Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran)

Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran) Vajihe Gholizade, Amir Saffari, Ali Ahmadabadi, Amir Karam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4172498/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The vulnerability of the Mashhad aquifer has been analyzed by spatial analysis approach using DRASTIC, SI, GODS and SINTACS models. The Mashhad aquifer in northeast Iran is now considered a critical area due to its special circumstances, the presence of industrial and agricultural activities, and large settlements. This study aims to evaluate the vulnerability zones of the Mashhad aquifer using four models DRASTIC, SI, GODS and SINTACS. The parameters of the models are explained and measured by GIS capabilities. After weighting, ranking, and integrating the layers in the ArcGIS software, we have produced vulnerability maps of the aquifer. The results have indicated that in the DRASTIC model, the study area is categorized into five vulnerability zones very low (5.81%), low (26.03%), moderate (44.45%), high (22.57%), and very high (1.13%). In the SI model, the study area is categorized into five vulnerability zones very low (0.40%), low (24.63%), moderate (23.98%), high (18.71%), and very high vulnerability (32.25%). In the GODS model, it is categorized into five vulnerability zones very low (0.93%), low (31.11%), moderate (11.45%), high (1.56%), and very high (54.95%). In the SINTACS model, the area is also categorized into the vulnerability five zones very low (0.44%), low (25.57%), moderate (28.58%), high (2.79%), and very high (42.61%). For validating the results, the vulnerability maps have been compared with the TDS quality index. The results showed that all four models have high accuracy in categorizing the vulnerability of the Mashhad aquifer. The comparison among the results of the models has indicated that the vulnerability of the aquifer generally increases from southeast to northwest and then decreases from the central region towards the northwestern areas. Vulnerability Mashhad aquifer DRASTIC SI GODS and SINTACS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The growth in population and the advancement of agriculture and industries (Bagheri et al., 2021 ; Khorrami and Malekmohammadi, 2021 ; Salehi et al., 2022 ; Karimi et al., 2023 ) have increased water consumption (Montoya et al., 2023 ; Xiangmei et al., 2021 ). This increased consumption of water can lead to a degradation in both the quantity and quality of underground water resources (Zendehbad et al., 2019 ; Kazemi et al., 2022 ; Jafarzadeh et al., 2023; Krishnamoonthy et al., 2023; Smida et al., 2023 ). The situation has been exacerbated by the decline in fresh underground water resources and reduced infiltration of surface water and rainfall into these resources, leading to a significant reduction in surface flows (Pan et al., 2023 ; Jafarzadeh et al., 2023). In Iran, underground water has now become the primary source for agriculture, drinking water, and industrial purposes (Asghari Moghadam et al., 2016; Jafarzadeh et al., 2023). However, the excessive extraction of underground water has caused a significant drop in aquifer water levels and the depletion of water layers in the earth (Kazemi et al., 2022 ; Salehi et al., 2022 ; Karimi et al., 2023 ; Zardosht et al., 2023 ; Thapinta, and Hudak, 2003 ). Furthermore, human activities have made the groundwater vulnerable to pollutants from industries (Kazemi et al., 2022 ; Salehi et al., 2022 ; Karimi et al., 2023 ) and agriculture (Zardosht et al., 2023 ). Currently, a significant portion of the Iran water consumption, particularly for drinking water, relies on underground water resources, mainly open aquifers (Jafarzadeh et al., 2023), which are highly susceptible to contamination from agricultural, industrial, and urban activities. Therefore, it is vital to assess the vulnerability of these aquifers for effective management, land use planning, quality monitoring, and groundwater pollution prevention and protection. There are various definitions of vulnerability. Turner et al. 2003 define it as the degree of probability of the system being damaged due to risk exposure. The vulnerability is considered as the level an entity is sensitive to damage and its potential for change or transformation (Gallopin,2006). Vulnerability is, in fact, an estimate of the type (management and environmental) and amount (amount) of damage to a system that is exposed to external or internal disturbances (Brand & Jax, 2007 ). Vulnerability assessment is a process during which information identifying vulnerability is combined and the areas with high vulnerability are distinguished from the areas with low vulnerability (Civita & Della, 1994; Kwami et al., 2023 ). In the quantitative assessment of the vulnerability of the aquifer, attention is paid to the transfer and flow models in the saturated and unsaturated zone, and the effect of the physical and hydrological characteristics of the soil, nutrition, and infiltration depth are evaluated to determine the distribution of sensitive or vulnerable areas (Almasri, 2008 ; Kwami et al., 2023 ; Smida et al., 2023 ). So far, a lot of research has been done in the field of vulnerability assessment of aquifers in the world and Iran. We discussed some of the research about the vulnerability of aquifers. Aneesh et al. ( 2022 ) investigated the vulnerability of an urban coastal aquifer in India using the DRASTIC model based on GIS. The study shows that pollution and vulnerability to pollutants is a major cause of concern for more than 3.82 million people living in the region. Abu-Bakr ( 2020 ) studied the vulnerability of groundwater in different types of aquifers in Egypt. The results of this study have shown the vulnerability of the aquifer in the three regions of Al-Minya, Wadi El-Trun, and Al-Kharga Oasis in low to medium zones. The research by Bordbar et al, 2019 about the vulnerability of the Qarasu-Garganrud coastal aquifer against the advance of saltwater revealed that the GALDIT model is superior to other methods such as SINTACS and DRASTIC. The application of surface feeding of the aquifer in the modification of the GALDIT method was examined by Faal et al. ( 2021 ) to assess the risk of saltwater advance in the Qom aquifer. The results of the research showed that the areas with high and medium vulnerability in the eastern part of the Qom aquifer with an area of about 14% of the total area of the aquifer are susceptible to the advance of salt water and can be considered as the range of salt water expansion for monitoring and optimal management of the coastal aquifer. In their research on the vulnerability of the karst aquifers of Kermanshah Plain and Bistun-Parav massif, Melki et al., (2018) employed the COP model. The results of their study indicated that 31.4% of the region is in the medium vulnerability 30.7% in the zone of low vulnerability, and 37.9% is located in the zone with very low vulnerability. The vulnerability of the Babol City aquifer was also investigated by Jaafari and Khoshrosh (2018) using the modified drastic model. Taking into account the main characteristics of the studied area (urban structure and paddy fields), the Drastic index was computed, and the degree of vulnerability of the Babol city aquifer was in the range of 127 to 176. Nediri et al., (2018) in their article assessed the vulnerability of the Dasht Khoi aquifer using the combined method with DRASTIC and SINTACS models. For validation, they used the measured nitrate values and concluded that the combined method showed a higher correlation index and correlation coefficient than the single DRASTIC and SINTACS methods. Hence, the combined method is more suitable for evaluating the vulnerability of this area. In the combined method, 19%, 42%, and 39% of the area of Dasht Khoi aquifer are in low, medium, and high vulnerability, respectively. Aftakhari et al., (2018), in an article titled "Evaluation of the qualitative vulnerability of the Birjand Plain aquifer using the SINTACS method", have concluded that 12.81% of the study area has medium to high vulnerability. They found that 80.47% of the area has high vulnerability and 71.6% of that has very high vulnerability. Faal Aghdam et al., (2016) in their research worked on the vulnerability of the Bilourdi plain aquifer of East Azerbaijan with DRASTIC and SINTACS methods and found that 36.5% of the total area of the study region is moderately vulnerable, 20% highly vulnerable and 43.5% is located in the medium vulnerability zone. Despite the higher efficiency of the SINTACS model to check the vulnerability, the combined methods provide better results. Seyf et al., (2013) in their study evaluated and prepared a map of the vulnerability of karst aquifers using the COP model, using three parameters of the covering layer, flow concentration, and precipitation regime to determine the vulnerability of the clean aquifer in Kermanshah. They found that 12.22%, 48.32%, and 39.46% of the area are categorized as very low, low and medium vulnerability, respectively. In the research entitled “Vulnerability assessment of Jovein plain aquifer using DRASTIC and GODS methods” by Khodaei et al., (2015), using vulnerability zoning, they compared the results of GODS and DRASTIC models. They argued that in both methods, the vulnerability of the Dasht Jovin aquifer is in two groups of low and medium vulnerability categories. In the GODS method, the range of low vulnerability is 35% and the medium vulnerability is 65%, and in the DRASTIC method, the low vulnerability is 49% and the medium vulnerability is 51% of the area of the plain. Zendehbad et al ( 2019 ) also conducted a study in Mashhad, Iran to identify the sources of high nitrate contamination in the urban aquifer. They used isotopic analysis of groundwater nitrate to determine its origins and provide information for future programs focused on improving groundwater quality. The study found that urbanization and land use had a greater influence on groundwater composition than interactions with the aquifer rock. The insufficient sewer system in certain parts of the study area directly contributed to the poor water quality. With limited natural processes to mitigate nitrate pollution, it is important to explore management strategies to reduce nitrogen input into the aquifer. In their study, Bagheri et al ( 2021 ) explored the development of karst and the flow directions of a transboundary aquifer in Northeast Iran using geochemical and multi-isotope evidence. They analyzed the hydrogeological and hydrogeochemical characteristics and isotopic signatures of the karstic aquifer to determine the origin of groundwater, flow directions, and the development of karst. The researchers found that understanding the geo-hydrogeological and tectonic settings, as well as using isotopic approaches, provides valuable insights into both groundwater flow directions and karst development. This knowledge can contribute to better assessments and management of water resources not only in the study area but also in other transboundary karstic regions. Smida et al ( 2023 ) conducted a critical review of the generic and modified DRASTIC models for assessing groundwater vulnerability and pollution hazard in the Braga shallow aquifer in Central Tunisia. They employed a GIS-based multicriteria and artificial neural network (ANN) analysis. The DRASTIC map generated from their study classified the vulnerability of the aquifer into four categories: very low (12.06%), low (81.88%), moderate (5.16%), and high (0.9%). The vulnerability index ranged between 43 and 159. The researchers further validated their findings and found that the DRSTIC-LU-based PHI (Pollution Hazard Index) was more reliable in accurately identifying the hazardous zones in the aquifer. Khorrami and Malekmohammadi ( 2021 ) conducted a study on the impact of excessive water extraction on groundwater ecosystem services (GSES) and developed a comprehensive framework to assess the vulnerability of GSES in the Mashhad Plain. They used a systematic approach to analyze the biophysical condition of the ecosystem and evaluated the spatial stability of GSES. By mapping the vulnerability intensity of GSES in the study area, they found that over 35% of the area was highly or very highly vulnerable. Additionally, approximately 18%, 30%, and 15% of the land exhibited moderate, low, and no vulnerability, respectively. The results indicated that excessive groundwater withdrawal had disrupted the provision of ecosystem services by the aquifer. This framework can be utilized for sustainable management of groundwater resources, particularly in arid and semi-arid regions faced with water resource depletion. The purpose of this research is to analyze the qualitative conditions of the Mashhad plain aquifer, to make our vulnerability analysis more reliable. Our analysis has been conducted by comparing the maps and the results of four models of DRASTIC, SI, GODS and SINTACS. Study area The Mashhad plain is formed like a wide valley in northeast Iran, Razavi Khorasan province, and is located amongst Binaloud and Hazar Masjid mountains. The Mashhad Plain aquifer (Zendehbad et al., 2019 ; Khorrami and Malekmohammadi, 2021 ) is located in the catchment area of the Kashafroud River. Mashhad metropolis with a population of about 3.5 million people is developed in this area as the second largest metropolis of Iran. The study area of Mashhad-Chenaran is 9957 km2, and the Mashhad plain aquifer is 2720 km2 (Fig. 1). The main drainage of this region is the Kashafroud River flowing from the northwest to the southeast into the Mashhad Plain. Various formations from the early epochs to the Quaternary period can be observed in the area. The Mashhad plain is covered with Quaternary alluvium, resulting from the activity of the Kashafroud river and the seasonal floods of the rivers such as Radkan, Kardeh, Torghabeh, and Shandiz which originate from the mountains of Hazar Masjid and Binaloud (Lashgaripour et al., 2014; Bagheri et al., 2021 ). An open water aquifer is spread throughout the Mashhad plain, which continues to the edge of Binaloud Heights. Due to the roughness of the bedrock and the existence of feeding areas from the mountains of Binaloud and Hazar Masjid, this aquifer is not homogeneous and it is not the same in terms of drainage condition (Akbari et al., 2008; Zendehbad et al., 2019 ). The area under study, as the main center of industrial and agricultural activities, was declared as a forbidden plain in 1988 due to the drop in the underground water level. Today, in addition to being in a critical prohibited situation in terms of the volume of resources, it is faced with serious challenges in terms of quality. Materials and Methods There are various methods to evaluate the vulnerability of an aquifer, including simulation-based methods, statistical methods, and overlapping index methods (Almasri and Kaluarachchi, 2005 ). Index and overlapping methods consist of hydrogeological parameters affecting the transfer of pollution to groundwater. To determine the relative importance, each of the parameters is compared with each other. Various methods for vulnerability assessment can be divided into three general categories including descriptive, statistical, and analytical ranking, and the combined methods (Niknam et al. 2007 ). The most important ranking methods are GOD, AVI, DRASTIC, COP, SINTACS and SI methods, developed based on combining the layers obtained from different parameters. These methods are different in the type and number of parameters but ultimately lead to recording a numerical index or score for each feature (Asghari Moghadam et al., 2016). In this study, we used four models of DRASTIC, SI, GODS, and SINTACS to estimate the vulnerability of the Mashhad plain aquifer, and to make vulnerability zoning of the plain aquifer. Finally, the parameters of the models have been examined based on the results of the model through ArcGIS tools to generate vulnerability zoning maps for the aquifer. In the following sections, we have discussed the individual methodologies of each model, including the definitions of variables, mathematical equations, and the steps to perform calculations of each model. DRASTIC model One of the effective methods in assessing the vulnerability of aquifers is the DRASTIC model. This model was designed by the Environmental Protection Agency of the United States of America, to evaluate the potential of groundwater pollution, for the entire United States. This model is based on the hydrogeological concept and describes all the important geological and hydrogeological factors affecting the movement of groundwater when it enters, passes through and exits the system (Wen et al. 2009 ). The DRASTIC method, as one of the most common methods for assessing the inherent vulnerability potential of underground water, is an overlap and index method. This method was developed by Aller et al. in 1987 with the aim of systematic assessment of groundwater contamination potential (Stigter et al., 2006 ). This method employs seven measurable hydrogeological system parameters including the depth of the water table (D), net nutrition (R), aquifer environment (A), soil environment (S), topography (T), the effect of the unsaturated environment (I) and the hydraulic conductivity of the aquifer (C) (Babiker, al, et. 2005). Eq. 1 is used to prepare the DRASTIC index map: \(DI=\sum _{j=1}^{7}{r}_{j}{w}_{j}={D}_{r}{D}_{w}+{R}_{r}{R}_{w}+{A}_{r}{A}_{w}+{S}_{r}{s}_{w}+{T}_{r}{T}_{w}+{I}_{r}{I}_{w}+{C}_{r}{C}_{w}\) Eq. 1 In the above equation, DI is the DRASTIC index, English uppercase letters represent the first letter of layer names, the index r: is the rank of the layer, and the index w: is the weight of the layer. The ratings of the sub-layers of each criterion vary from one to ten depending on their impact on the vulnerability potential. The weight of each layer is also a fixed value between one and five; which shows the relative importance of each layer compared to other layers in the vulnerability analysis of the aquifer (Rahman, 2008 ). In Fig. 2 , the structure of the DRASTIC model is illustrated to evaluate the vulnerability potential of the Mashhad Plain aquifer. SI model (Sensitivity Index) It is a method to evaluate the vulnerability potential of aquifers. The five parameters of this method are groundwater depth (D), net nutrition (R), aquifer lithology (A), topography (T), and land use (LU). The most important difference between this method and the DRASTIC method is the inclusion of the land use parameter. This method was presented by Ribeiro in 2000 to analyze the vulnerability of aquifers on a large to medium scale, from 1:50000 to 1:200000 (Ribeiro, 2000 ). In this method, after preparing the SI model layers and weighting each of the layer classes using the functions available in the ArcGIS environment, the sensitivity index is obtained from the weighted sum of the parameters mentioned in Eq. 2. \(DI={D}_{r}{D}_{w}+{R}_{r}{R}_{w}+{A}_{r}{A}_{w}+{T}_{r}{T}_{w}+{LU}_{r}{LU}_{w}\) Eq. 2 Where SI is the sensitivity index, capital letters represent the first letter of layer names, index r is the rank of layers and index w stands for the weight of layers. The numerical value of the sensitivity index can vary between zero and 100 (Ribeiro, 2000 ). Figure 2 shows the structure of the SI model for evaluating the vulnerability potential of the Mashhad Plain aquifer. GODS model This method is developed based on four parameters aquifer type, characteristics of unsaturated zone, depth of underground water, and type of soil surface texture. The value of different classes of parameters changes from zero to one and equal weight is assigned to all the parameters. The GODS vulnerability index is obtained from the product of the parameters (Khodai et al., 2015). The index of the vulnerability of underground water resources is considered with three main influential parameters and is obtained through Eq. 3: \({I}_{v}=G\times O\times D\times S\) Eq. 3 Where Iv is the vulnerability index, G is the score of the type of aquifer environment, O is the lithology score of the unsaturated zone (the lithology parameter is only calculated for open aquifers), and D is the score of the depth of the underground water level (Foster, 1987 ). The collected data are entered into the GIS application. The layers are valued based on their location. The zoning map is obtained based on the values of each parameter. The maps have been prepared using the Raster Calculator tool in ArcGIS and combined according to the relevant weight coefficients to generate the final map of vulnerability zones (Novinpour and Khezri, 2018). In Fig. 3 , the structure of the GODS model is presented to assess the vulnerability potential of the aquifer. SINTACS model One of the most widely used and well-known overlap index methods is the SINTACS model (Civita, 1994 ). This method was initially used by Civita et al. in 1990 to investigate the vulnerability of southern Italy. The SINTACS method is derived from the DRASTIC method. The parameters of this method are the same as the parameters of the DRASTIC method, but with the difference that the process of ranking the parameters in the SINTACS method is more flexible (Civita, 1990 ). One of the main advantages of the SINTACS model is to perform vulnerability assessment using a large number of layers, something that reduces the effect of errors or unknown factors on the final output (Pisciotta, et al, 2015 ). In this model, seven hydrogeological parameters are used to evaluate the relative vulnerability of groundwater pollution. The vulnerability index is obtained from the sum of the product of weights and ranks of the seven mentioned parameters, according to Eq. 4. The classification and valuation of different classes related to each of the parameters are executed in the GIS environment. A relative weight from 1 to 5 is given to each of the characteristics according to the importance of the effect on the pollution of the underground water system, which shows the relative effect of each characteristic on the transfer of pollution in the underground water. In this model, a rank from 1 to 10 is assigned to the ranges of each of the hydrological characteristics based on their impact on vulnerability, and it allows the user to use the SINTACS model for the study area. For the weighting and ranking of the SINTACS method, the ranks and weights provided by (Civita, 1994 ) have been used. $$\text{S}\text{I}\text{N}\text{T}\text{A}\text{C}\text{S} \text{I}\text{n}\text{d}\text{e}\text{x}={S}_{\text{O}\text{r}}{S}_{\text{O}\text{w}}+{I}_{\text{r}}{I}_{\text{w}}+{N}_{\text{r}}{N}_{\text{w}}+{T}_{\text{r}}{T}_{\text{w}}+{A}_{\text{r}}{A}_{\text{w}}+{C}_{\text{r}}{C}_{\text{w}}+{S}_{Vr}{S}_{Vw}$$ \(\sum _{i}^{7}{I}_{SINTACS}={P}_{i}\times {W}_{i}\) Eq. 4 Where S is the depth of underground water, I is the net nutrition, N is the effect of the unsaturated zone, T is the soil type, A is the aquifer environment, C is the hydraulic conductivity, SV is the topography (slope), w is the weight, and r is the rank of each of the model parameters. Figure 2 shows the structure of the SINTACS model, including the effective weights and ranks in the preparation of the qualitative vulnerability map of the Mashhad plain. Results and Discussion In this research, we employed four models DRASTIC, SI, GODS, and SINTACS to achieve the research goals and determine the level of vulnerability. Interpretation of DRASTIC model results In the DRASTIC method, weights have been assigned to seven parameters of groundwater depth (D), net recharge (R), aquifer environment (A), soil environment (S), topography (T), unsaturated environment (I), hydraulic conductivity (C) in GIS. Table 1 shows the weights of each of the classes of layers used in the DRASTIC method. Table (1) ranking and weight of DRASTIC model parameters Groundwater depth / meter Range 0–25 25–39 39–50 50–63 63–77 77–92 92-134.5 Rank 10 9 7 5 3 2 1 Net Nutrition / mm Range 3–5 5–7 7–9 9–11 11–13 - - Rank 1 3 5 8 10 - - Saturated zone Lithology Sand Sandstone Clay Gravel Shale Limestone Slit Rank 8 8 1 10 3 3 1 Soil environment Soil layer Clay Clay lime Lime Sandy lime Lime silt Silty - Rank 1 2 4 5 3 2 - Topography % Range 0–2 2–6 6–12 12–18 > 18 - - Rank 10 9 5 3 1 - - Unsaturated zone Lithology Sand Clay Gravel Sandstone Shale Marl Silt Rank 8 1 10 8 6 1 1 Hydraulic conductivity, meter / day Range 0.01–1.3 1.3–3.9 3.9–8.6 8.6–13 13-24.2 > 24.2 - Rank 1 2 4 6 8 10 - Depth of underground water (D): The distance between the surface of the earth and the water table is called the depth of the underground water (Ahmadi et al., 2012). As the level of depth increases, the likelihood of pollution decreases (Pakhstin Rouhi et al., 2016). In this research, the groundwater depth layer was prepared using the IDW interpolation method due to its higher accuracy than other methods in ArcGIS. According to Table (1), this layer is categorized into 7 ranges from 0–25 to 92-134.5, and for each of the ranges, a proportional weight from 1 to 10 is recorded. Net nutrition (R): Nutrition is the amount of water penetrating the surface of the earth and entering the water table (Rahman, 2008 ). The intensity and passage of dissolved substances depend on the intensity and vertical movement of water into the ground (Bouwer, 1978 ). In this research, the Piscopo method has been used to prepare the pure feeding layer. This method considers three factors rainfall, slope (percentage), and soil permeability. To prepare the rainfall map, the average annual rainfall statistics of the stations in the region and the IDW interpolation method have been used due to greater accuracy. The slope map has been prepared using the Digital Elevation Model (DEM) and the soil permeability map has also been created based on the soil map of the study area. The ranking of maps of soil permeability, precipitation, slope, and net nutrition are presented in Table (2). According to this table, the 1st rank has been assigned to the rainfall layer, because the average rainfall of the region is 276 mm per year. Finally, based on Piscopo's equation, the prioritized maps of slope, permeability, and rainfall rank have been processed in the ArcGIS software to generate a nutrition map. Table (2) Classification of the net nutrition layer (Piscopo, 2001 ) Soil Permeability Rainfall / mm Slope / Percentage Net Nutrition Factor Range / % Factor Range / % Factor Range / % Factor Range / % 5 High very 4 > 850 4 < 2 10 11–13 4 High 3 700–850 3 2–10 8 9–11 3 Moderate 2 500–700 2 10–33 5 7–9 2 Low 1 33 3 5–7 1 Very low - - - - 1 3–5 Aquifer environment (A): This parameter is related to the characteristics of the materials configured in the saturated zone, such as porosity, type, and size of particles (Brahim, et, al. 2012). The aquifer environment and its ingredients determine the length and trend of the underground water flow system in the aquifer (Voudouris, et, al. 2010). To prepare a map of this factor, information related to the type of saturated layer has been used in 39 drilling and piping sections and geophysical and geological explorations in the study area. The aquifer environment of the study area consists of sand, clay, shale, silt, gravel, and lime and each is weighted according to the permeability which is presented in Table (1). Coarse-grained particles such as sand and gravel have a higher weight, and finer-grained particles such as clay and silt have a lower weight. In areas with higher permeability, there is no reaction between the pollutant and the soil, and the potential for pollution can be higher due to the high speed of pollutant diffusion. Soil environment (S): The soil layer, usually with a thickness of about 0.5 to 2 meters, has a great potential to remove and reduce the concentration of pollutants due to the relatively high microbial activity, the presence of many organic substances, and the presence of plant roots (Amir Ahmadi et al., 2019). The potential of aquifer pollution depends on soil properties such as permeability, texture and proportion of soil organic matter (Kim and Hamm, 1999 ). In this research, the analysis results of 39 profiles taken from the region have been used to prepare the soil map. The soil environment is defined according to its textural classification and scored based on the pollution potential. The soil environment is classified in the study range from clay with a weight of 1 to loam-sandy with a weight of 5. Given that the potential of aquifer pollution depends on soil characteristics such as permeability, texture, and percentage of soil organic matter, the presence of fine-grained materials such as silt and clay has reduced the relative permeability of the soil and limited the movement of pollutants. The coarse-grained materials such as sand have facilitated the movement of pollutants in parts of the study area. Topography (T): The topography factor expresses the changes in the slope of the area (Stigter. al. et, 2006). Slopes that provide a higher infiltration opportunity have a higher contamination potential (Plymale, et. al, 2002). In this research, the slope layer has been prepared by a DEM layer with a spatial resolution of 12.5 meters in ArcGIS software. The average slope between the two points is obtained by dividing the vertical distance by the horizontal distance between them. Unsaturated zone (I): This area is unsaturated or continuously saturated so that it can control the passage of the pollutants and their dilution (Ahmadi and Aberoumand, 2009 ). An unsaturated zone controls the duration and manner of the movement of pollutants and, therefore, affects the time required for the quantity and concentration of substances that come into contact with pollution (Plymale, al. et, 2002). To prepare a map of this parameter, we used the data related to the soil type of the unsaturated zone in 39 drilling and pipe-laying sections in the Mashhad plain. The method of obtaining information related to the unsaturated zone has been the same as that of the aquifer environment. However, in this case, we considered the granularity and characteristics of the sediments between the underground water level and the ground surface. Accordingly, the more permeable the unsaturated zone is, the less reaction is formed between pollutants and soil particles, and the washing speed towards the aquifer is faster and higher. Therefore, the potential for contamination has increased. According to what can be seen in Table (1), the formations of the study area are clay, marl, and silt (weight 1), shale (weight 6), sand and gravel (weight 8), and gravel formation (weight 10). So, the permeability in the areas with gravel formation is greater than in other places. Hydraulic conductivity (C): Hydraulic conductivity is the ability of the aquifer environment to transport water and its associated pollutants. This parameter controls the transport and dispersion of the pollutants from the injection point inside the saturated zone (Rahman, 2008 ). The information related to hydraulic conductivity is obtained from pumping test calculations (Khodai et al., 2015). The transfer coefficient or transferability of an aquifer layer is the amount of water that passes through a unit cross-sectional area of the aquifer layer under a certain hydraulic slope, in square meters per day (meter per day in each meter of layer thickness). In general, because in pumping tests, the value of the parameter of the water transfer capability coefficient is measured, therefore, by using the saturation thickness of the aquifer, the value of hydraulic conductivity is obtained by dividing the water transfer capability coefficient by the saturation thickness. The transmission capability map has been obtained from the results of pumping tests and using the IDW interpolation method. The saturated layer thickness map has been obtained from the interpolation of the data related to the depth of the saturated region obtained from the drilling sections using the IDW method. Hence, the hydraulic conductivity of the study area has been obtained in 6 ranges from 0.01–1.3 to more than 24.2 and with weighting from 1 to 10 according to Table (1). Figure 4 shows each layer of the DRASTIC model and the rank of each class. After preparing each of the layers of the DRASTIC model to prepare the vulnerability map of the aquifer, the layers of the model have been combined in the ArcGIS software environment and the final map has been obtained (Figure 8). We, then, has been calculated the area of each category. Based on the results of the DRASTIC model, the studied area has been divided into five zones with very low, low, medium, high, and very high vulnerability, which are 5.81, 26.03, 44.45, 22.57, and 13.1 respectively. It includes 1% of the studied area (Table 7). Interpretation of SI model results In the SI (sensitivity index) method, five parameters of groundwater depth (D), net recharge (R), aquifer environment (A), topography (slope) (T) and land use (LU) have been weighted for the vulnerability of the aquifer using GIS. It should be noted that the four parameters of the saturated environment, groundwater depth, net nutrition, and topography are ranked in the SI method like the DRASTIC method, and the only difference is that in the SI method, the ranks below the criteria of these layers are multiplied by 10, that is, if the rank of one of the sub-layers in the DRASTIC method is 3, it becomes 30 in the SI method. The rank of the sub-sections of the layers of the SI method varies between zero and 100. Because its 4 parameters are the same as DRASTIC, it is avoided to describe them again and only the land use layer is described below. Land use (LU) The problems related to the increase of urban and industrial chemical pollutants new agricultural practices and changes in the use of land are serious threats to the environment in recent decades. Therefore, in arid and semi-arid areas where the dependence on underground water resources is greater, the destructive effect of the quality of these resources will be more intense due to the natural weakness of water and soil resources. To prepare the land use map, we have used the Landsat 8 OLI sensor satellite image, pass 159 and rows 34 and 35, related to May 14, 2021 (May 24, 1400), and with a spatial resolution of 30 meters (Fig. 1). The study area has rainfed agriculture, pastures, population centers, water agriculture, and gardens, forests, and rivers. The highest rating has been given to agricultural use because the chemical fertilizers used in the fields are washed by irrigation water and rainfall and easily penetrate the surface of the earth. The low slope in these areas also facilitates the penetration of pollutants. Residential areas, industrial facilities, and roads are in second place in terms of increasing the vulnerability potential of the aquifer. This can be a result of the pollution caused by anthropogenic factors. Also, due to its very low permeability, the river has been assigned zero rank in terms of increasing the vulnerability potential of the aquifer. The ranking of the criteria of this parameter is shown in Table (3). Table 5 shows the weight of each of the classes of layers used in the SI method. Table (3) Land use ranking in the SI model (Sensitivity Index) (Stigter et al., 2006 ) Landuse type Rank Agricultural areas Annual crop irrigation areas (rice fields) 90 Permanent crop areas (orchards, herbal gardens) 70 Heterogeneous agricultural areas 50 Pastures and agriculture-forests 50 Anthropogenic areas Industrial waste, landfill sites 100 Human-made areas (mines, shipbuilding factories, desert mines) 80 Continuous urban areas, airports, ports, highways, railways, industrial and commercial areas outside green spaces 75 Discontinuous urban areas 70 Natural areas Water environments (salt marshes, salt lakes, tidal areas) 50 Natural areas (forests, semi-natural areas) 0 Aquatic areas 0 Figure (5) shows the map of each layer of the SI model and the rank of each class. After preparing each of the layers of the SI model to prepare the vulnerability map of the aquifer, the layers of the model have been combined in the ArcGIS software to create the final map (Fig. 8 ). Then, the area of each category has been calculated. Based on the results of the Mashhad plain aquifer sensitivity index model, created from the linear combination of SI model parameters, a vulnerability map has been prepared in five zones with very low, low, medium, high, and very high vulnerability, respectively, with 0.4, 24.63, 23.98, 18.71 and 32.25 percent of this area (Table 7 ). Interpretation of GODS model results This method uses four parameters aquifer type (G), lithological characteristics of unsaturated zone (O), depth of underground water (D), and type of surface soil texture (S). To achieve the goals of the research and prepare each of the layers used in the GODS model, we employed capabilities presented by GIS and remote sensing technology. Table (4) shows the weight of each of the classes of layers used in the GODS method. Aquifer type (G): This parameter evaluates the type of aquifer in terms of whether it is free or confined. The information about the type of aquifer has been obtained using the information of the water resources map. The type of aquifer is weighted according to Table 4 , which includes free aquifer (weight 1), free covered (weight 0.5), semi-confined (weight 0.3), confined (weight 0.2), artesian (weight 1. 0) and without reservoir (weight 0). The type of aquifer throughout the study area is open aquifer type. Lithological characteristics of the unsaturated zone (O): Lithology examines the origin, structure, characteristics, and history of the solid layer of the earth studies rocks in terms of origin, age, composition, and distribution, and classifies them in terms of their physical and clinical properties. Geological maps of the studied area have been used to prepare the lithology layer. Thus, the Mashhad Plain aquifer study area includes three categories of materials in terms of lithological characteristics. the three categories are unhardened materials, hardened materials (porous rocks), and hardened materials (dense rocks). Unhardened materials (sediments) are formed by alluvial cone gravel, alluvial sand, glacial sand, windy sand, alluvial silt, and earthen soils. Hardened materials (porous rocks) are formed by chalky limestone, sandstone, siltstone, and mudstone. Hardened materials (dense rocks) are formed by karst limestone and recent lavas with igneous and metamorphic rocks. As can be seen in Table 4 , each of these elements is weighted from 0.4 to 1. The depth of the underground water level (D): The groundwater depth layer has been produced by the IDW interpolation method due to its higher accuracy than other methods in ArcGIS software. According to Table (4), this layer is classified into 7 ranges from ˂2 to > 100 and a proportional weight from 1 to 7 has been recorded for each of the ranges. Type of soil surface texture (S): Using the results of the analysis of 39 profiles taken from the region, the soil environment has been determined according to its textural classification and scored based on the pollution potential. The soil environment in the study area is classified as clay (weight 1), clay loam (weight 2), silt (weight 3), silty loam (weight 5), loam (weight 7), and sandy loam (weight 9). The potential of aquifer pollution depends on soil characteristics such as permeability, texture and proportion of soil organic matter. Thus, the presence of fine-grained materials such as silt and clay can reduce the relative permeability of the soil and can also decrease the movement of pollutants. In this situation, coarse-grained materials such as sand can facilitate the movement of pollutants in parts of the study area. Table 4 Rank and weight of GODS model parameters (PAEZ, 1990 ) Aquifer type No aquifer Artesian Confined Semi-confined Free and covered Free 0 0.1 0.2 0.3 0.5 1 Lithologic characteristics of unsaturated zones Intact soils Silty sand Aeolian sand Alluvial sand - Glacial sand and gravel Coarse-grained materials (sediments) and argillaceous rocks Argillaceous siltstone (sediments) Limestone with gypsum Siltstone Sandstone Limestone with gypsum Indurated materials (compact rocks) Igneous and metamorphic rocks Present-day lavas Karst limestone Indurated materials (compact rocks) 0.4 0.5 0.6 0.7 0.8 0.9 1 Depth of groundwater level / meters ˃ 100 100 − 50 50 − 20 20 − 10 10 − 5 5 − 2 ˂ 2 Texture of surface soil Clay Clay silt Silt Sandy silt Sand Coarse-grained gravel and sand Absence of soil 0.5 0.6 0.8 0.9 1 Figure 6 shows the maps of each of the layers of the GODS model and the rank of each class. After preparing the GODS model layers to prepare the aquifer vulnerability map, the model layers have been combined in ArcGIS to produce the final vulnerability map (Fig. 8 ). Then, the area of each category has been calculated (Table 7 ). Based on the results of the GODS model, the study area is divided into five zones with very low vulnerability (0.93%), low vulnerability (31.11%), medium vulnerability (11.45%), high vulnerability (1.56%) and very high vulnerability (54.95%). Interpretation of SINTACS model results In SINTACS method is executed with seven parameters of groundwater depth (S), net nutrition (I), effect of unsaturated zone (N), soil type (T), aquifer environment (A), hydraulic conductivity (C), and topography (slope) (SV). We have used GIS and remote sensing technology to achieve the research goals and prepare each of the layers according to the SINTACS model. Given the fact that the parameters used in the SINTACS model are the same as the DRASTIC model, it is avoided to mention again how they are weighted. These two models are the same in the type of investigated parameters, but they are different in the calculation method and mathematical relationships. Table (5) shows the weight of each of the classes of layers used in the SINTACS method. Table 5 Rank and weight of parameters in the SINTACS model (Eftekhari et al., 2019) Depth of ground water (meter) Range 0–3 3–5 5–7 7–10 10–13 13–20 20–30 30–36 36˂ Rank 9 8 7 6 5 4 3 2 1 Net nutrition (mm) Range 0–50 50–100 100–180 180–250 250> Rank 1 3 6 8 9 Unsaturated zone Formation type Clay, marl, silt Shale Sandstone and sand Gravel Rank 1 6 8 10 Soil environment Soil type Clay Lime clay Silt Silty lime Lime Sandy lime Rank 1 2 3 5 7 9 aquifer environment Formation type Clay and silt Limestone Sandstone and sand Gravel Rank 1 3 8 10 Hydraulic conductivity (meter/day) Range 0.01–1.3 1.3–3.9 3.9–8.6 8.6–13 13–24 24> Rank 1 2 4 6 8 10 Topography (slope %) Range 0.2 2–6 6–12 12–18 18˂ Rank 10 9 5 3 1 Figure 7 shows the maps of each layer of the SINTACS model and the rank of each class. After preparing each of the layers of the SINTACS model to prepare the vulnerability map of the aquifer, the layers of the model have been combined in the ArcGIS software environment to generate the final map (Fig. 8 ). The area of each category is presented in Table 7 . According to the SINTACS model, the Mashhad plain aquifer is divided into five vulnerability classes very low (0.44%), low (25.57%), moderate (28.58%), high (2.79%), very high (42.61%). Validation of DRASTIC, SI, GODS, and SINTACS vulnerability maps: To validate the vulnerability maps prepared from all four models, we have calculated the correlation coefficients between the vulnerability maps and the quality index of TDS. Information about TDS values of piezometric wells in the study area has been obtained from the Regional Water Company of Razavi Khorasan province. In this research, validation has been performed using statistical methods and calculating the correlation coefficient between vulnerability maps and TDS values in TerrSet software. In general, the concentration of TDS values of total dissolved solids is high in polluted groundwater and relatively low in drinking water (Ozler, 2003 & Mohammadi al. et, 2012). According to the classification (Todd, 1980 ), the TDS layer is divided into two categories of 0-1000 mg/liter class representing fresh water and 1000. 10,000 mg/liter class representing salty and polluted water. Validation results showed that all four DRASTIC, SI, GODS, and SINTACS models have high accuracy in zoning the vulnerability of Mashhad plain aquifer so that the correlation coefficients of vulnerability maps with TDS quality index are 0.996 in the DRASTIC model, and 0.995 in SI model, 0.85 in the GODS model, and 0.91 in the SINTACS model (Table 6 ). Table 6 Correlation coefficient of TDS values with DRASTIC, SI, GODS, and SINTACS vulnerability maps. Correlation coefficient Model 0.996 DRASTIC 0.995 SI 0.85 GODS 0.91 SINTACS Vulnerability analysis of Mashhad plain aquifer By comparing the vulnerability classes in the four DRASTIC, SI, GODS, and SINTACS models presented in Table 7 and Fig. 9 , it can be stated that the widest vulnerability in SI, GODS, and SINTACS models is related to the class of very high and in DRASTIC it is related to the class of moderate. The results of three of the four models have indicated that the class of very high vulnerability covers the largest area of the aquifer (Fig. 9 ). Table 7 Area and percentage of vulnerability classes in DRASTIC, SI, GODS, and SINTACS vulnerability maps. Model DRASTIC SI GODS SINTACS Vulnerability classes Area % Area km2 Area % Area km2 Area % Area km2 Area % Area km2 Very low 5.81 157.9 0.4 10.9 0.93 25.21 0.44 11.92 Low 26.03 707.38 24.63 669.54 31.11 840.02 25.57 690.5 Moderate 44.45 1207.97 23.98 651.9 11.45 309.2 28.58 771.73 High 22.57 613.42 18.71 508.5 1.56 42 2.79 75.38 Very high 1.13 30.72 32.25 876.57 54.95 1483.5 42.61 1150.4 Conclusion In the study conducted on the Mashhad plain aquifer, based on the DRASTIC model, the northwestern, central, and southeastern parts of the study area are among the areas with very low and low vulnerability, and the other areas have medium, high, and very high vulnerability. According to the SI model, the northwest, southeast, and parts of the central areas of the study area are among the classes with low and medium vulnerability. The southern and northern parts of the study area generally show high and very high vulnerability, and only very small spots scattered in the southwestern areas have a very low vulnerability value. According to the GODS model, certain areas in the northwestern and central parts of the study area are classified as high and very high vulnerability zones. On the other hand, low vulnerability zones are found in the eastern and southeastern parts, with scattered spots in the central region. Moderate and high vulnerability zones are also present in some parts of the inner and central places of the region. In the SINTACS model, the northwestern parts, as well as some central and southeastern areas, are classified as very low and low vulnerability zones, while other areas including the eastern, northeastern, and certain central regions are categorized as moderate, high, and very high vulnerability zones. There is acceptable compatibility and overlap between the vulnerability zones in the four models, but some discrepancies are observed in certain parts of the study area. For example, the adjacent area in the northwestern part of the study area is classified as a very high vulnerability zone in the GODS model, while it is considered a low and moderate vulnerability zone in the SINTACS model. There are also differences in the extent of vulnerability zones among the models, which can be attributed to variations in the type and number of parameters analyzed in each model. Generally, the vulnerability of the aquifer increases from the southeast to the northwest and then decreases from the central regions to the northwestern extremities, likely due to the groundwater flow direction from east to west and the higher static water level in these areas. It is also evident that the zones with moderate, high, and very high vulnerability are correlated with irrigated agriculture, orchards, and population and industrial centers. In terms of sediment types in the aquifer environment, sandy and sandy-muddy areas show higher vulnerability due to higher permeability, while the least vulnerable zones are found in silt and silty-sand areas. Based on the results obtained from the models and their validation, the findings of this study can be utilized in environmental assessments and various pollution analyses, serving as a basis for management decisions. As the results of this study revealed, the potential vulnerability of the Mashhad aquifer is relatively high. Since small changes in a vulnerable system can lead to significant destruction (Folke, 2006), it is necessary to take appropriate preventive measures to protect and manage these valuable resources and prevent further vulnerability and pollution of the Mashhad aquifer. If the quality management of groundwater is not implemented, the areas adjacent to the city of Mashhad, as well as the central and southeastern parts of the aquifer, which have the highest vulnerability, may face irreparable issues and problems in the future. Furthermore, considering the region's climate, low rainfall, recent droughts, rapid population growth, urbanization, increased agricultural and industrial activities, as well as the decline in water resources and the increase in pollution due to urban wastewater, agricultural and industrial effluents, and the increasing demand for drinking water, proper management can only be achieved through the cooperation of the people, experts, and relevant managers. Therefore, the following actions are suggested: Accurate and continuous monitoring of pollutant levels, especially nitrates, and determining the quality boundaries of water sources through further studies at the field and aquifer levels, to update and refine the regional pollution model. Considering the negative effects of pollution on the quality of groundwater, reducing the use of pesticides, chemical fertilizers, and nitrates, especially in areas with high vulnerability, and controlling agricultural, industrial, domestic, hospital, and other sources of pollution (by the monitoring program for managing water quality, control, and prevention of water pollution). Preventing the establishment of polluting industries and new units in areas with high vulnerability and ensuring that the disposal system for industrial wastewater and the burial or even recycling of industrial, urban, and rural waste comply with the standards. Declarations Author Contribution DeclarationAll authors of this article entitled: Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran) declare that this article does not contain any study on human body or animals.This manuscript is a part of the PhD thesis that was conducted in the Department of Natural Geography, Geomorphological Hazards, at Kharazmi University, Tehran, Iran.All data that support the findings of this study are available and presentable.This research did not receive any specific funding from any organization in the public, commercial or non-profit sectors.The authors declare that they have no competing financial interests or known personal relationships that would influence the work reported in this article.The main manuscript text was written by the first author, Vajihe Gholizade and Other authors have provided guidance and cooperation in the preparation and editing of all parts of the article as supervisors and advisors. Funding Declaration The authors of this article declare that this research did not receive any special funding from any organization in the public, commercial or non-profit sector, and all its costs were covered by the authors. The authors also declare that they have no competing financial interests or known personal relationships that could influence the work reported in this article. Data Availability declaration All primary and processed data that support the findings of this study are available and presentable. The library resources used in this article are referenced at the end of the article. Other primary and processed materials and data, including statistics, tables, maps, etc., are available and can be sent to researchers by sending a request to [email protected] . References Abu-Bakr, H. A. E. A. (2020). Groundwater vulnerability assessment in different types of aquifers. Agricultural Water Management, (240): 106275. Ahmadi, A., Aberoumand, M. (2009). 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GIS-based multicriteria and artificial neural network (ANN) investigation for the assessment of groundwater vulnerability and pollution hazard in the Braga shallow aquifer (Central Tunisia): A critical review of generic and modified DRASTIC models. Journal of Contaminant Hydrology, 259, 104245. https://doi.org/10.1016/j.jconhyd.2023.104245 Stigter, T. Y., Ribeiro, L., & Dill, A. M. M. (2006). Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeology journal. 14(1), 79-99. Thapinta, A., & Hudak, P. (2003). Use of geographic information systems for assessing groundwater pollution potential by pesticides in Central Thailand. Environmental International. 29, 87-93. Todd, P.K. (1980). Ground water, Hydrology. Kluwer Academic Publisher. p400. Turner et al. 2003. A framework for vulnerability analysis in sustainability science, Proc. Nat. Acad. Sci, 100(14), 8074-8079. Voudouris, K., Nazakis. N., Polemio, M., & Kareklas. K. (2010). Assessment of intrinsic vulnerability using the DRASTIC model and GIS in the Kiti aquifer. Cyprus. European Water. 30,13-24. Wen, Xiaohu; Wu, Jun; Si, Jianhua; 2009. A GIS-based DRASTIC model for assessing shallow groundwater vulnerability in the Zhangye Basin. northwestern China.. 57:1435–1442 Xiangmei, M., Leping, T., Chen, Y., & Lifeng, W. (2021). Forecast of annual water consumption in 31 regions of China considering GDP and population. Sustainable Production and Consumption, 27, 713-736. https://doi.org/10.1016/j.spc.2021.01.036 Yazdanpanahi, A., Khaledi Ahmadi, M., Gholafshani, M., & Heidari Alamdarlou, I. (2018). Investigating the land use effects on spatial and temporal variations of groundwater quality (Case study: Mashhad Plain). Iranian Journal of Watershed Management Science and Engineering, 43. Zardosht, Z., Khosravani, F., Rezaei, S., Ghaderi, S., & Hassani, G. (2023). The impact of two insecticides on the pollutant cycle and quality of surface and groundwater resources in the irrigated lands of Yasuj, Iran. Heliyon, 9(6), e17636. https://doi.org/10.1016/j.heliyon.2023.e17636 Zendehbad, M., Cepuder, P., Loiskandl, W., & Stumpp, C. (2019). Source identification of nitrate contamination in the urban aquifer of Mashhad, Iran. Journal of Hydrology: Regional Studies, 25, 100618. https://doi.org/10.1016/j.ejrh.2019.100618 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4172498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301201020,"identity":"d6da8f35-39ca-4802-b9a3-40cff00b4fb0","order_by":0,"name":"Vajihe Gholizade","email":"data:image/png;base64,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","orcid":"","institution":"Kharazmi University","correspondingAuthor":true,"prefix":"","firstName":"Vajihe","middleName":"","lastName":"Gholizade","suffix":""},{"id":301201021,"identity":"81d3aa82-88b1-48aa-bf54-95bdbea25fab","order_by":1,"name":"Amir Saffari","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Saffari","suffix":""},{"id":301201022,"identity":"46b2f374-d399-4fff-a1f1-50cd58d68cfd","order_by":2,"name":"Ali Ahmadabadi","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Ahmadabadi","suffix":""},{"id":301201023,"identity":"b0940298-c36e-4c55-a4d4-d74ab06cce6c","order_by":3,"name":"Amir Karam","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Karam","suffix":""}],"badges":[],"createdAt":"2024-03-26 23:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4172498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4172498/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56762106,"identity":"715f8e2f-d01d-4930-9633-2af77e7112e1","added_by":"auto","created_at":"2024-05-20 07:15:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99573,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of Mashhad plain aquifer in Razavi Khorasan province, Iran\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/5db30b4fea1b56f873f6583d.png"},{"id":56761623,"identity":"3e46b9a7-ecfd-4bf5-8136-dfbcd315916c","added_by":"auto","created_at":"2024-05-20 07:07:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161812,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of the DRASTIC, SI, SINACS models to assess the vulnerability potential of the Mashhad Plain aquifer\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/7467d69cc2cd98cd585684f3.png"},{"id":56761628,"identity":"48b0f559-64f3-415d-bafc-020b2cf21661","added_by":"auto","created_at":"2024-05-20 07:07:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":296373,"visible":true,"origin":"","legend":"\u003cp\u003eThe structure of GODS model to assess the vulnerability potential of Mashhad plain aquifer\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/b593a1fa51cfd021b45ea39c.png"},{"id":56762107,"identity":"f523000b-56d5-467b-9a69-4e0ba88ac0df","added_by":"auto","created_at":"2024-05-20 07:15:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133824,"visible":true,"origin":"","legend":"\u003cp\u003emaps 7 factors examined in the DRASTIC model, including groundwater depth, net nutrition, aquifer environment, soil environment, topography (slope), unsaturated environment, and hydraulic conductivity in the DRASTIC model\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/f80b8af290f77dc01b91fdd2.png"},{"id":56761626,"identity":"4f678943-4a0b-4f6c-a101-786dcec89748","added_by":"auto","created_at":"2024-05-20 07:07:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134710,"visible":true,"origin":"","legend":"\u003cp\u003emap of underground water depth layers, net nutrition, aquifer environment, topography (slope), and land use in SI model\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/4545352cdd9ed84a8e3204ef.png"},{"id":56762110,"identity":"eca20e1d-f100-47da-9b56-7a683d253275","added_by":"auto","created_at":"2024-05-20 07:15:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84236,"visible":true,"origin":"","legend":"\u003cp\u003eMap of aquifer-type layers, unsaturated zone characteristics, groundwater depth, and soil environment in GODS model\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/f1eb718ec499708acde7dffd.png"},{"id":56762422,"identity":"75ac143c-7179-41b1-8c6b-f7432edae346","added_by":"auto","created_at":"2024-05-20 07:23:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":127593,"visible":true,"origin":"","legend":"\u003cp\u003eMap of groundwater depth layers, net nutrition, unsaturated zone effect, soil type, aquifer environment, hydraulic conductivity, and topography (slope) in the SINTACS model\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/77c31e35a7355c301839b7b7.png"},{"id":56762109,"identity":"b7d47266-941d-4b86-a062-4f0c39e1124d","added_by":"auto","created_at":"2024-05-20 07:15:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":128852,"visible":true,"origin":"","legend":"\u003cp\u003eVulnerability potential zoning map of Mashhad plain based on DRASTIC, SI, GODS and SINTACS models\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/cff906859b7c7459db30630f.png"},{"id":56761630,"identity":"6a36f1cc-08e7-403f-b1e5-f0b71541f61b","added_by":"auto","created_at":"2024-05-20 07:07:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":9454,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical distribution of the area percentage of each of the aquifer vulnerability classes based on the results of DRASTIC, SI, GODS, and SINTACS models\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/b178b4e2dc5874ba712cc789.png"},{"id":58864150,"identity":"b85476b9-c6da-4325-881b-c7c61cb07781","added_by":"auto","created_at":"2024-06-22 13:44:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4172498/v1/35fbadce-26f5-4950-9c5d-6f2d7daa81ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe growth in population and the advancement of agriculture and industries (Bagheri et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khorrami and Malekmohammadi, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Salehi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karimi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) have increased water consumption (Montoya et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiangmei et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This increased consumption of water can lead to a degradation in both the quantity and quality of underground water resources (Zendehbad et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kazemi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jafarzadeh et al., 2023; Krishnamoonthy et al., 2023; Smida et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The situation has been exacerbated by the decline in fresh underground water resources and reduced infiltration of surface water and rainfall into these resources, leading to a significant reduction in surface flows (Pan et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jafarzadeh et al., 2023). In Iran, underground water has now become the primary source for agriculture, drinking water, and industrial purposes (Asghari Moghadam et al., 2016; Jafarzadeh et al., 2023). However, the excessive extraction of underground water has caused a significant drop in aquifer water levels and the depletion of water layers in the earth (Kazemi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salehi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karimi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zardosht et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thapinta, and Hudak, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Furthermore, human activities have made the groundwater vulnerable to pollutants from industries (Kazemi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salehi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karimi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and agriculture (Zardosht et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Currently, a significant portion of the Iran water consumption, particularly for drinking water, relies on underground water resources, mainly open aquifers (Jafarzadeh et al., 2023), which are highly susceptible to contamination from agricultural, industrial, and urban activities. Therefore, it is vital to assess the vulnerability of these aquifers for effective management, land use planning, quality monitoring, and groundwater pollution prevention and protection.\u003c/p\u003e\n\u003cp\u003eThere are various definitions of vulnerability. Turner et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e define it as the degree of probability of the system being damaged due to risk exposure. The vulnerability is considered as the level an entity is sensitive to damage and its potential for change or transformation (Gallopin,2006). Vulnerability is, in fact, an estimate of the type (management and environmental) and amount (amount) of damage to a system that is exposed to external or internal disturbances (Brand \u0026amp; Jax, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Vulnerability assessment is a process during which information identifying vulnerability is combined and the areas with high vulnerability are distinguished from the areas with low vulnerability (Civita \u0026amp; Della, 1994; Kwami et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the quantitative assessment of the vulnerability of the aquifer, attention is paid to the transfer and flow models in the saturated and unsaturated zone, and the effect of the physical and hydrological characteristics of the soil, nutrition, and infiltration depth are evaluated to determine the distribution of sensitive or vulnerable areas (Almasri, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kwami et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Smida et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSo far, a lot of research has been done in the field of vulnerability assessment of aquifers in the world and Iran. We discussed some of the research about the vulnerability of aquifers. Aneesh et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the vulnerability of an urban coastal aquifer in India using the DRASTIC model based on GIS. The study shows that pollution and vulnerability to pollutants is a major cause of concern for more than 3.82\u0026nbsp;million people living in the region. Abu-Bakr (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied the vulnerability of groundwater in different types of aquifers in Egypt. The results of this study have shown the vulnerability of the aquifer in the three regions of Al-Minya, Wadi El-Trun, and Al-Kharga Oasis in low to medium zones. The research by Bordbar et al, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e about the vulnerability of the Qarasu-Garganrud coastal aquifer against the advance of saltwater revealed that the GALDIT model is superior to other methods such as SINTACS and DRASTIC. The application of surface feeding of the aquifer in the modification of the GALDIT method was examined by Faal et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) to assess the risk of saltwater advance in the Qom aquifer. The results of the research showed that the areas with high and medium vulnerability in the eastern part of the Qom aquifer with an area of about 14% of the total area of the aquifer are susceptible to the advance of salt water and can be considered as the range of salt water expansion for monitoring and optimal management of the coastal aquifer. In their research on the vulnerability of the karst aquifers of Kermanshah Plain and Bistun-Parav massif, Melki et al., (2018) employed the COP model. The results of their study indicated that 31.4% of the region is in the medium vulnerability 30.7% in the zone of low vulnerability, and 37.9% is located in the zone with very low vulnerability. The vulnerability of the Babol City aquifer was also investigated by Jaafari and Khoshrosh (2018) using the modified drastic model. Taking into account the main characteristics of the studied area (urban structure and paddy fields), the Drastic index was computed, and the degree of vulnerability of the Babol city aquifer was in the range of 127 to 176. Nediri et al., (2018) in their article assessed the vulnerability of the Dasht Khoi aquifer using the combined method with DRASTIC and SINTACS models. For validation, they used the measured nitrate values and concluded that the combined method showed a higher correlation index and correlation coefficient than the single DRASTIC and SINTACS methods. Hence, the combined method is more suitable for evaluating the vulnerability of this area. In the combined method, 19%, 42%, and 39% of the area of Dasht Khoi aquifer are in low, medium, and high vulnerability, respectively. Aftakhari et al., (2018), in an article titled \u0026quot;Evaluation of the qualitative vulnerability of the Birjand Plain aquifer using the SINTACS method\u0026quot;, have concluded that 12.81% of the study area has medium to high vulnerability. They found that 80.47% of the area has high vulnerability and 71.6% of that has very high vulnerability. Faal Aghdam et al., (2016) in their research worked on the vulnerability of the Bilourdi plain aquifer of East Azerbaijan with DRASTIC and SINTACS methods and found that 36.5% of the total area of the study region is moderately vulnerable, 20% highly vulnerable and 43.5% is located in the medium vulnerability zone. Despite the higher efficiency of the SINTACS model to check the vulnerability, the combined methods provide better results. Seyf et al., (2013) in their study evaluated and prepared a map of the vulnerability of karst aquifers using the COP model, using three parameters of the covering layer, flow concentration, and precipitation regime to determine the vulnerability of the clean aquifer in Kermanshah. They found that 12.22%, 48.32%, and 39.46% of the area are categorized as very low, low and medium vulnerability, respectively. In the research entitled \u0026ldquo;Vulnerability assessment of Jovein plain aquifer using DRASTIC and GODS methods\u0026rdquo; by Khodaei et al., (2015), using vulnerability zoning, they compared the results of GODS and DRASTIC models. They argued that in both methods, the vulnerability of the Dasht Jovin aquifer is in two groups of low and medium vulnerability categories. In the GODS method, the range of low vulnerability is 35% and the medium vulnerability is 65%, and in the DRASTIC method, the low vulnerability is 49% and the medium vulnerability is 51% of the area of the plain. Zendehbad et al (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) also conducted a study in Mashhad, Iran to identify the sources of high nitrate contamination in the urban aquifer. They used isotopic analysis of groundwater nitrate to determine its origins and provide information for future programs focused on improving groundwater quality. The study found that urbanization and land use had a greater influence on groundwater composition than interactions with the aquifer rock. The insufficient sewer system in certain parts of the study area directly contributed to the poor water quality. With limited natural processes to mitigate nitrate pollution, it is important to explore management strategies to reduce nitrogen input into the aquifer. In their study, Bagheri et al (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) explored the development of karst and the flow directions of a transboundary aquifer in Northeast Iran using geochemical and multi-isotope evidence. They analyzed the hydrogeological and hydrogeochemical characteristics and isotopic signatures of the karstic aquifer to determine the origin of groundwater, flow directions, and the development of karst. The researchers found that understanding the geo-hydrogeological and tectonic settings, as well as using isotopic approaches, provides valuable insights into both groundwater flow directions and karst development. This knowledge can contribute to better assessments and management of water resources not only in the study area but also in other transboundary karstic regions. Smida et al (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a critical review of the generic and modified DRASTIC models for assessing groundwater vulnerability and pollution hazard in the Braga shallow aquifer in Central Tunisia. They employed a GIS-based multicriteria and artificial neural network (ANN) analysis. The DRASTIC map generated from their study classified the vulnerability of the aquifer into four categories: very low (12.06%), low (81.88%), moderate (5.16%), and high (0.9%). The vulnerability index ranged between 43 and 159. The researchers further validated their findings and found that the DRSTIC-LU-based PHI (Pollution Hazard Index) was more reliable in accurately identifying the hazardous zones in the aquifer. Khorrami and Malekmohammadi (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study on the impact of excessive water extraction on groundwater ecosystem services (GSES) and developed a comprehensive framework to assess the vulnerability of GSES in the Mashhad Plain. They used a systematic approach to analyze the biophysical condition of the ecosystem and evaluated the spatial stability of GSES. By mapping the vulnerability intensity of GSES in the study area, they found that over 35% of the area was highly or very highly vulnerable. Additionally, approximately 18%, 30%, and 15% of the land exhibited moderate, low, and no vulnerability, respectively. The results indicated that excessive groundwater withdrawal had disrupted the provision of ecosystem services by the aquifer. This framework can be utilized for sustainable management of groundwater resources, particularly in arid and semi-arid regions faced with water resource depletion.\u003c/p\u003e\n\u003cp\u003eThe purpose of this research is to analyze the qualitative conditions of the Mashhad plain aquifer, to make our vulnerability analysis more reliable. Our analysis has been conducted by comparing the maps and the results of four models of DRASTIC, SI, GODS and SINTACS.\u003c/p\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eThe Mashhad plain is formed like a wide valley in northeast Iran, Razavi Khorasan province, and is located amongst Binaloud and Hazar Masjid mountains. The Mashhad Plain aquifer (Zendehbad et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khorrami and Malekmohammadi, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) is located in the catchment area of the Kashafroud River. Mashhad metropolis with a population of about 3.5 million people is developed in this area as the second largest metropolis of Iran. The study area of Mashhad-Chenaran is 9957 km2, and the Mashhad plain aquifer is 2720 km2 (Fig. 1). The main drainage of this region is the Kashafroud River flowing from the northwest to the southeast into the Mashhad Plain. Various formations from the early epochs to the Quaternary period can be observed in the area. The Mashhad plain is covered with Quaternary alluvium, resulting from the activity of the Kashafroud river and the seasonal floods of the rivers such as Radkan, Kardeh, Torghabeh, and Shandiz which originate from the mountains of Hazar Masjid and Binaloud (Lashgaripour et al., 2014; Bagheri et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). An open water aquifer is spread throughout the Mashhad plain, which continues to the edge of Binaloud Heights. Due to the roughness of the bedrock and the existence of feeding areas from the mountains of Binaloud and Hazar Masjid, this aquifer is not homogeneous and it is not the same in terms of drainage condition (Akbari et al., 2008; Zendehbad et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The area under study, as the main center of industrial and agricultural activities, was declared as a forbidden plain in 1988 due to the drop in the underground water level. Today, in addition to being in a critical prohibited situation in terms of the volume of resources, it is faced with serious challenges in terms of quality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThere are various methods to evaluate the vulnerability of an aquifer, including simulation-based methods, statistical methods, and overlapping index methods (Almasri and Kaluarachchi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Index and overlapping methods consist of hydrogeological parameters affecting the transfer of pollution to groundwater. To determine the relative importance, each of the parameters is compared with each other. Various methods for vulnerability assessment can be divided into three general categories including descriptive, statistical, and analytical ranking, and the combined methods (Niknam et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The most important ranking methods are GOD, AVI, DRASTIC, COP, SINTACS and SI methods, developed based on combining the layers obtained from different parameters. These methods are different in the type and number of parameters but ultimately lead to recording a numerical index or score for each feature (Asghari Moghadam et al., 2016). In this study, we used four models of DRASTIC, SI, GODS, and SINTACS to estimate the vulnerability of the Mashhad plain aquifer, and to make vulnerability zoning of the plain aquifer. Finally, the parameters of the models have been examined based on the results of the model through ArcGIS tools to generate vulnerability zoning maps for the aquifer.\u003c/p\u003e \u003cp\u003eIn the following sections, we have discussed the individual methodologies of each model, including the definitions of variables, mathematical equations, and the steps to perform calculations of each model.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDRASTIC model\u003c/h2\u003e \u003cp\u003eOne of the effective methods in assessing the vulnerability of aquifers is the DRASTIC model. This model was designed by the Environmental Protection Agency of the United States of America, to evaluate the potential of groundwater pollution, for the entire United States. This model is based on the hydrogeological concept and describes all the important geological and hydrogeological factors affecting the movement of groundwater when it enters, passes through and exits the system (Wen et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The DRASTIC method, as one of the most common methods for assessing the inherent vulnerability potential of underground water, is an overlap and index method. This method was developed by Aller et al. in 1987 with the aim of systematic assessment of groundwater contamination potential (Stigter et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This method employs seven measurable hydrogeological system parameters including the depth of the water table (D), net nutrition (R), aquifer environment (A), soil environment (S), topography (T), the effect of the unsaturated environment (I) and the hydraulic conductivity of the aquifer (C) (Babiker, al, et. 2005). Eq.\u0026nbsp;1 is used to prepare the DRASTIC index map:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(DI=\\sum _{j=1}^{7}{r}_{j}{w}_{j}={D}_{r}{D}_{w}+{R}_{r}{R}_{w}+{A}_{r}{A}_{w}+{S}_{r}{s}_{w}+{T}_{r}{T}_{w}+{I}_{r}{I}_{w}+{C}_{r}{C}_{w}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eIn the above equation, DI is the DRASTIC index, English uppercase letters represent the first letter of layer names, the index r: is the rank of the layer, and the index w: is the weight of the layer. The ratings of the sub-layers of each criterion vary from one to ten depending on their impact on the vulnerability potential. The weight of each layer is also a fixed value between one and five; which shows the relative importance of each layer compared to other layers in the vulnerability analysis of the aquifer (Rahman, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the structure of the DRASTIC model is illustrated to evaluate the vulnerability potential of the Mashhad Plain aquifer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSI model (Sensitivity Index)\u003c/h2\u003e \u003cp\u003eIt is a method to evaluate the vulnerability potential of aquifers. The five parameters of this method are groundwater depth (D), net nutrition (R), aquifer lithology (A), topography (T), and land use (LU). The most important difference between this method and the DRASTIC method is the inclusion of the land use parameter. This method was presented by Ribeiro in 2000 to analyze the vulnerability of aquifers on a large to medium scale, from 1:50000 to 1:200000 (Ribeiro, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In this method, after preparing the SI model layers and weighting each of the layer classes using the functions available in the ArcGIS environment, the sensitivity index is obtained from the weighted sum of the parameters mentioned in Eq.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(DI={D}_{r}{D}_{w}+{R}_{r}{R}_{w}+{A}_{r}{A}_{w}+{T}_{r}{T}_{w}+{LU}_{r}{LU}_{w}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eWhere SI is the sensitivity index, capital letters represent the first letter of layer names, index r is the rank of layers and index w stands for the weight of layers. The numerical value of the sensitivity index can vary between zero and 100 (Ribeiro, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the structure of the SI model for evaluating the vulnerability potential of the Mashhad Plain aquifer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGODS model\u003c/h2\u003e \u003cp\u003eThis method is developed based on four parameters aquifer type, characteristics of unsaturated zone, depth of underground water, and type of soil surface texture. The value of different classes of parameters changes from zero to one and equal weight is assigned to all the parameters. The GODS vulnerability index is obtained from the product of the parameters (Khodai et al., 2015). The index of the vulnerability of underground water resources is considered with three main influential parameters and is obtained through Eq.\u0026nbsp;3:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({I}_{v}=G\\times O\\times D\\times S\\)\u003c/span\u003e \u003c/span\u003eEq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003eWhere Iv is the vulnerability index, G is the score of the type of aquifer environment, O is the lithology score of the unsaturated zone (the lithology parameter is only calculated for open aquifers), and D is the score of the depth of the underground water level (Foster, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe collected data are entered into the GIS application. The layers are valued based on their location. The zoning map is obtained based on the values of each parameter. The maps have been prepared using the Raster Calculator tool in ArcGIS and combined according to the relevant weight coefficients to generate the final map of vulnerability zones (Novinpour and Khezri, 2018). In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the structure of the GODS model is presented to assess the vulnerability potential of the aquifer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSINTACS model\u003c/h2\u003e \u003cp\u003eOne of the most widely used and well-known overlap index methods is the SINTACS model (Civita, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This method was initially used by Civita et al. in 1990 to investigate the vulnerability of southern Italy. The SINTACS method is derived from the DRASTIC method. The parameters of this method are the same as the parameters of the DRASTIC method, but with the difference that the process of ranking the parameters in the SINTACS method is more flexible (Civita, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). One of the main advantages of the SINTACS model is to perform vulnerability assessment using a large number of layers, something that reduces the effect of errors or unknown factors on the final output (Pisciotta, et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this model, seven hydrogeological parameters are used to evaluate the relative vulnerability of groundwater pollution. The vulnerability index is obtained from the sum of the product of weights and ranks of the seven mentioned parameters, according to Eq.\u0026nbsp;4. The classification and valuation of different classes related to each of the parameters are executed in the GIS environment. A relative weight from 1 to 5 is given to each of the characteristics according to the importance of the effect on the pollution of the underground water system, which shows the relative effect of each characteristic on the transfer of pollution in the underground water. In this model, a rank from 1 to 10 is assigned to the ranges of each of the hydrological characteristics based on their impact on vulnerability, and it allows the user to use the SINTACS model for the study area. For the weighting and ranking of the SINTACS method, the ranks and weights provided by (Civita, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) have been used.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{I}\\text{N}\\text{T}\\text{A}\\text{C}\\text{S} \\text{I}\\text{n}\\text{d}\\text{e}\\text{x}={S}_{\\text{O}\\text{r}}{S}_{\\text{O}\\text{w}}+{I}_{\\text{r}}{I}_{\\text{w}}+{N}_{\\text{r}}{N}_{\\text{w}}+{T}_{\\text{r}}{T}_{\\text{w}}+{A}_{\\text{r}}{A}_{\\text{w}}+{C}_{\\text{r}}{C}_{\\text{w}}+{S}_{Vr}{S}_{Vw}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\sum _{i}^{7}{I}_{SINTACS}={P}_{i}\\times {W}_{i}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;4\u003c/p\u003e \u003cp\u003eWhere S is the depth of underground water, I is the net nutrition, N is the effect of the unsaturated zone, T is the soil type, A is the aquifer environment, C is the hydraulic conductivity, SV is the topography (slope), w is the weight, and r is the rank of each of the model parameters. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the structure of the SINTACS model, including the effective weights and ranks in the preparation of the qualitative vulnerability map of the Mashhad plain.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eIn this research, we employed four models DRASTIC, SI, GODS, and SINTACS to achieve the research goals and determine the level of vulnerability.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eInterpretation of DRASTIC model results\u003c/h2\u003e\n \u003cp\u003eIn the DRASTIC method, weights have been assigned to seven parameters of groundwater depth (D), net recharge (R), aquifer environment (A), soil environment (S), topography (T), unsaturated environment (I), hydraulic conductivity (C) in GIS. Table\u0026nbsp;1 shows the weights of each of the classes of layers used in the DRASTIC method.\u003c/p\u003e\n \u003cp\u003eTable (1) ranking and weight of DRASTIC model parameters\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eGroundwater depth / meter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u0026ndash;77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u0026ndash;92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92-134.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eNet Nutrition / mm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eSaturated zone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eSoil environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay lime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandy lime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLime silt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eTopography %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026ndash;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eUnsaturated zone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eHydraulic conductivity, meter / day\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u0026ndash;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u0026ndash;8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13-24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDepth of underground water (D):\u003c/p\u003e\n \u003cp\u003eThe distance between the surface of the earth and the water table is called the depth of the underground water (Ahmadi et al., 2012). As the level of depth increases, the likelihood of pollution decreases (Pakhstin Rouhi et al., 2016). In this research, the groundwater depth layer was prepared using the IDW interpolation method due to its higher accuracy than other methods in ArcGIS. According to Table\u0026nbsp;(1), this layer is categorized into 7 ranges from 0\u0026ndash;25 to 92-134.5, and for each of the ranges, a proportional weight from 1 to 10 is recorded.\u003c/p\u003e\n \u003cp\u003eNet nutrition (R):\u003c/p\u003e\n \u003cp\u003eNutrition is the amount of water penetrating the surface of the earth and entering the water table (Rahman, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The intensity and passage of dissolved substances depend on the intensity and vertical movement of water into the ground (Bouwer, \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). In this research, the Piscopo method has been used to prepare the pure feeding layer. This method considers three factors rainfall, slope (percentage), and soil permeability. To prepare the rainfall map, the average annual rainfall statistics of the stations in the region and the IDW interpolation method have been used due to greater accuracy. The slope map has been prepared using the Digital Elevation Model (DEM) and the soil permeability map has also been created based on the soil map of the study area. The ranking of maps of soil permeability, precipitation, slope, and net nutrition are presented in Table\u0026nbsp;(2). According to this table, the 1st rank has been assigned to the rainfall layer, because the average rainfall of the region is 276 mm per year. Finally, based on Piscopo\u0026apos;s equation, the prioritized maps of slope, permeability, and rainfall rank have been processed in the ArcGIS software to generate a nutrition map.\u003c/p\u003e\n \u003cp\u003eTable (2) Classification of the net nutrition layer (Piscopo,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSoil Permeability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRainfall / mm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSlope / Percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNet Nutrition\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange / %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange / %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange / %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange / %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh very\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u0026ndash;850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u0026ndash;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAquifer environment (A):\u003c/p\u003e\n \u003cp\u003eThis parameter is related to the characteristics of the materials configured in the saturated zone, such as porosity, type, and size of particles (Brahim, et, al. 2012). The aquifer environment and its ingredients determine the length and trend of the underground water flow system in the aquifer (Voudouris, et, al. 2010). To prepare a map of this factor, information related to the type of saturated layer has been used in 39 drilling and piping sections and geophysical and geological explorations in the study area. The aquifer environment of the study area consists of sand, clay, shale, silt, gravel, and lime and each is weighted according to the permeability which is presented in Table\u0026nbsp;(1). Coarse-grained particles such as sand and gravel have a higher weight, and finer-grained particles such as clay and silt have a lower weight. In areas with higher permeability, there is no reaction between the pollutant and the soil, and the potential for pollution can be higher due to the high speed of pollutant diffusion.\u003c/p\u003e\n \u003cp\u003eSoil environment (S):\u003c/p\u003e\n \u003cp\u003eThe soil layer, usually with a thickness of about 0.5 to 2 meters, has a great potential to remove and reduce the concentration of pollutants due to the relatively high microbial activity, the presence of many organic substances, and the presence of plant roots (Amir Ahmadi et al., 2019). The potential of aquifer pollution depends on soil properties such as permeability, texture and proportion of soil organic matter (Kim and Hamm, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). In this research, the analysis results of 39 profiles taken from the region have been used to prepare the soil map. The soil environment is defined according to its textural classification and scored based on the pollution potential. The soil environment is classified in the study range from clay with a weight of 1 to loam-sandy with a weight of 5. Given that the potential of aquifer pollution depends on soil characteristics such as permeability, texture, and percentage of soil organic matter, the presence of fine-grained materials such as silt and clay has reduced the relative permeability of the soil and limited the movement of pollutants. The coarse-grained materials such as sand have facilitated the movement of pollutants in parts of the study area.\u003c/p\u003e\n \u003cp\u003eTopography (T):\u003c/p\u003e\n \u003cp\u003eThe topography factor expresses the changes in the slope of the area (Stigter. al. et, 2006). Slopes that provide a higher infiltration opportunity have a higher contamination potential (Plymale, et. al, 2002). In this research, the slope layer has been prepared by a DEM layer with a spatial resolution of 12.5 meters in ArcGIS software. The average slope between the two points is obtained by dividing the vertical distance by the horizontal distance between them.\u003c/p\u003e\n \u003cp\u003eUnsaturated zone (I):\u003c/p\u003e\n \u003cp\u003eThis area is unsaturated or continuously saturated so that it can control the passage of the pollutants and their dilution (Ahmadi and Aberoumand, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). An unsaturated zone controls the duration and manner of the movement of pollutants and, therefore, affects the time required for the quantity and concentration of substances that come into contact with pollution (Plymale, al. et, 2002). To prepare a map of this parameter, we used the data related to the soil type of the unsaturated zone in 39 drilling and pipe-laying sections in the Mashhad plain. The method of obtaining information related to the unsaturated zone has been the same as that of the aquifer environment. However, in this case, we considered the granularity and characteristics of the sediments between the underground water level and the ground surface. Accordingly, the more permeable the unsaturated zone is, the less reaction is formed between pollutants and soil particles, and the washing speed towards the aquifer is faster and higher. Therefore, the potential for contamination has increased. According to what can be seen in Table\u0026nbsp;(1), the formations of the study area are clay, marl, and silt (weight 1), shale (weight 6), sand and gravel (weight 8), and gravel formation (weight 10). So, the permeability in the areas with gravel formation is greater than in other places.\u003c/p\u003e\n \u003cp\u003eHydraulic conductivity (C):\u003c/p\u003e\n \u003cp\u003eHydraulic conductivity is the ability of the aquifer environment to transport water and its associated pollutants. This parameter controls the transport and dispersion of the pollutants from the injection point inside the saturated zone (Rahman, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The information related to hydraulic conductivity is obtained from pumping test calculations (Khodai et al., 2015). The transfer coefficient or transferability of an aquifer layer is the amount of water that passes through a unit cross-sectional area of the aquifer layer under a certain hydraulic slope, in square meters per day (meter per day in each meter of layer thickness). In general, because in pumping tests, the value of the parameter of the water transfer capability coefficient is measured, therefore, by using the saturation thickness of the aquifer, the value of hydraulic conductivity is obtained by dividing the water transfer capability coefficient by the saturation thickness. The transmission capability map has been obtained from the results of pumping tests and using the IDW interpolation method. The saturated layer thickness map has been obtained from the interpolation of the data related to the depth of the saturated region obtained from the drilling sections using the IDW method. Hence, the hydraulic conductivity of the study area has been obtained in 6 ranges from 0.01\u0026ndash;1.3 to more than 24.2 and with weighting from 1 to 10 according to Table\u0026nbsp;(1).\u003c/p\u003e\n \u003cp\u003eFigure 4 shows each layer of the DRASTIC model and the rank of each class. After preparing each of the layers of the DRASTIC model to prepare the vulnerability map of the aquifer, the layers of the model have been combined in the ArcGIS software environment and the final map has been obtained (Figure 8). We, then, has been calculated the area of each category. Based on the results of the DRASTIC model, the studied area has been divided into five zones with very low, low, medium, high, and very high vulnerability, which are 5.81, 26.03, 44.45, 22.57, and 13.1 respectively. It includes 1% of the studied area (Table 7).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eInterpretation of SI model results\u003c/h2\u003e\n \u003cp\u003eIn the SI (sensitivity index) method, five parameters of groundwater depth (D), net recharge (R), aquifer environment (A), topography (slope) (T) and land use (LU) have been weighted for the vulnerability of the aquifer using GIS. It should be noted that the four parameters of the saturated environment, groundwater depth, net nutrition, and topography are ranked in the SI method like the DRASTIC method, and the only difference is that in the SI method, the ranks below the criteria of these layers are multiplied by 10, that is, if the rank of one of the sub-layers in the DRASTIC method is 3, it becomes 30 in the SI method. The rank of the sub-sections of the layers of the SI method varies between zero and 100. Because its 4 parameters are the same as DRASTIC, it is avoided to describe them again and only the land use layer is described below.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLand use (LU)\u003c/h2\u003e\n \u003cp\u003eThe problems related to the increase of urban and industrial chemical pollutants new agricultural practices and changes in the use of land are serious threats to the environment in recent decades. Therefore, in arid and semi-arid areas where the dependence on underground water resources is greater, the destructive effect of the quality of these resources will be more intense due to the natural weakness of water and soil resources. To prepare the land use map, we have used the Landsat 8 OLI sensor satellite image, pass 159 and rows 34 and 35, related to May 14, 2021 (May 24, 1400), and with a spatial resolution of 30 meters (Fig. 1). The study area has rainfed agriculture, pastures, population centers, water agriculture, and gardens, forests, and rivers. The highest rating has been given to agricultural use because the chemical fertilizers used in the fields are washed by irrigation water and rainfall and easily penetrate the surface of the earth. The low slope in these areas also facilitates the penetration of pollutants. Residential areas, industrial facilities, and roads are in second place in terms of increasing the vulnerability potential of the aquifer. This can be a result of the pollution caused by anthropogenic factors. Also, due to its very low permeability, the river has been assigned zero rank in terms of increasing the vulnerability potential of the aquifer. The ranking of the criteria of this parameter is shown in Table (3). Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the weight of each of the classes of layers used in the SI method.\u003c/p\u003e\n \u003cp\u003eTable (3) Land use ranking in the SI model (Sensitivity Index) (Stigter et al.,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLanduse type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAgricultural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual crop irrigation areas (rice fields)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent crop areas (orchards, herbal gardens)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeterogeneous agricultural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePastures and agriculture-forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAnthropogenic areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial waste, landfill sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman-made areas (mines, shipbuilding factories, desert mines)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous urban areas, airports, ports, highways, railways, industrial and commercial areas outside green spaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscontinuous urban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNatural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater environments (salt marshes, salt lakes, tidal areas)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural areas (forests, semi-natural areas)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAquatic areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure (5) shows the map of each layer of the SI model and the rank of each class. After preparing each of the layers of the SI model to prepare the vulnerability map of the aquifer, the layers of the model have been combined in the ArcGIS software to create the final map (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Then, the area of each category has been calculated. Based on the results of the Mashhad plain aquifer sensitivity index model, created from the linear combination of SI model parameters, a vulnerability map has been prepared in five zones with very low, low, medium, high, and very high vulnerability, respectively, with 0.4, 24.63, 23.98, 18.71 and 32.25 percent of this area (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eInterpretation of GODS model results\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cp\u003eThis method uses four parameters aquifer type (G), lithological characteristics of unsaturated zone (O), depth of underground water (D), and type of surface soil texture (S). To achieve the goals of the research and prepare each of the layers used in the GODS model, we employed capabilities presented by GIS and remote sensing technology. Table\u0026nbsp;(4) shows the weight of each of the classes of layers used in the GODS method.\u003c/p\u003e\n \u003cp\u003eAquifer type (G):\u003c/p\u003e\n \u003cp\u003eThis parameter evaluates the type of aquifer in terms of whether it is free or confined. The information about the type of aquifer has been obtained using the information of the water resources map. The type of aquifer is weighted according to Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, which includes free aquifer (weight 1), free covered (weight 0.5), semi-confined (weight 0.3), confined (weight 0.2), artesian (weight 1. 0) and without reservoir (weight 0). The type of aquifer throughout the study area is open aquifer type.\u003c/p\u003e\n \u003cp\u003eLithological characteristics of the unsaturated zone (O):\u003c/p\u003e\n \u003cp\u003eLithology examines the origin, structure, characteristics, and history of the solid layer of the earth studies rocks in terms of origin, age, composition, and distribution, and classifies them in terms of their physical and clinical properties. Geological maps of the studied area have been used to prepare the lithology layer. Thus, the Mashhad Plain aquifer study area includes three categories of materials in terms of lithological characteristics. the three categories are unhardened materials, hardened materials (porous rocks), and hardened materials (dense rocks). Unhardened materials (sediments) are formed by alluvial cone gravel, alluvial sand, glacial sand, windy sand, alluvial silt, and earthen soils. Hardened materials (porous rocks) are formed by chalky limestone, sandstone, siltstone, and mudstone. Hardened materials (dense rocks) are formed by karst limestone and recent lavas with igneous and metamorphic rocks. As can be seen in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, each of these elements is weighted from 0.4 to 1.\u003c/p\u003e\n \u003cp\u003eThe depth of the underground water level (D):\u003c/p\u003e\n \u003cp\u003eThe groundwater depth layer has been produced by the IDW interpolation method due to its higher accuracy than other methods in ArcGIS software. According to Table\u0026nbsp;(4), this layer is classified into 7 ranges from ˂2 to \u0026gt;\u0026thinsp;100 and a proportional weight from 1 to 7 has been recorded for each of the ranges.\u003c/p\u003e\n \u003cp\u003eType of soil surface texture (S):\u003c/p\u003e\n \u003cp\u003eUsing the results of the analysis of 39 profiles taken from the region, the soil environment has been determined according to its textural classification and scored based on the pollution potential. The soil environment in the study area is classified as clay (weight 1), clay loam (weight 2), silt (weight 3), silty loam (weight 5), loam (weight 7), and sandy loam (weight 9). The potential of aquifer pollution depends on soil characteristics such as permeability, texture and proportion of soil organic matter. Thus, the presence of fine-grained materials such as silt and clay can reduce the relative permeability of the soil and can also decrease the movement of pollutants. In this situation, coarse-grained materials such as sand can facilitate the movement of pollutants in parts of the study area.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRank and weight of GODS model parameters (PAEZ, \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"19\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"19\"\u003e\n \u003cp\u003eAquifer type\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo aquifer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eArtesian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eConfined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSemi-confined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFree and covered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"19\"\u003e\n \u003cp\u003eLithologic characteristics of unsaturated zones\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIntact soils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSilty sand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAeolian sand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAlluvial sand\u003c/p\u003e\n \u003cp\u003e- Glacial sand and gravel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCoarse-grained materials (sediments) and argillaceous rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eArgillaceous siltstone (sediments)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLimestone with gypsum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSiltstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLimestone with gypsum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIndurated materials (compact rocks)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eIgneous and metamorphic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePresent-day lavas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eKarst limestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIndurated materials (compact rocks)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"19\"\u003e\n \u003cp\u003eDepth of groundwater level / meters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e˃ 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u0026thinsp;\u0026minus;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026minus;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e10\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e5\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e˂ 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"19\"\u003e\n \u003cp\u003eTexture of surface soil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eClay silt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSandy silt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCoarse-grained gravel and sand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAbsence of soil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the maps of each of the layers of the GODS model and the rank of each class. After preparing the GODS model layers to prepare the aquifer vulnerability map, the model layers have been combined in ArcGIS to produce the final vulnerability map (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Then, the area of each category has been calculated (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Based on the results of the GODS model, the study area is divided into five zones with very low vulnerability (0.93%), low vulnerability (31.11%), medium vulnerability (11.45%), high vulnerability (1.56%) and very high vulnerability (54.95%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eInterpretation of SINTACS model results\u003c/h2\u003e\n \u003cp\u003eIn SINTACS method is executed with seven parameters of groundwater depth (S), net nutrition (I), effect of unsaturated zone (N), soil type (T), aquifer environment (A), hydraulic conductivity (C), and topography (slope) (SV). We have used GIS and remote sensing technology to achieve the research goals and prepare each of the layers according to the SINTACS model. Given the fact that the parameters used in the SINTACS model are the same as the DRASTIC model, it is avoided to mention again how they are weighted. These two models are the same in the type of investigated parameters, but they are different in the calculation method and mathematical relationships. Table (5) shows the weight of each of the classes of layers used in the SINTACS method.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRank and weight of parameters in the SINTACS model (Eftekhari et al., 2019)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"27\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eDepth of ground water (meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e5\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e7\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e10\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e13\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e20\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e30\u0026ndash;36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36˂\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eNet nutrition (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e0\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e50\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e100\u0026ndash;180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e180\u0026ndash;250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e250\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eUnsaturated zone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormation type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eClay, marl, silt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eSandstone and sand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eGravel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eSoil environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSoil type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLime clay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSilty lime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSandy lime\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eaquifer environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFormation type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eClay and silt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eLimestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSandstone and sand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eGravel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eHydraulic conductivity (meter/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.01\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e1.3\u0026ndash;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e3.9\u0026ndash;8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e8.6\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e13\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e24\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"27\"\u003e\n \u003cp\u003eTopography (slope %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e2\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e6\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e12\u0026ndash;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e18˂\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the maps of each layer of the SINTACS model and the rank of each class. After preparing each of the layers of the SINTACS model to prepare the vulnerability map of the aquifer, the layers of the model have been combined in the ArcGIS software environment to generate the final map (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The area of each category is presented in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. According to the SINTACS model, the Mashhad plain aquifer is divided into five vulnerability classes very low (0.44%), low (25.57%), moderate (28.58%), high (2.79%), very high (42.61%).\u003c/p\u003e\n \u003cp\u003eValidation of DRASTIC, SI, GODS, and SINTACS vulnerability maps:\u003c/p\u003e\n \u003cp\u003eTo validate the vulnerability maps prepared from all four models, we have calculated the correlation coefficients between the vulnerability maps and the quality index of TDS. Information about TDS values of piezometric wells in the study area has been obtained from the Regional Water Company of Razavi Khorasan province. In this research, validation has been performed using statistical methods and calculating the correlation coefficient between vulnerability maps and TDS values in TerrSet software. In general, the concentration of TDS values of total dissolved solids is high in polluted groundwater and relatively low in drinking water (Ozler, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e \u0026amp; Mohammadi al. et, 2012). According to the classification (Todd, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e), the TDS layer is divided into two categories of 0-1000 mg/liter class representing fresh water and 1000. 10,000 mg/liter class representing salty and polluted water. Validation results showed that all four DRASTIC, SI, GODS, and SINTACS models have high accuracy in zoning the vulnerability of Mashhad plain aquifer so that the correlation coefficients of vulnerability maps with TDS quality index are 0.996 in the DRASTIC model, and 0.995 in SI model, 0.85 in the GODS model, and 0.91 in the SINTACS model (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation coefficient of TDS values with DRASTIC, SI, GODS, and SINTACS vulnerability maps.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrelation coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRASTIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGODS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSINTACS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eVulnerability analysis of Mashhad plain aquifer\u003c/p\u003e\n \u003cp\u003eBy comparing the vulnerability classes in the four DRASTIC, SI, GODS, and SINTACS models presented in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, it can be stated that the widest vulnerability in SI, GODS, and SINTACS models is related to the class of very high and in DRASTIC it is related to the class of moderate. The results of three of the four models have indicated that the class of very high vulnerability covers the largest area of the aquifer (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and percentage of vulnerability classes in DRASTIC, SI, GODS, and SINTACS vulnerability maps.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDRASTIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGODS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSINTACS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVulnerability classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea km2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea km2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea km2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea km2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e707.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e840.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e690.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1207.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e771.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e613.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e876.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1483.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1150.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the study conducted on the Mashhad plain aquifer, based on the DRASTIC model, the northwestern, central, and southeastern parts of the study area are among the areas with very low and low vulnerability, and the other areas have medium, high, and very high vulnerability. According to the SI model, the northwest, southeast, and parts of the central areas of the study area are among the classes with low and medium vulnerability. The southern and northern parts of the study area generally show high and very high vulnerability, and only very small spots scattered in the southwestern areas have a very low vulnerability value. According to the GODS model, certain areas in the northwestern and central parts of the study area are classified as high and very high vulnerability zones. On the other hand, low vulnerability zones are found in the eastern and southeastern parts, with scattered spots in the central region. Moderate and high vulnerability zones are also present in some parts of the inner and central places of the region. In the SINTACS model, the northwestern parts, as well as some central and southeastern areas, are classified as very low and low vulnerability zones, while other areas including the eastern, northeastern, and certain central regions are categorized as moderate, high, and very high vulnerability zones.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is acceptable compatibility and overlap between the vulnerability zones in the four models, but some discrepancies are observed in certain parts of the study area. For example, the adjacent area in the northwestern part of the study area is classified as a very high vulnerability zone in the GODS model, while it is considered a low and moderate vulnerability zone in the SINTACS model. There are also differences in the extent of vulnerability zones among the models, which can be attributed to variations in the type and number of parameters analyzed in each model. Generally, the vulnerability of the aquifer increases from the southeast to the northwest and then decreases from the central regions to the northwestern extremities, likely due to the groundwater flow direction from east to west and the higher static water level in these areas. It is also evident that the zones with moderate, high, and very high vulnerability are correlated with irrigated agriculture, orchards, and population and industrial centers. In terms of sediment types in the aquifer environment, sandy and sandy-muddy areas show higher vulnerability due to higher permeability, while the least vulnerable zones are found in silt and silty-sand areas. Based on the results obtained from the models and their validation, the findings of this study can be utilized in environmental assessments and various pollution analyses, serving as a basis for management decisions.\u003c/p\u003e\n\u003cp\u003eAs the results of this study revealed, the potential vulnerability of the Mashhad aquifer is relatively high. Since small changes in a vulnerable system can lead to significant destruction (Folke, 2006), it is necessary to take appropriate preventive measures to protect and manage these valuable resources and prevent further vulnerability and pollution of the Mashhad aquifer. If the quality management of groundwater is not implemented, the areas adjacent to the city of Mashhad, as well as the central and southeastern parts of the aquifer, which have the highest vulnerability, may face irreparable issues and problems in the future. Furthermore, considering the region's climate, low rainfall, recent droughts, rapid population growth, urbanization, increased agricultural and industrial activities, as well as the decline in water resources and the increase in pollution due to urban wastewater, agricultural and industrial effluents, and the increasing demand for drinking water, proper management can only be achieved through the cooperation of the people, experts, and relevant managers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, the following actions are suggested: Accurate and continuous monitoring of pollutant levels, especially nitrates, and determining the quality boundaries of water sources through further studies at the field and aquifer levels, to update and refine the regional pollution model. Considering the negative effects of pollution on the quality of groundwater, reducing the use of pesticides, chemical fertilizers, and nitrates, especially in areas with high vulnerability, and controlling agricultural, industrial, domestic, hospital, and other sources of pollution (by the monitoring program for managing water quality, control, and prevention of water pollution). Preventing the establishment of polluting industries and new units in areas with high vulnerability and ensuring that the disposal system for industrial wastewater and the burial or even recycling of industrial, urban, and rural waste comply with the standards.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDeclarationAll authors of this article entitled: Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran) declare that this article does not contain any study on human body or animals.This manuscript is a part of the PhD thesis that was conducted in the Department of Natural Geography, Geomorphological Hazards, at Kharazmi University, Tehran, Iran.All data that support the findings of this study are available and presentable.This research did not receive any specific funding from any organization in the public, commercial or non-profit sectors.The authors declare that they have no competing financial interests or known personal relationships that would influence the work reported in this article.The main manuscript text was written by the first author, Vajihe Gholizade and Other authors have provided guidance and cooperation in the preparation and editing of all parts of the article as supervisors and advisors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this article declare that this research did not receive any special funding from any organization in the public, commercial or non-profit sector, and all its costs were covered by the authors.\u003c/p\u003e\n\u003cp\u003eThe authors also declare that they have no competing financial interests or known personal relationships that could influence the work reported in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll primary and processed data that support the findings of this study are available and presentable. The library resources used in this article are referenced at the end of the article. Other primary and processed materials and data, including statistics, tables, maps, etc., are available and can be sent to researchers by sending a request to [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbu-Bakr, H. A. E. A. (2020). Groundwater vulnerability assessment in different types of aquifers. Agricultural Water Management, (240): 106275.\u003c/li\u003e\n \u003cli\u003eAhmadi, A., Aberoumand, M. (2009). Vulnerability of Khash-Plain Aquifer, Eastern Iran, to Pollution Using Geographic Information System (GIS). Journal of Geotechnical Geology, 5(1), 1-11(In Persian).\u003c/li\u003e\n \u003cli\u003eAhmadi, J., Akhoundi, L., Abbasi, H., Khashaei Siouki, A., \u0026amp; Alimadadi, M. (2013). Determining aquifer vulnerability using the DRASTIC model and applying sensitivity analysis, and elimination of parameters (case study: Saveh-Neyzar Plain). 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Journal of Hydrology: Regional Studies, 25, 100618. https://doi.org/10.1016/j.ejrh.2019.100618\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vulnerability, Mashhad aquifer, DRASTIC, SI, GODS and SINTACS","lastPublishedDoi":"10.21203/rs.3.rs-4172498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4172498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe vulnerability of the Mashhad aquifer has been analyzed by spatial analysis approach using DRASTIC, SI, GODS and SINTACS models. The Mashhad aquifer in northeast Iran is now considered a critical area due to its special circumstances, the presence of industrial and agricultural activities, and large settlements. This study aims to evaluate the vulnerability zones of the Mashhad aquifer using four models DRASTIC, SI, GODS and SINTACS. The parameters of the models are explained and measured by GIS capabilities. After weighting, ranking, and integrating the layers in the ArcGIS software, we have produced vulnerability maps of the aquifer. The results have indicated that in the DRASTIC model, the study area is categorized into five vulnerability zones very low (5.81%), low (26.03%), moderate (44.45%), high (22.57%), and very high (1.13%). In the SI model, the study area is categorized into five vulnerability zones very low (0.40%), low (24.63%), moderate (23.98%), high (18.71%), and very high vulnerability (32.25%). In the GODS model, it is categorized into five vulnerability zones very low (0.93%), low (31.11%), moderate (11.45%), high (1.56%), and very high (54.95%). In the SINTACS model, the area is also categorized into the vulnerability five zones very low (0.44%), low (25.57%), moderate (28.58%), high (2.79%), and very high (42.61%). For validating the results, the vulnerability maps have been compared with the TDS quality index. The results showed that all four models have high accuracy in categorizing the vulnerability of the Mashhad aquifer. The comparison among the results of the models has indicated that the vulnerability of the aquifer generally increases from southeast to northwest and then decreases from the central region towards the northwestern areas.\u003c/p\u003e","manuscriptTitle":"Spatial analysis of aquifer vulnerability using DRASTIC, SI, GODS and SINTACS models, (Study area: Mashhad Plain aquifer - Northeast Iran)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-20 07:07:07","doi":"10.21203/rs.3.rs-4172498/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c982295b-262f-4881-9abc-28b5bbc49cd1","owner":[],"postedDate":"May 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-22T13:36:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-20 07:07:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4172498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4172498","identity":"rs-4172498","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}