Geoscience Frontiers 13 (2022) 101365
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Geoscience Frontiers
journal homepage: www.elsevier.com/locate/gsf
Research Paper
Environmental fate and health exposures of the geogenic and
anthropogenic contaminants in potable groundwater of Lower Ganga
Basin, India
Indrani Mukherjee a,⇑, Umesh Kumar Singh b
a
b
Integrated Science Education and Research Centre (ISERC), Institute of Science, Visva-Bharati University, Santiniketan, Birbhum 731235, West Bengal, India
Department of Environmental Science, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Gaya 824236, Bihar, India
a r t i c l e
i n f o
Article history:
Received 23 July 2021
Revised 9 November 2021
Accepted 22 January 2022
Available online 26 January 2022
Handling Editor: E. Shaji
Keywords:
Cokriging
Health risks
Principal component analysis
Cluster analysis
Nitrate contamination
Fluoride contamination
a b s t r a c t
The study evaluated the sources and controlling factors of the groundwater contaminants in an agroeconomic region of Lower Ganga Basin using principal component analysis (PCA), multivariable linear
regressions (MLR), correlation analysis, and hierarchical cluster analysis, and evaluated the public health
risks using the Latin Hypercube Sampling, goodness-of-fit statistics, Monte Carlo simulation and Sobol
sensitivity analysis based on the 1000 samples collected in two sampling cycles (N = 1000). The study
reveals that the dissolution of fluoride-bearing minerals and semi-arid climate regulate the fluoride concentrations (0.10–18.25 mg/L) in groundwater. Extensive application of inorganic nitrogenous fertilizers
and livestock manure mainly contributed to elevated nitrate levels (up to 435.0 mg/L) in groundwater.
The health risks analysis indicates that fluoride exposure is more prevalent in the residents of each
age group than the nitrate and both contaminants exhibited higher non-carcinogenic health risks on
the infant and child (minor) age groups compared to adolescents and adults. Based on the cokriging interpolation mapping, the minor residents of 17.88%–23.15% of the total area (4545.0 km2) are vulnerable to
methemoglobinemia whereas the residents of all age-groups in 38.47%–44.45% of the total area are susceptible to mild to severe dental/skeletal fluorosis owing to consumption of untreated nitrate and fluoride enriched groundwater. The Sobol sensitivity indices revealed contaminant levels, groundwater
intake rate and their collective effects are the most influential factors to pose potential health risks on
the residents. Artificial recharge and rainwater harvesting practices should be adopted to improve the
groundwater quality and the residents are advised to drink purified groundwater.
Ó 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by
Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
licenses/by-nc-nd/4.0/).
1. Introduction
Groundwater is the substantial source of potable water for
50% of the world’s population and the world’s 2.5 billion individuals are exclusively reliant on groundwater to meet day-today water requirements (Kawo and Karuppannan, 2018; Ravi
et al., 2020; Soujanya Kamble et al., 2020). Aquifers of the arid/
semi-arid regions are under tremendous stress due to the overextraction of groundwater, mainly to fulfil the irrigation water supply demands which affect the climate resilience process of the
aquifers. In addition to the geogenic sources, such anthropogenic
activities have significantly augmented the groundwater contamination. Being the world’s largest consumer of groundwater, India is
⇑ Corresponding author.
already facing over-exploitation of the aquifers along with imperiled groundwater in many regions where fluoride, nitrate, iron and
other heavy-metals are identified as the major groundwater contaminants (Mohanakavitha et al., 2019a; Balamurugan et al.,
2020a; Mukherjee et al., 2021). Agricultural practices with intensive inorganic nitrogenous fertilizers, pesticides and herbicides
have been identified as the foremost cause of nitrate (NO
3 ) abundance in groundwater on a global scale. In addition, untreated sewage and industrial wastewater discharges, septic system leakage,
livestock waste, application of poultry manure for fish farming,
nitrifying bacterial activities, landfill leachate, atmospheric deposition and eutrophication phenomena are also responsible for nitrate
prevalence in potable groundwater (Torres-Martínez et al., 2020,
2021a, b). Amplified human interference left alluvial plains more
vulnerable to nitrate contamination and imperiled the groundwater resources for human consumption (Mukherjee and Singh,
E-mail address: indranimukherjee.envs@gmail.com (I. Mukherjee).
https://doi.org/10.1016/j.gsf.2022.101365
1674-9871/Ó 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
I. Mukherjee and Umesh Kumar Singh
Geoscience Frontiers 13 (2022) 101365
On the other hand, toxicity caused by fluoride on the residents
such as mild to severe dental anomalies, crippling deformities,
osteoporosis and death incidents in a few villages of this region
had been reported by a health survey conducted in 1995 where
the recorded maximum fluoride level in groundwater was
13.6 mg/L (Roy et al., 2018). The lack of alternative sources of
drinking water has compelled the residents of this region to consume raw groundwater and, as a result, the individuals are directly
exposed to the groundwater contaminants. Therefore, an understanding of the sources and influencing factors for augmentation
of groundwater NO
and their health exposure analysis
3 and F
on the residents may be useful in developing sustainable strategies
for curbing the derogation of groundwater resources from the
combined problems of NO
3 and F contamination. To our knowledge, such extensive source appointments and public health risks
analyses of NO
contamination at the LGB has not been
3 and F
undertaken in the past.
Several previous studies from China (Wu et al., 2014; Ren et al.,
2021), Pakistan (Khan et al., 2021), South America (Blarasin et al.,
2020), Canada (Loomer et al., 2019), West Africa (Egbi et al.,
2020), South Africa (Elumalai et al., 2020), Brazil (Silva et al.,
2021), Egypt (Masoud and Ali, 2020), Italy (Zanotti et al., 2022)
and India (Mohanakavitha et al., 2019b; Balamurugan et al.,
2020b; Panneerselvam et al., 2020) have adopted chemometric
methods, namely Principal component analysis (PCA), Cluster analysis (CA) and correlation coefficients to identify the sources of the
groundwater contaminants. However, the studies were conducted
based on a smaller sample size and physicochemical parameters
compared to the present study. Furthermore, the studies have
adopted the Pearson’s correlation coefficients which evaluates
the linear relationship among the groundwater facies. In reality,
groundwater facies usually follow non-normal distribution and
contain outliers. Therefore, correlation analysis based on the nonparametric methods would be more accurate for groundwater
facies with a larger sample size and physicochemical parameters
(Marín Celestino et al., 2018). Considering the large sample sizes,
the present study has opted for the Spearman’s correlation coefficients method over the Kendall’s s coefficients method (both are
nonparametric methods). The present study also applied the PCA
integrated with multiple linear regression (MLR) to quantify the
contamination sources. Some studies have performed the PCAMLR analysis to identify the contribution of the different sources
of PAHs in street dust (Feng et al., 2020), the atmosphere
(Soleimanian et al., 2020), and different sources of the pollutants
in river water (Meng et al., 2020) and a limited number of studies
performed this analysis on groundwater data to identify the contamination sources (Wu et al., 2020; Li et al., 2021a). The studies
found the PCA-MLR to be an effective approach to quantify the contribution of the contamination sources.
Several studies have also performed the non-carcinogenic
human health risks analysis (HHRA) using the USEPA HHRA
method for the exposure of fluoride (Li et al., 2019b;
Balamurugan et al., 2020c; Haji et al., 2021b; Karunanidhi et al.,
2021), nitrate exposure (Panneerselvam et al., 2021), both nitrate
and fluoride (Adimalla and Li, 2019; Adimalla et al., 2019; Ji
et al., 2020; He et al., 2021) and other contaminants (He et al.,
2019; Wang et al., 2021; Wei et al., 2021) in groundwater. Previous
studies have appointed the deterministic method using a mean
value of the population-related input parameters. Such an
approach ignores the differences between individuals which usually leads to errors and uncertainties in the evaluation. HHRA using
a probabilistic approach based on the Monte Carlo Simulations
(MCS), can overcome this problem and produce more realistic
results by reducing the errors and uncertainties (Mukherjee
et al., 2020). A few studies have analyzed the public health risks
related to fluoride or nitrate exposure in groundwater using the
2021a). Chronic consumption of nitrate contaminated potable
water has been hypothesized as the genesis of methemoglobinemia (blue baby syndrome), hypertension and thyroid dysfunctions
in infants, toddlers and pregnant women (Zhang et al., 2018;
Adimalla et al., 2019; He and Wu, 2019; Li et al., 2019a). Moreover,
numerous cross-sectional evidence associated it with two types of
birth defects and fifteen types of cancers (Schullehner et al., 2018;
Ward et al., 2018; Essien et al., 2020; Stayner et al., 2021).
Similarly, the occurrences of fluoride (F) in potable groundwater cause different types of dental and skeletal deformities (fluorosis) in humans including pitting and mottling of teeth, ligaments
calcification, osteoporosis, osteosclerosis, crippling, abdominal
pain, tingling sensations and calcification of blood vessels
(Mukherjee and Singh, 2018a, 2018b). Based on recent estimations,
about 0.2 billion people globally are under the dreadful fate of fluoride related health impairments, and India and China are among
the two worst-affected countries. In India, about 66 million individuals including 6 million children have been affected by fluoride
toxicity and about 411 million individuals are vulnerable to fluorosis due to consumption of fluoride-contaminated potable groundwater (Mukherjee and Singh, 2018c; Rasool et al., 2018). Fluoride
may get mobilized in groundwater from diverse geogenic sources
(such as fluoride-bearing minerals, marine aerosols and volcanic
eruptions) and anthropogenic sources (such as usage of fluoridecontaining irrigation water and application of phosphate fertilizers), and different geochemical processes (e.g., weathering of
fluoride-containing minerals, ion exchange/reverse ion exchange,
mixing and adsorption/desorption) and meteorological factors
(e.g., evaporation and precipitation), drive the enrichment of fluoride in groundwater systems (Adimalla and Li, 2019; Li et al.,
2019b; He et al., 2020, 2021; Ji et al., 2020; Mukherjee and
Singh, 2020a). Igneous (e.g., granitic-gneiss and pegmatite), sedimentary and metamorphic rocks usually contain significant
amounts of fluoride-bearing minerals such as amphiboles, fluorite,
muscovite, fluorapatite, topaz and hornblende. Several studies concluded that weathering of such host rocks is primarily responsible
for mobilizing fluoride into the groundwater systems of most
fluoride-endemic regions worldwide (Aravindan et al., 2011; Haji
et al., 2021a).
The present survey area is a typical agroeconomic semi-arid tract
of the Lower Ganga Basin (LGB) in India, with a geographical area of
4545 km2 of which 70.54% is under cultivation, and 52.54% and 15%
of the total area is underlain by the alluvial and granitic-gneiss aquifers (Mukherjee and Singh, 2020b). Extensive usage of irrigation
groundwater along with inorganic fertilizers and manure have
underpinned the agricultural activities of this region. Based on a
report published by the agriculture department of this region,
54.58% of the total area under cultivation is covered by irrigation,
primarily with groundwater. More than 36,786 MT nitrogen, 17,656
MT phosphate and 10,364 MT potash fertilizers are applied to the
agricultural fields each year with a rate of 128 kg/ha/yr, cumulatively (birbhum.nic.in). Extensive livestock (cattle, buffalo, sheep,
goat, pig, duck etc.) farming, poultry farming and fish farming practices with poultry-manure and improper sanitary and domestic
waste/wastewater management practices have also been reported
in this region (Mukherjee and Singh, 2021a). Furthermore, one
closed iron-mining site, one thermal power plant (produces 2.02
MT combustion residues/year which it releases to the environment),
one mini-steel plant, seven hot-springs and several pottery and
bricks industries, and sponge iron plants are situated within the survey area. Therefore, it is expected that local lithology, climatic conditions and anthropogenic influences control the groundwater
hydrogeochemistry of the region. Nitrate concentration in groundwater up to 182 mg/L has been reported in this survey area which
is four times greater than the national standard limit set by the BIS
(BIS, 2012; Mukherjee and Singh, 2018b, 2020a).
2
Geoscience Frontiers 13 (2022) 101365
I. Mukherjee and Umesh Kumar Singh
nomic area is 1430.5 mm, with about 75%–78% of the rainfall concentrated during the monsoon season. The aquifers of the survey
region get primarily recharged from the monsoon rainfall. The
annual potential evaporation is 1400–1600 mm which is greater
than the average yearly precipitation. The relative humidity of the
survey area varies between 43% and 83%.
MCS (Zhang et al., 2017; Shalyari et al., 2019; Bazeli et al., 2020;
Nakazawa et al., 2020). The present study has applied the MCS
and Sobol Sensitivity Analysis (SSA) methods to assess the combined exposure of groundwater fluoride and nitrate on the residents and has aimed to achieve higher accuracy in the evaluation
by applying the Latin Hypercube Sampling (LHS) method and
goodness-of-fit (GoF) statistics.
Previous studies related to groundwater contaminants analysis
usually applied inverse distance weighting, spline or ordinary kriging methods for interpolation (Aravindan and Shankar, 2011;
Shankar and Nafyad, 2020) and the application of the cokriging
method is still inadequately explored. The cokriging method predicts the variability of a parameter based on the multivariant statistical analysis and the correlated parameters. This technique
has been applied in other research areas and resulted in better prediction accuracy over conventional kriging and other methods (Li
et al., 2006; Wang et al., 2018; Yang et al., 2021). The study has
attempted to evaluate (i) the spatio-seasonal variations of ground
water NO
3 and F using the cokriging method, (ii) the sources and
factors driving the enrichment of NO
3 and F using the chemomet
ric approaches, (iii) the potential health risks of NO
in
3 and F
potable groundwater through direct ingestion using the MCS
method, and (iv) the sensitive parameters of the risk assessment
models using the SSA method.
2.1.2. Geology
Different heterogeneous geological units compose the local
geology of the study area. The Archaean rocks are exposed on the
southern and western parts of the survey region and comprised
of granite-gneisses, biotite-schists, calc-granulites and granite with
pegmatites and quartz veins (Fig. 1b). The Dubrajpur formation of
the Lower Jurassic age comprising of conglomerates, coarsegrained to medium-grained gritty and ferruginous sandstone, grey
siltstone, mottled shale and thin coal bands, are exposed as elongated patches in the south-eastern part overlying the Archaean
rocks with an unconformity or fault. The Rajmahal volcanic rocks
of the Middle Jurassic to Lower Cretaceous age are exposed on
the northern and western parts and composed of medium and
fine-grained basalts, amygdaloidal, vesicular and intertrappean
sediments. The ballistic flow rests either on the Dubrajpur formation of the Upper Gondwana age or the Lower Gondwana sediments. The Tertiary formation mainly overlies the Rajmahal
volcanic rocks and Archaean rocks in places and is comprised of
sandstone and clay beds. Laterite, mostly of the vesicular type,
occurs as a cap rock over the basalts and Tertiaries. Platy laterite
is also recorded in a few places and, in addition, the lateritic gravel
of detrital nature has an extensive presence.
2. Materials and methods
2.1. Description of the survey region
2.1.1. Location and climate
LGB is situated in the northeast of India. The survey area covers
a segment of 87°050 2500 E–88°010 4000 E longitudes and 23°320 3000 N–
24°350 0000 N latitudes (Fig. 1a). This region is categorized by the
subtropical subhumid (dry) climatic conditions with a yearly mean
temperature of 25.9 °C. The mean yearly rainfall in this agroeco-
2.1.3. Hydrogeology
The hydrographical system comprises numerous rainfed and
nonperennial streams (Fig. 1b). Groundwater prevails under an
unconfined state in the subsurface aquifers whereas it prevails
under a semi-confined to confined state in the deep aquifers
Fig. 1. (a) Study area and sampling locations, (b) geological units, (c) land use and land cover, (d) spatial distribution of fluoride during PRM, (e) spatial distribution of fluoride
during POM, (f) spatial distribution of nitrate during PRM and (g) spatial distribution of nitrate during POM.
3
I. Mukherjee and Umesh Kumar Singh
Geoscience Frontiers 13 (2022) 101365
ammonium (NH+4), and other anions, and the inorganic-cations,
separately. The filtered samples for inorganic-cation analysis were
preserved with ultrapure 6 N-nitric acid (NIST-CRM) at pH < 2 and
the samples for nitrate and nitrite analysis were preserved with
99.5% pure boric acid (NIST-CRM). Filtered raw water samples
were used for the remaining anions and NH+4 analysis. Electrical
Conductivity (EC), redox potential (Eh), temperature and pH were
measured on-site using a 371 Systronics water analyzer, equipped
with EC, ORP, temperature, and pH probes after calibrating using
appropriate standard solutions/procedures. Each preventative
course of action was followed during collection, carriage, preservation and analysis of the samples, as outlined by APHA (2012) to
prevent sample ageing and cross-contamination.
(Mukherjee and Singh, 2020b). Monsoon precipitation is the principal source of groundwater recharge. Discharge from the Massanjore and Bakreswar reservoirs, Tilpara, Deocha, Baidana and
Kendisala barrages/weirs, and Mayurakshi-Dwarka, MayurakshiBakreshwar and Bakreshwar-Kopai canals also supplement
groundwater recharge in the respective catchment area. Groundwater develops through open wells in the weathered zones and
bore wells in the deep aquifers with a yield potential rate of 3.5–
9.5 m3/hr and up to 20 m3/hr in places. Groundwater appears
under an unconfined state in the residuum of Gondwana sandstones yielding to the tune of 5.0–15.0 m3/hr and under a semiconfined state in the secondary porosity zone below the weathered
residuum of 12.0–20.0 m thickness. The bore wells taping fracture
zones (depth = 100 m) in the Gondwana sandstones have a yield
potentiality of 10–25 m3/hr (Ray and Shekhar, 2009).
Groundwater appears within 5–15 mbgl in the weathered mantle of granitic-gneissic aquifers overlain by the 8–10 m thick laterite layers at places which formed the shallow aquifers of yield
potentiality of up to 10 m3/hr. Groundwater develops under a confined state through bore wells in the potential fracture zones of the
granitic aquifers down to a depth of 70–100 mbgl with a groundwater discharge rate of 10.0–20.0 m3/hr (Mukherjee and Singh,
2022). Groundwater also develops within 12 mbgl in the porous
zone of the weathered mantle of the intertrappean basaltic aquifers (Rajmahal Trap) where the groundwater discharge rate of
the abstraction wells is of the order of 12 m3/hr. The groundwater
discharge rate of the potential fractures in basaltic aquifers in the
depth of 65–80 mbgl is 5–20 m3/hr. Groundwater develops in
the unconfined state in the near-surface aquifers and a semiconfined to confined state in the deep aquifers of the Tertiary sediments capped by laterites and Quaternary alluvium at places
with a yield potentiality rate of up to 50 m3/hr. However, unconsolidated and semi-consolidated Tertiary sediments appear at a
depth of 100–400 mbgl and when overlain by older alluvium have
yield potentiality of 80–100 m3/hr (Mukherjee et al., 2019).
Groundwater occurs both in the unconfined and confined states
within a depth of 600 mbgl in the unconsolidated/recent alluviums
with a large yield prospect of 150 m3/hr (Mukherjee and Singh,
2021b).
The annual replenishable groundwater resource of the survey
area has been estimated as 1.285 bcm with recharge from rainfall
being 0.84 bcm during the monsoon and 0.21 bcm during the nonmonsoon seasons. Recharge from other sources is 0.076 bcm during the monsoon and 0.161 bcm during the non-monsoon periods.
Considering the natural discharge of 0.119 bcm, the net annual
groundwater availability has been assessed as 1.166 bcm; out of
which 0.401 bcm groundwater is extracted annually (0.362 bcm
for irrigational use and 0.039 bcm for industrial and domestic
use) resulting in 34% of stages of groundwater development. Furthermore, the net groundwater availability for future use has been
estimated as 0.726 bcm. The basaltic and granitic aquifer regions
have been declared as drought-prone and southeastern, central
and northern alluvial aquifer regions have been declared as semicritical zones considering poor groundwater development in these
regions (CGWB, 2019).
2.3. Laboratory analysis
Analysis of Ca2+, Mg2+, Na+, K+ and Fe were performed by a Spectro Arcos multiview ICP-OES analyzer in automated mode for the
+
inorganic-cation samples. Analysis of F, Cl, NO
3 , NO2 , NH4,
3
2
PO4 , SO4 and Br was performed by an 883 Basic IC plus
(Metrohm) with an anion column (Metrosep A Supp 5–250/4.0)
for the anions and with a Metrosep C 6–250/2.0 microbore cation
column for NH+4 (Table 1). TA, CO2
3 and HCO3 of each anion sample
were measured by the titrimetric method using 0.02 N hydrochloric acid (NIST-CRM; 7647-01-0; Thermo Fisher Scientific; USA),
phenolphthalein indicator (77-09-8; Thermo Fisher Scientific;
USA) and methyl orange indicator (67-56-1; Thermo Fisher Scientific; USA).
2.4. Quality control and assurance
The quality assurance was attained through the execution of
stringent laboratory practices and quality control measures during
the preparation of the standards and samples, instrument calibration and sample measurements. NIST-CRMs and standard instruments were used to analyze the groundwater samples and to
achieve quality control measures. Millipore ultrapure water was
used for procedural blanks. The procedural, spiked and spiked sample blanks were measured in duplicate after the measurements of
every 10 actual samples to assess the measurement accuracy and
reproducibility. Recoveries of the ions were 95.5%–102.2% (±5%).
Multi-component reference material (1643d; NIST SRM; Thermo
Fisher Scientific, USA), ammonium standard (Certipur; 119812;
Merck; Germany) and IC multi-element standard V CertipurÒ
(Merck; Germany) were used as standard/stock solutions for the
cations and anions analyses. The limits of detection (LODs) of the
target ions were 0.0023, 0.0042, 0.0161, 0.0039, 0.019, 0.014,
0.066, 0.005, 0.031, 0.004, 0.002 and 0.002 mg/L for F, Cl, NO
3,
+
2+
+
2+
3
+
SO2
4 , PO4 , Br , Na , Mg , K , Ca , Fe and NH4, respectively. Analytical accuracy of the samples was ensured using the ion charge
balance which was within the standard limit of ±5% for all samples.
2.5. Source appointments by chemometric analysis
Considering the non-normal nature with outliers and extreme
values of the groundwater facies, the Wilcoxon Rank Sum Test
was performed to appraise the significance of seasonal variances.
The study employed several multivariate chemometric approaches
such as correlation coefficients (r), Principal Component Analysis–
Multilinear Regression (PCA-MLR) and Q-mode and R-mode
Agglomerative Hierarchical Clustering Analysis (AHCA, using
Ward’s linkage method, Euclidean distance and dendrogram) to
identify the interrelationship among the hydrochemical facies
and their hypothetical sources (Wu et al., 2014, 2020; Li et al.,
2021a; Ren et al., 2021). Before multivariate analysis, the normality
of all the facies was examined using Kolmogorov-Smirnov’s nor-
2.2. Field sampling and in-situ analysis
For this study, 1000 groundwater samples (Nsample = 500 2)
were collected in new and pre-cleaned high-density polyethylene
(HDPE) sterile containers from 500 distinct wells (Nwell = 500) in
the pre-monsoon (PRM) and post-monsoon (POM) periods of
2017–2018 (Fig. 1a). Prior to sample collection, the stagnant water
was pumped out from each well until the field parameters were
stabilized. The samples were sifted using a 0.45 lm Millipore filter
and collected in triplicate to analyze nitrate (NO
3 ), nitrite (NO2 ),
4
Geoscience Frontiers 13 (2022) 101365
I. Mukherjee and Umesh Kumar Singh
Table 1
Physico-chemical parameters of groundwater of the study area.
Parameter
Unit
Temperature
pH
°C
–
Electrical
Conductivity (EC)
Total Dissolved Solids
(TDS)
Total Hardness (TH)
lS/
–
6.5–
8.5
–
cm
mg/L
500
mg/L
150
Calcium (Ca )
mg/L
300
Magnesium (Mg2+)
mg/L
100
Sodium (Na+)
2+
mg/L
200
+
Potassium (K )
mg/L
10
Total iron (Fe)
mg/L
0.30
Carbonate (CO2
3 )
mg/L
–
mg/L
200
Chloride (Cl )
mg/L
200
Sulfate (SO2
4 )
mg/L
250
Fluoride (F)
Bicarbonate
(HCO
3)
mg/L
1.00
(NO
3)
mg/L
45.0
Nitrite (NO
2)
mg/L
3.00
Ammonium (NH+4)
mg/L
–
mg/L
–
Bromide (Br )
mg/L
–
Silica (SiO2)
mg/L
–
Nitrate
Phosphate
(PO3
4 )
y
MAC
PRM
Best fitted statistical distributiony
POM
Range
Mean ± SD
%
SEMAC*
Range
Mean ± SD
%
SEMAC*
26.0–35.6
6.49–8.65
30.9 ± 2.32
7.73 ± 0.34
61.56–
5438
38.8–
3427
2.80–
1300
12.0–
455.0
1.00–
87.48
1.20–
690.0
0.30–
140.1
BDL to
16.0
BDL to
36.0
24.4–
1141
3.40–
1311
BDL to
186.7
0.10–
18.25
BDL to
435.0
BDL to
14.92
BDL to
9.78
BDL to
54.75
BDL to
91.50
BDL to
48.00
–
0.20
18.7–31.0
6.49–8.45
20.5 ± 1.18
7.74 ± 0.34
723.7 ± 582.8
–
456.0 ± 367.2
25.8
236.0 ± 156.0
62.2
55.40–
6961
35.7–
4386
4.31–
1426
8.00–
440.0
3.15–
77.60
4.33–
578.0
0.40–
171.8
BDL to
17.6
BDL-48.0
62.59 ± 50.15
1.00
17.32 ± 12.45
0.00
66.04 ± 73.67
5.20
11.25 ± 23.21
27.6
0.88 ± 1.78
52.6
2.62 ± 7.59
–
235.9 ± 122.5
58.8
112.5 ± 145.8
13.8
15.60 ± 21.30
0.00
1.19 ± 2.03
25.6
29.72 ± 40.95
16.6
0.29 ± 0.21
0.00
0.28 ± 0.16
–
2.67 ± 1.84
–
1.63 ± 5.47
–
12.58 ± 10.33
–
30.5–
1305
1.55–
911.7
BDL to
216
0.03–
16.50
BDL to
486.7
BDL to
16.76
BDL to
13.17
BDL to
72.0
BDL to
78.13
BDL to
48.00
Type
Parameter(s)
–
0.00
Lognormal
Weibull
meanlog ± sdlog = 3.37 ± 1.1
Shape = 26.12, Scale = 7.89
872.9 ± 793.0
–
Lognormal
meanlog ± sdlog = 6.38 ± 0.61
676.0 ± 299.6
52.6
Lognormal
meanlog ± sdlog = 5.92 ± 0.61
245.9 ± 115.1
71.6
Lognormal
meanlog ± sdlog = 5.29 ± 0.60
68.99 ± 38.07
1.00
Lognormal
meanlog ± sdlog = 3.99 ± 0.66
18.14 ± 10.39
0.00
Lognormal
meanlog ± sdlog = 2.63 ± 0.69
70.52 ± 71.49
5.40
Lognormal
meanlog ± sdlog = 3.82 ± 0.83
15.73 ± 24.21
43.2
Weibull
Shape = 0.713, scale = 8.456
0.65 ± 0.47
38.0
Logistic
Location = 0.57, scale = 0.58
7.15 ± 20.2
–
Lognormal
meanlog ± sdlog = 0.96 ± 0.02
288.7 ± 188.5
64.2
Logistic
Location = 227.1,scale = 61.9
133.1 ± 153.7
18.2
Lognormal
meanlog ± sdlog = 4.10 ± 1.16
Logistic
Location = 11.68,scale = 9.12
20.93 ± 24.75
0.00
1.02 ± 0.87
23.4
Lognormal
meanlog ± sdlog = 1.19 ± 0.95
15.60 ± 21.27
24.0
Logistic
Location = 14.79,scale = 13.6
0.38 ± 0.25
0.00
Lognormal
meanlog ± sdlog = 0.09 ± 0.21
0.43 ± 0.29
–
Lognormal
meanlog ± sdlog = 0.15 ± 0.25
5.86 ± 2.24
–
Lognormal
meanlog ± sdlog = 0.56 ± 0.54
1.28 ± 4.26
–
Lognormal
meanlog ± sdlog = 0.49 ± 1.70
10.28 ± 9.97
–
Lognormal
meanlog ± sdlog = 2.33 ± 2.29
*
MAC: Maximum Admissible Limit based on BIS (2012) and WHO (2017).
Evaluated based on the GoF statistics on the average values.
the kth source of the ith parameter, and Skj denotes the influence
degree of the kth source on the jth sample (Balamurugan et al.,
2020c; Feng et al., 2020). The parameter with greater loading possesses more importance in the particular component irrespective
of the sign. The component loadings were characterized as strong,
moderate and weak corresponding to their absolute loading values
of >0.75, 0.75–0.50 and 0.50–0.30, respectively.
The MLR was performed based on the obtained component
scores to evaluate the %contribution of each groundwater contamination source (%Ci). The analysis was set up by considering the
total standardized concentration Cij of ith groundwater facies in
the jth sample (Ci) as the dependent parameter and Skj as the independent parameter, as expressed by Eqs. (2) and (3).
mality test (K-S test). As the groundwater facies defied the
assumption of normality, the study analyzed correlations among
the parameters using nonparametric statistics using the ‘corx’
package in R-platform.
The initial datasets for PCA were standardized through transformation to reduce the influence of high values and different units
among the parameters and obtain their normal distributions. The
datasets were tested for appropriateness of applying PCA using
the Kaiser Meyer Olkin (KMO) test of sample adequacy with cutoff greater than 0.5 and Bartlett’s test of sphericity at q 0.05.
The physicochemical parameters which had attained the appropriateness tests were considered for further analysis after performing
communality checks, and the parameters with communality 0.5).
The principal components (PCs) were extracted from the scree plot
based on an eigenvalue >1 and adjusted by orthogonal varimax
rotation to attain rationalized rotated components. The PCs are
the linear combination of the input parameters, as expressed by
Eq. (1).
C ij ¼
X
aik Skj
Cj ¼
X
Bk Skj
ð2Þ
C ij
ð3Þ
k
Cj ¼
X
i
ð1Þ
k
Where Bk is the coefficient of the regression for the ith factor which
represents the overall contribution rate of kth contamination source
to the entire survey region. The Bk values were obtained by the fit-
Where Cij denotes the z-score standard concentration of the ith
parameter of the jth sample, aik denotes the influence degree of
5
I. Mukherjee and Umesh Kumar Singh
Geoscience Frontiers 13 (2022) 101365
ting linear regression in R-platform and the %Ci values were calculated by Eq. (4) (Wu et al., 2020; Haghnazar et al., 2022).
X %C i ¼ Bk =
Bk 100
divided into four groups according to age, namely adults (>21 yr),
teenagers (11–21 yr), children (1–10 years) and infants (<1 yr). The
characterization of the health hazards was performed by measuring the Chronic Daily Intake (CDI) of nitrate and fluoride-based
on Eq. (11), and comparing them with their respective threshold
limits/reference doses (RfD), which is demonstrated as the hazard
quotient (HQ) (Eq. (12)). Furthermore, the overall non-carcinogenic
risks of F and NO
3 is evaluated by the hazard index (HI), as
expressed by Eq. (13) (Wu and Sun, 2016; He et al., 2019; Li
et al., 2021b; Wang et al., 2021; Wei et al., 2021).
ð4Þ
Furthermore, the parameters were standardized using z-scores
before performing the AHCA. The study had employed R-mode
AHCA to obtain the optimal groups of parameters and Q-mode
AHCA to obtain the optimal groups of the samples with analogous
and heterologous characteristics. Moreover, a dendrogram was
constructed for a visual representation of the analysis. The aptness
tests of the datasets for PCA, PCA and AHCA were conducted using
the ‘psych’ and ‘cluster’ packages in R software.
8
<
CDIF ¼ ðCF
IREFEDÞ
: CDI
NO3
2.6. Spatio-seasonal analysis
8
<
NO
3
¼
ðBWATÞ
ðCNO
3
ð11Þ
IREFEDÞ
ðBWATÞ
CDIF
HQ F ¼ RfD
F
ð12Þ
The spatial variances of F and
in groundwater systems
were evaluated based on the cokriging method wherein several
correlated auxiliary parameters can jointly estimate the geospatial variability of a primary parameter. To surpass the prediction accuracy over the traditional kriging techniques, the cokriging
contemplates the geospatial auto-correlation of the primary
parameter and the spatial cross-correlation between the primary
parameter and the better sampled auxiliary parameters (up to
three). This method can accurately estimate a parameter even at
the regions with poor data coverage. The kriging method estimates
a parameter at an unmeasured location based on Eqs. (5)–(7) (Xie
et al., 2018; Murugesan et al., 2020; Smith et al., 2020).
The variables to determine the CDIs and HQs are explained in Tables
1 and 2. When the estimated HQs or HI values are > unity, the noncarcinogenic risks to public health are significant (USEPA, 1989).
The health risks analyses were performed based on the Monte Carlo
method in R-platform (‘EnvStats’ package) with 50,000 iterations
and the LHS technique where the statistical distribution pattern of
F and NO
3 levels were evaluated using the GoF statistics.
n
X
Z 0 xp ¼
ki Z ðxi Þ
ð5Þ
2.8. Sobol sensitivity analysis
ki cðxi ; yj Þ l ¼ cðxi ; xÞ
ð6Þ
Sobol indices is a variance decomposition-based approach to
perform the global sensitivity analysis of a model with n input
parameters (Sobol, 2001; Saltelli et al., 2010), as expressed by Eq.
(14).
ki ¼ 1
ð7Þ
: HQ NO
3
CDINO
¼ RfDNO3 3
HI ¼ HQ NO3 þ HQ F
ð13Þ
i¼1
n
X
i¼1
n
X
y ¼ f ðxÞ ¼ ðx1 ; x2 ; x3 ; ::::; xn Þ 2 Rn
i¼1
The overall variance of the model output V(y) can be expressed by
Eq. (15) where Vi represents the variance of the ith parameter (Eq.
(16)), Vij represents the partial variance of ith and jth parameters
(Eq. (17)).
Where Z’(xp) and Z(xi) are the estimated (kriged) and measured values of a parameter at locations xp and xi, respectively; ki is the associated weight; l is the Lagrange multiplier; c(xi, xj) is the variogram
of a vector with origin and extremity at xi and xj, respectively. However, cokriging estimates a parameter based on Eqs. (5), (8) and (9),
where u and v are the primary and auxiliary parameters, respectively (Cellmer and Zrobek, 2017).
nm
v X
X
kim cmv ðxi ; xj Þ lv ¼ cuv ðxj ; xÞ
VðyÞ ¼
kim ¼
i¼1
1; when m ¼ u
0; when m–u
ð8Þ
ð9Þ
cuv ðhÞ ¼
1
2NðhÞ
ðm1
XÞ
m
X
i¼1
j¼ðiþ1Þ
V ij þ ::: þ V 1:::m
ð15Þ
V i ¼ V xi Exi ðyjxi Þ
ð16Þ
h
i
V ij ¼ V xij Exij yjxi ; xj V i V j
ð17Þ
1¼
½Z u ðxi Þ Z u ðxi þ hÞ½Z v ðxi Þ Z v ðxi þ hÞ
Vi þ
The Eq. (15) can be normalized as Eq. (18) where each term represents the variance up to the mth order and are called Sensitivity
Indices (SIs), xi represents all parameters of the parameter-space
except xi. V xi Exi ðyjxi Þ denotes the variance of the expected value
of y conditional on the values of all input parameters of the parameter space except xi.
Before performing the cokriging analysis, cross semivariograms were determined based on Eq. (10). The geostatistical
analysis was accomplished using ArcGIS software (v10.5) based
on the ordinary cokriging method and the results were crossvalidated.
NðhÞ
X
n
X
i¼1
m¼1 i¼1
nm
X
ð14Þ
ð10Þ
n
X
i¼1
i¼1
V i =VðyÞ þ
ðm1Þ
X
m
X
i¼1
j¼ðiþ1Þ
V ij =V ðyÞ þ ::: þ V 1:::m =V ðyÞ
ð18Þ
The variance contribution of the individual parameter of the model
is represented by the first term of Eq. (18) and is known as the firstorder sensitivity index (FOSI) of the ith variable. The influence of the
interaction between the ith and jth variables (xi and xj) on the overall
output variance is represented by the second term of Eq. (18), also
known as the second-order sensitivity index (SOSI). The overall
2.7. Non-carcinogenic human health risks
The probable non-cancer health risks of F and NO
3 due to
direct ingestion of groundwater was evaluated for the inhabitants
6
Geoscience Frontiers 13 (2022) 101365
I. Mukherjee and Umesh Kumar Singh
Table 2
Exposure risks assessment input parameters for Monte Carlo Simulations and estimated risk levels associated with groundwater fluoride and nitrate.
Population
Simulation parameters
Scenario
CDI (mg/kg-bw/d)
HQ
HI
Name
Description
Unit
Distribution
Value
Nitrate
Fluoride
Nitrate
Fluoride
Infants
IR
EF
ED
BW
AT
Ingestion rate
Exposure frequency
Exposure duration
Body weight
Average time
L/d
d/yr
yr
kg
d
Lognormal
Triangular
Fixed
Lognormal
Fixed
0.61 ± 0.27
Min: 180, max: 364, mode: 345
1
7.04 ± 1.49
365
Minimum
Average
50th %ile
95th %ile
Maximum
0.794
1.720
1.630
4.640
13.99
0.006
0.138
0.102
0.374
0.398
0.50
1.08
1.02
2.90
8.74
0.10
2.30
1.70
6.23
6.63
0.60
3.38
2.72
9.13
15.4
Children
IR
EF
ED
BW
AT
Ingestion rate
Exposure frequency
Exposure duration
Body weight
Average time
L/d
d/yr
yr
kg
d
Lognormal
Triangular
Fixed
Lognormal
Fixed
1.25 ± 0.57
Min: 180, max: 364, mode: 345
6
16.68 ± 1.48
2190
Minimum
Average
50th %ile
95th %ile
Maximum
0.432
1.443
1.254
2.785
9.900
0.002
0.118
0.096
0.216
0.254
0.27
0.90
0.78
1.74
6.19
0.03
1.97
1.60
3.60
4.23
0.30
2.87
2.38
5.34
10.4
Teens
IR
EF
ED
BW
AT
Ingestion rate
Exposure frequency
Exposure duration
Body weight
Average time
L/d
d/yr
yr
kg
d
Lognormal
Triangular
Fixed
Lognormal
Fixed
1.58 ± 0.69
Min: 180, max: 364, mode: 345
6
46.25 ± 1.18
2190
Minimum
Average
50th %ile
95th %ile
Maximum
0.189
0.428
0.357
1.802
6.668
0.001
0.061
0.052
0.169
0.193
0.12
0.27
0.22
1.13
4.17
0.02
1.02
0.87
2.82
3.22
0.13
1.28
1.09
3.94
7.38
Adults
IR
EF
ED
BW
AT
Ingestion rate
Exposure frequency
Exposure duration
Body weight
Average time
L/d
d/yr
yr
kg
dy
Lognormal
Triangular
Fixed
Lognormal
Fixed
1.95 ± 0.64
Min: 180, max: 364, mode: 345
6
57.03 ± 1.10
9125
Minimum
Average
50th %ile
95th %ile
Maximum
0.075
0.377
0.338
1.517
5.440
0.001
0.033
0.025
0.099
0.124
0.05
0.24
0.21
0.95
3.40
0.02
0.55
0.42
1.65
2.07
0.06
0.79
0.63
2.60
5.47
influence of the ith parameter on V(y) is termed as the total-order
sensitivity index (TOSI) and is represented by Eq. (19).
Ex i ½V ðyjx i Þ VðyÞ V x i Ex i ðyjx i Þ
¼
VðyÞ
VðyÞ
V x i Ex i ðyjx i Þ
¼1
VðyÞ
About 1.20% of PRM samples and none of the POM samples
were plunged into the Cl-Na+ zone signifying the influence of
potential evaporation on the groundwater systems of the study
area or signifying a longer residence time. About 5.6% and 5.8% of
+
samples were plunged into the HCO
3 -Na zone during the preand post-monsoon periods which might be associated with the base
ion exchange phenomena. About 24.6% of PRM and 32.4% of POM
samples were plunged into the mixed water-type zone (Cl2+
2+
+
2+
SO2
and HCO
4 -Mg -Ca
3 -Na -Ca ), indicating the effects of
chemical weathering processes/rock-water interactions, ion
exchange/reverse ion exchange and human-induced activities.
POM samples plotted in these zones also indicates recently
recharged water during the monsoon period. The diagrams also
revealed that the alkali elements such as Na+ and K+ surpassed
alkaline earth elements such as Ca2+ and Mg2+ in 83.2% of PRM
samples and 85.6% of POM samples whereas the weak acids such
as CO2
and HCO
and
3
3 surpassed the strong acids such as Cl
2
SO4 in 79.0% of PRM samples and 69.0% of POM samples.
TOSI ¼
ð19Þ
SI >0.1, 0.1–0.01 and <0.01 indicate highly sensitive, moderately
sensitive and insensitive parameters or interaction between the
parameters. The sensitivity analysis was conducted on the Python
platform (v3.9) with SciPy (v1.6.3) and SALib (v1.3.13) libraries.
3. Results and discussion
3.1. Characterization of groundwater facies
The seasonal concentrations of the groundwater facies exhibited wide and significant variations suggesting that the hydrogeochemistry is heterogeneous and controlled by different processes
(Table 1). Nevertheless, the groundwater of the survey area is
found to be largely palatable and near-neutral to weakly alkaline,
although shallow groundwater at certain places showed orangebrown slime with a bad odor and taste due to the presence of iron
bacteria. The cations composition of groundwater is dominated by
Ca2+ followed by Na+ and Mg2+ whereas the HCO
3 is the most
abundant anion followed by Cl and SO2
during both seasons.
4
The samples are mainly freshwater to brackish water and soft to
moderately hard (Supplementary Data Fig. S1).
Piper’s diagram (Piper, 1944) was adopted to determine the
overall characteristics of the groundwater systems (Fig. 2a, b).
Approximately 68.0% PRM and 60.2% POM samples fall in the zone
2+
2+
of HCO
type indicating the supremacy of HCO
type
3 -Ca
3 -Ca
groundwater across the study area, primarily in the shallow aquifers. Percolated meteoric groundwater usually contains high proportions of calcium and bicarbonate contents whereas many
hidden faults, fractures and lineaments are located throughout
the survey region, particularly in the granitic-gneissic aquifers of
the geothermal area (Mukherjee and Singh, 2018a, b). Thus, the
predominance of Ca2+-HCO
3 type groundwater in deep aquifers
implies mixing of percolated meteoric groundwater with deep circulating groundwater with a long rock-water interaction time.
3.2. Cooccurrences of nitrate and fluoride
Cooccurrences of nitrate and fluoride in groundwater systems
of the survey area have been depicted using the cokriging method
(Fig. 1), based on the correlation matrix described in Supplementary Data Table S1. The concentration of nitrate was Below Detection Limit (BDL) in 15.2% of PRM samples and 14.7% of POM
samples, where the fluoride concentration of these samples was
less than the required minimum level of fluoride in potable water
(>BDL and 0.5 mg/L) to prevent dental decay. The samples were
classified as Ca2+-HCO
3 water type, representing deep confined
aquifers. Fluoride and nitrate cooccurred in the remaining samples
during both seasons. Fluoride was recurrently detected at <0.5 mg/
L in 55.8% of wells majorly representing groundwater of Ca2+-HCO
3
type where nitrate concentration varied within BDL and MAC.
Co-occurrences of fluoride and nitrate had exceeded their preintervention limits defined by BIS in 16.6% of PRM samples and 21.0%
of POM samples where the highest nitrate concentration was
detected in a shallow alluvium aquifer of agriculture LULC type
and the highest fluoride level was detected in the deep alluvium
aquifer overlain by a laterite/lateritic layer. Elevated nitrate levels
were detected significantly in shallow wells of the agricultural
7
I. Mukherjee and Umesh Kumar Singh
Geoscience Frontiers 13 (2022) 101365
Fig. 2. (a) major groundwater facies during PRM based on Piper’s diagram, (b) major groundwater facies during POM based on Piper’s diagram, (c) dendrogram of the R-mode
AHCA for the PRM dataset, (d) dendrogram of the R-mode AHCA for the POM dataset, (e) dendrogram of the Q-mode AHCA during PRM dataset, and (f) dendrogram of the Qmode AHCA during POM dataset.
regions of the survey area, and fluoride concentrations of >2.5 mg/L
were detected in the deep fractured granitic-gneiss parts of the
study area (Fig. 1d, e). Based on the groundwater facies, groundwater of Na+-HCO
3 type exhibited fluoride concentration >10 mg/L
whereas it was within 2.5–10 mg/L in groundwater of Na+-Cl
and mixed water types and BDL–3.8 mg/L in groundwater Ca2+2+
2
HCO
3 and Ca -Cl -SO4 water types. On the other hand, a higher
nitrate level (>45 mg/L) was detected in groundwater other than
Ca2+-HCO
3 water-type.
3.3. PCA-MLR
Before carrying out the PCA-MLR, the aptness tests for PCA were
conducted on the PRM and POM datasets to appraise the sphericity
8
Geoscience Frontiers 13 (2022) 101365
I. Mukherjee and Umesh Kumar Singh
and sampling adequacy. The q-value of Bartlett’s sphericity test on
the PRM and POM datasets were almost zero (q < 0.05) signifying
that the PCs are mutually independent and the physicochemical
parameters exhibited adequate correlations to evaluate the component loadings. Moreover, the values of the KMO test for the
PRM and POM datasets were 0.73 and 0.72 (>0.65, 1) indicating
that the samples were adequate to conduct PCA-MLR. CO2
and
3
temperature exhibited communality < 0.5. Therefore, both parameters were discarded from further analysis (Table 3) and the PCAMLR was performed based on nineteen hydrogeochemical observations (Nparam = 19) of 500 groundwater samples each season (Nprmsample = Npom-sample = 500). Based on the Scree plots, the first four
PCs (PC1 to PC4) were extracted as they exhibited an eigenvalue > 1
for both seasonal datasets (Supplementary Data Fig. S2) and cumulatively explained 78.33% and 79.25% of the total variances of the
PRM and POM datasets (Table 3).
PC1 has explained about 45.7% and 51.4% of the total variances
of the PRM and POM datasets, respectively and is predominantly
weighted by TH, TDS, Na+, Ca2+, Mg2+, F and HCO
3 , and moderately weighted by SiO2, Cl, pH, SO2
4 and Fe during both the seasons. This describes the ionic strength resulting from the rock–
water interactions, dissolution and weathering of silicate and carbonate minerals and evaporites. This further indicates that weathering of minerals is a prominent process in regulating the
hydrogeochemistry of the survey region. The soils of the survey
region are moderately reactive. Therefore, a greater positive component loading for HCO
3 in the post-monsoon period indicates
the interactions of meteoric water with soils during in-filtration
and the recharge of the groundwater systems with meteoric water.
PC2 has explained about 18.8% and 20.6% of the total variances
during the pre- and post-monsoon periods. The component has
+
exhibited moderate to strong positive loadings of PO3
4 , Br , K ,
2
Cl , NO3 , Eh and SO4 , and positive loading with F , NO2 and
NH+4 which suggest that the anthropogenic activities (e.g., excessive use of pesticides and fertilizers, domestic and septic tank effluent, livestock waste and inappropriate sanitation practices) have
influenced the hydrogeochemistry of groundwater. PC1 (representing weathering of minerals) and PC2 (representing anthropogenic
influences) have both exhibited significant positive loading of K+,
indicating that the occurrence of K+ in groundwater is associated
with the anthropogenic sources and dissolution/weathering of
muscovite and feldspar present in the granite rocks.
PC3 has explained about 7.26% and 5.05% of the total variances
during PRM and POM seasons. The component showed positive
loadings for pH, K+, Na+, F and HCO
3 and weak negative loadings
for TH, SiO2, Ca2+ and Mg2+ signifying that the reverse cation
exchange reactions also govern the solute acquisition of these ions.
The positive loading for F as well as pH, Na+ and HCO
3 and negative loading for Ca2+ reveal the aqueous geochemistry of calcite+
fluorite. The positive correlation of F with pH, HCO
3 and Na ,
2+
and negative correlation of F with Ca also explain the same phenomena (Supplementary Data Table S1). PC4 has explained 6.57%
and 2.20% of the total variability during the pre- and postmonsoon seasons. The component loadings of Cl and F on PC4
are positive and significant and the loadings were relatively higher
in the pre-monsoon period than in the post-monsoon period and the
positive correlations of F with Cl indicate the influence of evaporation on shallow groundwater and mixing of brackish shallow
water with deep groundwater of the survey region. The MLR analysis revealed the minerals dissolution and weathering as the key
sources of groundwater facies accounting for 64% and 65.7% of
the overall contribution during the pre- and post-monsoon periods,
respectively whereas the anthropogenic sources viz. pesticides, fertilizers, domestic and septic tank effluent, livestock waste and
inappropriate sanitation practices accounted for 23.5% and 26.3%
of the overall contribution. In addition, the ion-exchange/reverse
ion-exchange reactions, and evaporation/mixing phenomena
accounted for 6.8% and 5.7% of the overall contribution during
the PRM season and 5.1% and 2.9% of the overall contribution during the POM season.
3.4. Source appointments by AHCA
3.4.1. R-mode AHCA
The study considered 21 parameters, namely EC, TDS, pH, Eh,
2
3
TH, Ca2+, Mg2+, Na+, K+, Fe, NH+4, CO2
3 , HCO3 , SO4 , Cl , PO4 , F ,
NO3 , NO2 , Br and SiO2 of the seasonal datasets for R-mode HCA.
The Dendrograms (Fig. 2c, d) exhibited three major clusters during
the pre- and post-monsoon periods, respectively. The first cluster
(RC1) showed associations among pH, Eh, EC, TDS, Na+, CO2
3 ,
SO2
4 , HCO3 , Cl , Fe, SiO2 and F indicating the influence of the geogenic anomalies, especially minerals weathering to control the
hydrochemical compositions of groundwater. These parameters
have significantly contributed to the groundwater TDS. This cluster
Table 3
Varimax rotation component matrix of the parameters during the PRM and POM seasons.
Parameter
PRM
Communality
pH
0.684
Eh
0.568
TDS
0.968
TH
0.896
Ca2+
0.805
2+
Mg
0.692
Na+
0.724
+
K
0.578
NH+4
0.634
HCO
0.758
3
Cl
0.591
NO
0.538
3
2
SO4
0.645
F
0.797
3
PO4
0.563
Br
0.508
NO
0.543
2
SiO2
0.762
Fe
0.553
Eigen value
Variance (%)
POM
Validation
PC1
PC2
PC3
PC4
Communality
Validation
PC1
PC2
PC3
PC4
KMO = 0.73,
Bartlett’s test: chi–
sq = 2013.5, q = 0,
df = 59
0.504
0.278
0.978
0.838
0.764
0.602
0.756
0.292
–0.02
0.797
0.513
–0.09
0.632
0.754
0.206
0.415
–0.01
0.506
0.611
5.725
45.70
0.220
0.570
0.182
0.170
0.101
0.080
–0.09
0.405
0.322
0.042
0.521
0.709
0.532
0.224
0.520
0.501
0.356
–0.16
0.211
1.665
18.80
0.164
0.027
0.027
–0.12
–0.20
–0.16
0.118
0.112
–0.01
0.139
0.004
–0.01
0.023
0.103
0.033
–0.06
–0.03
–0.21
–0.33
1.136
7.26
–0.19
0.020
0.100
0.052
0.071
–0.01
0.013
–0.01
0.009
0.053
0.182
0.004
0.001
0.118
–0.02
0.003
–0.06
0.007
0.030
1.038
6.57
0.760
0.298
0.988
0.875
0.837
0.704
0.721
0.614
0.664
0.728
0.714
0.639
0.582
0.704
0.602
0.583
0.668
0.686
0.615
Eigen value
Variance (%)
KMO = 0.72,
Bartlett’s test: chi–
sq = 3122.8, q = 0,
df = 59
0.517
0.172
0.981
0.925
0.871
0.789
0.812
0.270
0.004
0.811
0.517
0.032
0.527
0.756
–0.22
0.027
–0.02
0.551
0.605
5.878
51.40
–0.22
0.553
0.087
0.165
0.021
0.154
–0.06
0.522
0.431
0.085
0.509
0.745
0.504
0.243
0.512
0.525
0.214
–0.01
0.252
1.654
20.60
0.149
–0.02
0.105
–0.15
–0.18
–0.11
0.159
0.137
0.054
0.133
0.101
0.031
–0.21
0.109
–0.31
0.007
0.001
–0.12
0.047
1.225
5.05
0.001
–0.03
–0.01
0.01
–0.07
0.015
0.045
–0.31
–0.03
0.113
0.001
0.018
0.071
0.104
–0.05
0.070
0.087
–0.08
0.050
1.003
2.20
9
I. Mukherjee and Umesh Kumar Singh
Geoscience Frontiers 13 (2022) 101365
reveals that fluoride reported in the groundwater of the survey
area is primarily of geogenic origin. The second cluster (RC2) is
associated with TH, Ca2+ and Mg2+ and indicates that the groundwater hardness is governed by the mineral dissolution process.
+
+
The third cluster (RC3) comprises PO3
4 , Br , K , NH4, NO3 and
NO
2 and indicates the influences of the anthropogenic anomalies
and nitrification processes.
ð20Þ
KMg3 ðAlSi3 O10 ÞF2 þ2OH ! KMg3 ðAlSi3 O10 ÞðOHÞ2 þ2F
ð21Þ
CaF2 þ2OH
ðfluoriteÞ
ð22Þ
ðmuscoviteÞ
ðphlogopiteÞ
! CaðOHÞ2 þ 2F
dissolution
CaF2 þ 2HCO3 !
ðhostrockÞ
ðgroundwaterÞ
CaCO3 # þ2F þ CO2 þ H2 O
ð24Þ
ðgroundwaterÞ
ðgroundwaterÞ
hydrolysis
!
1:5Al2 Si2 O5 ðOHÞ4 þKþ þ 2F þ HCO3 ð25Þ
ðkaoliniteÞ
NaMg3 AlSi3 O10 ðOH; FÞ2 þ7CO2 þ 7:5H2 O
ðbiotiteÞ
hydrolysis
!
0:5Al2 Si2 O5 ðOHÞ4 þNaþ þ 3Mg2þ þ 2F
ðkaoliniteÞ
þ
2H4 SiO4 þ7HCO3
ðorthosilicic - acidÞ
ð26Þ
Moreover, positive correlations of F with NO
3 indicate an influence
of anthropogenic activities, especially intensive use of phosphate
fertilizers in the agricultural field, in enriching the concentrations
of F and NO
3 (Table 3). During reverse cation exchange, calcium
and magnesium of groundwater replace sodium and potassium of
the mineral matrix. As a result, it demotes the concentrations of calcium and magnesium in groundwater with the simultaneous dissolution of Na+, K+ and F. Therefore, positive correlations of F with
rNa+/rCa2+ (rprm = 0.23; rpom = 0.26), rNa+/rMg2+ (rprm = 0.11;
rpom = 0.10) and (rNa+ – 0.65rCl)/(rCa2+ + rMg2+) (rprm = 0.28;
rpom = 0.21) indicate enrichment of fluoride in groundwater also
through the reverse cation exchange mechanism where r indicates
measurements in meq/L (Xiao et al., 2021). The significantly higher
positive correlations between F and Cl during the pre-monsoon
period (r = 0.13) compared to the post-monsoon period (r = 0.11)
indicates the effects of evaporation also responsible for fluoride
and nitrate enrichment in groundwater.
The occurrence of nitrate in groundwater systems is usually
considered an anthropogenic indicator. About 66.4%–85.2% of samples exhibited nitrate concentration > 3 mg/L signifying the influences of different anthropogenic sources on groundwater
systems of the study area. A significant positive correlation of
NO
3 with Cl during both seasons indicates the effects of domestic
wastewater infiltration and septic tank leakage to enrich the concentrations of nitrate and chloride in groundwater (Table 3). NO
3
has also exhibited positive correlations with K+ and Ca2+ implying
the excessive usage of inorganic and organic fertilizers as pollution
sources of nitrate, whereas it has displayed a negative correlation
+
with NO
2 and positive correlation with NH4 indicating contribution of nitrification and denitrification process to control its
enrichment.
Fluoride levels in groundwater are controlled by the lithology
and climatic condition of an area including weathering of the host
rocks, anion and cation exchange processes, residence time and
evaporation. The lithology data of the sampled wells indicate the
presence of fluorite, and different micas such as biotite and muscovite in the aquifer systems. A positive correlation of fluoride with
pH indicates (rprm = 0.32; rpom = 0.28) the mobilization of F from
these minerals with hydroxyl ion (OH) in an alkaline environment
as indicated by Eqs. (20)–(22) (Mukherjee and Singh, 2018c).
ðphlogopiteÞ
ð23Þ
ðmuscviteÞ
3.5. Nitrate and fluoride contamination circumstances
ðbiotiteÞ
CaCO3 # þ2F
KAl3 Si3 O10 ðOH; FÞ2 þCO2 þ 2:5H2 O
3.4.2. Q-mode HCA
The study has excluded the physicochemical parameters which
exhibited poor correlations with other parameters and performed
the Q-mode HCA based on the parameters TH, TDS, Na+, K+, Ca2+,
2
Mg2+, HCO
3 , Cl , SO4 and NO3 of 500 samples for both seasons.
The dendrogram indicated five prominent clusters of the samples
(Fig. 2e, f). The first cluster (QC1) consists of 57.0% of PRM and
58.2% of POM samples and represented relatively a less contaminated cluster, whereas the second cluster (QC2) contains 7.2% of
PRM and 16.8% of POM samples, the third cluster (QC3) contains
22.6% of PRM and 19.2% of POM samples, the fourth cluster
(QC4) contains 3.8% of PRM and 0.2% of POM samples and the fifth
cluster (QC5) contains 9.4% of PRM and 5.6% of POM samples. QC2,
QC3, QC4 and QC5 indicated low, moderate, high and very high
contamination of groundwater concerning fluoride, nitrate and
TDS.
KMg3 ðAlSi3 O10 ÞF2 þ2OH ! KMg3 ðAlSi3 O10 ÞðOHÞ2 þ2F
dissolution
CaF2 þ CO3 2
!
ðhostrockÞ
ðgroundwaterÞ
3.6. Health risks assessment
The health hazards associated with the direct consumption of
contaminated groundwater concerning fluoride and nitrate
became a vital issue in the arid/semi-arid segments of India
(Mukherjee et al., 2021). The analytical results revealed the ele
vated levels of NO
3 and F in the groundwater of the survey area
at places. As health morbidities associated with NO
in
3 and F
potable water are chronic, the study considered the average value
of both seasons in each sampling location to perform the GoF analysis which indicated logistic and lognormal distributions to better
describe the variability of NO
3 and F concentrations in groundwater of the survey area, respectively (Table 1). Based on the Monte
Carlo simulations, populations of each age group intake higher
nitrate on a daily basis, compared to fluoride under the minimum,
maximum, average and 95th-% exposure conditions (Table 2). The
HQ values of nitrate and fluoride exposures indicate greater noncarcinogenic health risks to the infant and child populations which
reduce with increasing age (infant > child > teen > adult) under all
exposure conditions (Table 2).
The results further indicate that HQ for fluoride was greater
than unity for infant, child and teen-age groups under the average,
95th-% and maximum exposure scenarios whereas it was > 1 for
the adult age group under the 95th-% and maximum exposure scenario. On the other hand, HQ values for nitrate exceeded unity for
infants under the mean, 95th-% and maximum, for children and
teens under the 95th-% and maximum, and adults under the maximum exposure conditions, implying infants and children of the
survey area are at potential risk of dental and skeletal fluorosis
Fluoride also exhibited significant positive correlations with
Na+ (rprm = 0.58; rpom = 0.41), K+ (rprm = 0.12; rpom = 0.11) and HCO
3
(rprm = 0.57; rpom = 0.55), and a negative correlation with Ca2+
(rprm = 0.15; rpom = 0.38) indicating rock-water interaction
mechanisms such as weathering of fluoride-containing minerals
and hydrolysis to enrich the fluoride levels in groundwater as indicated by Eqs. (23)–(26).
10
Geoscience Frontiers 13 (2022) 101365
I. Mukherjee and Umesh Kumar Singh
Fig. 3. (a) FOSI and TOSI of the exposure models for infants, (b) FOSI and TOSI of the exposure models for children, (c) FOSI and TOSI of the exposure models for teens, (d) FOSI
and TOSI of the exposure models for adults, and (e) SOSI of the exposure models.
approach (represents only the average scenario), for the residents
of Dali county (Li et al., 2021b), Wuqi County (He and Wu, 2019;
He et al., 2019), Guanzhong Plain (Wu and Sun, 2016; Zhang
et al., 2018) and Hancheng City (Ji et al., 2020) of the Shaanxi Province in China, Yercaud of the Salem District (Balamurugan et al.,
2020b; Panneerselvam et al., 2021), Sulur taluk of the Coimbatore
district (Karunanidhi et al., 2021) of Tamil Nadu in India, Showt
city of the West Azerbaijan province in Iran (Yousefi et al., 2019)
and Lower Volta River Basin of Ghana (Egbi et al., 2020). These
studies also reported greater non-cancer risks of groundwater fluoride and/or nitrate on the minor age groups (10 yr) compared to
and the infants are considerably susceptible to methemoglobinemia due to consumption of untreated groundwater. In the probabilistic health risks assessment, 50th-% and 95th-% statistics
represent the typical individual and the conceivable worst-case
exposure scenarios. Considering the total HI values, infants, children and teens are vulnerable under both exposure scenarios and
the adult population is vulnerable under the worst-case exposure
conditions to the combined adverse non-carcinogenic effects of
nitrate and fluoride.
Non-cancer health risks due to exposure to fluoride and/or
nitrate in groundwater were assessed, based on the deterministic
11
I. Mukherjee and Umesh Kumar Singh
Geoscience Frontiers 13 (2022) 101365
the monsoon recharge (Fig. 1). Therefore, groundwater quality of
certain regions of the survey area can be improved through artificial recharge using percolation ponds, check dams and riverbank
filtration and rainwater harvesting using percolation tanks and
recharge pits. Groundwater recharge through the existing dugwells and rejuvenation of the existing ponds has also been
reported as a cost-effective measure to dilute the concentrations
of the groundwater contaminants (Mukherjee and Singh, 2018c,
2021b). On the other hand, higher concentrations of the groundwater contaminants were recorded at certain areas during the postmonsoon period than in the pre-monsoon period (Fig. 1). Therefore, treatment techniques such as adsorption or ion exchange,
reverse osmosis, electrodialysis, biochemical denitrification and
catalytic reduction/denitrification can be adopted to remove the
excess fluoride and nitrate from groundwater. Substitution of the
groundwater sources with piped water supply systems can be an
effective mitigation measure to address the public health issues
caused by the harmful waterborne contaminants, specifically fluoride and nitrate for the survey region. To supply safe drinking
water to the residents, a comprehensive piped water supply
scheme has been commissioned by the groundwater management
bodies for the survey region but the coverage is still poor
(wbphed.gov.in).
the teen and adult age groups. Nonetheless, the results of the present study are consistent with the health risks assessment of NO
3
or F in potable water based on the probabilistic method on the
residents of Iranshahr city, Sistan, and the Baluchistan province
in Iran (Shalyari et al., 2019), Central Java, Indonesia (Sadler
et al., 2016) and Behbahan city, Khuzestan Province, Iran
(Badeenezhad et al., 2021). The current study has reported higher
exposure of groundwater fluoride and nitrate on the residents
compared to the aforementioned studies.
3.7. Sobol sensitivity analysis (SSA)
SSA was conducted with a sample size of 1 105 (6 105
model iterations) to recognize the impact of the health risk predictor parameters in the estimation of the non-carcinogenic risks
(Fig. 3). It exemplifies the utmost significant parameters that influence the outcomes of the health risk analysis. The FOSIs and TOSIs
of the nitrate and fluoride risks assessment for all age groups imply
that their concentrations in groundwater are the most influential
risk predictor parameter (SI 0.1). IR has also been recognized
as an important parameter for fluoride exposure on the inhabitants
of all age groups considering TOSI, and in view of FOSI, it is moderately sensitive (SI = 0.1–0.01) for minor age groups and highly sensitive (SI > 0.1) for teen and adult populations. However, IR
exhibited moderate to high influence on every population group
for nitrate exposure considering FOSI and TOSI, respectively. The
sensitivity of EF and BW are moderate for fluoride exposure and
insignificant for nitrate exposure on the minor populations considering FOSI, and in view of TOSI, their sensitivities are moderate for
both exposures on the minor populations.
On the other hand, both the parameters are insensitive on the
teen and adult residents considering FOSI of both exposures. EF
was found to be moderately sensitive and BW was found to be
insensitive in light of TOSI for both the age-groups and exposures.
The SOSI which determines the pairwise influence of the parameters on the outcomes of the risk assessment models (Fig. 3),
explained that the combination of nitrate and fluoride concentrations in groundwater with IR has significant influences on the
exposure risks (SOSI > 0.1) of all populations, whereas the pairwise
interactions of contaminant (F and NO
3 ) levels with BW and EF
are moderately sensitive for fluoride and nitrate exposures on
the minor populations. In addition, the combined sensitivity of C
and EF is also moderate for nitrate exposure on the teen and adult
populations. The influences of the remaining pairwise parameters
are insignificant for both exposures on each population group.
4. Conclusions
The residents of this agroeconomic, semi-arid and rural region
are extensively dependent on groundwater and are compelled to
intake untreated groundwater due to poor socio-economic conditions. The present study employed different chemometric methods
to evaluate the sources and influencing factors of groundwater
contaminants and appraised the health risks on the inhabitants
using the probabilistic approach. The following conclusions have
been attained.
(i) A greater part of the survey area was identified as endemic
to the cooccurrences of elevated NO
3 (>50 mg/L) and F
(>1.5 mg/L) in groundwater mainly of mixed, Na+-HCO
3
and Na+-Cl-types, although groundwater of Ca2+-HCO
3type with nitrate 50.0 mg/L and fluoride 2.50 mg/L
was prevalent in the PRM and POM periods. Nitrate and fluoride levels exceeded their maximum permissible limits for
human consumption in about 23.4%–25.6% and 16.6%–24.0%
of samples. Nitrate at higher concentrations was prevalent in
the shallow groundwater whereas higher fluoride was
prevalent in the shallow, medium and deep groundwater.
(ii) The results of the Spearman’s rank correlation, PCA-MLR and
AHCA revealed incipient chemical weathering and a semiarid climate followed by reverse-ion exchange and mixing
phenomenon to control the hydrogeochemical profile of fluoride, whereas intensive cultivation with high nitrogenous
fertilizers and pesticides, domestic and septic tank effluent,
livestock waste and inappropriate sanitation practices were
identified for NO
3 enrichment. Based on the MLR analysis,
the chemical weathering/dissolution of minerals, anthropogenic sources, ion exchange/reverse ion exchange processes and evaporation/mixing processes accounted for
64%–65.7%, 23.5%–26.3%, 5.1%–6.8% and 2.9%–5.7% of the
overall contribution for the groundwater contaminants.
(iii) Based on the public health risks assessment and cokriging
interpolation of fluoride and nitrate, minor populations from
approximately 17.88%–23.15% of the total area are susceptible to methemoglobinemia whereas the populations of
every age group in about 38.47%–44.45% of the total survey
area are susceptible to mild to severe fluorosis (dental and
3.8. Mitigation measures
The analysis of the contamination sources and public health
risks indicate that the survey region, especially the deteriorated
areas require the adoption of sustainable management practices
of the groundwater resources in order to improve the groundwater
quality and health profile of the residents. Several non-treatment
measures such as blending groundwater of poor quality with good
quality water, diluting the groundwater contaminants through
artificial recharge techniques and rainwater harvesting can be
adopted for the survey area. A study conducted by Bhagavan and
Raghu (2005) revealed that the construction of check dams was
able to reduce the fluoride concentrations in groundwater and
improve the public health conditions of the Anantapur District,
Andhra Pradesh, India. Gowrisankar et al. (2017) also reported that
artificial recharge through the construction of check dams was able
to improve the groundwater quality of Krishnagiri District, Tamil
Nadu State, India. The spatio-seasonal variations of fluoride indicated lower fluoride levels during the post-monsoon period at a
few places of the study area due to the dilution effects during
12
Geoscience Frontiers 13 (2022) 101365
I. Mukherjee and Umesh Kumar Singh
skeletal) due to intake of untreated groundwater. Compared
to teenagers and adults, the populations of minor age groups
are found to be more vulnerable to nitrate and fluoride exposures (infants > children > teens > adults).
(iv) The Sobol sensitivity analysis indicates that the concentration of the contaminants, ingestion rate of the potable
groundwater and their combined effects are the most influential parameters for the potential non-cancer health risks
on the residents. Therefore, the study recommends the
inhabitants drink purified groundwater. Moreover, frequent
monitoring and public awareness campaigns should be conducted in the survey area along with the adaptation of artificial recharge and surface water storage practices to
improve the groundwater quality. Regulation of the extreme
use of fertilizers in the farming lands and adaptation of the
scientific sanitary and domestic waste management practices are also advisable for the survey area to improve the
groundwater quality.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.gsf.2022.101365.
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The study aimed to attain a greater accuracy in public health
risks assessment for the exposures of the groundwater contaminants using the higher sizes of seasonal samples, quality control
and assurance procedures, MCS with higher simulations, LHS,
GoF statistics and SSA. However, it still might have encountered
a trivial but unavoidable inaccuracy in the estimation as, in reality,
groundwater contaminants do not follow any theoretical statistical
distribution pattern exactly. Furthermore, the study has adopted
the USEPA provided default values of RfDs for the groundwater
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on the inhabitants of the study area is required to calculate the
RfDs of the contaminants and to improve the public health risks
analysis. Despite these constraints, the methodologies, results,
analysis and findings of the present study are reliable and were
able to obtain reproducible results with higher accuracy. Therefore,
the study may be useful for groundwater management and to
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areas with similar geological and climatic conditions and can be
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The study recommends analyzing the spatio-seasonal variability of the groundwater contaminants using different interpolation
methods, including the cokriging method, and determining the
best interpolation technique based on their interpolation accuracy
level and cross-validation results. The study further recommends
conducting pilot studies to improve the quality of groundwater
using the managed aquifer recharge structures and conducting
health surveys on the residents to understand the exposure status.
Analysis of the microbial activities in the groundwater systems and
isotopic investigation of groundwater facies is also recommended
to endorse the findings of the present study.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared
to influence the work reported in this paper.
Acknowledgements
The first author (IM) would like to acknowledge Ms. M. Fernandez (London, UK) for her help to improve the quality of the manuscript. The anonymous reviewers and the Editor are sincerely
acknowledged for their useful comments.
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