Prediction of copper content in waste dump of Sarcheshmeh copper mine using visible and near-infrared reflectance spectroscopy

The pore structure characteristics of soil are closely related to soil engineering properties. For saline soil distributed in seasonally frozen areas, existing studies have focused on the influence of freeze–thaw cycles on pore structure, while the influence of soluble salt in the soil is not well understood. This study aims to explore the influence of salt content and salt type on the pore structure of freeze-thawed soil. Soil samples with different salt contents (0–2%) and types (bicarbonate salt and sulfate salt) were subjected to 10 freeze–thaw tests, and their pore size distributions (PSDs) were obtained by mercury intrusion porosimetry tests. In addition, the PSDs were quantitatively analyzed by fractal theory. For both salts, the PSDs of the tested soil samples were bimodal after the freeze–thaw cycles, and the porosity of saline soil samples increased with increasing salt content overall. However, the contents of various types of pores in soil samples with two salt types were quite different. The variation in bicarbonate salt content mainly affected the mesopore and macropore contents in the soil samples, and their change trends were opposite to each other. For soil samples with sulfate salt, the porosity and macropore content increased significantly when the salt content exceeded 1%. In addition, the pore structures in saline soil presented fractal characteristics after the freeze–thaw cycles, and the fractal dimension was positively correlated with macropore content. This study may provide references for understanding the engineering properties of saline soil in seasonally frozen areas at the microscale.     

Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine,Southeast Iran

Water is one of the basic and fundamental requirements for the survival of human beings. Mining of the sulphide mines usually produce a significant amount of acid mine drainage (AMD) contributing to huge amounts of chemical components and heavy metals in the receiving waters. Prediction of the heavy metals in the AMD is important in developing any appropriate remediation strategy. This paper attempts to predict heavy metals (Cu, Fe, Mn, Zn) from the AMD using backpropagation neural network (BPNN), general regression neural network (GRNN) and multiple linear regression (MLR), by taking pH, sulphate (SO₄) and magnesium (Mg) concentrations in the AMD into account in Shur River, Sarcheshmeh porphyry copper deposit, southeast Iran. The comparison between the predicted concentrations and the measured data resulted in the correlation coefficients, R, 0.92, 0.22, 0.92 and 0.92 for Cu, Fe, Mn and Zn ions using BPNN method. Moreover, the R values were 0.89, 0.37, 0.9 and 0.91 for Cu, Fe, Mn, and Zn taking the GRNN method into consideration. However, the correlation coefficients were low for the results predicted by MLR method (0.83, 0.14, 0.9 and 0.85 for Cu, Fe, Mn and Zn ions, respectively). The results further indicate that the ANN can be used as a viable method to rapidly and cost-effectively predict heavy metals in the AMD. The results obtained from this paper can be considered as an easy and cost-effective method to monitor groundwater and surface water affected by AMD.     

R-mod factor analysis, a popular multivariate statistical technique to evaluate water quality in Khaf-Sangan basin, Mashhad, Northeast of Iran

Water quality data are required in order to compare chemical water analyses and identify water masses. R-mode factor analysis, a popular multivariate statistical tool, has been effectively used for groundwater quality studies. In this paper, the R-mode factor analysis was applied to 50 groundwater samples collected from pumping wells in the Sangan-Khaf basin which is located in the southeast of Mashhad, northeast Iran. The groundwater samples were analysed for chemical parameters. The factor analysis was then performed on the chemical data set. It can be suggested that four factors in R-mode analysis explain more than 94.31% of the total variance. The contribution of each factor at sample points, factor score, was calculated. The spatial distribution of the factor scores for each factor was mapped separately. Since the Sangan iron mine south of the study area probably affects groundwater aquifer, therefore, such studies can be used to manage the groundwater quality in the study area.     

Anisotropy and bedding effects on the hydro geological regime in a confined aquifer to design an appropriate dewatering system

Prediction of groundwater inflow into mining excavations is very important in order to design an effective dewatering system to keep the mine workings dry and create prolonged cone of depression. The effects of anisotropy ratio and bedding on the hydraulic head and drawdown curves of a dewatering test carried out in a fully penetrating well in a confined aquifer have been investigated. An existing numerical finite element model has been used to perform the simulations. The results of the numerical model are compared to those from analytical Jacob and Lohman solution for estimating hydraulic heads and drawdown curves. It was found that the anisotropy ratio and bedding should not have a significant effect on drawdown and the quantity of inflow into a confined aquifer. It was further found that taking the simultaneous effects of anisotropy and bedding into account reduces the differences in the results of analytical and numerical methods. Comparison of the field data and model predictions showed that, the modelling results for a three layer anisotropic aquifer fit well to the field data than those results obtained for a single layer aquifer and the relative error decreased from 4.81 % to 2.98 %.     

The weight of interaction of mining activities: groundwater in environmental impact assessment using fuzzy analytical hierarchy process (FAHP)

Mining operations threaten the environment if the monitoring and controlling steps are not implemented completely. One of the important methods for control of the environmental situation in the mining district is the environmental impact assessment (EIA) method, which is performed by matrix calculations. In this method, the environmental problem is broken into several parts as the Impacting Factors that is evaluated their influences on Environmental Components by the mathematical calculations. For these calculations, the weight of each Impacting Factor must be evaluated by using comprehensive scenarios that are involved all the predicable environmental issues. Based on literature, it has not been organized a comprehensive scenario about “Interference with groundwater” as an Impacting Factor yet. By consideration of the importance degree of groundwater and its role in supply the drinking water resource, it is felt to demand for an organization a developed scenario in relation with groundwater pollution in mining district. Therefore, the main aim of this study is developing a new scenario to weight the “Interference with groundwater” in EIA matrix. For this purpose, the 8 criteria and 63 subcriteria are defined and their weights are determined using the Fuzzy Analytic Hierarchy Process. The proposed scenario can be successfully evaluated the weight of “Interference with groundwater” Impacting Factor more exactly than the former one, because it considers 8 criteria and 63 subcriteria instead of 2 criteria in the former scenario. Finally, the application of proposed scenario is illustrated by an imaginary ideal case study. Such studies can be used by mining engineers and planners to design an appropriate environmental plan for the mining districts.     

Using robust staged R-mode factor analysis and logistic function to identify probable Cu-mineralization zones in Khusf 1:100,000 sheets,east of Iran

Factor analysis method is a multivariate analysis technique that is widely used for the interpretation of stream sediment geochemical data. The purpose of factor analysis is describing the changes in a set of multi-element geochemical data by reducing the dimension of the data and variables to a number of factors that can present the hidden association between elements. Differences in mobility, physical, and chemical properties of the elements and the nature of the factor analysis method in which the matrix of all data is used cause paragenes elements not to be found on the output of factor analysis. In this research, to improve the output of factor analysis for deriving the best reagent multi-element mineralization, robust staged factor analysis method was used according to the close nature of geochemical data in order to identify the Cu-mineralization potential in Khusf 1:100,000 sheets located at the east of Iran. The robust staged factor analysis enhances the recognition of anomalous geochemical signatures and increases geochemical anomaly intensity and the percentage of the total explained variability of data. As indicated by the results of the study, few anomalous zones have been found in the study area. The observation of chalcopyrite and malachite mineralization in andesite and dacite–andesite rocks in a region during the field study confirms the effectiveness of the robust SFA technique. Such studies can be used by mine engineers and geologists for designing an optimum grid exploration on the next exploration steps.