water

Article
Assessing Groundwater Quality for Sustainable Drinking and
Irrigation: A GIS-Based Hydro-Chemical and Health Risk
Study in Kovilpatti Taluk, Tamil Nadu
Vivek Sivakumar 1 , Venkada Lakshmi Ramamoorthy 2 , Uma Maguesvari Muthaiyan 3 ,
Shumugapriya Kaliyappan 4 , Gokulan Ravindiran 5 , Sethuraman Shanmugam 6 , Priya Velusamy 1 ,
Logesh Natarajan 7 , Hussein Almohamad 8, * , Motrih Al-Mutiry 9 and Hazem Ghassan Abdo 10

1 Department of Civil Engineering, GMR Institute of Technology, Razam 532127, Andhra Pradesh, India;
vivek.s@gmrit.edu.in or 1717vivek@gmail.com (V.S.); priya.v@gmrit.edu.in or vrpriyaashree@gmail.com (P.V.)
2 Department of Civil Engineering, SRM Madurai College for Engineering & Technology, Madurai 630612,
Tamil Nadu, India; venkadalakshmir@gmail.com
3 Department of Civil Engineering, Rajalakshmi Engineering College, Chennai 602105, Tamil Nadu, India;
umamaguesvari.m@rajalakshmi.edu.in
4 Department of Civil Engineering, Nehru Institute of Technology, Coimbatore 641105, Tamil Nadu, India;
queenpriya@gmail.com
5 Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology,
Hyderabad 500090, Telengana, India; gokulravi4455@gmail.com
6 Department of Civil Engineering, M.Kumarasamy College of Engineering,
Thalavapalayam, Karur 639113, Tamil Nadu, India; shansethu83@gmail.com
7 National Centre for Coastal Research, Ministry of Earth Sciences, Government of India,
Chennai 600100, Tamil Nadu, India; logesh@nccr.gov.in
Citation: Sivakumar, V.; 8 Department of Geography, College of Arabic Language and Social Studies, Qassim University,
Ramamoorthy, V.L.; Muthaiyan, Buraydah 51452, Saudi Arabia
9 Department of Geography, College of Arts, Princess Nourah bint Abdulrahman University,
U.M.; Kaliyappan, S.; Ravindiran, G.;
Shanmugam, S.; Velusamy, P.; Riyadh 11671, Saudi Arabia; mkalmutairy@pnu.edu.sa
10 Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous P.O. Box 2147, Syria;
Natarajan, L.; Almohamad, H.;
hazemabdo@tartous-univ.edu.sy
Al-Mutiry, M.; et al. Assessing
* Correspondence: h.almohamad@qu.edu.sa
Groundwater Quality for Sustainable
Drinking and Irrigation: A GIS-Based
Abstract: The continuous investigation of water resources is essential to assess pollution risks. This
Hydro-Chemical and Health Risk
Study in Kovilpatti Taluk, Tamil
study investigated a groundwater assessment in the coastal belt of Tamil Nadu’s Kovilpatti Taluk,
Nadu. Water 2023, 15, 3916. Thoothukudi district. Twenty-one groundwater samples were collected during the pre-monsoon and
https://doi.org/10.3390/ post-monsoon seasons, analyzing water quality parameters, namely pH, EC, Cl− , SO4 2− , Ca2+ , Mg2+ ,
w15223916 HCO3 − , TH, Na2+ , and K+ . The Water Quality Index (WQI) was computed and it is observed that 5%
of pre-monsoon and 9% of post-monsoon samples were unsuitable for drinking. SAR, MHR, RSC,
Academic Editors: Guilin Han,
Peiyue Li, Busawan Bidorn and
%Na and Kelley’s index were used to determine irrigation suitability. Pre-monsoon shows 29% (MHR)
Balamurugan Paneerselvam and 71% (RSC) unsuitable, and post-monsoon shows 59% (MHR) and 9% (RSC) unsuitable. Coastal
activity, urbanization, and industrialization in Kovilpatti resulted in the degradation of groundwater
Received: 15 September 2023
quality. Solving this coastal issue requires sustainable wastewater treatment and strict industrial
Revised: 20 October 2023
discharge guidelines. Spatial distribution plots, Box plots, Gibbs plots, Piper plots, Wilcox plots and
Accepted: 2 November 2023
Correlation Matrices had similar results to the computed WQI and its physical–chemical parameters.
Published: 9 November 2023
According to the human health risk assessment, the Mooppanpatti, Illuppaiurani, and Vijayapuri
regions show high health risks due to the nitrate and fluoride concentration in the groundwater.
Kadambu, Melparaipatti, Therkuilandhaikulam, and Vadakku Vandanam have low levels, posing
Copyright: © 2023 by the authors. a minimal health risk.
Licensee MDPI, Basel, Switzerland.
This article is an open access article Keywords: water; groundwater pollution; water Quality Index; human health risk assessment
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).

Water 2023, 15, 3916. https://doi.org/10.3390/w15223916 https://www.mdpi.com/journal/water

Water 2023, 15, 3916 2 of 22

1. Introduction
Freshwater resources have decreased recently due to urbanization and insufficient
rainfall [1,2]. Groundwater is a significant source of water since the depletion of surface
water and subsurface water resources have become increasingly important in many areas
of India in recent years [3]. The dangers of using groundwater includes contamination
and pollution. Drinking contaminated groundwater can have serious health consequences,
including blue baby disorder from nitrate and fluorosis from fluoride [4]. To protect
against pollution, the water resources must be constantly monitored. Understanding wa-
ter management and the sustainable utilization of water resources requires monitoring
groundwater quality [5]. Pollution typically refers to introducing harmful or hazardous
substances into the environment on a larger scale, due to human activities. Contamination,
on the other hand, is a broader term that refers to introducing undesirable elements or
impurities on a smaller scale, often localized. Groundwater quality in Kovilpatti, a coastal
town, has declined significantly, mainly due to coastal activities [6,7]. The local people
and the environment have expressed alarm over the deterioration of the groundwater.
The uncontrolled release of non-purified household and industrial effluents into nearby
natural waters, notably the coastal areas, is one of the primary causes of poor groundwater
quality [8]. Groundwater pollution is due to the discharge of hazardous chemicals and
pollutants into the environment by industrial activities around Kovilpatti. Inappropriate
waste management procedures and the careless application of chemical pesticides and
fertilizers in agriculture have also worsened matters [9]. These substances may leak into
the groundwater, lowering its quality and irrigation suitability and making it unfit for
human consumption. Additionally, excessive groundwater withdrawal for agriculture
and other uses has caused the aquifers to become contaminated with saltwater [10]. As a
result, the groundwater is starting to taste salty, making it unusable for many purposes
and seriously harming agricultural production [11,12]. A comprehensive strategy, includ-
ing strict legislation, the monitoring of industrial discharges, better waste management
systems, and the promotion of sustainable agriculture practices, is required to address the
problem. To protect the groundwater resources in Kovilpatti and provide a sustainable and
healthy environment for its citizens, cooperation between the government, industries, and
neighborhood groups is crucial [13,14].
The recharge area beneath determines the quality of the groundwater. The seasonal
and regional variations in groundwater quality are controlled by these geochemical pro-
cesses [15–17]. The primary cause of groundwater fluoride pollution is the weathering
and leaching of rocks. On the other hand, agricultural practices such the use of pesticides
are the main cause of nitrate contamination in groundwater [18]. One instrument for
evaluating the quality of groundwater and surface water is the Water Quality Index (WQI).
Numerous studies have used a range of indicators, such as sodium percentage (%Na+),
sodium adsorption ratio (SAR), residual sodium carbonate (RSC), and magnesium hazard
ratio (MHR), to determine whether water is suitable for irrigation [19–21]. When evaluating
data on water quality, GIS is a crucial tool for comprehending the geographical distribution
of contaminants and changes in water quality over time [22]. Innovations in involving local
communities, decision-makers, and stakeholders in the groundwater management process
help in ensuring sustainable resource use and minimizing conflicts [23–28].
The research scope encompasses the cotton and matchstick production sectors. Con-
tamination sources in this study area are typically divided into organic and inorganic
categories. The chemical waste emanating from industrial discharge, particularly sub-
stances such as red phosphorous and nitrogen, poses significant health risks to the local
population, manifesting as fluorosis, cancer, and respiratory complications, among oth-
ers. Residents in close proximity to these industries frequently suffer from symptoms
including nausea, vomiting, and abdominal pain due to groundwater pollution, which
primarily results from the release of red phosphorus by matchstick manufacturers and other
pollution sources.
Water 2023, 15, 3916 3 of 22

Moreover, the infiltration of chemical contaminants into groundwater from rainwater

percolating through rock layers adds another layer of health concerns. As a result, the need
for effective groundwater treatment in Kovilpatti before it is considered safe for drinking
becomes paramount [29]. It is worth noting that this study marks the first comprehensive
exploration of these severe issues within the study area and southern Tamil Nadu. The
objective of this investigation is to assess the suitability of groundwater in Kovilpatti taluk,
Thoothukudi district, for both drinking and industrial purposes. The primary goals of the
study include: 1. Conducting an initial assessment and interpretation of the groundwater
quality in Kovilpatti Taluk. 2. Determining the suitability of the groundwater for drinking
and irrigation. 3. Employing spatial maps to identify areas at risk of contamination. The
study involves an analysis of water quality in comparison to WHO and BIS standards.
Additionally, it assesses the suitability for irrigation using parameters such as RSC, SAR,
Na%, KR, PI, MHR, and PS. The study also seeks to identify the water type and composition
through the use of Chandha plots, Piper timber plots, and Gibbs plots. Ultimately, the
primary goal is to identify sources of pollution.

2. Study Area
The Tuticorin district is situated in Tamil Nadu’s southernmost region. Eight talukas
make up the district’s administrative division. Kovilpatti, the headquarter of the talukas,
was chosen amongst the eight talukas for the study and the sample was collected from
different locations in the Kovilpatti region. The taluk’s population density is about
390 people per square kilometer, and the total area was about 823.37 Sq.km. Kovilpatti Taluk
has a tropical climate with 761 mm of annual precipitation. In this study area, the average
annual temperature is 28.8 ◦ C. There are no larger bodies of water in the Tuticorin district,
and the Tamirabarani river provides water for all essential uses, including irrigation.
Water is supplied from public wells and private boreholes to meet the needs of people,
industries, and irrigation activity. Based on the local recharge mode, the groundwater
drawn up through each borehole will have unique properties. According to their con-
centration values, physical and chemical characteristics are typically used to categorize
groundwater. Direct groundwater consumption is no longer advised. Because of the
untreated introduction of contaminants from industries to the ground, water contains
cations and anions in varying concentrations above the recommended limits. Most of the
research region is covered by hornblende biotite gneiss geology features. Calcareous gritty
sandstone and clay, charnockite, and quartzite are some of the features covered.
Over time, changes in the locality’s water quality standards over a region contribute
to the emergence of new diseases. These kinds of activities are also made easier by the
geological conditions of the soil there. In this taluk, there were almost 82 villages. To learn
about the features of the groundwater, we framed a grid measuring 6 × 6 sq. Km and
fixed 21 stations as our sampling points. Even though there are many small, medium, and
giant tanks in this area, they are essentially dry for around 6 to 7 months of the year, and
starting in March, farmers mostly rely on accessible groundwater resources. On a gross
basis, just 15% of the area seeded is watered under tanks. Murappanad and Srivaikundam
have various canal networks along the Tamiraparani river. The groundwater beneath
the irrigation tanks and along riverbanks has recently been tapped by farmers using big-
diameter dugwells and dug-cum bore wells. The location map for the chosen research
region, Kovilpatti Taluk, is shown in Figure 1.
Water 2023, 15, 3916 4 of 22

Figure 1. Study area location map of Kovilpatti Taluk.

The geographical characteristics and climate of the area have an impact on its hydro-
logical formation. The Thamirabarani river, which runs through the area and has its source
in the Western Ghats, is the taluk’s primary water source. The hydrology of Kovilpatti is
also influenced by the numerous tiny rivers, streams, and irrigation tanks there. The region
has a tropical climate, with rainfall primarily coming from the southwest and northeast
monsoons. Groundwater is essential for maintaining agriculture and providing the needs
of the people, but overuse has raised issues with groundwater quality and depletion.
In Kovilpatti Taluk, sulfate (SO4 2− ) sources can originate from both natural and anthro-
pogenic (human-induced) processes. Naturally, sulfate can be present in groundwater and
surface water due to the weathering of sulfur minerals, volcanic emissions, and atmospheric
deposition. Additionally, organic matter decay in soils can release sulfates. Anthropogenic
sources may include industrial activities, especially those related to mining and metal
processing, as well as agricultural practices involving sulfur-containing fertilizers and
wastewater discharge. Monitoring and managing sulfate sources are essential to ensure
water quality and prevent environmental contamination in Kovilpatti Taluk.

3. Materials and Methods

Pre-monsoon (July to December 2018) and post-monsoon (January to June 2018)
groundwater samples were taken from 21 stations using hand pumps whose depths ranged
between 150 and 100 feet below the ground level (bgl). Figure 2 shows the sampling
location of the study area of Kovilpatti Taluk.
Water 2023, 15, x FOR PEER REVIEW 5 of 22
Water 2023, 15,
Water 2023, 15, 3916
x FOR PEER REVIEW 5 5of
of 22
22

Figure 2. Location map of the samples in the study area of Kovilpatti Taluk.
Location map
Figure 2. Location map of the samples in the study area of Kovilpatti Taluk.
High-density polyethene (HDPE) bottles measuring 1 L were used to collect the sam-
High-density
ples.High-density polyethene
Specific parameters
polyethene (HDPE)
such(HDPE) bottles
as pH, TDS,
bottles andmeasuring
EC were11measured
measuring LL were
were used
used
usingtocollect
to collectthe
standard theporta-
sam-
sam-
ples.
ples. Specific
ble digital parameters
Specificmeters (EC/ORP
parameters such as pH,
suchmeter,
as pH,pHTDS,
TDS, and
meter, EC
and EC were
TDSwere measured
meter) using
in the using
measured standard
station. portable
Following
standard the
porta-
digital
ble meters
recommended
digital (EC/ORP
meters protocol
(EC/ORP meter,
by APHA,pH meter,
meter, pH TDS meter)
the meter,
samples TDSwere in the in
analyzed
meter) station. Following
to station.
the determine theconcen-
the
Following recom-
the
mended
tration ofprotocol
recommended chloride by APHA,
(Cl
protocol −), by the samples
alkalinity
APHA, (CO , were
HCO3analyzed
the3−samples −), and total
were to determine
hardness
analyzed the2+concentration
(Ca
to determine , Mgthe
2+). Sulphate
concen- of
chloride − − − 2+ 2+
tration of(Cl
was determined ), alkalinity
chloride using (CO 3 , HCO
(Cl−), aalkalinity
spectrophotometer ), and
(CO33−, HCO total
and hardness
barium
3−), and
(Ca as
totalchloride
hardness ,(Ca
Mg ). Sulphate
an2+additive.
, Mg was
Sodium
2+). Sulphate
determined
wasdetermined
was using
examined using a spectrophotometer
usinga aflame and
photometer. Figure
spectrophotometer barium chloride
3 displays
and barium as an additive.
the flowchart
chloride Sodium
for thisSodium
as an additive. was
investi-
examined
gation.
was usingusing
examined a flame photometer.
a flame Figure
photometer. 3 displays
Figure the flowchart
3 displays for this
the flowchart forinvestigation.
this investi-
gation.

Figure3.3. The
Figure Theentire
entiremethodology
methodologyflow
flowchart
chartfor
forthis
thisresearch
researchregion
regionfor
forKovilpatti
KovilpattiTaluk.
Taluk.
Figure 3. The entire methodology flow chart for this research region for Kovilpatti Taluk.
3.1.
3.1. Water
Water Quality
Quality Index
Index
The
The WQI is anumerical
3.1. Water WQI is
Quality a numerical expression
Index expression for
for characterizing
characterizing water
water quality
quality that
that is
is cumula-
cumula-
tively
tively calculated
Thecalculated and
WQI is aand based on measuring
based expression
numerical on measuring several water quality
several water quality
for characterizing parameters.
that isThe
parameters.
water quality The WQI
WQI
cumula-
measures the overall effect of numerous water quality metrics and considers whether sur-
measures
tively the overall
calculated and effect
basedofon numerous water
measuring quality
several metrics
water and parameters.
quality considers whether
The WQIsur-
face and groundwater suit their intended uses. The Water Quality Index is a measure used
face and groundwater
measures suit of
the overall effect their intended
numerous uses.quality
water The Water Quality
metrics and Index is a measure
considers whether used
sur-
to classify surface and groundwater pollution levels. Based on WQI scores of 50, 50–100,
to classify
face surface andsuit
and groundwater groundwater pollution
their intended levels.
uses. The Based
Water on WQI
Quality Indexscores of 50, 50–100,
is a measure used
100–200, 200–300, and >300, water samples are categorized as too good, good, bad, ex-
100–200,
to classify200–300,
surface andandgroundwater
>300, water samples
pollutionare categorized
levels. Based onasWQI
too good,
scores good,
of 50, bad, ex-
50–100,
tremely bad, and no use for drinking, respectively [30,31]. The WQI is calculated using the
tremely bad,
100–200, and no
200–300, anduse for drinking,
>300, respectively
water samples [30,31]. TheasWQI
are categorized too is calculated
good, good, using the
bad, ex-
procedures below:
procedures
tremely bad,below:
and no use for drinking, respectively [30,31]. The WQI is calculated using the
Step 1: Calculation of relative weight by using Equation (1).
Step 1:below:
procedures Calculation of relative weight by using Equation (1).
Step 1: Calculation of relative weight by using wi Equation (1).
Wi = n (1)
∑i wi
Water 2023, 15, 3916 6 of 22

where Wi is the relative weight of each parameter, wi is the parameter’s weight in terms of
wi, and n is the parameters in their whole.
Step 2: Calculation of the Qi value by using Equation (2).

Ci × 00
Qi = (2)
Si
where Qi stands for quality rating, Si is the WHO water quality standard, and Ci is the
concentration of each parameter (mg/L).
Step 3: Equation (3) was then used to derive the Water Quality Index.

WQI = ∑ Wi × Qi (3)

3.2. Irrigation Water Quality

The chemical composition, or the mineral composition of irrigation water, is the
primary determinant of its quality. Certain physical and biological traits, such as turbidity
and the presence of algae, bacteria, or viruses, can also affect the appropriateness of water
which could be used for irrigation. Relative to Ca2+ and Mg2+ ions, the concentration of the
bicarbonate (HCO3 − ) and carbonate (CO3 2− ) anions is in residual sodium carbonates (RSC).
Excessive amounts of chemicals render plants poisonous or disturb their ionic equilibrium.
The metrics of irrigation water quality were assessed using the sodium adsorption ratio
(SAR), residual sodium carbonate (RSC) [7], sodium percentage [32], Kelly’s ratio [12], and
magnesium hazard ratio (MHR) [33]. Each value was expressed as mg/L.

Na+
SAR = r  (4)
Ca2+ + Mg2+ /2

Na+
%Na =   × 100 (5)
Ca2+ + Mg2+ + Na2+

 
RSC = HCO3 − + CO3 − − Ca2+ + Mg2+ (6)

Mg2+
MHR =   × 100 (7)
Ca2+ + Mg2+

Na2+
KI =   (8)
Ca2+ + Mg2+

3.3. Spatial Analysis

Kriging interpolation can be used to display groundwater quality data spatially. The
predicted values come from the weighted averages of the neighboring sample locations.
This spatial interpolation worked when the sample points were dense enough to capture
the regional variance [34,35]. For making kriging software such as Excel (version 2016) and
ArcGIS, the data list could be prepared in an Excel format before using the ArcGIS software
10.8 version, using the ARC GIS toolbar, to run the data and process the interpolation.

3.4. Human Health Risk Assessment (HHRA)

An HHRA is studied to determine the effects of water quality characteristics on health,
and it is looked at in both adults and children. Pollutants can enter the human body by
ingestion, contact with the skin, or inhalation. The current investigation examined the
Water 2023, 15, 3916 7 of 22

cutaneous and ingested modes of transformation. The average daily exposure dose for
ingestion and cutaneous absorption by water was calculated using Equations (9)–(13).

Cw × IR × EF × ED
ADDing = (9)
BW × AT

Cw × SA × Kp × ET × EF × ED × 10−3
ADDder = (10)
BW × AT

ADD ingestion
HQing = (11)
RfD

ADD dermal
HQder = (12)
RfD

H I = HQing + HQder (13)

ADDing and ADDder are the daily exposure doses to water (in mg/kg/day) by in-
gesting and dermal activity; Cw is the actual concentration of the water samples in mg/L;
ED is the exposure duration in years; IR is the ingestion rate (L/day); EF is the exposure
frequency (day/year); BW stands for body weight on average (kg); AT denotes the typical
time (days). (Table 1 reveals the standard values for the calculation of HI). The average skin
surface area in square centimeters that is exposed to water is SA; Kp is the water’s coeffi-
cient for dermal activity and is 0.001 for Na2+ , F− , NO3 − , and Cl; ET stands for exposure
time (day/hour); the water quality parameters’ reference dosage, or RfD, is measured in
mg/kg/day (F− is 0.06 and NO3 − is 1.6); the Hazard Index is also known as HI; and the
Hazard Quotient is also known as HQ ingestion and dermal.

Table 1. Standard values for the calculation of HI.

Description IR (L/day) EF (day/year) ED (year) BW (kg) AT (day) SA (cm2 ) ET (h/day)

Adults 2 350 40 70 14,000 18,000 0.58
Children 0.78 350 4 15 1400 6600 1

4. Results and Discussion

4.1. Quality of Water for Drinking
The WHO created international guidelines and customized them for regional use,
including drinking water quality criteria. For the pre-monsoon (July to September 2018)
and post-monsoon (January to March 2018) seasons, the groundwater quality metrics are
statistically summarized in Table 2. Table 3 displays the percentage of samples that are
above the allowable limit. The current analysis indicates that the study region’s ground-
water has an acidic pH (6.4–8.3) in the post-monsoon samples but is slightly alkaline
(7.5–8.7) in the pre-monsoon samples. From the pre-monsoon group, one sample (Therku
Ilandhaikulam) had a pH value of 8.7, beyond the upper acceptable pH limit (8.5). In
contrast, from the post-monsoon group, one sample (Uttuppatti) had a pH value of 6.4,
above the lower permissible pH limit (6.5). Pre-monsoon TDS values range from 103 to
2020 mg/L in the analyzed water samples, whereas post-monsoon TDS levels range from 70
to 2240 mg/L. The standards stipulate that TDS may not exceed 1000 ppm [2]. Oxidation-
reduction potential, with the abbreviation ORP, refers to evaluating a lakes or river’s
capacity to remove pollutants or decompose wastes as animal and plant remains. The
amount of oxygen in the water is high when the ORP value is high.
Water 2023, 15, 3916 8 of 22

Table 2. Kovilpatti Taluk’s physiological chemical parameters’ minimum, maximum, and ave-
rage values.

Pre-Monsoon Post-Monsoon
Concentration Concentration
Water Quality Parameters Avg SD Avg SD
Max Min Max Min
pH 8.70 7.50 8.14 0.80 8.30 6.40 7.44 0.44
TDS 2020.00 103.00 642.71 743.72 2240.0 70.00 733.33 836.53
EC 4303.00 198.00 1290.33 1528.0 4670.0 144.00 1481.43 1694.88
ORP −22.00 −80.00 −54.48 63.50 12.00 −63.00 −25.29 41.60
DO 2.90 1.00 1.82 5.34 2.60 1.10 1.94 5.20
Cl 1684.00 30.00 290.52 442.15 1853.0 6.00 501.71 663.87
Alkalinity 1484.00 85.00 608.48 574.13 720.00 24.00 307.90 311.18
Total Hardness 223.00 34.00 93.00 90.57 224.00 42.00 122.00 113.82
Calcium 139.00 27.00 70.90 63.59 155.00 32.00 79.00 71.14
Magnesium 98.00 5.00 22.86 26.22 129.00 7.00 43.00 43.64
Sodium 95.00 5.00 28.62 33.01 98.00 3.00 28.95 34.05
Potassium 22.00 1.00 6.86 5.32 25.00 1.00 7.62 6.19
Sulphate 547.00 20.00 173.10 203.67 584.00 15.00 213.29 230.88
WQI 418.00 48.00 132.10 147.75 436.09 27.15 156.51 170.53
Nitrate 45 10 23.952 11.112 42 7 19.571 10.630
Fluoride 1.47 0.9 1.244 0.170 1.26 0.7 1.109 0.139

Table 3. Percentage of samples that go over the allowed limit.

WHO 2004 % of the Sample % of the Sample

Water Quality
Parameters (mg/L) Maximum Exceeds the Permitted Exceeds the Permitted
Most Desirable Limits Limit in the PRM Limit in the POM
Permissible Limit
pH (no unit) 6.5 8.5 4.76 -
TDS 500 1500 9.52 4.76
EC (µS/cm) - 600 61.9 80.95
DO 5 - - -
Cl 250 1000 4.76 9.52
Alkalinity 137 287 90.47 57.14
Total Hardness (mg/L) 200 600 - -
Calcium (mg/L) 75 200 - -
Magnesium (mg/L) 30 150 - -
Sodium (mg/L) - 200 - -
Potassium (mg/L) - 10 23.8 14.28
Sulphate (mg/L) 200 400 9.52 9.52
Nitrate (mg/L) 10 45 10 6
Fluoride (mg/L) 1.5 1 5 3

The TDS permitted limit was exceeded by 24% of the samples collected before the
monsoon and 19% of the samples collected after it. The pre-monsoon and post-monsoon
measurements of Attikulam’s TDS levels were higher, at 2020 and 2240 ppm, respectively.
The ranges for calcium concentrations (PRM and POM) are 27 to 139 mg/L and 32 to
155 mg/L, respectively. Following the calcium ion in both sessions, the concentrations
of the magnesium cation range from 5 to 98 mg/L (pre-monsoon) and 7 to 129 mg/L
(post-monsoon). Ca2+ cannot be more than 75 mg/L, and Mg2+ cannot be more than
30 mg/L.
In both seasons, the levels of Ca2+ and Mg2+ are higher than these allowed thresholds.
Na, which dominates in the examined samples and is found in amounts ranging from 5 to
95 mg/L (pre-monsoon) and 3 to 98 mg/L (post-monsoon), comes after Ca2+ and Mg2+ .
The principal cations in the groundwater from the study area are in the following order:
Ca2+ > Mg2+ > Na+ > K+ . The calcium and magnesium ions have reached levels beyond
Water 2023, 15, 3916 9 of 22

the permitted limit due to the leaching of limestone, dolomites, gypsum, and anhydrite,
while the calcium ions may have come from the cation exchange process.
The attentiveness of Cl− , SO4 2− , and HCO3 − varies from 30 to 1684 mg/L, 20 to
547 mg/L, and 85 to 1484 mg/L, respectively, during the pre-monsoon. Cl− , SO4 2− ,
and HCO3 − concentrations during the post-monsoon vary from 6 to 1853 mg/L, 15 to
584 mg/L, and 24 to 720 mg/L, respectively. Chloride and sulfate concentrations exceed
the permitted limit of 200 mg/L in both seasons. HCO3 − > SO4 2− > Cl− is the preferred
order for anion concentration during the pre-monsoon, and Cl− > HCO3 − > SO4 2− is
the preferred order post-monsoon. The subsurface leaching of rocks may cause a higher
chloride concentration during the pre-monsoon. The physiological chemical parameters of
Kovilpatti Taluk show minimum, maximum, and average values in Table 3.

4.2. Water Quality Index

The pre-monsoon WQI values range from 37 to 212, while the post-monsoon WQI
values range from 21 to 224. The WQI rating places 10% of the pre-monsoon groundwater
samples in the excellent category and 52% in the good water category for drinking. Just
5% of the groundwater samples were deemed hazardous for ingestion, as opposed to
23% of the samples having terrible water quality and 10% having poor water quality. The
post-monsoon seasons saw a drop in water quality from outstanding to sound, from good
to awful to extremely poor. The percentages for too good, terrible, extremely bad, and no
use for drinking water quality are 10%, 29%, 23%, 29%, and 9%, respectively.

4.2.1. Groundwater Suitability Assessment for Irrigation

The suitability of the water for agriculture was assessed using the SAR, Na percentage,
RSC, and MHR. The categorization of irrigation water quality is shown in Tables 4 and 5.
Because of rain in the post-monsoon season, most irrigation suitability measurements
had higher values during the pre-monsoon seasons. Figures 4 and 5 exhibits the spatial
distribution of the water quality index for the pre- and post-monsoon seasons.

Table 4. Irrigation water quality parameters in Kovilpatti Taluk.

Pre-Monsoon Post-Monsoon
S.No Station Name
SAR %Na MHR KI RSC SAR %Na MHR KI RSC
1 Mooppanpatti 0.9 20 56 0.24 15 0.8 18 50 0.22 1
2 Illuppaiurani 0.9 24 10 0.31 9 0.6 13 38 0.15 −6
3 Vijayapuri 0.3 14 14 0.16 10 0.2 6 56 0.06 −2
4 Sivandhipatti 0.2 7 59 0.07 4 0.1 2 59 0.02 −2
5 Theethampatti 0.2 8 51 0.09 6 0.3 10 63 0.11 2
6 VadakkuVandanam 0.1 6 16 0.07 6 0.3 8 50 0.09 −4
7 Chokkalingapuram 0.1 4 30 0.04 7 0.2 5 58 0.05 −6
8 Kadambur 0.1 5 32 0.05 8 0.2 6 30 0.06 −3
9 Melparaipatti 0.1 3 17 0.03 −2 0.1 3 24 0.03 −5
10 Uttuppatti 0.1 5 15 0.06 3 0.1 4 17 0.04 −4
11 Mandithoppu 0.3 14 50 0.16 −1 0.1 5 25 0.06 −2
12 Thalavaipuram 0.2 10 39 0.11 3 0.3 8 68 0.09 3
13 Idaiseval 1.1 29 43 0.42 6 1.2 31 41 0.46 −2
14 Akilandapuram 0.3 12 34 0.13 −1 0.2 9 32 0.09 −3
15 Kayathar 1.0 29 20 0.41 0 1.0 21 50 0.27 −5
16 Rajapuddukudi 0.3 15 18 0.17 7 0.3 8 53 0.09 0
17 Attikulam 1.6 32 42 0.46 3 1.5 32 31 0.47 −3
18 Therkuilandhaikulam 0.3 12 37 0.13 4 0.3 8 40 0.09 −2
19 Chidambarampatti 1.4 39 16 0.64 1 0.9 17 66 0.21 −7
20 Kumarettiyapuram 0.9 17 66 0.20 −2 0.8 15 69 0.17 −9
21 Kalankaraippatti 1.3 35 15 0.54 9 1.2 32 18 0.47 6
 Total Hardness (mg/L) 200 600 - -
Calcium (mg/L) 75 200 - -
Magnesium (mg/L) 30 150 - -
Sodium (mg/L) - 200 - -
Potassium (mg/L) - 10 23.8 14.28
Water 2023, 15, 3916 10 of 22
Sulphate (mg/L) 200 400 9.52 9.52
Nitrate (mg/L) 10 45 10 6
Fluoride (mg/L) 1.5 1 5 3
Table 5. Classification of Kovilpatti Taluk’s irrigation water quality parameters.

4.2. Water Quality Index % of Water Samples

Parameters Range Water Classification
PRM POM
The pre-monsoon WQI values range from 37 to 212, while the post-monsoon WQI values
rangeMHR <50 WQI rating places
from 21 to 224. The Suitable 81
10% of the pre-monsoon 41 sam-
groundwater
>50 Unsuitable 19 49
ples in the excellent category and 52% in the good water category for drinking. Just 5%
<1.25 Good 29 86
of the RSC
groundwater samples
1.25–2.5 were deemed hazardous for ingestion,
Doubtful - as opposed 5to 23% of
the samples having terrible
>2.5 water quality and
Unsuitable10% having poor
71 water quality. The
9 post-
monsoon seasons saw <10 a drop in water Too
quality
Goodfrom outstanding
100 to sound, from100good to
awful SAR 10–18 Good - -
to extremely poor. The percentages for too good, terrible, extremely bad, and no
use for drinking water18–26
quality are 10%,Average
29%, 23%, 29%, and 9%, - respectively. -
>26 Bad - -
<20% Too Good 71 81
4.2.1. Groundwater Suitability
20–40% Assessment for Irrigation
Good 29 19
The
%Nasuitability of the water for agriculture
40–60% Allowable was assessed using
- the SAR, Na- percent-
age, RSC, and MHR.60–80% Suspectful
The categorization -
of irrigation water quality is shown in- Tables 4
and 5. Because of rain in the post-monsoon season, most irrigation suitability -measure-
>80% Not Suitable -
<1 Suitable 100 100
Kelly
ments ratio
had higher values during the pre-monsoon seasons. Figures 4 and 5 exhibits the
>1 Unsuitable - -
spatial distribution of the water quality index for the pre- and post-monsoon seasons.

Water 2023, 15, x FOR PEER REVIEW 10 of 22

Figure 4. The geographic distribution of the Water

Water Quality
Quality Index
Index (pre-monsoon).
(pre-monsoon).

Figure 5. The geographic distribution of the Water

Water Quality
Quality Index
Index (post-monsoon).
(post-monsoon).

4.2.2. Sodium Absorption Ratio

Table 4. Irrigation water quality parameters in Kovilpatti Taluk.
Groundwater with high levels of salt causes alkaline soil. Sodium and salinity dangers
Pre-Monsoon
are significant factors Post-Monsoon
to consider when evaluating the groundwater utilized for irrigation.
S.No Station Name
SAR %Na MHR KI RSC SAR %Na MHR KI RSC
1 Mooppanpatti 0.9 20 56 0.24 15 0.8 18 50 0.22 1
2 Illuppaiurani 0.9 24 10 0.31 9 0.6 13 38 0.15 −6
3 Vijayapuri 0.3 14 14 0.16 10 0.2 6 56 0.06 −2
4 Sivandhipatti 0.2 7 59 0.07 4 0.1 2 59 0.02 −2
Water 2023, 15, 3916 11 of 22

The SAR ratio is expressed in the Equation (4). The soil structure deteriorates when water
with a high SAR is used continuously. One can use the sodium adsorption ratio in water to
determine the cation-exchange processes in soil. According to the Sodium Adsorption Ratio
(SAR), groundwater could be described as very friendly (10), excellent (10–18), confused
(18–26), and not suitable (>26) [26–28,36]. The SAR in samples from Kovilpatti Taluk ranges
from 0.1 to 1.6 mg/L and from 0.1 to 1.5 mg/L, with average values of 0.6 and 0.5 mg/L in
the pre- and post-monsoon seasons, respectively. The Sodium Absorption Ratio results for
all samples in Kovilpatti Taluk are good.
The salinity hazard parameter (EC), with a range of 198 to 4303 µS/cm and an average
value of 1290 µS/cm, is used in the Wilcox diagram. According to the Wilcox diagram,
67% of the groundwater samples collected during the monsoon are labelled as C3S1 water,
which denotes dangerous water with a high salinity and low sodium content. Almost 14%
of the groundwater samples in the research region were given the C2S1 classification, which
denotes medium salinity and low sodium risks. Without any further salinity concern, this
water may be used for irrigation. Pre-monsoon groundwater samples classified as C3S1
had a 47% risk of excessive salinity and low sodium. In the research region, almost 19% of
the groundwater samples were classified as C2S1, which means the water is acceptable for
irrigation and has a medium salinity and low sodium danger.

4.2.3. Sodium Percent (Na%)

Extra sodium in the groundwater alters the properties of the soil and decreases its
permeability. Based on the percentage of sodium, groundwater was categorized as highly
suitable (20%), suitable (20–40%), permissible (40–60%), not suitable (60–80%), and entirely
not suited (>80%) [37–39]. With an average value of 16 and 12 in the pre- and post-monsoon
seasons, the Na% in the study area ranges from 3 to 39 and 2 to 32. According to the Indian
Standards [2], irrigation water should have an extreme Na+ content of 60%. A %Na level
above 60 may lead to Na+ accumulations, which will deteriorate the soil’s physical qualities.
The groundwater samples collected from Kovilpatti Taluk have sodium concentrations that
fall into the excellent and good ranges.

4.2.4. Bicarbonate Hazard

Water with a high bicarbonate concentration can combine calcium and magnesium
to form salts. RSC assigns the following categories to groundwater: fair (1.25), not fair
(1.25–2.5), and extreme not fair (>2.5) [40–43]. In Kovilpatti Taluk, of the analyzed samples,
29% have an adequate pre-monsoon irrigation water quality, 71% have an unsuitable pre-
monsoon irrigation water quality, and 86% have a suitable post-monsoon irrigation water
quality. In total, 5% of the irrigation water is questionable, and 9% is inappropriate for
irrigation. The reason for the differences in alkalinity between the pre- and post-monsoon
seasons is the diluting of groundwater caused by rainfall [44–46].

4.2.5. Magnesium Hazard Ratio

Mg2+ concentrations over Ca2+ and Mg2+ concentrations are referred to as MHR. Nor-
mal circumstances will create an equilibrium between the Ca2+ and Mg2+ concentrations in
natural water [47,48]. The typical MAR values are 32 mg/L for the pre-monsoon season
and 45 mg/L for the post-monsoon season. The MAR values are between 10 and 66 mg/L
and 17 and 69 mg/L. In the pre- and post-monsoon seasons, Table 3 demonstrates that 81%
and 41% of the samples surpass the allowed limit (50 mg/L). Additionally, the research
area employed the Kelly index [2]. KI (1) (Table 4) indicates that all groundwater samples
in the research area are suitable for irrigation.

4.3. Primary Elements Governing Groundwater Chemistry

4.3.1. Gibbs Plot
The association between the lithological characteristics of the aquifer and its water
composition was discovered using the Gibbs plot. Figure 6a,b shows the Gibbs plot,
 area employed the Kelly index [2]. KI (1) (Table 4) indicates that all groundwater samples
in the research area are suitable for irrigation.

4.3. Primary Elements Governing Groundwater Chemistry

Water 2023, 15, 3916 4.3.1. Gibbs Plot 12 of 22

The association between the lithological characteristics of the aquifer and its water
composition was discovered using the Gibbs plot. Figure 6a,b shows the Gibbs plot, high-
highlighting regions
lighting regions wherewhere precipitation,
precipitation, evaporation,
evaporation, and rock–water
and rock–water contact
contact are theare the
domi-
dominant processes
nant processes [49]. [49]. The prominent
The prominent samplessamples are identified
are identified in theindominant
the dominant
fieldsfields of
of evap-
evaporation and the rock–water interaction in both seasons of the Gibbs plots.
oration and the rock–water interaction in both seasons of the Gibbs plots. Because ground- Because
groundwater percolation
water percolation predominates
predominates with rockwith rock infiltration
infiltration for Cl (Cl+HCO
for Cl (Cl+HCO 3) and Na 3 )(Na+Ca),
and Na
(Na+Ca), weathering and evaporation conditions are present before and after
weathering and evaporation conditions are present before and after the monsoon. Post- the monsoon.
Post-monsoon
monsoon seasonsseasons are less
are less weathered
weathered regarding
regarding Cl (Cl+HCO
Cl (Cl+HCO 3) and3 ) and Na (Na+Ca)
Na (Na+Ca) thanthan
pre-
pre-
and and post-monsoon
post-monsoon seasons.
seasons.

(a)

(b)

Figure 6. (a) Gibbs plot for samples from the pre-monsoon. (b) Gibbs plot for samples from the
post-monsoon.

4.3.2. Piper Plot

Based on the predominate cations and anions, the piper [22] classification is used to
express similarities and differences in the chemistry of various water samples. According
to a Piper trilinear diagram (Figure 7a,b) during the pre-monsoon period, 29% of the water
samples take on a Ca-Cl type, 38% of the samples have a mixed Mg-HCO3 and Ca-Cl type,
and the remaining 33% of the samples have a Mg-HCO3 type of water dominance. In total,
90% of the samples taken during the post-monsoon have the water type Ca-Cl, while the
remaining samples have a mixed water type. It implies that, in both sessions, alkaline earth
minerals outnumbered alkalis [50–52].
 samples take on a Ca-Cl type, 38% of the samples have a mixed Mg-HCO3 and Ca-C
and the remaining 33% of the samples have a Mg-HCO3 type of water dominance. In
90% of the samples taken during the post-monsoon have the water type Ca-Cl, wh
remaining samples have a mixed water type. It implies that, in both sessions, al
Water 2023, 15, 3916 earth minerals outnumbered alkalis [50–52]. 13 of 22

(a)

(b)
Figure
Figure7.
7. (a) Piperdiagram
(a) Piper diagramfor for pre-monsoon
pre-monsoon samples.
samples. (b) Piper(b) Piperfor
diagram diagram for post-monsoon
post-monsoon samples. sa
4.3.3. Wilcox Plot
4.3.3. Wilcox Plot
The groundwater’s ratio of Na to EC computed for both seasons was shown on
the The groundwater’s
Wilcox diagram (Figureratio
8a,b).ofAn
Na to EC computed
improper for both
sample was taken seasons
in both was
the pre- andshown
post-monsoon seasons, as seen in this graph [53,54]. A few small patches
Wilcox diagram (Figure 8a,b). An improper sample was taken in both the pre- and are the only
areas where the salinity and sodium have not increased significantly enough to make the
monsoon seasons, as seen in this graph [53,54]. A few small patches are the only
groundwater region unsuitable for irrigation. Most samples are S1 and were collected in
C1, C21, C3, and C4.
 where the salinity and sodium have not increased significantly enough to ma
Water 2023, 15, 3916
groundwater region unsuitable for irrigation. Most samples are S1 and14 were
of 22
collec
C1, C21, C3, and C4.

(a)

(b)
Figure
Figure 8. (a)
(a)Wilcox
Wilcox plot
plot for for pre-monsoon.
pre-monsoon. (b) plot
(b) Wilcox Wilcox plot for pre-monsoon.
for pre-monsoon.

4.3.4. Box and Whisker Plot

4.3.4. Box and Whisker Plot
Box and whisker plots visually represent how a data set might vary. During the
Box andseasons,
pre-monsoon whisker plots visually
parameters represent
such as Na show upper how a data
quartile set might
maximum vary.
values, During th
while
others including Mg, Cl, HCO 2− and SO 2− show lower quartile maximum values. The
monsoon seasons, parameters 3 , such 4as Na show upper quartile maximum values,

others including Mg, Cl, HCO32−, same,

remaining parameter Ca remains the
and SOin both the lower and the upper quartiles.
42− show lower quartile maximum value
During the post-monsoon seasons, parameters such as Na show upper quartile maximum
remaining
values, whileparameter Ca remains
others including the2−same,
Mg, Cl, HCO in 2both
− showthe lower andmaximum
the upper qu
3 , and SO4 lower quartile
During
values. the
Thepost-monsoon seasons,
remaining parameter parameters
Ca remains suchinas
the same Nathe
both show upper
lower quartile max
and upper
values, while
quartiles. Due others including
to changes Mg, Cl,precipitation
in post-monsoon HCO32−, and SO42− show
occurrences, the lower quartile max
pre-monsoon
seasonsThe
values. haveremaining
higher values than post-monsoon
parameter Ca remainsseasons.
the Figure
same 9a,b shows
in both thethelower
variation
and upper
plot for pre-monsoon and post-monsoon seasons in the selected research region of the
tiles. Due to changes in post-monsoon precipitation occurrences, the pre-monsoon s
Kovilpatti Taluk region [55–59].
have higher values than post-monsoon seasons. Figure 9a,b shows the variation p
pre-monsoon and post-monsoon seasons in the selected research region of the Kov
Taluk region [55–59].
x FOR PEER REVIEW

Water 2023, 15, 3916 15 of 22

(a)

(b)
9. (a) Box plot for pre-monsoon. (b) Box plot for post-monsoon.
Figure 9.Figure
(a) Box plot for pre-monsoon. (b) Box plot for post-monsoon.
4.3.5. Correlation Matrix
The correlation coefficient (r) has a value between +1 and −1. The correlation between
4.3.5. Correlation Matrix
the water quality metrics is well-linked when the r value is between 0.8 and 1, moderate
Thebetween 0.5 and 0.8,coefficient
correlation and weak between
(r) 0.5
hasand
−
a0.value
The strong positive correlation
between +1−
and between
−1. The corr
TDS and EC (0.995), Na (0.901), and SO4 (0.957), and EC with SO4 (0.956) and Na+
+ 2 2

the water quality

(0.893), as well metrics
as Na+ withis
K+well-linked
(0.820), and SO4 2− when the rduring
were present value the is between 0.8
pre-monsoon
− −
TDS with Cl (0.678) and Mg (0.518), and EC with Cl (0.701), Mg (0.5), and K+
2+ 2+
between(0.890).
0.5 and 0.8, and weak between 0.5 and 0. The strong positive corr
(0.784), Ca2+ with Cl− (0.548) and Na+ (0.519), and SO4 2− with K+ all have a moderately
TDS and EC correlation
positive (0.995),(0.660).
Na+ The
(0.901),
parametersand SO
listed 42− (0.957),
in Table and
6a show some weakEC with SO42−
correlations
(0.893), with one another.
as well as Na+ with K+ (0.820), and SO42− were present during th
(0.890). TDS with Cl− (0.678) and Mg2+ (0.518), and EC with Cl− (0.701), M
(0.784), Ca2+ with Cl− (0.548) and Na+ (0.519), and SO42− with K+ all have a
itive correlation (0.660). The parameters listed in Table 6a show some w
with one another.
There is a significant positive correlation between TDS and EC (0.9
Water 2023, 15, 3916 16 of 22

Table 6. (a) Correlation between water quality parameters in pre-monsoon samples. (b) Correlation
between water quality parameters in post-monsoon samples.

(a)
pH TDS EC Cl− Alkalinity Ca2+ Mg2+ Na+ K+ SO4 2−
pH 1
TDS −0.167 1
EC −0.185 0.995 1
Cl− −0.114 0.678 0.71 1
Alkalinity −0.381 0.481 0.478 0.36 1
Ca2+ −0.346 0.461 0.437 0.548 0.328 1
Mg2+ 0.138 0.519 0.5 0.426 0.336 0.11 1
Na+ −0.89 0.91 0.893 0.61 0.345 0.519 0.45 1
K+ −0.33 0.786 0.784 0.635 0.158 0.383 0.218 0.82 1
SO4 2− −0.17 0.957 0.956 0.643 0.435 0.381 0.556 0.89 0.668 1
(b)
pH TDS EC Cl− Alkalinity Ca2+ Mg2+ Na+ K+ SO4 2−
pH 1
TDS 0.5 1
EC −0.2 0.998 1
Cl− −0.19 0.614 0.635 1
Alkalinity 0.22 0.546 0.536 0.23 1
Ca2+ −0.19 0.564 0.559 0.32 0.24 1
Mg2+ 0.36 0.427 0.414 0.146 0.277 0.251 1
Na+ −0.32 0.893 0.89 0.468 0.497 0.537 0.389 1
K+ −0.139 0.771 0.773 0.653 0.229 0.371 0.212 0.818 1
SO4 2− −0.9 0.977 0.973 0.53 0.651 0.582 0.434 0.886 0.715 1

There is a significant positive correlation between TDS and EC (0.998), Na+ (0.893), and
2−
SO4 (0.977), and EC with SO4 2 (0.973) and Na+ (0.890), as well as Na+ with K+ (0.818) and
SO4 2− during the post-monsoon (0.886). The TDS with Cl− (0.614), Alkalinity (0.546), and
Ca2+ (0.564), and EC with Cl− (0.635), Alkalinity (0.536), Ca2+ (0.559), and K+ (0.773), and
Cl− with K+ (0.653) and SO4 2 (0.530), and Ca2+ with Na+ (0.537), SO4 2− (0.582), as well as
K+ with SO4 2 , have a moderate correlation (0.715). Table 6b shows some weak correlations
that exist as well. There was a correlation of 0.995 between EC and TDS, a correlation
of 0.675 and 0.701 for Cl− along with TDS and EC, a 0.548 correlation between Ca2+ and
Cl− , a 0.519 and 0.500 correlation between Mg2+ along with TDS and EC, 0.901, 0.893, and
0.519 correlates with Na+ along with TDS, EC, and Ca2+ , 0.784 and 0.820 correlates K+
along with EC and Na+ , and 0.957, 0.956, 0.890, and 0.668 correlates SO4 2− along with TDS,
EC, Na+ , and K+ ; these have occurred in the pre-monsoon samples collection. There was a
correlation of 0.998 between EC and TDS, a 0.614 and 0.635 correlation for Cl− along with
TDS and EC, a 0.546 and 0.536 correlation with alkalinity along with TDS and EC, 0.564 and
0.559 correlation between Ca2+ with TDS and EC, 0.893, 0.890, and 0.537 correlates with
Na+ along with TDS, EC, and Ca2+ , 0.771, 0.773, 0.653, and 0.818 correlates K+ along with
TDS, EC, Cl− and Na+ , and 0.977, 0.973, 0.530, and 0.582 correlates SO4 2− along with TDS,
EC, Cl− , Na+ , Ca2+ , and K+ ; these are occurred in the post-monsoon samples collection.

4.4. Human Health Risk Assessment (HHRA)

Fluoride and nitrate exposure were evaluated with Average Daily Exposure Dose
(ADDing) and Dermal Exposure Dose (ADDder) for each location. Table 7 summarizes
the Hazardous Human Health Risk Assessment (HHRA) for adults, with ADDing ranging
from 0.84 to 2.3 mg/kg/day for fluoride and 0.1 to 0.66 mg/kg/day for nitrate, averaging
1.518 mg/kg/day. ADDder for nitrate and fluoride ranged from 0.0001 to 0.0004 mg/kg/day
and averaged 0.0002 mg/kg/day. Table 8 outlines the HHRA for children, with ADDing for
nitrate and fluoride ranging from 0.130 to 0.690 mg/kg/day and 4.800 to 6.260 mg/kg/day
Water 2023, 15, 3916 17 of 22

(average: 5.48 mg/kg/day). ADDder for fluoride and nitrate ranged from 0.026 to
0.051 mg/kg/day and 0.0003 to 0.001 mg/kg/day (average: 0.0004 mg/kg/day). Health
Impact (HI) values exceeding 1 suggest severity. For adults, fluoride’s HI ranged from
0.68 to 1.98 (average: 1.338), while children ranged from 2.276 to 3.556 (average: 2.93). The
nitrate HI for adults ranged from 0.12 to 0.82 (average: 0.450), and for children, 0.480 to
1.180 (average: 0.81). Children had higher HI values due to body weight differences [60–66].

Table 7. Different parameters of human health risk assessment for Adults.

Nitrate (NO3 ) Fluoride (F)

S.No Station Name
ADDing ADDder HQing HQder HI ADDing ADDder HQing HQder HI
1 Mooppanpatti 0.6 0.0003 0.82 0.0059 0.82 1.89 0.0023 1.95 0.0009 1.98
2 Illuppaiurani 0.66 0.0004 0.79 0.0068 0.79 2.1 0.0025 1.25 0.0009 1.25
3 Vijayapuri 0.5 0.0003 0.81 0.0036 0.81 1.98 0.0255 1.5 0.0008 1.51
4 Sivandhipatti 0.4 0.0003 0.69 0.0056 0.69 1.45 0.0036 1.36 0.0006 1.36
5 Theethampatti 0.4 0.0004 0.59 0.0024 0.59 1.23 0.0142 1.25 0.0005 1.26
6 VadakkuVandanam 0.3 0.0002 0.45 0.0085 0.45 2.01 0.0223 1.15 0.0001 1.15
7 Chokkalingapuram 0.2 0.0001 0.32 0.0042 0.32 1.96 0.0012 1.26 0.0002 1.28
8 Kadambur 0.1 0.0002 0.42 0.0036 0.42 1.25 0.0023 1.34 0.0004 1.34
9 Melparaipatti 0.1 0.0003 0.25 0.0047 0.25 0.98 0.0015 1.75 0.0003 1.76
10 Uttuppatti 0.2 0.0002 0.36 0.0025 0.36 1.58 0.0215 1.23 0.0002 1.23
11 Mandithoppu 0.3 0.0001 0.25 0.0049 0.25 1.63 0.0123 0.98 0.0001 0.99
12 Thalavaipuram 0.2 0.0001 0.39 0.0036 0.39 1.42 0.0213 0.68 0.0003 0.68
13 Idaiseval 0.2 0.0001 0.12 0.0045 0.12 0.84 0.0021 0.96 0.0005 0.98
14 Akilandapuram 0.1 0.0003 0.25 0.0085 0.25 1.69 0.0036 0.97 0.0004 0.97
15 Kayathar 0.3 0.0002 0.36 0.0061 0.36 1.32 0.0021 0.99 0.0002 1.02
16 Rajapuddukudi 0.4 0.0001 0.46 0.0036 0.46 1.45 0.0034 1.36 0.0003 1.36
17 Attikulam 0.3 0.0001 0.45 0.0042 0.45 1.68 0.0003 1.02 0.001 1.03
18 Therkuilandhaikulam 0.5 0.0002 0.32 0.0074 0.32 0.94 0.0025 1.36 0.0002 1.38
19 Chidambarampatti 0.2 0.0002 0.12 0.0065 0.12 0.96 0.0041 1.96 0.0003 1.97
20 Kumarettiyapuram 0.1 0.0003 0.53 0.0035 0.53 2.3 0.0039 1.74 0.0002 1.75
21 Kalankaraippatti 0.3 0.0002 0.72 0.0014 0.72 1.23 0.0009 1.85 0.0001 1.85
Average 0.302 0.0002 0.450 0.004 0.450 1.518 0.007 1.329 0.0004 1.338
Minimum 0.1 0.0001 0.12 0.0014 0.12 0.84 0.0003 0.68 0.0001 0.68
Maximum 0.66 0.0004 0.82 0.0085 0.82 2.3 0.0255 1.96 0.001 1.98

Table 8. Different parameters of human health risk assessment for Children.

Nitrate (NO3 ) Fluoride (F)

S.No Station Name
ADDing ADDder HQing HQder HI ADDing ADDder HQing HQder HI
1 Mooppanpatti 0.63 0.0005 1.18 0.008 1.18 5.85 0.0276 3.51 0.0246 3.55
2 Illuppaiurani 0.69 0.0006 1.15 0.0089 1.15 6.06 0.0278 2.81 0.0246 2.85
3 Vijayapuri 0.53 0.0005 1.17 0.0057 1.17 5.94 0.0508 3.06 0.0245 3.10
4 Sivandhipatti 0.43 0.0005 1.05 0.0077 1.05 5.41 0.0289 2.92 0.0243 2.96
5 Theethampatti 0.43 0.0006 0.95 0.0045 0.95 5.19 0.0395 2.81 0.0242 2.85
6 VadakkuVandanam 0.33 0.0004 0.81 0.0106 0.81 5.96 0.0476 2.71 0.0238 2.75
7 Chokkalingapuram 0.23 0.0003 0.68 0.0063 0.68 5.92 0.0265 2.82 0.0239 2.86
8 Kadambur 0.13 0.0004 0.78 0.0057 0.78 5.21 0.0276 2.9 0.0241 2.94
9 Melparaipatti 0.13 0.0005 0.61 0.0068 0.61 4.94 0.0268 3.31 0.024 3.35
10 Uttuppatti 0.23 0.0004 0.72 0.0046 0.72 5.54 0.0468 2.79 0.0239 2.83
11 Mandithoppu 0.33 0.0003 0.61 0.007 0.61 5.59 0.0376 2.54 0.0238 2.58
12 Thalavaipuram 0.23 0.0003 0.75 0.0057 0.75 5.38 0.0466 2.24 0.024 2.28
13 Idaiseval 0.23 0.0003 0.48 0.0066 0.48 4.8 0.0274 2.52 0.0242 2.56
14 Akilandapuram 0.13 0.0005 0.61 0.0106 0.61 5.65 0.0289 2.53 0.0241 2.57
15 Kayathar 0.33 0.0004 0.72 0.0082 0.72 5.28 0.0274 2.55 0.0239 2.59
16 Rajapuddukudi 0.43 0.0003 0.82 0.0057 0.82 5.41 0.0287 2.92 0.024 2.96
17 Attikulam 0.33 0.0003 0.81 0.0063 0.81 5.64 0.0256 2.58 0.0247 2.62
18 Therkuilandhaikulam 0.53 0.0004 0.68 0.0095 0.68 4.9 0.0278 2.92 0.0239 2.96
19 Chidambarampatti 0.23 0.0004 0.48 0.0086 0.48 4.92 0.0294 3.52 0.024 3.56
20 Kumarettiyapuram 0.13 0.0005 0.89 0.0056 0.89 6.26 0.0292 3.3 0.0239 3.34
21 Kalankaraippatti 0.33 0.0004 1.08 0.0035 1.08 5.19 0.0262 3.41 0.0238 3.45
Average 0.33 0.00 0.81 0.01 0.81 5.48 0.03 2.89 0.02 2.93
Minimum 0.130 0.0003 0.480 0.004 0.480 4.800 0.026 2.240 0.024 2.276
Maximum 0.690 0.001 1.180 0.011 1.180 6.260 0.051 3.520 0.025 3.556
 11 Mandithoppu 0.33 0.0003 0.61 0.007 0.61 5.59 0.0376 2.54 0.0238 2.58
12 Thalavaipuram 0.23 0.0003 0.75 0.0057 0.75 5.38 0.0466 2.24 0.024 2.28
13 Idaiseval 0.23 0.0003 0.48 0.0066 0.48 4.8 0.0274 2.52 0.0242 2.56
14 Akilandapuram 0.13 0.0005 0.61 0.0106 0.61 5.65 0.0289 2.53 0.0241 2.57
15
Water 2023, 15, 3916 Kayathar 0.33 0.0004 0.72 0.0082 0.72 5.28 0.0274 2.55 0.0239 2.59
18 of 22
16 Rajapuddukudi 0.43 0.0003 0.82 0.0057 0.82 5.41 0.0287 2.92 0.024 2.96
17 Attikulam 0.33 0.0003 0.81 0.0063 0.81 5.64 0.0256 2.58 0.0247 2.62
18 Therkuilandhaikulam Fluoride’s
0.53 0.0004 0.68 for 0.0095
HI values 0.68under4.9
adults were 0.0278 no
1, indicating 2.92 0.0239
adverse effects.2.96
For
19 Chidambarampattinitrate, 0.23 0.0004 0.48 0.0086 0.48 4.92 0.0294 3.52 0.024
high HI values (exceeding 1) correlated with the highest groundwater nitrate 3.56
20 Kumarettiyapuramconcentrations,
0.13 0.0005 caused0.89 0.0056 organic
by fertilizers, 0.89 manure,
6.26 untreated
0.0292 sewage,
3.3 and0.0239 3.34
soil bacteria.
21 Kalankaraippatti Nitrate
0.33 0.0004 impacted
and fluoride 1.08 0.0035 1.08 (70%)
children more 5.19than 0.0262 3.41 High
adults (30%). 0.0238
nitrate 3.45
levels
Average 0.33
risk diseases 0.00
including 0.81
stomach 0.01 0.81 5.48
cancer, methemoglobinemia, 0.03goitre,2.89 0.02
metabolic issues,2.93
birth
Minimum abnormalities,
0.130 0.0003hypertension,
0.480 and cattle poisoning.
0.004 0.480 4.800 Figures 10 and2.240
0.026 11 depict fluoride
0.024 and
2.276
Maximum nitrate HI values
0.690 0.001[67–71].
1.180 0.011 1.180 6.260 0.051 3.520 0.025 3.556

Water 2023, 15, x FOR PEER REVIEW 19 of 22

Figure 10. Health Index (HI) values of nitrate for Adults and Children.
Figure 10. Health Index (HI) values of nitrate for Adults and Children.

Figure 11. Health Index (HI) values of fluoride for Adults and Children.
Figure 11. Health Index (HI) values of fluoride for Adults and Children.

5. Conclusions
According to the outcome of the spatial distribution of the WQI, Kovilpatti Taluk
• better
has As perquality
the Water
fromQuality
July to Index (WQI),
December 5% of pre-monsoon
(pre-monsoon) andrainfall
due to the 9% of post-monsoon
pattern in the
post-monsoon
samples are season. The for
unsuitable Kovilpatti
human Taluk region has been a contaminant during the
consumption.
•
post-monsoon seasons.
All Kovilpatti TalukInwater
thesesamples
regions,meet
sound samplesquality
irrigation are higher duringsuch
indicators pre-monsoon
as the so-
seasons
diumforabsorption
irrigation, ratio
which cansodium
and be determined
percent. by analyzing the RSC, MHR, SAR, Na%,
• Kelly
and ratios. Inthe
Nonetheless, pre-monsoon,
Magnesium Mooppanpatti,
Hazard Ratio and Illuppaiurani, and Vijayapuri
Residual Sodium Carbonatehad high
values
nitrate and fluoride. The human health risk assessment results from the
indicate that 29% of pre-monsoon samples and 59% of post-monsoon samples are analysis of fluoride
and nitrate showfor
unsuitable that the majority
irrigation, whileshow
71%the highest valuesand
of pre-monsoon faced9%byof children than adults.
post-monsoon sam-
So, there
ples is
meeta need for more criteria.
the required proper regulation for the respective zones. Rainfall reduced
•
some risks.
The Kadambu,
observed Melparaipatti,
variation Therkuilandhaikulam,
can be attributed andofVadakku
to the interaction alkaline Vandanam had
earth elements
with both rocks and water, which surpasses the influence of alkali elements, as
demonstrated by the data from the Piper and Gibbs plots. Additionally, the correla-
tion matrix reveals a positive correlation between TDS and EC with chloride, sodium,
and sulfate.
Water 2023, 15, 3916 19 of 22

low risks. Mooppanpatti, Illuppaiurani, and Vijayapuri remained high post-monsoon,

while other zones lowered risks. These regions need proper management plans and reme-
dial measures to reduce the risk. Effective groundwater contaminant management plans
involve regular monitoring, identifying contamination sources, implementing containment
measures and considering remediation options. They also require regulatory compliance,
public awareness and sustainable practices to protect and restore groundwater quality
while safeguarding public health and the environment.

5. Conclusions
• As per the Water Quality Index (WQI), 5% of pre-monsoon and 9% of post-monsoon
samples are unsuitable for human consumption.
• All Kovilpatti Taluk water samples meet irrigation quality indicators such as the
sodium absorption ratio and sodium percent.
• Nonetheless, the Magnesium Hazard Ratio and Residual Sodium Carbonate values
indicate that 29% of pre-monsoon samples and 59% of post-monsoon samples are
unsuitable for irrigation, while 71% of pre-monsoon and 9% of post-monsoon samples
meet the required criteria.
• The observed variation can be attributed to the interaction of alkaline earth elements
with both rocks and water, which surpasses the influence of alkali elements, as demon-
strated by the data from the Piper and Gibbs plots. Additionally, the correlation matrix
reveals a positive correlation between TDS and EC with chloride, sodium, and sulfate.
• The Gibbs plots reveal a comparison between the pre- and post-monsoon seasons,
indicating increased evaporation and decreased weathering, particularly in the case of
Cl+HCO3 and Na+Ca, during the post-monsoon period. The majority of the samples,
such as C1, C21, C3, and C4, fall within the S1 category.
• Box and whisker plots show more pre-monsoon values due to post-monsoon alter-
ations from rainfall.
• Kovilpatti Taluk is moderate primarily for drinking and irrigation, with the pre-
monsoon showing moderate to poor conditions due to industrialization.
• The post-monsoon improves due to precipitation. Due to high nitrate and fluoride
pre-monsoon, Mooppanpatti, Illuppaiurani, and Vijayapuri pose serious health risks.
• Kadambu, Melparaipatti, Therkuilandhaikulam, and Vadakku Vandanam have low risks.
With the post-monsoon, there are higher risks in Mooppanpatti and Illuppaiurani.

Author Contributions: V.S., V.L.R. and U.M.M.—Worked for Article Writing and Software Validation;
S.K. and G.R.—helped for grammer and plagiarism checks; S.S., P.V. and L.N.—supported for plots
preparation and field data collections; H.A.—supported for funding and reviewing the methodology;
M.A.-M. and H.G.A.—contributed for language check and reviewing the results and discussions part.
All authors have read and agreed to the published version of the manuscript.
Funding: The researchers would like to thank the Deanship of Scientific Research, Qassim University
for funding the publication of this project.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data generated or analyzed during this study are included in this
publishing article.
Conflicts of Interest: The authors declare no conflict of interest.

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