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.     

Hydride generation coupled to microfunnel-assisted headspace liquid-phase microextraction for the determination of arsenic with UV–Vis spectrophotometry

In this research, a microfunnel-assisted headspace liquid-phase microextraction technique has been used in combination with hydride generation to determine arsenic (As) by UV–Vis spectrophotometry. The method is based on the reduction of As to arsine (AsH₃) in acidic media by sodium tetrahydroborate (NaBH₄) followed by its subsequent reaction with silver diethyldithiocarbamate (AgDDC) to give an absorbing complex at 510 nm. The complexing reagent (AgDDC) has been dissolved in a 1:1 (by the volume ratio) mixture of chloroform/chlorobenzene microdroplet and exposed to the generated gaseous arsine via a reversed microfunnel in the headspace of the sample solution. Several operating parameters affecting the performance of the method have been examined and optimized. Acetonitrile solvent has been added to the working samples as a sensitivity enhancement agent. Under the optimized operating conditions, the detection limit has been measured to be 0.2 ng mL^?1 (based on 3s_b/m criterion, n _b?=?8), and the calibration curve was linear in the range of 0.5–12 ng mL^?1. The relative standard deviation for eight replicate measurements was 1.9 %. Also, the effects of several potential interferences have been studied. The accuracy of the method was validated through the analysis of JR-1 geological standard reference material. The method has been successfully applied for the determination of arsenic in raw and spiked soft drink and water samples with the recoveries that ranged from 91 to 106 %.     

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.     

Transition index maps for urban growth simulation: application of artificial neural networks,weight of evidence and fuzzy multi-criteria evaluation

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.     

Green synthesis of 1,3-dihydrobenzimidazol-2-ones from aromatic diamines by microwave in a tetrabutylammonium bromide–ethanol molten salt paste

Environmental Chemistry Letters - The replacement of hazardous solvents by greener alternatives is a major goal in green chemistry. Benzimidazol-2-ones are heterocycles exhibiting a wide range of...