Background: Groundwater protection against pollution is a very important issue. Groundwater vulnerability maps are useful tools for protecting aquifers and assessing the potential for contamination. Therefore, the purpose of this study was to prepare vulnerability maps and perform sensitivity analysis to identify the most influential factors in the vulnerability of the studied aquifer. Methods: In this study, groundwater vulnerability to pollution was evaluated using DRASTIC, composite DRASTIC, and nitrate vulnerability (NV) models. Drastic is an index for the systematic assessment of potential groundwater pollution. In this method, the drastic index was calculated from the total weight and rank of the factors. For this purpose, first the factors affecting the pollution transfer, were weighted, ranked, and merged using GIS software. Then, using the overlapping techniques and after applying the necessary weight coefficients on each layer, a map of the vulnerability area of the study aquifer was prepared. Results: It was revealed that there is a significant linear relationship between all three models with the distribution of nitrate concentration. Accordingly, it was the most efficient NV model, followed by the composite DRASTIC (CD) and DRASTIC models, respectively. Also, in the studied aquifer, the DRASTIC index was between 147 and 136, the combined DRASTIC index was between 70 and 190, and the nitrate index was between 13 and 132. Conclusion: Vulnerability assessment of the studied aquifer using DRASTIC, and combined DRASTIC, and NV indices shows that, according to the DRASTIC index, 69. 7% of the studied aquifer was in the medium vulnerability class. Also, according to the combined DRASTIC index, the largest area (53. 62%) has low vulnerability and 31. 56% has moderate vulnerability. But according to the nitrate index, 77. 16% of the aquifers had very low vulnerability.

Background and Purposes: Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. In this study, the optimized of DRASTIC, Pesticide DRASTIC model parameters and land use layers (LU) were used to assess of pollution risk in catchment basin aquifer in south of Namak lake using of statistical methods. Methods: Information layers were prepared, rated (deterministic and fuzzy-statistical), weighted (original and statistical) and combined (by Index-Overlay method) in GIS environment. For modeling, from nonlinear regression for fuzzy-statistical rating (scaling) and the Pearson correlation coefficients between of nitrate concentrations with scaling parameters of DRASTIC, P-DRASTIC model and sensitivity analysis (removal and single-parameter) were performed to determine and modify of parameters weighted. Results: As result P-RASIC-LU and RASIC-LU model with statistical rating and weighting, removal-parameter sensitivity analysis, determine as best selection model based on correlation coefficient = 62%, P-value = 0. 01 and with parameters of net recharge, aquifer media, soil media, impact of vadose zone, hydraulic conductivity and land use with the weighty values of 3. 1, 4. 0, 4. 1, 3. 1, 2, 2 and 2. 5, 4. 63, 4. 15, 3. 03, 2, 1. 96 consequently. According to this model, western and southern parts of the aquifer has high pollution risk due to high net recharge and coarse-grain material in the impact of vadose zone, soil and aquifer media. Conclusion: Since reviewing of weight and rank of model parameters is limited personal opinions and increased model validation using statistical methods and GIS, It can be expected that favorable results to be followed for optimization of pollution risk model.

Miandoab Plain is part of the Urmia Lake basin, which has significantly reduced the quantity and quality of its water resources in recent years. Groundwater vulnerability assessments provide a useful method for analyzing the sensitivity of aquifer to the contaminant. The purpose of this study was determining the vulnerability of the aquifer using AVI, GODS, DRASTIC and modified DRASTIC models so that the vulnerability potential to pollution can be determined more exactly. In this study, the data and information provided by the Regional Water Company of East Azarbaijan were used to provide layers needed for the preparation of each vulnerability model in the ArcGIS software. The new rates of modified DRASTIC model calculated using a correlation between each parameter and the nitrate concentration obtained from the analysis of 16 water samples taken in July 2016. The final mapping of the vulnerability maps was also verified by nitrate values. Also, validation of the final vulnerability maps was done with nitrate values. The coefficients of determined values between the nitrate concentration and vulnerability models of AVI, GODS, DRASTIC and modified DRASTIC were estimated to be 0. 004, 0. 13, 0. 18, and 0. 33, respectively. The results showed that after calibrating the model with the nitrate data, it is the best model for assessing the vulnerability of Aquifer. Based on this, it was observed that the most vulnerable groundwater pollution is in the northeast and parts of the west of the plain and the least vulnerable in the southern and southeastern parts of the plain.

Assessing the vulnerability of groundwater is an effective tool to identify vulnerable areas and conservation activities in order to maintain water quality in these areas. DRASTIC method has been used for vulnerability assessment more than other methods in scale area. The weakness of this method is estimating the weights of the model parameter. The aim of this research is to optimize the weights of DRASTIC model by using statistical nonparametric method. In this regard, the appropriate inputs of DARSTIC model were collected from Dezful area, and then vulnerability of the groundwater of this area was determined based on the primary weights of the model parameters. The final weights of the parameters were optimized using nonparametric test and the concentration of the nitrates in Dezful area. Results show better performance of optimized DRASTIC model than original DRASTIC model.

TYasuj city is the capital of Chaharmahal and Bakhtiari province and is located in the center of Yasuj plain. The development of Yasuj city has increased the possibility of pollution and vulnerability of surface and groundwater in the plain. DRASTIC method which is the most popular of overlaying methods apply in order to producing the vulnerability map of the Yasooj aquifer. Vulnerability Zoning map aquifer based on the integration of raster maps of seven DRASTIC Parameters (water depth, Aquifer recharge, environment, groundwater, soil type, topography, the saturated and unsaturated hydraulic conductivity) in GIS shows that16%, 3% and 1% of aquifer has moderate to high, high and very high risk of contamination, respectively and the rest are pollution-free or low pollution. To assessment the accuracy of the study, the vulnerability map was calibrated with the nitrate concentration and land use maps. The coefficient correlation vulnerability index is about 49%. Also, the results were evaluated by land use map, which indicate the acceptable accuracy of the model.

Background: Groundwater nitrate pollution is an important environmental problem in water resources management. In this regard, specific measures aiming at prevention of water pollution will be helpful to managers and decision-makers. Identification of aquifers’ vulnerable areas and determination of groundwater protection zones using most widely used models, such as DRASTIC and CD, are one of the most useful approaches in water resources’ hygiene. Objective: The present study aimed to assess the vulnerability of Shiraz plain’s unconfined aquifer using the above-mentioned models. Methods: The main hydro-geologic factors affecting the transmission of pollution, including depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, aquifer hydraulic conductivity, and land use parameters were rated, weighted, and integrated using GIS 9.3. Finally, the maps of Shiraz plain’s unconfined aquifer vulnerability were prepared. Results: The vulnerability maps based on these two indexes showed very similar results, identifying the southeastern part of the aquifer, around Maharlu Lake, as the vulnerable zone. The observed nitrate concentrations from the wells in the underlying aquifer were in accordance with these findings. The results of sensitivity analyses indicated the depth parameter as the most effective parameter in vulnerability assessment of Shiraz plain. Conclusion: As Shiraz plain has been covered with fine-grained sediments, except for some central and south-east regions which have moderate vulnerability and high nitrate concentration, its vulnerability is low. Given the intensive agricultural activities and also the rise in groundwater level in southeastern regions, more attention should be paid to these areas.