S. Salavati-Nik; S. Saadat; M. Alameh
Abstract
Introduction: Sheshtamad area is located in the western part of Khorasan Razavi province and includes Sheshtamad city and its surrounding villages. A variety of sedimentary and igneous rock units, mainly belonging to the Cretaceous or younger age, are found in the area. A series of orogenic movements, ...
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Introduction: Sheshtamad area is located in the western part of Khorasan Razavi province and includes Sheshtamad city and its surrounding villages. A variety of sedimentary and igneous rock units, mainly belonging to the Cretaceous or younger age, are found in the area. A series of orogenic movements, lithofacies changes and magmatic activities led to the distribution of heavy elements that can cause environmental problems in the area. This study aimed to assess the heavy metal contamination of the soil in the extent of the study area. It has been attempted to investigate the origin of pollution and its impact on the environment using geochemical data.
Materials and Methods: In this study distribution and possibility of contamination from arsenic (As), lead (Pb), zinc (Zn), copper (Cu), chromium (Cr), nickel (Ni), cobalt (Co), antimony (Sb), strontium (Sr) and vanadium (V) were investigated. These variables were determined using chemical analysis of the sediments. For this purpose, network sampling was carried out at 1.5×1.5 km and 3 to 4 samples were taken from each cell. Throughout the study area (geological sheet of 1:100,000 Sheshtamad), 1248 samples of sediments were collected by Jiangxi Company and analyzed by ICP-MS method. The analysis results were summarized in ArcGIS software and elemental concentration zoning was performed by Kriging and Inverse Distance Weighting methods. Some indices have been proposed to evaluate heavy metal contamination in sediments and soils. In order to determine soil contamination with heavy metals, some parameters such as Contamination Factor and Pollution Index were calculated. Multivariate statistical analyses such as correlation analysis have been applied to identify the geochemical behaviors of different geochemical groups.
Results and Discussion: Surface distribution of As with values above 270 mg kg-1 was observed in the drainages of the southern part of the study area. Sr and Sb were measured with values above 2900 mg kg-1 in the drainages from south to eastern part, and above 4 mg kg-1 in south and central drainages, respectively. Surface dispersion of Zn above 210 mg kg-1 was present in the drainages from central, south, southwest and northwestern parts of the region. Cu and Pb were measured with values greater than 240 mg kg-1 in North and Northwestern parts, and greater than 110 mg kg-1 in East, Central and southwest, respectively. Surface distribution of Co element with values above 58 mg kg-1 from drainages in central, north and southwest and V with values higher than 85 mg kg-1 in central and southwest part were also observed. Finally, distribution of Cr element with values above 7400 mg kg-1 in east central and southwestern part and Ni with values higher than 890 mg kg-1 in center and north section were found.
Pb had significant contamination only in a small area of the central part. Cu also showed little pollution only in a small area of the center of the region on the fringes of mining activities. According to the Pollution Index, As had a high pollution in the south and west parts. The central and southwestern parts of the study area show moderate Cr pollution, based on the Contamination Factor. Based on the correlation matrix used to determine the relationship of heavy elements with each other, some elements have a positive correlation with each other, which may indicate their common origin. For example, Co had a positive correlation with Ni and Cr indicating a common origin associated with the distribution of mafic rock units in the region. Pb and Zn have a positive correlation with each other as well as with Co and V, whereas the absence or negative correlation of As with these elements indicates a separate source for this element compared to Pb, Zn, V and Co. The presence of Cu element, despite its limited correlation with zinc, appears to be independent and likely to be more closely related to mineralization processes and mining activities in the region. Sr was negatively or very weakly correlated with most elements. It is chemically similar to calcium and can be present in carbonate sediments with gypsum in addition to substituting for plagioclases. The results of correlation coefficients with elemental zoning maps, geological maps and results of pollution coefficients showed very good agreement. The results of this study indicated that heavy metal contamination in the region was mainly due to the geological characteristics of this area. In other words, that was mainly “lithogenic”.
Conclusion: The results showed that Cr in the central and southwestern parts of the region had moderate contamination and it should be considered by experts and residents of the area to prevent Cr entering the biological cycle of the region in the future. It is one of the most toxic heavy metals and contaminated areas should receive proper attention. Studies were also indicated the presence of As in the stream sediments, especially in the southern parts of the region, and it is necessary to measure the permeability of this element into groundwater and surface waters in addition to the soil. V, Sb, Zn and Co had no significant contamination and specific environmental problems at current concentrations, is unlikely. Anthropogenic contamination, except from limited mining activities, did not play a significant role in the contamination of this region.
mohammad banayan
Abstract
Detection of rainfall characteristics in arid and semi-arid regions such as northeast of Iran has a critical role in any adaptation and mitigation plan to drought. The main goal of this study was to determine the rainy season starting date and daily rainfall threshold by using Rainfall Uncertainty Evaluation ...
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Detection of rainfall characteristics in arid and semi-arid regions such as northeast of Iran has a critical role in any adaptation and mitigation plan to drought. The main goal of this study was to determine the rainy season starting date and daily rainfall threshold by using Rainfall Uncertainty Evaluation Model. The Starting Analysis Date (SAD) characterize the beginning of a new rainy season in a given region, whereas, the Rainy Season Beginning Date and the Rainy Season Ending Date are used to determine the Rainy Season Length. In this study, seventeen locations in northeast of Iran with available long daily rainfall data (24 to 48 years) were selected. Our results indicated three annual courses of rainy season, 12 locations showed a uni-model annual course with shorter values in summer (A courses), 4 locations indicated uni-model with shorter rainy season length across April and March (B courses) and only 1 location showed bi-model annual course (C courses). These classifications were confirmed by multivariate statistical methods analysis. SAD was determined by differences between shortest median RSL and the shortest median RSL of the first day of each month. According to this approach SAD values for different classes determined as: July 1st in (A) region, March 1st in (B) region and February 1st in (C) region. Appropriate daily rainfall threshold was 1.0 mm for only 3 locations, and the highest value of this index obtained in Torbat-j location (2.2 mm), therefore 1.0 mm value which traditionally is in use as daily rainfall threshold need to be revised.
