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OPEN
Probabilistic modelling
is superior to deterministic
approaches in the human health
risk assessment: an example
from a tribal stretch in central India
Rajkumar Herojeet
1
, Rakesh K. Dewangan 2, Pradeep K. Naik
2,3*
& Janak R. Verma 2
This case drew national attention in 2018. About 100 people died and more than 300 hospitalized
in a span of few years in a village of 1200 people in a tribal stretch in central India. Medical teams
visiting the area reported severe renal failure and blamed the local eating and drinking habits as
causative factors. This human health assessment based on geochemical investigations finds nitrate
(NO3−) and fluoride (F−) pollution as well in village’s groundwater. Both deterministic and probabilistic
techniques are employed to decipher the contamination pathways and extent of contamination.
Source apportionments of NO3− and F− and their relationship with other ions in groundwater are
carried out through chemometric modelling. Latent factors controlling the hydrogeochemistry of
groundwater too are explored. While hazard quotients (HQ) of the chemical parameters (HQNO−
3
and HQF−) identify ingestion as the prominent pathway, the calculated risk certainty levels (RCL) of
the hazard index (HI) values above unity are compared between the deterministic and probabilistic
approaches. Deterministic model overestimates the HI values and magnify the contamination
problems. Probabilistic model gives realistic results that stand at infants (HINO− = 34.03%,
3
HIF− = 24.17%) > children (HINO− = 23.01%, HIF− = 10.56%) > teens (HINO− = 13.17%, HIF− = 2.00%) > adults
3
3
(HINO− = 11.62%, HIF− = 1.25%). Geochemically, about 90% of the samples are controlled by rock3
water interaction with Ca2+–Mg2+–HCO3− (~ 56%) as the dominant hydrochemical facies. Chemometric
modelling confirms Ca2+, Mg2+, HCO3−, F−, and SO42− to originate from geogenic sources, Cl− and NO3−
from anthropogenic inputs and Na+ and K+ from mixed factors. The area needs treated groundwater
for human consumption.
Globally, consumption of nitrate (NO3−) and fluoride (F−) contaminated groundwater is a serious concern due
to their role in causing clinical diseases in humans1–5. Among the different inorganic forms of nitrogen (NO3−,
NO2− and NH4+) that exist in aquifers, NO3− concentrations are higher than those of NO2− and NH4+ due to their
high solubility and mobility rates as well as higher stable oxidative state in water6,7. Both NO2− and NH4+ are
easily oxidized and converted to NO3−; thus, they have lower contents in groundwater8. Anthropogenic sources
that contribute to excess NO3− in groundwater system are overuse of N-fertilizers, excreta from livestock farms,
municipal wastewater irrigation, runoff from urban and agricultural land, leaching from waste dumping sites
and discharge of untreated sewage and industrial effluents9–13.
The natural sources of NO3− in groundwater are the dissolution and oxidation of nitrogenous minerals in
the sedimentary and metasedimentary rocks. The bedrock nitrogen minerals, such as nitraline, nitre, suhalite
and tobelite, have three possible origins: organic matter, ammonium silicates and nitrate and ammonium salts14.
Dissolution of these sources release ammonium from their crystal lattices into the soil horizon, the chemical
form which can be easily assimilated by soil micro-organisms or get converted to NO3− through the nitrification
1
Department of Environmental Studies, Post Graduate Government College, Sector-11, Chandigarh 160011,
India. 2Central Ground Water Board, North Central Chhattisgarh Region, Ministry of Jal Shakti, Govt. of India, LK
Corporates Tower, Dumartarai, Dhamtari Road, Raipur 492015, India. 3Present address: Centre for Hydrological
Sciences and Communication, Bhubaneswar, India. *email: pradeep.naik@water.net.in
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process for nitrogen fixation by leguminous plants15–17. On the other hand, the weathering process of organic
nitrogen present in bedrocks are mineralized and converted to ammonium, which is readily used by the soil biota.
Recently, many workers highlighted the worldwide contamination of NO3− in groundwater and its adverse
effect on human health. Some locational examples are Loess Plateau, Northwest China18, Weining Plain, Northwest China2, Matanza-Riachuelo River Basin, Argentina19, Donsheng district, Inner Mongolia20, Catalan Region,
Spain21, Gorveh-Dehgelan, Western Iran22, Shanmuganadhi river basin, southern India23, Jalandhar district,
Punjab, India24, Panipat district, Haryana, India25, Nagpur, Western Maharashtra, India26, Gaya district, Bihar,
India8, Tiruppur, Tamil Nadu, India27.
Water with NO3− concentrations between 45 and 100 mg/L and above 100 mg/L are consumed daily for
drinking purposes by ~ 118 million and ~ 108 million people, respectively, in India28,29. The common and predominant effect of excess NO3− content (> 45 mg/L) in bottle-fed infants and children is Methemoglobinemia
disease24,30. Almasri concludes that the clinical symptom of methemoglobinemia is normally encountered as
body dehydration and gastrointestinal infections31. Further, the biochemical effects of NO3− occur in the human
body as follows: (a) NO3− is converted to NO2− under reducing conditions, (b) haemoglobin (Hb) combines with
NO2− to form methemoglobin, (c) the effect of methemoglobin reduces the oxygen supply in red blood cells and
drops the oxygen level in the body and (d) higher rate of methemoglobin formations (> 10%) leads to the blueish
colouration of the skin, known as a blue-baby syndrome (cyanosis)4,32. The prolonged exposure to high NO3− content in water has other health risks, such as multiple sclerosis, nitrosamines and non-hodgkin lymphoma33–35.
The fluoride deposition on the earth’s crust is approximately 0.32% and occurs mainly in rocks, such as
granites and gneisses. Both natural and anthropogenic inputs contribute to F− contamination in groundwater.
However, the higher concentrations of F− in groundwater is predominantly from geogenic sources and their
exposure is a threat to human health36. The geogenic sources include presence of fluoride-bearing minerals, such
as fluorite, amphiboles, topaz, apatite, fluorapatite, etc. in rocks, sediments and soils, evapotranspiration and
atmospheric deposition37–39. Prominent anthropogenic sources are the applications of pesticides and phosphatic
fertilizers, industrial effluents and landfills40–42.
Lower F− concentrations (< 0.5 mg/L) in drinking water cause dental carries and concentrations between 0.6
and 1.5 mg/L are essential for bone formation and development of skeleton and teeth in the human body4,24,43.
The long-term exposure to F− concentrations above the recommended guideline/permissible limit (1.5 mg/L)
may cause dental fluorosis, discoloration, pitting and mottling of teeth, skeleton fluorosis (4–8 mg/L), osteoporosis, arthritis, thyroid, rheumatic pain, kidney problem, muscle stiffness and abnormalities in red blood cells
(> 10 mg/L)38,44–47. Globally, at least 200 million individuals are affected by acute fluorosis in 28 different nations
due to the consumption of F− contaminated groundwater48. In India alone, ~ 25 million individuals are affected
by endemic fluorosis in 20 states besides ~ 66 million people in the risk of developing fluorosis, including ~ 6
million children below 14 years of age49–52. Mukherjee and Singh have made a detailed review of F− contamination in groundwater in different states of India53.
Supebeda, the study area of this contribution, is a small village situated in the border of Chhattisgarh and Odisha States in a tribal stretch in central India (Fig. 1). Groundwater is the primary source of water in this region.
In recent years, the local inhabitants have been facing numerous medical problems related to severe renal issues,
kidney diseases and fluorosis. As per the media reports, there have been more than 100 causalities till date due
to these diseases in recent years and around 300 villagers are battling for life. Thus, the study area has become
a hotspot to understand the real reason for the peoples’ health problems. Several research organizations, such
as the Indian Council of Medical Research, Geological Survey of India, Indira Gandhi Agricultural University,
Chhattisgarh State Public Health and Engineering Department, Pandit Ravishankar Shukla University, National
Institute Technology (Raipur), have already visited the village for investigation purposes. The research angles by
many of these organizations have been genetic genesis, food habits, consumption of spurious liquor and other
medicinal causes54–56. Presently, there is no literature available on the geochemistry of groundwater quality
and associated health hazard risks to the local population. Preliminary sampling suggested NO3− and F− contamination in groundwater57. The present investigation, therefore, is aimed at making a detailed appraisal of the
groundwater quality, non-carcinogenic health risk assessment in humans based on deterministic and probabilistic approaches, hydrochemical characterization, source apportionment of NO3− and F− through chemometric
techniques and their relationship with other ions in groundwater.
Materials and methods
Study area
The study area, village Supebeda, lies between North latitudes 19° 50′ and 19° 54′ and East longitudes 82° 38′ and
82° 42′ occupying a geographical area of 3 km2 in the administrative block of Deobhog in Gariyaband district of
Chhattisgarh State, India (Fig. 1). Situated on the bank of the river Tel, it borders the State of Odisha in the east.
With a population of about 1200 people, it has nearly equal male–female sex ratio and literacy rate of 50.51%.
The village has a Gram Panchayat (village council). The region is endowed with a sub-tropical monsoon climate
with three distinct seasons: the southwest monsoon starts from mid-June to September; the winter season spreads
from October to February and the summer season extends from March to mid-June. The average annual rainfall
is 1200 mm, and the temperature in winter varies from 5 to 25 °C and in summer from 29 °C to 46 °C.
Local geology
Gupta et al.58 and Neogi and Das59 have conducted detailed study on the geology of the area. As per this study,
there are three major lithological units in the area from east to west, i.e., (i) migmatiticquartzo feldspathic gneiss,
(ii) banded augen gneiss and (iii) hornblende granite (Fig. 2). Migmatiticquartzo feldspathic gneisses are greycolored, medium-grained rocks with finely laminated alternations of felsic (quartz + plagioclase + K-feldspar:
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Figure 1. Groundwater sampling around village Supebeda in Chhattisgarh State, India: The village borders the
state of Odisha on its east. Groundwater samples were collected from 27 locations marked by black dots. The
map was prepared based on MapInfo 8.5 (https://www.precisely.com/product/precisely-mapinfo/mapinfo-pro).
Qtz + Pl + Kfs) and mafic (Bt + Hbl-rich) bands. Leucocratic segregations are found extensively and are generally
stromatically folded into or parallel to the layering. There is occasional presence of orthopyroxenes in migmatite gneisses as greasy, green patches with diffuse margins (‘patchy charnockite’). Bands of migmatized mafic
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Figure 2. Geology and hydrogeology of the area around village Supebeda in Chhattisgarh State, India: The area
represents a metamorphic terrain with a complex geology58. The arrow marks show the groundwater flow in
different directions. Well drilled in the charnokite-khondalite complex are high-yielding with a yield potential of
3–5 L per second. The map was prepared based on MapInfo 8.5 (https://www.precisely.com/product/preciselymapinfo/mapinfo-pro).
granulites, metapelitic rocks (infrequently sapphirine-bearing) and rare calcsilicate granulites, besides isolated
appearance blastoporphyritic charnockite, occur congruently with the gneisses.
Banded augen gneisses are pink-colored, medium- to coarse-grained rocks. The bandings within them are
defined by mafic and felsic layers with K-feldspar (Kfs) augen and quartz lenticles. There is occasional occurrence
of leucosomes in a narrow zone closer to the migmatitic quartzofeldspathic gneiss unit with sharp abetment to the
west. The gneissic fabric generally precedes the leucosomes in banded augen gneiss. Hbl-rich and Pl + Cpx-rich
layers are hosted thinly within banded gneisses. Amphibolites (Hbl + Pl ± Grt ± Cpx) and calc-silicate gneisses
with these thin layers are mesoscopic to the regional scale bands.
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Intruding into the banded gneiss is the pink-colored, coarse-grained hornblende granite that consists of
microcline, quartz, hornblende, and biotite. With intense shearing and mylonitization along its eastern fringe,
it has poor presence further westward.
Hydrogeology
Groundwater occurs under unconfined condition in weathered portions of rocks and semi-confined to confined
conditions in their fractured parts, i.e., in charnockite and khondalite, at depth. The shallow aquifer occurs within
an average depth of 16 m. The configuration of water table in the shallow aquifer follows the topography due to
which the groundwater movement is generally toward valleys or topographic lows. The water bodies, such as
tanks, canals, and streams. also influence the occurrence and movement of groundwater in shallow aquifer. This
aquifer is developed mostly by dugwells in the area with their depth ranging between 7 and 16 m. In general,
the yield of dugwells ranges from 25 to 40 m3/day. Deeper aquifer in the area is formed mainly of granitic rocks
and is developed by borewells with a depth range of 50–80 m. In general, the yield of borewells ranges from 85
to 430 m3/day.
The groundwater flow is analyzed based on the water table elevation contours. In northern part of the study
area, groundwater flow is toward the south, i.e., the Tel River, while the flow is toward the north in the southern
part. The water table elevations in the study area range between 240 and 260 m above mean sea level with northern part having higher groundwater table elevation. Transmissivity ranges from 15 to 45 m2/day in charnockite
and khondalite and at favourable places it goes up to 100 m2/day. The potential fractures for boreholes up to
80 m depth are recorded at various depths, i.e., 40–45, 60–65, 75–80 m, and are 3–4 in numbers. Hydrogeology
of the study area is shown in Fig. 2.
Water sampling and analysis
Groundwater samples from twenty-seven locations were collected from the dugwells and borewells in and around
the Supebeda area during pre-monsoon season (May 2020) (Fig. 1). Plastic bottles (HDPE) of 1000 ml capacity
were used. These bottles were prewashed with HNO3 (10%) and rinsed with double deionised water. At the time
of sample collection, groundwater sources were flushed for 10–15 min to obtain a fresh solution by removing the
stagnant water in the pipe. The sampling bottles were thoroughly rinsed 2–3 times with the fresh groundwater
to be collected to preserve the original characteristics of the sampled water. Some basic parameters, such as pH,
electrical conductivity (EC) and total dissolved solids (TDS), were immediately measured onsite after the collection of groundwater samples using a pH/EC/TDS meter (Hanna HI 9811-5). Whatman filter paper (0.45 μm) was
used to remove the suspended particulate matter. The samples were preserved by acidifying (pH ~ 2 with HNO3)
and kept at a temperature of 4 °C. Standard protocol prescribed by the American Public Health Association was
followed for the investigation of major cations (Ca2+, Mg2+, Na+, and K+) and anions (HCO3−, Cl−, SO42−, F−,
and NO3−). Merck-GR grade chemicals and reagents were used to prepare the chemical solutions using double
deionized water. All the glassware and apparatus were soaked with 10% hydrochloric acid (HCl) for one day and
cleaned with double deionized water. Blank samples were prepared from the stock solutions of each parameter
for instrumental calibration. The accuracy of analysing datasets was computed using the charge balance error
(CBE) equation (Eq. 1), and each sample value was within its error limit of ± 5%60.
(Cations)meq/L − (Anions)meq/L
CBE% =
× 100
(1)
(Cations)meq/L + (Anions)meq/L
Human health risk assessment (HHRA)
Human health risk assessment (HHRA) is the quantitative risk analysis of potentially harmful chemical parameters present in water on human health through various pathways and specific time periods61,62. It has four
distinct steps: (1) hazard identification, (2) exposure assessment, (3) dose–response assessment and (4) risk
characterization4,63.
The significant pathways for risk analysis on human health from chemical exposure are ingestion and dermal
contact. In the present study, the average daily dose (ADD) of ingestion and dermal pathways for target chemicals,
namely NO3− and F−, are employed to determine the non-carcinogenic HHRA as shown in Eqs. (2) and (3)64.
The assessments of ADDingestion and ADDdermal are computed on four different age groups, i.e., infants (< 1 year),
children (1–11 years), teens (11–18 years) and adults (above 18 years). The adverse impact of the target parameters on human health may vary due to physiological and behavioural attributes, organ development factors and
tolerance responses to the specific chemicals in the human body.
CM × IRw × EFr × ED
BW × ATr
(2)
CM × SA × Kp × EFr × ED × ET × CF
BW × ATr
(3)
ADDingestion =
ADDdermal =
(The parameters/variables used in these equations are defined in Supplementary Table S1).
The ratio of the potential adverse non-carcinogenic risk from each exposure pathway (ingestion and dermal)
with respect to the corresponding reference dose of a chemical parameter is estimated through hazard quotient
(HQ)64, as shown in Eqs. (4)–(5). Hazard index (HI) is the combined non-carcinogenic hazard risks of a particular
parameter from all different possible exposure routes65,66. Both HI and HQ are unitless values. When HQ > 1, it
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is indicative of potential health effects from a specific exposure route67. Similarly, the value of HI > 1 depicts the
adverse non-carcinogenic toxicity in each target age group67.
HQingestion =
ADDingestion
RfDi
(4)
HQdermal =
ADDdermal
RfDd
(5)
HIM =
n
HQingestion +
i=1
n
HQdermal
(6)
i=1
where HIM indicates the total hazard index of a specific parameter, and i represents the exposure route of a specific subpopulation group considered in the present study (Eq. 6). The ingestion and dermal pathways reference
dose (RfD) values for NO3− and F− are 1.6 mg/kg per day and 0.06 mg/kg per day, respectively4,68–70.
Both deterministic and probabilistic approaches are applied to determine the potential non-carcinogenic
HHRA in the present study. The deterministic approach simply incorporates fixed values on the mathematical
formula developed by USEPA for different exposure pathways71. The point estimation results generate only a
single value that may underestimate or overestimate the risk analysis. Normally, the values of the variables of
the point estimation vary with respect to climatic conditions, place, time, chemical concentrations in water
and receptor types (i.e., body weight, exposure frequency and different subpopulation groups)72, but since the
uncertainty of the deterministic model considers only a fixed value for every input variable, this technique is a
conservative risk assessment approach.
Probabilistic technique, namely Monte Carlo Simulation (MCS), is an alternative statistical model that offers
a sound methodology and provides holistic information for risk assessment suggested by USEPA72. Monte Carlo
Simulation is a computer software application configuring a statistical distribution array in the form of probabilistic approximation of a mathematical equation to generate more corroborated reproducibility results and
reduces the uncertainty associated in risk analysis4. Oracle Crystal Ball software version (11.1.2.4.850) is used
for the MCS study. The operation of MCS requires prearrangement of input variables/parameters with respect to
their maximum, minimum, mean, and standard deviation (SD) values to define best-fitted statistical distribution
types to generate their probability distribution functions (PDFs)72. The input parameters, such as ingestion rate
(IRw), exposure frequency (EF), exposure duration (ED), expose skin surface area (SA), exposure time (ET) and
body weight (BW), generally have 10,000 repetitions for the computation of risks from oral ingestion and dermal
contact for each subpopulation group. Thus, the numerical stability of MCS is obtained at 10,000 permutations
for HQ and HI4,73,74. The sensitivity analysis is also employed to extract the significant input variables impacting
the outcome of a simulation model for potential risks.
In this work, the target parameters, i.e., NO3− and F−, are defined by the auto-select to determine the bestfitted probability distribution pattern based on their concentration values. Their goodness of fit (GoF) statistical
outcomes are presented in Table 1. The values and types of distribution of various input variables for ingestion
and dermal pathways for the deterministic and probabilistic models are provided in Supplementary Table S1.
Chemometric analysis
Chemometric statistical models, such as principal component analysis (PCA) and cluster analysis (CA),
are widely used by many researchers to distinguish among the probable sources of chemical parameters in
water11,75–78. Principal component analysis enables extraction of valuable information and better interpretation of
statistically significant parameters from large, complex datasets79. The present study uses z-scale standardization
of all chemical parameters to generate dimensionless values80–82. Varimax rotation method has been employed to
extract the principal components (PCs). The PCs with eigenvalues > 1 are statistically significant for interpreting
the hidden factors in water quality83,84.
Cluster analysis has been used to create similar groups from a different set of objects or variables85. Ward’s
linkage and squared Euclidean distance have been applied on z-transformation data to obtain different clusters86.
The cluster significance has been assessed using Sneath’s test method87. Minitab 17 and MS Office 2021 have been
employed to perform the statistical analysis.
Parameters
Distribution types
and their parameter
values
Anderson–Darling
test
Anderson–Darling
test (p value)
Kolmogorov–
Smirnov test
Kolmogorov–
Smirnov test (p value) Chi-square test
Chi-square test (p
value)
0.001
Premonsoon
Nitrate
Logistic (Mean = 34.25,
14,593
Scale = 24.04)
0.000
0.1940
0.000
Fluoride
Uniform (Min = 0.01,
Max = 1.97)
0.543
0.1815
0.250
0.5670
14.667
5.0370
0.081
Table 1. Best fitted and goodness of fit (GoF) outcomes of the probability distribution of Nitrate and Fluoride
in the groundwater around village Supebeda in Chhattisgarh State, India.
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Results and discussions
Table S2 lists the concentrations of various physicochemical parameters in analyzed groundwater samples. Table 2
gives the statistical description of physicochemical parameters [range, mean, and standard deviation (SD)] and
percentage of samples above the BIS46 and WHO45 standards. Water samples are neutral to slightly alkaline in
nature with the pH values ranging from 7.2 to 8.3 with a mean of 7.9 (mean ± SD = 7.9 ± 0.3). EC values show wide
variation from 313.0 to 3446.0 µS/cm with 11.11% samples above the guideline value of 1500 µS/cm45. High EC
values at some locations cause salinity due to excessive mineralization in groundwater. The water quality classification based on EC values88 indicates that 62.96% of the samples are moderately saline, 26.63% are medium
to highly saline and 7.41% are highly saline for irrigation purposes (Table S3). Further, classification by FAO89
shows that 7.41% samples are above the standard EC range (0–3000µS/cm) for irrigational use (Table 2).
Groundwater samples with TDS values above acceptable limit of 500 mg/L and permissible limit of 2000 mg/
L46 for drinking purposes are 22.22% and 11.11%, respectively, of all collected samples. These 11.11% of the
samples are above the normal range of 0–2000 mg/L89 for irrigational use as well. As per Davis and DeWiest90,
about 63% of the samples are within the desirable limit for drinking purposes and about 26% in between desirable
and permissible limit of 1000 mg/L (Table S3). Further, the TDS classification by Freeze and Cherry91 indicates
that majority of the groundwater samples (88.89%) falls under freshwater and the rest 11.11% under brackish
water category (Table S3).
Total hardness (TH) values vary from 65.0 to 755.0 mg/L with 33.33% and 11.11% samples above the acceptable (200 mg/L) and permissible limits (600 mg/L), respectively46. The elevated level of TH is primarily linked
with the excess concentrations of Ca2+, Mg2+ and HCO3− ions in groundwater11,76. Classification of groundwater
based on TH values by Sawyer and McCarty92 divulges that 3.70% of the samples are soft, 22.22% are moderately
hard, 44.44% are hard and 29.63% are very hard in nature (Table S3). Further, TDS versus TH plot depicts that
the groundwater is fresh to brackish water types with moderately hard to very hard in nature (Fig. S1). Sindhu
concludes that the prolonged consumption of very hard water is associated with calcification of arteries, urolithiasis, anencephaly, and gastrointestinal tract irritation93. Box-Whisker plot shows the relative abundance and
dominance of various cations (Ca2+ > Na+ > Mg2+ > K+) and anions (HCO3− > Cl− > NO3− > SO42− > F−) in groundwater (Fig. S2).
Major parameters
About 26.9% and 3.7% samples show Ca2+ contents above the acceptable limit of 75 mg/L and permissible limit
of 200 mg/L, respectively46. The Mg2+ mean ± SD is 21 ± 13.2 with 11.11% of samples above the acceptable limit
of 30 mg/L46 (Table 2). The alkali metals, i.e., Na+ and K+, are within their respective guideline values (200 mg/L
and 12 mg/L)45. HCO3− concentrations range from 85 to 519 mg/L with 3.7% of samples above the guideline
value of 500 mg/L45. Chloride (Cl−) concentrations vary from 7.1 to 408.3 mg/L, with 11.11% of samples above
the acceptable limit of 250 mg/L46. The excess concentrations of Ca2+, Mg2+, HCO3− and Cl− ions are the key
chemicals resulting hardness of groundwater24. The level of SO42− ions in groundwater is within the acceptable
limit of 200 mg/L46. The concentrations of cations (Ca2+, Mg2+, and Na+) and anions (HCO3−, Cl−, and SO42−)
are within their normal ranges for irrigational use (Table 2)89.
FAO89
Premonsoon
% of sample above BIS46
and WHO45 Standards
BIS46 standards
Parameter
AL
PL
Standards (usual range for irrigation use) Range
Mean ± SD
AL
PL
% of sample above FAO89 Standards
Physical parameters
pH
6.5–8.5
6.5–8.4
7.2–8.3
7.9 ± 0.3
NIL
EC
1500a
0–3000
313.0–3446.0
941 ± 795
11.11% (3)
NIL
TDS
500
2000
0–2000
200.32–2205.44
602.2 ± 509.0
22.22% (6)
11.11% (3)
11.11% (3)
TH
200
600
–
65.0–755.0
257 ± 178
33.33% (9)
11.11% (3)
–
Ca2+
75
200
0–400
20.0–214.0
67 ± 53
29.6% (8)
3.7% (1)
NIL
Mg2+
30
100
0–60
3.6–52.8
21 ± 13.2
11.11% (3)
NIL
NIL
Na+
200a
0–920
16.4–185.5
65 ± 43.9
NIL
NIL
K+
12a
–
0.6–11.4
2.2 ± 2.1
NIL
–
7.4% (2)
Major cations
Major anions
HCO3−
500a
0–610
85.0–519.0
297 ± 109
3.7% (1)
Cl−
250
1000
0–1063
7.1–408.3
73.8 ± 109.2
11.11% (3)
NIL
NIL
SO42−
200
400
0–960
4.8–105.5
29 ± 30.6
NIL
NIL
NIL
NO3−
45
0–45
0–128.3
39 ± 40
37.0% (10)
F−
1
0–20
0–1.9
0.9 ± 0.6
14.8% (4)
1.5
NIL
37.0% (10)
25.9% (7)
NIL
Table 2. Descriptive statistics of chemical parameters of groundwater samples collected from Supebeda,
district Gariyaband, Chhattisgarh, India. AL and PL stands for acceptable limits and permissible limits in the
absence of alternative source of water (BIS46). a Indicates parameters guideline values as per WHO45.
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Health risk parameters
Consumption of NO3− and F−-rich water causes various health risks in humans. The NO3− content in groundwater
in Supebeda exceeds its guideline value of 45 mg/L for drinking and irrigation purposes46,89 in 37.0% of samples
(Table 2). The classification of NO3− concentrations based on Adimalla43 signifies that 62.96% of the samples
have no risk (< 45 mg/L), 29.63% of samples have high risk (45–100 mg/L) and the remaining 7.41% of samples
have very high risk to human health (> 100 mg/L) (Table S4).
The range of F− concentrations varies from 0 to 1.9 mg/L with 14.8% and 25.9% of samples above the acceptable limit of 1.0 mg/L and permissible limit of 1.5 mg/L, respectively46. Although about 55.56% of samples have
the required F− level (0.6–1.5 mg/L) for human health, as per Adimalla43, 22.22% of samples may cause dental
caries (< 0.5 mg/L) and an equal percent dental fluorosis (1.6–2.0 mg/L) (Table S4).
Health risk assessment (HRA)
Table S5 provides the calculated average daily dose (ADD) values of NO3− and F− through ingestion and dermal
contact of groundwater using deterministic and probabilistic approaches for different age groups. Tables 3 and
4 show estimates of the non-carcinogenic HRA with respect to hazard quotient ( HQ ) and hazard index ( HI )
parameters, respectively. The deterministically calculated mean, median, 5th percentile (minimum) and 95th
percentile (maximum) values of HQingestion, HQdermal and HI for NO3− and F− are relatively more than those of
the probabilistically estimated values in all target population groups. The mean and 95th percentile of HQNO− for
3
ingestion pathway are above the acceptable limit (i.e., HQ > 1) in the deterministic study, which indicates that the
potential non-carcinogenic risk shall affect the larger sections in all target populations. On the other hand, the
probabilistically calculated HQNO− for ingestion pathway is above the threshold limit (i.e., HQ > 1) only at 95th
3
percentile, which shows that the clinical risk of non-carcinogenic effect is a concern to the sensitive sections of
people in all subpopulation groups at the extreme point (Table 3).
In deterministic estimate, the ingestion route of HQF− shows mean and 95th percentile above the safety
limit ( HQ > 1) only in infants, and rest of the population groups (Children, Teens, and Adults) have HQF− > 1
in 95th percentile. In probabilistic study, the threat of non-carcinogenic hazard divulges at the maximum point
( HQF− 95th percentile > 1) through ingestion pathway in the infants and children’s groups (Table 3). In dermal
contact, the deterministically and probabilistically calculated mean, median, 5th percentile and 95th percentile
values HQNO− and HQF− are less than the threshold limit (HQ < 1) in all target population groups. This indicates
3
that there is no potential non-carcinogenic health risk through dermal contact from the indicator parameters
(Table 3).
Risk certainty level (RCL) is assessed to generate the likelihood percentage scenarios of non-cancer hazard
quotient risk above the threshold value ( HQ > 1) in all individual datasets of a particular pathway. It is always
advantageous to determine the RCL value in HRA for any exposure pathway, even if the mean, 5th percentile
and 95th percentile values of different age groups are below their threshold limits. Among the target age groups,
the order of deterministic RCL ( HQ > 1) for NO3− and F− through the ingestion route is infants ( HQNO− =
3
51.85% and HQF− = 66.67%) > children ( HQNO− 48.15% and HQF− = 33.33%) > adults ( HQNO− = 40.74% and
3
3
Premonsoon
Age group
Deterministic value (ingestion)
Parameter Mean
Median
SD
5th
percentile
Risk
certainty
level
(RCL) %
Probabilistic value (ingestion)
Risk
certainty
level
(RCL) %
95th
percentile
HQ > 1
Mean
Median
SD
5th
percentile
95th
percentile HQ > 1
Infants
2.20E+00
1.22E+00
2.26E+00
1.69E−02
5.88E+00
51.85%
9.04E−01
5.27E−01
2.49E+00
− 7.45E−01 3.74E+00
34.02%
Children
1.43E+00
7.90E−01
1.46E+00
1.10E−02
3.81E+00
48.15%
5.47E−01
3.52E−01
1.45E+00
− 5.54E−01 2.24E+00
23.00%
1.03E+00
5.70E−01
1.06E+00
7.91E−03
2.75E+00
37.04%
3.84E−01
2.51E−01
1.01E+01
− 3.92E−01 1.54E+00
13.16%
Adults
1.11E+00
6.13E−01
1.14E+00
8.52E−03
2.96E+00
40.74%
3.25E−01
2.43E−01
9.26E−01
− 3.72E−01 1.45E+00
11.62%
Infants
1.41E+00
1.05E+00
8.84E−01
3.01E−01
2.82E+00
66.67%
7.32E−01
4.92E−01
1.54E+00
4.16E−02
2.23E+00
24.17%
9.14E−01
6.83E−01
5.73E−01
1.95E−01
1.82E+00
33.33%
4.68E−01
3.53E−01
8.95E−01
3.12E−02
1.30E+00
10.55%
6.59E−01
4.92E−01
4.13E−01
1.41E−01
1.31E+00
25.93%
2.86E−01
2.03E−01
5.53E−01
9.03E−03
8.36E−01
2.00%
7.09E−01
5.30E−01
4.44E−01
1.51E−01
1.42E+00
33.33%
2.76E−01
2.02E−01
5.27E−01
9.08E−03
7.85E−01
HQ > 1
Probabilistic value (dermal)
Teens
Children
Teens
HQNO−
3
HQF −
Adults
Parameter Deterministic value (dermal)
1.25%
HQ > 1
Infants
6.65E−03
3.68E−03
6.81E−03
5.11E−05
1.77E−02
NIL
4.09E−04
2.63E−04
1.06E−03
− 3.64E−04 1.63E−03
NIL
Children
4.99E−03
2.76E−03
5.11E−03
3.83E−05
1.33E−02
NIL
4.54E−04
2.94E−04
1.19E−03
− 3.96E−04 1.77E−03
NIL
4.73E−03
2.62E−03
4.85E−03
3.64E−05
1.26E−02
NIL
3.54E−04
2.38E−04
9.20E−04
− 3.22E−04 1.40E−03
NIL
Adults
5.71E−03
3.16E−03
5.85E−03
4.39E−05
1.52E−02
NIL
3.16E−04
2.13E−04
8.22E−04
− 3.21E−04 1.26E−03
NIL
Infants
4.25E−03
3.18E−03
2.66E−03
9.08E−04
8.89E−03
NIL
3.30E−04
2.46E−04
6.34E−04
2.38E−05
9.15E−04
NIL
3.19E−03
2.38E−03
2.00E−03
6.81E−04
6.37E−03
NIL
3.63E−04
2.66E−04
7.04E−04
2.42E−05
1.02E−03
NIL
3.03E−03
2.26E−03
1.90E−03
6.46E−04
6.04E−03
NIL
2.64E−04
1.94E−04
5.03E−04
9.71E−06
7.51E−04
NIL
3.65E−03
2.73E−03
2.29E−03
7.80E−04
7.29E−03
NIL
2.41E−04
1.79E−04
4.62E−04
7.47E−06
6.87E−04
NIL
Teens
Children
Teens
HQNO−
3
HQF −
Adults
Table 3. Statistical description of deterministically and probabilistically calculated hazard quotient (HQ) for
ingestion and dermal pathways in different age groups.
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Deterministic value
95th
percentile
HI > 1
9.05E−01 5.27E−01 2.49E+00 − 7.45E−01
3.74E+00
34.03
48.15%
5.48E−01 3.35E−01 1.45E+00 − 5.55E−01
2.24E+00
23.01
40.74%
3.84E−01 2.51E−01 1.02E+00 − 3.92E01
1.54E+00
13.17
2.97E+00
40.74%
3.52E−01 2.43E−01 9.26E−01 − 3.72E−01
1.45E+00
11.62
1.41E+00 1.06E+00 8.87E−01 3.02E−01
2.82E+00
66.67%
7.32E−01 4.92E−01 1.54E+00 4.18E−02
2.23E+00
24.17
9.17E−01 6.85E−01 5.75E−01 1.96E−01
1.83E+00
37.04%
4.68E−01 3.53E−01 8.95E−01 3.15E−02
1.30E+00
10.56
6.62E−01 4.94E−01 4.15E−01 1.41E−01
1.32E+00
25.23%
2.86E−01 2,04E−01 5.53E−01 9.34E−03
8.36E−01
2.00
7.13E−01 5.33E−01 4.47E−01 1.52E−01
1.42E+00
33.33%
2.76E−01 2.02E−01 5.27E−01 9.34E−03
7.85E−01
1.25
95th
percentile
HI > 1
Mean
2.21E+00 1.22E+00 2.27E+00 1.70E−02
5.90E+00
51.85%
1.43E+00 7.93E+00 1.47E+00 1.10E−02
3.83E+00
1.03E+00 5.72E−01 1.06E+00 7.95E−03
2.76E+00
Adults
1.11E+00 6.16E−01 1.14E+00 8.56E−03
Infants
Age
group
Pathways
(ingestion + dermal)
Infants
Children
Teens
Children
Teens
HINO−
3
HIF−
Adults
Risk
certainty
level
(RCL)
(%)
Risk
certainty
level
(RCL)
(%)
Probabilistic value
Mean
Median
5th
percentile
SD
Median
SD
5th
percentile
Table 4. Statistical description of deterministically and probabilistically calculated hazard index (HI) for
ingestion and dermal pathways in different age groups.
HQF− = 33.33%) > teens (HQNO− = 37.04% and HQF− = 25.93%) (Table 3). Similar findings of NO3− and F− non-car3
cinogenic health risk for groundwater ingestion pathways are found in Jiangcungou, Northwest China (i.e., chil1
dren > adults > teenagers) and Nalagarh valley, Himachal Pradesh, India (i.e., infants > children > adults > teenagers)4. On the other hand, the probabilistic RCL ( HQ > 1) orders for NO3− and F− through ingestion pathway
are infants ( HQNO− = 34.02% and HQF− = 24.17%) > children ( HQNO− = 23.00% and HQF− = 10.55%) > teens
3
3
( HQNO− = 13.16% and HQF− = 2.00%) > adults ( HQNO− = 11.62% and HQF− = 1.25%) (Table 3).
3
3
The deterministic and probabilistic RCLs ( HQ > 1) indicate trivial non-carcinogenic risks from the indicator parameters (NO3− and F−) through the dermal route. Therefore, the perusal of Table 3 shows that NO3− and
F− exposure through direct groundwater consumption has higher non-carcinogenic HQ by several orders of
magnitude than that of the dermal route in all age groups. Liu get similar findings of non-cancerous health risks
from the groundwater of Weining plain, China72. Further, among the indicator parameters, the mean, median
and 95th percentile values of HQNO− are more than those of HQF− through the groundwater ingestion pathway
3
within each stratified age group in both deterministic and probabilistic approaches (Table 3).
Hazard index (HI)
The non-carcinogenic HI is the combination of non-carcinogenic hazard quotient risk factors of each indicator
parameter (NO3− or F−) through multi-exposure pathways (ingestion and dermal) of groundwater, as shown in
Table 4. The mean, median and 95th percentile values of infants and children in the deterministic result exceed
the safety reference level of HINO− > 1, divulging prominent threat level of non-carcinogenic HHR from NO3− in
3
these age groups. The remaining population groups (teens and adults) in deterministic study and all the target
population groups in probabilistic estimate reveal the non-carcinogenic risk of NO3− at 95th percentile values
only ( HINO− > 1).
3
With respect to F−, in the deterministic study, the potential non-cancerous effect is prominent in infants
since the mean, median and 95th percentile values are above the safe reference limit (i.e., HIF− > 1), but the rest
of the subpopulation groups show HIF− > 1 in 95th percentile only, which shows that the threat of health risk is
still persistent in the sensitive sections of the stratified age groups at the extreme value. On the other hand, in
the probabilistic estimate, the HIF− results indicate that the infants and children’s groups are at the risk of noncarcinogenic effect at 95th percentile values, i.e., HIF− > 1.
Accordingly, in the deterministic output, the RCL magnitude of non-carcinogenic HINO− risk stands at infants
3
(51.85%) > children (48.15%) > teens (40.74%) = adults (40.74%), and that of HIF− at infants (66.67%) > children (37.04%) > adults (33.33%) > teens (25.23%) (Table 4). The probabilistically calculated RCL health risks
in the subpopulation groups are in the following order: infants ( HINO− = 34.03% and HIF− = 24.17%) > children
3
( HINO− = 23.01% and HIF− = 10.56%) > teens ( HINO− = 13.17% and HIF− = 2.00%) > adults ( HINO− = 11.62% and
3
3
3
HIF− = 1.25%).
The deterministic RCL for HI is more than the probabilistic RCL in all age groups divulging that the deterministic estimation is based on the extreme (single point) values (please see Table S1, fifth column) for all input
variables individually at different concentration levels of the indicator parameters. Since these extreme (single
point) values may not always represent the actual field conditions, the deterministic estimates often lead to
overestimation of the output results (Table 4). Therefore, the deterministic approach cannot cater to the holistic
scenario of risk assessment for the inclusive members of the population interests due to differences in personto-person characteristics and dynamism prevailing in the environment.
The probabilistic approach gives a range of values to choose from depending on the most likelihood field
conditions (please see Table S1, eighth column). Therefore, the probability approximation of events reduces
the uncertainties by providing more accurate and prospective risk assessment outcomes than those of the conventional deterministic approach. Liu72 too conclude that the health risk assessment in groundwater through
probabilistic simulation provides more comprehensive results.
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The present study, however, suggests that the HRA of the indicator parameters should be studied using both
deterministic and probabilistic approaches mutually to obtain more holistic outputs, thereby reducing the uncertainties and overcoming the conservative risk analysis of the point estimation. In a similar line, Kaur25 conclude
that the deterministic and probabilistic methods may be studied independently to assess non-carcinogenic
HHRA (NO3− and F−) in groundwater.
Sensitivity and uncertainty analysis
Deterministic technique does not provide any provision for sensitivity and uncertainty analysis. Therefore,
sensitivity analysis has been carried out in the probabilistic process of working using the Monte Carlo Simulation (MCS) approach to extract the most influential input variables for the non-carcinogenic risk prediction.
Figure 3a,b represents the tornado plots showing the percentage scales of all input variables for non-carcinogenic
HINO− and HIF− in the stratified age groups. The sensitivity analysis validates that the variables of dermal route
3
are not vividly influenced in the overall contribution of non-carcinogenic HI in all subpopulation groups and
that the input variables of the ingestion pathway have more potential non-carcinogenic health effects than those
of the dermal contact. The HQ results are further supported and validated by the sensitivity analysis of tornado
plots. For HINO− sensitivity output, the parameter concentration (CM ingestion) is the most influential variable fol3
lowed by exposure duration (EDingestion) with minor contributions from ingestion rate (IRingestion) and exposure
frequency (EFingestion) in all target populations. It indicates that higher NO3− content in ingested water will have
more health implications, but as per Carlsson94, 60–70% of the intake NO3− dose is generally excreted within
the first 23 h in urine. Therefore, possibly the clinical NO3− toxicity in humans is less significant because of the
limited exposure duration of NO3− intake dose in the body.
The results of sensitivity analysis HIF− for infants and children stand in the order of
EDingestion > CMingestion > IRingestion > EFingestion. In infants and children, 80% of the oral F− intake is absorbed in the
body with storage in the bones and95. Thus, exposure duration is the most significant input variable due to high
retention of F− intake dose in infants and children. The tornado HIF− plots for teens and adults show the percentage of contribution variables as CM ingestion > EDingestion > IRingestion > EFingestion. For teens and adults, ~ 50% of an orally
ingested F− is retained in the body95,96. Thus, the lower retention potential of F− dose in teens and adults compared
to that in infants and children indicates that the parameter concentration is the main driving force for fluoride
toxicity in the sensitivity outputs. The body weight (BWingestion) variable negatively infers non-carcinogenic HINO−
3
and HIF− simulations in all age groups (Fig. 3a,b).
Uncertainty analysis is crucial in determining the conservatism, ramification, and certainty accuracy level of
the risk analysis results97. In this study, the application of MCS is notably enhanced to identify and quantify the
uncertainties in the non-cancer HRA. Nevertheless, there are still other uncertainties that remain unaccounted
in the model input variables, thereby limiting the validity of the whole scenario study. For example, (i) the daily
water intake and dermal contact of target population groups are not measured during the groundwater sampling,
(ii) body weights of the local population are not evaluated (instead, the representative data of the Indian Council
of Medical Research (ICMR) and USEPA are used), (iii) average time, dermal permeability and conversion factor
are considered as the same, fixed or similar values for deterministic and probabilistic approaches for different age
groups, (iv) the variables data to generate the probability distribution functions (PDFs) using MCS are acquired
from the USEPA and other relevant published literatures, (v) assumption that the concentrations of specific
chemical parameters in groundwater are fully bio-absorbed in the human body may lead to ambiguity in risk
analysis, and finally (vi) the reference doses (RfD) for ingestion and dermal exposures are obtained from USEPA.
Hydrogeochemical processes
Gibbs diagram is applied to elucidate the mechanism controlling groundwater chemistry in the study area98.
This diagram enables understanding of the relationship between cation ratio [Na+/(Na+ + Ca2+)] or anion ratio
[Cl−/(Cl− + HCO3−)] versus TDS, thereby defining three distinct areas, namely evaporation, rock-water and
precipitation zones (Fig. 4) that depicts that majority of the groundwater samples (88.89%) are clustered in the
rock dominance zone and the remaining samples (11.11%) fall in the evaporation zone.
Groundwater chemistry is primarily influenced by various geochemical processes, especially the interaction
of percolating water with subsurface rocks and the chemical solute exchange processes of aquifer minerals in the
study area. Many researchers conclude that the elevated concentrations of F− in groundwater are proportionately
related to rock-water interaction99–102. Besides the rock weathering processes, climatic factors too play a critical role in regulating the evaporation in the semiarid region26. The scattering of samples in the Gibbs diagram
signifies the impact of anthropogenic inputs in the aquifer systems. The role of the evaporation factor enhances
the groundwater salinity by elevating the Na+ and Cl− ions, resulting in the higher TDS concentrations, which
are further abetted by anthropogenic activities103.
Piper diagram is a widely used graphical interpolation to characterize the hydrochemical interaction,
water genesis and groundwater contamination sources29,104,105. Figure 5 depicts that the groundwater is predominantly dominated by alkaline earths over the alkalies and weak acids over the strong acids. This is represented by three hydrochemical facies, namely Ca2+–Mg2+–HCO3− (55.56%), Ca2+–Mg2+–Cl−–SO42− (29.63%)
and Na+–K+–HCO3− (14.81%). Also, the groundwater samples are further classified into four water types, i.e.,
Ca2+–HCO3− (55.56%), Ca2+–Cl− (7.40%), Ca2+–Mg2+–Cl− (22.22%) and Ca2+–Na+–HCO3− (14.81%). The highest
percentage of Ca2+–HCO3− water type indicates dissolution of carbonate minerals with percolating water from
irrigation runoff and precipitation in the subsurface aquifers11,76. The cations triangle shows that majority of the
samples (70.37%) belong to no-dominant zone, and the remaining samples of 11.11%, 14.82% and 3.70% represent water types in Ca2+, Na+ and Mg2+ dominated zones, respectively. In the anions triangle, around 70.37% samples fall in HCO3− water type, which indicates weathering of carbonates and silicates minerals and ion exchange
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Figure 3. Tornado plots illustrating sensitivity analysis of input variables to the non-carcinogenic hazard index
(HI) of groundwater: (a) NO3− and F− ingestion and (b) for dermal contact: This sensitivity analysis figure is
drawn by the probabilistic approach using the Monte Carlo Simulation (MCS) technique to extract the most
influential input variables for the non-carcinogenic risk prediction for human health. The length of horizontal
bars indicates the percentage contribution of various input variables to extract the non-carcinogenic hazard
index (HI) of different age groups.
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Figure 4. Gibbs diagram representing the factors controlling groundwater chemistry: This diagram enables to
understand the relationship between cation ratio [Na+/(Na+ + Ca2+)] or anion ratio [Cl−/(Cl− + HCO3−)] versus
TDS defining three distinct areas, namely evaporation, rock-water and precipitation zones to elucidate the
dominant mechanism influencing the groundwater chemistry of the study area.
Figure 5. Piper diagram illustrating hydrochemical facies and water types: This graphical interpolation enables
characterization of the hydrochemical interaction, genesis of water and groundwater contamination sources.
Black arrows signify the conversion of water types due to anthropogenic and geogenic factors.
processes in the groundwater106. Approximately 22.22% of the samples belonging to Cl− water type depict the role
of anthropogenic factors and dissolution of evaporities in the groundwater26. The transformation of water types
from Ca2+–HCO3− to Ca2+–Cl− and Ca2+–Mg2+–Cl− types divulges the adverse impacts of human activities and
applications of N-chemicals on cultivated lands, thereby elevating the NO3− concentrations in groundwater107,108.
Further, the conversion of water from Ca2+–HCO3− to Ca2+Mg2+–Cl− and Ca2+–Na+–HCO3− types is due to the
dissolution of fluorite minerals (CaF2) and cation exchange between Ca2+ and Na+109,110. Subba Rao suggests that
the weathering of rocks, higher Na+ and HCO3− (or NaHCO3) and alkaline nature of water favour the gradual
increase of F− concentrations in groundwater100,111.
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Source apportionment and geochemical relationships of NO3− and F− with other parameters
Many workers have studied the relationship of nitrate and fluoride with specific parameters through scatter plots.
For example, for nitrate: NO3− versus pH76, NO3− versus Cl−17, NO3− versus K+, NO3− versus Ca2+, NO3− versus
SO42−, NO3− versus Cl−112, NO3− versus EC, NO3− versus Cl−, NO3− versus K+, NO3− versus SO42−, NO3− versus Na+,
NO3− versus Ca2+, NO3− versus Mg2+, NO3− versus HCO3−27, and for fluoride: F versus pH, F− versus HCO3−76,
F− versus HCO3−, F− versus Na+, F− versus NO3−102, F− versus pH, F− versus Ca2+113, F− versus pH, F− versus Na+,
F− versus K+, F− versus HCO3−, F− versus Ca2+114. However, these studies have not evaluated NO3− and F− holistically for their geochemical relationships with physical parameters and major cations and anions and also their
source apportionment with site-specific datasets available. The present study is unique in the sense that it uses
scatter plots to correlate NO3− and F− with other physicochemical parameters independently (pH, EC, TH, Ca2+,
Mg2+, Na+, K+, Cl−, HCO3−, SO42−, and F− versus NO3) to achieve these objectives.
Source apportionment and geochemical relationship of NO3− with other parameters
A strong inverse correlation between NO3− and pH (r2 = 0.688 and y = − 0.0061x + 8.0993) indicates decreasing
pH values with increasing NO3− concentrations (Fig. 6a). Dadgar and Payandeh115 too report this relationship
in Tabriz province, Iran. The oxidation of dissolved CO2 in groundwater forms carbonic acid and readily dissociates into H+ and HCO3− ions is an intensive process24. Further, NO3− ions rapidly react with free H+ ions to
form HNO3 resulting in acidic conditions at higher NO3− concentrations (Eq. 7).
CO2 + H2 O → H2 CO3 (Carbonic acid)
H2 CO3 → H+ + HCO−
(7)
3
−
+
NO3 + H ↔ HNO3 (Nitric acid)
The scatter plot of NO3− versus EC shows a positive correlation (r2 = 0.5185), divulging higher mineralization of dissolved substances, including excess NO3− concentrations in groundwater (Fig. 6b). The samples with
NO3− contents above the guideline value of 45 mg/L46 have higher EC in groundwater. Such a relationship is
often associated with anthropogenic inputs, such as agricultural runoff, domestic sewage, poultry farming and
unplanned urbanization, which release an enormous quantity of organic nitrogen and ammonia76,116–118. Ammonia is affectively absorbed in the soil particles that restrict its movement. During the limited aerobic condition in
the soil, the nitrification process converts the immobilized ammonia into nitrate by bacterial activities, as shown
in Eq. (8). Anthropogenic inputs accelerate the nitrification process that enhances easy leaching of NO3− from
the soil in the percolating water recharging the aquifers.
+
2NH3 + 3O2 → 2NO−
2 +2H + 2H2 O
(Ammonia)
(Nitrite)
(8)
2NO2 + O2 → 2NO3− (Nitrate)
The possible mineral source contributing Ca2+ and Mg2+ in the groundwater is determined by Ca2+/Mg2+
ratio119. Figure 6d,e depicts the positive relationship of NO3− with Ca2+ (r2 = 0.6998) and Mg2+ (r2 = 0.5672),
which indicates cation exchange processes in the groundwater due to prolonged application of N-fertilizers for
crop production120. This cation exchange process significantly enhances the mineralization of Ca2+ and Mg2+ and
elevates NO3− concentrations. Also, the nitrification process increases the NO3− level and acidity in groundwater
resulting in Ca2+ and Mg2+ enrichment by the dissolution of carbonate minerals76,121, as illustrated in Fig. S3a,
i.e., 14.8% samples by dolomite and 48.2% by calcite in our study area. The remaining 37% samples have Ca2+/
Mg2+ ratio values > 2 depicting the influence of silicate weathering in groundwater122. The study area is a metamorphic terrain with a rich deposition of calcsilicate, hornblende, quartz and biotite57. Therefore, Ca2+ and Mg2+
concentrations in groundwater are influenced by carbonate and silicate rock-water interaction as expressed in
Eqs. (9)–(12)24,123–125.
CaCO3 + H2 CO3 ↔ Ca2+ + 2HCO−
3 (calcite dissolution)
(9)
CaMg(CO3 )2 + 2H+ ↔ CaCO3 + Mg2+ + H2 CO3 (dolomite dissolution)
(10)
CaSO4 + CaMgCO3 + 6H+ ↔ CaCO3 + Ca2+ + Mg2+ + SO2−
4 + H2 CO3 Anhydrite and dolomite dissolution
(11)
+
+
Na, K, Ca, Mg silicate + H2 CO3 → Na + K + Ca
−
2+
2+
+ Mg
2+
+ H4 SiO4 + HCO−
3
+ Clay
(12)
2+
Since NO3 has a strong positive loading with Ca and Mg , it exhibits a significant positive correlation
with TH (r2 = 0.7247) (Fig. 6c). Water hardness is attributed to the elevated concentrations of dissolved alkaline
earth elements (Ca2+ and Mg2+) in the aquifer system79. The scatter plots of NO3− with Na+ (r2 = 0.085) (Fig. 6f)
and K+ (r2 = 0.0627) (Fig. 6g) signify a very weak positive relationship and suggest that the anthropogenic inputs
are not the only primary source of alkali ions contents in groundwater. The bivariate plot of Na+ + K+ versus TZ+
(Fig. S3b) depicts that the entire groundwater samples fall below the 1:1 aquiline. This indicates the weathering effect of silicate minerals besides the anthropogenic impacts, such as the application of NPK fertilizers and
discharge of untreated sewerage water on the open ground, which elevate the Na+ and K+ concentrations in
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Figure 6. Scatter plot correlations between NO3− and (a) pH, (b) EC, (c) TH, (d) Ca2+, (e) Mg2+, (f) Na+, (g)
K+, (h) HCO3−, (i) Cl−, (j) SO42− in groundwater samples: Each plot signifies the relationship of NO3− with a
particular physicochemical parameter to understand their geochemical interaction. Source apportionment of
NO3− is carried out with the help of such interactions.
groundwater11,27,126,127. In the study area, albite, microcline and alunite dissolution are the key sources of Na+
and K+ ions through rock-water interactions, as shown in Eqs. (13)–(15).
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2NaAlSi3 O8 +2CO2 + 11H2 O → Al2 Si2 O5 (OH)4 +4H4 SiO4 + 2Na+ + 2HCO−
3
(Albite)
(Kaolinite)
2KAlSi3 O8 +2CO2 + 11H2 O → Al2 Si2 O5 (OH)4 +4H4 SiO4 + 2K+ + 2HCO−
3
(Microcline)
(Kaolinite)
2−
+
KAl3 (SO4 )2 (OH)6 +3CO2 + H2 O → 3Al(OH)3 +3HCO−
3 + K + 2SO4
(Alunite)
(Gibbsite)
(13)
(14)
(15)
The scatter plot of NO3− versus HCO3− shows the least positive loading (r2 = 0.012) among the anions
(Fig. 6h). This relationship suggests that the HCO3− does not exhibit much variation with increasing or decreasing NO3− concentrations. The fact that HCO3− ions are the dominant anions in the groundwater samples confirms
that its primary source is possibly carbonate and silicate weathering26,76,122,128, as shown in Eqs. (9)–(12).
In NO3− versus Cl− plot (Fig. 6i), their positive correlation (r2 = 0.5943) implies a common source, such as
a combination of oxidation of animal and human waste44, application of manure and nitrogenous fertilizers129,
septic tank seepages130, agricultural runoff131, etc. Similar findings are reported in the semiarid regions of many
Indian States, such as Punjab24, Rajasthan132, Andhra Pradesh133, and Telangana134.
Figure 6j depicts the weak positive loading between NO3− and SO42− (r2 = 0.1622) due to two separate sets of
NO3− and SO42− concentrations in the groundwater samples. The samples having low or high NO3− levels have
both low and high SO4− concentrations, thus neglecting the influence of the anthropogenic activities on SO42−.
The plot of Ca2+ versus SO42− (Fig. S3c) is meant to identify the minerals that contribute to higher amount of
Ca2+ and SO42− ions in groundwater76. Majority of the samples (92.6%) are below the equiline (1:1), indicating
that the role of gypsum (CaSO4·2H2O) dissolution is insignificant. The remaining samples (7.4%) falling along the
equiline depict the dissolution of anhydrite (CaSO4) mineral in the groundwater135,136. The gypsum precipitation
in the groundwater occurs through direct hydration of anhydrite and dissolution of calcium-bearing minerals
oxidized with sulphate and hydronium ions137, as expressed in Eqs. (16) and (17). Hence, the weak positive correlation between Ca2+ and SO42− (r2 = 0.197) (Fig. S3c) suggests that the limited concentrations of Ca2+ ions in
the groundwater may be due to the precipitation of gypsum138. If the study area lacks gypsum mineral, then the
biologically oxidized sulphur containing compounds deposited by the rainwater and nitrogen compounds in the
soil leach down to groundwater as SO42− and NO3− ions139. Thus, the positive regression line between NO3− and
SO42− (y = 0.3084x + 16.957) (Fig. 6j) is found in the groundwater samples of the study area. Karunanidhi27 report
similar findings on the positive relationship between NO3− and SO42− in the groundwater samples of Tiruppur
region, India. Moreover, the dissolution of alunite [KAl3(SO4)2(OH)6], as expressed in Eq. (15), will also contribute to the SO42− ions in groundwater.
CaSO4 + 2H2 O → CaSO4 · 2H2 O
(16)
CaCO3 + 2H+ + SO2−
4 + H2 O → CaSO4 · 2H2 O + CO2
(17)
Source apportionment and geochemical relationship of F− with other parameters
Normally, high pH in groundwater depicts its alkaline nature, resulting in elevated concentrations of HCO3− and
high hydroxyl (OH−) ions (Eq. 18, Tables 2 and S2, Fig. S2). A fairly positive relationship between pH and
F− (r2 = 0.2607; Fig. 7a) indicates that the alkaline water favours dissolution and mobilization of F− bearing minerals in groundwater140. The weathering processes of fluoride-bearing rocks to replace F− ions with OH− ions
in the lattices of different minerals, namely muscovite, biotite, amphibole, and hornblende, has enriched the
F− concentrations in groundwater. Xiao141 and Karunanidhi142 express the displacement mechanism of F− ions
by OH− ions in the muscovite, biotite, and hornblende minerals as follows (Eqs. 19–21).
−
HCO−
3 + H2 O = H2 CO3 + OH
(18)
KAl2 [AlSi3 O10 ]F2 + 2OH− = KAl2 [AlSi3 O10 ](OH)2 + 2F−
(19)
KMg3 [AlSi3 O10 ]F2 + 2OH− = KMg3 [AlSi3 O10 ](OH)2 + 2F−
(20)
NaCa2 (Mg, Fe, Al)5 (Al, Si)8 O22 F2 + 2OH− → NaCa2 (Mg, Fe, Al)5 (Al, Si)8 O22 (OH)2 + 2F−
(21)
Figure 7b shows a negative trend between the EC and F− (r2 = 0.0118; y = -147.3x + 1079), indicating no major
influence of EC on F− ion concentrations. A rather weak, but negative relationship of F− with Ca2+ (r2 = 0.1262,
y = -31.957x + 97.575) and Mg2+ (r2 = 0.0922, y = -6.8172x + 27.41) indicates decreasing concentrations of Ca2+
and Mg2+ ions in groundwater with increasing F− content (Fig. 7d,e). Various workers report similar findings
between F− versus Ca2+ elsewhere53,110. The excess concentrations of HCO3− with high pH contribute to the
alkaline water, thus favouring the dissolution of fluorite (CaF2) in groundwater due to precipitation of CaCO3143,
as shown in Eq. (22).
−
CaF2 + 2HCO−
3 = CaCO3 + 2F + H2 O + CO2
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Figure 7. Scatter plot correlations between F− and (a) pH, (b) EC, (c) TH, (d) Ca2+, (e) Mg2+, (f) Na+, (g) K+,
(h) HCO3−, (i) Cl−, (j) NO3−, (k) SO42− in groundwater samples: Each plot signifies the relationship of F− with a
particular physicochemical parameter to understand their geochemical interaction. Source apportionment of
F− is carried out with the help of such interactions.
Jack suggests that the rock-water interaction of fluoride-bearing minerals from a recharge area through the
facture zone would precipitate the Ca2+ and Mg2+ ions as calcite, Mg-Calcite dolomite, and dolomite fluorite,
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respectively, along the groundwater flow path to a discharge area144. Hem states that because Ca2+ and Mg2+ ions
are divalent cations with similar properties, they possess the same stability with other ion pairs (SO42−, CO32− and
HCO3−) and contribute similarly to water hardness145. Thus, the inverse relationship between TH and F− (Fig. 7c)
is due to decreased Ca2+ and Mg2+ ion concentrations or precipitation of calcium carbonate and Mg-calcite
dolomite causing enhanced solubility of fluoride-bearing minerals in the study area139,146.
While examining the role of Na+, it is found that the Na+/Ca2+ ratio helps in understanding the probable
reason for lowering of Ca2+ activity in groundwater144. Around 37% of water samples have Na+/Ca2+ ratio > 1,
indicating that evapotranspiration is possibly affecting the Ca2+ activity by precipitating it and increasing the Na+
concentrations, thus favouring the enrichment of F− content in groundwater (Fig. S3d). The study area is a semiarid region characterized by drier climatic conditions where the dissolved constituents are readily concentrated
and precipitated by evaporation, thereby leading to groundwater salinity147,148. The remaining 63% groundwater
samples show Na+/Ca2+ ratio < 1, which depicts that rock-water interaction is another key contributing factor of
generation of Ca2+ and F− ions due to the dissolution of fluorite minerals in the groundwater. However, Ca2+ ions
subsequently react with NaHCO3 to form CaCO3 precipitation (Eq. 23). In a similar line, Arveti99 report that high
F− content in groundwater is directly related to the dissolution of fluoride enriched minerals due to prolonged
residence time of water due to physiographic conditions or low hydraulic conductivity in aquifers providing a
longer contact period. The plot Na+ versus F− (r2 = 0.1475) with a positive slope (y = 28.723x + 38.045) indicates
gradual increase of F− concentrations with elevated Na+ content in groundwater (Fig. 7f). The higher concentrations of NaHCO3 or Na+ ions with alkaline pH in groundwater allows dissolution of F− ions from fluorite (CaF2)
through rock–water interaction143,149 (Eq. 23).
CaF2 + 2NaHCO3 = CaCO3 + 2Na+ + 2F− + H2 O + CO2
+
(23)
−
In Fig. 7g, the plot K versus F divulges that there is no significant positive or negative relationship between
them. The flat linear regression (r2 = 0.0001) indicates that the K+ does not have much influence on the fluoride
mineralization in groundwater. The orthoclase feldspar (KAlSiO3O8) is generally resistant to attack by water,
but apparently gets altered to silica, clay, and K+ ions145. In the study area, rapid precipitation of alunite occurs
in the aquifers due to the high degree of stability of potassium-bearing alumino-silicate minerals resulting in
low content of K+ in groundwater.
The positive trend between HCO3− and F− (r2 = 0.1108; y = 61.992x + 239.04) divulges that the increase in
HCO3− content supports the dissolution of F− bearing minerals in groundwater (Fig. 7h). However, few samples have low HCO3− concentrations with high F− values which indicates that the F− enrichment in groundwater is affected by a combination of processes, such as evapotranspiration and calcite precipitation150. The
HCO3−/Ca2+ratio predicts the likelihood of F− enrichment in groundwater140. About 85% of samples show
HCO3−/Ca2+ratio > 1 (Fig. S3e), signifying that groundwater hydrological conditions are still favourable for further enrichment of fluoride minerals in the study area with their saturation index prevailing in the order of -2.66
to -0.68 (undersaturated condition).
The application of phosphatic and chloride containing fertilizers are the main anthropogenic sources of
high F−, NO3− and Cl− contamination in the groundwater24,150. Figure 7i,j shows inverse relationship of F− with
Cl− (r2 = 0.038, y = -36.228x + 107.74) and NO3− (r2 = 0.2135, y = -31.485x + 68.551), respectively. These plots signify
that F− contamination in groundwater is from a different source than that of Cl− and NO3; thus, the role of agricultural inputs for F− generation is neglected. In some cases, when the redox potential falls below a certain value in
groundwater, the denitrification process of NO3− by the nitrate-reducing bacteria, accompanied by increased pH
value, enhances the precipitation of Ca2+ resulting in the high F− and HCO3− concentrations in water (Eq. 24)139.
The inverse correlation between SO42− and F− (r2 = 0.0456; y = − 11.144x + 39.442) indicates two different sets
of SO42− and F− contents in the groundwater samples (Fig. 7k). The samples having low or high F− levels have
both low and high SO4− concentrations, thus neglecting the influence of anthropogenic activities. In groundwater,
when the redox potential is below a specific value due to high evapotranspiration, sulphate-reducing bacteria
initiate desulphurisation process that results in the loss of SO42− ions (Eq. 25). Further, the desulphurisation
process raises the pH value, thus favouring the fluorite solubility leading to the high concentrations of F− and
HCO3− ions and precipitation of Ca2+ ions as CaCO3 in groundwater139. Many researchers have observed similar
relationship between SO42− versus F− elsewhere144,150,151, because the decrease in solubility of fluorite minerals is
affected by the presence of SO42− ions in groundwater.
2NO3 → 2HO −N = O → HO − N = N − OH → N2
↓
NH3
ց
−
−
SO2−
4 + CH4 → HS + HCO3 + H2 O
ր
N2 O
(24)
(25)
Chemometric analysis
Principal component analysis
Principal component analysis (PCA) is applied on the 13 chemical parameters to extract the significant principal
components (PCs) that define the hydrogeochemistry in the study area and help in identifying the probable
sources of these parameters in groundwater. A scree plot is generated to determine the eigenvalues of the PCs
using the varimax rotation method. Three PCs were considered as significant from the entire extracted PCs whose
eigenvalues are greater than 1. The eigenvalue of PC1, PC2 and PC3 are 57.60, 18.60 and 9.90, respectively, and
their cumulative variance is 86.10% of all analyzed parameters (Table 5). The significant PCs having parameters
loading scores of > 0.75 (strong, marked bold) and between 0.50 and 0.75 (moderate, marked bold with italics)
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are considered for the PCA interpretation. The first principal component (PC1) that explains 57.60% of the
cumulative variance shows strong positive loading on EC, TDS, TH, Ca2+, Mg2+, Cl− and NO3− and a strong
inverse relationship with pH (Table 5).
The loading TH (0.98) is directly related to Ca2+ (0.96) and Mg2+ (0.86) scores that indicate that water hardness
is influenced by the alkaline earths concentrations in aquifers77,104. The weathering and dissolution of carbonate
(calcite and dolomite) and silicate minerals through rock-water interaction are the probable sources of Ca2+ and
Mg2+ in groundwater, which is also supported by Ca2+/Mg2+ ratio24. The weak loading of alkalis (Na+: 0.45 and K+:
0.18) with respect to alkaline earths (Ca2+ and Mg2+) supports the cation ion exchange process in groundwater79.
Both Cl− (0.93) and high loading of NO3− (0.90) indicate the effect of agrochemicals and domestic sewage in
groundwater29,152. The application of chemical fertilizers, namely anhydrous ammonium chloride, ammonium
nitrate and urea containing inorganic chlorine and nitrogen, is a matter of concern11. The inverse loading of pH
(-0.92) is due to the oxidation of dissolved CO2 and organic matter forming carbonic acids, thereby releasing
free H+ ions153. The inorganic chlorine and nitrogen react with H+ ions rapidly to form HCl and HNO3, which
decrease pH in groundwater. The high scores of EC (0.89) and TDS (0.89) are due to the elevated concentrations
of Ca2+, Mg2+, Cl− and NO3− ions, which enhance the mineralization of groundwater in the study area. Therefore,
PC1 is controlled by lithogenic (Ca2+ and Mg2+) and anthropogenic (Cl− and NO3−) factors.
The second principal component (PC2) explains 18.60% of the total variance. It is positively weighed on
Na+ (0.85) and F− (0.77), moderately weighed on HCO3− (0.74) and has insignificant loading on Ca2+ (0.10)
indicating lithogenic sources of these elements (Table 5). PC2 indicates that the dissolution of fluoride-bearing
minerals is influenced by the elevated concentrations of Na+ and HCO3− or NaHCO3− in the aquifer system. On
the other hand, the weak correlation of Ca2+ with F− (Fig. 7d) suggests that high Ca2+ content in groundwater
inhibits fluoride mineralization at alkaline pH25,154,155). Therefore, PC2 deals with fluoride dissolution through
rock-water interaction.
Lastly, in the principal component 3 (PC3), a variance of 9.90% depicts positive correlation with K+ (high:
0.79) and HCO3− (moderate: 0.50), and negative loading on SO42− (moderate: − 0.59) (Table 5). The main sources
of K+ and HCO3− are the weathering of silicate, muscovite, biotite, and microcline minerals found in the study
area. The negative score of SO42− is due to the leaching of inorganic sulphides present in the sediments through
percolating water, weathering of pyrite-sulphides bearing minerals, namely pyroxene, amphiboles, magnetite and
olivine156 and biological oxidation of sulphur containing compounds in soil139. The oxidation of these minerals
present in the soil profile or subsurface layers is operated through oxygen transport, viz., convection process and
direct exposure of air, because of lowering of groundwater levels through evapotranspiration and groundwater
extraction157. Further, the inverse correlations of SO42− with K+ and HCO3− reflect the different minerals sources
contributing to these ions in the aquifer system. The concentrations of K+, HCO3− and SO42− are well within the
acceptable limits or guideline values of BIS46 and WHO45, thus indicating geogenic inputs.
Cluster analysis
Cluster analysis (CA) is employed on the 27 groundwater samples to create different clusters by grouping similar samples in the form of a dendrogram. The samples grouped in each cluster are marked by certain specific
parameters controlling them. Therefore, the variation in the clusters can be identified by computing the average
value of each parameter of the sample(s) within a cluster to assess the specific tracers for each cluster82,86. Figure 8
Variable
PC1
PC2
PC3
pH
− 0.92
0.17
0.08
Communality
0.89
EC
0.89
0.42
0.03
0.98
TDS
0.89
0.42
0.03
0.98
TH
0.98
0.09
0.04
0.96
Ca2+
0.96
0.10
− 0.02
0.93
Mg2+
0.86
0.06
0.17
0.78
Na+
0.45
0.85
0.13
0.94
K+
0.18
0.08
0.79
0.67
HCO3−
0.26
0.74
0.50
0.87
Cl−
0.93
0.31
− 0.02
0.97
SO42−
0.49
− 0.09
− 0.59
0.59
NO3−
0.90
− 0.17
0.09
0.85
F−
− 0.45
0.77
− 0.07
0.81
Eigen values
7.48
2.41
1.29
% of variance
57.60
18.60
9.90
Cumulative % of variance
57.60
76.20
86.10
Probable identified sources
Mixed factors (lithogenic and anthropogenic inputs)
Fluoride dissolution through rock-water
interaction
Weathering of bedrocks, evapotranspiration and groundwater extraction
11.19
Table 5. Rotated varimax component matrix of the analysed groundwater samples around village Supebeda in
Chhattisgarh State, India. Bold indicates strong loading between parameters. Bold-Italics indicates moderate
loading between parameters. PC Principal component.
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depicts three significant clusters [(Dlimk/Dmax) * 100 < 105] from the dendrogram. Table 6 provides the average
values of the groundwater parameters for each cluster. Cluster 1 (C1) is formed by the largest number of samples
(G1, G4, G8, G9, G12, G20, G14, G21, G2, G3, G5, G6, G7, G10, G11, G13, G15) with highest values of pH and
F−, higher values of Na+ and HCO3− and lowest value of Ca2+ that indicate fluoride enrichment. The average
values of the parameters belonging to C1 are below their respective standard limits of BIS46 and WHO45, except
for F− (1.14) (Table 6). Thus, the groundwater quality of C1 is influenced by the dissolution of fluoride-bearing
minerals and fits well with PC2.
Cluster 2 (C2) denotes the higher values of TDS, TH, Ca2+, K+, SO42−and NO3− and the lowest value of F−. The
average values of TDS (627.47), TH (316.43), Ca2+ (88.29) and NO3− (65.11) are above the acceptable limits of
BIS46 due to their excess concentrations in samples G19, G22, G23, G24, G26, and G27 (Tables S2 and Table 6).
Figure 8. Dendrogram of groundwater sampling locations around village Supebeda in Chhattisgarh State,
India: Three different clusters (C1, C2, and C3) are identified by Ward’s method and the Euclidean distance
to determine the similarity/dissimilarity. The relatively homogenous samples are grouped in each cluster and
marked by certain specific parameters controlling them. In the y-axis, (Dlimk/Dmax) * 100 represents the quotient
between the linkage distances for a particular case divided by the maximal linkage distance. The quotient is then
multiplied by 100 to standardize the linkage distance represented by the y-axis.
Parameters
C1
C2
C3
pH
8.03
7.69
7.30
EC
572.00
980.43
2939.67
TDS
366.08
627.47
1881.39
TH
156.18
316.43
686.67
Ca2+
37.06
88.29
192.67
Mg2+
15.25
22.97
49.20
Na+
57.29
51.17
140.57
K+
HCO3−
2.12
2.41
2.60
293.49
252.86
421.00
355.03
Cl−
19.66
84.73
SO42−
15.82
48.33
58.57
NO3−
16.10
65.11
108.27
1.14
0.53
0.73
F−
Table 6. Average values of the physicochemical parameters for each cluster. Bold indicates the highest average
value of a parameter among the three clusters. Bold-Italics indicates the second highest average value of a
parameter to identify the special tracer. Italics indicates the lowest average value of a parameter among the
three clusters.
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The lowest value of F− (0.53) in C2 among the three significant clusters are due to only one sample (G22) that just
touches the BIS46 acceptable limit of F− (Table S2). The groundwater samples (G16, G23, G19, G22, G26, G24,
G27) that represent the C2 have K+ and SO42− concentrations below their respective guideline values of WHO45
and BIS46. Therefore, C2 is influenced by both geogenic and anthropogenic factors.
Finally, C3 is the smallest cluster (G17,G18,G25) and is marked by the highest values of EC, TDS, TH,
Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42− and NO3− and the lowest value of pH (Table 6). The average values of
EC (2939.67), TDS (1881.39), TH (686.67), Ca2+ (192.67), Mg2+ (49.20), Cl− (355.03) and NO3−(108.27) are
above their respective guideline or acceptable limits of BIS46 and WHO45, except for Na+ (140.57), K+ (2.60),
HCO3− (421.00) and F− (0.73), due to their elevated contents in sample numbers G17,G18 and G25 that decrease
the pH in groundwater. On the other hand, only sample G18 has excess concentrations of HCO3− and F− above
their acceptable limits defined by BIS46 (Table S2). Therefore, the specific parameters that majorly influence
the C3 are EC, TDS, TH, Ca2+, Mg2+, Na+, K+, Cl− and NO3− that indicate geogenic and anthropogenic inputs
enhancing the mineralization of groundwater. Finally, C2 and C3 correspond to the combination of PC1 and PC3.
Conclusions
This paper highlights the non-carcinogenic human health risk assessment (HHRA) of NO3− and F− contamination in groundwater on four different age groups (infants, children, teens and adult) through ingestion and
dermal contact using deterministic and probabilistic approaches, source apportionment of NO3− and F− with
multiple parameters and chemometric modelling to extract the latent factors controlling the groundwater
chemistry. Results of the deterministic and probabilistic hazard quotients ( HQ ) of nitrate ( HQNO−) and fluo3
ride ( HQF−) signify that the ingestion pathway has the potential non-carcinogenic health implications on all
target populations. The deterministic results of the risk certainty levels (RCL) of the hazard index ( HI ) above
unity for nitrate ( HINO−) stand at infants (51.85%) > children (48.15%) > teens (40.74%) = adults (40.74%)
3
and for fluoride ( HIF− ) at infants (66.67%) > children (37.04%) > adults (33.33%) > teens (25.23%). However, the probabilistically calculated RCL health risks in the subpopulation groups are in the order of infants
( HINO− = 34.03% and HIF− = 24.17%) > children ( HINO− = 23.01% and HIF− = 10.56%) > teens ( HINO− = 13.17%
3
3
3
and HIF− = 2.00%) > adults (HINO− = 11.62% and HIF− = 1.25%). These figures reveal that there exist higher degrees
3
of potential human health risks in all the subpopulation groups in the deterministic outputs compared to those
of the probabilistic model. Field observations do not support deterministic conclusions, but they do approve
the probabilistic RCL values. This may be because the deterministic estimation is based on the assumption of an
extreme (single point) value for all input variables individually at different concentration levels of the indicator
parameters, thus possibly leading to overestimation of the output results since the extreme value may not represent the actual field conditions. Also, since the deterministic approach does not have any provision for validation of its output results, the analysis coming out of it is speculative by nature. On the contrary, the probabilistic
approach provides options to choose from a range of values depending on the most likelihood field conditions
besides a provision for sensitivity analysis, which enables validation of the input variables affecting the output
results among the various exposure pathways. Due to all these considerations, this study concludes that probabilistic modelling is superior to deterministic approaches in human health risk assessment.
Strong positive correlation of scatter plots between NO3− with multiple parameters (EC, TH, Ca2+, Mg2+ and
−
Cl ) indicate anthropogenic inputs, such as domestic sewage, agricultural runoff, oxidation of poultry wastes,
etc. Prolonged application of N fertilizers has developed cation exchange processes between NH3 and Ca2+ and
Mg2+ enhancing the mineralization of Ca2+ and Mg2+ in groundwater, thus leading to water hardness and elevated
NO3− concentrations. The positive regression lines between F− and pH, Na+ and HCO3−, respectively, infer that
the alkaline pH with higher concentrations of NaHCO3 or Na+ or HCO3− ions in groundwater allows dissolution
of fluoride-bearing rocks, such as muscovite, biotite, amphibole, fluorite, and hornblende through rock-water
interaction. Therefore, the fact that about 85% of samples show HCO3−/Ca2+ ratio > 1 indicates that there exist
favourable groundwater conditions for further enrichment of fluoride minerals in the study area. This finding
certainly shall be detrimental to the human health risks, especially of infants and children, in the long run, which
is a matter of great concern for the entire study area. Chemometric modelling confirms that Ca2+, Mg2+, HCO3−,
F− and SO42− are derived from geogenic sources, Cl− and NO3− from anthropogenic inputs and Na+ and K+ from
mixed factors. Further, integration of extracted principal components (PCs) with each significant cluster enables
prediction of the latent parameters influencing the sampling locations and confirmation of the various sources.
The study area needs clean drinking water free from NO3− and F− for better human health. Based on the
unique findings of the present work, socio-enviro conditions and hydrogeological setup, treatment of groundwater through various membrane techniques (reverse osmosis and electrodialysis), ion exchange, adsorption,
coagulation, and precipitation processes are highly recommended prior to human consumption. Also, since
literacy rate in the area is about 50%, effort needs to be made for mass awareness through various IEC (information, education, and communication) techniques to apprise people of the local groundwater conditions and what
is best for their longevity. Further, to tackle similar problems elsewhere in the world, the evaluation of HHRA
must be carried out both deterministically and probabilistically to get a holistic picture of groundwater vulnerability. Source apportionment of the contaminants too must be conducted with the help of the chemometric
techniques for better human judgement.
Data availability
The datasets generated during and/or analysed during the current study are already presented in the form of
tables and figures in the manuscript. In case of any specific requirement, the corresponding author may please
be contacted for the needful.
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Received: 16 May 2023; Accepted: 21 October 2023
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Author contributions
H.R.: Conceptualization, software, formal analysis, data curation, methodology, writing—original draft preparation. R.K.D.: Field investigations, water sampling and chemical analysis. P.K.N.: Visualization, supervision,
validation, writing—reviewing and editing. J.R.V.: Hydrogeological investigations, water sampling and preparation of maps. All authors have read and approved the final manuscript. The authors affirm that human research
participants provided informed consent for publication of the images in Graphical Abstract. In case of minors,
informed consent was obtained from all subjects and/or their legal guardian(s).
Competing interests
The authors declare no competing interests.
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