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 Keywords Aquifer vulnerability · Chengalpattu ·
source of water supply due to its relatively low vulnera- DRASTIC model · GIS · Specific vulnerability index
bility to pollution in comparison to surface water, and its
huge storage capacity. Because of the known health and eco-
nomic impacts associated with groundwater contamination,
steps to measure the vulnerability of groundwater must be
taken for sustainable groundwater protection and manage-
ment planning. Susceptibility of groundwater refers to the
intrinsic characteristics that determine the sensitivity of the
water to being adversely affected by an imposed contam-
inant 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 rela-
tion to groundwater quality data and results have shown a
strong relationship between DRASTIC specific vulnerabil-
ity index and nitrate-as-nitrogen concentrations. A ground-
water 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 clas-
sified as a high vulnerable zone and the south and northeast- 1 Introduction
ern parts show medium vulnerable zones, and record higher
nitrate values. The theory of groundwater vulnerability was introduced by
the end of 1960s to create an alertness of groundwater con-
S. Kumar (B) · N. Radhakrishnan
tamination [1]. It can be defined as the possibility of perco-
Department of Civil Engineering, National Institute of Technology, lation and diffusion of contaminants from the ground sur-
Tiruchirappalli 620 015, India face into the groundwater system. Vulnerability is usually
e-mail: geosat08@gmail.com considered as an “intrinsic” property of a groundwater sys-
tem that depends on its sensitivity to human and/or natural
D. Thirumalaivasan
Institute of Remote Sensing, College of Engineering, Guindy Campus, impacts [2]. “Specific” or “integrated” vulnerability, on the
Anna University, Chennai 600 025, India other hand, combines intrinsic vulnerability with the risk of

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208 Arab J Sci Eng (2014) 39:207–216

the groundwater being exposed to the loading of pollutants The method was originally developed for manual overlay
from certain sources [1]. Groundwater vulnerability deals of semi quantitative data layers, however the simple defi-
only with the hydrogeological setting and does not include nition of its vulnerability index as a linear combination of
pollutant attenuation. The natural hydrogeological factors factors shows the feasibility of the computation using Geo-
affect the different pollutants in different ways depending graphical Information System (GIS) [11–15].
on their interactions and chemical properties. Groundwater represents a main resource for supply in
Many approaches have been developed to evaluate aquifer Chengalpattu. The need for protection and management of
vulnerability. They include process based methods, statisti- groundwater has been recognized. Agricultural pesticides
cal methods, and overlay and index methods [3]. The process and wastewater are the main causes of the degradation of
based methods use simulation models to estimate the conta- groundwater quality in the study area. Also, insecure landfill
minant migration but they are constrained by data shortage of municipal wastes on permeable aquifer units and uncon-
and computational difficulties [4]. Statistical methods use trolled discharge of sewage affect groundwater quality neg-
statistics to determine associations between spatial variables atively. As a result of surface and groundwater flow, the
and the actual occurrence of pollutants in the groundwater. variety of contaminants and their mixing in surface water
Their limitations include insufficient water quality observa- and groundwater threaten the groundwater quality. Hence,
tions, data accuracy and careful selection of spatial variables. groundwater vulnerability in the area should be determined
Overlay and index methods combine factors controlling the for the protection of the groundwater. The main aim of this
movement of pollutants from the ground surface into the satu- study is to evaluate the intrinsic and specific groundwater
rated zone resulting in vulnerability indices at different loca- vulnerability index for the study area using the DRASTIC
tions. Their main advantage is that some of the factors such as model based on GIS.
rainfall and depth to groundwater can be available over large
areas, which makes them suitable for regional scale assess-
ments [5]. However, their major drawback is the subjectivity 2 Study Area
in assigning numerical values to the descriptive entities and
relative weights for the different attributes. Chengalpattu, in Kancheepuram district, located on the
DRASTIC is an index model designed to produce vul- northern East Coast of Tamil Nadu (Fig. 1) is one of the
nerability scores for different locations by combining sev- largest industrial areas in Tamil Nadu, India, covering an
eral thematic layers (Depth-to-water, net Recharge, Aquifer area of 764 km2 . It is bounded in the west by the Vellore and
media, Soil media, Topography, Impact of vadose zone, Thiruvannamalai districts, in the north by the Thiruvallur and
and hydraulic Conductivity) [6–8]. The DRASTIC method Chennai districts, in the south by the Villuppuram district in
assumes that (1) any contaminant is introduced at the ground the east by the Bay of Bengal. The geographical location of
surface; (2) the contaminant is flushed into the groundwa- Chengalpattu lies between 11◦ 00 to 12◦ 00 North latitudes
ter by precipitation; (3) the contaminant has the mobility of and 77◦ 28 to 78◦ 50 East longitudes. The town of Chengal-
water; (4) the areas evaluated using DRASTIC are 0.4 km2 pattu had a population of 412,289. It is the second largest
or larger [9,10]. town in the district of Kancheepuram.

Fig. 1 Location map of study

area [16]

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Arab J Sci Eng (2014) 39:207–216 209

In the summer season, maximum and minimum tempera- The above seven parameters are used to define the hydro-
tures are 37.5 ◦ C and 20.0 ◦ C, respectively. In winter season logical setting of an area. These seven parameters are fur-
maximum and minimum temperature is 28.7 ◦ C, 19.8 ◦ C ther subdivided into ranges (or) zones, representing various
respectively. The pre-monsoon rainfall is almost uniform hydrological settings and are assigned different ratings on
throughout the district. The coastal taluk gets more rain than a scale of 1 in 10 (Table 1). The rating assigned to each
do the interior regions. The prevailing wind direction is south- of these ranges or zones indicates their relative importance
west in the morning and southeast in the evening. The town within each parameter, in contributing to aquifer vulnera-
gets rain from both SW and NE monsoons. Average annual bility. DRASTIC is a standardized non-subjective method to
rainfall is 1,125 mm. The NE and SW monsoons are the major compare the vulnerability to contaminate over various hydro-
donors with 54 and 36 % contribution each, to the total annual logical settings. For this reason the method is rigid in the
rainfall [17]. assignment of weights and ratings to the parameters.
In the study area, the major geomorphological related
features are sandstone and shales, Charnockite, Garnet-
3.2 DRASTIC Intrinsic Vulnerability Index (DIVI)
Sillimanite gneiss, clayey sand, and sand and silt. The area
exposes crystalline rocks of Achaean age and sedimentary
The seven parameters themselves not considered to be
rocks of Gondwana and the Cuddalore Formation of Mio-
equally important in vulnerability assessment. In order to
Pliocene age. A gravel and shingle bed locally known as the
reflect the relative importance of these parameters, weights
Kanjeevaram Gravels belongs has a Pliocene to early Pleis-
in the scale of 1–5 are assigned to each of these parameters
tocene age.
(Table 2). The seven hydrological parameters—with their rat-
ing and weights are linearly combined, additively, to derive
the weighted map—indicate the non-dimensional intrinsic
3 Methodology
vulnerability index. The DRASTIC intrinsic vulnerability
index (DIVI) is computed using the following equation [20].
The aquifer vulnerability assessment, ever since it was first
introduced in 1968, has evolved considerably in pursuit of
DIVI = D r D w + Rr Rw + Ar Aw
methods, which are realistic, effective, and accurate. Conse-
quently, numerous models for vulnerability assessment have +S r S w + T r T w + I r I w + C r C w , (1)
been developed varying in their approaches, data require-
where the capital letters indicate the respective parameter,
ments, and wider applicability. The overlay and index meth-
and the subscripts “r” and “w” refer to their rating and weight,
ods in general and the DRASTIC model in particular, are the
respectively. The index is useful at a regional scale to prior-
most widely used techniques for vulnerability assessment
ities area of high, moderate, low and very low vulnerability
studies at regional scales.
regions.
3.1 DRASTIC Model
3.3 DRASTIC Specific Vulnerability Index (DSVI)
The DRASTIC model was developed for United States Envi-
ronmental Protection Agency [18] by Aller et al. [19] of the In order to assess specific vulnerability, the model is to be
National Water Well Association. It was originally designed modified by involving an additional parameter reflecting the
as an easy-to-use model that would allow a user with a basic anthropogenic impact. The choice of the additional parameter
knowledge of hydrology to assess the relative potential for depends on the type of contamination for which the specific
groundwater contamination. The method is a standardized vulnerable assessment is to be made. For studies involving
system for evaluating groundwater pollution based on the the nitrate as contaminant, land use is a surrogate parameter.
hydrological setting of an area. Hydrological setting is a com- The land use parameter is further subdivided in to ranges (or)
posite description of the entire major geologic and hydrologic zones as agricultural, built-up (or) settlement, wastelands,
factors that affect and control groundwater movement into, and water body. Then these ranges are assigned different rat-
through, and out of an area [19]. It is defined as a map- ings depending on the potential of nitrate contamination from
pable unit with common hydrological characteristics, and as their different sources (Table 2). This additional parameter
a consequence, common vulnerability to contamination by is linearly combined additively with DRASTIC vulnerability
introduced pollutants. The acronyms DRASTIC stands for index to calculate the specific DRASTIC vulnerability index
the seven parameters used in the model which are: Depth- [21]. The DRASTIC specific vulnerability index (DSVI) is
to-water (D), Recharge (R), Aquifer media (A), Soil media calculated using the following equation.
(S), Topography (T), Impact of vadose zone (I), Hydraulic
conductivity (C). DSVI = DIVI + AIr AIw (2)

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 Table 1 DRASTIC rating and weighting values for the various hydrogeological parameter settings for the study area
210

Depth to water (ft) Net recharge (in.) Aquifer media Soil media Topography (% slope) Impact of vadose zone Hydraulic conductivity (gpd/ft2 )

Interval R Interval R Permeability classes R Pedologic Classes R Interval R Classes R Interval R

0–5 10 0–2 1 Massive shale 2 Thin or absent 10 0–2 10 Confining layer 1 1–100 1

123
5–15 9 2–4 3 Metamorphic/igneous 3 Gravel 10 2–6 9 Silt/clay 3 100–300 2
15–30 7 4–7 6 Weathered 4 Sand 9 6–12 5 Shale 3 300–700 4
30–50 5 7–10 8 Glacial Till 5 Peat 8 12–18 3 Limestone 3 700–1,000 6
50–75 3 >10 9 Bedded Stone 6 Shrinking and/or 7 >18 1 Sandstone 6 1,000–2,000 8
Aggregated Clay
75–100 2 0–2 1 Massive Sandstone 6 Sandy loam 6 Weight 1 Bedded Limestone, 6 >2, 000 10
Sandstone
Weight 5 Weight 4 Massive Limestone 8 Loam 5 Sand and gravel 6 Weight 3
with silt
Sand and gravel 8 Silty loam 4 Sand and gravel 8
Basalt 9 Clay loam 3 Basalt 9
Karst Limestone 10 Muck 2 Karst Limestone 10
Weight 3 Non–Shrinking and 1 Weight 5
Non-aggregated Clay
Weight 2

Table 2 DRASTIC rating and weighting values for the various hydrogeological parameter settings for the study area
Depth to water (m) Net recharge (mm) Aquifer media Soil media Topography (% slope) Impact of vadose zone Hydraulic conductivity
(m/day)

Interval R Interval R Permeability classes R Pedologic classes R Interval R Classes R Interval R

2.60–4.57 9 0–50.8 1 Gneiss 4 Sandy loam 6 0–2 10 Confining layer 1 0.45–4.89 1

4.57–6.50 7 50.8–101.6 3 Sandstone 3 Clay loam 3 2–6 9 Silt/clay 3 4.89–8.3 2
Weight 5 101.6–103.12 6 Weight 3 Non-shrinking 1 6–12 5 Sandstone 6 Weight 3
Weight 4 Weight 2 12–18 3 Sand and gravel 8
>18 1 Weight 5
Weight 1
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 3.4.3 Aquifer Media (A)
“r” and “w” indicate the corresponding rating and weight,
respectively. An aquifer is geological information that contains sufficient
A high vulnerability index indicates an area that it is more saturated permeable material to yield significant quantities
vulnerable to groundwater contamination than areas where of water to wells (or) springs. The aquifer media parameter
the indexvalue is lower. The range of vulnerability index is was prepared using a subsurface geology map. The ratings
divided into very low, low, moderate and high vulnerability assigned as per DRASTIC model to the aquifer media para-
zones. meters are given in Table 2.

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 bio-
The depth from the ground level of the water table is con- logical activity. The types of soil influence the amount of
sidered as the depth-to-water table. The depth-to-water table recharge or contaminant that will reach the aquifer. Various
parameter was derived from water level data of eight control soil types have the ability to attenuate or retard a contaminant
wells from the public work department (PWD). The depth- as it moves through the soil profile. The attenuation charac-
to-water table from ground level point information was inter- ter of soil media varies widely depending on the soil texture
polated to derive the depth to groundwater table surface. This and with regard to the different type of contaminants. The
surface has a maximum value of 6.50 m and minimum value soil media parameter was prepared using a geological map
of 2.6 m. Then these values are classified into ranges accord- from the Soil Survey and Land Use Organization, Depart-
ing to the DRASTIC model fit only in the two ranges with ment of Agriculture, Tamil Nadu. The soil media types were
rating from 9 to 7 as shown in Table 2. The eight observation then assigned ratings from 1 to 10 as per DRASTIC model
wells used in the preparation of depth-to-water map is shown (Table 2).
schematically in Fig. 2.
3.4.5 Topography (T)
3.4.2 Net Recharge (R)
Topography parameter refers to the slope of the bed and
has an influence on vulnerability assessment with regard to
Net recharge represents the amount of water per unit area
whether water and pollutant will preferably run off or remain
of land which penetrates the ground surface and reaches the
on the surface long enough to infiltrate. The contour details
water table. This recharge water is thus available to transport
available in the Survey of India topography maps at 20 m
a contaminant vertically to the water table and horizontally
contour intervals were used to derive the slope map. The
within the aquifer. The greater the recharge, the greater the
rating assigned as per DRASTIC model to the topography
potential for ground-water pollution [19].
parameters are given in Table 2.
In this study, net recharge parameter was calculated using
the Groundwater Estimation Committee (GEC) norms [22],
3.4.6 Impact of Vadose Zone (I)
which are based on the groundwater balance method. The
Ministry of Water Resources, Government of India, consti-
The vadose zone is described as the zone below the typical
tuted a high power committee, to set out the policy framework
soil horizon and above the water table, which is unsaturated or
for groundwater estimation methodology, which is referred
discontinuously saturated. The vadose zone parameter is one
to as the Groundwater Estimation Committee (GEC). The
of the most significant parameters in vulnerability assessment
GEC norms give detailed guidelines regarding estimation of
and hence it has a weight of 5. The ratings assigned per the
recharge, which is based on the groundwater balance method.
DRASTIC model to the impact of vadose zone parameters
As per the GEC norms the recharge is to be calculated based
are given in Table 2.
on water table fluctuation and by rainfall infiltration method.
The net recharge is computed using the following equation
3.4.7 Hydraulic Conductivity (C)
Rr = s + Aag + Aip − Ri , (3)
Hydraulic conductivity is a measure of ability of the aquifer
where Rr is net recharge, s is change in groundwater stor- to transmit water. Higher conductivity values typically cor-
age, Aag is groundwater abstraction for irrigation, Aip is respond to high vulnerability to contaminant, this parameter
groundwater abstraction for industrial and public supply and controls the rate at which groundwater will flow under a given
Ri is return flow from irrigation. hydraulic gradient. The rate at which groundwater flows also

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Fig. 2 Observation well

locations

control the rate at which contaminant moves away from the Table 3 Rating of anthro-
Land use categories Rating
point it entered the aquifer. The hydraulic conductivity value pogenic impact as per DRASTIC
abstained from the Public Works Department (PWD) and model
Agriculture 8
the Tamil Nadu Water Supply Board (TWSB). The values of Built-up 5
hydraulic conductivity are used to develop the hydraulic con-
Wastelands 2
ductivity surface. The hydraulic conductivity surface having
Water bodies 1
a range from 0.45 to 8.3 m/day fits only into two ranges with
a rating of 1 and 2 (Table 2).
model to the priority area according to their vulnerability
3.4.8 Anthropogenic Impact (AI) to contamination. The DRASTIC model used to perform a
specific vulnerability assessment is the product of eight para-
The seven parameters discussed above were used to arrive the meters.
intrinsic vulnerability. The anthropogenic impact parameter The depth-to-water table level from eight observation
reflects the human impact with regard to a specific (or) group wells was used to derive the surface of depth-to-water table
of contaminants and used to assess the specific vulnerability. parameter. The area around Kannivakkam has the shallow
In this study area the specific vulnerability was carried out water table (2.6–4.57 m) and the other area has a very high
with regard to nitrate. The major source of nitrate contamina- depth-to-water (4.57–6.57 m). A high rating (9) was assigned
tion in the study area is from the use of fertilizers and settle- to low depth-to-water table areas. The depth-to-water table
ment areas. Hammerlinck and Arneson [20] have used a land map is shown in Fig. 3.
use map as a surrogate parameter for reflecting the anthro- The Groundwater Estimation Committee norms were
pogenic impact of nitrate. The land use map was prepared used to derive the parameter map using weighted Thiessen
from IRS-lC satellite data collected in 2009. The land use cat- polygon approach. Recharge values are higher in the Kan-
egories, namely agricultural, built-up (or) settlement, waste- nivakkam area. The area of Keezhkottaiyoor has a moderate
lands and water body were assigned ratings based on sources recharge (50.8–101.6 mm) and the rest of the area has rel-
of nitrate. The rating assigned as per DRASTIC model to atively low recharge values. A high rating (6) was assigned
Anthropogenic Impact parameters are given in Table 3. 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
4 Results and Discussion in Fig. 5. The soil available in the study area was categorized
into three texture ranges namely, rock, sandy loam, and clay
The objectives formed for the present study involves carrying loam. Clay loam covers more than 80 % in the study area. The
out a specific vulnerability assessment using the DRASTIC soil medium parameter is shown in Fig. 6. The topographical

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Fig. 3 Depth-to-water table parameter map

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

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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 Fig. 11 DRASTIC SVI map for nitrate
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 nat-
ural breaks method available in ArcView GIS capture the
natural grouping of ratings in to proposed four categories cover around 18, 29, 22 and 31% of the study area, respec-
namely, very low, low, moderate and high. The DSVI map tively (Table 4). The risk map shows a high risk of ground-
for nitrate is delineated as very low (75–99), low (100–119), water contamination where agricultural and human activi-
moderate (120–149) and high (150–179). DSVI map shows ties are concentrated. In the rest of the area the absence of
four classes of vulnerability: very low, low, moderate (or) agricultural and human activities, placed in moderate and
medium and high (Fig. 11). The high, medium, low and very low vulnerability category, implicate a moderate and low
low groundwater vulnerability risk zones of the study area risk.

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20
Table 4 Vulnerability categories and their areas
18

Nitrate-as-Nitrogen (ppm)
R² = 0.9187
Range Category Area in km2 % area 16
14
<99 Very Low 236.84 31 12
100–119 Low 168.08 22 10
8
120–149 Moderate 221.56 29
6
150–179 High 137.52 18 4
2
0
Table 5 Values of Nitrate-as-nitrogen and DRASTIC SVI in various 90 110 130 150 170
well locations Vulnerability Index

Well no. Nitrate-as-nitrogen DSVI values Fig. 12 Scatter plot graph for DSVI with nitrate-as-nitrogen
(ppm)

13,167 10 99
to concentration above 10 ppm is 0, 1, 2 and 1. All the wells
13,202 12 119
in the high vulnerability category have concentration above
13,166 13 134
10 ppm while all wells in the low vulnerability category have
13,011 8 99
concentration less than 10 ppm.
13,238 17 162
23,047 9 108
13,237 7 98
13,239 15 137 6 Conclusions

This study was performed using a GIS model and the DRAS-
TIC method to determine the vulnerability of groundwater in
5 Validation
the Chengalpattu region, which is located in the Kancheep-
uram District. Seven parameter maps were prepared in a
Water quality data is neither necessary nor sufficient for val-
GIS environment, and a vulnerability classification was per-
idation of a vulnerability index. The index may be high but
formed using GIS techniques. The DRASTIC Vulnerability
in the absence of a contaminant source the outcome may
Index was computed and the values were reclassified into
be nil. Vulnerability can only be validated in a relative man-
four classes, namely, high (150–179), medium (120–149),
ner, comparing responses to identical contaminant sources. In
low (100–119), and very low (75–99) vulnerable areas, which
the present study, the evaluated vulnerability was carried out
cover 18, 29, 22 and 31 % of the study area, respectively. The
with the water quality data with respect to nitrate. The water
Nitrate concentration of groundwater was evaluated for val-
quality database used in this study was collected from gov-
idation of the DRASTIC results. Our survey indicates that
ernment departments. The water quality database consists of
the obtained results are realistic and representative of the
well water samples collected during the period of 2001–2010
actual situation in the field. The very low vulnerable areas
from eight well locations. The concentration of nitrate was
are outside of the agricultural areas in the study region. Spe-
determined using a spectrophotometer following the proce-
cific vulnerability index maps are to be used as screening
dures described by Parsons et al. [23]. Nitrate concentration
tools to spotlight trouble spots and not as an alternate for
in groundwater is commonly reported as “nitrate-as-nitrogen
detailed site-specific analysis. As detailed site specific analy-
(NO3-N)” in ppm. The nitrate-as-nitrogen value at each well
sis is costly, these assessments can be used as tools, which
location is shown in Table 5.
identify the zones of concern and as a tool which decides the
The validation of the model was attempted against the
need for a detailed assessment into such zones of concern. In
permissible limit of nitrate, i.e. 10 ppm, for drinking water
addition, these vulnerability assessment maps find important
as per Bureau of Indian Standards code no. 10500–1991. The
uses in decision-making, preparing groundwater protection
data obtained from the field, show that the DSVI values when
plans, and water quality investigations.
compared with the standard nitrate values (ppm) produces the
expected results thereby the model gets validated. A scatter Acknowledgments The authors are grateful to Institute of Remote
plot diagram of DSVI with a nitrate concentration has shown Sensing, Guindy, Chennai for providing the necessary IRS data for
the linear relationship evident and displayed in Fig. 12. The the study. We are also grateful to the Public Work Department, Tamil
Nadu Water Supply Board, Soil Survey and Land Use organization,
number of wells with less than 10 ppm nitrate concentration the Geological Survey of India, Chennai and the Central Groundwater
in the very low, low, moderate and high vulnerability category Board of Chennai for providing the necessary data and permitting us
is 3, 1, 0 and 0, respectively, whereas the same with regard to use it for our study. We thank two anonymous reviewers for their

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valuable comments the draft manuscript, and Mike Kaminski (KFUPM) 13. Wen, X.; J Wu, J.; Si, J.: A GIS-based DRASTIC model for assess-
for corrections to the English. ing shallow groundwater vulnerability in the Zhangye Basin, north-
western China. Environ. Geol. 57, 1435–1442 (2009)
References 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)
1. Vrba, J.; Zoporozec, A.: Guidebook on mapping groundwater vul-
15. Fabbri, A.; Napolitano, P.: The use of database management and
nerability. IAH International Contribution for Hydrogeology. 16,
geographical information systems for aquifer vulnerability analy-
131, Hannover7 Heise (1994)
sis. Contribution to the International Scientific Conference on the
2. Insaf, S.; Babiker., Mohamed, A.; Hiyama, T.; Kato, K.: A GIS-
occasion of the 50th Anniversary of the founding of the Vysoka
based DRASTIC model for assessing aquifer vulnerability in
Skola Banska, Ostrava, Czech Republic (1995)
Kakamigahara Heights, Gifu Prefecture, central Japan. Sci. Total
16. National Informatics Centre (NIC), Government of India: http://
Environ. 345, 127–140 (2005)
tnmaps.tn.nic.in/district.php. Accessed 10 October 2011
3. Tesoriero, A.J.; Inkpen, E.L.; Voss, F.D.: Assessing ground-water
17. National Informatics Centre (NIC), Government of India: http://
vulnerability using logistic regression. In: Proceedings for the
www.kanchi.nic.in/district_profile_pro.html. Accessed 13 Octo-
Source Water Assessment and Protection 98 Conference, Dallas,
ber 2011
TX, pp. 157–65 (1998)
18. US EPA (Environmental Protection Agency): DRASTIC: A stan-
4. Barbash, J.E.; Resek, E.A.: Pesticides in ground water: Distribu-
dard system for evaluating groundwater potential using hydrogeo-
tion, trends, and governing factors. MI7 Ann Arbor Press, Chelsea
logical settings. Oklahoma WA/EPA Series, Ada.163 (1985)
(1996)
19. Aller, L.; Bennett, T.; Petty, R.J.: DRASTIC: A standardized sys-
5. Thapinta, A.; Hudak, P.F.: Use of geographic information systems
tem for evaluating ground water pollution potential using hydro-
for assessing groundwater pollution potential by pesticides in Cen-
geologic settings. R.S. Kerr Environmental Research Laboratory,
tral Thailand. Environ Int. 29(1), 87–93 (2003)
US Environmental Protection Agency (1987)
6. Srinivasamoorthy, K.; Vijayaraghavan, K.; Vasanthavigar, M.;
20. Hamerlinck, J.D.; Arneson, C.S.: (eds.) Wyoming Ground Water
Sarma, V.S.; Rajivgandhi, R.; Chidambaram, S.; Anandhan, P.;
Vulnerability Assessment Handbook, Volume 2: Assessing Ground
Manivannan, R.: Assessment of groundwater vulnerability in Met-
Water Vulnerability to Pesticides. Spatial Data and Visualiza-
tur region, Tamilnadu, India using DRASTIC and GIS techniques.
tion Center Publication, SDVC 98-01-2, University of Wyoming,
Arab J Geosci. 4, 1215–1228 (2011)
Laramie, Wyoming (1998)
7. Hasiniaina, F.; Zhou, J.; Guoyi, L.: Regional assessment of ground-
21. Thirumalaivasan, D.; Karmegam, M.; Venugopal, K.: AHP-
water vulnerability in Tamtsag basin, Mongolia using DRASTIC
DRASTIC: software for specific aquifer vulnerability assessment
model. J. Am. Sci. 6(11), 65–78 (2010)
using DRASTIC model and GIS. Environ. Model. Softw. 18, 645–
8. Lima, M.L.; Zelaya, K.; Massone, H.: Groundwater vulnerability
656 (2003)
assessment combining the DRASTIC and Dyna-Clue model in the
22. CGWB: Detailed Guidelines for implementing the Ground Water
Argentine Pampas. Environ. Manag. 47, 828–839 (2011)
Estimation Methodology—1997. Central Ground Water Board,
9. Rahman, A.: A GIS based model for assessing groundwater vul-
Ministry of Water Resources, Government of India (1998)
nerability in shallow aquifer in Algarh, India. Appl. Geogr. 28(1),
23. Parsons, T.R.; Maita, Y.; Lalli, C.M.: Manual of Chemical and
32–53 (2007)
Biological Methods for Seawater Analysis. Pergamon Press, New
10. Al-Adamat, R.; Foster, I.D.L.; Baban, S.M.J.: Groundwater vul-
York, p. 173 (1984)
nerability 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 vulner-
ability based on GIS and DRASTIC methods: a case study of
the Senirkent-Uluborlu Basin (Isparta, Turkey). Hydrogeol. J. 17,
2023–2035 (2009)

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