Saeed Saadat
Abstract
In this work, the results of nearly 1400 stream sediment sample analysis are processed to better understand environmental pollution caused by mining activities in Eastern Iran. The stream sediment samples are analyzed for As, Sb, Fe, Cr, Ni, Co, Cu, Zn, Pb, Sr, and Hg. The mean concentration of these ...
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In this work, the results of nearly 1400 stream sediment sample analysis are processed to better understand environmental pollution caused by mining activities in Eastern Iran. The stream sediment samples are analyzed for As, Sb, Fe, Cr, Ni, Co, Cu, Zn, Pb, Sr, and Hg. The mean concentration of these elements follows the decreasing order of Fe > Sr > Zn > Cr > Cu > Ni > Co > Pb > As > Sb > Hg. Based on the assessment of pollution, extremely severe enrichment factor Co (EF > 25), and high enrichment of Sb, Hg, Cr, and Sr (EF > 10) are detected. Specifically, Cr and Ni in southern stream sediments show significantly elevated concentrations compared to the others. The range of the contamination factor varies from CF < 1 to CF > 6 for most elements. Geo-accumulation index shows high contamination levels by Cr and Co and high to severe contamination by Sb. The risk indices are low for all elements except for As and Co in the eastern part of the studied area. Principal component analysis, Spearman correlation coefficient, and cluster analysis are used to demonstrate similarities and differences between the elements. Pollution indices show that contaminations in some samples are the consequence of gold mineralization. The high correlation of Cu, Zn, and Sb is due to the sulfide mineralization of gold. The high correlation of Cr and Ni corresponds to ultramafic rocks and ophiolitic series. This study focuses on the impact of mining activities, even at early stages on the dispersion of some heavy metals in stream sediments. Based on the results presented here, while most contamination in the target area is rooted in geochemical and mineralization processes, mining activity also contributes to soil pollution for certain elements such as Cu and Zn. The most affected stream sediments are those within the vicinity of mining areas and attention should be paid to potential risks to the environment particularly during gold mining activities.
Afrodita Zendelska; Adrijana Trajanova; Mirjana Golomeova; Blagoj Golomeov; Dejan Mirakovski; Nikolinka Doneva; Marija Hadzi-Nikolova
Abstract
The treatment of acid mine drainages is usually based on two basic technologies, active and passive treatment technologies. Whichever acid mine drainage (AMD) treatment method is employed, a neutralizing procedure that raises the water's pH over 7.0 using alkaline agents is required prior to discharge. ...
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The treatment of acid mine drainages is usually based on two basic technologies, active and passive treatment technologies. Whichever acid mine drainage (AMD) treatment method is employed, a neutralizing procedure that raises the water's pH over 7.0 using alkaline agents is required prior to discharge. A comparison of eight different agents (BaCO3, Na2CO3, NaOH, KOH, K2CO3, MgO, CaCO3, and Ba(OH)2) was performed in order to choose the most effective neutralizing agent for acid mine drainage treatment. The experiments were performed using a multi-component synthetic aqueous solution with an initial concentration of 10 mg/L of the Cu, Mn, Zn, Fe, and Pb ions and an initial pH value of 1.9. According to the research, the most effective neutralizing agent for the removal of heavy metals from a multi-component aqueous solution is MgO, while the least effective agent was Na2CO3. The obtained series of effective neutralizing agents for the removal of heavy metals from a multi-component aqueous solution are presented in the work. The effect of the studied concentration of neutralizing agents depends on the neutralizing agents and heavy metals that are used. The percentage of heavy metals removed from aqueous solutions increases along with rising pH values. The consumption of the neutralizing agent decreases as the concentration of the neutralizing agent is increased. In addition, the time taken to achieve pH depends on the agent concentration. In particular, as the concentration of the neutralizing agent increases, the time to reach the pH decreases.
R. Dabiri; M. Bakhshi Mazdeh; H. Mollai
Abstract
The aim of this study was to determine the extent of metal pollutions and the identification of their major sources in the vicinity of the Sangan iron mine occurring in NE Iran. Soil samples were collected from the vicinity of the mine site and analyzed for heavy metals. In addition, the chemical speciation ...
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The aim of this study was to determine the extent of metal pollutions and the identification of their major sources in the vicinity of the Sangan iron mine occurring in NE Iran. Soil samples were collected from the vicinity of the mine site and analyzed for heavy metals. In addition, the chemical speciation of these metals was investigated by means of the sequential extraction procedure. The statistical and spatial variability of the metal concentrations and other soil parameters were also analyzed by the multivariate statistical methods (principal component analysis and cluster analysis). Contaminant Factor (CF) and Enrichment Factor (EF) were used to evaluate soil pollution in the samples. By this study, one can conclude that a notable enrichment of heavy metals happened in the margin of the mining area. The data obtained reveal that soils in the area are contaminated, showing higher levels of Fe, Sn, Co, Cu, Sb, S, and Cd in comparison with their normal distributions. The results of sequential extraction analysis and multivariate (geo)statistical methods show that the variability of Fe, Sn, Co, Cu, Sb, S, and Cd is predominately controlled by the anthropogenic source (mining activity), whereas Pb, Cr, and Zn are mainly of natural (geogenic) origin.
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.
R. Marandi; F. Doulati Ardejani; H. Amir Afshar
Abstract
The biosorption of heavy metals can be an effective process for the removal of such metal ions from aqueous solutions. In this study, the adsorption properties of nonliving biomass of phanerochaete chrysosporium for Pb (II) and Zn (II) were investigated by the use of batch adsorption techniques. The ...
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The biosorption of heavy metals can be an effective process for the removal of such metal ions from aqueous solutions. In this study, the adsorption properties of nonliving biomass of phanerochaete chrysosporium for Pb (II) and Zn (II) were investigated by the use of batch adsorption techniques. The effects of initial metal ion concentration, initial pH, biosorbent concentration, stirring speed, temperature and contact time on the biosorption efficiency were studied. The experimental results indicated that the uptake capacity and adsorption yield of one the metal ion were reduced by the presence of the other one. The optimum pH was obtained as 6.0. The experimental adsorption data were fitted to both Langmuir and Frundlich adsorption models for Pb (II) and to the Langmuir model for Zn (II) ion. The highest metals uptake values of 57 and 87 mg/g were calculated for Zn (II) and Pb (II) respectively. Desorption of heavy metal ions was performed by 50 mM HNO3 solution. The results indicated that the biomass of phanerochaete chrysosporium is a suitable biosorbent for the removal of heavy metal ions from the aqueous solutions.
