Arab J Sci Eng (2014) 39:207–216 DOI 10.1007/s13369-013-0843-3

RESEARCH ARTICLE - EARTH SCIENCES

GIS Based Assessment of Groundwater Vulnerability Using Drastic Model Sathees Kumar · D. Thirumalaivasan · Nisha Radhakrishnan

Received: 30 May 2012 / Accepted: 15 February 2013 / Published online: 1 November 2013 © King Fahd University of Petroleum and Minerals 2013

Abstract Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The DRASTIC model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. In this study the DRASTIC model is applied for a part of Kancheepuram district, Tamil Nadu, India, to generate a small-scale map of groundwater vulnerability to contamination. The whole area is classified on a scale of very low, low, moderate and high susceptibility to pollution. The model is considered in relation to groundwater quality data and results have shown a strong relationship between DRASTIC specific vulnerability index and nitrate-as-nitrogen concentrations. A groundwater vulnerability map is developed by using the DRASTIC model in a computer based Geographic Information System. The results show that the central part of the study area is classified as a high vulnerable zone and the south and northeastern parts show medium vulnerable zones, and record higher nitrate values. S. Kumar (B) · N. Radhakrishnan Department of Civil Engineering, National Institute of Technology, Tiruchirappalli 620 015, India e-mail: [email protected] D. Thirumalaivasan Institute of Remote Sensing, College of Engineering, Guindy Campus, Anna University, Chennai 600 025, India

Keywords Aquifer vulnerability · Chengalpattu · DRASTIC model · GIS · Specific vulnerability index

1 Introduction The theory of groundwater vulnerability was introduced by the end of 1960s to create an alertness of groundwater contamination [1]. It can be defined as the possibility of percolation and diffusion of contaminants from the ground surface into the groundwater system. Vulnerability is usually considered as an “intrinsic” property of a groundwater system that depends on its sensitivity to human and/or natural impacts [2]. “Specific” or “integrated” vulnerability, on the other hand, combines intrinsic vulnerability with the risk of

123

208

Arab J Sci Eng (2014) 39:207–216

the groundwater being exposed to the loading of pollutants from certain sources [1]. Groundwater vulnerability deals only with the hydrogeological setting and does not include pollutant attenuation. The natural hydrogeological factors affect the different pollutants in different ways depending on their interactions and chemical properties. Many approaches have been developed to evaluate aquifer vulnerability. They include process based methods, statistical methods, and overlay and index methods [3]. The process based methods use simulation models to estimate the contaminant migration but they are constrained by data shortage and computational difficulties [4]. Statistical methods use statistics to determine associations between spatial variables and the actual occurrence of pollutants in the groundwater. Their limitations include insufficient water quality observations, data accuracy and careful selection of spatial variables. Overlay and index methods combine factors controlling the movement of pollutants from the ground surface into the saturated zone resulting in vulnerability indices at different locations. Their main advantage is that some of the factors such as rainfall and depth to groundwater can be available over large areas, which makes them suitable for regional scale assessments [5]. However, their major drawback is the subjectivity in assigning numerical values to the descriptive entities and relative weights for the different attributes. DRASTIC is an index model designed to produce vulnerability scores for different locations by combining several thematic layers (Depth-to-water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity) [6–8]. The DRASTIC method assumes that (1) any contaminant is introduced at the ground surface; (2) the contaminant is flushed into the groundwater by precipitation; (3) the contaminant has the mobility of water; (4) the areas evaluated using DRASTIC are 0.4 km2 or larger [9,10]. Fig. 1 Location map of study area [16]

123

The method was originally developed for manual overlay of semi quantitative data layers, however the simple definition of its vulnerability index as a linear combination of factors shows the feasibility of the computation using Geographical Information System (GIS) [11–15]. Groundwater represents a main resource for supply in Chengalpattu. The need for protection and management of groundwater has been recognized. Agricultural pesticides and wastewater are the main causes of the degradation of groundwater quality in the study area. Also, insecure landfill of municipal wastes on permeable aquifer units and uncontrolled discharge of sewage affect groundwater quality negatively. As a result of surface and groundwater flow, the variety of contaminants and their mixing in surface water and groundwater threaten the groundwater quality. Hence, groundwater vulnerability in the area should be determined for the protection of the groundwater. The main aim of this study is to evaluate the intrinsic and specific groundwater vulnerability index for the study area using the DRASTIC model based on GIS.

2 Study Area Chengalpattu, in Kancheepuram district, located on the northern East Coast of Tamil Nadu (Fig. 1) is one of the largest industrial areas in Tamil Nadu, India, covering an area of 764 km2 . It is bounded in the west by the Vellore and Thiruvannamalai districts, in the north by the Thiruvallur and Chennai districts, in the south by the Villuppuram district in the east by the Bay of Bengal. The geographical location of Chengalpattu lies between 11◦ 00 to 12◦ 00 North latitudes and 77◦ 28 to 78◦ 50 East longitudes. The town of Chengalpattu had a population of 412,289. It is the second largest town in the district of Kancheepuram.

Arab J Sci Eng (2014) 39:207–216

In the summer season, maximum and minimum temperatures are 37.5 ◦ C and 20.0 ◦ C, respectively. In winter season maximum and minimum temperature is 28.7 ◦ C, 19.8 ◦ C respectively. The pre-monsoon rainfall is almost uniform throughout the district. The coastal taluk gets more rain than do the interior regions. The prevailing wind direction is southwest in the morning and southeast in the evening. The town gets rain from both SW and NE monsoons. Average annual rainfall is 1,125 mm. The NE and SW monsoons are the major donors with 54 and 36 % contribution each, to the total annual rainfall [17]. In the study area, the major geomorphological related features are sandstone and shales, Charnockite, GarnetSillimanite gneiss, clayey sand, and sand and silt. The area exposes crystalline rocks of Achaean age and sedimentary rocks of Gondwana and the Cuddalore Formation of MioPliocene age. A gravel and shingle bed locally known as the Kanjeevaram Gravels belongs has a Pliocene to early Pleistocene age.

3 Methodology The aquifer vulnerability assessment, ever since it was first introduced in 1968, has evolved considerably in pursuit of methods, which are realistic, effective, and accurate. Consequently, numerous models for vulnerability assessment have been developed varying in their approaches, data requirements, and wider applicability. The overlay and index methods in general and the DRASTIC model in particular, are the most widely used techniques for vulnerability assessment studies at regional scales.

209

The above seven parameters are used to define the hydrological setting of an area. These seven parameters are further subdivided into ranges (or) zones, representing various hydrological settings and are assigned different ratings on a scale of 1 in 10 (Table 1). The rating assigned to each of these ranges or zones indicates their relative importance within each parameter, in contributing to aquifer vulnerability. DRASTIC is a standardized non-subjective method to compare the vulnerability to contaminate over various hydrological settings. For this reason the method is rigid in the assignment of weights and ratings to the parameters. 3.2 DRASTIC Intrinsic Vulnerability Index (DIVI) The seven parameters themselves not considered to be equally important in vulnerability assessment. In order to reflect the relative importance of these parameters, weights in the scale of 1–5 are assigned to each of these parameters (Table 2). The seven hydrological parameters—with their rating and weights are linearly combined, additively, to derive the weighted map—indicate the non-dimensional intrinsic vulnerability index. The DRASTIC intrinsic vulnerability index (DIVI) is computed using the following equation [20]. DIVI = D r D w + Rr Rw + Ar Aw +S r S w + T r T w + I r I w + C r C w ,

(1)

where the capital letters indicate the respective parameter, and the subscripts “r” and “w” refer to their rating and weight, respectively. The index is useful at a regional scale to priorities area of high, moderate, low and very low vulnerability regions.

3.1 DRASTIC Model 3.3 DRASTIC Specific Vulnerability Index (DSVI) The DRASTIC model was developed for United States Environmental Protection Agency [18] by Aller et al. [19] of the National Water Well Association. It was originally designed as an easy-to-use model that would allow a user with a basic knowledge of hydrology to assess the relative potential for groundwater contamination. The method is a standardized system for evaluating groundwater pollution based on the hydrological setting of an area. Hydrological setting is a composite description of the entire major geologic and hydrologic factors that affect and control groundwater movement into, through, and out of an area [19]. It is defined as a mappable unit with common hydrological characteristics, and as a consequence, common vulnerability to contamination by introduced pollutants. The acronyms DRASTIC stands for the seven parameters used in the model which are: Depthto-water (D), Recharge (R), Aquifer media (A), Soil media (S), Topography (T), Impact of vadose zone (I), Hydraulic conductivity (C).

In order to assess specific vulnerability, the model is to be modified by involving an additional parameter reflecting the anthropogenic impact. The choice of the additional parameter depends on the type of contamination for which the specific vulnerable assessment is to be made. For studies involving the nitrate as contaminant, land use is a surrogate parameter. The land use parameter is further subdivided in to ranges (or) zones as agricultural, built-up (or) settlement, wastelands, and water body. Then these ranges are assigned different ratings depending on the potential of nitrate contamination from their different sources (Table 2). This additional parameter is linearly combined additively with DRASTIC vulnerability index to calculate the specific DRASTIC vulnerability index [21]. The DRASTIC specific vulnerability index (DSVI) is calculated using the following equation. DSVI = DIVI + AIr AIw

(2)

123

123

2

5

75–100

Weight

Weight

4

1

9

0–2

8

>10

6

3

1

R

7–10

4–7

2–4

0–2

Interval

Net recharge (in.)

Basalt Weight

3

10

8 9

Sand and gravel Karst Limestone

8

6

6

5

4

3

2

R

Massive Limestone

Massive Sandstone

Bedded Stone

Glacial Till

Weathered

Metamorphic/igneous

Massive shale

Permeability classes

Aquifer media

Non–Shrinking and Non-aggregated Clay Weight

Muck

Clay loam

Silty loam

Loam

Shrinking and/or Aggregated Clay Sandy loam

Peat

Sand

Gravel

Thin or absent

Pedologic Classes

Soil media

2

1

2

3

4

5

6

7

8

9

10

10

R

Weight

>18

12–18

6–12

2–6

0–2

Interval

Interval

R

9

7

5

Interval

2.60–4.57

4.57–6.50

Weight

6 4

Weight

3

1

R

101.6–103.12

50.8–101.6

0–50.8

Net recharge (mm)

Depth to water (m)

Weight

Sandstone

Gneiss

Permeability classes

Aquifer media

3

3

4

R

Weight

Non-shrinking

Clay loam

Sandy loam

Pedologic classes

Soil media

2

1

3

6

R

Weight

3 1 1

>18 Weight

5

9

10

R

12–18

6–12

2–6

0–2

Interval

5

10

9

8

6

6

6

3

3

3

1

R

Weight

Sand and gravel

Sandstone

Silt/clay

Confining layer

Classes

5

8

6

3

1

R

3

10

8

6

4

2

1

R

Weight

4.89–8.3

0.45–4.89

Interval

3

2

1

R

Hydraulic conductivity (m/day)

Weight

>2, 000

1,000–2,000

700–1,000

300–700

100–300

1–100

Interval

Hydraulic conductivity (gpd/ft2 )

Impact of vadose zone

Karst Limestone

Basalt

Bedded Limestone, Sandstone Sand and gravel with silt Sand and gravel

Sandstone

Limestone

Shale

Silt/clay

Confining layer

Classes

Impact of vadose zone

Topography (% slope)

1

1

3

5

9

10

R

Topography (% slope)

Table 2 DRASTIC rating and weighting values for the various hydrogeological parameter settings for the study area

5

7

15–30

3

9

5–15

50–75

10

0–5

30–50

R

Interval

Depth to water (ft)

Table 1 DRASTIC rating and weighting values for the various hydrogeological parameter settings for the study area

210 Arab J Sci Eng (2014) 39:207–216

Arab J Sci Eng (2014) 39:207–216

211

where, AI is the anthropogenic parameter and the subscripts “r” and “w” indicate the corresponding rating and weight, respectively. A high vulnerability index indicates an area that it is more vulnerable to groundwater contamination than areas where the indexvalue is lower. The range of vulnerability index is divided into very low, low, moderate and high vulnerability zones.

3.4.3 Aquifer Media (A)

3.4 Preparation of the Parameter Maps

3.4.4 Soil Media (S)

3.4.1 Depth-to-Water Table (D)

The soil media refers to the top 1 m of the unsaturated zone referred as top soil which is characterized by significant biological activity. The types of soil influence the amount of recharge or contaminant that will reach the aquifer. Various soil types have the ability to attenuate or retard a contaminant as it moves through the soil profile. The attenuation character of soil media varies widely depending on the soil texture and with regard to the different type of contaminants. The soil media parameter was prepared using a geological map from the Soil Survey and Land Use Organization, Department of Agriculture, Tamil Nadu. The soil media types were then assigned ratings from 1 to 10 as per DRASTIC model (Table 2).

The depth from the ground level of the water table is considered as the depth-to-water table. The depth-to-water table parameter was derived from water level data of eight control wells from the public work department (PWD). The depthto-water table from ground level point information was interpolated to derive the depth to groundwater table surface. This surface has a maximum value of 6.50 m and minimum value of 2.6 m. Then these values are classified into ranges according to the DRASTIC model fit only in the two ranges with rating from 9 to 7 as shown in Table 2. The eight observation wells used in the preparation of depth-to-water map is shown schematically in Fig. 2.

An aquifer is geological information that contains sufficient saturated permeable material to yield significant quantities of water to wells (or) springs. The aquifer media parameter was prepared using a subsurface geology map. The ratings assigned as per DRASTIC model to the aquifer media parameters are given in Table 2.

3.4.5 Topography (T) 3.4.2 Net Recharge (R) Net recharge represents the amount of water per unit area of land which penetrates the ground surface and reaches the water table. This recharge water is thus available to transport a contaminant vertically to the water table and horizontally within the aquifer. The greater the recharge, the greater the potential for ground-water pollution [19]. In this study, net recharge parameter was calculated using the Groundwater Estimation Committee (GEC) norms [22], which are based on the groundwater balance method. The Ministry of Water Resources, Government of India, constituted a high power committee, to set out the policy framework for groundwater estimation methodology, which is referred to as the Groundwater Estimation Committee (GEC). The GEC norms give detailed guidelines regarding estimation of recharge, which is based on the groundwater balance method. As per the GEC norms the recharge is to be calculated based on water table fluctuation and by rainfall infiltration method. The net recharge is computed using the following equation Rr = s + Aag + Aip − Ri ,

Topography parameter refers to the slope of the bed and has an influence on vulnerability assessment with regard to whether water and pollutant will preferably run off or remain on the surface long enough to infiltrate. The contour details available in the Survey of India topography maps at 20 m contour intervals were used to derive the slope map. The rating assigned as per DRASTIC model to the topography parameters are given in Table 2. 3.4.6 Impact of Vadose Zone (I) The vadose zone is described as the zone below the typical soil horizon and above the water table, which is unsaturated or discontinuously saturated. The vadose zone parameter is one of the most significant parameters in vulnerability assessment and hence it has a weight of 5. The ratings assigned per the DRASTIC model to the impact of vadose zone parameters are given in Table 2. 3.4.7 Hydraulic Conductivity (C)

(3)

where Rr is net recharge, s is change in groundwater storage, Aag is groundwater abstraction for irrigation, Aip is groundwater abstraction for industrial and public supply and Ri is return flow from irrigation.

Hydraulic conductivity is a measure of ability of the aquifer to transmit water. Higher conductivity values typically correspond to high vulnerability to contaminant, this parameter controls the rate at which groundwater will flow under a given hydraulic gradient. The rate at which groundwater flows also

123

212

Arab J Sci Eng (2014) 39:207–216

Fig. 2 Observation well locations

control the rate at which contaminant moves away from the point it entered the aquifer. The hydraulic conductivity value abstained from the Public Works Department (PWD) and the Tamil Nadu Water Supply Board (TWSB). The values of hydraulic conductivity are used to develop the hydraulic conductivity surface. The hydraulic conductivity surface having a range from 0.45 to 8.3 m/day fits only into two ranges with a rating of 1 and 2 (Table 2). 3.4.8 Anthropogenic Impact (AI) The seven parameters discussed above were used to arrive the intrinsic vulnerability. The anthropogenic impact parameter reflects the human impact with regard to a specific (or) group of contaminants and used to assess the specific vulnerability. In this study area the specific vulnerability was carried out with regard to nitrate. The major source of nitrate contamination in the study area is from the use of fertilizers and settlement areas. Hammerlinck and Arneson [20] have used a land use map as a surrogate parameter for reflecting the anthropogenic impact of nitrate. The land use map was prepared from IRS-lC satellite data collected in 2009. The land use categories, namely agricultural, built-up (or) settlement, wastelands and water body were assigned ratings based on sources of nitrate. The rating assigned as per DRASTIC model to Anthropogenic Impact parameters are given in Table 3.

4 Results and Discussion The objectives formed for the present study involves carrying out a specific vulnerability assessment using the DRASTIC

123

Table 3 Rating of anthropogenic impact as per DRASTIC model

Land use categories

Rating

Agriculture

8

Built-up

5

Wastelands

2

Water bodies

1

model to the priority area according to their vulnerability to contamination. The DRASTIC model used to perform a specific vulnerability assessment is the product of eight parameters. The depth-to-water table level from eight observation wells was used to derive the surface of depth-to-water table parameter. The area around Kannivakkam has the shallow water table (2.6–4.57 m) and the other area has a very high depth-to-water (4.57–6.57 m). A high rating (9) was assigned to low depth-to-water table areas. The depth-to-water table map is shown in Fig. 3. The Groundwater Estimation Committee norms were used to derive the parameter map using weighted Thiessen polygon approach. Recharge values are higher in the Kannivakkam area. The area of Keezhkottaiyoor has a moderate recharge (50.8–101.6 mm) and the rest of the area has relatively low recharge values. A high rating (6) was assigned to the high recharge area. The net recharge map is shown in Fig. 4. In the study area the aquifer media is classified as charnokite and sandstone. The aquifer media map is shown in Fig. 5. The soil available in the study area was categorized into three texture ranges namely, rock, sandy loam, and clay loam. Clay loam covers more than 80 % in the study area. The soil medium parameter is shown in Fig. 6. The topographical

Arab J Sci Eng (2014) 39:207–216

Fig. 3 Depth-to-water table parameter map

213

Fig. 5 Aquifer media parameter map

Fig. 4 Net Recharge parameter map Fig. 6 Soil media parameter map

layer displays a gentle slope (0–8 %) over most of the study area which has been assigned the DRASTIC ratings of 5, 9, and 10 (Table 2). The topography parameter map is shown in Fig. 7. The impact of the vadose zone layer, the gravels were assigned a high rating value (8), the sandstone was assigned moderate rating value (6) while the lowest rating values 1 and 3 were assigned to the confining layer and silt/clay respectively. The impact of the vadose zone parameter map is shown in Fig. 8. The hydraulic conductivity parameter was described based on the pump test details available at eight locations in the study area The study area has low hydraulic conductivity values ranging from 0.45–9 (m/day) hence was assigned low rating values, 1 and 2. The resulting hydraulic conductivity map is shown in Fig. 9. The land use map of the study area was used to derive the anthropogenic impact parameter map with regards to nitrate. The resulting

Fig. 7 Topographic parameter map

123

214

Arab J Sci Eng (2014) 39:207–216

Fig. 8 Vadose zone parameter map Fig. 10 Anthropogenic impact parameter map

Fig. 9 Hydraulic conductivity parameter map

anthropogenic impact parameter map with regards to nitrate is shown in Fig. 10. The parameter maps derived above were overlaid and the DSVI was calculated using Eqs. 1 and 2. The resulting DSVI map is an index map where in the vulnerability index in the number reflecting the specific vulnerability of the aquifer to the specific contaminant under consideration. The natural breaks method available in ArcView GIS capture the natural grouping of ratings in to proposed four categories namely, very low, low, moderate and high. The DSVI map for nitrate is delineated as very low (75–99), low (100–119), moderate (120–149) and high (150–179). DSVI map shows four classes of vulnerability: very low, low, moderate (or) medium and high (Fig. 11). The high, medium, low and very low groundwater vulnerability risk zones of the study area

123

Fig. 11 DRASTIC SVI map for nitrate

cover around 18, 29, 22 and 31% of the study area, respectively (Table 4). The risk map shows a high risk of groundwater contamination where agricultural and human activities are concentrated. In the rest of the area the absence of agricultural and human activities, placed in moderate and low vulnerability category, implicate a moderate and low risk.

Arab J Sci Eng (2014) 39:207–216

215 20

Range

Category

Area in km2

% area

<99

Very Low

236.84

31

100–119

Low

168.08

22

120–149

Moderate

221.56

29

150–179

High

137.52

18

Nitrate-as-Nitrogen (ppm)

Table 4 Vulnerability categories and their areas

18

R² = 0.9187

16 14 12 10 8 6 4 2 0

Table 5 Values of Nitrate-as-nitrogen and DRASTIC SVI in various well locations Well no.

Nitrate-as-nitrogen (ppm)

DSVI values

13,167

10

99

13,202

12

119

13,166

13

134

13,011

8

99

13,238

17

162

23,047

9

108

13,237

7

98

13,239

15

137

5 Validation Water quality data is neither necessary nor sufficient for validation of a vulnerability index. The index may be high but in the absence of a contaminant source the outcome may be nil. Vulnerability can only be validated in a relative manner, comparing responses to identical contaminant sources. In the present study, the evaluated vulnerability was carried out with the water quality data with respect to nitrate. The water quality database used in this study was collected from government departments. The water quality database consists of well water samples collected during the period of 2001–2010 from eight well locations. The concentration of nitrate was determined using a spectrophotometer following the procedures described by Parsons et al. [23]. Nitrate concentration in groundwater is commonly reported as “nitrate-as-nitrogen (NO3-N)” in ppm. The nitrate-as-nitrogen value at each well location is shown in Table 5. The validation of the model was attempted against the permissible limit of nitrate, i.e. 10 ppm, for drinking water as per Bureau of Indian Standards code no. 10500–1991. The data obtained from the field, show that the DSVI values when compared with the standard nitrate values (ppm) produces the expected results thereby the model gets validated. A scatter plot diagram of DSVI with a nitrate concentration has shown the linear relationship evident and displayed in Fig. 12. The number of wells with less than 10 ppm nitrate concentration in the very low, low, moderate and high vulnerability category is 3, 1, 0 and 0, respectively, whereas the same with regard

90

110

130

150

170

Vulnerability Index

Fig. 12 Scatter plot graph for DSVI with nitrate-as-nitrogen

to concentration above 10 ppm is 0, 1, 2 and 1. All the wells in the high vulnerability category have concentration above 10 ppm while all wells in the low vulnerability category have concentration less than 10 ppm.

6 Conclusions This study was performed using a GIS model and the DRASTIC method to determine the vulnerability of groundwater in the Chengalpattu region, which is located in the Kancheepuram District. Seven parameter maps were prepared in a GIS environment, and a vulnerability classification was performed using GIS techniques. The DRASTIC Vulnerability Index was computed and the values were reclassified into four classes, namely, high (150–179), medium (120–149), low (100–119), and very low (75–99) vulnerable areas, which cover 18, 29, 22 and 31 % of the study area, respectively. The Nitrate concentration of groundwater was evaluated for validation of the DRASTIC results. Our survey indicates that the obtained results are realistic and representative of the actual situation in the field. The very low vulnerable areas are outside of the agricultural areas in the study region. Specific vulnerability index maps are to be used as screening tools to spotlight trouble spots and not as an alternate for detailed site-specific analysis. As detailed site specific analysis is costly, these assessments can be used as tools, which identify the zones of concern and as a tool which decides the need for a detailed assessment into such zones of concern. In addition, these vulnerability assessment maps find important uses in decision-making, preparing groundwater protection plans, and water quality investigations. Acknowledgments The authors are grateful to Institute of Remote Sensing, Guindy, Chennai for providing the necessary IRS data for the study. We are also grateful to the Public Work Department, Tamil Nadu Water Supply Board, Soil Survey and Land Use organization, the Geological Survey of India, Chennai and the Central Groundwater Board of Chennai for providing the necessary data and permitting us to use it for our study. We thank two anonymous reviewers for their

123

216

Arab J Sci Eng (2014) 39:207–216

valuable comments the draft manuscript, and Mike Kaminski (KFUPM) for corrections to the English.

References 1. Vrba, J.; Zoporozec, A.: Guidebook on mapping groundwater vulnerability. IAH International Contribution for Hydrogeology. 16, 131, Hannover7 Heise (1994) 2. Insaf, S.; Babiker., Mohamed, A.; Hiyama, T.; Kato, K.: A GISbased DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Sci. Total Environ. 345, 127–140 (2005) 3. Tesoriero, A.J.; Inkpen, E.L.; Voss, F.D.: Assessing ground-water vulnerability using logistic regression. In: Proceedings for the Source Water Assessment and Protection 98 Conference, Dallas, TX, pp. 157–65 (1998) 4. Barbash, J.E.; Resek, E.A.: Pesticides in ground water: Distribution, trends, and governing factors. MI7 Ann Arbor Press, Chelsea (1996) 5. Thapinta, A.; Hudak, P.F.: Use of geographic information systems for assessing groundwater pollution potential by pesticides in Central Thailand. Environ Int. 29(1), 87–93 (2003) 6. Srinivasamoorthy, K.; Vijayaraghavan, K.; Vasanthavigar, M.; Sarma, V.S.; Rajivgandhi, R.; Chidambaram, S.; Anandhan, P.; Manivannan, R.: Assessment of groundwater vulnerability in Mettur region, Tamilnadu, India using DRASTIC and GIS techniques. Arab J Geosci. 4, 1215–1228 (2011) 7. Hasiniaina, F.; Zhou, J.; Guoyi, L.: Regional assessment of groundwater vulnerability in Tamtsag basin, Mongolia using DRASTIC model. J. Am. Sci. 6(11), 65–78 (2010) 8. Lima, M.L.; Zelaya, K.; Massone, H.: Groundwater vulnerability assessment combining the DRASTIC and Dyna-Clue model in the Argentine Pampas. Environ. Manag. 47, 828–839 (2011) 9. Rahman, A.: A GIS based model for assessing groundwater vulnerability in shallow aquifer in Algarh, India. Appl. Geogr. 28(1), 32–53 (2007) 10. Al-Adamat, R.; Foster, I.D.L.; Baban, S.M.J.: Groundwater vulnerability and risk mapping for the basaltic aquifer of the Azraq Basin of Jordan using GIS, remote sensing and DRASTIC. Appl. Geogr. 23, 303–324 (2003) 11. Alwathaf, Y.; Mansouri, B.E.: Assessment of aquifer vulnerability based on GIS and ARCGIS methods: A case study of the Sana’a Basin (Yemen). J. Water Resour. Protect. 3, 845–855 (2011) 12. Sener, E.; Sener, S.; Davraz, A.: Assessment of aquifer vulnerability based on GIS and DRASTIC methods: a case study of the Senirkent-Uluborlu Basin (Isparta, Turkey). Hydrogeol. J. 17, 2023–2035 (2009)

123

13. Wen, X.; J Wu, J.; Si, J.: A GIS-based DRASTIC model for assessing shallow groundwater vulnerability in the Zhangye Basin, northwestern China. Environ. Geol. 57, 1435–1442 (2009) 14. Ahmed, A.A.: Using Generic and Pesticide DRASTIC GIS-based models for vulnerability assessment of the Quaternary aquifer at Sohag, Egypt. Hydrogeology Journal. 17, 1203–1217 (2009) 15. Fabbri, A.; Napolitano, P.: The use of database management and geographical information systems for aquifer vulnerability analysis. Contribution to the International Scientific Conference on the occasion of the 50th Anniversary of the founding of the Vysoka Skola Banska, Ostrava, Czech Republic (1995) 16. National Informatics Centre (NIC), Government of India: http:// tnmaps.tn.nic.in/district.php. Accessed 10 October 2011 17. National Informatics Centre (NIC), Government of India: http:// www.kanchi.nic.in/district_profile_pro.html. Accessed 13 October 2011 18. US EPA (Environmental Protection Agency): DRASTIC: A standard system for evaluating groundwater potential using hydrogeological settings. Oklahoma WA/EPA Series, Ada.163 (1985) 19. Aller, L.; Bennett, T.; Petty, R.J.: DRASTIC: A standardized system for evaluating ground water pollution potential using hydrogeologic settings. R.S. Kerr Environmental Research Laboratory, US Environmental Protection Agency (1987) 20. Hamerlinck, J.D.; Arneson, C.S.: (eds.) Wyoming Ground Water Vulnerability Assessment Handbook, Volume 2: Assessing Ground Water Vulnerability to Pesticides. Spatial Data and Visualization Center Publication, SDVC 98-01-2, University of Wyoming, Laramie, Wyoming (1998) 21. Thirumalaivasan, D.; Karmegam, M.; Venugopal, K.: AHPDRASTIC: software for specific aquifer vulnerability assessment using DRASTIC model and GIS. Environ. Model. Softw. 18, 645– 656 (2003) 22. CGWB: Detailed Guidelines for implementing the Ground Water Estimation Methodology—1997. Central Ground Water Board, Ministry of Water Resources, Government of India (1998) 23. Parsons, T.R.; Maita, Y.; Lalli, C.M.: Manual of Chemical and Biological Methods for Seawater Analysis. Pergamon Press, New York, p. 173 (1984)