Content uploaded by Dipankar Saha
Author content
All content in this area was uploaded by Dipankar Saha on Feb 07, 2016
Content may be subject to copyright.
Groundwater vulnerability assessment
using DRASTIC and Pesticide DRASTIC models in intense
agriculture area of the Gangetic plains, India
Dipankar Saha &Fakhre Alam
Received: 12 April 2014 /Accepted: 1 September 2014
#Springer International Publishing Switzerland 2014
Abstract Delineating areas susceptible to contamina-
tion from anthropogenic sources form an important
component of sustainable management of groundwater
resources. The present research aims at estimating vul-
nerability of groundwater by application of DRASTIC
and Pesticide DRASTIC models in the southern part of
the Gangetic plains in the state of Bihar. The DRASTIC
and Pesticide DRASTIC models have considered seven
parameters viz. depth to water level, net recharge, aqui-
fer material, soil material, topography, impact of vadose
zone and hydraulic conductivity. A third model,
Pesticide DRASTIC LU has been adopted by adding
land use as an additional parameter, to assess its impact
on vulnerability zonation. The DRASTIC model indi-
cated two vulnerable categories, moderate and high,
while the Pesticide DRASTIC model revealed moder-
ate, high and very high vulnerable categories. Out of the
parameters used, depth to water level affected the vul-
nerability most. The parameter caused least impact was
topography in DRASTIC, while in case of Pesticide
DRASTIC and Pesticide DRASTIC LU models, the
parameter was hydraulic conductivity. A linear regres-
sion between groundwater NO
3
concentrations and the
vulnerability zonation revealed better correlation for
Pesticide DRASTIC model, emphasising the effective-
ness of the model in assessing groundwater vulnerability
in the study region. Considering all three models, the
most vulnerable areas were found to be concentrated
mainly in two zones, (i) in the south-western part along
Ekangarsarai-Islampur patch and (ii) around
Biharsharif-Nagarnausa area in the central part. Both
zones were characterised by intensive vegetable cultiva-
tion with urban areas in between.
Keywords DRASTIC .Vulnerability mapping .
Groundwater pollution .Gangetic plains .Aquifers .
India
Introduction
Quality deterioration by both geogenic processes and
anthropogenic activities is a major challenge to the
sustainable management of groundwater resources in
different parts of the world. One of the major anthropo-
genic inputs responsible for physical and chemical con-
tamination of groundwater is urban and industrial efflu-
ents, which is increasing volumetrically with population
growth, urbanisation and change in lifestyle (Rahman
2008). Leachates from urban landfills and seepage from
untreated sewage discharged into open land or in river/
streams are contaminating groundwater. In rural areas,
poor sanitation practice and open disposal of human and
animal wastes result in bacteriological and nitrate (NO
3
)
pollution of shallow groundwater (Chakraborty et al.
2011). NO
3
is not naturally found in surface water and
groundwater, as it is introduced from anthropogenic
sources. This compound is considered as an indicator
of contaminant movement from the source like from
Environ Monit Assess
DOI 10.1007/s10661-014-4041-x
D. Saha (*):F. Al am
Central Ground Water Board,
Mid-Eastern RegionPatna 800001, India
e-mail: dsaha002@gmail.com
agricultural land, land fill sites and places of open
defacation (Javadi et al. 2011a,b;Neshatetal.2013).
Agricultural activities involving chemical fertilisers and
synthetic pesticides are the main reasons for elevated
NO
3
level, as well as alarmingly high pesticides in
groundwater at some places (Thapinta and Hudak
2003;Chaeetal.2004;Sahaetal.2008; Ghose et al.
2009). NO
3
concentration in groundwater exceeding the
permissible limit of 45 mg L
−1
adopted in India (BIS
2012) is widespread and has been reported from 11 out
of 28 states (Mehta 2006). Significant increase in pesti-
cide consumption in agricultural sectors has also been
recorded in the last five decades. Pesticides like DDT
and HCH were used extensively in India till recently,
both for agricultural and sanitary purposes. It is estimat-
ed that about 25,000 metric tons of chlorinated pesti-
cides are used annually in India (Sankararamakrishnan
et al. 2005), a part of which accumulates in soil, reaches
aquifer with percolating groundwater, to remain there
for a longer period. In view of widely reported ground-
water quality deterioration from various anthropogenic
activities, the issue of aquifer protection against contam-
ination and that of remediation of aquifers are of crucial
significance (Zektser et al. 2004).
In India, as also observed in the other parts of the
world, the drinking water sector heavily depends on
aquifers. Presently, 85 % of rural domestic needs is
catered from groundwater (CGWB 2011). On the other
hand, in urban areas, where reservoir-based water sup-
ply is generally the source, now-a-days groundwater is
also playing a significant role. The component of
groundwater in the total water supply ranges from 30
to 100 and 7 to 30 % for the towns/cities located in
alluvial and hard rock areas, respectively (NIUA 2005).
Unlike surface water, once an aquifer gets polluted, it
is difficult to remediate and may persist for centuries or
can be even irrecoverable (Freeze and Cherry 1979). As
a concept, groundwater vulnerability defines the sensi-
tivity of an aquifer to get contaminated from anthropo-
genic activities on the land surface (Vrba and Zeporozec
1994). Assessment of vulnerability provides stepping
stones in evaluating the sensitivity and risk of an aquifer
to get polluted and forms an essential component of
management options to preserve the groundwater qual-
ity (Worrall et al. 2002).
Foster (1987) defined vulnerability as ‘the intrinsic
characteristics which determine the sensitivity of vari-
ous parts of an aquifer to being adversely affected by an
imposed contamination load’. Vulnerability can be
classed into intrinsic and specific. Intrinsic vulnerability
represents the physical and hydrogeological character-
istics of an area those play a role in the process of an
aquifer getting contaminated. On the other hand, specif-
ic vulnerability defines the likelihood of groundwater to
be affected by a particular pollutant or a group of pol-
lutants. However, there is a difference between vulner-
ability and pollution risk. Pollution risk is defined as ‘the
interaction between the natural vulnerability of the aqui-
fer and the pollution loading that is being or will be
applied on the surface environment as a result of human
activity’(Foster 1987). It is possible that an area with
high aquifer vulnerability may have low pollution risk,
if there is no significant pollution load. On the other
hand, a significant pollution creating industry located in
a low vulnerable area may render high pollution risk.
Many approaches have been developed to evaluate
aquifer vulnerability. They include process-based, sta-
tistical, and overlay and index methods (Tesoriero et al.
1998). Each group of methods has its strengths and
weaknesses with respect to its suitability under a partic-
ular set of factors. Approaches based on simulation are
part of process-based methods which require large vol-
ume of input data and high computing power (Iqbal
et al. 2012). The process-based methods are also
constrained by computational difficulties as well as
requirement of intensive calibration in the field itself to
assess the fate of pollutants in the vadose zone.
Statistical methods are simpler in application as they
obtain a correlation between various explanatory param-
eters with the pollutant concentration (McLay et al.
2001). Statistical methods employ careful selection of
spatial variability and are useful if error-free data are
available in sufficient volume (Babiker et al. 2005).
Most widely used methods are based on index and
overlay technique, which consider different physical
and hydrogeological factors that control movement of
pollutants through the unsaturated zone till they reach
the water table and their further spread (Aller et al.
1987). Depending upon the relative importance of each
factors considered in conjunction with the area charac-
teristics, a numerical value is assigned to each factor.
Weighted attribute ratings are then added to get an
overall numerical score which express the level of
groundwater vulnerability. Finally, the similar numerical
scores are clubbed together to prepare vulnerability map
in a geographic information system (GIS) platform.
Index and overlay methods are widely used and accept-
ed because of the two inherent advantages, (i) required
Environ Monit Assess
data are generally available and (ii) do not need incor-
porating and explaining complex processes related to
pollution of groundwater (Thapinta and Hudak 2003).
DRASTIC is one of the widely used index and
overlay methods, developed by Aller et al. (1987)for
US Environmental Protection Agency (EPA) in order to
perform a systematic evaluation of groundwater pollu-
tion potential of any hydrogeological setting. This meth-
od is widely used in countries around the world (Stigtter
et al. 2006). DRASTIC involves seven physical and
hydrogeological factors, viz., depth of water (D), net
recharge (R), aquifer media (A), soil media (S), topog-
raphy (T), impact of vadose zone (I) and hydraulic
conductivity (C). A two-tier numerical ranking system
is adopted in this method, weight and factor. The final
index is obtained by the weighted sum of the factors.
Each of the physical/hydrogeological parameters is giv-
en a weight based on its importance (most important as 5
and least as 1). Depending upon the relative prominent
role in impacting pollution potential, a factor score is
given for each parameter with a rating between 1 and 10.
Important assumptions made when applying DRASTIC
are that the contaminant is introduced at the ground
surface, entered into the groundwater by precipitation
and has the mobility of water (Aller et al. 1987).
Besides DRASTIC, some other index and overlay
methods like Pesticide DRASTIC and Susceptibility
Index (SI) are also widely used to assess vulnerability,
specifically to study the impact of agricultural activity
and other anthropogenic activities (Anane et al. 2013).
Pesticide DRASTIC method adopts the same parame-
ters as DRASTIC, but with different weights, whereas
SI is marked by inclusion of a new factor land use and
exclusion of the three factors, soil media, vadose zone
and aquifer hydraulic conductivity (Ribeiro 2000).
The researchers in India has applied DRASTIC,
Pesticide DRASTIC and SI in different hydrogeologic
environments during the last two decades (Jha and
Sebastian 2005;Rahman2008;Umaretal.2009;
Alam et al. 2014).
The Gangetic plains in India, where the present study
area is located, covers about 250,000 km
2
and mainly
extends over the three large and populous states, Uttar
Pradesh, Bihar and West Bengal. The area represents
one of the most densely populated regions of the world
(829–1,500 persons km
−2
) and under intensive cultiva-
tion because of the inherent fertility of soil and richness
in water resources. The plains are endowed with poten-
tial aquifers made up of unconsolidated fluvio-lacustrine
deposits of Quaternary age within a depth of 200 m
(Saha et al. 2007; Mukhejee et al. 2007;Sahaetal.
2013). Because of shallow water table (generally
<10 m below ground level (bgl)), copious rainfall and
good hydraulic potentiality, the shallow aquifers
(<60 mbgl) are widely used for agriculture and drinking
(Saha et al. 2007,2010a).
The shallow aquifers in the Gangetic plains are facing
pollution risk mainly from two sectors, (i) rapid increase
in use of chemical fertiliser and synthetic pesticides for
agriculture and (ii) unplanned dumping of solid and
liquid urban wastes. Sharp increase in chemical fertiliser
consumption can be gauged from the fact that in Bihar
state its consumption in early 1960s was 4 kg ha
−1
,
which has increased to 90 kg in 1975–1976 and further
to 200 kg in 2010–2011 (Singh 2011). To cope up with
the rising demand, the production of pesticides in India
has increased from 5,000 to 85,000 metric tons between
the period 1958 and 2004 (Gupta 2004). The urbanisa-
tion is also progressing rapidly, which is evident from
the fact that presently there are nine urban with more
than one million population each are located in the
Gangetic plains. The solid wastes are often dumped in
an unplanned manner without any hydrogeological con-
siderations, while the sewages are untreated or partially
treated before discharging into the rivers and open lands.
The entire water demand for agriculture, domestic
and industry of the study area is dependent on ground-
water. The hydrogeologic conditions, groundwater re-
source availability and subsurface flow regime of the
area have been studied by Saha et al. (2007,2008,
2010). The area is so critically dependent on groundwa-
ter, that any deterioration of quality will create an im-
mediate adverse impact on drinking water supply.
Vulnerability assessment of groundwater is thus partic-
ularly important in view of several factors mentioned
above. The present research attempts to understand the
hydrogeological characteristics of the area and related
factors like topography, soil characteristics, vadose zone
characteristics etc., which might have bearing on
groundwater- quality deterioration. The objective of
the study is to assess vulnerability of groundwater oc-
curring in the unconsolidated alluvial aquifer system in
areas under extensive use. No such study has been
undertaken in this part of the Gangetic plains with pre-
dominantly rural background representing agrarian econ-
omy. The outcomes of the study can be used in other parts
of the Gangetic plains with similar hydrogeologic and
socio-economic conditions.
Environ Monit Assess
Study area
The axial river Ganga divides the Gangetic plains in the
state of Bihar and Uttar Pradesh into two halves, the
North and South Ganga Plain. The research area covers
2,200 km
2
of the South Ganga Plain, with its southern
border abuts against the Precambrian Highlands, while
the northern eastern and western boundaries merge with
the vast Plains. The Precambrian rocks exposed in the
south, dips towards north, under a sequence of
Quaternarty fluvial deposits, made up of alternate layers
of sand, clay and sandy clay (Saha et al. 2007).
Fig. 1 Location map of the study area
Environ Monit Assess
Administratively, the area forms a part of the Nalanda
district of Bihar (Fig. 1), representing a gently sloping
topography towards north with an elevation range of
51–73 m above mean sea level. The area represents a
typical agrarian economy with 74 % of its total popula-
tion of 0.26 million live in rural areas. Biharsharif, the
largest urban area, with a population of 0.3 million, is
the headquarters of the Nalanda district. About 84 % of
the geographical area is under cultivation with three
cropping seasons, summer (April–June), winter
(November–March) and monsoon (July–October).
More than 80 % of the total groundwater extrac-
tion (1.710 mcm km
−2
) is for agriculture need.
The area receives south-western monsoon during
June to September, when 82 % of total annual
rainfall (mean 858 mm) occurs. Monsoon rainfall
is the main source of aquifer recharge, constituting
76 % of the total annual recharge of 2.796 mcm km
−2
(CGWB 2011).
The sand layers within the Quaternary alluvial se-
quence form the potential aquifer system. Hand pumps
(depth, 30–60 m) are generally used for drinking in rural
areas, while urban water supply is mostly dependent on
deep tube wells (depth, 100–150 m), which are directly
connected to stand posts. Irrigation is primarily relying
on dug wells (depth, 10–20 m) and mechanised
tube wells (depth, 30–60 m). The area is devoid of
any major industry. The consumption of chemical
fertiliser in the area is reported as 223 kg ha
−1
.
The use of pesticide is also significant, though no
authentic data is available on their consumption.
The solid waste from urban and semi-urban areas
are dumped without taking into consideration the litho-
logical character of the subsurface formation, while the
sewage is discharged into streams/open lands without
proper treatment.
Methodology
DRASTIC is an empirical method developed for evalu-
ating the pollution potential of groundwater. The method
is being adopted increasingly in a variety of
hydrogeological and climatic conditions in different parts
Tabl e 1 The DRASTIC, Pesticide DRASTIC and Pesticide DRASTIC LU model parameters (after Aller et al. 1987)
Parameters Characteristics DRASTIC Pesticide
DRASTIC
Pesticide
DRASTIC LU
Depth to water level Refers to depth to water level from ground surface. As the
groundwater level goes deeper, the lesser chance for
contamination to occur
555
Net recharge Refers to volume of water infiltrates through ground
surface and joins groundwater body through percolation.
The more the volume of recharge, the higher the level
ofcontaminationtooccur
444
Aquifer media Refers to the aquifer framework material. This controls
the pollution attenuation process
333
Soil media Soil represents the uppermost profile; it lies over the
Vados zone and controls the recharge rate
255
Topography Slope of the land surface is considered under topography.
The higher the slope, the higher the runoff and the lower
the infiltration. Lower infiltration means the chance
of percolation of contaminants is less
133
Impact of Vados zone Refers to the material between soil profile and water table.
It controls the attenuation of the contaminants as the
water flows through this zone
544
Hydraulic conductivity This refers to the rate at which water flows along the gradient
of water table. The higher the conductivity, the more the
chance of spread of the contaminants in the groundwater
system
322
Land use Refers to what type of human activity is going on.
The nature and level of contaminants depend on
land use types
––5
Environ Monit Assess
of the world (Lobo-Ferreira and Oliveira 2003;Ramos-
Leal and Rodrı’guez-Castillo 2003; Shirazi et al. 2013).
This method is adopted for the first time in the Gangetic
plains of Bihar, where the entire societal water demand is
extracted from the aquifers. Considering the significant
dependence on aquifers, which are quite potential in
nature, it is imperative to assess the vulnerability of
groundwater for its sustainable use.. The Pesticide
DRASTIC model uses the same parameters as
DRASTIC (Table 1). The present study has also
considered land use as an additional parameter in
Pesticide DRASTIC model (Pesticide DRASTIC LU
model).
Each parameter has been assigned a rating between 1
and 10, based on their relative impact on the pollution
potential. Weights have been assigned to each
parameter, ranging from 1 to 5, depending on their
relative importance. Aller et al. (1987) established the
numerical weights using Delphi technique, by utilising
the practical and research experiences of professionals
Fig. 2 Flow chart elaborating the methodology for groundwater vulnerability analysis using DRASTIC, Pesticide DRASTICand Pesticide
DRASTIC LU models
Environ Monit Assess
worldwide. Typically, the experts were asked to rate the
risk level of certain activities under a set of initial
conditions (Rahman 2008). The analyses were carried
out in a GIS environment, by converting the data/map
set to raster dataset, with a cell size of 50×50 m. The
vulnerability assessment of an individual cell is based
on the index value (D
i
), which has been worked out
based on weight and rating of each parameter for that
Fig. 3 Depth to water level map of the study area
Tabl e 2 Assigned weights and rating used in DRASTIC, Pesticide DRASTIC and Pesticide DRASTIC LU (after Aller et al. 1987)
Factor DRASTIC Pesticide DRASTIC Pesticide DRASTIC LU
Rating (R) Weight (W) Rating (R) Weight (W) Rating (R) Weight (W)
Depth to water (D) 7, 9, 10 5 7, 9, 10 5 7, 9, 10 5
Net recharge (R) 9 4 9 4 9 4
Aquifer media (A) 8 3 8 3 8 3
Soil (S) media 3, 4, 5, 6 2 3, 4, 5, 6 5 3, 4, 5, 6 5
Topography(T) 1,5,7,10 1 1,5,7,10 3 1,5,7,10 3
Impact of vadose zone (I) 1,8 5 1,8 4 1,8 4
Hydraulic conductivity (C) 2, 3, 4, 5 3 2,3, 4, 5 2 2,3, 4, 5 2
Land use (LU) —— —— 1,5, 7, 8 5
Environ Monit Assess
particular cell. The following equation has been used in
DRASTIC model to work out D
i
Di¼DrDwþRrRwþArAwþSrSwþTrTw
þIrIwþCrCwð1Þ
Where, D,R,A,S,T,Iand Care the seven parameters
and subscripts rand ware the corresponding ratings and
weights. Pesticide DRASTIC index (PD
i
) has been
worked out using the same equation, considering the
same ratings of all parameters but with different S
w
,T
w
,
I
w
and C
w
. The Pesticide DRASTIC LU index (PDLU
i
)
adopted the same weights and ratings as that of the seven
parameters from Pesticide DRASTIC. The PDLU
i
has
been worked out using the following equation.
PDLUi¼DrDwþRrRwþArAw
þSrSwþTrTwþIrIw
þCrCwþLrLwð2Þ
Where, D,R,A,S,T,I,Cand Lare the same
parameters, as Eq. (1)andLrepresent land use.
The flow charts of the three models are shown in
Fig. 2.
Detailed field works were carried out to mea-
sure water levels and groundwater sample collec-
tion for NO
3
analyses. Water levels were measured
in the month of November 2012 from 62 selected
wells. The vulnerability maps produced by the
models were validated with NO
3
concentration in
groundwater. Water samples were collected from
the same 62 wells during the month of May
2012 and were analysed in the chemical laboratory
of Central Ground Water Board, Patna (detection
limit 2.0 mg L
−1
). The correlation between NO
3
concentration and the index values of the cell
where the sampling station is located were worked
out by first order linear regression analysis. Since
the D
i
,PD
i
and PDLU
i
were not found to be in
same range, they were normalised before
Fig. 4 Net recharge map of the study area
Environ Monit Assess
regression analyses, to smoothen the anomalies
(Senar and Davraz 2012). The normalisation was
performed based on the following relation:
Xnorm ¼X–Xmin
ðÞ=Xmax–Xmin
ðÞ½100 ð3Þ
Where, X
norm
is normalised data, X
max
is maxi-
mum index value and X
min
is minimum index
value.
Results and discussion
Eight thematic layers were prepared representing
each parameter on the GIS platform based on the
data generated through field work and collected
from different Government departments.
Vulnerability assessment parameters
Depth to water level
Depth to water level, which defines the uppermost sur-
face of the zone of saturation, is important because it
determines the length of a path which a contaminant
must travel before reaching the water level. The duration
of contact between the percolating water and the solid/
semi-solid constituents in the vadose zone determines to
what extent the pollutants undergo chemical and biolog-
ical reactions like dispersion, oxidation and sorption,
which cause natural attenuation. Deeper the water level,
greater is the chances of attenuation of the pollutants.
In the study region, monsoon being the main source
of groundwater recharge (CGWB 2011), water level
follows the season, with shallowest during the middle
of the monsoon (month, August) and deepest before the
onset of monsoon (month, May). Considering that the
Fig. 5 Soil map of the study area
Environ Monit Assess
major pumping season starts in November to irrigate the
winter crop, water levels were measured during the 1st
week of November from the wells, when it was found to
ranged between 1.44 and 5.86 m bgl. A depth (below
ground) to water level map was prepared using the water
levels measured from 62 monitoring stations (Fig. 3).
The water levels were found to be clustered in the first
three groups, <2 m bgl (rating, 10), 2–4 m bgl (rating, 9)
and >4 m bgl (rating, 7) proposed by Aller et al. (1987).
This parameter was assigned a weight of 5 (Table 2)for
all three models.
Net recharge
Net recharge represents the volume of water which
infiltrates through the surface and reaches the aquifer.
This component is the principal vehicle that transport
the contaminants through percolation (Voudouris et al.
2010). The higher the volume of net recharge, the more
is the vulnerability of the aquifer. The weight and rating
of the parameter have been adopted on the basis of
annual rainfall of the area. The mean (30 years) annual
rainfall recorded in five stations within the area ranged
from 778 to 945 mm; the spatial distribution worked out
by Thyssen Polygon method is shown in Fig. 4.
Considering the rainfall distribution of the area
(>254 mm year
−1
), the weight and rating of the param-
eter has been considered to be 4 and 9, respectively, for
all three models, following Aller et al. (1987).
Aquifer media
Aquifer media refers to the nature of geologic formation
which serves as aquifer like sand and gravel in case of
alluvium while weathered zone and secondary porosi-
ties (fracture/joint) in case of hard rock. The nature and
rate of flow (hydraulic conductivity) of an aquifer is
controlled by its framework material called media. The
media also exert a major control over the pollutant’s
route and path length. The time available during the
flow for the attenuation process to remain active de-
pends on the characters of the aquifer media like sorp-
tion, reactivity, dispersion and effective surface area of
the aquifer framework material (Aller et al. 1987). The
Fig. 6 Topography map of the study area
Environ Monit Assess
nature and type of aquifer media were determined from
available lithological logs of 28 borewells collected
from Government departments. The aquifers in the area
are made up of medium to coarse sand with thin and
localised gravel beds. The weight for aquifer media has
been considered as 3 while the rating has been taken as 8
(Table 2) for all three models.
Soil media
The soil characteristics influence the rate of infiltration,
which in turn controls attenuation processes like filtra-
tion, biodegradation, sorption and volatilisation during
the process of percolation through the soil (Aller et al.
1987). Presence of fine-grained materials in soil like
clay, silt, peat and organic matter decrease the perme-
ability and help effectively in reducing the contamina-
tion load. Based on the data available from the National
Bureau of Soil Survey and Land Use Planning
(NBSSLUP 2003), the area was classified into four soil
types, clay loam, fine loam, loam and coarse loam
(Fig. 5). In the central part and in a narrow zone along
Biharsharif-Rajgir tract, the soil is coarse-loam type. On
the other hand, fine-grained soil like clay loam is found
along the northern border and also as small patches in
the south-western part. The rating varies from 3 (clay
loam) to 6 (coarse loam) for the models. The weight has
been considered as 2 for DRASTIC and 5 for both
Pesticide DRASTIC and Pesticide DRASTIC LU
(Table 2).
Topography
The slope of land surface and its variation is referred as
topography. In areas with low slope, runoff water is
retained for longer periods, allowing higher infiltration,
thus having a greater pollution potential. Slope data of
the area was obtained from Shuttle Radar Topography
Fig. 7 Vadose zone map of the study area
Environ Monit Assess
Mission Digital Elevation Model (SRTMDEM), with a
resolution of 90 m. Not much slope variation was ob-
served (Fig. 6) in the study area. For both the models,
the ratings of 1, 5, 7 and 10 were considered for the
slope percentages as follows, 0–2, 2–8, 8–16 and
>16 %, respectively (Table 2). The weight was taken
as 1 for DRASTIC, while 3 for both Pesticide
DRASTIC and Pesticide DRASTIC LU models.
Impact of vadose zone
The unsaturated zone lying between the ground surface
and water level is termed as vadose zone. This zone has
an important role on percolating water. The type of
material in vadose zone determines the pollution atten-
uation characteristics like biodegradation, mechanical
filtration, sorption, volatilisation and dispersion (Aller
et al. 1987). The information on vadose zone was ex-
tracted from the lithological logs of 28 borewells
collected from Government departments as well as
from the geological maps available from Geological
Survey of India (GSI 1998).Basedonthelitholog-
ical logs of 28 borewells, the predominant litholo-
gy, representing the vadose zone, up to 5.86 m bgl
(max depth to water level) has been grouped into
two classes, clayey sand and sand (Fig. 7). The
weight for this parameter was considered as 5 for
DRASTIC and 4 for the other two models. The
rating was taken as 1 for clayey sand and 8 for sand
(Table 2) for all the models.
Hydraulic conductivity
Groundwater always remains under movement, and hy-
draulic conductivity expresses the ability of aquifer to
transmit water. This component thus determines at
which rate the pollutants move through an aquifer
(Aller et al. 1987). The hydraulic conductivity of an
Fig. 8 Hydraulic conductivity distribution in the study area
Environ Monit Assess
unconsolidated aquifer depends upon the porosity as
well as inter-connectivity among the inter-granular void
spaces. In general, the smaller the grain size, the lower is
the hydraulic conductivity. However, besides the grain
size, two other factors that impart effect on hydraulic
conductivity are sphericity of the grains and their pack-
ing. Hydraulic conductivity values for seven locations
(range, 18.2 to 43.7 m day
−1
) are available for the study
area (Saha et al. 2007,2013). The zones with equal
hydraulic conductivity values are shown in Fig. 8.For
both DRASTIC and Pesticide DRASTIC models, the
range values (18.2–43.7 m day
−1
) are divided into four
equal segments, and the ratings were applied as
follows: 2 for 12–20 mday
−1
, 3 for 20–28 m day
−1
, 4 for
28–36 m day
−1
and 5 for 36–44 m day
−1
. The weights
were considered as follows, 3 for DRASTIC, while 2 for
both Pesticide DRASTIC and Pesticide DRASTIC LU
models.
Land use
Land use is an important human intervention that influ-
ences vulnerability assessment (Anane et al. 2013). A
land use map of the area was prepared by interpreting
2011 satellite data of IRS IC, LISS-III scanner (spatial
resolution, 23.5 m). The main land use classes demar-
cated were agricultural land followed by urban areas
covering 87 and 11 % of the geographical area, respec-
tively (Fig. 9). Waste land (including fallow land) was
also demarcated in isolated patches. The agricultural
land was further classified into (i) predominantly under
cereals in all three cropping seasons (monsoon, winter
and summer) and (ii) largely under vegetable cultiva-
tion, particularly as winter and summer crops. The land
use parameter was given a uniform weight of 5, while
the ratings were adopted as follows, 5 for urban areas, 7
for cultivated area (other than vegetables), 8 for areas
Fig. 9 Land use map of the study area
Environ Monit Assess
under predominantly vegetables and 1 for wasteland
(including fallow land) (Table 2).
Vulnerability mapping
After preparing the layers, the vulnerability maps were
prepared by overlying the layers in a GIS environment,
where the indices were calculated for each cell of 50×
50 m. The DRASTIC index scores ranged from 135 to
186, whereas Pesticide DRASTIC showed a wider var-
iation of 144 to 211. Maximum variation in index scores
(169–251) was observed in Pesticide DRASTIC LU.
Based on the classification by Engel et al. (1996), as
referred in Anane et al. (2013), the DRASTIC exhibited
only two vulnerability categories, ‘moderate’and ‘high’
(Fig. 10;Table3), while the Pesticide DRASTIC
expressed three categories, ‘moderate’,‘high’and ‘very
high’(Fig. 11). Parameter-wise salient statistics of the
scores of the cells viz., minimum, maximum, mean,
standard deviation and coefficient of variation, for
DRASTIC and Pesticide DRASTIC are produced in
Tab les 4and 5. An assessment of means of the param-
eters revealed that the depth to water level (mean= 44)
has the highest contribution to vulnerability index,
closely followed by net recharge (mean= 40). The four
parameters viz., soil media, topography, impact of va-
dose zone and hydraulic conductivity, were considered
with different weights in DRASTIC and Pesticide
Fig. 10 Groundwater vulnerability zone using DRASTIC model
Tabl e 3 Vulnerability categories
for DRASTIC and Pesticide
DRASTIC (Engel et al. 1996)
Vulnerability
category
Index
core
Low 1–120
Moderate 121–160
High 161–200
Very high >200
Environ Monit Assess
DRASTIC. The mean of these four parameters revealed
theimportanceofvadosezoneasmaximumin
DRASTIC while topography was most prominent in
Pesticide DRASTIC. Considering the spatial variation
of index values, the standard deviation indicated impact
of vadose zone as the most significant, both in
DRASTIC and Pesticide DRASTIC (Tables 4and 5).
The DRASTIC vulnerability map revealed ‘high’
vulnerability covering 40 % of the area, clustering in
two patches, (i) south-western corner in Ekangarsarai-
Islampur zone and (ii) in Biharsharif-Noorsarai area,
and further extending in two directions, (iia) eastward
towards Asthawan and (iib) south-eastward through
Pawapuri and Giriak (Fig. 10). Pesticide DRASTIC
model exhibited ‘very high’category zone, cumulative-
ly covering 11 % of the area, but were distributed as
isolated patches mainly in the central, eastern and west-
Fig. 11 Groundwater vulnerability zone using Pesticide DRASTIC model
Tabl e 4 Statistical summary of the DRASTIC parameter
DRAS T I C
Min3540246156
Max50402412104015
Mean 44 40 24 9 8 22 11
SD 2.7 0 0 2.3 2.9 17.6 3
CV % 6.1 0 0 24.5 35.7 80.4 26.3
Tab l e 5 Statistical summary of the Pesticide DRASTIC
parameter
DRAS T I C
Min 35 402415 3 4 4
Max50402430303210
Mean 44 40 24 23 24 18 8
SD 2.7 0 0 5.7 8.7 14.1 2
CV % 6.1 0 0 24.5 35.7 80.4 26.3
Environ Monit Assess
ern parts (Fig. 11). All such ‘very high’category areas
were found to coincide with the ‘high’vulnerability
zones delineated by DRASTIC. In Pesticide
DRASTIC, area under ‘high’vulnerability (80 % of
total area) was found to be double of the area demarcat-
ed by DRASTIC. Several researchers have reported
comparatively higher groundwater vulnerability rating
by Pesticide DRASTIC than DRASTIC (Ahmed 2009).
Such variations are due to different weights considered
for few parameters in the models. The two parameters,
soil media and topography were given weights of 5 and
3, respectively, in Pesticide DRASTIC against to 2 and 1
in DRASTIC model, helping an overall increase of the
index values. This happens despite the marginally lower
weight assigned to two other parameters, impact of
vadose zone and hydraulic conductivity. Considering
both the models, higher groundwater vulnerability cate-
gories were found to be confined mainly in two areas, (i)
in the western part, in an elongated zone between
Ekangarsarai and Islampur, and (ii) in Biharsharif-
Fig. 12 Groundwater vulnerability zone using Pesticide DRASTIC model incorporating land Use
Tabl e 6 Statistical summary of
the Pesticide DRASTIC LU
parameter
DRAS T I C LandUse
Min354024153445
Max5040243030321040
Mean 44 40 24 23 24 18 8 31
SD 2.7 0 0 5.7 8.7 14.1 2 6.79
CV % 6.1 0 0 24.5 35.7 80.4 26.3 21.9
Environ Monit Assess
Noorsarai area. These areas were marked with
shallow water level (2–3 m bgl) with soil type as loam
to coarse loam.
‘Very high’vulnerability category has not been de-
tected in DRASTIC model. However, vulnerability in-
dex values have no positive relation with pollution risk.
Higher pollution risk occurs due to anthropogenic ac-
tivities like intense agriculture and certain industries,
even in low vulnerable areas (Anane et al. 2013). At
cases, such underestimation of vulnerability emanates
from the fact that the DRASTIC expresses the intrinsic
vulnerability and does not include the contribution of
specific anthropogenic activity to groundwater pollution
(Almsari 2008; Bai et al. 2012).
Pesticide DRASTIC LU model
Land use is an important anthropogenic intervention on
the earth surface, which significantly affects groundwa-
ter vulnerability. This is particularly true for the
Gangetic plains, characterised by intense agriculture
and dense population distribution. The PDLU
i
scores
(169–251) were found in two categories (Anane et al.
2013; Engel et al. 1996), ‘very high’and ‘high’,witha
geographical coverage of 71 and 29 %, respectively
(Fig. 12). The salient statistical parameters of the scores
are shown in Table 6. An evaluation of the mean values
revealed that the depth to water level (mean, 44)
has the highest contribution to the vulnerability
index, closely followed by net recharge (mean, 40) and
land use (mean, 31). The three parameters viz.,
soil media, topography and impact of vadose zone
contribute moderately, while hydraulic conductivity
has the lowest contribution. The variation of vul-
nerability index has been impacted by vadose zone
(80.4 %), followed by topography, hydraulic con-
ductivity and soil media as indicated by coefficient
of variation values.
The ‘very high’vulnerable areas were spread over
the central, eastern and south-western part, while ‘high’
category areas were found in four patches, but all were
found to be confined within the ‘moderate’category
areas under DRASTIC model.
Tabl e 7 Statistics of single-pa-
rameter sensitivity analysis for
DRASTIC
Parameters Theoretical weight Theoretical weight % Effective weight %
Mean Min Max SD
D 5 21.7 28.78 23.49 31.25 1.56
R 4 17.4 25.95 24.69 27.59 0.75
A 3 13 15.57 14.81 16.55 0.45
S 2 8.7 5.983.757.891.39
T 1 4.3 5.22 0.67 6.9 1.84
I 5 21.7 9.73 9.26 10.34 0.28
C 3 13 8.66 5.81 10.34 1.43
Tabl e 8 Statistics of
single-parameter sensitivity
analysis for Pesticide DRASTIC
Parameters Theoretical weight Theoretical weight % Effective weight %
Mean Min Max SD
D 5 19.23 24.79 17.86 31.25 2.9
R 4 15.38 22.35 18.96 27.78 2.4
A 3 11.54 13.41 11.37 16.67 1.4
S 5 19.23 12.69 7.73 16.95 2.5
T 3 11.54 13.36 1.68 18.99 4.8
I 4 15.38 9.11 2.19 17.88 7.0
C 2 7.69 4.26 1.95 6.85 1.3
Environ Monit Assess
Sensitivity analyses
The DRASTIC and Pesticide DRASTIC models attract
debate on two issues, (i) unavoidable subjectivity related
with the seven parameters used and (ii) whether it is
really necessary to use all parameters (Babiker et al.
2005). Though, it is also believed that a number of input
data layers adopted are constrained by impacts of errors
Tabl e 9 Statistics of single-pa-
rameter sensitivity analysis for
Pesticide DRASTIC LU
Parameters Theoretical weight Theoretical weight % Effective weight %
Mean Min Max SD
D 5 16.12 21.17 14.83 26.63 2.44
R 4 12.9 19.08 15.94 23.67 2.03
A 3 9.67 11.45 9.56 14.2 1.22
S 5 16.12 10.85 6.67 14.85 2.14
T 3 9.67 11.46 1.37 16.39 4.13
I 4 12.9 7.77 1.85 14.95 5.93
C 2 6.45 3.63 1.7 5.85 1.11
LU 5 16.12 14.59 10.68 21.51 2.62
Fig. 13 Nitrate distribution in groundwater in the study area
Environ Monit Assess
or uncertainties of the individual parameters when final
model output is produced (Rosen 1994). The rates and
weights used for various parameters have also been
debated (Napolitano and Fabbri 1996). Regarding the
necessity of all seven parameters, few authors opined
that DRASTIC-equivalent result can be obtained by
using less number of parameters (Merchant 1994). To
address these issues, a sensitivity analysis known as
single-parameter sensitivity analyses (SPSA)
(Napolitano and Fabbri 1996) was carried out. In
SPSA, all the parameters were evaluated for their inter-
dependence and variability, as independency of param-
eters decreases the risk of judgement, and then the
effective weight of each parameter was worked out
(Rosen 1994; Babiker et al. 2005). The real or effective
weight of each parameter was then compared with the
theoretical weight assigned during the model workout.
The effective weight has been worked out using the
following relation.
W¼Pr=Pw
ðÞ=v½Þ
i100 ðivÞ
Where, Wrefers tothe effective weight, P
r
and P
w
are
the respective rating and weight of each parameters and
vdenotes overall vulnerability index.
The theoretical and effective weights for DRASTIC,
Pesticide DRASTIC and Pesticide DRASTIC LU are
shown in Tables 7,8and 9. In DRASTIC, the parameter
depth to water was the most prominent, as reflected by
its effective weight (mean, 28.7 %), which exceeded the
theoretical weight by (mean, 21.7 %). Similarly, the net
recharge has also shown higher effective weight (mean,
25.9 %) than the theoretical weight (mean, 17.4 %). The
impact of vadose zone, on the other hand, has shown
Fig. 14 Relationship between
NO
3
concentration and aquifer
vulnerability map by DRASTIC
Fig. 15 Relationship between
NO3 concentration and aquifer
vulnerability map by DRASTIC
Pesticide
Environ Monit Assess
significantly lower effective weight (mean, 9.73 %) than
the theoretical weight assigned (mean, 21.7 %). Higher
effective weight of the parameters like depth to water
and net recharge highlighted their importance in the
DRASTIC model output. Information about these pa-
rameters should be accurate and detailed for better vul-
nerability assessment of groundwater.
In Pesticide DRASTIC model also, the same two
parameters depth to water level and net recharge, were
found to be the most effective. In DRASTIC model, soil
media and topography were the least important parame-
ters whereas in Pesticide DRASTIC model, impact of
vadose zone and hydraulic conductivity were the two
parameters with least impact. In the case of Pesticide
DRASTIC LU model, the two most important parameters
worked out were depth to water level and net recharge.
Validation of the vulnerability maps
As already discussed, the area is intensively cultivated
(cropping intensity, 154 %), where nitrogen-based
fertilisers like urea are generously used. Researchers have
correlated elevated concentrations of NO
3
at places in the
Gangetic plains to excessive application of nitrogen-based
fertilisers (Sankararamakrishnan et al. 2007;Handa1983).
In the study area, the validation of the model output was
carried out by comparing the vulnerable zones with NO
3
concentration in groundwater. The NO
3
concentration
ranged between 20.2 and 140.8 mg L
−1
with a mean of
56.1 mg L
−1
, where 60 % of the samples exceeded
45 mg L
−1
(Fig. 13). The reason for the high NO
3
con-
centration in groundwater in the area is related to the use
of NO
3
-based fertilisers for agriculture (Saha et al. 2008).
The NO
3
flows with the return seepage of irrigation water
and percolates till it joins the groundwater. This effect is
pronounced in areas underlain by formations with high
percolation rate. The areas under vegetable cultivation are
particularly marked with higher concentartion because of
a generous dose of fertilisers used to boast production.
The correlation was worked out by considering the index
values (D
i
,PD
i
and PDLU
i
) of the cells where groundwa-
ter sampling stations are located, as independent variable
and NO
3
concentration(>40mgL
−1
) as dependent vari-
able. The correlation coefficient for DRASTIC, Pesticide
DRASTIC and Pesticide DRASTIC LU were found to be
0.324, 0.409 and 0.268, respectively (Figs. 14,15 and 16).
Higher correlation coefficient values for Pesticide
DRASTIC indicated better applicability of this model
for demarcating the vulnerability zones.
Conclusions and recommendations
The present research has attempted to assess groundwa-
ter vulnerability in the Nalanda district of Bihar state,
located in the southern part of the Gangetic plains, using
DRASTIC, Pesticide DRASTIC and Pesticide
DRASTIC LU models. The groundwater management
issues in the Gangetic plains mainly concerned with the
volumetric budgeting of the resource, ignoring the
chemical quality aspects. Reporting of anthropogenic
pollution of groundwater in recent years has emphasised
the need of incorporating the chemical quality aspects in
the management issues and also understanding of vul-
nerability of aquifers as a prerequisite to prevent/
Fig. 16 Relationship between
NO
3
concentration and aquifer
vulnerability map using
DRASTIC Pesticide LU
Environ Monit Assess
minimise such pollution. The present study is the first
endeavour to assess the groundwater vulnerability in the
Gangetic plains in the state of Bihar.
Seven parameters representing the hydrogeological
settings and physical characteristics have been consid-
ered for DRASTIC and Pesticide DRASTIC. The role
of land use (in addition to the seven parameters) has also
been assessed by considering it as an additional param-
eter as Pesticide DRASTIC LU model. Higher vulnera-
ble areas worked out using DRASTIC and Pesticide
DRASTIC have been found to be clustered in two zones
(i) Biharsharif-Noorsarai area in the central part and (ii)
along the south-western border between Ekangarsari
and Islampur. Besides, three small patches have
also been delineated in the eastern and southern
parts. The two parameters, depth to water and net
recharge inflicted maximum impact on the intrinsic
vulnerability of the aquifer system, while the aqui-
fer media has a moderate impact. The two parameters,
soil media and topography, have exerted moderate im-
pact on Pesticide DRASTIC, while a low impact in case
of DRASTIC.
No significant value addition has been observed by
clubbing land use parameter with parameters adopted in
Pesticide DRASTIC model. In this case (Pesticide
DRASTIC LU), 71 % of the study area has been delin-
eated under ‘very high’category. This category area,
were already incorporated in higher vulnerable areas
demarcated by DRASTIC and Pesticide DRASTIC
models.
The sensitivity analysis has revealed the role of depth
to water and net recharge as most significant parameters
in vulnerability assessment, emphasising that the data
regarding these two parameters should be representative
and accurate. The aquifer vulnerability maps that were
prepared using different models were again compared
with NO
3
concentration in groundwater. The spatial
distribution revealed high NO
3
(>70 mg L
−1
) concen-
trations confined in five zones, three of which
(Biharsharif-Noorsarai, east of Ashtawan and south of
Ekangarsarai) were by and large, coinciding with higher
vulnerable areas detected by DRASTIC and Pesticide
DRASTIC. However, the remaining two zones, partic-
ularly the one with larger aerial extent in the west of
Hilsa, were located beyond the higher vulnerable areas.
The linear regression analyses between NO
3
concentra-
tion (>40 mg L
−1
), and the index values of the cells
revealed better correlation in case of Pesticide
DRASTIC (R
2
=0.409), emphasising that this model
produces better vulnerable zonation than the other
models. The land use parameter has got no significant
contribution in vulnerable zonation.
The groundwater vulnerability zonation should form
an integral part of any sustainable groundwater manage-
ment plan of the area. High NO
3
concentration in areas
of the west of Hilsa, which is under intense agriculture
but falling within the comparatively low vulnerable
zones delineated by both DRASTIC and Pesticide
DRASTIC, indicated that the pollution load (fertilisers)
also played a significant role in contaminating ground-
water even in low vulnerable areas. Detailed time-
domain groundwater-quality monitoring is essential to
update the changing levels of pollutants. It is recom-
mended that similar research should be undertaken in
other areas of the Gangetic plains to have a wider
understanding of vulnerability of aquifers from anthro-
pogenic sources.
Acknowledgements The research forms a part the PhD thesis of
the first author. The authors extend thanks to R.C Jain, K M
Najeeb and K C Naik of CGWB for their support. The views
expressed by the authors are their own and not of the Department.
The discussion made with Rashid Umar, G.K. Roy, R.R. Shukla
and S.N. Dwivedi helped in improving the manuscript.
References
Ahmed, A. A. (2009). Using generic and Pesticide DRASTIC
GIS-based models for vulnerability assessment of the
Quaternary aquifer at Sohag, Egypt. Hydrogeology Journal,
17, 1203–1217.
Alam, F., Umar, R., Ahmed, S., & Dar, F. A. (2014). A new model
(DRASTIC-LU) for evaluating groundwater vulnerability in
parts of central Ganga Plain, India. Arabian Journal of
Geoscience, 7,927–937.
Aller, L., Bennett, T., Lehr, J.H., & Petty, R.J. (1987) DRASTIC: a
standardized system for evaluating groundwater pollution
potential using hydrogeologic settings. U.S.-EPA/600/2-85/
018.
Almsari, M. N. (2008). Assessment of intrinsic vulnerability to
contamination for Gaza coastal aquifer, Palestine. Journal
Environmental Management, 88,577–593.
Anane, M., Abidi, B., Lachaal, F., Limam, A., & Jellali, S. (2013).
GIS-based DRASTIC, Pesticide DRASTIC and
Susceptibility Index (SI): comparative study for evaluation
of pollution potential in the Nabeul-Hammamet shallow
aquifer Tunisia. Hydrogeology Journal, 21,715–731.
Babiker, I. S., Mohamed, M. A. A., Hiyama, T., & Kato, K.
(2005). A GIS based DRASTIC model for assessing aquifer
vulnerability in Kakamigahara Heights Gifu Prefecture,
Central Japan. Science of the Total Environment, 345,127–
140.
Environ Monit Assess
Bai, L., Wang, Y., & Meng, F. (2012). Application of DRASTIC
and extension theory in the groundwater vulnerability evalu-
ation. Water Environment Journal, 26,381–391.
BIS (2012) Specification for drinking water, ISI: 10500, Bureau of
Indian Standards, Second Revision, New Delhi
CGWB (2011) Dynamic groundwater resources of India, Central
Ground Water Board, Ministry of Water Resources,
Government of India
Chae, G., Kim, K., Yun, S., Kim, K., Kim, S., Choi, B., Kim, H., &
Rhee, C. W. (2004). Hydrogeochemistry of alluvial ground-
waters in an agricultural area: an implication for groundwater
contamination susceptibility. Chemosphere, 55,369–378.
Chakraborty, D., Das, B., & Murril, M. T. (2011). Examining
India’s ground water quality management. Environmental
Science and Technology, 45(1), 27–32.
Engel, B. A., Navulur, K. C. S., Cooper, B. S., & Hahn, L. (1996).
Estimating groundwater vulnerability to non-point source
pollution from nitrates and pesticides on a regional scale.
Wallingford, UK: IAHS Publ. no. 235, IAHS.
Foster, S. S. D. (1987) Fundamental concepts in aquifer vulnerabil-
ity, pollution risk and protection strategy. In W. Van
Duijevenboden& H. G.VanWaegeningh (Eds.), Vulnerability
of soil and groundwater to pollutants (pp. 69–86). Proceedings
and information of the TNO Committee on Hydrogeological
Research: Vol. 38. The Hague
Freeze, R. A., & Cherry, A. J. (1979). Groundwater (pp. 262–
265). New Jersey: Prentice Hall, Inc.
Ghose, N.C., Saha, D., & Gupta, A. (2009) Synthetic detergents
(surfactants) and organochlorine pesticide signatures in sur-
face water and groundwater of Greater Kolkata, India, doi:10.
4236/jwarp.2009.14036.
GSI 1998
Gupta, P.K. (2004) Pesticide exposure–Indian Scene. Elsevier
Ireland Ltd
Handa, B.K. (1983) Effect of fertilizer on ground water quality in
India. In: Symp. On Ground water Development—a perspec-
tive for the year 2000 A.D. University of Roorkee, India.
Iqbal, J., Gorai, A. K., Tirkey, P. N., & Pathak, G. (2012).
Approaches to groundwater vulnerability to pollution: a lit-
erature review. Asian Journal of Water Environment
Pollution, 9,105–115.
Javadi, S., Kavehkar, N., Mousavizadeh, M. H., & Mohammadi,
K. (2011a). Modification of DRASTIC model to map
groundwater vulnerability to pollution using nitrate measure-
ments in agricultural areas. Journal of Agr Sci Tech, 13(2),
239–249.
Javadi, S., Kavehkar, N., Mohammadi, K., Khodadi, A., &
Kahawita, K. (2011b). Calibration DRASTIC using field
measurements, sensitivity analysis and statistical method to
assess groundwater vulnerability. Water International, 36(6),
719–732.
Jha, M. K., & Sebastian, J. (2005). Vulnerability study of pollution
upon shallow groundwater using DRASTIC/GIS. New Delhi:
Map India.
Lobo-Ferreira, J. P., & Oliveira, M. M. (2003). On the experience
of groundwater vulnerability assessment in Portugal: aquifer
vulnerability and risk international workshop AVR03,
Salamanca, Gto. Mexico (p. 10). Thailand: Meteorological
Department.
McLay, C. D. A., Dragten, R., Sparling, G., & Selvarajah, N.
(2001). Predicting groundwater nitrate concentrations in a
region of mixed agricultural land use: a comparison of three
approaches. Environmental Pollution, 115,191–204.
Mehta, M. (2006). Status of ground water and policy issues for its
sustainable development in India. In B. R. Sharma, K. G.
Villholth, & K. D. Sharma (Eds.), Proceedings; ground water
research and management, integrating science into manage-
ment and decisions (pp. 62–74). Colombo: International
Water Management Institute.
Merchant, J. W. (1994). GIS-based groundwater pollution hazard
assessment: a critical review of the DRASTIC model.
Photogramm Engineer and Remote Sensing, 60( 9), 1117–
1127.
Mukhejee, A., Fryer, A. E., & Howell, P. D. (2007). Regional
hydrostratigraphy and ground water flow modeling in the
arsenic affected areas of Western Bengal basin; West
Bengal. Hydrogeology Journal. doi:10.1007/s10040-007-
0208-7.
Napolitano, P., & Fabbri, A.G. (1996) Single-parameters
sensitivity analysis for aquifer vulnerability assessmet
using DRASTIC and SINTACS. Proceedings of the
Vienna conference on HydroGIS96: application of geo-
graphic information systems in hydrology and water re-
sources management, IAHS Pub. No. 235, April 1996, p.
559–556.
NBSSLUP. (2003). Soil map of Bihar. Kolkata: National Bureau
of Soil Survey and Land UsePlanning. Govt of India, Eastern
Region.
Neshat, A., Pradhan, B., Pirasteh, S., & Shafri, H. Z. M. (2013).
Estimating groundwater vulnerability to pollution using a
modified DRASTIC model in the Kerman agricultural area,
Iran. Environmental Earth Science, 71,3119–3131.
NIUA. (2005). Status of water supply, sanitation and solid waste
management in urban areas. New Delhi: National Institute of
Urban Affairs.
Rahman, A. (2008). A GIS based DRASTIC mode for assessing
groundwater vulnerability in shallow aquifer in Aligarh,
India. Applied Geography, 28,32–53.
Ramos-Leal, J. A., & Rodrı’guez-Castillo, R. (2003). Aquifer
vulnerability mapping in the Turbio river valley, Mexico, a
validation study. Geofı’Int, 42,141–156.
Ribeiro, L. (2000) SI: a new index of aquifer susceptibility to
agricultural pollution. Internal report, ER-SHA/CVRM
Lisbon Portugal
Rosen, L. (1994) Study of the DRASTIC methodology with the
emphasis on Swedish conditions. In: Program and abstracts
of the 37th conference of the International Association for
Great Lakes Research and Estuarine Research Federation.
Buffalo, NY:IAGLR. p. 166.
Saha, D., Upadhyay, S., Dhar, Y. R., & Singh, R. (2007). The
aquifer system and evaluation of its hydraulic parameters in
parts of South Ganga Plain, Bihar. Journal of the Geological
Society of India, 69,1031–1041.
Saha, D., Dhar, Y. R., & Sikdar, P. K. (2008). Geochemical
evolution of groundwater in the Pleistocene aquifers of
South Ganga Plain Bihar. Journal Geological Society of
India, 71,473–482.
Saha, D., Dhar, Y. R., & Vittala, S. S. (2010a). Delineation of
groundwater development potential zones in parts of margin-
al Ganga Alluvial Plain in South Bihar, Eastern India.
Environmental Monitoring and Assessment. doi:10.1007/
s10661-009-0937-2.
Environ Monit Assess
Saha, D., Sahu, S., & Chandra, P. C. (2010b). Arsenic safe alter-
nate aquifers and their hydraulic characteristics in contami-
nated areas of Middle Ganga Plain, Eastern India.
Environmental Monitoring and Assessment. doi:10.1007/
s10661-010-1535-z.
Saha, D., Dwivedi, S. N., & Singh, R. K. (2013). Aquifer system
response to intensive pumping in urban areas of the Gangetic
plains, India: the case study of Patna. Environmental Earth
Science.doi:10.1007/s12665-013-2577-7.
Sankararamakrishnan, N., Sharma, A. K., & Sanghvi, R. (2005).
Orgenochloride andorganophosphorous pesticide residues in
groundwater and surface water of Kanpur, U.P, India.
Environment International. doi:10.1016/J.envint.2004.08.
001.
Sankararamakrishnan, N., Sharma, A. K., & Iyenger, L.
(2007). Contamination of nitrate and fluoride along
the Ganges alluvial plain of Kanpur district, Uttar
Pradesh, India. Environ. Moni. Assess.doi:10.1007/s10661-
007-0085-5.
Senar, E., & Davraz, A. (2012). Assessment of groundwater
vulnerability based on modified DRASTIC model, GIS and
analytical hierarchy process (AHP) method; the case of
Egiridir Lake Basin (Isparta, Turkey). Hydrogeology
Journal, 21,701–714.
Shirazi, S. M., Imran, M. H., Akib, S., Yusop, Z., & Harun, Z. B.
(2013). Groundwater vulnerability assessment in Melaka
state of Malaysia using DRASTIC and GIS techniques.
Environmental Earth Sciences, 70, 2293–2304. doi:10.
1007/s12665-013-2360-9.
Singh, R.K.P. (2011) Changing face of agriculture in Bihar. http://
www.bihartimes.in/articles/RKP/agriculture.html
Stigtter, T. Y., Ribeiro, L., & Carvaiho Dill, A. M. M. (2006).
Evaluation of an intrinsic and a specific vulnerability assess-
ment method in comparison with groundwater salinization
and nitrate contamination levels intwo agricultural regions in
the south of Portugal. Hydrogeology Journal, 14,79–99.
Tesoriero, A.J., Inkpen, E. L., & Voss, F. D. (1998) Assessing ground
water vulnerability using logistic regression. Proceedings for
the Source Water Assessment and Protection 98 conference,
Dallas, TX p 157–165
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.
Umar, R., Ahmed, I., & Alam, F. (2009). Mapping groundwater
vulnerable zones using modified DRASTIC approach of an
alluvial aquifer in parts of Central Ganga Plain, Western Uttar
Pradesh. Journal Geological Society of India, 73,193–201.
Voudouris, K., Kazakis, 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. EW Publications.
Vrba, J., & Zeporozec, A. (1994). Guidebook on mapping ground-
water vulnerability. International Contributions to
Hydrogeology, 16,129.
Worrall, F., Besien, T., & Kolpin, D. D. (2002). Groundwater
vulnerability: interactions of chemical and site properties.
The Science of the Total Environment, 299,131–143.
Zektser, I. S., Karimova, O. A., Bujuoli, J., & Bucci, M. (2004).
Regional estimation of fresh groundwater vulnerability:
methodological aspects and practical applications. Water
Resources, 31(6), 645–650.
Environ Monit Assess
