L. Akpan; A. Celestine Tse; F. dumbari Giadom; C. Iorfa Adamu
Abstract
In this study, the chemical composition of water and soils contiguous to two abandoned coal mines in southeastern Nigeria, was assessed to evaluate the impact of water flow from the mines ponds on the geoenvironment and potential for acid mine drainage (AMD). Parameters including the pH, anions and cations, ...
Read More
In this study, the chemical composition of water and soils contiguous to two abandoned coal mines in southeastern Nigeria, was assessed to evaluate the impact of water flow from the mines ponds on the geoenvironment and potential for acid mine drainage (AMD). Parameters including the pH, anions and cations, and the heavy metals were measured. These were used to evaluate contamination/pollution using hybrid factors including Pollution Load Index, factors, enrichment factors, pollution load index and index of geoaccumulation. The pH range of 3.4 to 5.9 classified the water as weakly to strongly acidic, typical of AMD. The SO42– ion, which indicates pollution by mine waters, showed moderate to high concentrations. Iron, zinc lead and copper were the most abundant heavy metals. Pollution Load Index values were greater than unity which show progressive deterioration in water and sediment quality. The Enrichment Factor values of up to 1 indicated enrichment through lithogenic and anthropogenic sources. The mine dumps serve as pools that can release toxic heavy metals into the water bodies by various processes of remobilization. Based on the lithology, mineralogy, chemical concentrations and environmental factors, the study has shown that there exists a potential for the generation of AMD. The heavy metals enriched mine flow, especially iron, empty into the nearby water bodies which serve as sources of municipal water supply. Consumption of untreated water over a prolonged period from these water sources may be detrimental to health. Remedial measure and continuous monitoring are recommended for good environmental stewardship.
K. Sultan Shah; I. Mithal Jiskani; N. Muhammad Shahani; H. Ur Rehman; N. Muhammad Khan; S. Hussain
Abstract
In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect ...
Read More
In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surface and underground mining in Pakistan. The original application of the ARIMA model provides that how the number of accidents and fatalities is influenced by the implementation of various approaches to promote an effective safety management system. The ARIMA model requires the data series of the predicted elements with a random pattern over time and produce an equation. After the model identification, it may forecast the future pattern of the events based on its existing and future values. In this research work, the accident data for the period of 2006-2019-is collected from Inspectorate of Mines and Minerals (Pakistan), Mine Workers Federation, and newspapers in order to evaluate the long-term forecast. The results obtained reveal that ARIMA (2, 1, 0) is a suitable model for both the mining accidents and the workers’ fatalities. The number of accidents and fatalities are forecasted from 2020 to 2025. The results obtained suggest that the policy-makers should take a systematic consideration by evaluating the possible risks associated with an increased number of accidents and fatalities, and develop a safe and effective working platform.
Mine Economic and Management
H. Dehghani
Abstract
Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent ...
Read More
Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to solve this problem, it is necessary to use the artificial algorithms, which have a good ability to predict the volatility of various phenomena. In the present work, the gene expression programming (GEP) method was used to predict the copper price volatility. In order to understand the ability of this method, the results obtained were compared with the other classical prediction methods. The results indicated that the GEP method was much better than the time series and multivariate regression methods in terms of the prediction accuracy.
