Environment
N. Zandy Ilghani; F. Ghadimi; M. Ghomi
Abstract
The Haft-Savaran Pb-Zn mineralization zone with the lower Jurassic age is located in the southern basin of Arak and Malayer-Isfahan metallogenic belt of Iran. Based upon the geological map of the Haft-Savaran area, the sandstone and shale of lower Jurassic are the main rocks of Pb-Zn deposit. In this ...
Read More
The Haft-Savaran Pb-Zn mineralization zone with the lower Jurassic age is located in the southern basin of Arak and Malayer-Isfahan metallogenic belt of Iran. Based upon the geological map of the Haft-Savaran area, the sandstone and shale of lower Jurassic are the main rocks of Pb-Zn deposit. In this area, 170samples were taken from 33 boreholes, and44 elements were measured by the ICP-MS method. Adaptation of the alteration index and Pb–Zn mineralization was investigated in this work. The model was created based on the Sericitic, Spitz-Darling, Alkali, Hashimoto, and Silicification Indices in all boreholes. This work showed that the Sericite, Hashimoto, Spitz-Darling, and Silicification indices increased around mineralization, and the alkali index decreased around it. Development of the alteration indices indicates that direction of the ore-bearing solution is NE-SW, and that this trend is consistent with the faults in the area. Based upon the 3D models and other data interpretations, Pb–Zn and elements such as Fe, Mn, Cr, and Ni have deposited within the alteration zones.
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 ...
Read More
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.
Feridon ghadimi; Mohammad Ghomi; Abdolmotaleb Hajati
Abstract
Altogether 20 groundwater samples were collected around the Lakan Pb and Zn mine in Iran. Samples were analyzed for 8 constituents including Fe, Pb, Hg, Mn, Zn, CN, SO4 and Cl using standard method. The results show that the average concentrations of constituents were 0.01, 0.60, 0.10, 0.01, 0.40, 35, ...
Read More
Altogether 20 groundwater samples were collected around the Lakan Pb and Zn mine in Iran. Samples were analyzed for 8 constituents including Fe, Pb, Hg, Mn, Zn, CN, SO4 and Cl using standard method. The results show that the average concentrations of constituents were 0.01, 0.60, 0.10, 0.01, 0.40, 35, 0.01 and 5.95 mg/kg for Fe, Mn, Pb, Zn, Hg, SO4, CN and Cl, respectively. The computed contamination index ranged between 2.38 and 443. It was concluded that contamination index shows a medium to high contaminated situation for Pb and Hg in groundwater around the tailings dam. Based on a multivariate analysis, four main sources of these hydrochemical data were identified. (1) Zn, Mn, TDS and SO4 have both natural and anthropogenic sources; (2) Hg constituent represents a natural source and Pb shows a anthropogenic source due to Lakan mine; (3) CN and Fe have anthropogenic source and mainly originated from the plant processing; (4) Cl represents a natural source.
