Spectroscopic-based assessment of the content and geochemical behaviour of arsenic in a highly heterogeneous sulphide-rich mine waste dump

Soil pollution by arsenic is a serious environmental problem in many mining areas. Quick identification of the amount and extent of the pollution is an important basis for developing appropriate remediation strategies. In a case study, 55 soil samples were collected from a highly heterogeneous waste dump around the Sarcheshmeh copper mine, south east Iran. Samples’ visible and near-infrared (VNIR) reflectance spectra were measured, transformed to absorbance and then pre-processed using Savitzky–Golay first-derivative (FD) and Savitzky–Golay second-derivative (SD) transformation methods. The obtained spectra were then subjected to three regression models including principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) for predicting arsenic concentration. The best prediction accuracies were obtained by SVR and PLSR methods applied on first-derivative pre-processed spectra with R ² values of 0.81 and 0.69, respectively. It was found that VNIR spectroscopy is a successful method for predicting As concentration in contaminated soils of the dumpsites. Study of the prediction mechanism showed that the intercorrelation between arsenic and spectral features of soil including iron oxy/hydroxides and clay minerals was the major mechanism enabling the prediction of arsenic concentration. However, higher values of correlation coefficients at ~460, ~560 and ~590 nm suggested the internal association between arsenic and iron minerals as the more important mechanism for prediction. This conclusion supported previous speciation studies conducted in the same waste dump using improved correlation analysis and chemical sequential extraction method.     

Universe with Time Dependent Deceleration Parameter and Λ Term in General Relativity

A new class of exact solutions of Einstein's field equations with perfect fluid for an LRS Bianchi type-I spacetime is obtained by using a time dependent deceleration parameter. We have obtained a general solution of the field equations from which three models of the universe are derived: exponential, polynomial and sinusoidal form respectively. The behaviour of these models of the universe are also discussed in the frame of reference of recent supernovae Ia observations.     

Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River,U.S.

Monitoring sediment transport is essential for managing and maintaining rivers.Estimation of the sediment load in rivers is fundamental for the study of sediment movement,erosion,and flood control.In the current study,three machine learning models-multi-layer perceptron(MLP),multi-layer perceptron-stochastic gradient descent(MLP-SGD),and gradient boosted tree(GBT)-were utilized to estimate the suspended sediment load(SSL)at the St.Louis(SL)and Chester(CH)stations on the Mississippi River,U.S.Four evaluation criteria including the Correlation Coefficient(CC),Nash Sutcliffe Efficiency(NSE),Scatter Index(SI),and Willmott’s Index(WI)were utilized to evaluate the performance of the used models.A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations,but in the absence of current-day discharge data(Qt),a combination of input parameters including SSL_t-3,SSL_t-2,SSL_t-1,Q_t-3,Q_t-2,Q_t-1 can be used to estimate the SSL.The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983,SI of 0.254,WI of 0.991,and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933,SI of 0.576,WI of 0.961,and NSE of 0.867,respectively,at station SL.The results of MLP models were improved by SGD optimization.Therefore,the MLP-SGD method is recommended as the most accurate model for SSL estimation.     

High-resolution spatiotemporal distribution of precipitation in Iran: a comparative study with three global-precipitation datasets

High-resolution precipitation datasets are used for numerous applications. However, depending on the procedures for obtaining these products, such as number of observations, quality checking, error-correction procedures, and interpolation techniques, they include many uncertainties. Therefore, the accuracy of these products needs to be evaluated over different regions. In this study, the Iranian National Dataset (INDS), a new 1?×?1 km precipitation dataset based on precipitation data of 1,441 quality-controlled stations for the climatic period from 1961 to 2005, was constructed using the digital elevation model, correlation method, and Kriging interpolation procedure. Iran's annual precipitation values at grids and stations were extracted from Climatic Research Unit (CRU) CL 2.0, CRU TS 3.10.01, and WorldClim datasets, and differences between corresponding values in each of the three datasets and INDS were calculated and analyzed. The coefficient of determination (R ²) between the national network stations' data and the CRU CL 2.0, CRU TS 3.10.01, and WorldClim datasets were 0.50, 0.13, and 0.62, respectively. Moreover, R ² values between the grids of each dataset and INDS were 0.51, 0.40, and 0.60, respectively. To determine the global datasets' efficiency for displaying temporal patterns of precipitation, the monthly values gathered from them at 11 stations (as representative of Iran's various precipitation regimes) were compared with the real values at these stations. The results showed that in term of temporal patterns, the concurrences among the three global datasets and the INDS was more acceptable, especially in the case of CRU CL 2.0. In general, it is concluded that the global datasets could be deployed for the primary assessment of the annual precipitation distribution; however, for more precise studies, use of local data is highly recommended.     

Determining the groundwater potential recharge zone and karst springs catchment area: Saldoran region,western Iran

Assessing the groundwater recharge potential zone and differentiation of the spring catchment area are extremely important to effective management of groundwater systems and protection of water quality. The study area is located in the Saldoran karstic region, western Iran. It is characterized by a high rate of precipitation and recharge via highly permeable fractured karstic formations. Pire-Ghar, Sarabe-Babaheydar and Baghe-rostam are three major karstic springs which drain the Saldoran anticline. The mean discharge rate and electrical conductivity values for these springs were 3, 1.9 and 0.98 m³/s, and 475, 438 and 347 μS/cm, respectively. Geology, hydrogeology and geographical information system (GIS) methods were used to define the catchment areas of the major karstic springs and to map recharge zones in the Saldoran anticline. Seven major influencing factors on groundwater recharge rates (lithology, slope value and aspect, drainage, precipitation, fracture density and karstic domains) were integrated using GIS. Geology maps and field verification were used to determine the weights of factors. The final map was produced to reveal major zones of recharge potential. More than 80 % of the study area is terrain that has a recharge rate of 55–70 % (average 63 %). Evaluating the water budget of Saldoran Mountain showed that the total volume of karst water emerging from the Saldoran karst springs is equal to the total annual recharge on the anticline. Therefore, based on the geological and hydrogeological investigations, the catchment area of the mentioned karst springs includes the whole Saldoran anticline.     

Effects of climate change on wind energy production in Iran

Arabian Journal of Geosciences - This study explores the effect of future climate change on wind energy conversion in Iran, based on data from existing wind farms. In an effort to estimate and...     

Towards simulation of 3D nonlinear high-speed vessels motion

A numerical simulation algorithm based on the finite volume discretisation is presented for analyzing ship motions. The algorithm employs a fractional step method to deal with the coupling between the pressure and velocity fields. The free surface capturing is fulfilled by using a volume of fluid method in which the interface between the liquid (water) and gas (air) phases are computed by solving a scalar transport equation for the volume fraction of the liquid phase. The computed velocity field is employed to evaluate the acting forces and moments on the vessel. Using the strategy of boundary-fitted body-attached mesh and calculating all six degrees-of-freedom of motion in each time step, time history of ship motions including displacements, velocities and accelerations are evaluated.To verify the proposed algorithm, a series of verification tests are conducted. First, a two-dimensional asymmetrical wedge slamming is simulated as a simple type of a common case for high-speed vessels. Then, the steady-state forward motion of a high-speed planing catamaran is investigated. Results of both test cases show good agreement with experimental data. It is concluded that the proposed algorithm can be a promising strategy for both performance prediction and design of high-speed vessels.