Exploration
Shoopala Uugulu; Nazlene Poulton; Akaha Tse; Martin Harris; Taiwo Bolaji
Abstract
The long mining history in Namibia has resulted in numerous abandoned mining sites scattered throughout the country. Past research around the Klein Aub abandoned Copper mine highlighted environmental concerns related to past mining. Considering that residents of Klein Aub depend solely on groundwater ...
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The long mining history in Namibia has resulted in numerous abandoned mining sites scattered throughout the country. Past research around the Klein Aub abandoned Copper mine highlighted environmental concerns related to past mining. Considering that residents of Klein Aub depend solely on groundwater for their domestic, irrigation and other uses, there is a need to thoroughly investigate groundwater quality in the area to ascertain the extent of the contamination. This study characterises groundwater quality using a comprehensive quality assessment approach. On-site parameters reveal that pH ranges between 6.82-7.8, electrical conductivity ranges between 678 - 2270 μS/cm, and dissolved oxygen ranges between 1.4 -5.77 mg/L. With the exception of two samples, the onsite parameters indicate that water is of excellent quality according to the Namibian guidelines. The stable isotopic composition ranges from -7.26 to -5.82‰ and -45.1 to -35.9‰ for δ18O and δ2H, respectively. The groundwater plots on and above the Global Meteoric Water Line, and the best-fit line is characterised by a slope of 4.9, implying the evaporation effect. Hydrochemical analyses indicate bicarbonate and chloride as dominant anions, while calcium and sodium are dominant cations, indicating groundwater dissolving halite and mixing with water from a recharge zone. The Heavy Metal Pollution Index suggested that the water samples are free from heavy metal pollution. The Heavy Metal Evaluation Index clustered around 3, implying that heavy metals moderately affect groundwater. The groundwater quality is suitable for irrigation purposes. The findings offer valuable insights into the area's hydrochemistry and highlight potential environmental risks; hence, groundwater monitoring is recommended.
Environment
Azadeh Agah; Faramarz Doulati Ardejani
Abstract
This study aimed to develop a model to illustrate the migration of petroleum hydrocarbons that penetrate the underground environment due to leakage from storage tanks located below the surface.The transport model for non-aqueous phase liquids was integrated with contaminant transport models in two dimensions ...
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This study aimed to develop a model to illustrate the migration of petroleum hydrocarbons that penetrate the underground environment due to leakage from storage tanks located below the surface.The transport model for non-aqueous phase liquids was integrated with contaminant transport models in two dimensions to forecast the contamination of groundwater and soil-gas resulting from the migration of light non-aqueous phase liquids on the water surface. The finite volume method was employed to obtain numerical solutions. The findings indicated that evaporation significantly influences the migration of non-aqueous phase liquids. The soluble plume's production and movement were impacted by the geological features of the location and the existence of the free phase plume. Comparing the model predictions and the results from the field studies for the thickness of non-aqueous phase liquids plume over water indicates a good agreement between the results of the two methods with an average error of less than 5%. The maximum thickness of non-aqueous phase liquids plume between 7 and 7.5 meters was obtained at a distance of 2250 meters from the beginning of the investigated profile. Although 36 years have passed since the leakage occurred, a significant amount of the spilled mass still remained in the non-aqueous phase liquids. The prolonged migration of non-aqueous phase liquids over this time period has led to the contamination of groundwater and the accumulation of significant quantities of contaminated soil.
Exploration
shirin Jahanmiri; Ali Aalianvari; Malihehe Abbaszadeh
Abstract
Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and ...
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Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and subsequent operational phases. Groundwater inflows, often perceived as rare geological hazards, can induce instability in the surrounding rock formations, leading to severe consequences such as injuries, fatalities, and substantial financial expenditures. The primary objective of this research is to explore the application of machine learning techniques to identify the most accurate method of forecasting tunnel water seepage. The prediction of water loss into the tunnel during the forecasting phase employed a tree equation based on gene expression programming (GEP). These results were compared with those obtained from a hybrid model comprising particle swarm optimization (PSO) and artificial neural networks (ANN). The Whale Optimization Algorithm (WOA) was selected and developed during the optimization phase. Upon contrasting the aforementioned methods, the Whale Optimization Algorithm demonstrated superior performance, precisely forecasting the volume of water lost into the tunnel with a correlation coefficient of 0.99. This underscores the effectiveness of advanced optimization techniques in enhancing the accuracy of groundwater inflow predictions and mitigating potential risks associated with tunneling activities.
V. Mwango Bowa; W. Samiselo; E. Manda; Y. Lei; W. Zhou; A. Shane; S. Chinyanta
Abstract
The influence of variable groundwater has been overlooked in the available literature. Yet, wedge failure induced by variable groundwater is still commonly experienced in sedimentary rock formation in many commercial dams, highways, and surface mine slopes around the world. In this article, a robust ...
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The influence of variable groundwater has been overlooked in the available literature. Yet, wedge failure induced by variable groundwater is still commonly experienced in sedimentary rock formation in many commercial dams, highways, and surface mine slopes around the world. In this article, a robust analytical model for stability analysis of the rock slopes subjected to wedge slope failure induced by variable groundwater is presented. This involves modifying the existing analytical model for estimating the safety factor of the rock slope subjected to wedge failure by incorporating the effects of variable groundwater. The proposed analytical model is validated using a numerical simulation model using the Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D) software. Furthermore, a real wedge slope instability at the Chingola Open-Pit Mine (COP F&D) induced by the presence of variable groundwater case history is studied in order to illustrate the effectiveness of the presented analytical model. The investigation results indicate that the presence of variable groundwater has a direct impact on the computed factor of safety of the rock slope subjected to wedge failure. The results obtained entail that the presented analytical model can provide a robust analytical model for the stability analyses of the rock slope subjected to wedge failure considering the presence of variable groundwater.
Z. Bayatzadeh Fard; F. Ghadimi; H. Fattahi
Abstract
Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization ...
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Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization (ANN-BBO), and multi-output adaptive neural fuzzy inference system (MANFIS) to estimate the distribution of heavy metals in groundwater of Lakan lead-zinc mine is demonstrated.For this purpose, the contamination groundwater resources were determined using the existing groundwater quality monitoring data, and several models were trained and tested using the collected data to determine the optimum model that used three inputs and four outputs. A comparison between the predicted and measured data indicated that the MANFIS model had the mostpotential to estimate the distribution of heavy metals in groundwater with a high degree of accuracy and robustness.
M. Sakizadeh; R. Mirzaei
Abstract
The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan Province, Iran. For this purpose, 17 groundwater quality variables including EC, TDS, turbidity, pH, total ...
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The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan Province, Iran. For this purpose, 17 groundwater quality variables including EC, TDS, turbidity, pH, total hardness, Ca, Mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, Fe, Mn, Cu, and Cr(VI) from 41 wells and springs were used during an eight-year time period (2006 to 2013). The cluster analysis was used, leading to a dendrogram that differentiated two distinct groups. The factor analysis extracted eight factors accumulatively, accounting for 90.97% of the total variance. Thus the variations in 17 variables could be covered by just eight factors. K-NN and SVMs were applied for the classification of the aquifer under study. The results of SVMs indicated that the best performed model was related to an exponent of degree one with an accuracy of 94% for the test data set, in which the sensitivity and specificity were 1.00 and 0.87, respectively. In addition, there was no significant difference among the results of different kernels, indicating that an acceptable result can be achieved by selecting the optimum parameters for a kernel. The results of K-NN showed roughly a lower efficiency compared with those of SVMs, where the sensitivity and specificity was reduced to 0.90 and 0.88, respectively, although the accuracy of the model was 93%. A sensitivity analysis was performed on the groundwater quality variables, suggesting that calcium next to nitrate were the most influential parameters in the classification of this aquifer.
C. Bempah; H-J. Voigt; A. Ewusi
Abstract
The focus of this research work is on the determination of the impact of mining on the groundwater quality in the historical mining region of SW Ashanti region in Ghana. This work describes the characteristics of the groundwater chemistry and pollution of the aquifer in the gold-ore bearing formation, ...
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The focus of this research work is on the determination of the impact of mining on the groundwater quality in the historical mining region of SW Ashanti region in Ghana. This work describes the characteristics of the groundwater chemistry and pollution of the aquifer in the gold-ore bearing formation, which is highly weathered and fractured. The fractures control the permeability and depth of the groundwater within the studied area. The concentrations of the major ions and trace elements (As, Fe, Cu, Mn, and Zn) present are determined in 63 groundwater wells at dry and wet seasons. The results obtained showed that the concentrations of these ions and elements were below the World Health Organization (WHO) guideline values for drinking water. However, concentrations of the As and Fe ions were very high above the guideline values. The wells with high As and Fe concentration levels might be located at an apparent rock fractured zone that extends to a nearby mine. Such fractured zones allow groundwater to move more rapidly away from a mine, creating more severe mine-drainage pollution in their paths. The results obtained from this study suggested a possible risk to the population of the studied area, given the toxicities of the As and Fe ions, and the fact that for many people living in the studied area, groundwater is a main source of their water supply.
