Evaluation of Heavy Metal Pollution Indices for Surface Water of the Sarcheshmeh Copper Mine using Multivariate Statistical Methods and GIS | ||
| تحقیقات آب و خاک ایران | ||
| Article 14, Volume 50, Issue 1, March and April 2019, Pages 161-176 PDF (1.4 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijswr.2018.254261.667869 | ||
| Authors | ||
| Akram Seifi* ; Hossien Riahi | ||
| Assistant Professor, Department of Water Engineering, Vali-e-Asr University, Rafsanjan, Iran | ||
| Abstract | ||
| Sarcheshmeh copper mine is the second largest open-pit copper mine in the world which its mining activities, dewatering operations, and dam construction could cause pollution to the surface and groundwater of the region. In this study, the heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and degree of contamination (Cd) were used to evaluate heavy metal concentration in the 82 samples of surface water. Also, the main effective parameters on the heavy metal pollution indices were investigated using principal component analysis (PCA). The hybrid multiple linear regression (MLR) and PCA model was used to develop new equations for HPI, HEI, and Cd indices using minimum number of heavy metal variables. The study area was divided into three sub-sections with different mining activities. The concentrations of elements in water samples were compared with the maximum admissible concentration values of WHO standard for drinking purposes. Based on the spatial distribution maps in GIS, the highest concentrations of heavy metals were found in mining sites and sedimentary dam, and the lowest ones found in the Shour River. Based on the HPI values, 70% of the samples were in the critical range of 100- 482245.3 and only 30% of samples were classified as having low pollution levels. The HEI and Cd results revealed that the 79 (96%) and 69 (84.2%) samples were polluted with heavy metals, respectively. The PCA extracted four components, of which the first component with 63.3% of the total variance contains high loadings for Al, Cd, Co, Fe, Zn, Mn, and Ni elements. Despite of very wide ranges of indices variation, the accuracy of proposed MLR-PCA model was confirmed for less number variables in the study area. Findings of this study can be used for investigating preventive measures and to control pollution in the study area and similar regions for drinking purposes in the future. | ||
| Keywords | ||
| Cluster Analysis; Principal Component Analysis; Pollutant critical limit; Degree of contamination; Quality classification | ||
| References | ||
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