American Journal of Humanities and Social Sciences Research (AJHSSR)
2018
American Journal of Humanities and Social Sciences Research (AJHSSR) e-ISSN :2378-703X Volume-02, Issue-08, pp-136-153 www.ajhssr.com
Research Paper
Open Access
Monitoring Kuhdasht Plain Aquifer Using the Drastic Model (Water Quality Index of the US National Institutes of Health) In the GIS Environment Daryoush Rahmatiyan1, Ghorbanali Mohammadpour2, Haniyeh Kaveh3, Furoud Geravand4*, Mahdiyeh Momenpour5, MostafaFallah Mojaver6,maryam Saemi7 1
Master in Environmental Pollution 2 PhD. in Environment 3 Ph.D. Student of Environmental Pollution, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran 4 Ph.D. Student of Environmental Pollution, Tonekabon Branch, Islamic Azad University, Tonekabon, IranAuthor 5 Ph.D. Student of Environmental Pollution, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran 6 Master in Science in Habitats and Biodiversity 7 Master in Geology
ABSTRACT:Identification and provision of zoning of vulnerable aquifers, i.e. areas where pollutants can penetrate and distribute from ground surface to groundwater system, is an appropriate management tool to prevent the contamination of groundwater resources. There are several methods to assess the vulnerability of aquifers, which are generally divided into three main groups: statistical methods, mathematical methods, and overlapping indexes methods. In this research, due to the importance of plain aquifer for agricultural and drinking water supply of the region, drastic method is one of the most applicable overlapping methods used. The plain aquifer vulnerability map derived from the combination of seven raster maps of drastic model parameters (water table depth, pure supply, aquifer type, soil type, topographic slope, non-saturated environment ingredients and hydraulic conductivity) is used; it has been developed in seven layers in ArcGis software. The final aquifer vulnerability map was prepared for contamination by weighting and ranking and combining the seven layers above. Matching the nitrate ion on the final drastic map, it was determined that all points with high nitrate are in the high contamination range, approving the accuracy of the model. According to the zoning map obtained, about 0.98%, 12.98%, 62.56%, and 23.48% of the study area were within the low, moderate, moderate to high, and high vulnerability ranges, respectively. The results of this study indicate that the highest vulnerability potential is in the moderate to high class, and the northern, northwestern and western areas of the plain have a high potential, while the southwest areas have the lowest potential. KEYWORDS: Vulnerability, Drastic Model, Groundwater, Kuhdasht Plain
I. INTRODUCTION The increasing development of human societies and the expansion of industrial activities have a major contribution to environmental pollution, especially water (Rahman 23: 2008). Therefore, human activity in some cases affects the balance of existing capacity in nature. (Konudal et al., 423: 2007) Aquifers are one of the most important water sources vulnerable to contamination. These sources are exposed to contamination in various forms, making it more difficult and costly to detect and control contamination compared to surface water. Also, due to the continuing contamination in these sources, the polluter and vulnerable sources, it is appropriate to prepare vulnerability zoning maps and adopt management policies. (Konudal et al., 235: 2007) (Biker et al., 130: 2005) The concept of vulnerability was first introduced in France in late 1960 in order to provide information on groundwater contamination. (Zwiorozek and Verba 58: 1994) Vulnerability is a relative, non-dimensional, and unmeasurable property that depends on the characteristics of the aquifer, the geological and hydrological environment. (Antacus and Lambrexi 290: 2007) There are several ways to investigate and assess the potential for contamination in an aquifer: processing methods, overlapping recognition methods, and statistical methods. (Alma Serie 579: 2008)
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