Preconcentration and ultra-trace determination of hexavalent chromium ions using tailor-made polymer nanoparticles coupled with graphite furnace atomic absorption spectrometry: Ultrasonic assisted-dispersive solid-phase extraction

We describe ultrasonic assisted-dispersive solid-phase extraction based on tailor-made polymer (UA-DSPE-TMP) nanoparticles for selective extraction of Cr(vi) ions. Ultrasound is a robust method to facilitate extraction of target ions in the sorption step and elution of target ions in the desorption step. The TMP nanoparticles used in UA-DSPE-TMP were prepared by precipitation polymerization. TMP nanoparticles were synthesized using methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a crosslinker, 2,2�-azobisisobutyronitrile as an initiator, 1,5-diphenylcarbazide as a ligand, methanol as the solvent, and Cr(vi) as a template ion, through a precipitation polymerization technique. TMP nanoparticles were characterized by Fourier-transformed infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy (SEM), elemental analyses and energy-dispersive X-ray spectroscopy. The effect of influencing parameters on extraction of Cr(vi) by the prepared TMP nanoparticles was evaluated and optimized. The optimum conditions for the method were: pH of solution = 1.0; sonication time for sorption = 10 min; TMP amount = 10 mg; type and concentration of eluent: NaOH/EDTA 0.5/0.1 mol L-1; volume of eluent = 200 μL; sonication time for desorption = 2 min. Under the optimized conditions, the limit of detection and relative standard deviation for the detection of Cr(vi) by the method was 8.0 ng L-1 and <7, respectively. © 2018 The Royal Society of Chemistry and the Centre National de la Recherche Scientifique

Preconcentration and ultra-trace determination of hexavalent chromium ions using tailor-made polymer nanoparticles coupled with graphite furnace atomic absorption spectrometry: Ultrasonic assisted-dispersive solid-phase extraction

We describe ultrasonic assisted-dispersive solid-phase extraction based on tailor-made polymer (UA-DSPE-TMP) nanoparticles for selective extraction of Cr(vi) ions. Ultrasound is a robust method to facilitate extraction of target ions in the sorption step and elution of target ions in the desorption step. The TMP nanoparticles used in UA-DSPE-TMP were prepared by precipitation polymerization. TMP nanoparticles were synthesized using methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a crosslinker, 2,2�-azobisisobutyronitrile as an initiator, 1,5-diphenylcarbazide as a ligand, methanol as the solvent, and Cr(vi) as a template ion, through a precipitation polymerization technique. TMP nanoparticles were characterized by Fourier-transformed infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy (SEM), elemental analyses and energy-dispersive X-ray spectroscopy. The effect of influencing parameters on extraction of Cr(vi) by the prepared TMP nanoparticles was evaluated and optimized. The optimum conditions for the method were: pH of solution = 1.0; sonication time for sorption = 10 min; TMP amount = 10 mg; type and concentration of eluent: NaOH/EDTA 0.5/0.1 mol L-1; volume of eluent = 200 μL; sonication time for desorption = 2 min. Under the optimized conditions, the limit of detection and relative standard deviation for the detection of Cr(vi) by the method was 8.0 ng L-1 and <7, respectively. © 2018 The Royal Society of Chemistry and the Centre National de la Recherche Scientifique

Determining the best ISUM (Improved Stock Unearthing Method) sampling point number to model long-term soil transport and micro-topographical changes in vineyards

Advances in soil erosion measuring tools and micro-topography modelling will contribute to our understanding of land degradation processes and help to design correct erosion mitigation measures in agricultural fields. Vineyards being one of the most degraded agricultural landscapes, it is necessary to accurately predict soil erosion levels within them. One possible method to achieve this goal in vine plantations is ISUM (improved stock unearthing method). To apply ISUM, it is necessary to detect the graft unions which are recognised as passive bioindicators of the original micro-topography at the time of planting. In this paper, we propose a methodology to determine: (i) how many measuring points are necessary to reach the best estimate of soil erosion for developing current soil surface level maps; and (ii) which spatial interpolation method is the best to map the micro-topographical changes. ISUM was applied in the Ruwer-Mosel valley vineyards (Germany) using 18 measuring points at 10 cm intervals between opposite pair graft unions of 1.7 m inter-row distance. Several interpolation methods were used to map the micro-topography changes and anisotropic ordinary kriging (OK) emerged as the best as judged by the performance statistics of the coefficient of determination and the root-mean-square-error. Our findings demonstrated that soil erosion rates were 40.1, 39.4, 25.0, 38.9, 37.9, to 64.8 Mg ha −1 yr −1 over the 40 years since the establishment of the vineyard studied, when using 18, 15, 10, 7, 5 and 2 measuring points, respectively. We propose that ISUM can be standardised as using measuring points at 10 cm intervals. © 2019 Elsevier B.V

Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning

Evaluation of spatial variability and mapping of soil properties is critical for sustainable agricultural production in arid lands. The main objectives of the present study were to spatialize soil organic carbon (SOC), soil particle size distribution(clay, sand, and silt contents), and calcium carbonate equivalent (CCE) by integrating multisource environmental covariates, including digital elevation model (DEM) and remote sensing data by machine learning (Cubist, Cu and random forest, RF) in an arid region of Iran. Additionally, Synthetic Soil Images (SySI) were achieved from multi-temporal images of bare soil pixels based on Landsats 4, 5, 7, 8, and a DEM. Three hundred topsoil samples (0–30 cm depth) were collected based on the conditioned Latin hypercube sampling (cLHS) approach in Afzar district, Fars province, southern Iran. The models were calibrated and validated by the 10-fold cross-validation approach, and the performance was evaluated using root mean square error (RMSE), the ratio of the performance to interquartile distance (RPIQ), and coefficient of determination (R2). Also, the prediction accuracy was assessed by the relative RMSE (rRMSE). The performance of the best models based on the RPIQ index showed that the model for predicting clay (1.89) had a good prediction, for sand (1.64), SOC (1.55), and CCE (1.59) had a fair prediction, while the model for silt (1.13) performed poorly. We found that the Cu and RF models had the highest and lowest prediction accuracies for CCE (rRMSE = 14.31%) and SOC (rRMSE = 43.93%), respectively. We discovered that a combination of high-quality RS data (SySI) and variables derived from DEM were reasonably able to predict soil properties. We revealed a strong promise to enhance the accuracy of digital soil mapping, especially in regions with limited soil data. Moreover, the application of RS data can reduce the soil sampling cost and, accordingly, soil mapping