The optimal dam site selection using a group decision-making method through fuzzy TOPSIS model

The complex and controversial task of selecting a dam site in a river basin can be successfully achieved using science-informed multi-criteria decision-making (MCDM) techniques. In this paper, we describe the application of the group fuzzy TOPSIS model for optimal ranking of the case study of Kandoleh dam sites in Kermanshah province, Iran, involving 18 input criteria. In this study, decision-making committee was made up of 20 involved decision makers. The comments of four non-biased, external experts in dam site selection were also used. The triangular fuzzy numbers were used to apply experts’ opinions on the selection criteria. In total, four alternative sites were assessed based on the technical, economic, social and environmental considerations and the data were analyzed using fuzzy TOPSIS MCDM model. Ranking results were compared with multi-criteria decision-making models, including the ELimination and Choice Expressing the REality and simple additive weighting. This logical, open and transparent framework provides a science-informed decision-making approach for complex problems such as optimal dam site selection. Finally, using sensitivity analysis, local studies and group discussions, we demonstrated the multiple benefits of the proposed novel method for a science-informed, open and transparent method for optimal ranking of the dam site candidates.     

Options for sustainable development planning based on “GHGs emissions reduction allocation (GERA)” from a national perspective

Climate change phenomenon can be seen as a simple but daunting problem. The lack of equity in emission reduction burden sharing regime, will cause a need for a greater sacrifice for poor or less developed countries. Thus, the evaluation of different aspects of equity at a national scale and presenting a top–down model of equity for allocation of GHGs emission (such as GERA) in line with sustainable development is the main objective of this study. In this study, the five equity principles proposed in the literature namely (1) population distribution, (2) GHGs emissions, (3) GDP, (4) trend of economic growth and (5) per capita of carbon productivity as appropriate criteria of equity estimation. Due to the different decision makers' preferences, different weights are allocated to indicators and analyzed. Iran has been considered as a case study, and these criteria were applied at national level to propose an allowance allocation scheme. The result of applying GERA for Iran, at provincial level and under the five equity criteria, determines which provinces have to shoulder higher reduction burdens, and makes a room for less developed provinces for growth. Based on these results, this model demonstrated to be more sensitive to criteria selection rather than to the weight factors. In addition, shifting to low carbon technologies or renewables, careful evaluation of current emission–income pattern, improving of energy intensity and finally, adjustment of secondary industries (manufacturing) based on ecological and natural resources of each region are suggested as the most efficient approaches toward sustainability and green development for the case study.     

Synthesis and antibacterial activity of a Fe3O4–AgCl nanocomposite against Escherichia coli

In this investigation, Fe₃O₄ magnetic nanoparticles (MNPs) were prepared by the alkalinization of an aqueous medium containing ferrous sulfate and ferric chloride. In the next step, a Fe₃O₄–AgCl magnetic nanocomposite was fabricated by the drop-by-drop addition of silver nitrate solution into a NaCl solution containing Fe₃O₄ MNPs. All prepared nanoparticles were characterized by transition electron microscopy (TEM), X-ray diffraction (XRD), and energy-dispersive X-ray spectroscopy (EDS). Both particle types varied in size from 2.5 to 20?nm, with an average size of 7.5?nm for Fe₃O₄ MNPs and 12.5?nm for Fe₃O₄–AgCl nanocomposites. The antibacterial effect of the Fe₃O₄ MNPs and fabricated Fe₃O₄–AgCl nanocomposites against Escherichia coli (ATCC 35218) were investigated by conventional serial agar dilution method using the Müller–Hinton Agar medium. The minimum inhibitory concentration was 4?mg?mL^?1 for Fe₃O₄ MNPs and 2?mg?mL^?1 for the Fe₃O₄–AgCl magnetic nanocomposites. Time-kill course assays showed that the Fe₃O₄–AgCl magnetic nanocomposites successfully killed all inoculated bacterial cells during an exposure time of 60?min. The antibacterial activity of recycled Fe₃O₄–AgCl magnetic nanocomposites over four 60?min cycles of antibacterial treatment was further tested against E. coli by the colony-forming unit (CFU) method. The antibacterial efficiency of the nanocomposites was constant over two cycles of antibacterial testing.     

Experimental and Numerical Analysis of CO Concentration Dispersion of Vehicular Exhaust Emissions in Isolated Environment

Numerical and experimental analyses were applied to carbon monoxide (CO) concentration dispersion to monitor air quality in an enclosed residential complex parking area in Tehran. Firstly, the parking area was preliminary assessed through verifying the characteristics of the problem including the geometry and boundary conditions. Then, proportion of vehicular exhaust emissions was estimated and eventually experimental and numerical analyses were performed. In order to perform numerical calculation, a three-dimensional model was created to numerically simulate the enclosed residential complex parking area by FLUENT software that solves flow governing equations with finite volume method. In FLUENT, species model was selected to assess the dispersion of CO in flow domain. In experimental analysis, CO concentration was measured using sampling bags with a volume of 10 l in 4 min at 6 different points. The sample air was drawn into sampling bags by electric pumps. The findings show that the maximum amount of CO concentration is above the permissible standard recommended by the World Health Organization. Pollutant accumulation was significant in confined areas. In the place where openings exist, the level of accumulation was lower than other areas. The findings obtained from numerical simulation are in complete accord with experimental results.     

A well to wire LCA model development and sensitivity analysis for carbon footprint of combined cycle power plants in Iranian electricity network

The present research introduces a well to wire pseudo comprehensive carbon footprint model for combined cycle power plants. The mentioned model integrates land use change model, operational model and transmission and distribution model into one comprehensive model. The parameters which their effects are considered in the integrated model are: fuel type, fuel transmission type, emission for fuel extraction and processing, own consumption of the plant, degradation, site ambient condition, transmission and distribution losses. For quantifying the effectiveness of each parameter, sensitivity analyses based on different life cycle scenarios are performed. The result shows that the effect of land use change is negligible. The carbon footprint of electrical energy produced in combined cycle plant until it is delivered to the end users varies from 321 to 522 g CO₂ eq/kWh.     

Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration

This study aims to predict daily carbon monoxide (CO) concentration in the atmosphere of Tehran by means of developed artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Forward selection (FS) and Gamma test (GT) methods are used for selecting input variables and developing hybrid models with ANN and ANFIS. From 12 input candidates, 7 and 9 variables are selected using FS and GT, respectively. Evaluation of developed hybrid models and its comparison with ANN and ANFIS models fed with all input variables shows that both FS and GT techniques reduce not only the output error, but also computational cost due to less inputs. FS–ANN and FS–ANFIS models are selected as the best models considering R², mean absolute error and also developed discrepancy ratio statistics. It is also shown that these two models are superior in predicting pollution episodes. Finally, uncertainty analysis based on Monte-Carlo simulation is carried out for FS–ANN and FS–ANFIS models which shows that FS–ANN model has less uncertainty; i.e. it is the best model which forecasts satisfactorily the trends in daily CO concentration levels.     

Eco-friendly dyeing of wool using natural dye from weld as co-partner with synthetic dye

Since the last decade, the application of natural dyes on textile materials is gaining popularity all over the world, possibly because of increasing awareness of environment, ecology, and pollution control. In this research, extraction of dyes from weld using soxhlet apparatus has been studied. The color components extracted and isolated from weld plant were characterized by Column Chromatography, Thin Layer Chromatography (TLC), NMR, mass, IR techniques. The natural dye extract obtained from the weld was used for the dyeing of wool fiber. The results indicated that the exhaustion rate for the extracted dye increases by 49% compared to the raw dye. This, in turn, leads to a considerable decrease in the amount of the extracted dye used to reach the same desired results. Finally, for comparative studies between synthetic and this natural dye, all dyed specimens were tested for wash and light fastness properties, making weld a viable alternative to synthetic acid dyes.