions that aid long-term monitoring efforts. Such strategies will be critical tools in resource-scarce semi-arid regions such as those found in the current study region of Texas, as well as similar semi-arid regions such as northern China and northeastern Brazil. Overall, the results of this study provide direction for long-term soil health monitoring at this site, as well as a critical evaluation of relationships between soil health indicator measurements that aids interpretation and management planning

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Not AvailableIndian agriculture has seen a paradigm shift from earlier days of begging bowl to a modern era of food self-sufficiency. Pesticides as plant protection agents play an important role in securing food for a nation of 1.22 billion people. If the credits of pesticides include enhanced economic potential in terms of increased production of food and fibre, and amelioration of vector-borne diseases, then their debits have resulted in serious health implications to man and his environment. The general concept of “if little is good, a lot more will be better” has violated the basic concept of need based application of pesticide and hence have become one factor of environmental contamination. This article is aimed to give some light on the evolution of pesticides, there importance and environmental contamination with emphasis on some management strategies.Not Availabl

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement