Moho depth determination beneath the Zagros Mountains from 3D inversion of gravity data

Due to its geological and economic importance, the Zagros Mountains have been investigated by many researchers during the last decades. Nevertheless, in spite of all the studies conducted on the region, there are still some controversial problems concerning the structure of the Zagros Mountains, including crustal depths, demanding more insights into understanding the crustal constraints of the region. Accordingly, we have conducted a gravity study to determine Moho depth map of the Zagros Mountains region, including its major structural domains from the coastal plain of the Persian Gulf to central Iran. The employed data are the densest and most accurate terrestrial gravity data set observed until now with the precision of 5 μGal and resolution of 5 arc-minute by 5 arc-minute. To image Moho depth variations, gravity inversion software GROWTH2.0 is used, proposing the possibility to model stratified structures by means of a semi-objective exploratory 3D inversion approach. The obtained results reveal the crustal thickness of ~?30–35 km underneath the southwestern most Zagros Fold-Thrust Belt increasing northeastward to 48 km. The maximum Moho depth is estimated ~?62 km below the Zagros Mountains belt along the Main Zagros Thrust. Northeast of the study area, an average crustal thickness of 46 km is computed beneath Urumieh–Dokhtar magmatic arc and central Iran.     

Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels

Collapse settlement is one of the main geotechnical hazards, which should be controlled during first impoundment stage in embankment dams. Imposing large deformations and significant damages to dams makes it an important phenomenon, which should be checked during design phases. Also, existence of a variety of contributing parameters in this phenomenon makes it difficult and complicated to well predict the potential of collapse settlement. Thus, artificial neural networks, which are commonly applied by majority of geotechnical engineers in predicting various perplexing problems, can be efficiently used to calculate the value of collapse settlement. In this paper, feedforward backpropagation neural networks are considered. And three-layered FFBPNNs with the architectures of 4–6–2 and 4–9–2 accurately predicted the coefficient of stress release and collapse settlement value, respectively. These networks were trained using 180 datasets gained from large-scale direct shear test, which were carried out on gravel materials. High correlation between measured and predicted values for both collapse settlement and coefficient of stress release can be easily understood from the coefficient of determination and root mean square error. It is shown that sand content and normal stress applied to the specimens, respectively, are most effective parameters on the collapse settlement value and coefficient of stress release.     

Effect of treated municipal wastewater on bean growth, soil chemical properties, and chemical fractions of zinc and copper

The current study was carried out in order to investigate the short-term effect of different dilutions of wastewater on soil chemical properties, chemical fractions of zinc (Zn) and copper (Cu), and to assess the chemical buildup of heavy metal on two bean species. The experiment was performed as factorial based on completely randomized design with different dilutions of wastewater on two bean cultivars in two soil textures. The treatments consisted of irrigation with treated wastewater over all growing season, irrigation with wastewater and freshwater in equal proportions, and irrigation with well water only as control. The result showed that soil parameters are significantly affected by application of wastewater irrigation. Irrigation with wastewater increased the concentrations of organic matter, electrical conductivity, N, K, P, Fe, Cu, Zn, Mn, and Ni in soils compared to the control treatment. However, their values were all below international standards. Application of wastewater decreased soil pH and calcium carbonate equivalent. Plant tissue analysis showed increases in N, P, K, Zn, Cu, Zn, and Mn concentrations in grain and frond of beans in wastewater treatment as compared to the control. The concentrations of all elements in plants were lower than the toxic threshold. Chemical fractionations of Zn and Cu indicated that chemical forms of these metals were affected by irrigation with wastewater. Irrigating with wastewater resulted in the movement of Zn from the labile fractions towards the nonlabile fractions. In turn, mobility factor of Cu increased with application of wastewater. Maximum fresh and dry yields of beans were obtained from wastewater treatment compared to the control treatment. This study indicated that wastewater irrigation improves soil properties, plant growth, and yield without any contamination in soil and toxicity in plants.     

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)

Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteorological drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R ²) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet.     

Modeling of forest soil and litter health using disturbance and landscape heterogeneity indicators in northern Iran

This paper focuses on the indicators of soil and litter health, disturbance, and landscape heterogeneity as a tool for prediction of ecosystem sustainability in the northern forests of Iran. The study area was divided into spatial homogenous sites using slope, aspect, and soil humidity classes. Then a range of sites along the disturbance gradient was selected for sampling. Chemical and physical indicators of soil and litter health were measured at random points within these sites. Structural equation modeling (SEM) was applied to link six constructs of landscape heterogeneity, three constructs of disturbance (harvest, livestock, and human accessibility), and soil and litter health. The results showed that with decreasing accessibility, the total N and organic matter content of soil increased and effective bulk density decreased. Harvesting activities increased soil organic matter. Therefore, it is concluded that disturbances through harvesting and accessibility inversely affect the soil health. Unexpectedly, it was found that the litter total C and C:N ratio improved with an increase in the harvest and accessibility disturbances, whereas litter bulk density decreased. Investigation of tree composition revealed that in the climax communities, which are normally affected more by harvesting activities, some species like Fagus orientalis Lipsky with low decomposition rate are dominant. The research results showed that changes in disturbance intensity are reflected in litter and soil indicators, whereas the SEM indicated that landscape heterogeneity has a moderator effect on the disturbance to both litter and soil paths.     

Removal of cadmium and humic acid from aqueous solutions using surface modified nanozeolite A

The sorption of cadmium and humic acids from aqueous solutions using surface-modified nanozeolite A has been investigated under various examination conditions. The morphology of untreated and treated nanozeolite was studied under scanning electron microscope and transmission electron microscope. Isotherms of cadmium adsorption onto surface-modified nanozeolite A were studied at different pH, solid to liquid ratio, adsorbate concentration and interaction time. Kinetic and equilibrium studies were conducted and the equilibrium data have been analyzed using Langmuir and Freundlich isotherm models. The study revealed that experimental results were in agreement with the Freundlich model. The Langmuir monolayer adsorption capacity was found to be 1666.67 g cadmium and 6.75 g humic acid per gram of modified nanozeolite A, which is higher than that of reported value for other zeolites. The sorption ability was enhanced by surface modification and reduction in size and enabled the zeolite to adsorb cadmium. The adsorption of cadmium and humic acid on nanozeolite was found to be the highest at pH 6 and 3, respectively. Results showed that solid to liquid ratio and pH are the most important factors for cadmium and humic acid removal, respectively. Effect of competitive ions was studied and results showed that there is no competition between cadmium and humic acid sorption and presence of these ions.     

Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971–2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015–2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

     

Ocean Chemistry Revealed by Mineralogical and Geochemical Evidence at the Permian–Triassic Mass Extinction, Offshore the Persian Gulf, Iran

The Upper Permian Dalan Formation and the Lower Triassic Kangan Formation in the Persian Gulf area are mainly composed of shallow marine facies limestone and dolomite. Two subsurface-cored intervals were investigated in order to understand the original mineralogy and paleoceanic conditions. The decreasing trend of Sr concentration in these deposits shows that aragonite was precipitated during the Late Permian and then gradually changed to calcite toward the Permian–Triassic boundary (PTB). The dissolution rate of aragonite decreased from 60 m below the PTB toward the boundary, with the only exception at 10 m below the Permian-Triassic Boundary (PTB) due to the Permian–Triassic unconformity in this region. The increasing trend of Mg/Ca ratio in a global scale at the end-Permian time shows that the interpreted variation of mineralogy does not result from the change of this ratio. The increasing pCO2 and decreasing pH are considered to be the main controlling factors. The increase of Ca2+ at the end-Permian time due to the input of meteoric waters is too little to fully compensate this effect. A local maximum of the Si content just at the PTB confirms the input of runoff waters.