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ORIGINAL PAPER
An investigation on the assessment of mercury concentration and its
spatial distribution in Kodaikanal Lake sediments, South India
Balamurugan Palani
1
&Sivaprakasam Vasudevan
1
&Thirunavukkarasu Ramkumar
1
&Selvaganapathi Rajendiran
1
Received: 31 December 2020 /Accepted: 15 July 2021
#Saudi Society for Geosciences 2021
Abstract
In this study, the anthropogenic sources, the toxic concentration of mercury (Hg), and its spatial distribution were investigated by
using fourteen different sediment samples, collected from different locations in Kodaikanal Lake. The research was carried out by
evaluating different parameters such as sediment characteristics and the concentration of mercury. The study also involves estab-
lishing pollution indices like enrichment factor, index of geo-accumulation, contamination factor, and a potential ecological risk
factor for the sediment samples. The characterization studies were done by energy dispersive X-ray fluorescence (EDXRF) and the
particle size analyzer to determine the concentration of mercury and to classify the sediments based on the size of the particles,
respectively. Statistical analysis and the spatial distribution of mercury were assessed by using two different software tools such as
Geostatistic (SPSS Statistics software 17.0) and Geostatistics analyst module (ArcGIS 10.1). The weight percentage of sand, silt,
and clay in the collected sediment samples were found to be 61.24 to 83.55%, 15.24 to 36.78%, and 0.92 to 1.98% respectively. The
weight percentage of organic matter was from 6.00 to 16.00% and total carbonate content varies from 2.22 to 7.54%. The results
obtained from the EDXRF confirmed that the concentration of mercury in the collected samples ranges from 19 to 30 mg/kg of
sediment and it indicates that almost all parts of the lake exhibit notably higher concentration. Pearson’s correlation coefficient value
of 0.74 signifies the association of Hg to the depth of the lake. The high loading values of mud (0.92), Hg (0.91), and water depth
(0.86) for PC1 are concerning the 61.10% expressed, the same source for Hg and fine particles and they were, transported and
deposited together in deeper depth. The Hg content in the sample to enrichment factor exhibits high to very high (20.95 to 33.81)
and index of geo-accumulation with moderate to strongly polluted nature of the sediments (2.34 to 3.00). The fine-grained
sediments, water depth, and organic matter were found to be significant controlling factors of Hg distribution in the sediments of
the lake. The values of EF and I
geo
show that the enhancement of sediment by heavy metal (Hg) was by anthropogenic activities
such as discharge of the solid waste from the thermometer factory. Additionally, the contamination factor and potential ecological
risk factors were calculated as 47.52 to 75 and 1900 to 3000 respectively, and express the prevalence of very high contamination
factors and very high ecological risk. The results also suggest that Hg in lake sediments represents its polluted nature; it could also be
influenced by industrial and human activities in the catchment.
Keywords Mercury pollution .Industrial activities .Spatial distribution .Kodaikanal Lake
Introduction
The exponential growth of industrialization and urbanization
leads to the deposition of the huge amount of heavy metals in
the aquatic system (Frignani and Bellucci 2004; Almeida et al.
2007;Ubwa2013; Abuduwaili et al. 2015; Islam et al. 2015;
Yan et al. 2016; Balamurugan et al. 2017;Emmanueletal.
2018, Cao et al. 2021). The aquatic contamination of mercury
assesses mainly due to effluents released from the thermome-
ters, barometers, and fluorescent lamp factories. Mercury (Hg)
is a liquid heavy metal that has the property of highly volatile
and also easily bio-accumulates in the aquatic species (Coelho
et al. 2005; García-Rico et al. 2006; Shi et al. 2010; Torres
Escribano et al. 2011). The huge amount of mercury which
was released into the environment diffused into the remote
Arctic Circle due to the long range of atmospheric transport
Responsible Editor: Amjad Kallel
*Balamurugan Palani
palanibala2@gmail.com
1
Department of Earth Sciences, Annamalai University, -608 002,
Annamalai Nagar, India
Arabian Journal of Geosciences (2021) 14:1629
https://doi.org/10.1007/s12517-021-08033-y
(Fitzgerald et al. 1998; Cheng and Schroeder 2000;Durnford
et al. 2010). In addition to that, mining, smelting combustion
of fossil fuels, agricultural fertilizer, and industries are the
major reason for increasing mercury concentration in the
aquatic regions and it is ultimately deposited on the soil/
surface sediment and surface water (Zhang and Wong 2007;
Chen et al. 2012; Pan et al. 2018). Sediments are excellent
indicators of mercury and important in showing natural vari-
ability and responsiveness to changes in mercury loading to
watersheds.
Generally, the sediments with a higher amount of silt and
clays have a great capacity to accumulate mercury even in
lower concentrations and act as the environmental indicators
of mercury contamination which helps to map, trace, and
monitor the anthropogenic source of mercury pollution caused
by the geochemical natural process. Many authors prefer to
express the mercury pollution in surface sediments of shel-
tered environments, like lakes, reservoirs, estuaries, lagoons,
and bays (Pereira et al. 1998; Schintu and Degetto 1999;
Harland et al. 2001; Hortellani et al. 2005), due to human
(anthropogenic) activities.
Geographical Information Systems (GIS)–based spatial
distribution pattern gives more knowledge for the assessment
of heavy metals (mercury) on the lake sediment and to delin-
eate contamination zones (Omran and Abd El Razek 2012;
Wu et al. 2014; Balamurugan et al. 2017;Dingetal.2018).
Zheng (2006) expresses the use of inverse distance weight
(IDW) procedures assists spatial interpolation of heavy metal
distribution patterns in the sediments.
The main scope of the study has to evaluate mercury (Hg)
concentration in the Kodaikanal Lake sediments and distin-
guish between natural and anthropogenic sources of the mer-
cury using different indexes like enrichment factor (EF), index
of geo-accumulation (I
geo
), contamination factor (C
f
), and po-
tential ecological risk factor (E
r
). The spatial distributions of
mercury concentration for EF, I
geo
,C
f
,andE
r
were also
adopted in the present study to establish variation in its distri-
butional pattern and the factors that influence the spatial dif-
ferences within the Kodaikanal Lake.
Materials and methods
Study area
The research area, Kodaikanal Lake, is situated at Palani Hills,
Dindigul district, Southern India, and it looks star-shaped
amid evergreen lush slopes (about 2130 m above mean sea
level). Kodaikanal Lake is a man-made lake located in the
heart of Kodaikanal, and it is a closed basin with a total area
of about 24 ha. Sir Vere Henry Levinge, the then Collector of
Madurai, was instrumental in creating the lake in 1863. The
lake is said to be Kodaikanal’s most popular geographic
landmark and tourist attraction. The lake falls between the
latitudes 10° 13′N and 10° 14′N and longitudes 77° 28′E
and 77° 29′E of Kodaikanal (Fig. 1). The bathymetry map also
reveals that the lake basin is having undulated bottom floor
with a maximum water depth of 11 m.
The lake is located in the north of the thermometer factory
(Hindustan Unilever Limited). Chesebrough-Pond’s moved
the thermometer factory from the USA to India in 1982 due
to the increased awareness about its potential toxic effect in
developed countries. In 1987, Hindustan Unilever Limited
(HUL) acquires the thermometer factory from Pond’sIndia.
The factory imported mercury from the US and exported pro-
duced thermometers to markets in the USA and Europe. The
factory produced 100,000 to 150,000 thermometers per
month. After 18 years of operation, the Hindustan Unilever
thermometer factory shut down in 2001 due to the public
agitation and complaints of factory workers. The lake and
waste dump site are situated within a 1-km radius from the
thermometer factory (Hindustan Unilever Limited,
Kodaikanal) and fall in the southern catchment area of the
lake. In 2001, the thermometer factory was responsible for a
7-tonne mercury spill resulting from the company's improper
storage and disposal of mercury. Mody (2001)alsoreported
that Kodaikanal Lake is heavily polluted due to the dumping
of solid waste and discharging of mercury-contaminated ef-
fluent from the thermometer factory.
Sample collection and analytical procedures
In these research works, fourteen surface sediment samples
(approximately 2 kg in each location) were collected at differ-
ent locations of the lake by using a Van Veen grab sampler
onboard hired fishing traveler. The Van Veen grab sampler
can penetrate up to 20 cm and collect the sample representing
the surface sediment. The sampling point locations were
mapped by using GPS (Global Position System). The sedi-
ment samples were collected in clean and sealed Ziploc-
polyethylene plastic bags and stored in the deep freezer to
avoid contamination. Before the sedimentological and mercu-
ry analysis, the samples were dried at 45°C for 48 h. Finally,
the amount of organic matter (OM), total carbonate content
(TCC), and mercury (Hg) were measured. To understand the
anomalous metal concentration, geochemical normalization
of heavy metal data to the conservative elements such as Fe
was used. Several authors have successfully used Fe to obtain
normalized heavy metal contaminants (Mucha et al. 2003).
Hence, in the present study, Fe was used as a conservative
tracer to differentiate nature from anthropogenic components.
The represent amount of samples for analysis were obtained
through the cone and quartering method.
The particle sizes of the samples were analyzed by using
Horiba LA-300 scattering particle size analyzer. In order to
obtain particle size measurement, the sediment samples were
1629 Page 2 of 12 Arab J Geosci (2021) 14:1629
pretreated with H
2
O
2
and HCl to reduce the influence of or-
ganic matter and carbonates. A small amount of sodium
hexametaphosphate (NaPO
3
) was also added to completely
disperse the fine particle (Aasif et al. 2018). From the obtained
result, the sediment samples were classified based on the par-
ticle size according to Folk’s(1974). In addition to that, the
sediment samples were standardized by the Wentworth grade
scale method to determine the sediment size fraction
(Wentworth 1922). The reference values are in the ranges
for sand size > 62.5 μm to 2 mm, silt size > 4 μmto62.5
μm, and clay size > 1 μmto4μm.
The percentage of the organic matter and carbonate content
present in the collected sediment samples was evaluated by
the wet oxidation method. The wet oxidation method was
preferred to avoid the destruction of grains during pretreat-
ment. The most preferred oxidants are potassium dichromate
(Walkley and Black 1934) and hydrogen peroxide
(Schumacher 2002; Allen and Thornley 2004; Mikutta et al.
2005). In this experiment, known amounts (100 g) of sediment
samples were initially treated with 30% hydrogen peroxide
(H
2
O
2
). Hydrogen peroxide was continually added to the sam-
ple until the sample frothing ceased and then samples were
dried at 110°C for 24 h (Tiit Vaasma 2008). Further, the dried
samples were treated with concentrated HCl to determine the
percentage of total carbonate content in the sediment
(Schumacher 2002). The percentage of the organic matter
and carbonate content was determined by taking the difference
between the weights of sediments before and after treating
with H
2
O
2
and concentrate HCl respectively.
For heavy metal analyses, all representative samples were
analyzed for the concentration of mercury (Hg) and iron (Fe)
using energy-dispersive X-ray fluorescence spectrometry
(EDXRF) in the Department of Analytical Chemistry,
Bhabha Atomic Research Center (BARC), Mumbai, India.
For the EDXRF analyses, the sediment samples were pow-
dered and then allowed to pass through the size of the mesh
sieve ASTM-230. The calibration of X-ray fluorescence was
carried out by using a thin-film XRF calibration standard
(Micrometer
TM
). A standard soil (NIST SRM 2781 and
2702) was used as standard reference material for standardiz-
ing the instrument. Chemically pure silica (SiO
2
) was used as
a blank and the elemental analysis were carried out with
triplets.
Data interpretation
The spatial distribution maps are being created by using the
Geostatistical analyst tool, ArcGIS v 10.1 by ESRI software.
From the statistical method of analysis, the minimum, maxi-
mum, mean, standard deviation, Pearson’s correlation coeffi-
cient, and principal component analysis were evaluated by
using Microsoft Excel and SPSS Statistics software v 17.0.
Fig. 1 Location map of the Kodaikanal Lake
Arab J Geosci (2021) 14:1629 Page 3 of 12 1629
Pearson’s correlation coefficient and principal component
analysis was utilized to evaluate the relationship between sed-
iment characteristics and mercury concentration and their pos-
sible source.
Data analysis
The enrichment factor (EF) for an element was calculated by
using the Earth’s crustal average (Taylor 1964). To identify
anomalous metal concentrations, a geochemical standardiza-
tion of heavy metal data into conservative elements, such as
Fe, was used. Inthis study, EF was calculated by the given Eq.
(1):
EF ¼El=FeðÞ
sample =El=FeðÞ
crust ð1Þ
where (El/Fe)
sample
is the ratio between Hg and Fe content
in the sediment sample and (El/Fe)
crust
is the ratio between Hg
and Fe average abundant in the continental crust (Taylor
1964). The enrichment factor values are divided into six con-
tamination categories (Birth 2003): no enrichment for EF < 1,
minor enrichment for EF < 3, moderate enrichment for EF =
3–5, moderately high enrichment for EF= 5–10, high enrich-
ment for EF = 10–25, very high enrichment for EF = 25–50,
and extremely high enrichment for EF > 50.
Muller and Suess (1979) clearly defined that the intensity
of contamination in sediments was evaluated by using the
index of geo-accumulation (I
geo
). The index of geo-
accumulation was calculated by the following Eq. (2):
Igeo ¼log2Cn=1:5Bn
ðÞ ð2Þ
where C
n
is the calculated concentration of examined metal
nin the sediment, B
n
is the geochemical background value in
average shale (Turekian and Wedepohl 1961) of the metal n,
and 1.5 is the background matrix correlation factor of
lithogenic effect. Muller (1969) has distinguished into seven
classes of the index of geo-accumulation as follows: I
geo
≤0is
unpolluted (class 0), 0 to 1 is unpolluted to moderately pollut-
ed (class 1), 1 to 2 is moderately polluted (class 2), 2 to 3 is
moderate to strongly polluted (class 3), 3 to 4 is strongly
polluted (class 4), 4 to 5 is strong to very strongly polluted
(class 5), > 5 is very strongly polluted (class 6).
The contamination factor (C
f
)ismainlyusedtoevaluate
the ratio between the amounts of metal concentration present
in sediments with respect to the concentration of metal in the
crust which is given in Eq. (3).
Cf¼Cmsample
=Cmcrust ð3Þ
where C
msample
is the metal concentration in the sediment
and C
mcrust
is the pre-industrial background values of metal.
Hakanson (1980) defined four classes of contamination factor
as follows: C
f
< 1 (low contamination), 1 ≤C
f
<3 (moderate
contamination), 3 ≤C
f
< 6 (considerable contamination), and
C
f
≥6 (very high contamination).
The potential ecological risk factor (E
r
) assesses the risk of
heavy metal concentration (Hakanson 1980). The potential
ecological risk factor is considered a synergy, heavy metal
concentration, high level toxic, and ecological sensitivity of
heavy metals (Nabholz 1991; Singh et al. 2010; Douay et al.
2013). The parameter is expressed as follows:
Er¼TrCfð4Þ
where T
r
is the toxicity response factor of heavy metal and
toxicity response for Hg = 40 (Hakanson 1980), and the C
f
is
the contamination factor of heavy metal. The permissible limit
of the potential ecological risk factor for heavy metals is as
follows: < 40 is low-risk factor, 40 to 80 is a moderate risk
factor, 80 to 160 is moderateto the high-risk factor, 160 to 320
is high-risk factor, and >320 is very-high-risk factor
(Hakanson 1980).
Result and discussion
The experimental investigations include parameters such as
sediment grain size, organic matter, total carbonate content,
and concentration of mercury. Pearson’s correlation coeffi-
cient, principal component analysis, enrichment factor, index
of geoaccumulation, contamination factor, and potential eco-
logical risk factor were also evolved by using fourteen differ-
ent sediment samples collected from Kodaikanal Lake to un-
derstand the natural abundance, anthropogenic influence, and
its affinity with other parameters. The calculated values from
the obtained data are given in Table 1.
Sediment characteristics
In this study, the sediment grain size was determined and the
percentage of sand, silt, and clay were given in Table 1.The
obtained result reveals that the sediment samples contain from
61.24 to 83.55 weight % of sand, from 15.24 to 36.78 weight
% of silt, and from 0.92 to 1.98 weight % of clay. The mean
content of sand, silt, and clay were found to be 72.29 weight
%, 26.40 weight %, and 1.31 weight % respectively.
Additionally, the sediments were classified according to the
grain size by using Folk’s(1974) classification method.
Figure 2clearly shows that the distribution patterns of the
sediments of the collected samples are clustered tightly in
the field of sandy silt nature. Moreover, the spatial distribution
pattern also exhibits that the Kodaikanal Lake sediments are
mostly dominated by sandy silt fraction. The grain sizes are
increased near the confluence of the lake and the fine particles
of mud (silt + clay) are deposited in the deeper parts of the
lake.
1629 Page 4 of 12 Arab J Geosci (2021) 14:1629
The amount of organic matter and carbonate contents in the
sediment was evaluated and the results are given in Table 1.It
depicts that the minimum and maximum percentage of organ-
ic matter present in all the fourteen sediment samples was
from 6.00 to 16.00 weight % and the mean values were
10.65 weight %. The obtained values confirm that the organic
matter controlled by the grain size pattern and the accumula-
tion of organic matter in the sediments has been identified as
the main compound which plays a significant role in the
control and transport of heavy metal concentration (Gaiero
et al. 1997). The higher organic matter in Kodaikanal Lake
sediments is mainly due to the inflow of organic waste from
the industries and terrigenous sources. The weight percent of
carbonate content in the collected sediments was around 2.22
to 7.54 weight % and the mean value was determined as 4.28
weight %. The weight percentage of carbonate contents ex-
hibits that the sediments were occupied by shells and frag-
ments of bivalves and also the presence of shell in the surface
Table 1 Sediment characteristics, organic matter, total carbonate content, water depth, heavy metals, and ecological risk assessment in the Kodaikanal
Lake sediments
Sample
no.
Sand (%) Silt (%) Clay (%) Mud (%) OM (%) TCC (%) Water depth (m) Fe (%) Hg (mg/kg) EF I
geo
C
f
E
r
1 68.30 30.66 1.04 31.70 7.50 2.22 4.50 10.34 28 31.95 2.90 70.00 2800
2 66.93 32.14 0.93 33.07 6.00 2.93 4.10 10.35 27 30.78 2.85 67.50 2700
3 77.27 21.75 0.98 22.73 10.50 5.42 4.30 10.30 28 32.08 2.90 70.00 2800
4 75.03 23.78 1.19 24.97 9.95 3.89 3.30 10.22 26 30.02 2.79 65.00 2600
5 77.88 20.24 1.88 22.12 12.00 2.78 3.20 10.20 24 27.76 2.68 60.00 2400
6 62.76 36.32 0.92 37.24 10.50 7.54 4.60 10.21 29 33.52 2.95 72.50 2900
7 61.24 36.78 1.98 38.76 15.50 5.86 4.75 10.90 30 32.48 3.00 75.00 3000
8 63.31 35.57 1.12 36.69 12.00 4.43 4.40 10.12 29 33.81 2.95 72.50 2900
9 76.28 21.85 1.87 23.72 16.00 6.96 4.60 10.52 28 31.41 2.90 70.00 2800
10 80.39 17.69 1.92 19.61 9.00 5.77 3.90 10.46 20 22.56 2.42 50.00 2000
11 81.88 17.15 0.97 18.12 10.20 2.40 3.50 10.70 19 20.95 2.34 47.50 1900
12 83.55 15.24 1.21 16.45 9.50 2.39 3.70 10.72 22 24.22 2.55 55.00 2200
13 68.09 30.76 1.15 31.91 9.60 2.64 4.40 10.70 28 30.88 2.90 70.00 2800
14 69.21 29.66 1.13 30.79 10.22 3.54 4.50 10.66 28 30.99 2.90 70.00 2800
Min 61.24 15.24 0.92 16.45 6.00 2.22 3.20 10.12 19 20.95 2.34 47.50 1900
Max 83.55 36.78 1.98 38.76 16.00 7.54 4.75 10.90 30 33.81 3.00 75.00 3000
Mean 72.29 26.40 1.31 27.71 10.65 4.28 4.13 10.46 26.14 29.53 2.79 65.36 2614.28
Fig. 2 Ternary plot (Folk’s1974)
showing relative percentage of
sand, silt, and clay fractions in the
Kodaikanal Lake sediments
Arab J Geosci (2021) 14:1629 Page 5 of 12 1629
sediments are mainly due to the accumulation of calcium car-
bonate. It suggests that the levels of carbonate contents in
Kodaikanal Lake are of biogenic origin.
Mercury concentration in the surface sediments
The concentration of mercury in the surface sediment con-
firms that the concentration of mercury in the sediments
ranges from 19 mg/kg as a minimum in location 11 and 30
mg/kg as a maximum in location 7 (Fig. 3) with the mean of
26.14 mg/kg. The mercury concentrations have been com-
pared with permissible limits and standards like Post
Archean Australian Shale (PAAS) standard by Taylor and
McLennan (1985), and NASC by Condie (1993), and upper
continental crust (UGC) standards by Taylor and McLennan
(1985). The comparison of mercury level in the lake sedi-
ments with the standards expresses that the concentration is
higher than the permissible limit. The spatial distributional
pattern (Fig. 4) of Hg concentration showed that higher con-
centration spreads in almost all parts of the lake, moderate
concentration in the central part of the lake (location 5), and
lesser concentration in the western part of the lake. The prev-
alence of higher mercury concentration in the major parts of
the lake is mainly due to the discharge of wastewater from the
thermometer factory (Hindustan Unilever Limited) which is
located within the proximity limit of 1.0 km and falls in the
catchment area of the lake. The waste dump site of the indus-
try also necessitates the Hg inflow into the lake along with the
water and sediments.
Statistical analysis
Pearson’s correlation coefficient clarifies the relationship of
heavy metals and the importance of the main cause in the
geoenvironment (Gopal et al. 2017; Karthikeyan et al. 2018;
Godson et al. 2018; Vasiliu et al. 2020). It was employed to
identify the relationship between textural characteristics and
Hg concentration. The Pearson’s correlation coefficients used
among sand, mud, organic matter (OM), total carbonate
content (TCC), water depth, and concentration of Hg are giv-
en in Table 2. The correlation of the surface sediments shows
that the Hg is negatively correlated with sand and has a high
positive correlation with mud. It is mainly due to the higher
adsorption capacity of mud because of its fine size and higher
surface-volume ratio. The relationship between variables
identifies a clear comparison with the mud vs Hg (r
2
=
0.83), water depth (r
2
= 0.69), TCC (r
2
= 0.26), and OM (r
2
= 0.10); positive correlation of OM vs Hg (r
2
=0.28),water
depth (r
2
=0.27),andTCC(r
2
= 0.56) respectively; and in
addition to that, the coefficient of TCC vs Hg (r
2
= 0.35) and
water depth (r
2
= 0.49). The observed relationship between
OM and TCC shows a positive correlation (r
2
=0.56).This
implies that the carbonate content in the lake sediments are
sourced from the biogenic shell fragments of bivalves along
with the calcium carbonate accumulation. The good relation-
ship between the water depth vs Hg (r
2
= 0.74) signifies the
prevalence of favorable conditions for the deposition of finer
particles in deeper depths. The high positive correlation hap-
pens between Hg and mud (r
2
= 0.83), Hg and water depth (r
2
= 0.74), and mud and water depth (r
2
= 0.69), which expresses
that the depth favors the accumulation of Hg along with the
finer particles.
Principal component analysis (PCA) is a general multivar-
iate analysis which is used in various environmental studies to
identify the source of the metal in sediments and interpreting
their spatial variations (Bai et al. 2011; Anju and Banerjee
2012; Islam et al. 2015; Gopal et al. 2017; Karthikeyan et al.
2018). The relationship among the metals analyzed based on
the three principle components was presented in three-
dimensional spaces (Fig. 5;Table3). The PC1 and PC2 were
obtained with eigenvalues > 1, explaining more than 84% of
the total variance. In the present study, the 6 variables from
KodaikanalLake sediments are summarized by three principal
components (PCs), with the percentage of cumulative of
61.10, 84.09, and 91.91 respectively. PC1 explains 61.10%
of the total variance and reveals high loading values for mud,
Hg, and water depth with 0.92, 0.91, and 0.60, respectively.
The significant positive correlation between the fine particles
Fig. 3 Relationship between
mercury concentration (mg/kg)
and different sampling sites in the
Kodaikanal Lake
1629 Page 6 of 12 Arab J Geosci (2021) 14:1629
and Hg exhibits that they were transported from the same
source and accumulated together into the lake at the favorable
deeper depth. PC2 (23% of the total variance) is the strong
loading of organic matter and total carbonate content with
0.81 and 0.70 respectively and shows high positive loading
mainly because of the source from the biochemical process,
such as agriculture, shola forest land use, rainwater runoff, and
human activities. PC3 (sand) shows no factor loading with
any parameter. Therefore, in this study, it is observed that
Hg, OM, and TCC are positively correlated, interconnected,
and bound to the fine particles.
Ecological risk assessment
Enrichment factor (EF) is a common approach for evaluating
the anthropogenic impact on sediments which has to analyze
normalized enrichment factor for metal concentrations above
the uncontaminated reference level (Salomons and Forstner
1984; Dickinson et al. 1996; Hornung et al. 1989). Table 1
depicts that the value of enrichment factor of mercury (Hg) in
the Kodaikanal Lake sediments ranges from 20.95 to 33.81
with a mean of 29.53 and falls within the category of high to
Fig. 4 Spatial distributions of mercury (Hg) concentration in the Kodaikanal Lake
Table 2 Pearson (r) correlation coefficients of sediment texture, organic
matter, total carbonate content, water depth, and Hg in the Kodaikanal
Lake sediments
Sand Mud OM TCC Water depth Hg
Sand 1
Mud −1.0** 1
OM −0.10 0.10 1
TCC −0.26 0.26 0.56* 1
Water depth −0.69** 0.69** 0.27 0.49 1
Hg −0.83** 0.83** 0.28 0.35 0.74** 1
Bold numbers mark the high positive correlation
*Correlation is significant at the 0.05 level (2-tailed)
**Correlation is significant at the 0.01 level (2-tailed)
Table 3 Principal component analysis loading of sediment texture,
organic matter, total carbonate content, water depth, and Hg in the
Kodaikanal Lake sediments
Parameters Component
123
Sand −0.92 0.33 0.08
Mud 0.92 −0.33 −0.08
OM 0.36 0.81 −0.45
TCC 0.53 0.70 0.41
Water depth 0.86 0.05 0.26
Hg 0.91 −0.11 −0.15
Eigenvalue % 3.67 1.38 0.47
Variance % 61.10 23.00 7.82
Cumulative % 61.10 84.09 91.91
Arab J Geosci (2021) 14:1629 Page 7 of 12 1629
very high enrichment. As shown in Fig. 6, the spatial distri-
bution pattern of EF values of Hg reveals that the high enrich-
ment factor was found in the western part and very high en-
richment factor in the rest of the lake. The direct inflow of Hg
along with sediment and water took place in the southern part;
the rest of the region of the lake received its mercury pollutant
through the atmospheric wet deposition apart from its direct
inflow.
The index of geo-accumulation (I
geo
) was used to define
the degree of anthropogenic pollution in the sediments. As
shown in Table 1, the index of geo-accumulation for Hg in
the Kodaikanal Lake sediments ranges from 2.34 to 3.00 with
ameanof2.79.TheI
geo
index reveals that all the samples fell
in the category of moderate to strongly polluted by mercury.
Visualizing the degree of mercury contamination in the
Kodaikanal Lake sediments through spatial interpolation ex-
poses the pollution status of the lake ecosystem (Fig. 7). In the
spatial distribution of Hg parades, the Kodaikanal Lake was
moderately polluted in nature in the western part and strongly
polluted in rest of the parts of the lake which was relatively
Fig. 5 Rotated loading showing
correlation of sediment texture,
organic matter, total organic
content, water depth, and Hg
Fig. 6 Spatial distributions of enrichment factor in the Kodaikanal Lake
1629 Page 8 of 12 Arab J Geosci (2021) 14:1629
similar to the result obtained in EF. The spatial distributional
patterns of the mercury express its deviation to its concentra-
tion over space. The whole lake is moderate to very strongly
polluted by mercury which confirms the Hg originating from
anthropogenic inputs, including industrial activities.
In this study, the contamination factor (C
f
) was determined
for all the collected sediments and the values are given in
Table 1. From the obtained values, the contamination factor
of Hg in Kodaikanal Lake sediments are ranging from 47.50
to 75.00 with a mean of 65.36 which falls under the category
of very high contamination factor. The spatial distribution of
C
f
values of Hg is shown in Fig. 8. It expresses that the west-
ern part of the lake is having a low contamination factor (C
f
)
value and the northeastern part expresses the highest C
f
value.
It suggests that the lake is experiencing very high contamina-
tion factor and indicates a serious anthropogenic influence.
The potential ecological risk posed by mercury in the sed-
iments from Kodaikanal Lake was assessed by the method
followed by Hakanson (1980). The potential ecological risk
values for all fourteen samples were calculated individually
and given in Table 1. From obtained values, it exhibits the
potential ecological risk factor contribution of Hg in
Kodaikanal Lake sediments which ranges from 1900 to
3000 with the mean of 2614.28. It represents that all the col-
lected sediment samples come under very high risk with re-
spect to Hg concentration. Therefore, the ecological risk of
mercury in sediments of Kodaikanal Lake is very high level.
The interacted mercury from the Hindustan Unilever Limited
increased the ecological risk component of the Kodaikanal
Lake.
Conclusions
From the experimental study, we calculated that the lake basin
at deeper depth favors the settlement of finer fractions like silt
and clay (mud). Organic matter content in the lake suggests
the inflow of organic wastes from the industries and terrige-
nous sources. The lake is medium-grained carbonate sands;
most are of biogenic origin. The mercury concentration was
analyzed for all fourteen samples by using EDXRF and the
concentration of mercury was found in the range of 19 to 30
mg/kg. The major reason for the higher degree of mercury
contamination at downstream reservoirs (Kodaikanal Lake)
is mainly due to floating material that comes from the streams
which were highly influenced by mercury concentration. The
Kodaikanal Lake is fed by the Pambar stream by Periyakulam
Plains. The Pambar Stream which feeds several ponds in
Periyakulam before joining Varaha River and emptying into
the Vaigai runs beneath Hindustan Unilever’s mercury-
contaminated thermometer factory site (Nityanand et al.
2017). The mercury vapor can be released into the air and
migrates long distances which leads to widespread pollution.
And also, the thermometer factory site is situated at a deeper
elevation and drainage is also likely to carry the Hg extensive-
ly deposited in the soil of the factory premises (Dames URS
Fig. 7 Spatial distributions of the index of geo-accumulation in the Kodaikanal Lake
Arab J Geosci (2021) 14:1629 Page 9 of 12 1629
and Moore 2002; Karunasagar et al. 2006) to the lake. The
Pearson correlation coefficient (0.74) signifies the association
of Hg with mud is too high at deeper depths of the Kodaikanal
Lake. The obtained result illustrates that the degree of mercury
contamination in the sediments has a high to very high nature
of EF and the index of geo-accumulation is moderate to very
strongly polluted with very high contamination factor and
potential ecological risk is very high risk. Eventually, the
study reveals that Kodaikanal Lake is highly contaminated
with the Hg and also heavily polluted due to the solid waste
from the waste dumpsite and the thermometer factory. The
grain size is the dominant factor in controlling the distribution
of Hg as a function of the sedimentation process of water
depth. Industry and other anthropogenic activities accelerate
the rate of inflow of Hg into the lake through the greater
erosion of soils by tourism, agriculture, and road develop-
ment, etc. in the catchment area with proximity to industry.
The study reveals that the Hg in the lake sediments was
sourced from industrial and anthropogenic activities.
Acknowledgements The authors are thankful to the Analytical
Spectroscopy Section, Department of Analytical Chemistry Division,
Bhabha Atomic Research Centre (BARC), Mumbai, India, for the sup-
port at varying stages of this work. The manuscript was greatly improved
by the valuable comments from the editors and anonymous reviewers.
Funding The authors gratefully acknowledge the Promotion of
University Research and Scientific Excellence (PURSE), and the
Science and Engineering Research Board (SERB), Department of
Science and Technology, New Delhi, for its financial support in the form
of a research grant.
Declarations
Conflict of interest The authors declare that they have no competing
interests.
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