1. Introduction
Identifying and preparing the aquifer zoning map of vulnerable areas, where the pollutants from surface water can infiltrate and spread into groundwater system, is an important management tool for preventing groundwater resource contamination. One of the most important vulnerable aquatic resources against the contamination is the aquifer. The purpose of this research is making the vulnerability map of the region and also detecting the regions with the highest amount of ground water contamination characters in the study area. Regarding to the importance of groundwater resources and its uses for different purposes such as drinking, agriculture and industry, we do the research and also study on the aquifer vulnerability. Also, protecting the region is necessary in developing optimal management of ground water resources.
2. Study Area
Neishaboor plain, with an area of 3351/38 square kilometers, is one part of the aquatic basin of the central desert. The plain is located in the southern mountains of Binalood situated in northeastern part of central desert and its climate is mid-dry. The plain is located in the longitude of to eastern hemisphere and the latitude of to northern hemisphere. The highest height of Neishaboor Plain is 3200 m above the sea level over Binalood Mountains and the lowest point is 1050 m above the sea level in the exterior part of the plain. The rainfall is reported to be 292 mm as an average for the plain area.
3. Material and Methods
In this research, we have used different information including topographical maps or numerical topographical maps with 1:25000 scale, geometrical maps with 1:100000 scale, soil map, meteorological statistics, hydrology groundwater level, the result of pumping test, digging logarithm and observational well. We also utilized the wells and their data. The statistics of geographical studies maps and water consumption are all included in the research based on khorasan Razavi water stock company. Seven factors or characters that are measurable by the hydrological system are determined in this method. These factors included groundwater depth, net recharge, aquifer media, soil type, topography, impact of vadose zone and the hydraulic conductivity. These characters have numerically been estimated, so a special rate from 1 to 10 is specified to each factor relating the potential contaminants.
4. Results and Discussion
Regarding to the layers in DRASTIC model and their special weight, the weight overlapping function was used by the Raster calculator menu in ArcGIS software to combine and compare the layers and integrate the data. The final map of the aquifer in comparison to the contamination was categorized into 5 boundaries or ranges, including extremely low vulnerability, low vulnerability, medium vulnerability, high vulnerability and extremely high vulnerability. One of the flaws noticed in DRASTIC model is that the categorizing and parameter evaluation is fairly expertized and the model should be calibrated by the distribution concentration of natural contaminants such as Nitrate, which mainly leads to special vulnerability of the aquifer. In this research, we have used the data related to Nitrate concentration driven from 57 samples of ground water wells to estimate the model again.
5. Conclusion
Regarding to the zoning map, we found that nearly 23.55 percent of the studied Region is located in the lowest vulnerability range, 17.16 percent is located in the low vulnerability range, 19.21 percent is located in the average vulnerability range, 28.72 percent is located in high vulnerability range and finally 11.36 percent is located in extremely high vulnerability range. The results show that the highest vulnerability potential range is high in the southern and western parts of the plain while the northern areas demonstrate the lowest potential. Comparing and contrasting the nitrate ion on the final map of DRASTIC model, it was concluded that areas with the highest nitrate ion concentrations are located in extremely high contaminate force range, emphasizing the precision and accuracy of the model.