Abstract: The 2013/39/EU Directive of the European Parliament provided a list of substances (organic and inorganic compounds); these so-called priority and dangerous priority substances affect the quality status of surface waters. Due to their high level of toxicity, these contaminants have legal limits in the order of µg/L and even some in the order of ng/L and pg/L. To this aim, an effective and sensitive analytical method for monitoring these contaminants was deemed necessary. In this experimental process, a highly effective workflow represented by a pre-analytical and an analytical phase was developed and validated. The pre-analytical phase comprises a liquid-liquid microextraction and a quick, easy, cheap, effective, rugged, and safe purification. The analytical part was performed by a very sensitive and robust multi-residual GC-MS/MS method without the need for derivatization. This method simultaneously identified and quantified most of these substances (represented by pesticides, chloroalkane hydrocarbons, and polycyclic aromatic hydrocarbons) at very low concentration levels while respecting the analytical concentration limits required by the European directive.
Abstract: Biodiesel is a green diesel fuel that is synthesized via the transesterification reaction of plant oils or animal fats with light alcohols, mainly methanol, in the presence of commonly homogeneous alkaline catalysts. One way to make the biodiesel synthesis process more eco-friendly is the use of heterogeneous catalysts as they are reusable. But with these catalysts, the transesterification reaction mixture will be a three-phase mixture containing methanol, oil and catalyst that leads to long reaction times and high process costs. In this study, the ultrasound waves were used to synthesis of the biodiesel from the Jatropha oil in the presence of CaO as a heterogeneous catalyst. The effect of two different co-solvents (acetone & hexane) on improving the homogeneity of the reaction mixture and therefore to enhance the conversion yield of the oil to biodiesel was considered. Experiments were carried out by a probe type ultrasonic instrument at an output power of 200 W and frequency of 20 kHz. The results showed that both of the considered co-solvents had positive effect on developing the conversion yield and the acetone was the more efficient one due to its moderate polarity, which could improve the miscibility of the oil with the methanol. A biodiesel sample with a conversion yield of 90.33% was synthesized under optimal operating conditions including 25% v/v acetone to methanol, 12:1 methanol to oil molar ratio, 4% w/w catalyst to oil, 55°C reaction temperature and 60 min reaction time. Hnmr and densitometry were used to determine the percentage of the synthesized biodiesel samples. Also, some of the physicochemical properties of the synthesized biodiesel were analyzed, which were in accordance with the ASTM and EN standards.
Abstract: Environmental sustainability apart from spoken at different levels need to investigate the management of industrial activities in terms of eco friendliness. It is well known that effluents from various industries becomes a major source of environmental degradation in particular water pollution. Thus water analysis report turns to be more significant, to determine the quality and standard of life maintained in a typical ecosystem. This study includes the analysis of physico-chemical factors of raw as well as treated water samples from Korattur Lake employing nano sized adsorbents like titania with tuned structural and morphological properties through organic plant extract. The XRD analysis, BET analysis, DRS-UV analysis, SEM and TEM analysis have been carried out and reported. The usual indicators of contamination level at a given source namely Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), Chloride, Nitrate and Phosphate contents etc. were used to ascertain the efficiency of contaminants removal using tea leaf extract mediated nano titania. The reliability factor of the treatment process was found to be less than 1, revealed the effectiveness and reliability of nano material.
Abstract: During recent decades, the excessive use of water has led to the scarcity of the available surface and groundwater resources. Quantitative and qualitative surveys of groundwater resources indicate that accurate and efficient optimization methods can help to overcome the numerous challenges in assessment of groundwater quality. For this purpose, three optimization meta-heuristic algorithms, including imperialist competitive (ICA), election (EA), and grey wolf (GWO), as well as the support vector regression method (SVR), were used to simulate the groundwater quality of the Salmas Plain. To achieve this goal, the data of the groundwater quality for the Salmas plain were utilized in a statistical period of 10 years (2002-2011). The results were evaluated according to Wilcox, Schuler, and Piper standards. The results indicated higher accuracy of the GWO-SVR method compared to the other two methods with values of R2=0.981, RMSE=0.020 and NSE=0.975. In general, a comparison of the results obtained from the hybrid methods and different diagrams showed that the samples had low hardness and corrosion. Also, the results indicated the high capability and accuracy of the GWO-SVR method in estimating and simulating the groundwater quality.
Abstract: Evaluating the energy and environmental indicators allows for identifying the strengths and weaknesses of a system for optimizing material and energy consumption and developing strategies to reduce environmental impacts. This study determined and assessed the energy and environmental indicators of wheat flour production systems. The input and output materials and corresponding energy equivalents were calculated and then the energy indicators and forms. The environmental indicators were assessed by the life cycle assessment method in SimaPro software. The total input and output energies per year of flour production were 287935007 and 286675200 MJ, respectively. Wheat had the highest share (99.19%) of energy consumption in flour production; the energy ratio, productivity, intensity, and net energy gain indexes were equal to 1.02, 0.07 kg.MJ-1, 13.84, MJ.kg-1, and 0.31 MJ.kg-1, respectively. In the flour factory, the share of direct and indirect energy was 0.27 and 99.73%, respectively; the share of renewable and nonrenewable energy was 99.19 and 0.81%, respectively. Wheat input had the largest share of environmental indicators in flour production. The normalization step showed that the most important environmental indicator was marine water ecotoxicity (1.53×105 kg 1.4 DB eq.ton-1) followed by terrestrial ecotoxicity (36.59×105 kg 1.4 DB eq.ton-1), eutrophication (5.83 kg PO4 eq.ton-1), and acidification potential (6.57 kg SO2 eq.ton-1) indicator.
Abstract: It is essential to assess flood risk mapping for sustainable development. The present study aimed to identify the causes of flooding and predict the extent of damage caused in the area of Miyandoab, Iran. The Driver, Pressure, State, Impacts, response (DPSIR) conceptual framework model was used to analyze the factors affecting flooding in the region. DPSIR is a system approach that identifies key relationships between humans and the environment, and its combination with the simple additive weighting (SAW) model identifies a new strategy for achieving sustainable development. The DPSIR method for flood susceptibility analysis in the region, examined the social, economic, physical, and environmental factors as the driving force. The flood risk level was then determined for the region by preparing the driving force map and mapping the region. For this purpose, the decision-making trial and evaluation laboratory (Dematel) and SAW models were used to investigate the causal relationship between the factors and calculate the weight of layers; Matlab software was used to implement the models. Finally, based on the weights extracted from the SAW method, risk mapping was performed in the geographic information system (GIS) environment. The results showed that out of the total area of the study area, about 78,462 hectares have a high risk, 91,542 hectares have medium risk, and 2,952 hectares have a low risk of flood. The results from combining the models of decision support systems and GIS indicated high efficiency in determining the areas with a high risk of flood.
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