Ecotoxicology and Environmental Safety 214 (2021) 112085

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Ecotoxicology and Environmental Safety

journal homepage: www.elsevier.com/locate/ecoenv

Groundwater vulnerability assessment using GIS-based DRASTIC model in

Nangasai River Basin, India with special emphasis on
agricultural contamination
Amit Bera a, *, 1, Bhabani Prasad Mukhopadhyay a, Puja Chowdhury a, Argha Ghosh b,
Swagata Biswas a
a
Department of Earth Sciences, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, West Bengal, India
b
Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur 741252, West Bengal, India

A R T I C L E I N F O A B S T R A C T

Edited by: Paul Sibley Nangasai basin is a semi-arid watershed where agriculture is the main source of economy. In present day,
increasing population demands increase in food productivity which leads to increase use of fertilizers and
Keywords: chemical pesticides in agriculture. These fertilizers on the other hand mix up with the groundwater and increase
Thematic layers the pollution, which affects human health adversely. So, for controlling the groundwater contamination risk
DRASTIC
proper water resource management and assessment of groundwater vulnerability is extremely important. Total 7
GIS
hydrogeological parameters have been considered for this study, and the final groundwater vulnerability map
Groundwater contamination
Nitrate and TDS concentration has been prepared by overlay weighted method with the help of DRASTIC index, which is classified into 5
Nangasai basin vulnerable classes (very high, high, moderate, low, and very low). In the south and south-eastern regions of the
basin namely Deghi, Bankada, Baram, Macha, Katin, Tilabani high groundwater contamination is been observed.
For validating the model, the water quality parameters-nitrate and TDS have been used with the accuracy of 89%
and 86% respectively. Using effective as well as scientifically approved methods, the anthropogenic and agri­
cultural contamination can be controlled and managed which will lower the risk of contamination. This map can
be further utilized as a base map for management of groundwater pollution and its planning.

1. Introduction ground water quality due to contamination with toxic materials has
been gaining importance day by day. Ground water pollution are re­
Groundwater is the prime source of fresh water available from the ported to occur in several parts of India due to anthropogenic activities
natural resources on the earth. This water is utilized by the human be­ which drastically changed the chemical quality of ground water
ings for drinking, domestic and agriculture purposes. In India, nearly (Gumma et al., 2013; Kulshreshtha et al., 2013; Radhapyari et al., 2013;
84% of rural population uses ground water for drinking and domestic Srivastava et al., 2013; Bhattacharjee et al., 2013). Agricultural inten­
purposes while more than half of the urban water requirement is met sification induces the application of chemical fertilizers and pesticides to
from groundwater (CGWB, 2013). It is essential to monitor the ground the crop fields which exhibits adverse effects on the non-target elements
water storage in any place to understand the hydrological cycle taking like soil, water, air, human beings and other organisms. Ground water
place along with the change in climatic conditions and also to achieve storage is one of the most vulnerable natural resources that become
proper sustainable water management for a continuous increase in degraded due to the residual toxicity and persistence of the chemicals
population (Tiwari et al., 2009). Over exploitation of ground water applied in crop field. It is essential to measure the ground water
causes declination of ground water table and lowers the quantity as well contamination in any area for assessing the ground water vulnerability
as quality of the available ground water for domestic, industrial and as well as for taking remedial steps (Arias-Estévez et al., 2008).
agricultural purposes (Bera et al., 2020; Biswas et al., 2020). In addition Groundwater vulnerability assessment has been reported in various
to the continuous depletion of ground water table, degradation of climatic region of the world, such as semi-arid regions (Djoudi et al.,

* Corresponding author.
E-mail address: amit.rs2017@geology.iiests.ac.in (A. Bera).
1
ORCID ID: 0000-0002-9437-2491.

https://doi.org/10.1016/j.ecoenv.2021.112085
Received 27 September 2020; Received in revised form 19 January 2021; Accepted 18 February 2021
Available online 6 March 2021
0147-6513/© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

2019; Arya et al., 2020; Meng et al., 2020), humid tropical regions to cross the permissible limit in Purulia district. Research related to
(Seabra et al., 2009; Omotola et al., 2020), sub-tropical regions (Singh groundwater management in Purulia district has been previously per­
et al., 2015; Xiaoyu et al., 2018), temperate regions (Luoma et al., 2017; formed by some researchers. Ghosh et al. (2016) delineated the
Haidu and Nistor, 2020), arid regions (Ghazavi and Ebrahimi, 2015; groundwater potential zones in Kumari watershed, on the basis of 7
Heiß et al., 2020). The assessment has been also reported in various physical parameters by using MIF and GIS techniques. Mura (2014)
hydrogeological environments, i.e. Karst aquifers (Vías et al., 2010; discussed about the land and water resource management techniques,
Nanou and Zagana, 2018), Coastal region (Kardan Moghaddam et al., under which both groundwater and surface water management have
2017; Motevalli et al., 2018), Alluvial aquifers (Alam et al., 2014; been studied thoroughly. Nag (2005) delineate the groundwater po­
Hussain et al., 2017), Hard rock aquifers (Shekhar et al., 2015; Jenifer tential zones in Bagmundi block of Purulia district, here lineament and
and Jha, 2018). The phenomenon, groundwater vulnerability is the hydro-geomorphological parameters were given the most impor­
conceptualized assuming that the physical environment might act as a tance. Farooq et al. (2018) worked on the fluoride contamination in the
protector to groundwater against natural impacts to some extent, groundwater of Purulia-I and II blocks of Purulia district. Mondal et al.
especially regarding the entry of contaminants into the subsurface (2013) studied the water quality and stated the fluoride contamination
(Baalousha, 2006; Hasiniaina et al., 2010). Ground water vulnerability zones in Purulia district. Mandal and Sanyal (2019) used GIS analysis
to contamination or pollution depends on the soil characteristics (soil techniques to study the fluoride concentration in the groundwater of
structure, texture, infiltration rate etc.), hydrological characteristics Purulia. Researches related to nitrate contamination and uses of
(drainage density, runoff volume, slope etc.), climatological condition of DRASTIC vulnerability model in the groundwater of Purulia has not
the area, land use pattern and nature of the pollutants that enter into the been performed in a detailed manner previously. The main objective of
ground water table (Arias-Estévez et al., 2008; Rajendran and Mansiya, the present research work is to assess groundwater vulnerability of
2015; Zhai et al., 2017; Adimalla and Qian, 2019; Hossain and Patra, Nangasai river basin by the use of GIS based DRASTIC model and also
2020). Soil depth and the sub-surface geological formations affect the discuss the spatial distribution of nitrate concentration in the ground­
natural chemical composition along with the quality of the ground­ water. The result obtained from this research would further help the
water. The vulnerability of groundwater in a given area is assessed to police makers and planners for preparing groundwater management
identify the zones that are susceptible to contamination due to anthro­ plan in the near future.
pogenic activities.
Vulnerability assessment and vulnerability maps can be used as an 2. Study area
important predictive tool for decision-makers in regard to the restora­
tion of groundwater quality (Jaseela et al., 2016). Prior researchers have Nangasai is a right bank tributary of Kumari River with a total area of
used various models for evaluating the vulnerability of groundwater, 391.07 sq. km. Geographically the basin is located between the latitudes
such as: EPIK (Doerfliger and Zwahlen, 1997; Nekkoub et al., 2020); 22º52ꞌ1ꞌꞌ N to 23º6ꞌ4ꞌꞌ N and longitudes 86º10ꞌ13ꞌꞌ E to 86º27ꞌ35ꞌꞌ E
SINTACS (Civita and De Maio, 1997; Noori et al., 2019), FIS (Pathak and (Fig. 1). This basin is located at Balarampur -Barabazar blocks of Purulia
Bhandary, 2020); GOD (Foster, 1987; Boulabeiz et al., 2019; Taazzouzte district, West Bengal, and Boram-Patamda blocks of East Singhbhum
et al., 2020); SIGA (Vrba, 1991); PCA technique (Rahmani et al., 2019); district of Jharkhand. The drainage pattern of Nangasai is directly
Decision random forest (Lahjouj et al., 2020), tree-based data mining influenced by topography and is structural control. This river emerges
(Yoo et al., 2016); Fuzzy Clustering (Javadi et al., 2020); Boosted from Bagmundi hill and flows eastwards towards Bardaha village of
regression tree (Motevalli et al., 2019), though the choice of model Barabazar block and joins the master stream Kumari river. Sona nadi
depends on the aquifer type and data availability in that region. In order (river) is the main tributary of this river that emerges near Chhamara hill
to study the groundwater vulnerability to pollution, DRASTIC model of East Singhbhum and further flows north-easterly to the Nangasai river
was developed in collaboration with the National Water Well Associa­ near Bardaha village of Barabazar block. Lithologically the area is a hard
tion (NWWA) and the U.S. Environmental Protection Agency (EPA) rock terrain, so the primary porosity and permeability are limited within
(Aller et al., 1987). DRASTIC model accounts for seven hydrological the weathered and saprolitic zones which are relatively less. Mica schist,
factors which strongly influence vulnerability of the groundwater. This shale and epidiorite are the principal rock types of the area along with
model evaluate groundwater vulnerability in a vertical scale assuming sparse occurrences of quartzite, granite, migmatite. The elevation of the
that the contaminant is applied at the ground surface that propagates region ranges from 185 to 555 m. Majority of the area is used as agri­
with the similar mobility rate as water to the groundwater table by cultural land and the agricultural activities are monsoon dependent. The
precipitation or infiltration and the study area is generally larger than south-western region of the basin is scrubland and bare land, besides a
0.4 sq.km (Kazakis and Voudouris, 2015). In the present days, DRASTIC small pocket of deciduous forest and plantation noticed along some
has been considered as a standardized system to assess groundwater places. The major soil type of the region are Typic endoaqualfs, Typic
pollution potentiality (Breabăn and Paiu, 2012) and has been applied by haplustalfs, Typic Haplustepts and Typic paleustalfs. Since the region is
several researchers (Neshat et al., 2014; Singh et al., 2015; Tiwari et al., semi-arid the dry seasons are well marked by scarcity of water. The
2016; Al-Abadi et al., 2017; Ahada and Suthar, 2018; Mondal et al., average annual rainfall of the region varies from 12.75 to 15.65 mm.
2019; Liang et al., 2019). Application of Geographic Information System Maximum rainfall is experienced in the south-west monsoon in the
(GIS) has been reported to be used with DRASTIC for obtaining maps of months of June to September.
groundwater potential zones and identification of the vulnerable zones
which are prone to contamination (Pathak et al., 2009; Saida et al., 3. Materials and methods
2017; Chandoul et al., 2015). This multi-layers information can be
successfully integrated and analyzed in GIS environment to delineate DRASTIC is a popular method for assessing aquifer vulnerability.
ground water potential zones in a low cost and time effective manner This method was originally developed by US Environmental Protection
(Selvam et al., 2014; Ghosh et al., 2016). Agency. Hydrogeological parameters are used for assessment of aquifer
The present study was conducted in the Nangasai river basin of vulnerability for this model. The field was conducted during the post-
Purulia district of West Bengal India to assess the ground water monsoon period (December). Total 40 well location have been
vulnerability and identify the risk prone zones. Though the annual selected in the total basin area for measuring depth to water level and on
rainfall in this region is high, but the major parts of the Purulia district spot measurement of Nitrate and TDS has been done in the field. Nitrate
experiences drought and comes under limited yield prospect zones was measured using AQUASOL Nitrate test kit (Code: AE308). The in­
(Ghosh et al., 2016). According to CGWB (2013), concentration of strument HANNA HI 98192 (EC/TDS/Nacl) has been used for measuring
fluoride, iron, and other heavy metals in the groundwater was reported the amount TDS, and HANNA Quick Calibrate Solution has been used for

2
A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

Fig. 1. Location of the study area.

calibrating the device. The base map of the study area has been prepared physical range scale and contamination risk factor the 7 parameters
from the Survey of India Toposheet (73 I/4, 73 I/8, 73J/1, 73J/5) and have been assigned specific weights. Furthermore, the sub criteria of the
hydrogeological map of Purulia district. The GPS locations of the wells parameters were rated in a scale of 1 (least significant) to 10 (most
were imported to ArcGIS 10.0 environment. The spatial distribution significant) according to their relative importance. The ratings of each
map of TDS, Nitrate and Depth to water level map were prepared using sub criteria were multiplied with the weight of the main factor for
the IDW Interpolation technique, with the help of spatial analysis tool of calculating the total weight (Table 1). Each thematic map has been
ArcGIS 10.0. Rainfall data has been collected from Worldclim global prepared using 30 m × 30 m pixel cell size and reclassified according to
climate data with spatial resolution of 1sq. km. Then these pixel data their respective weights. With the help of DRASTIC index weighted sum
were converted to point data. The annual rainfall values of these point overlay method the final vulnerability map has been generated (Fig. 2).
data were used to calculate the recharge using modified Chaturvedi DRASTIC Index (DI) was computed using the formula:
(1973) method of UP irrigation research institute.
DI=DrDw + RrRw + ArAw + SrSw + TrTw + IrIw + CrCw
R=1⋅35 (P− 14)0.5
Where, D, R, A, S, T, I, C represents the 7 parameters, r is the rating, and
Then interpolation method was used for developing the recharge w is the weight assigned to the respective parameters.
map. For the aquifer media map hydrogeological map of CGWB has been High Drastic index number indicates high risk of groundwater
used. Soil texture data published by NBSS and LUP has been used for the contamination, here nitrate and TDS values has been used for validation.
preparation of soil media map. For vadose zone media map, soil data of
NBSS and LUP, and litholog data of CGWB has been used. ASTER DEM 4. Result and discussion
(30 m spatial resolution) has been used for generating slope map using
the surface tools of ArcGIS 10.0. Hydraulic conductivity has been 4.1. Depth to water level
measured from transmissibility data of pumping test.
This DRASTIC Model depends on certain assumptions such as: 1) The The distance between the ground surfaces to the water table is
aquifer would get contaminated from surface contaminant source. 2) referred to as the depth to water table. This is the actual distance
Post precipitation the contaminants should have enough mobility to mix covered by the contaminants and pollutants prior to dissolution in the
up with the recharge water to reach the water table. 3) The study area groundwater. Greater the depth to water level, lower is possibility of
must be more than 100 acre (Aller et al., 1987). Depending on the contamination of groundwater (Aller et al., 1987), here attenuation

3
A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

Table 1 recharge indicates higher vulnerability to contamination and vice-versa.

DRASTIC rating and weighting values for the various hydrogeological settings in This is due to the fact that more water migrating inwards has the po­
the study area. tentiality of carrying more contaminants along with its flow. The net
Parameters Range Rating Weight Total weight recharge of the study area varies from 8.15 to 8.84 in/year (Fig. S2, see
(rating×weight) supplementary file). Total 0.33% of the study area has been assigned a
Depth to water 7.87–16.44 9 5 45 weightage of 36 for having a net recharge of 8.53–8.84 in/year. In the
level (in feet) 16.44–20.72 8 40 upper catchment regions of the basin the recharge rate is high. Espe­
20.72–24.86 6 30 cially, the regions Bareda, Bamni, Newdih, Amaru have moderately high
24.86–30.43 4 20
to high recharge rate ranging from 8.28 to 8.84 inch/year. In the north
30.43–44.28 2 10
Net recharge (in/ 8.53–8.84 9 4 36 eastern part of the lower catchment covering the areas Ransi, Barabhum,
year) 8.37–8.53 7 28 Herbana, Kerro the recharge rate is low. So, the vulnerability to
8.28–8.37 6 24 contamination is relatively low.
8.24–8.28 4 16
8.15–8.24 2 8
Aquifer media Mica schist 10 3 30
4.3. Aquifer media
Epidiorite, 8 24
Hornblende The consolidated formation and unconsolidated rocks and pebbles in
schist which the water is contained is referred to as aquifer media, which also
Mica schist and 7 21
includes pores and fractures. The underlying rock structures highly
shale
Granite gneiss, 5 15 affect the rate of permeability and dissolution of the contaminants into
Migmatite the groundwater thereafter. Sorption, cation exchange, filtration, and
Intrusive granites 3 9 other processes take place while the water seeps inwards. The aquifer
Quartzite 2 6 media hence is an important tool in assessing the quality of ground­
Soil media Sandy skeletal 10 2 20
Loamy skeletal 8 16
water. The transportation of the pollutants, therefore, depends on the
Coarse loamy 7 14 thickness and permeability of the formation. Lower permeability and
Loamy 5 10 greater thickness of the formation is categorized by lower risk of
Mixed fine loamy 3 6 contamination with higher dissolution and dilution of the contaminants
Fine loamy 2 4
(Edet, 2004). The study area has 6 different types of lithological for­
Topography 0–1.72 10 1 10
(Slope in %) 1.72–2.07 8 8 mations namely mica schist, epidiorite-hornblende schist, mica schist,
2.07–3.80 6 6 and shale, granite gneiss and migmatite, intrusive granite and quartzite
3.80–12.53 3 3 which have been assigned weightage according to their influence on the
12.53–56.61 1 1 quality of groundwater (Fig. S3, see supplementary file).
Vadose zone Sand and gravel 9 5 45
media Coarse loamy 7 35
Epidiorite-hornblende schist covers 72.85% of the study area only
Loamy 5 25 1.29% of the study area is covered by intrusive granite.
Sandy silt 2 10
Hydraulic 39.71–50 10 3 30 4.4. Soil media
conductivity 20.74–39.71 8 24
10.45–20.74 6 18
4.87–10.45 4 12 The uppermost part of vadose zone with active biological activities is
1.84–4.87 2 6 the soil media. The soil media actively operates in the permeation of
0.20–1.84 1 3 contaminants through the formations and controls the recharge of the
area. Soil media plays a crucial role in removal of the pollutants by
effective adsorption, and attenuation. Being chemically active and rich
plays an important role in filtering the pollutants from the seeping
in organic matter the soil media helps in greater cationic exchange and
through the soil media. Hence, this factor depth to water table bears
removal of heavy metals. The soil highly affects the presence of con­
significance in ranking and assigning weightage to the parameters
taminants and in their passage vertically into the vadose zone (Aller
concerned. The depth to water level in the study area varies from 7.87 to
et al., 1987). The soil is highly affected by the presence of contaminants
44.28 m (Fig. S1, see supplementary file). The depth to water table in
which lowers the permeability vertically down the vadose zone (Aller
the study area is categorized into 5 categories ranging from
et al., 1987). In the study area the predominant soil types are mixed fine
(7.87–16.44) m, (16.44–20.72) m, (20.72–29.86) m, (29.86–30.43) m,
loamy and sandy skeletal (Fig. S4, see supplementary file). Each soil type
to (30.43–44.28) m having weightages of 45, 40, 30, 20, 10 respectively.
has been ranked according to their weightage. Sandy skeletal covering
The depth to water level data is collected from 40 shallow tube wells at
an area of 114.41 sq km has been ranked first and assigned a weightage
different location of the Basin. In the southern and eastern side (Bir­
of 30. Fine loamy covers a petty 4.82% of the study area. In the southern
amdih, Paharpur, Jaba, Raidih), upper catchment and small pockets of
side (Jharbani, Bamni, Jaba, Muchidi) loamy skeletal is found and in the
the middle portion of the basin (Mukuldih, Bamnidih), the groundwater
middle catchment (Gordih, Hatiadih, Bankada, Loadih, Macha) sandy
depth is high, ranging from 24.86 to 44.28, so the risk of contamination
skeletal soil is found. These soils have high infiltration rate and are
is low. Whereas, in the south eastern and middle catchment region,
therefore more vulnerable to contamination. On the other hand, the
groundwater depth is much shallower, the areas Gordih, Katin, Gengara,
western and southern parts of the basin (Raidih and Hija) have fine
Mirudih, Deghi lying in that region are quite vulnerable to groundwater
loamy soil. The upper and lower catchments of the northern part have
contamination.
mixed fine loamy soil, with relatively lesser infiltration rate. So the re­
gions, Amaru, Fatepur, Hubung, Salbani are less vulnerable to contam­
4.2. Net recharge ination risk.

Water from precipitation and various other artificial sources seep 4.5. Topography
into the soil and reach down to the groundwater table. This amount of
water percolating per unit area of soil is referred to as net recharge. This Topography simply implies the slope of an area (Sener et al., 2009).
recharge depends on various factors such as slope, permeability, rainfall, This factor highly influences the velocity of surface runoff, thereafter
land cover, amount of water seeping in (Shirazi et al., 2013). High influencing the rate of infiltration. Topography gives a clear view of

4
A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

Datasets

Field Conventional Climatic Satellite

Investigation data data images

Observation
well Rainfall
Data ASTER DEM

Soil data Geological

(NBSS & LUP) map (GSI)

Depth to
Pumping Litholog Recharge
water level
test (CGWB)

Topography
Hydraulic
Soil Vadose zone Aquifer
Conductivity
media media media

Raster conversion

DRASTIC weight and

ranking

Consistency No
Yes

Reclassification

Overlay analysis

Nitrate

Validation Groundwater vulnerability

TDS zone

Fig. 2. Methodological flow chart for GVZ mapping.

where the pollutants tend to concentrate, infiltrate and contaminate the basin the slope is highly steep ranging from 3.80% to 56.61%, in these
subsurface water. Higher the slope of an area, lower is the rate of regions the risk of contamination is less since the runoff is more than the
infiltration and lesser are the chances of contaminants to seep down­ infiltration rate. On the other hand, in the lower catchment the valley fill
wards. The slope of the study area ranges from 1.72% to 56.61% and and agricultural lands have gentle slope, so the contamination risk is
have been categorized into 5 categories (Fig. S5, see supplementary file). greater.
1453.80 sq km of the study area has slope % ranging from 2.07 to 3.80
and the assigned weightage is 6. A weightage of 10 has been assigned to 4.6. Impact of vadose zone
an area of 103.10 sq km covering 26.36% of the study area, for having a
slope % of 1.72. Low slope % value indicates greater residence time and The under saturated zone above the water table is vadose zone
helps in higher seepage of contaminant rich water. On the southwestern (Ahmed et al., 2015). The vadose zone is an extremely important factor
side (Beareda, Paharpur, Muchidih, Jaba) of the upper catchment of the for influencing the amount of contaminant rich water percolating down.

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A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

Hence, the vadose zone media determines the amount of contaminants conductivity ranging from 0.20 to 4.87 m/day. So the contamination
migrating to the water table and its attenuation by acting as a risk is lower in these regions. On the other hand the hydraulic conduc­
passageway. Straining, filtration, and various bio-chemical degradation tivity in the southern and middle catchment regions (Bankada, Pahar­
processes take place in this zone. The vadose zone of the study area pur, Tilabani, Macha, Katin) is relatively higher ranging from 20.74 to
comprised of sand and gravel, coarse loamy, loamy, and sandy silt. Sand 50 m/day, so the risk of contamination is higher.
and gravel covered 114.41 sq. km of the study area and the maximum
weightage has been assigned to the zone, 45. A low weightage of 10 has
4.8. Groundwater vulnerable zone
been assigned to the sandy silt covering 4.82% of the study area. The
northern and north western region (Amaru, Shalbani, Ransi, Kerro) of
The groundwater vulnerability of Nangasai basin has been calculated
the basin consists of loamy type vadose media with moderate rate of
using Drastic Index (DI) equation with the help of 7 thematic layers
contamination (Fig. S6, see supplementary file). Besides, the southern
integration. In the study area the DI ranges from 74 to 198. In the study
and middle portions (Madhabpur, Baram, Loadih, Katin, Mukrudih) can
area, the spatial distribution of groundwater contamination has been
be characterized by loamy and sand-gravel vadose media, with high
classified into 5 classes: very high, high, moderate, low, very low
infiltration rate and higher risk of contamination. The north eastern and
(Fig. 3). The southern portion of the basin shows very high contami­
western parts consist of sandy silt vadose media with relatively lower
nation. Around 16.80% of the area covering the regions Bankada,
vulnerability to contamination.
Baram, Deghi, Macha, Katin, Gordih, Loadih are located in this zone.
The main landuse pattern of this zone are cropland and fallow land, the
4.7. Hydraulic conductivity main soil type is sandy skeletal, and the hydraulic conductivity range is
high, so groundwater infiltration rate is also high. Besides, the aquifer
Hydraulic Conductivity is an important parameter that influences depth is also shallow, so the agricultural contaminants mix up easily
the rate of mobility of the groundwater into the saturation zone hence with the recharge water which further contaminates the groundwater.
determining the amount of contaminants moving downwards, shows the On the other side, the western and northeastern sides of the basin show
rate of transmission of the pollutant rich water into the aquifer. Higher very low groundwater contamination covering 18.21% of the study area
the values of hydraulic conductivity higher are the risks of contamina­ (Table 2). In this zone the regions Raidih, Biramdih, Bamnidih, Ransi,
tion. The study area has hydraulic conductivity values ranging from 0.20 Hijla are located. These regions are characterized by relatively less
to 50 m/day categorized into 6 classes (Fig. S7, see supplementary file). groundwater infiltration rate, due to low hydraulic conductivity and
Total 20.02% of the study area has hydraulic conductivity values moderate recharge rate. Besides groundwater depth is also deep, so the
ranging from 39.71 to 50 m/day, covering an area of 78.29 sq km, with contaminants mixed recharge water takes a longer time to reach the
the assigned weightage 30. A low weightage of 3 has been assigned to groundwater table through the vadose zone, longer is the time of
the hydraulic conductivity values ranging from 0.20 to 1.84 m/day attenuation lower is the risk of contamination. In the north-western,
covering 14.07% of the study area. The northern regions mainly Kerro, middle and eastern side the regions such as Amaru, Murugora, Kerro,
Salbani, Bamnidih, Amaru, Hullung show relatively lesser hydraulic Bara Sushi, Gidighati, Bela have low to moderate risk of contamination.

Fig. 3. Groundwater vulnerability zone of Nangasai River Basin.

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A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

Table 2 moderate and fairly negative values. Therefore, it can inferred that the
DRASTIC index areas vulnerable to groundwater contamination. quantitative factors among which hydraulic conductivity and depth to
Aquifer DRASTIC Area Area Well locations water level plays a vital role for determining the groundwater vulner­
vulnerability index (sq. (%) ability. On the other hand among the qualitative parameters such as
classes classes km.) aquifer media in which are mica schist and shale; soil media among
Very low 74–105 70.80 18.21 Hijla, Herbana, Ransi, which are sandy skeletal and loamy skeletal; vadose zone media which
Kurni, Biramdih, are sand and gravel all plays an important role for determination of the
Bamnidih, Raidih, groundwater vulnerability (Fig. S8, see supplementary file).
Ruchap
Low 105–125 106.87 27.49 Bela, Neudih, Salbani,
Fatehpur, Kerro, 5. Validation
Murugora, Barabhum
Hattola, Hullung, For assessing the accuracy of the DRASTIC model used for generating
Mukundapur, Mukrudih
the final groundwater vulnerability map, it has been validated using
Moderate 125–148 62.63 16.11 Madhabpur, Bara Sushni,
Gidighati, Amaru, Bamni Nitrate and TDS parameter. Nitrate and TDS were measured from
High 148–180 83.12 21.38 Bareda, Hathiadih, shallow tube wells collected from 40 location sites of the basin area. The
Paharpur, Gulubani, nitrate and TDS values in the study area varies from 0.50 to 50 ppm and
Gengara, Charak Pathar, 108.12–1125.64 ppm respectively (Figs. 4 and 5). It can be seen from
Jaba, Jharbani
Very high 180–198 65.31 16.80 Katin, Macha, Tilabani,
the spatial distribution of the parameters in the southern and south
Deghi, Bankada, Baram, eastern regions namely Macha, Loadih, Baran, Tilabani, Deghi show
Muchidih, Gordih, Loadih very high concentration of nitrate ranging from 30.12 to 50 ppm, and
similarly high to very high concentration of TDS ranging from 433.02 to
1125.64 ppm. These regions possess high groundwater vulnerability
In this study correlation between groundwater vulnerability with the
scale. The agricultural contaminants mixes up with the recharge water
other factors has been represented by different bar graphs and scatter
and further contaminates the groundwater. On the other hand, in the
plots. The Pearson’s correlation coefficient between the groundwater
western part of the upper catchment and north eastern part of the lower
vulnerability and the various quantitative parameters such as depth to
catchment shows very low concentrations of TDS and nitrate ranging
water level, recharge, topography and conductivity has been calculated.
from 108.12 to 304.74 ppm and 0.5–6.74 ppm respectively. These re­
It has been observed that conductivity shows the highest r value
gions such as Kurni, Raidih, Murugora, Herbana, Mukundapur show
(r = 0.87), a strong positive correlation value. The correlation coeffi­
relatively lower groundwater vulnerability.
cient for the parameter recharge indicates moderate positive r value
The relationship between groundwater vulnerability with nitrate and
(r = 0.46). Whereas the r value for depth to water level (r = − 0.54) and
TDS concentrations are estimated using Pearson Correlation Coefficient
topography (r = − 0.02) with groundwater vulnerability which indicates
(r). The Pearson Correlation Coefficient (r) is calculated using the for

Fig. 4. Spatial distribution of Total Dissolved Solids (TDS) of Nangasai River Basin.

7
A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

Fig. 5. Spatial distribution of Nitrate of Nangasai River Basin.

mula given below- researchers applied ROC for DRASTIC vulnerability modeling (Mogaji
∑ ∑ ∑ and San Lim, 2017; Khosravi et al., 2018; Kumar and Pramod Krishna,
n( xy) − ( x)( y)
r = √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
∑ ∑ 2 } ∑ ∑ 2 }̅ 2020; Torkashvand et al., 2020). This ROC curve examines the quality of
{n x2 }− ( x) {n y2 }− ( y) deterministic and probabilistic forecasting system. ROC analysis better
than other methods because of its cutoff- independence, and its oper­
Where, r = Pearson Coefficient, n represents the number of pairs, x and y ating range value also provides better result (Frattini et al., 2010). AUC
are the two variables. (Area Under the Curve) value mainly range from 0.5 to 1. Higher AUC
It has been observed from the correlation analysis that the r value value (close to 1) indicates its success rate of better prediction accuracy
between groundwater vulnerability with nitrate and TDS are r = 0.80 (Shadman Roodposhti et al., 2016). Yesilnacar, and Yesilnacar and
and r = 0.74 respectively which indicates a strong positive correlation Topal (2005, 2005) in their paper have classified AUC values with
(Fig. 6). respect to prediction accuracy into 5 quantitative ranges, which are
To assess the accuracy of this model Receiver operating character­ (0.5–0.6) low/poor, (0.6–0.7) moderate/average, (0.7–0.8) good,
istics (ROC) curve analysis has been done using the nitrate and TDS (0.8–0.9) very good, and (0.9–1) excellent. AUC has been used to
concentrations with the groundwater vulnerability. Many previous represent the percentage of prediction value. In ROC, plotting is done
based on specificity and sensitivity on x-axis and y-axis respectively. It
has been observed from the ROC analysis that the AUS values of nitrate
1000 60
and TDS are 0.89 and 0.86 which determines that the prediction accu­
900
50 racy are 89% and 86% respectively (Fig. S9, see supplementary file).
800
Therefore, it can be stated that DRASTIC is an ideal model for assessing
700 y = 0.2592x - 17.397
40 groundwater vulnerability.
Nitrate (ppm)

y = 3.8201x - 138.95 R² = 0.6434

TDS (ppm)

600
R² = 0.555
500 30 6. Conclusion
400
300
20 For assessing the groundwater vulnerability of the Nangasai basin,
200 DRASTIC model has been used with GIS integration and 7 hydro­
10
100 geological parameters have been considered. In this study it is observed
0 0
that the DRASTIC vulnerability index value varies from 74 to 198 and
70 90 110 130 150 170 190 the study area can be classified into five vulnerable classes. The very
GVZ (DRASTIC index) high and high vulnerable class covers about 16.80% and 21.38% portion
TDS Nitrate Linear (TDS) Linear (Nitrate) of the study area and the very low and low vulnerable classes covers
18.21% and 27.49 area respectively. The Pearson correlation coefficient
Fig. 6. Relationship between the Nitrate, TDS and Groundwater vulnerability
of DRASTIC vulnerability with the other DRASTIC parameters are
zone (GVZ).

8
A. Bera et al. Ecotoxicology and Environmental Safety 214 (2021) 112085

calculated and it is observed that the depth to water level and hydraulic Ahmed, I., Nazzal, Y., Zaidi, F.K., Al-Arifi, N.S., Ghrefat, H., Naeem, M., 2015.
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