The effect of fluorescent tracers on droplet spectrum,viscosity, and density of pesticide formulations

The most important factor affecting efficacy and drift of pesticide applications is the droplet spectrum. To measure pesticide drift, researchers utilize fluorescent tracers to rapidly quantify spray deposition. Although fluorescent tracers have been used for more than 50 years, no experiments have been performed on the effect they have on the properties of pesticide formulations (density and viscosity) or droplet spectrum, which affect the drift of pesticides. Therefore, we examined the effect of an oil- and water-based tracer on the volume median diameter (VMD), viscosity, and density of oil- and water-based pesticide formulations. In addition, we experimentally fit and demonstrate the utility of using distributions to characterize pesticide droplet spectra. The addition of tracers to both water- and oil-based formulations did not significantly alter the VMD, viscosity, and density. Lognormal distributions provided the best fit for the water- and oil-based formulations with and without tracer. Our results demonstrated that the addition of oil- and water-based tracers do not significantly alter pesticide formulations properties and droplet spectrum, and most likely do not alter the movement of pesticide droplets in the environment.     

Cr(VI) sorption by free and immobilised chromate-reducing bacterial cells in PVA–alginate matrix: equilibrium isotherms and kinetic studies

Chromate-resistant bacterial strain isolated from the soil of tannery was studied for Cr(VI) bioaccumulation in free and immobilised cells to evaluate its applicability in chromium removal from aqueous solution. Based on the comparative analysis of the 16S rRNA gene, and phenotypic and biochemical characterization, this strain was identified as Paenibacillus xylanilyticus MR12. Mechanism of Cr adsorption was also ascertained by chemical modifications of the bacterial biomass followed by Fourier transform infrared spectroscopy analysis of the cell wall constituents. The equilibrium biosorption analysed using isotherms (Langmuir, Freundlich and Dubinin–Redushkevich) and kinetics models (pseudo-first-order, second-order and Weber–Morris) revealed that the Langmuir model best correlated to experimental data, and Weber–Morris equation well described Cr(VI) biosorption kinetics. Polyvinyl alcohol alginate immobilised cells had the highest Cr(VI) removal efficiency than that of free cells and could also be reused four times for Cr(VI) removal. Complete reduction of chromate in simulated effluent containing Cu²⁺, Mg²⁺, Mn²⁺ and Zn²⁺ by immobilised cells, demonstrated potential applications of a novel immobilised bacterial strain MR12, as a vital bioresource in Cr(VI) bioremediation technology.     

Sorption mechanisms of cephapirin, a veterinary antibiotic, onto quartz and feldspar minerals as detected by Raman spectroscopy

Raman spectroscopy was used to investigate sorption mechanisms of cephapirin (CHP), a veterinary antibiotic, onto quartz (SiO₂) and feldspar (KAlSi₃O₈) at different pH. Sorption occurs by electrostatic attraction, monodentate and bidentate complexation. The zwitterion (CHP^o) adsorbs to a quartz⁽⁺⁾ surface by electrostatic attraction of the carboxylate anion group (-COO⁻) at low pH, but adsorbs to a quartz⁽⁻⁾ surface through electrostatic attraction of the pyridinium cation, and possibly COO⁻ bridge complexes, at higher pH. CHP⁻ bonds to quartz⁽⁻⁾ surfaces by bidentate complexation between one oxygen of -COO⁻ and oxygen from carbonyl of an acetoxymethyl group. On a feldspar^(+/−) surface, CHP^o forms monodentate complexes between CO, and possible -COO⁻ bridges and/or electrostatic attachments to localized edge (hydr)oxy-Al surfaces. CHP⁻ adsorbs to feldspar⁽⁻⁾ through monodentate CO complexation. Similar mechanisms may operate for other cephalosporins. Results demonstrate, for the first time, that Raman techniques can be effective for evaluating sorption mechanisms of antibiotics.     

Adsorption and breakdown of penicillin antibiotic in the presence of titanium oxide nanoparticles in water

The fate and transport of antibiotics in natural water systems is controlled in part by interactions with nanometer (10⁻⁹ m) metal oxide particles. Experiments were performed by mixing solutions of ampicillin (AMP), a common, penicillin-class human and veterinary antibiotic, with 25 nm-TiO₂ (anatase) nanoparticles at different pH conditions. Both sorption and degradation of AMP were observed in the AMP-nanoparticle solutions. For AMP concentrations from ∼3 μM to 2.9 mM the overall AMP removal from solution can be described by linear isotherms with removal coefficients (K_r) of 3028 (±267) L kg⁻¹ at pH 2, 11,533 (±823) L kg⁻¹ at pH 4, 12,712 (±672) L kg⁻¹ at pH 6, and 1941 (±342) L kg⁻¹ at pH 8. Mass spectral analysis of AMP solutions after removal of the solid nanoparticles yielded ions that indicate the presence of peniclloic acid, penilloic acid and related de-ammoniated by-products as possible compounds resulting from the degradation of AMP at the TiO₂ surface.     

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles

Purpose

The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium.

Method

Iron?Csilver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique.

Results

The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10?mg?g^?1, respectively, as compared to the experimental value of 54.0?mg?g^?1 with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set.

Conclusion

Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.     

Comparing contaminated property redevelopment for mandatory and Voluntary Cleanup Programs in California

This study uses California data to compare redevelopment for properties subject to mandatory and voluntary cleanup. CalSites are subject to the CERCLA liability approach, while properties in the Voluntary Cleanup Program (VCP) are subject to a risk-based approach (RBA) that allows some contamination to remain for non-residential redevelopment. The expectation is that VCPs will show a greater proportion of industrial redevelopment, which has the least stringent standard, and a smaller proportion of residential redevelopment. The results show an overall trend toward more residential redevelopment of contaminated properties, but consistent with expectations, the trend is weaker for VCP properties than CalSites.     

Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ ₂₅₄ as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.     

Biosorption kinetics of heavy metals by leaf biomass of Jatropha curcas in single and multi-metal system

Biosorption of Cu²⁺, Zn²⁺, and Cr⁶⁺ from aqueous solutions by leaf biomass of Jatropha curcas was investigated as a function of biomass concentration, initial metal ion concentration, contact time, and pH of the solution systematically. The aim of this study was to optimize biosorption process and find out a suitable kinetic model for the metal removal in single and multi-metal system. The experimental data were analyzed using two sorption kinetic models, viz., pseudo-first- and pseudo-second-order equations, to determine the best fit equation for the biosorption of metal ions Cu²⁺, Zn²⁺, and Cr⁶⁺ onto the leaf biomass of J. curcas in different metal systems. The experimental data fitted well the pseudo-second-order equation and provided the best correlation for the biosorption process. The findings of the present investigation revealed that J. curcas leaf biomass was an eco-friendly and cost-effective biosorbent for the removal of heavy metal ions from wastewater.     

Smart nanopackaging for the enhancement of food shelf life

Environmental Chemistry Letters - Food spoilage is a major global concern due to the lack of proper packaging technology. Nanotechnology is expected to improve food packaging. Indeed, novel...