D2EHPA-impregnated alumina for adsorption of strontium ions in environmental samples

The alumina impregnated by the di-2-ethyl hexyl phosphoric acid was introduced to make more adsorption of strontium as well as to determine the optimal conditions. The influence of various parameters such as pH, equilibrium time, adsorbent mass, interfering ions, and various eluant agents for the desorption of the strontium ions, initial concentration, and temperature was investigated to find out the adsorption behavior of the adsorbent under different conditions. The adsorbent was characterized by the Fourier transform infrared spectroscopy. The experimental data were fitted on the two-parameter and three-parameter adsorption isotherm models. The Freundlich and Redlich–Peterson models have suitable fitting on the experimental data (R ² = 0.9307). The kinetic models of adsorption were analyzed by the pseudo-first- and pseudo-second-order models. The results have been indicated that the pseudo-second-order kinetic model is more appropriate than the others. Advantages of our method were simple operation, less time for preparation of adsorbent, rapid phase separation, and capability to combine with various detection techniques. The method has been utilized to extract and the recovery of strontium ions in environmental aqueous samples.     

Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.     

The effects of human activities and different land-use on trace element pollution in urban topsoil of Isfahan (Iran)

The excessive input of trace elements into urban soil has become one of the most important concerns in industrial and crowded cities all over the world. The contamination of urban soils can affect the health of people living in urban areas, and the surrounding ecosystems. Current study was conducted to assess the effects of human activities as well as different land-use on accumulation of trace elements in urban topsoil and also identify the potential risks to human health in Isfahan (Iran). A total of 95 topsoil samples were taken from different localities of Isfahan City and analyzed for Zn, Pb, Cu and Cr using the atomic absorption spectrophotometric method. Pollution index (PI) was calculated for each trace element to identify the rate of trace element accumulation with respect to the background values. Land-use map and geochemical maps were also created for evaluating of spatial distribution of pollution index and trace elements’ concentration in the studied area. Overlapping the concentrations’ map and land-use map revealed that the highest values of pollution index and trace elements’ concentration were located in central part of the city and highways with a great vehicle traffic load and also in the vicinity of industrial factories that increased potential health hazards to the local community. On the other hand, lowest values of trace elements were located in green-lands with strict vehicle traffic laws. These results indicated that different land-use and human activities have affected quality of urban topsoil of Isfahan resulting in great apprehensions regarding public health in crowded parts of the city.     

Hydrothermal Alteration Mapping in Northern Khur, Iran, Using ASTER Image Processing: a New Insight to the Type of Copper Mineralization

Eastern Iran has great potential for the discovery of different types of mineralization. The study area encompasses Tertiary magmatism in the northern Lut block located in northern Khur, South Khorasan, eastern Iran and is mostly covered by volcanic rocks, which are intruded by porphyritic subvolcanic intrusions in some places. Application of the spectral angle mapper (SAM) technique to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images detected sericitic, argillic, and propylitic alterations, silicification, and secondary iron oxides. The alteration is linear and associated within vein-type mineralization. Twelve prospective areas are selected for detailed exploration and based on our processing results, in addition to NW-SE faults, which are associated with Cu mineralization indications, NE-SW faults are also shown to be important. Based on the presence of subvolcanic rocks and numerous Cu ± Pb-Zn vein-type mineralizations, extensive alteration, high anomaly of Cu and Zn (up to 100 ppm), the age (43.6 to 31.4 Ma) and the initial 87Sr/86Sr ratio (0.7047 to 0.7065) of the igneous rocks, and the metallogenic epoch of the Lut block (middle Eocene-lower Oligocene) for the formation of porphyry Cu and epithermal deposits, the studied area shows great potential for porphyry copper deposits.     

Two-dimensional numerical finite volume modeling of processes controlling distribution and natural attenuation of BTX in the saturated zone of a simulated semi-confined aquifer

This paper presents a two-dimensional finite volume model to predict multi-species reactive transport processes in the saturated zone of a simulated semi-confined aquifer. A multipurpose commercial software called PHOENICS was used to solve model equations numerically. Capability of the present model was first confirmed using experimental data and the results obtained by a published one-dimensional finite element reactive transport model by other researchers taking different scenarios into consideration. The model was then expanded to a two-dimensional case to simulate reactive transport of BTX compounds with discontinuous source in the saturated zone of the groundwater flow system. In addition to the physical transport processes, the two-dimensional model also incorporates linear and nonlinear adsorption isotherms, first order and Monod kinetics. The two-dimensional model considers both static and dynamics modes into account. The results show that considering chemical reactions during reactive transport of contaminants could successfully predict the contaminated zone. The results of such studies can be used for monitoring of contaminated areas, designing methods to control pollution transport, and minimize its harmful effects on aquifer systems.     

Statistical fusion of two-scale images of porous media

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.     

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Predicting the geometry of channels and alluvial rivers is of primary importance in river engineering science. Appropriately designing channels and predicting stable river cross‐sections can decrease costs and prevent the destruction of installations and agricultural land by rivers. Consequently, researchers have applied different empirical and regression methods to achieve relations for predicting stable channel and river geometry. In this study, Group Method of Data Handling ]GMDH) models are used to predict three geometric variables of stable channels, namely width (w), depth (h) and slope (s). The effect of different input parameters, such discharge (Q), median grain size (d₅₀) and the Shields parameter (τ^*) on the GMDH models is assessed with regard to predicting stable channel geometry. The results indicate that the GMDH model with mean absolute percentage error (MAPE) of 5.53%, 4.05% and 4.89% for channel width, depth and slope prediction respectively, exhibits good accuracy. Moreover, a comparison of the GMDH models with previous theoretical equations (based on regression analysis) indicates the superiority of GMDH model performance, with error reductions of one‐fifth, one‐eighth and one‐sixth compared with the regression equations for channel width, depth and slope prediction, respectively. Copyright © 2016 John Wiley & Sons, Ltd.     

Spatiotemporal trends of aridity index in arid and semi-arid regions of Iran

The spatiotemporal trends of aridity index in the arid and semi-arid regions of Iran in 1966–2005 were investigated using the Mann–Kendall test and Theil–Sen’s slope estimator. The results of the analysis showed negative trends in annual aridity index at 55 % of the stations, while just one site had a statistically significant (α?=?0.1) negative trend. Furthermore, the positive trends in the annual aridity index series were significant at the 95 % confidence level at Bushehr and Isfahan stations. The significant negative trend in the annual aridity index was obtained over Mashhad at the rate of ?0.004. In the seasonal series, the negative trends in the spring and winter aridity index were larger compared with those in the other seasonal series. A noticeable decrease in the winter aridity index series was observed mostly in the southeast of the study area. In the summer and autumn aridity index, two significant positive trends were found.     

Characterization of alkanes, hopanes, and polycyclic aromatic hydrocarbons (PAHs) in tar-balls collected from the East Coast of Peninsular Malaysia

The East Coast of Peninsular Malaysia faces the South China Sea and is vulnerable to oil pollution because of intense petroleum production activities in the area. The South China Sea is also a favored route for supertankers carrying crude oil to the Far East. Consequently, oil spills can occur, causing pollution and contamination in the surrounding areas. Residual oil spills stranded on coastal beaches usually end up as tar-balls. Elucidating the sources of tar-balls using a molecular marker approach is essential in assessing environmental impacts and perhaps settling legal liabilities for affected parties. This study utilizes a multimodal molecular marker approach through the use of diagnostic ratios of alkanes, hopanes, and polycyclic aromatic hydrocarbons (PAHs) to determine the source, distribution and weathering of tar-balls. Hopane ratios (e.g., C29/C30, and summation C31-C35/C30 ratios) were used to identify the sources of tar-balls. The weathering effects were distinguished by using alkanes, namely the unresolved complex mixture (UCM) and low molecular weight/high molecular weight (L/H) ratios. Similarly, PAHs were also used for the determination of weathering processes undergone by the tar-balls. This multimodal molecular marker gave a very strong indication of the sources of tar-balls in this study. For example, 16 out of 17 samples originated from South East Asian Crude Oil (SEACO) with one sample from Merang, Terengganu originating from North Sea Oil (Troll). The TRME-2 sample may have come from a supertanker's ballast water discharge. The second possibility is that the tar-ball may have been transported via oceanographic currents. All 'weathered' sample characterizations were based on the presence of UCM and other ratios. The multimodal molecular marker approach applied in this study has enabled us to partially understand the transport behavior of tar-balls in the marine environment and has revealed insights into the weathering process of tar-balls.     

Evaluation of floodplain variability considering impacts of climate change

In this study, an approach is presented for handling hydraulic uncertainties in the prediction of floodplain. Different factors affect river flood characteristics. Furthermore, the high changeability of flooding conditions leads to high variability of the inundation. River morphology is one of the most effective factors in river flood characteristics. This factor is influenced by sedimentation and erosion in the river cross sections, which affects the discharge variation. The depth and the width of the river cross section lead to an increase or decrease in the river flow path. This results in changes in the extent of the floodplain based on the generated rainfall. The inundated region boundaries are determined by utilizing the mean first‐order second‐moment analysis. The proposed method is applied to the Kajoo River in the south‐eastern part of Iran. Determination of floodplain uncertainty is a damage‐reduction policy in this region. Also, it is useful to prepare the necessary activities for overcoming the flood hazards. Climate change is the second effective factor on the floodplain uncertainties. Climate change affects the magnitude, extent and depth of inundation and it may intensify the flood problem. Therefore, the future rainfall pattern of the study area under climate change is simulated to evaluate its impacts on the river flow characteristic. Subsequently, a hydraulic routing model is used to determine floodplain. Finally, the copula function is used to estimate the joint probability of the changes in the inundation area due to changes in river morphology and the rainfall changes due to impacts of climate change. Results show that the uncertainties of the extent of floodplain are affected by climate change and river morphology, leading to noticeable changes in the magnitude and frequency of floods. Evaluating these impacts and estimating corresponding river discharges will help in the study of river dynamics, and will also contribute towards devising effective mitigation and management strategies. Copyright © 2010 John Wiley & Sons, Ltd.