Biosorption of Cr(VI) and Zn(II) ions from aqueous solution onto the solid biodiesel waste residue: mechanistic, kinetic and thermodynamic studies

In this present study, the biosorption of Cr(VI) and Zn(II) ions from synthetic aqueous solution on defatted J atropha oil cake (DJOC) was investigated. The effect of various process parameters such as the initial pH, adsorbent dosage, initial metal ion concentration and contact time has been studied in batch-stirred experiments. Maximum removal of Cr(VI) and Zn(II) ions in aqueous solution was observed at pH 2.0 and pH. 5.0, respectively. The removal efficiency of Cr(VI) and Zn(II) ions from the aqueous solution was found to be 72.56 and 79.81 %, respectively, for initial metal ion concentration of 500 mg/L at 6 g/L dosage concentration. The biosorbent was characterized by Fourier transform infrared, scanning electron microscopy and zero point charge. Equilibrium data were fitted to the Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherm models and the best fit is found to be with the Freundlich isotherm for both Cr(VI) and Zn(II) metal ions. The kinetic data obtained at different metal ion concentration have been analysed using the pseudo-first-order, pseudo-second-order and intraparticle diffusion models and were found to follow the pseudo-second-order kinetic model. The values of mass transfer diffusion coefficients (D _e) were determined by Boyd model and compared with literature values. Various thermodynamic parameters, such as ΔG°, ΔH° and ΔS°, were analysed using the equilibrium constant values (K _e) obtained from experimental data at different temperatures. The results showed that biosorption of Cr(VI) and Zn(II) ions onto the DJOC system is more spontaneous and exothermic in nature. The results indicate that DJOC was shown to be a promising adsorbent for the removal of Cr(VI) and Zn(II) ions from aqueous solution.     

Parametric study of MSW landfill settlement model

A newly developed and validated constitutive model that accounts for primary compression and time-dependent mechanical creep and biodegradation is used for parametric study to investigate the effects of model parameters on the predicted settlement of municipal solid waste (MSW) with time. The model enables the prediction of stress strain response and yield surfaces for three components of settlement: primary compression, mechanical creep, and biodegradation. The MSW parameters investigated include compression index, coefficient of earth pressure at-rest, overconsolidation ratio, and biodegradation parameters of MSW. A comparison of the predicted settlements for typical MSW landfill conditions showed significant differences in time-settlement response depending on the selected model input parameters. The effect of lift thickness of MSW on predicted settlement is also investigated. Overall, the study shows that the variation in the model parameters can lead to significantly different results; therefore, the model parameter values should be carefully selected to predict landfill settlements accurately. It is shown that the proposed model captures the time settlement response which is in general agreement with the results obtained from the other two reported models having similar features.     

Re-examining log law velocity profile in smooth open channel flows

Environmental Fluid Mechanics - A comprehensive investigation of velocity distribution is presented, and the log law is re-examined using experimental data from a smooth uniform open channel flow....     

Challenges and possible solutions in dental practice during and post COVID-19

Environmental Science and Pollution Research - The pandemic outbreak of coronavirus disease (COVID-19) created a significant impact on the oral healthcare profession, and dentists are reported to...     

Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.

Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.     

Short-term influence of phosphate and nitrate on heavy metal accumulation by red alga Acrosorium uncinatum

Heavy metal accumulation (Cu, Zn, Ni, and Pb) in common marine macroalga, Acrosorium uncinatum under nutrient (phosphate and nitrate) enriched (experiment 1) and starved (experiment 2) conditions over a short exposure period (12 h) were examined in this study. Control was maintained in seawater contained nutrient solution without addition of metals and in seawater alone for experiment 1 and 2, respectively. Among the four metals studied, the accumulation of Zn, Ni, and Pb was considerably lower than Cu. The accumulation factor for all metals varies greatly in different nutrient concentrations, but it increases as the exposure of metal concentration decreases in both the experiments. The results of the present findings established that this macroalga is an accumulator of metals Cu, Zn, Ni, and Pb and have the potential to accumulate these metals even in a short time exposure period (12 h). Even though metal accumulation by A. uncinatum largely depends on the available concentration in the medium, nutrients like phosphate and nitrate can affect the accumulation significantly.     

Direct radiative forcing of urban aerosols over Pretoria (25.75°S, 28.28°E) using AERONET Sunphotometer data: First scientific results and environmental impact

The present study uses the data collected from Cimel Sunphotometer of Aerosol Robotic Network（AERONET） for the period from January to December, 2012 over an urban site,Pretoria（PTR; 25.75°S, 28.28°E, 1449 m above sea level）, South Africa. We found that monthly mean aerosol optical depth（AOD, τa） exhibits two maxima that occurred in summer（February） and winter（August） having values of 0.36 ± 0.19 and 0.25 ± 0.14,respectively, high-to-moderate values in spring and thereafter, decreases from autumn with a minima in early winter（June） 0.12 ± 0.07. The Angstrom exponents（α440–870） likewise,have its peak in summer（January） 1.70 ± 0.21 and lowest in early winter（June） 1.38 ± 0.26,while the columnar water vapor（CWV） followed AOD pattern with high values（summer） at the beginning of the year（February, 2.10 ± 0.37 cm） and low values（winter） in the middle of the year（July, 0.66 ± 0.21 cm）. The volume size distribution（VSD） in the fine-mode is higher in the summer and spring seasons, whereas in the coarse mode the VSD is higher in the winter and lower in the summer due to the hygroscopic growth of aerosol particles.The single scattering albedo（SSA） ranged from 0.85 to 0.96 at 440 nm over PTR for the entire study period. The averaged aerosol radiative forcing（ARF） computed using SBDART model at the top of the atmosphere（TOA） was- 8.78 ± 3.1 W/m2, while at the surface it was- 25.69 ± 8.1 W/m2 leading to an atmospheric forcing of ＋ 16.91 ± 6.8 W/m2, indicating significant heating of the atmosphere with a mean of 0.47 K/day.     

Photocatalytic degradation of atrazine in aqueous solution using La-doped ZnO/PAN nanofibers

Environmental Science and Pollution Research - Polyacrylonitrile (PAN)-based-modified zinc oxide (ZnO) nanofibers were synthesized by using electrospinning and hydrothermal techniques. The...