Biomonitoring of unmetabolized polycyclic aromatic hydrocarbons (PAHs) in urine of waterpipe/cigarette café workers

Environmental Science and Pollution Research - Fresh tobacco or the smoke resulting from waterpipe and cigarette contains large amounts of polycyclic aromatic hydrocarbons (PAHs), which consumption...     

DRASTIC framework improvement using Stepwise Weight Assessment Ratio Analysis (SWARA) and combination of Genetic Algorithm and Entropy

Environmental Science and Pollution Research - Hybrid and integrated techniques can be used to investigate intrinsic uncertainties of the overlay and index groundwater vulnerability assessment...     

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.     

Indigenous knowledge about climate change and sustainability of nomadic livelihoods: understanding adaptability coping strategies

Environment, Development and Sustainability - Indigenous knowledge about climate change that makes adaptability necessary by coping strategies leads to the sustainability of nomadic livelihoods....     

Performance of activated carbon coated graphite bipolar electrodes on capacitive deionization method for salinity reduction

• Graphite bipolar electrodes act as an appropriate bed for the CDI process. • Activated carbon Coating improves the application of the electrodes. • CDI is an environmentally friendly method to apply for brackish water. • Initial concentration is the most important parameter in the CDI method. • CDI process in a batch-mode setup needs more development. This research investigates a capacitive deionization method for salinity reduction in a batch reactor as a new approach for desalination. Reductions of cost and energy compared with conventional desalination methods are the significant advantages of this approach. In this research, experiments were performed with a pair of graphite bipolar electrodes that were coated with a one-gram activated carbon solution. After completing preliminary tests, the impacts of four parameters on electrical conductivity reduction, including (1) the initial concentration of feed solution, (2) the duration of the tests, (3) the applied voltage, and (4) the pH of the solution, were examined. The results show that the maximum efficiency of electrical conductivity reduction in this laboratory-scale reactor is about 55%. Furthermore, the effects of the initial concentration of feed solution are more significant than the other parameters. Thus, using the capacitive deionization method for water desalination with low and moderate salt concentrations (i.e., brackish water) is proposed as an affordable method. Compared with conventional desalination methods, capacitive deionization is not only more efficient but also potentially more environmentally friendly.     

Assessment of temporal and spatial water quality in international Gomishan Lagoon,Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.     

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.     

Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province,Iran

Wind energy is a renewable energy resource that has increased in usage in most countries. Site selection for the establishment of large wind turbines, called wind farms, like any other engineering project, requires basic information and careful planning. This study assessed the possibility of establishing wind farms in Ardabil province in northwestern Iran by using a combination of analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) methods in a geographical information system (GIS) environment. DEMATEL was used to determine the criteria relationships. The weights of the criteria were determined using ANP and the overlaying process was done on GIS. Using 13 information layers in three main criteria including environmental, technical and economical, the land suitability map was produced and reclassified into 5 equally scored divisions from least suitable to most suitable areas. The results showed that about 6.68 % of the area of Ardabil province is most suitable for establishment of wind turbines. Sensitivity analysis shows that significant portions of these most suitable zones coincide with suitable divisions of the input layers. The efficiency and accuracy of the hybrid model (ANP-DEMATEL) was evaluated and the results were compared to the ANP model. The sensitivity analysis, map classification, and factor weights for the two methods showed satisfactory results for the ANP-DEMATEL model in wind power plant site selection.