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.     

The <Emphasis Type="Italic">AhRR</Emphasis>-c.565C&gt;G transversion may increase total antioxidant capacity levels of the seminal plasma in infertile men

The Aryl hydrocarbon receptor (AhR)-repressor (AhRR) is a regulator of the AhR pathway, which plays an important role in xenobiotic and reactive oxygen species (ROS) metabolism. Total antioxidant capacity (TAC) is a major factor in semen quality that protects sperm against ROS stress. Malondialdehyde (MDA) is the indicator of lipid peroxidation damage that is occurred due to ROSs. In this study, we determined and compared the MDA and TAC levels of infertile men’s semen and blood plasma regarding genotype groups of AhRR-c.565C>G transversion. Semen and blood samples of 123 infertile males were collected from the Fatemeh Zahra IVF Centre, Babol, Iran. TAC and MDA levels of seminal and blood plasma were measured by TBARS and FRAP methods, respectively. Cases were genotyped by the PCR-RFLP method. The frequency of c.565C>G genotypes was determined as CC (34.14%), CG (55.28%) and GG (10.58%). Mean levels of TAC μm/L and MDA nmol/mL in semen plasma of CC, CG and GG groups were (1365.7, 1.28), (1542.8, 1.51) and (1860.2, 0.82), respectively. Also, mean levels of TAC μm/L and MDA nmol/mL in blood plasma samples in CC, CG and GG genotypes were (806.14, 1.168), (727.1, 1.006) and (635.7, 0.83), respectively. Comparison of marker levels between genotypes revealed that the TAC level of semen plasma in the GG genotype was significantly higher than its level in the CC group (p < 0.05). Our findings showed that in seminal plasma of infertile men with the GG genotype of AhRR-c.565C>G transversion, the level of total antioxidant capacity is significantly higher in comparison with the CC genotype. Then, the G allele of AhRR-c.565C>G transversion may have a role in the increase in antioxidant capacity of seminal plasma.     

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 %.     

Statistical source identification of major and trace elements in groundwater downward the tailings dam of Miduk Copper Complex, Kerman, Iran

Identifying the possible sources of potential harmful metals in groundwater systems plays a crucial role in evaluating the potential risks to residents and local plant cover. An attempt was made to define the origin of Al, Cd, Cu, Fe, Mo, Ni, and Pb in groundwater using multivariate statistic approaches [principal component analysis (PCA), hierarchical cluster analysis], and tailings sequential extraction by the method of Tessier et al. The concentrations of studied elements were measured in 42 samples collected from 15 stations surrounding and downward the tailings dam of Miduk Copper Complex, central province of Kerman, Iran. According to the PCA results, confirmed by cluster dendrogram and metal content measurement of tailings sequential extracts, two components accounting for nearly 73% of the total variance, controlled the heavy metal variability and classified the possible source of groundwater contamination into two categories: (1) upper seepage which controls the variability of Cd, Cu, Fe, Ni, and Pb and (2) toe seepage of tailings dump affecting on Mo and Al concentration in downstream groundwater.     

Water pinch analysis for water and wastewater minimization in Tehran oil refinery considering three contaminants

This study aims to find an appropriate way to minimize water utility in the petrochemical and petroleum industries due to high rate of water consumption. For this purpose, Tehran oil refinery has been well studied. In this research, three key contaminants including suspended solid, hardness as well as COD have been considered to analyze the water network. In addition, the potential of water reuse was studied for all methods. These key contaminants once were analyzed separately as a single contaminant and the amount of required freshwater was calculated for them. In this stage, amount of freshwater was reduced to about 60.9 (17%), 203 (59.7%) and 143 m³/h (42.5%) in terms of suspended solids, hardness, and COD, respectively. Water minimization within operations for suspended solids is less than two others. Therefore, this is a limiting contaminant and can be selected as a key contaminant. In the next stage, three contaminants were analyzed two by two based on their mass transfer. Results show that, in the targeting for minimization based on the suspended solids and hardness, the amount of required water is reduced to 142.74 m³/h or 42%. This amount for suspended solids and COD is equal to 86.3 m³/h (26%) and for COD and hardness is 124 m³/h (37%). Analyzing the methods shows that the method based on the double contaminant gives more precise results rather than single contaminant.     

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.     

Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer,Iran

Geostatistical methods are one of the advanced techniques to interpolate groundwater quality data. Geostatistical interpolation techniques employ both the mathematical and the statistical properties of the measured points. Compiling the data distribution on spatial and temporal domain is of crucial importance in order to evaluate its quality and safety. The main purpose of this paper is to assess groundwater quality of Arak plain, Iran, by an unbiased interpolated method so called Kriging. Therefore, seven quality variables of Arak plain aquifer including TDS, SAR, EC, Na⁺, TH, Cl^?, and SO₄ ^2? have been analyzed, studied, and interpreted statistically and geostatistically. Utilized data in this study were collected from 97 water well samples in Arak plain, in 2012. After normalizing data, variogram as a geostatistical tool for defining spatial regression was calculated and experimental variograms have been plotted by GS⁺ software, then the best theoretical model was fitted to each variogram based on minimum RSS error. Cross validation was used to determine the accuracy of the estimated data. The uncertainty of the method could be well assessed via this method since the method not only gave the average error (around 0 in this study) but also gave the standard deviation of the estimations. Therefore, more than 3800 points were estimated by ordinary Kriging algorithm in places which have not been sampled. Finally, estimation maps of groundwater quality were prepared and map of estimation variance, EV, has been presented to assess the quality of estimation in each estimated point. Results showed that the Kriging method is more accurate than the traditional interpolation algorithms not honoring the spatial properties of the database.