Analyzing material flow in Alborz industrial estate,Ghazvin, Iran

Journal of Material Cycles and Waste Management - Industrial ecology and models such as material flow analysis make operational approaches in order to reuse produced waste as primary resources in...     

Accumulation and elimination of cadmium and lead in juvenile milkfish during sublethal exposure

Pollution of aquatic environments by trace metals is a worldwide environmental problem. Metal pollutants are increasingly being released into the environment as a result of industrialization. In this study, the bioaccumulation of cadmium and lead in young juvenile milkfish liver (Chanos chanos) was investigated after exposure to three sublethal concentration of each pollutants (1/20, 1/10, and 1/5 LC₅₀ of 96-h LC₅₀) for acute time 12, 24, and 96 h and subchronically for 7, 14, and 21 days. Cadmium and lead accumulation in liver increased with the exposure period and concentrations of pollutant. Compared to controls, the uptake of cadmium is much higher than that of lead. Accumulation factors showed an increase with exposure time and for lead an inverse relationship between accumulation factor and exposure concentration. The elimination of the two pollutants during the 30 days depuration was investigated after 30 days depuration time. During this phase, cadmium and lead concentrations decreased.     

A novel polyacrylonitrile–zeolite nanocomposite to clean Cs and Sr from radioactive waste

Radioactive wastes containing Cs⁺ and Sr²⁺ are among the most dangerous environmental pollutants. Therefore, removing Cs⁺ and Sr²⁺ from environmental media is needed. Removal can be done by nanocrystalline ion exchangers. Nanocrystalline ion exchangers are studied in depth for the treatment of nuclear wastes because these exchangers have high exchange capacity and fast kinetics. However, operating the columns of these exchangers is very difficult. This issue may be overcome by the preparation and use of nanocomposites. Here, we prepared a novel polyacrylonitrile–zeolite nanocomposite for the removal of Cs⁺ and Sr²⁺ in a fixed-bed column operation. We studied the effect of influent flow rate, nanocomposite bed height and initial concentrations. Experimental data were analysed using the Thomas model and the bed-depth service time model. The results reveal that total adsorbed ion and bed capacity increased with increasing initial ions concentration and bed height; and decreased with increasing influent flow rate. The maximum bed capacity was 0.085 meq/g for Cs⁺ and 0.128 meq/g for Sr²⁺. The critical bed height (Z ₀) was 4.35 cm for Cs⁺ and 2.89 cm for Sr²⁺. These findings demonstrate that the new nanocomposite is suitable for removal of Cs⁺ and Sr²⁺.     

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca²⁺), magnesium ions (Mg²⁺), sodium ions (Na⁺), chloride ions (Cl^?), sulfate (), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R ²) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R ² between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.     

Field application of SPME as a novel tool for occupational exposure assessment with inhalational anesthetics

Occupational exposure to inhalational anesthetics occurs routinely in operating rooms. It could induce serious health hazards and diseases. This exposure assessment is a crucial step in determining risks. In this study, a pen-shaped holder for solid-phase microextraction (SPME) sampler was successfully applied as a time-weighted average sampling tool for workshift exposure assessment of operation room staff to halothane. It proved to be very convenient for use in occupational environments such as operation rooms. Samples were analyzed by a gas chromatography-mass spectrometry. The validity of the SPME method was checked in real-world conditions with Occupational Safety and Health Administration (OSHA) 103 standard method for the determination of inhalational anesthetics. A good agreement between OSHA 103 and SPME methods was obtained and results demonstrated no statistically significant differences in anesthetic concentrations determined by the two analytical methods (p?≥?0.05). It is concluded that SPME in retracted mode could successfully be applied in occupational exposure assessment purposes.     

GIS aided prediction of CO2 emission dispersion from geothermal electricity production

CO₂ is the dominant constituent of non-condensable gases in the steam phase of most geothermal fluids. This paper attempts to present the results of a study conducted to develop prediction modeling of CO₂ dispersion in the surrounding atmosphere from a planned 50 MWe geothermal power plant prior to its production. Dispersion models are widely used for predicting future concentrations of environmental emissions on a range of geographic scales. The dispersion type for gases and their average removal rate depends on the meteorological conditions such as wind direction, wind speed, precipitation, atmospheric stability, and surface roughness and topography. Geographic Information System (GIS) capabilities were used for quality visualization of the model outputs and presenting an accurate numerical copy of the study area. The results by the prediction model show that the natural transfer of CO₂ will be from the power plant toward east and northeast and CO₂ concentration trends after the power plant utilization will be similar to the background conditions with minor changes. This dispersion test was carried out to validate and field test the GIS aided dispersion modeling approach described in the paper and the procedure may be applicable for other studies assessing the emission dispersion of pollutants from a point source.     

Minimum quantity lubrication in grinding: effects of abrasive and coolant–lubricant types

This research has been conducted to study the influence of the abrasive and coolant–lubricant types on the minimum quantity lubrication (MQL) grinding performance. One type of CBN and three types of conventional wheels (corundum) have been tested. The tests have been performed in presence of fluid, air jet and eleven types of coolant–lubricants, as well as, in dry condition. The results indicate that the finest surface quality and lower grinding forces could be obtained while grinding with CBN wheel. In case of conventional wheels, the coarser and high porosity wheels induce much proper grinding results. Furthermore, grinding when utilizing MQL with oil results in higher grinding performance.     

Long-term variations of water quality parameters in the Maroon River, Iran

Sixteen water quality parameters have been monitored at four stations located along the Maroon River during 1989?C2008. The trend analysis was performed on seasonal and annual time-scales using the Mann?CKendall test, the Sen??s slope estimator and the linear regression. The relationships of the water quality parameters to river discharge were also investigated. The statistical methods showed both positive and negative trends in annual water quality data. However, significant trends were detected by the statistical methods only in calcium, magnesium, sodium absorption ratio, pH, and turbidity series. The results indicated that the concentrations of the water quality parameters increased in spring and winter seasons, while the concentrations were diluted in summer and autumn seasons in the last two decades. Moreover, the highest numbers of significant trends were found in the spring and summer series, respectively. According to the regression analysis, most of the water quality parameters were negatively correlated with river discharge.