International Journal of Environmental Science and Technology

The effects of some chelating agents and electricity on cadmium phytoextraction from a mine soil were examined in pot culture of sunflower to achieve more remediation efficiency. At the beginning of the flowering stage, ethylene-diamine-tetra-acetic acid (EDTA) as a chemical chelator, cow manure extract (CME) and poultry manure extract (PME) as organic chelators were applied (2 g kg−1 soil) during irrigation. Seven days later, Helianthus annuus was negatively charged by inserting a stainless steel needle in the lowest part of the stem with 10 and 30 V direct current electricity for 1 h each day for 14 days. Afterward, concentration of cadmium in roots and shoots, cadmium translocation factor (TF), cadmium uptake index (UI) and soil available (diethylene-triamine-penta-acetic acid extractable) cadmium were measured. Results indicated that EDTA reduced roots dry weight while none of the roots and shoots was affected by other chelating agents and by electrical treatment as well. Highest concentration of cadmium in shoots was measured in 10 V-control with no significant differences with 30 V-PME and 30 V-EDTA. Utilization of chelating agents did not increase the cadmium TF and cadmium UI while highest values for cadmium TF and cadmium UI were observed in 10 and 30 V treatments, respectively. Available cadmium in the soil near root system treated with 10 and 30 V was relatively lower compared with the soil far from root system. Results of this experiment indicated that charging the plant with direct current electricity ameliorated the efficiency of cadmium phytoremediation.

Wheat being a glycophyte crop, responds differently to saline-sodic soil environmental conditions. The application of calcium is multidimensional with respect to sodium ion and plant part response. This study was conducted to record the response of shoot and root to sodium and calcium interaction under saline environment. Wheat seed of variety Punjab 85 were raised in quartz sand. Later on the seedlings were transplanted to pots containing Hoagland’s nutrient solution along with NaCl at 0 mM. and 50 mM. Calcium was applied as CaSO2 2H2O at 3 mM. and 6 mM. Under saline conditions shoot showed positive response to sodium ion in the presence of higher calcium. Relative water contents were higher in the root system at 6 mM of CaSO4. 2H2O under saline condition. Growth responses to potassium and Magnesium in the presence of sodium induced salinity with calcium ion interaction remained variable.

Metal oxide nanoparticles have becoming increasingly popular as an additive to diesel blend due to their numerous advantages. The current study aims to optimize and analyse the diesel engine characteristics by applying RSM optimization tool operating with Al2O3 nanoparticles incorporated fuel blends at various compression ratio and loads. The diesel engine’s input parameters were selected are compression-ratio, load, and Al2O3 nanoparticle concentration. The diesel engine compression-ratio was differed by 16.5–18.5, load varied by 25–100% and concentration of nanoparticles varied by 1–100 ppm. With RSM, multi-objective optimization has been done. The output responses were predicted by the mathematical models. The expected co-efficient of determinations for selected responses like BTE, BSFC, CO, HC, NOx, and CO2 are 98.94%, 97.65%, 96.27%, 95.38%, 99.76%, and 99.38%, respectively. Experimental evaluation of the engine output responses is established to fall within the acceptable range of error (< 5%). So, On the basis of study, it was concluded that the mathematically created model could be successfully used to forecast the aforesaid engine performance characteristics and found statistically fit.

A hybrid anaerobic–aerobic treatability of real textile wastewater was investigated by using a lab-scale upflow anaerobic sludge blanket (UASB) coupled with a sequencing batch reactor (SBR). The performance of the integrated system was determined under various operational conditions. In phase I of the study, the UASB reactor was operated and optimized on real textile wastewater. The UASB was operated at a hydraulic retention time of 48 h in a semi-continuous mode with 12-h feeding and 12-h non-feeding cycle. Volatile fatty acid to alkalinity ratio was kept in the range of 0.42–0.72, while pH was strictly maintained between 6.8 and 7.2 under mesophilic conditions (35  $$^\circ$$ C). In phase II of the study, SBR was coupled with a UASB reactor and operated on real textile wastewater. The SBR was operated at an HRT of 6 h and solids retention time of 20 days. The integrated system was optimized with maximum removal efficiencies of 98% for chemical oxygen demand, 94% for color, 93% for total Kjeldahl nitrogen, and 89% for orthophosphate phosphorus. The results indicated that the integrated system is robust and flexible for the treatment of textile wastewater.

The remediation of soil, contaminated by organic pollutants, in a cylinder-to-plane dielectric barrier discharge reactor at atmospheric air pressure was reported. Two model organic pollutants were selected; a solid pollutant (2,6-dichloropyridine) and a liquid pollutant (n-dodecane). The effects of the contaminant’s initial concentration and state, the energy consumption, and the soil type on the pollutant removal efficiency were investigated. To that scope, various contaminated samples of both quartz sand and loamy sandy soil were treated by plasma for various treatment times and initial 2,6-dichloropyridine/n-dodecane concentrations. The results revealed that (1) the removal efficiency of 2,6-dichloropyridine was higher compared to that of n-dodecane at a given plasma treatment time and (2) the removal efficiency increased with the energy density increasing, but decreased as the soil heterogeneity, organic matter and pollutant concentration were enhanced. The main removal mechanism proposed is the evaporation of pollutant molecules coupled with their oxidation by plasma species in the gas and solid/liquid phase.

The aim of this study was to compare the performance of support vector machine and artificial neural network techniques to predict the soil cation exchange capacity of an agricultural research station in terms of soil characteristics (clay, silt, sand, gypsum, organic matter). The data consist of 380 soil samples collected from different horizons of 80 soil profiles located in the Khoja (Khajeh) region of Azerbaijani provinces, Iran. The support vector machine and artificial neural network models predict the cation exchange capacity from the above soil characteristics of the samples. The models’ results are compared using three criteria, i.e., root-mean-square errors, Nash–Sutcliffe and the correlation coefficient. A comparison of support vector machine results with artificial neural network method indicates that artificial neural network is better than the support vector machine method in prediction of the cation exchange capacity.

The performance of boehmite granules in the adsorption of thorium ions from aqueous solution was studied in batch and continuous processes. The effective parameters such as pH and temperature were evaluated. First, the pH value was optimized, and then, several experiments were carried out in the optimized pH value and various temperatures (30, 40 and 55 °C). The equilibrium adsorption data were analyzed using Weber–van Vliet, Fritz–Schlunder and Hill isotherm models to find the best-fit isotherm. Thermodynamic parameters including standard enthalpy change (∆H°), standard entropy change (ΔS°) and standard Gibbs free energy change (∆G°) were determined in the batch mode. The application of adsorbent for thorium removal from aqueous solutions was examined through column breakthrough studies. The effect of various parameters such as inlet concentration, flow rate and bed height on the breakthrough curves was investigated. The maximum column capacity was found to be about 57.78 mg thorium per gram of boehmite. Three widely used models, including Thomas, Adams–Bohart and Bed Depth Service Time (BDST), were used to fit the data obtained from the experiments. The results indicated that the Thomas model could describe the column dynamics in all operating situations. But, the Adams–Bohart model can only estimate the primary section of the breakthrough curves.

Wise prediction of discharge is vital for effective utilization of water resources and hydropower generation. There is no doubt that the future climate change will substantially affect precipitation amount, discharge and hydro-meteorology which are considered as major sources for hydropower energy. In this study, we present an application of downscaling technique on prediction of discharge of a local gage station in Turkey. Variable input set of the Third Generation Coupled Global Climate Model variables are statistically downscaled and coupled with variable forms of local discharge (Q, LnQ, MavQ, StdQ, and Q/Qmax) by using five different models as Gene-expression programming, Group Method of Data Handling, K-nearest neighbour, logistic regression and linear regression. Different sub-models are developed and calibrated based on the training period, and the best model is selected based on the testing period. Future projecting of discharge is done based on the Third Generation Coupled Global Climate Model A2 scenario, and the financial analysis is carried out by using this forecasted Q values.

Simultaneous investigation of kerf quality and fume emissions during laser cutting process is essential to achieve efficient and hygienic cutting process. In this study, an experimental investigation on process factors of CO2 laser cutting (CLC) of polyvinyl chloride (PVC) sheets has been carried out followed by an optimization study to enhance the kerf geometry and reduce the emitted harmful fumes. Laser power, transverse cutting speed, and sheet thickness have been considered as the process factors. The kerf geometry has been measured using optical microscope. The emitted fumes are considered to be proportional to the generated kerf volume. An L9 experimental design plan was designed using Taguchi method, and the results were evaluated using analysis of variance (ANOVA). Second-order regression models have been developed to correlate the input parameters to the process responses. Genetic algorithm (GA) was implemented to obtain the optimum process parameters that should be used to minimize the kerf volume and consequently the emitted fumes. A significant decrease in kerf volume has been obtained using the optimum process parameters. Confirmation experiments have been conducted to verify the accuracy of the proposed approach. The proposed optimization method shows a considerable decrease in the kerf volume and the emitted fumes, especially for large sheet thicknesses. The emitted fumes can be reduced by about 39% and 61% for sheet thickness of 7 mm and 10 mm, respectively, by applying the optimum cutting parameters obtained from this study.