ORIGINAL ARTICLE

Assessment of groundwater quality with special referenceto arsenic in Nawalparasi district, Nepal using multivariatestatistical techniques

Ishwar Chandra Yadav • Ningombam Linthoingambi Devi •

Devendra Mohan • Qi Shihua • Surendra Singh

Received: 11 April 2013 / Accepted: 11 November 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Groundwater is a precious resource for

humankind not only in Nepal but also across the globe due

to its diverse functions. A total of 48 groundwater samples

were collected from three villages of Nawalparasi district,

Nepal, during pre-monsoon and monsoon to estimate the

overall groundwater quality and to identify the sources of

contamination with emphasis on arsenic (As). The average

concentrations of all tested groundwater quality parameters

(temp., pH, EC, ORP, Ca2?, Mg2?, Na?, K?, Cl-,

F-,SO42-, PO4

3-, HCO3-, NO3

-, Cu, Ni, Mn, Cd, Pb, Fe,

Zn, Cr, and As) were well within permissible limits of

WHO for drinking water, except for Ni, Cd, Pb, Cr, and As.

Concentration of As ranged from 60 to 3,100 lg L-1 and

155 to 1,338 lg L-1 in pre-monsoon and monsoon,

respectively. The Piper diagram of the groundwater

chemistry showed groundwater of Nawalparasi belongs to

Ca–Mg–HCO3 and Mg–HCO3 water type with HCO3- as

dominant ions. As content in the study area was negatively

correlated with Fe in pre-monsoon, while it was positively

correlated in monsoon. Furthermore, As was negatively

correlated with oxidation reduction potential suggesting

reducing condition of groundwater. Principal component

analysis revealed seven major factors that explained 81.996

and 83.763 % of total variance in water quality in pre-

monsoon and monsoon, respectively. The variance of water

quality was related mainly with the degree of water–rock

interaction, mineralization, and anthropogenic inputs.

Keywords Multivariate � Hierarchical �Groundwater � Arsenic � Terai � Nepal

Introduction

Groundwater (often called ‘the hidden asset’) is the most

abundant and readily accessible source of freshwater in the

earth making up 97 % of all freshwater (excluding glaciers

and polar caps). It fulfills several diverse functions of

humankind across the globe. Use of groundwater for drink-

ing purposes is one of the best known and perhaps the most

important function in many countries (Todd 1980; Grath

et al. 2009). Besides drinking water, groundwater also rep-

resents the most significant source of irrigation water. Its use

in irrigation, industries, municipalities, and rural homes

continues to increase in modern days as the surface water

gets polluted by various anthropogenic activities (Meyer and

Wyrick 1966; Shah et al. 2003). Groundwater alone can

support about two-thirds of the world’s population by sup-

plying freshwater water (Jousma and Roelofsen 2004). In

India, China, Pakistan, Bangladesh and Nepal, approxi-

mately one billion people rely on groundwater, the only

source for irrigation (Villholth and Sharma 2006). With

I. C. Yadav � S. Singh (&)

Centre of Advanced Study in Botany, Banaras Hindu University,

Varanasi 221005, UP, India

e-mail: surendrasingh.bhu@gmail.com

I. C. Yadav

e-mail: icyadav.bhu@gmail.com

N. L. Devi

Centre for Environmental Sciences, Central University of Bihar,

BIT Campus, Patna 800014, India

D. Mohan

Department of Civil Engineering, Indian Institute of Technology,

Banaras Hindu University, Varanasi 221005, India

Q. Shihua

School of Environmental Studies, State Key Laboratory of

Bio-geology and Environmental Geology, China University of

Geosciences, 388, Lumo Road, Wuhan 430074, China

123

Environ Earth Sci

DOI 10.1007/s12665-013-2952-4

gradual increase in world population, many of the available

groundwater systems have been put under stress due to over

exploitation and contamination (Stephan 2009).

Water quality refers to the suitability of water to sustain

living organisms. For humans, it is used for drinking,

bathing, washing, irrigation, and in industry. Generally, a

person requires two liters of water per day for his/her

physiological functions (WHO 1996). The physical, bio-

logical, and chemical conditions of groundwater reflect the

nature of water quality. In case there is change in water

quality, these in turn are influenced by natural and

anthropogenic activities. The quantity and quality of water

directly and indirectly affect human activity, health, and

sanitation and these in turn depend on the water sources.

Groundwater, the most abundant in the Terai aquifers, is

the major source of drinking water which fulfils the need of

over 90 % of Terai population (ADB/ICIMOD 2006).

People living in Terai extract groundwater through various

means such as dug wells, hand pumps, deep and shallow

wells. The lithology of the sediments in the Terai can decide

the chemistry of shallow groundwater to a large extent. The

shallow aquifers are vulnerable to pollution if surface sed-

iments are sandy and permeable, however, it is less vul-

nerable or so if a protective layer of low permeability (clay

or silt) is present (NERC/WAN 2001). It may also influence

the amount of oxygen present in the shallow aquifers, which

in turn will affect the water chemistry. The shallow aquifer

seems to be unconfined (highly vulnerable to surface pol-

lution) and ill-developed in most of the Terai regions.

However, it is thin or even absent in Nawalparasi and

Kapilvastu (Upadhyay 1993). It is reported that the deep

aquifer ([200 m) of the Terai is artesian, i.e., free flowing

(Basnyat 2001). Groundwater quality remains typically

constant over time; however, changes in hydrogeological

conditions can lead to differences in water quality over a

relatively short distance. Much of the Terai physiographic

region and some parts of Siwalik valleys are underlain by

shallow or deep aquifers. Likewise, many of them are fur-

ther underlain by thick sediments of alluvial origin which

make this area one of the most potential productive aquifers

(MoPE/UNEP 2004; ADB/ICIMOD 2006).

It is estimated that the Terai region has a potential of

about 12 billion m3 of groundwater, with an estimate

annual recharge of 5.8–9.6 billion m3, i.e., the maximum

that may be extracted annually without any adverse effect

(WECS 1999). Current groundwater withdrawal is about

0.52 billion m3 per year (MoPE 2001). However, recent

study by Kansakar (2005) showed that the overall annual

extraction of groundwater in the Terai region is little more

than 10 % of the estimated annual recharge of

8.8 9 109 m3. This indicates the possibility of increase in

consumption of groundwater in the near future thereby

declining the water table. The dropping of water tables

beyond the shallow aquifer leads to potential pollution of

groundwater, both from extraction and from agricultural

inputs.

In Nawalparasi too, groundwater is the main source of

drinking and irrigation water. Shallow groundwater aqui-

fers are mostly used for drinking purposes. Arsenic (As) is

the newly emerging problem in the Terai groundwater,

especially in Nawalparasi (one of the worst As-affected

districts), which is facing severe challenges to safe drinking

water. Despite the importance of groundwater in Nawal-

parasi, assessment of groundwater quality was not given

much attention because of lack of scientific and techno-

logical development in the region. Water quality data are

not available in these areas. Hence, the present study is

aimed to assess the overall characteristics of groundwater

quality and to identify the possible sources of contamina-

tion with major emphasis on As.

Geology and hydrogeology of Nawalparasi

Nawalparasi, the present study area lies in the southern

Nepal which is the continuation of the Indo-Gangetic plain

(also called Terai plain). Terai plain has gentle slope

towards south from an elevation of as high as 200–300 m in

the north to as low as 63 m in the south near the Indian

border, above mean sea level (Upreti 2001). From the

Indian border, Nawalparasi district extends northward

across Narayani River (one of the major rivers of Nepal)

alluvium and then across the low gradient fan of locally

derived alluvium and finally into the Himalayan foothills

(also known as Churia hills) (Hagen 1969; Pathak 1991). It

consists of quaternary to recent alluvium including channel

sand and gravel deposit and outwash deposit that were

deposited over Siwalik rock (Sharma 1990). These fluviatile

deposits are cross-bedded, eroded, reworked and re-depos-

ited due to constant shifting of stream channel (Tibbitts

et al. 1973). These sediments are estimated to be more than

1500 m thick (Upreti 2001; Bhattacharya et al. 2003). The

main lithology of the Churia hill north to study area is fine-

grained sandstone, shale, conglomerate and freshwater

limestone. The grain size ranges from clay fraction to

boulder. The Churia formation of the study area is com-

paratively thinner (about 2 km) than the eastern part and

continues to get deeper towards the northern part (Upreti

and Fort 1999). Their geology is very much comparable

with geology of Bengal Delta Plain (BDP) in Bangladesh

and West Bengal, India.

Geomorphologically, the Terai plain is divided into two

zones, i.e., the Bhabar zone in the north and Gangetic

alluvial plain (GAP) in the south (Hagen 1969; Sharma

1990). Bhabar zone is narrow stretch of recent alluvium

and colluvial fan deposit at the foot of Siwalik hill. It

Environ Earth Sci

123

consists of thick deposit of gravel pebble and boulder

mixed with sand and silt. Bhabar zone consists mostly of

reworked sediments from the Siwalik rock except for the

alluvial plain of first and second grade rivers. It is the main

recharge area for aquifer in the Terai region. The alluvial

and colluvial fan in Bhabar coalesce into piedmont slope

and merge with the Gangetic alluvial in the south. The

surface gradient is higher compared to GAP. Sediments in

the GAP were deposited by braided river which changed

their frequency in course of time; as a result, clay, silt, sand

and gravel deposit of varying thickness occur interlayer

with each other (Sharma 1990). In general, sediment size

decreases toward the south, although there are some

exceptions. Subsurface layer of sand and gravel forms good

aquifers which often are the buried river channel and are

therefore interconnected.

Terai is a geographic, and not a hydrographic term, for

district in the plain or near bordering the Siwalik. It has

multiple aquifer system; shallow aquifers are generally

unconfined or semi-confined while deeper ones are normally

confined aquifer. The Churia formations are source rock for

most of the alluvium underlying the Bhabar zone and the

GAP to the south. The major rivers in Nawalparasi are Na-

rayani, Jharai, Bhaluhi, and Rawan Khand. It receives an

average annual rainfall of 2,145 mm. The temperature ran-

ges from 5 �C in winter to 44 �C in summer (DPCC/NDDC

2000). Hydrologically, not only in Nepal but also in entire

South Asia, it is unusual in having a relatively brief, massive

period of annual aquifer recharge (monsoon), followed by

steady drying for the remaining three-quarters of the year

(Malik et al. 2010). Surface recharge through high monsoon

precipitation (1,800–2,000 mm year-1) and local Narayani

River streams (Jharai and Bhaluhi) supply groundwater to

the shallow (less than 200 m deep) aquifers in study area.

The hydraulic conductivity of the shallow aquifer materials

in the eastern part of Nawalparasi is high (GWRDB/UNDP

1989; Rao and Pathak 1996). High hydraulic conductivity

values are characteristic for clean coarse-grained sand and

gravel. In western part, hydraulic conductivity is much

inferior to rest of the direction (GWRDB/UNDP 1989). The

recharge comes from local infiltration of rainfall everywhere

where more or less permeable surface permits. The infiltra-

tion percentage may be high, but there may be plenty of

rejected recharge in monsoon season because of over satu-

ration of the soil immediately underlying the land surface.

Materials and methods

Study area

Nawalparasi district, a part of Lumbini zone (one of the 75

districts of Nepal), is located in the southwestern Terai

region of Nepal. It lies 147 km west of the capital city

Kathmandu and is surrounded by Chitwan district in the

east, Tanahun district in the north, Palpa and Rupandehi

districts in the west, and the Indian state of Uttar Pradesh in

the south. The present study was conducted in three vil-

lages (Thulokunwar, Kasia, and Panchgawa) of Nawal-

parasi district that lies east to the capital city Parasi Bazar

(Fig. 1) where the existence of groundwater contamination

especially with As has been commonly reported by Gurung

et al. (2005) and Yadav et al. (2011, 2012).

Sample collection

A total of 48 groundwater samples were collected during

the two consecutive seasons, i.e., pre-monsoon and mon-

soon seasons in the year 2010. About 250 ml groundwater

samples were collected in meticulously cleaned polyeth-

ylene bottle (TARSON, India) for subsequent laboratory

chemical analysis. Tubewells were flushed well before the

collection of water sample to remove any stagnant water.

Two sets of samples were collected from each location.

One set of samples contained 1 ml of concentrated HNO3

as preservative for cations, metals and total As analysis.

The other sets didn’t contain any preservative and were

stored for analysis of anions. The water samples were not

filtered at the time of collection because residents in the

areas were using tubewell water for drinking without fil-

tering. The water samples were then preserved in ice box

and brought to the laboratory of Centre of Advanced Study

in Botany, Banaras Hindu University where it was stored at

4 �C until analysis. Standard procedures were followed for

all the samples (APHA 1992).

Physico-chemical analysis

The water samples were analyzed as per standard methods of

APHA (1998). The temperature, pH, electrical conductivity

(EC), and oxidation reduction potential (ORP) were mea-

sured onsite using portable field kits. Water temperature, pH,

and ORP were measured using Hanna made portable kit (HI

9812 pH/ORP/temperature) and values were reported as an

electrical potential of water sample relative to the reference

electrode. The EC values were recorded using Milwaukee

sharp EC tester (M111751). All portable testing kits were

calibrated on each sampling day with the respective standard

solution prior to sample analysis. The groundwater samples

analyzed in laboratory consist of major cations (Ca2?,

Mg2?, Na? and K?), major anions (Cl-, F-, SO42-, PO4

3-,

HCO3-, and NO3

-) and heavy metals (Cu, Ni, Mn, Cd, Pb,

Fe, Zn, Cr, and As). The major cations were analyzed by

flame photometry (Systronic Flame Photometer 128; Model

Compressor 126). SO42-, PO4

3- and NO3- were analyzed

by spectrophotometry method (Systronic Visiscan 167)

Environ Earth Sci

123

while F- was quantified using ion selective electrode

method (lp pH/ion/temperature, Toshniwal). The Cl- and

HCO3- were analyzed titrimetrically. Heavy metals (Fe,

Mn, Ni, Cr, Cu, Pb, Zn and Cd were determined by flame

atomic absorption spectrometer (Aanalyst 800, Perkin

Elmer).

Total As was analyzed using atomic absorption spec-

trometry coupled with hydride generator (AAS-HG). As

(III) was pre-reduced to As (V) by adding 10 ml each of

conc. HCl and 5 % (w/v) reducing solutions (KI and

ascorbic acid) in 50 ml volumetric flask and the reaction

mixture was allowed to stand at room temperature for

Fig. 1 Map of Nepal showing study area

Environ Earth Sci

123

45 min for complete reduction of As (III). The contents

were diluted with 1.5 % HCl to the mark and were ana-

lyzed by AAS-HG (AAnalyst 800; Perkin Elmer). The

detection limit of AAS-HG was 1 ppb. The multivariate

statistical analysis such as descriptive analysis; Pearson’s

correlation coefficient, cluster analysis (CA), principle

component analysis (PCA) and factor analysis (FA) were

performed using SPSS 16, and Minitab 14.

Quality assurance and quality control (QA/QC)

Quality of data set was evaluated through the use of field

blanks, duplicate, spike samples and replicates (Sundaram

et al. 2009). Prior to sample collection, all equipments were

pre-cleaned with 10 % nitric acid followed by doubled

distilled water. Quality assurance of metal data was per-

formed by inter-laboratory comparisons at Department of

Chemical Engineering, Indian Institute of Technology,

Varanasi. No discrepancy occurred between the original

and new results. Measured values showed relative standard

deviations lower than 10 % for all elements in the analyzed

samples. In general, no major analytical problems were

encountered in analysis of these samples, and consequently

the data set is considered acceptable.

Multivariate statistical analysis

Multivariate statistical techniques such as PCA and CA are

such tools, which enable us to understand the relationship

among huge numbers of variables (Andrade et al. 1992;

Chen et al. 2007; Shrestha and Kazama 2007; Lokhande

et al. 2008). Surface and groundwater quality can also be

evaluated by applying PCA and CA through data reduction

and classification (Singh et al. 2005; Papatheodorou et al.

2006; Shrestha and Kazama 2007; Omo-Irabor et al. 2008;

Yang et al. 2009; Belkhiri et al. 2010; Fataei 2011). These

techniques can also be used to acquire relationships

between parameters and sampling sites, to categorize the

important factors and sources responsible for groundwater

quality and to suggest valuable tools for managing water

resources and monitoring groundwater quality.

Principle component analysis studies the association

between variables with reducing dimensionality of data

structure. It involves the transformation of the original

variables into new uncorrelated ones called principle

components (PCs), which are accomplished on the diago-

nalization of the correlation matrix of the data, structure

(Vega et al. 1998; Helena et al. 2000). It is used for

extraction of different factors.

Cluster analysis is a group of multivariate techniques

whose prime objective is to assemble objects based on their

characteristics. CA classifies objects in such a way that

each object is similar to the others in the cluster with

respect to a predetermined selection criterion. Hierarchical

agglomerative clustering is the most popular approach,

which provides intuitive similarity relationships between

any one sample and the entire data set, and can be illus-

trated by a dendrogram (tree diagram) (McKenna 2003;

Lokhande et al. 2008). The dendrogram provides a visual

summary of the clustering processes, presenting a picture

of the groups and their proximity, with a dramatic reduc-

tion in dimensionality of the original data.

Results and discussions

Characteristics of groundwater

A total of 48 groundwater samples were analyzed for

various physico-chemical characteristics during pre-mon-

soon and monsoon and are presented in Table 1. Ground-

water temperature generally tends to remain relatively

constant except in tropical regions. Water temperature

ranged from 24.5 to 28.2 �C (mean 26.6 �C) in pre-mon-

soon and 26.1 �C to 29.1 �C in monsoon (mean 27.4 �C).

The pH measurement of groundwater provides a very quick

and easy way to obtain appraisal of acid–base equilibrium

in groundwater. Environmental protection agency (EPA)

suggests that groundwater should have pH within 6–9.5.

The pH of the water samples ranged from 6.9 to 7.7 and 7.2

to 7.5 in pre-monsoon and monsoon, respectively.

Electric conductivity (EC) is an indication of water

salinity. The EC varied widely from 449 to 1,200 ls cm-1

(mean 622.4 ls cm-1) and 391 to 1,438 ls cm-1 (mean

572 ls cm-1) in pre-monsoon and monsoon, respectively.

ORP was found to be lower in groundwater and it ranged

from -99 to 143 mv (mean -38.8 mv) in pre-monsoon

and -129 to 97 mv (mean 81.5 mv) in monsoon. The

negative ORP value observed in either of seasons indicates

reducing groundwater condition in study area. Chloride

(Cl-) and sulfate (SO42-) do not have health-based

guidelines but may cause concern due to taste if found high

in groundwater. WHO suggested that Cl- level exceeding

250 mg L-1 may decrease its acceptability for drinking

due to bad taste and corrosion of metals. The concentration

of Cl- ranged from ND to 75.5 mg L-1 (mean

11.5 mg L-1), 5.0 to 166.1 mg L-1 (mean 27.9 mg L-1)

in pre-monsoon and monsoon, respectively. Concentration

of Cl- with respect to seasons was well within the per-

missible limit of WHO for drinking water.

WHO recommended that concentration of SO42- higher

than 500 mg L-1 in groundwater may create gastrointes-

tinal tract problem (WHO 2004). Concentration of SO42-

in groundwater ranged from 0.1 to 0.5 mg L-1 (mean

0.2 mg L-1) and 0.1 to 1.1 mg L-1 (mean 0.2 mg L-1) in

pre-monsoon and monsoon, respectively. High

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123

concentration NO3- in groundwater is considered to be

contaminant due to its health effect especially on infants

such as ‘‘blue-baby syndrome’’. Naturally, low level of

NO3- is likely to be present in groundwater; however, high

level may result from anthropogenic sources such as sew-

age discharge or fertilizers. NO3- in the groundwater

ranged from 0.1 to 0.7 mg L-1 (mean 0.3 mg L-1) and 0.1

to 0.6 mg L-1 (mean 0.3 mg L-1) in pre-monsoon and

monsoon, respectively.

Fluoride (F-) helps to protect against dental caries at

low concentration but may cause serious problems such as

dental and skeletal fluorosis. Groundwater samples in the

study area contained average F- concentration of

0.3 mg L-1 in pre-monsoon, and were found to be

increased to a concentration of 2.0 mg L-1 in monsoon

against WHO permissible limit (1.5 mg L-1) of F- in

drinking water. Fe and Mn are naturally present throughout

the environment and are generally perceived as safe as

often taste will determine users from drinking water rich in

these compounds. Concentration of Fe ranged from ND to

0.97 mg L-1 (mean 0.27 mg L-1) in pre-monsoon and

3.41 to 4.14 mg L-1 (mean 3.64 mg L-1) in monsoon. The

average Mn concentration in the water samples was well

within the WHO permissible limit (0.4 mg L-1) for

drinking water and it ranged from 0.02 mg L-1 in pre-

monsoon to 0.11 mg L-1 in monsoon. High level of Pb

was observed both in pre-monsoon (0.03 mg L-1) and

monsoon season (0.12 mg L-1) against WHO permissible

limit (0.01 mg L-1) for drinking water. Concentration of

total As ranged from 60 to 3,100 lg L-1 (mean

595 lg L-1) and 155 to 1,338 lg L-1 (mean 516 lg L-1)

in pre-monsoon and monsoon, respectively. The average

concentration of As detected irrespective of seasons

showed many fold (from 6 to 300 times) higher than the

WHO permissible limit (10 lL-1) for drinking water.

The characteristics of groundwaters were compared with

standard value for drinking water prescribed by WHO.

Mean concentrations of Ni, Cd, Pb, Cr and As were found

to be higher than WHO permissible limit (0.02 mg L-1),

(0.003 mg L-1), (0.01 mg L-1), (0.05 mg L-1) and

(0.01 mg L-1), respectively, for drinking water. However,

others parameters such as Fe, Mn and Zn were well below

Table 1 Characteristics of groundwater quality parameters and their variations

Variable Pre-monsoon season Monsoon season

Min. Max. Mean SD Med. Min. Max. Mean SD Med.

Temp 24.5 28.2 26.6 0.9 26.9 26.1 29.1 27.4 0.8 27.3

pH 6.9 7.7 7.3 0.2 7.3 7.2 7.9 7.5 0.2 7.5

EC 449.0 1,200.0 622.4 183.1 549.5 391.0 1,438.0 572.0 209.8 529.5

ORP -99.0 143.0 -38.8 78.8 -74.0 -129.0 97.0 -81.5 47.0 -94.5

Ca2? 10.1 19.7 14.7 2.7 13.7 38.9 132.3 71.4 20.0 68.4

Mg2? 11.8 43.1 18.2 6.3 17.3 5.2 37.0 16.7 7.0 17.3

Na? 3.5 39.4 14.2 7.6 11.7 7.4 93.5 51.4 18.5 54.8

K? 0.2 20.9 2.3 5.5 0.3 0.9 43.0 3.7 8.6 1.3

Cl- ND 75.5 11.5 20.8 5.0 5.0 166.1 27.9 34.0 17.6

F- 0.1 0.5 0.3 0.1 0.3 0.9 3.1 2.0 0.6 1.8

SO42- 0.1 0.5 0.2 0.1 0.1 0.1 1.1 0.2 0.2 0.1

PO43- 0.01 0.02 0.01 ND 0.01 0.01 0.03 0.01 ND 0.01

HCO3- 153.0 510.0 257.1 91.8 242.3 402.0 920.0 551.0 117.4 546.3

NO3- 0.1 0.7 0.3 0.1 0.3 0.1 0.6 0.3 0.1 0.2

Cu 0.01 0.14 0.03 0.02 0.02 ND ND ND ND ND

Ni 0.08 0.14 0.11 0.02 0.11 0.04 0.09 0.06 0.01 0.06

Mn ND 0.09 0.02 0.03 ND ND 0.73 0.11 0.16 0.06

Cd 0.05 0.07 0.06 0.00 0.06 0.01 0.03 0.02 ND 0.02

Pb ND 0.12 0.03 0.03 0.02 0.08 0.16 0.12 0.02 0.12

Fe ND 0.97 0.27 0.22 0.20 3.41 4.14 3.64 0.17 3.62

Zn 0.09 0.20 0.12 0.03 0.10 0.04 0.38 0.09 0.07 0.07

Cr 0.06 0.15 0.11 0.03 0.11 0.01 0.08 0.05 0.02 0.05

As 60.0 3,100.0 595.0 607.0 490.0 155.0 1,338.0 516.0 282.0 427.0

All values are in mg L-1 except pH, EC (ls cm-1), ORP (mv) and As (lg L-1)

SD standard deviation, ND not detectable

Environ Earth Sci

123

the permissible limit of WHO for drinking water. The

average concentrations of Cu were observed low

(0.03 mg L-1) in pre-monsoon and were non-detectable

(ND) in monsoon. The ND concentration of Cu in monsoon

may possible due to dilution effect of Cu.

Hydrochemical facies of groundwater

Hydrochemical facies are employed for basic character-

ization of the chemical composition of groundwater. It

differentiates chemical types of water and helps to identify

the dominant ion types. A trilinear diagram (also called

Piper plot) developed by Piper is a very useful and popular

method for primary characterization of water quality (Piper

1944). The diagram is useful in screening and sorting large

numbers of chemical data and hence makes interpretation

easier. It utilizes the triangle (also known as ‘Ferre’ tri-

angle from Niggli’s classification of igneous rock) one for

anions and one for cations. The cations expressed as per-

centage of total cations in mill equivalents per liter are

plotted as a single point on the left triangle; whereas

anions, also expressed as percentage of total anions are

plotted as single points on the right triangle. These two

points are then projected into the central diamond shaped

area parallel to the outer axis of each triangular plot and

then the intersection of these points is plotted. The dia-

mond shaped graph is used to represent the composition of

water with respect to both cations and anions. This trilinear

diagram suitably discloses the similarity and differences

among groundwater samples because those with similar

quality will tend to plot together as group.

The trilinear piper diagram showed most of the

groundwater samples analyzed in pre-monsoon and mon-

soon seasons belongs to Ca–Mg–HCO3 and Mg–HCO3

water types with small representation from Ca–HCO3 and

Na–HCO3 (Fig. 2). This may probably be due to the dis-

solution of carbonate minerals such as calcite, dolomite

and aragonite. From the plot, it is evident that Mg–HCO3

water type is dominated in the general water chemistry of

the study area. Anions are strongly dominated by bicar-

bonate, which reflects a primarily meteoric source (Kumar

et al. 2010; Umar and Alam 2012). There is no significant

change in hydrochemistry of groundwater collected

between pre-monsoon and monsoon seasons. This suggests

that most of the major ions present in groundwater of

present study area originated from natural sources.

Interrelationship

The Pearson’s correlation coefficient was applied in

groundwater quality data to see the interrelationship and

coherence pattern among water quality parameters with

reference to seasons. The correlation coefficient values of the

analyzed water quality parameter are given in Tables 2 and

3. ORP was positively correlated with K?, Cl- and SO42-,

however, it was negatively correlated with As content. The

concentration of As was found to be increased with decrease

in ORP. This indicates reducing groundwater condition is

Fig. 2 Piper diagram showing

hydrochemical characteristics of

groundwater in pre-monsoon

and monsoon

Environ Earth Sci

123

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Pb

Fe

Zn

Cr

As

Tem

p1

pH

-0.4

51

EC

0.0

3-

0.0

81

OR

P0.0

6-

0.0

20.3

91

Ca2

?-

0.2

1-

0.0

10.3

20.5

51

Mg

2?

-0.0

4-

0.2

30.1

60.5

50.3

81

Na?

-0.0

30.3

30.3

90.5

80.6

0.3

01

K?

0.0

0-

0.1

50.5

70.7

10.4

40.6

80.2

51

Cl-

0.0

8-

0.2

40.6

90.6

90.4

80.5

40.2

60.9

41

F-

0.2

3-

0.1

10.1

90.0

90.0

50.0

60.3

2-

0.1

1-

0.1

11

SO

42-

-0.1

9-

0.0

70.6

00.6

00.5

40.5

30.3

10.8

90.8

8-

0.2

41

PO

43-

0.2

0-

0.3

10.4

7-

0.0

10.0

20.0

0-

0.0

80.0

40.1

90.2

50.0

31

HC

O3-

0.3

20.1

50.3

50.0

3-

0.0

10.1

20.3

90.0

00.0

60.1

4-

0.0

70.3

71

NO

3-

-0.0

0-

0.1

2-

0.0

3-

0.1

30.1

1-

0.0

1-

0.1

50.0

40.1

4-

0.3

50.1

60.0

4-

0.0

81

Cu

0.0

7-

0.2

0-

0.1

4-

0.1

9-

0.0

3-

0.1

6-

0.1

4-

0.0

7-

0.0

30.1

4-

0.0

9-

0.1

3-

0.2

0-

0.1

1

Ni

-0.0

40.4

2-

0.2

8-

0.2

5-

0.2

3-

0.5

2-

0.2

7-

0.2

9-

0.3

4-

0.2

9-

0.2

9-

0.3

6-

0.2

1-

0.1

40.1

81

Mn

0.3

6-

0.6

00.2

00.5

00.3

50.6

10.2

40.6

00.6

3-

0.1

20.5

1-

0.0

60.1

10.0

5-

0.0

2-

0.4

61

Cd

-0.0

10.4

3-

0.4

0-

0.2

5-

0.2

1-

0.4

1-

0.2

2-

0.3

5-

0.3

9-

0.1

1-

0.3

2-

0.3

3-

0.2

3-

0.1

60.3

00.7

7-

0.5

11

Pb

0.3

0-

0.5

50.4

30.3

70.1

90.6

00.1

33

0.4

60.5

10.2

90.2

90.6

00.3

00.0

3-

0.0

6-

0.6

40.5

2-

0.6

21

Fe

0.1

0-

0.5

90.0

3-

0.0

40.2

40.0

8-

0.2

20.0

90.2

1-

0.2

00.0

50.3

5-

0.0

10.2

00.1

7-

0.1

30.4

0-

0.3

00.4

11

Zn

0.1

10.0

30.1

30.1

70.0

20.0

30.5

7-

0.0

8-

0.0

90.3

3-

0.1

2-

0.0

60.3

4-

0.3

1-

0.0

9-

0.2

40.2

2-

0.4

10.1

40.0

61

Cr

-0.1

70.4

7-

0.4

3-

0.4

5-

0.2

5-

0.5

5-

0.3

4-

0.3

9-

0.4

8-

0.3

0-

0.3

2-

0.3

9-

0.3

00.0

10.2

30.8

8-

0.6

00.8

0-

0.7

3-

0.1

6-

0.3

21

As

0.3

00.0

5-

0.0

1-

0.2

0-

0.1

4-

0.1

0-

0.1

3-

0.1

8-

0.1

90.0

2-

0.2

20.3

70.1

2-

0.2

1-

0.1

30.2

8-

0.2

10.2

90.0

0-

0.1

1-

0.1

60.2

21

Environ Earth Sci

123

Ta

ble

3P

ears

on

’sco

rrel

atio

nco

effi

cien

to

fg

rou

nd

wat

erch

arac

teri

stic

sin

mo

nso

on

Tem

pp

HE

CO

RP

Ca2

?M

g2

?N

a?K

?C

lF

-S

O4

2-

PO

43

-H

CO

3-

NO

3-

Ni

Mn

Cd

Pb

Fe

Zn

Cr

As

Tem

p1

pH

0.4

51

EC

-0

.06

-0

.29

1

OR

P0

.14

0.4

8-

0.2

21

Ca2

?0

.04

-0

.52

0.5

4-

0.5

11

Mg

2?

0.2

30

.05

0.3

6-

0.3

10

.26

1

Na?

0.2

60

.13

0.1

4-

0.1

20

.06

0.4

01

K?

-0

.04

-0

.16

0.8

8-

0.0

50

.53

0.2

4-

0.1

51

Cl-

0.3

3-

0.3

20

.32

-0

.07

0.7

20

.20

0.1

20

.34

1

F-

0.0

00

.21

0.1

70

.10

-0

.33

-0

.17

0.6

5-

0.0

3-

0.3

71

SO

42-

0.4

2-

0.1

60

.00

0.1

30

.51

0.0

30

.10

0.0

40

.91

-0

.43

1

PO

43-

-0

.09

0.2

7-

0.1

30

.50

-0

.33

0.2

5-

0.0

1-

0.1

1-

0.0

90

.19

0.0

11

HC

O3

-0

.05

-0

.05

0.1

8-

0.2

90

.14

0.2

30

.64

-0

.02

0.1

00

.26

0.0

1-

0.2

61

NO

3-

0.0

0-

0.3

00

.11

-0

.04

0.2

6-

0.1

2-

0.3

70

.26

0.2

1-

0.3

10

.20

0.0

3-

0.2

91

Ni

-0

.25

-0

.12

0.3

0-

0.1

30

.10

0.3

4-

0.0

20

.21

0.0

8-

0.1

9-

0.0

60

.08

0.0

3-

0.2

01

Mn

0.3

1-

0.1

80

.19

-0

.09

0.6

40

.16

0.2

50

.22

0.8

4-

0.1

80

.79

-0

.09

0.1

40

.10

0.0

91

Cd

-0

.12

-0

.04

-0

.55

0.0

0-

0.3

1-

0.5

2-

0.3

7-

0.4

8-

0.4

9-

0.0

0-

0.3

0-

0.0

0-

0.3

30

.19

-0

.60

-0

.43

1

Pb

0.0

4-

0.0

2-

0.4

00

.04

-0

.33

-0

.47

-0

.25

-0

.42

-0

.50

0.0

6-

0.3

4-

0.0

4-

0.1

80

.16

-0

.49

-0

.36

0.8

21

Fe

-0

.37

-0

.44

0.2

3-

0.3

30

.21

0.0

5-

0.4

70

.27

-0

.02

-0

.35

-0

.18

0.0

7-

0.2

90

.42

0.5

7-

0.0

60

.00

-0

.02

1

Zn

0.0

40

.13

-0

.27

0.1

1-

0.1

5-

0.1

7-

0.3

2-

0.1

7-

0.1

6-

0.1

7-

0.1

1-

0.0

3-

0.1

70

.16

-0

.00

-0

.08

0.3

20

.21

0.1

21

Cr

-0

.14

-0

.08

-0

.48

0.0

2-

0.3

3-

0.5

2-

0.2

6-

0.4

5-

0.5

20

.12

-0

.36

-0

.02

-0

.22

0.1

8-

0.6

8-

0.4

80

.94

-0

.86

-0

.10

0.1

71

As

-0

.08

-0

.46

0.6

3-

0.5

30

.43

0.5

30

.21

0.3

70

.36

-0

.06

0.1

1-

0.1

40

.35

-0

.04

0.5

20

.13

-0

.58

-0

.50

0.3

6-

0.3

6-

0.5

51

Environ Earth Sci

123

prevalent in the study area. Further, positive correlation of

As with PO43-and HCO3

- also supports reducing condition

of the groundwater. SO42- was found to be significantly and

positively correlated with Cl-, K?, ORP and EC. Cl- con-

tents in the groundwater were also significantly and posi-

tively correlated with K?, ORP and EC. Fe in the

groundwater samples was negatively correlated with As in

pre-monsoon while positive correlation was established in

monsoon. Cr content of the water samples was negatively

correlated with Pb, Mn and Mg2?. Pb was also very much

negatively correlated with Cd, Ni, and pH. Significant and

positive correlation was also observed between EC and

Ca2?, K?, and SO42-, indicating source of groundwater

contamination could be from weathering of carbonate min-

erals and some anthropogenic wastes.

PCA

Altogether, seven major PCs were extracted with eigen-

value greater than 1 from all the 23 water quality param-

eters which accounted 81.996 and 83.763 % in pre-

monsoon and monsoon season, respectively. There are

several criteria to identify the number of PCs to be retained

in order to understand the fundamental data composition. A

scree plot (Fig. 3) is used here, which showed a change of

slope after the seventh eigenvalue. The factor loadings

obtained after varimax orthogonal rotation from the pre-

monsoon and monsoon data are given in Tables 4 and 5.

The degree of association between each variable showedFig. 3 Scree plots of the eigenvalues of PCA

Table 4 Principal component analysis of groundwater quality in pre-monsoon

Variables PC1 PC2 PC3 PC4 PC5 PC6 PC7

Temp 0.141 -0.527 -0.056 0.211 0.511 -0.382 0.203

pH -0.419 0.529 0.595 0.168 -0.258 -0.026 0.060

EC 0.627 0.077 0.234 0.483 -0.117 0.287 0.119

ORP 0.696 0.403 0.224 0.022 0.259 -0.089 -0.046

Ca2? 0.539 0.416 0.089 -0.036 0.027 0.318 0.265

Mg2? 0.720 0.160 -0.031 -0.105 0.094 -0.247 -0.323

Na? 0.460 0.250 0.733 -0.127 0.077 0.061 0.267

K? 0.781 0.459 -0.158 0.203 0.153 -0.064 -0.105

Cl- 0.828 0.358 -0.209 0.265 0.108 0.043 0.041

F- 0.144 -0.426 0.519 -0.045 0.287 0.398 -0.338

SO42- 0.708 0.582 -0.167 0.186 -0.038 0.046 -0.019

PO43- 0.331 -0.574 -0.042 0.587 -0.242 0.292 0.008

HCO3- 0.258 -0.357 0.479 0.272 -0.127 -0.227 0.384

NO3- 0.075 0.091 -0.479 -0.031 -0.513 -0.054 0.256

Cu -0.180 -0.031 -0.257 -0.156 0.568 0.541 0.078

Ni -0.718 0.347 -0.050 0.269 0.304 -0.051 0.266

Mn 0.758 -0.012 -0.263 -0.260 0.306 -0.336 0.174

Cd -0.733 0.320 -0.004 0.266 0.387 0.017 -0.005

Pb 0.775 -0.441 -0.111 0.137 0.033 0.052 -0.127

Fe 0.290 -0.309 -0.563 -0.100 0.022 0.240 0.491

Zn 0.238 -0.273 0.597 -0.437 0.089 -0.043 0.335

Cr -0.830 0.355 -0.119 0.172 0.137 0.042 0.188

As -0.240 -0.260 0.082 0.681 0.182 -0.238 -0.050

Eigen values 7.239 3.092 2.680 1.850 1.599 1.238 1.161

% of variance 31.47 13.44 11.651 8.046 6.951 5.383 5.049

Cumulative % 31.473 44.917 56.568 64.613 71.565 76.948 81.996

Environ Earth Sci

123

the respective loading factor for each component. During

pre-monsoon, PC 1 explained 31.47 % of total variance.

PC 1 has strong positive loading on Cl-, K?, Pb, Mn,

Mg2?, and SO42-, a strong negative loading on Cr, Cd and

Ni, and a moderate positive loading on ORP, EC and Ca.

PC 1 represents the natural hydro-geochemical evolution of

groundwater by groundwater–geological interaction which

can be elucidated by the dissolution of rocks and minerals

in sediments by chemical weathering (Belkhiri et al. 2010;

Nosrati and Eeckhaut 2012). This component explains the

erosion from upland area. The dissolution of dolomite is

possible source of Mg2? (Lasaga 1984). Mg2? accompa-

nies EC, Cl-, K?, and Ca2?, as positive loading in PC1

and, therefore, we can conclude that the source of Mg2? is

groundwater–geological interaction. Weathering of gran-

ites and magmatic rocks can enrich K? and Cl- in natural

waters (Laurent et al. 2010; Nosrati and Eeckhaut 2012). In

the weathering of igneous rocks, potassium feldspars are

usually the main sources K? ion (Kim et al. 2004). EC

exhibits moderate positive loadings indicating anthropo-

genic pollution sources, such as effluents of domestic ori-

gin, septic tanks, fertilizers and pesticides application in

agriculture (Chapagain et al. 2010; Nosrati and Eeckhaut

2012). Dissolution of gypsum mineral could increase

SO42- concentration in groundwater (Laurent et al. 2010;

Yidana 2010). Trace elements (Pb, Mn, Cr, Cd, and Ni)

with strong loading in PC 1 indicate anthropogenic source

(Helena et al. 2000; Levins and Gosk 2008); however,

these elements have a common source of origin in

groundwater, hence its occurrence is more likely from the

natural sources.

Table 5 Principal component analysis of groundwater quality in monsoon

Variable PC1 PC2 PC3 PC4 PC5 PC6 PC7

Temp 0.127 0.425 0.498 -0.051 0.238 -0.244 0.513

pH -0.310 0.614 0.012 -0.477 0.196 -0.298 0.238

EC 0.675 -0.185 -0.347 0.061 0.516 0.116 0.005

ORP -0.324 0.330 0.249 -0.663 0.231 0.127 -0.296

Ca2? 0.712 -0.385 0.273 0.317 0.143 -0.009 0.057

Mg2? 0.570 0.268 -0.302 -0.064 -0.048 0.202 0.534

Na? 0.336 0.747 -0.116 0.388 0.016 0.103 0.049

K? 0.563 -0.331 -0.187 -0.168 0.658 -0.025 -0.017

Cl- 0.764 -0.077 0.613 -0.009 0.013 0.061 -0.080

F- -0.129 0.611 -0.407 0.243 0.296 0.380 0.036

SO42- 0.523 0.039 0.803 -0.083 -0.093 0.054 -0.124

PO43- -0.121 0.221 -0.057 -0.577 -0.084 0.691 0.051

HCO3- 0.339 0.430 -0.191 0.516 -0.086 -0.193 -0.037

NO3- -0.013 -0.611 0.310 -0.046 0.311 0.260 0.258

Cu 0.451 0.312 0.276 0.143 -0.402 0.424 0.050

Ni 0.498 -0.169 -0.477 -0.411 -0.431 -0.127 0.002

Mn 0.659 0.067 0.607 0.043 -0.072 0.079 -0.021

Cd -0.842 -0.260 0.192 0.296 -0.026 0.148 0.108

Pb -0.761 -0.159 0.146 0.338 0.047 0.143 0.160

Fe 0.150 -0.774 -0.333 -0.195 -0.187 0.125 0.196

Zn -0.324 -0.184 0.186 -0.194 -0.243 -0.170 0.474

Cr -0.840 -0.177 0.132 0.409 0.085 0.211 0.012

As 0.750 -0.177 -0.391 0.190 -0.155 0.075 0.062

Eigen values 6.507 3.488 3.014 2.296 1.559 1.302 1.100

% of variance 28.290 15.164 13.106 9.984 6.776 5.659 4.484

Cumulative % 28.290 43.545 56.560 66.544 77.320 78.979 83.763

Table 6 Comparison of PC between pre-monsoon and monsoon

PCs Pre-monsoon Monsoon

PC1 Cl-, K, Pb, Mn, Mg, SO42-, ORP,

EC, Cr, Ni, Cd

Cl-, As, Ca, EC, Mn,

Cd, Cr, Pb

PC2 SO42-, pH Na, pH, F-

PC3 Na, Zn, pH, F- SO42-, Cl-, Mn

PC4 As, PO43- ORP, PO4

3-

PC5 Cu, Temp K, EC

PC6 Cu PO43-

PC7 Nil Mg, Temp

Environ Earth Sci

123

Principle component 2 accounted for 13.44 % of total

variance in groundwater quality and is positively dominated

by pH and SO42-. This may be possibly due to anthropogenic

pollution source mainly resulting from fertilizers and pesti-

cides used in agricultural fields which later may percolate

and find their way to groundwater. The source of SO42- in

groundwater may also result from other various sources such

as atmospheric deposition (Wayland et al. 2003), SO42-

bearing fertilizer and bacterial oxidation of sulphur com-

pounds (Sidle et al. 2000). PC 3 represented 11.65 % of

variance in water quality with high loading on Na?, Zn, pH,

and F-, indicating contribution of domestic wastewater in

shallow and unconfined aquifers. PC 4 accounted for 8.04 %

of the total variation in hydrochemistry and had high abso-

lute positive loading on As and PO43-. This is because the

mineral contents in the groundwater get dissolved due to

dissolution processes of geological formation under reduced

condition hence, releasing As in groundwater. PC 5

accounted for 6.95 % of total variation in groundwater

quality with high loading on Cu and temperature. PC 6 and

PC7 did not contain distinctive sources of variance in

hydrochemistry because of not having loading value greater

than 0.50. Hence, they are dropped and not taken into con-

sideration in groundwater water quality.

The loading component and communality of each vari-

able showed there is no significant seasonal variation in

hydrochemistry with few exceptions which control the

groundwater quality in the study area. The only property

that shows significant seasonal variation is As which

accounts for significant loading in PC 4 in pre-monsoon

and got increased its weighting in monsoon season

(Table 6). This indicates the weathering of As-bearing

minerals, mostly arseniosiderite (also confirmed by XRD

analysis (data not shown here) are predominant in study

area (Yadav 2012). The first three PCs accounted for more

than 50 % of the explained variance in the groundwater

quality both in pre-monsoon and monsoon season (Fig. 4).

Fig. 4 Rotated loading plots of first three PC in pre-monsoon (top)

and monsoon (bottom)

Fig. 5 Hierarchical dendrogram of groundwater in pre-monsoon

Fig. 6 Hierarchical dendrogram of groundwater in monsoon

Environ Earth Sci

123

The main contributors for changing the geochemical

composition of the groundwater are water–rock interaction,

mineralization and anthropogenic inputs.

Cluster analysis

The hierarchical CA is performed for hydrochemical water

quality parameter using Ward’s method. Squared Euclid-

ean distance is used as measure of distance which is one of

the most commonly adopted measures (Fovell and Fovell

1993). CA of the groundwater parameters is depicted in

Figs. 5 and 6. The variables cluster into two major groups.

The dendrogram (Figs. 5, 6) showed strong similarities

between major ions (Ca2?, Mg2?, Na?, HCO3-, Cl-, and

SO42-). These parameters also indicated high loadings in

PC 1 (Table 4). Hence, cluster I is interpreted as rock–

water interaction or natural mineralization (Subyani and

Ahmadi 2010; Ishaku and Matazu 2012). Also, cluster I

showed similarities between trace elements, NO3-, K? and

Mn that most likely represent effect of weathering of

feldspar and mica, in addition to agricultural fertilizers

(Subyani and Ahmadi 2010). Other group showed close

similarities between EC and As and are clustered II.

Conclusions

The groundwater quality of the Nawalparasi district was

evaluated based on multivariate statistical techniques. It

was found the most of the groundwater quality parameters

were well within permissible limit of WHO for drinking

water, except for Ni, Cd, Pb, Cr, and As. The average

concentration of As was found many fold higher than

WHO permissible limit of As in drinking water irre-

spective of seasons. Hydrochemical facies analysis

showed the groundwater of study area belongs to Ca–Mg–

HCO3 and Mg–CO3–HCO3 water type with HCO3- as

dominant ion. The negative correlation of As with ORP

inferred the reducing condition of groundwater. PCA

indicated that the parameters responsible for characteris-

tics of groundwater quality are related to various factors

such as natural processes (rock–water interaction and

mineralization), and anthropogenic inputs (fertilizer, pes-

ticides, and domestic wastewater discharge). Hence, an

integrated groundwater management and appropriate

application of fertilizer and pesticides in agriculture fields

is essential to ensure acceptable groundwater quality in

the study area.

Acknowledgments This study was supported by Indian Council for

Cultural Relations (ICCR) to ICY in the form of South Asian Asso-

ciation for Regional Cooperation (SAARC) fellowship (F.N.8-2/09

10/SAARC/ISD-II).

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