Northwestern Part Of Study Area Research Articles

Vertical geochemical zonation in the Masjed Daghi porphyry copper-gold deposit, northwestern Iran: implications for exploration of blind mineral deposits

The geochemical zonation method allows distinction between sub-ore and supra-ore primary halos, prediction of erosion level of mineralization, and exploration of blind mineral deposits. In this study, the geochemical zonation of the Masjed Daghi porphyry-Cu deposit, located in the NW of Iran, was investigated to estimate the level of erosion of this deposit based on the geochemical data obtained from 46 drill cores. We used vertical zonation indices (Pb.Zn/Cu.Mo) and (Pb.Zn/Cu.Ag) as geochemical indicators in exploration of blind porphyry-Cu deposits. The values of Vz 1 varied in different parts of the study area. Its values are considerably higher in the eastern and southeastern parts of the area (7-100), and lower in the northwestern parts of the area (0.03–1), indicating the least amount of erosion in the east and SE in comparison with the northwestern parts of study area. Vertical variations of the Pb.Zn/Cu.Mo index show a significant decrease with increased depth; for example, the level decreases to 0.001 at a depth of 580 metres in the NW of the area, indicating a sub-ore zone of halo. With respect to the present level of erosion, high values of Vz indices imply the presence of sub-cropping to blind porphyry-Cu deposits in this area.

Combined use of multivariate statistical analysis and hydrochemical analysis for groundwater quality evolution: A case study in north chain plain

Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis (MA) and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis (DA) method, which can afford 100% correct assignation according to the 3 different clusters (good water (GW), poor water (PW), and very poor water (VPW)) obtained from cluster analysis (CA). According to factor analysis (FA), 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.