assessment of heavy metal contamination in the surface sediments
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Heavy metals, surface sediments, Gulf of Kachchh, Gujarat, IndiaTRANSCRIPT
ORIGINAL ARTICLE
Assessment of heavy metal contamination in the surface sedimentsin the mangrove ecosystem of Gulf of Kachchh, West Coastof India
Goutam Kumar • Manoj Kumar • A. L. Ramanathan
Received: 22 May 2014 / Accepted: 11 January 2015
� Springer-Verlag Berlin Heidelberg 2015
Abstract This study was an attempt to understand the
role of biogeochemical processes in controlling
the heavy metal distribution in the mangroves and to
assess heavy metal pollution load (viz. Cr, Fe, Mn, Co, Ni,
Cu, Zn, Cd, and Pb) in the Gulf of Kachchh, India. Vari-
ous biogeochemical processes and anthropogenic factors
were playing an important role in altering the concentration
of heavy metals in the sediments. A significant correlation
of Mn, Fe and Pb with Zn implies the role of diffused
anthropogenic activities. The organic carbon content
(1.47–3.25 %) and clay content (0.1–2.5 %) seem to play
a significant role in the metal concentration. Factor analysis
suggested two different processes, Factor I strongly indi-
cating anthropogenic activities and Factor II indicating the
combination of natural, marine in situ reduction processes
(biogeochemical) with partial anthropogenic influence.
The enrichment factor (EF) and Geochemical Index (Igeo)
of Cu were higher at Old Bedi Port (S5), whereas both
these values were higher for Cu as well as Cd at Jam-
Salaya (S8). Igeo values indicate that sediments around the
estuarine mouths were polluted with heavy metals as
compared to other locations. Except Chodeshwar (S9), PLI
value [1 was reported in almost all the locations, with a
slightly higher value reported at Narara (S7); (1.57) and
New Bedi Port (S4); (1.56). The findings of this study
would help in formulating guidelines for controlling the
pollution and suggest the ways by which the mangroves of
the Gulf of Kachchh could be revitalized.
Keywords Gulf of Kachchh � Sediment � Heavy metals �Pollution Load Index (PLI) � Average Shale Values
(ASV) � All Composite Locations (ACL)
Introduction
Coastal and marine ecosystems are potentially at risk due
to a high concentration of heavy metals in the sediments.
Salinity (Coakley et al. 1993), freshwater discharge
(Forstner and Whittmann 1981; Chakraborty et al. 2012),
flow rates (Schoellhamer 1995) and geomorphological
setup are important factors which affect the concentration
of heavy metals in the sediments within estuaries. Sedi-
ments made up of different grain size such as clay, silt and
sand, and other geochemical phases like organic material,
oxides of iron and manganese, carbonates and sulphide
complexes, act as potential binding sites for metals which
enter in an estuarine system (Jonathan et al. 2004; Kumar
et al. 2013a, b). Sediments have the ability to accumulate
and assimilate heavy metals even from low concentrations
in the overlying water column (Tam and Wong 2000; El
Nemr et al. 2007). Seawater quality could be directly
affected by the metal contamination of surface sediments.
This may be potentially harmful to the sensitive lowest
levels of the food chain and consequently pose a risk to
human health. Complex processes of exchange of materials
determine the distribution of metals within the aquatic
environments. These processes are influenced by various
anthropogenic activities and by natural processes like
coastal and seafloor erosion, and inputs from riverine,
biological activities, water drainage, industrial effluents
and air-borne matter precipitation (Leivouri 1998; Ip et al.
2007). The toxicity, resistance to degradation and tendency
to bio-accumulate make heavy metals ecologically critical
G. Kumar � M. Kumar � A. L. Ramanathan (&)
School of Environmental Sciences, Jawaharlal Nehru University,
New Delhi 110067, India
e-mail: [email protected]
123
Environ Earth Sci
DOI 10.1007/s12665-015-4062-y
(Diagomanolin et al. 2004). Anthropogenic sources of
heavy metals are textile industries, pipes, metal smelting
industries, fungicide or pesticide industries, landfill leach-
ates and secondary precipitation of polluted airborne matter
(Bandl 1995; Pesticide Information Office 2005).
Estuarine environment of the Gulf of Kachchh has not
been extensively explored with reference to metal con-
taminations. Very limited studies focused on physico-
chemical aspects of water and sediments (GUIDE 2000;
Saravanakumar et al. 2008; Kumar et al. 2010), sediment
dynamics (Pradhan et al. 2004), abundance and seasonal
variations of phytoplankton in creek water (Saravanakumar
et al. 2008) and microbial diversity in the surface sedi-
ments (Kumar et al. 2013a, b) have been done. There is a
high fluctuation in physicochemical conditions due to
active interaction of fluvial-marine environment (Kumar
et al. 2010), surface washout from nearby cities and pre-
cipitation of airborne particles (Takeoka and Murano
1993). The main objective of the current study was to know
about the spatial variation of the various heavy metal
contamination in the estuarine environment of the Gulf of
Kachchh.
Material and method
Study area
The Gulf of Kachchh lies approximately between lati-
tudes 22� to 23�N and longitudes 68� to 70� 300E, with
an area of approximately 7,300 km2 (Fig. 1). The major
portion of the study area (Jamanagar district; Okha to
Jodiya) has already been declared as the Marine National
Park. The climate is semi-arid and the maximum rainfall
is of the order of 50 cm yr-1. There are no major river
flows into the Gulf of Kachchh, but a seasonal runoff has
been observed from some small rivers (viz. Nagavanti
river, Und river and Ghi river). Shore material is the
major source of the sediment in the study area as the
damming of the Indus River has considerably decreased
the amount of sediment delivered to the Arabian Sea
(Giosan et al. 2005, 2006). Many industries like metal
smelting, cement, salt industries, textile and ship dis-
mantling are situated in nearby towns like Jamnagar,
Kandla, Mundra Mandvi, Sikka and Salaya which are the
potential source of heavy metal pollution in the estuary
of the Gulf of Kachchh.
Sample collection and preservation
The representative samples, in triplicate, from nine dif-
ferent locations (n = 27; 1 kg each) of surface sediments
(up to 10 cm depth) were collected in October 2008 from
the inter-tidal regions of the mangroves. The sampling
locations were selected in such a manner as to gain an
insight into the pristine biogeochemical processes and
interaction between the anthropogenic and natural com-
ponents in the Gulf of Kachchh. Samples were collected
from the vicinity of the river mouth to the Gulf, consid-
ering the influence of rivers viz. Nagavanti river (Mundra:
S1), Und river (Jodiya: S2) and Ghi river (Jam-Salaya: S8).
Samples were also collected from Sachana (S3), New Bedi
Port (S4), Old Bedi Port (S5), Sikka (S6), Narara (S7), and
Chodeshwar (S9) to study the anthropogenic influence of
surface washout as well as dry deposition. The samples
were collected in pre cleaned polythene bags in such a
manner to avoid any contamination followed by stored in
the ice chest and transported to the laboratory at Jawaharlal
Nehru University, New Delhi, for further analysis.
Sample analysis
The final powdered form of sediment samples were ana-
lyzed with Energy Dispersive X-ray Fluorescence (ED-
XRF) (model: PANalytical Epsilon 5). The accuracy of the
analytical procedure, which was checked by analyzing the
Standard Reference Materials (SRMs) of Canadian soil
standards (SO-1, SO-2, SO-3 and SO-4), showed a varia-
tion between 5 and 10 %. The dry sieving was carried out
by electromagnetic sieve shaker (Fritsch Analysette-3) into
250 lm (medium sands), 125 lm (fine sands), 63 lm (very
fine sands), 37 lm (very coarse silt) and\37 lm (clay) for
grain size analysis. The further separation of 63 lm was
done by wet sieving by Attenburg Sedimentation Cylin-
der’s Method based on Stokes’ law. The textural parame-
ters were computed using the formulae as defined by Folk
and Ward (1957) (Kumar et al. 2010). Organic carbon of
the sediment samples was estimated using the Walkley–
Black method (1934).
Enrichment factor (EF), Index of Geo-accumulation
(Igeo) and Pollution Load Index (PLI)
The extent of sediment contamination was assessed using
the Enrichment Factor (EF) and Geo-accumulation Index
(Igeo). EF is a good tool to differentiate the occurrence of
metal pollution through natural or anthropogenic activities
(Morillo et al. 2004; Selvaraj et al. 2004). Using EF, the
concentrations of the metals in the sediments were nor-
malized to the textural characteristic of the sediments. The
normalization of the metals in the sediments is usually
done with Al which is found in the form of aluminasilicates
and is the principal component of costal sediments. The EF
can also be used to determine the degree of sedimentation
(Lee et al. 1998; Huang and Lin 2003; Woitke et al. 2003).
For the metals under consideration, the EF values were
Environ Earth Sci
123
interpreted with respect to crust average values (Taylor and
Kolbe 1964; Birth 2003). According to Dragovic et al.
(2008) the metal EF calculations can be performed using
Eq. (1):
EF ¼ M½ �= Al½ �ð Þsoil= M½ �= Al½ �ð Þcrust ð1Þ
where [M] is the concentration of desired element under
investigation and [Al] is the concentration of Al in soil and
crust, respectively. Concentration of each element of crust
has been taken from Taylor and Mclennan (1995).
According to Taylor and Kolbe (1964) there is no enrich-
ment if the EF \ 1, there is a minor enrichment if EF \ 3,
a moderate enrichment if EF = 3–5 and extremely severe
enrichment if EF [ 50.
The Igeo is calculated to assess the metal pollution in
soils (Muller 1979) using Eq. (2):
Igeo ¼ log2 Cn=1:5� Bnð Þ; ð2Þ
Fig. 1 Sampling locations (IRS
P6-LISS III image, October,
2008) of the study area
Environ Earth Sci
123
where Cn is the measured concentration of heavy metal in
the soil, Bn is the geochemical background value in average
shale (Taylor and Mclennan 1995) of element n and 1.5 is
the background matrix correction in factor due to litho-
genic effects.
Value of Igeo indicates the level of contamination. Based
on the Igeo data/Muller’s geo-accumulation indexes and
their respective classes, the contamination level with
respect to each metal at various locations is ranked with\0
(class: 0) uncontaminated, 0–1 (class: 1) uncontaminated to
moderately contaminated, 1–2 (class: 2) moderately con-
taminated and sequentially [5 (class: 6) indicates extre-
mely contaminated.
Tomlinson et al. (1980) had utilized a method based on
PLI to evaluate the degree of pollution by metals. PLI
provides a comparative means to assess the quality of a site
or estuarine. PLI was calculated using the following
equation:
CF ¼ Cmetal=Cbackground ð3Þ
PLI ¼n p CF1 � CF2 � CF3 � . . . CFnð Þ; ð4Þ
where CF = contamination factor, Cmetal = concentration
of pollutant, Cbackgrounds = background value for the
metal and n is the number of metals. A value of 0 on the
PLI would indicate no pollutants; a value of 1 would
suggest the presence of baseline levels of pollutants,
while values that exceed 1 would indicate a progressive
deterioration of the site and estuary (Tomlinson et al.
1980). These values can be obtained as Concentration
Factor (CF). Concentration Factor is the quotient obtained
by dividing the concentration of each metal. While
computing the CF of sediments of the study area, world
average concentrations of these elements reported for
Shale (Turekian and Wedepohl 1961) were taken as the
background values.
Statistical analysis
Pearson Correlation analysis is a bivariate method com-
monly used to measure and establish the relationship
between two variables. It is generally used to measure the
degree of dependency of one variable to the other (Kumar
et al. 2013a, b). This statistical tool was applied to the
results of heavy metals. Factor Analysis was done using
IBM SPSS statistics software (Version 19.0.1). Varimax
rotation with Kaiser Normalization scheme was used for
extraction of factors to explain the observed relationship
among the variables. R-mode factor analysis was used to
identify the major factors controlling the geochemistry of
heavy metals in the surface sediments of the Gulf of Ka-
chchh. If the Eigen value is greater than 1, it reflects a
significant contribution of the corresponding factor.
Results and discussion
Grain-size distribution and organic carbon
in the surface sediments
Shore material and the load brought by the Indus River are
the major sources of sediment into the Gulf of Kachchh
(Zingde 1999). The damming of the Indus River has sig-
nificantly lowered the amount of sediment delivered to the
Arabian Sea (Giosan et al. 2005, 2006). The decrease in the
supply of sediment of the Indus River has resulted in a
change in the Indus delta. The Gulf of Kachchh still
receives sediments through the tidal erosion of the aban-
doned delta. The material eroded from this region can be
brought to the Gulf of Kachchh by both tidal and long-
shore currents. The import of fine-grained sediments into
high tidal areas is aided by tidal processes, particularly by
settling lag effects (Bartholdy 2000). A major portion of all
sediment samples contained sand in the range of
77.20–97.93 %. It was also observed that the silt content
ranged from 0.60 to 21.45 % and the clay content ranged
from 0.1 to 2.5 % (Table 1).
Major element geochemistry
Industrial development and urbanization have resulted in
the intertidal flats in the estuary to be contaminated with a
large concentration of heavy metals. Sediments and residue
from the mangroves are major sinks for metals (Du et al.
2006). The extent of industrial effluents and atmospheric
deposition in the catchment area is important in deter-
mining the metal binding in the sediments; it is also
dependent on the degree to which the sediment can bind
and release metals. This is influenced by the physical and
chemical characteristics of the soil and the sediment (Du
Laing et al. 2002).
Table 1 Average particle size
and organic carbon content (%)
of the surface sediments
Types S1 S2 S3 S4 S5 S6 S7 S8 S9
Sand 77.20 78.50 94.80 95.50 96.40 96.30 97.59 96.36 97.93
Silt 21.45 19.0 4.88 4.17 3.50 3.46 0.60 2.80 1.65
Clay 1.35 2.5 0.32 0.33 0.1 0.24 1.81 0.84 0.42
Organic carbon 3.25 1.78 2.80 2.09 2.18 1.47 2.54 2.92 2.82
Environ Earth Sci
123
Average content of clay and organic carbon at Jodiya
(S2) were observed at 2.5 and 1.78 %, respectively; while
at Mundra (S1) it was 1.35 and 3.25 %, respectively.
Heavy metal concentration in the sediments of Gulf of
Kachchh has been estimated. The estimated concentration
of heavy metals excedded the Average Shale Value (ASV)
to the mean values of All Composite Locations (ACL)
except for Pb (Table 2).
Chromium (Cr)
The Cr concentration at all the locations crossed the ASV
of 90 mg/kg. The highest mean Cr concentration was
observed at Jodiya (S2) while the lowest was at Mundra
(S1) with a mean values of 255 (ranged 166–362) and
120 mg/kg (ranged 113–123 mg/kg), respectively. Greater
anthropogenic influence (Lo and Fung 1992) and higher
clay content in the sediment lead to a higher accumulation
of Cr at Jodiya. The lowest mean concentration of Cr at
Mundra (S1), may be due to less anthropogenic influence
and comparatively low clay proportion. The mean con-
centration of ACL was 163 mg/kg (ranged 119–269 mg/
kg).
Copper (Cu)
The highest mean Cu concentration was observed at Old
Bedi Port (S5) with a mean value of 138 mg/kg (ranged
122–153 mg/kg) and the lowest was observed at Mundra
(S1) with a mean value of 44 mg/kg (ranged 37–50 mg/
kg). The mean Cu concentration at Mundra (S1) was less
than the ASV of 45 mg/kg which is almost half of the ACL
value of 86 mg/kg. The plot of heavy metals versus EF
(Fig. 2) indicated the moderate enrichment of Cu at Old
Bedi Port (S5) and Jam-Salaya (S8), and Igeo, computed
also showed Cu in moderate contamination (Fig. 3).
Organic carbon concentration (Ramos et al. 1999; Zhou
et al. 1998) along with the tendency of the sediments of the
mangrove ecosystem to capture the fine suspended solid
and chemical precipitation (Chakraborty et al. 2014) and
existence of old port may lead to higher Cu concentration
at Old Bedi Port. Higher mean Cu concentration in the
surface sediments at Jam-Salaya (S8) may be due inorganic
chemical complex of Tata Group’s Tata Chemical Ltd
(Business Standard 2013, online sources) and existence of
some favorable cusp-shaped geomorphological formation
on the location.
Cadmium (Cd)
The mean Cd concentration exceeded the ASV of
0.30 mg/kg at all the locations except Sachana (S3)
which had a mean value of 0.173 mg/kg. The highest
mean Cd concentration (23 mg/kg) was observed at Jam-
Salaya (S8). Further, higher mean Cd concentration
(18 mg/kg) was observed at Chodeshwar (S9). Discharge
of industrial and agricultural waste was found to be
responsible for Cd contamination in Sikka and Vadinar,
Gulf of Kachchh (Ghrefat and Yusuf 2006; Chakraborty
et al. 2014). It was observed from Fig. 2, Jam-Salaya
(S8) was moderatly enriched with Cd while Chodeshwar
(S9) was minor, also supported by Igeo plot (Fig. 3). The
reason for higher enrichment of the Cd at Jam-Salaya
(S8) may be similar to that of Cu. Further presence of
higher organic carbon in the sediment also may be the
reason for higher concentration of Cd in the sediments of
both the locations. Man et al. (2004) reported that Cd has
the greatest tendency toward remobilization from the
sediment phase to the more bio-available pore water
phase.
Iron (Fe) and manganese (Mn)
Iron is found as an abundant element in the continental
crust; enrichment of Fe in the form of Fe oxyhydroxides
occurs due to the root aeration of mangrove and tidal
influence (Otero et al. 2009). The mean Fe concentration at
all the locations exceeded the ASV of 46,000 mg/kg except
at Jam-Salaya (S8) and Chodeshwar (S9). The mean Fe
ACL was observed to be 56,563 mg/kg. The mean Mn
concentration of ACL was observed to be 1,020 mg/kg
(ranged 853–1,170 mg/kg) which is higher than the ASV
of 850 mg/kg.
Cobalt (Co)
The mean Co concentration at almost all the locations
exceeded the ASV of 19.01 mg/kg, but it was nearly
equivalent at Mundra (S1). The mean ACL value of Co was
observed at 23 mg/kg (ranged 17–27 mg/kg).
Zinc (Zn)
The highest mean Zn concentration (160 mg/kg) was
observed at Old Bedi Port (S5) with a ranged of
149–171 mg/kg. The mean Zn concentration was reported
lower than that for the ASV of 95 mg/kg at Mundra (S1),
Jodiya (S2), New Bedi Port, Jam-Salaya (S8) and Cho-
deshwar (S9), while its values exceeded at the remaining
locations. Higher concentration of Zn in the estuarine
sediments has been attributed to anthropogenic activities
viz. industrial tailing and wastes (Donazzolo et al. 1984).
The higher concentration of Zn at various locations did not
show any riverine influence. Further comprehensive
investigations may reveal the exact source of Zn input in
the estuarine system of the current study area.
Environ Earth Sci
123
Ta
ble
2H
eav
ym
etal
con
cen
trat
ion
inth
esu
rfac
ese
dim
ent
wit
hav
erag
esh
ale
val
ues
(un
its:
mg
/kg
)
S1
S2
S3
S4
S5
S6
S7
S8
S9
AC
LA
SV
**
Cr
Mea
n1
20
25
51
35
14
11
45
18
21
32
19
91
64
16
39
0
Ran
ge
11
3–
12
31
66
–3
62
11
8–
15
11
37
–1
49
12
8–
15
71
67
–1
92
12
0–
14
21
91
–2
12
14
2–
18
31
19
–2
69
Cu
Mea
n4
47
17
16
21
38
10
96
51
30
87
86
45
Ran
ge
37
–5
06
7–
73
67
–7
54
4–
77
12
0–
15
38
9–
11
95
9–
76
11
2–
14
46
9–
11
14
4–
13
7
Cd
Mea
n0
.33
10
.39
80
.17
30
.40
80
.45
90
.42
60
.46
22
3.0
88
18
.11
24
.80
.30
Ran
ge
0.2
0–
0.4
20
.07
–0
.66
0.1
0–
0.2
20
.31
–0
.56
0.2
2–
0.6
60
.21
–0
.54
0.2
4–
0.6
61
2–
36
12
–2
20
.15
–2
4
Fe
Mea
n5
6,9
10
69
,62
07
3,1
40
72
,97
07
8,3
80
82
,34
07
2,2
30
1,1
40
98
05
6,5
63
46
,00
0
Ran
ge
47
,89
2–
64
,36
55
8,9
32
–7
8,3
64
62
,51
6–
88
,46
86
4,3
82
–8
8,4
34
72
,68
0–
88
,68
05
3,7
66
–1
03
,62
86
6,2
89
–7
6,8
40
86
0–
1,4
10
96
0–
1,0
20
95
3–
86
,21
6
Co
Mea
n1
82
32
32
22
32
52
32
62
52
31
9.0
1
Ran
ge
10
–2
21
6–
34
18
–2
61
7–
26
21
–2
62
0–
29
20
–2
42
2–
28
21
–3
01
7–
27
Zn
Mea
n8
68
31
03
86
16
01
23
10
40
.70
0.2
98
29
5
Ran
ge
77
–9
37
0–
98
89
–1
14
80
–8
91
49
–1
71
11
5–
13
69
8–
11
00
.46
–0
.95
0.1
2–
0.5
60
.3–
16
0
Mn
Mea
n8
50
1,0
00
1,1
30
1,0
40
1,1
10
1,0
00
98
01
,14
09
80
1,0
20
85
0
Ran
ge
81
0–
90
08
80
–1
,10
01
,05
0–
1,2
10
92
0–
1,1
20
89
0–
1,2
60
81
0–
1,2
10
89
0–
1,0
30
98
0–
1,3
20
89
0–
1,0
30
85
3–
1,1
70
Pb
Mea
n1
51
41
81
82
53
41
62
31
82
02
0
Ran
ge
14
–1
71
2–
15
16
–2
11
2–
23
19
–3
02
9–
38
15
–1
82
1–
27
8.7
–2
51
3–
33
Ni
Mea
n5
17
06
96
76
67
76
87
87
76
95
0
Ran
ge
44
–6
46
1–
77
42
–8
85
6–
72
49
–8
17
3–
80
65
–7
26
9–
89
73
–8
05
2–
80
AS
Vav
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,A
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all
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61
)
Environ Earth Sci
123
Lead (Pb)
The highest mean Pb concentration (34 mg/kg) was
observed at Sikka (S6) with a range of 19–38 mg/kg. The
average concentration of Pb in the sediment of Indian
rivers has been reported at *14 mg g-1 (Dekov et al.
1999). Few locations (viz. Old Bedi Port (S5), Sikka (S6)
and Jam-Salaya(S8)) exceeded the ASV of 20 mg/kg. This
can be explained by these locations being greatly influ-
enced by atmospheric inputs and industrial influences
(Notling and Helder 1991). The mean ACL was observed
equal to ASV.
Nickel (Ni)
The mean Ni concentrations at all the locations exceeded
the ASV of 50 mg/kg and the ACL value of 69 mg/kg
(ranged 52–80 mg/kg).
There is no much salinity variation in different locations
due to absence of Perennial Rivers in the study area. Hence
there is not a specific variation observed in heavy metals
except Cu and Cd, which is higher in at Narara (S7), Jam-
salaya (S8) and Choweswar (S9) in comparison to Jodiya
(S2), Sachana (S3) and New Bedi Port (S4). It may be
attributed due to flocculation and coagulation processes
along with favorable geomorphological formations. High
concentrations of these metals in mangrove sediments
indicate that the mangrove systems trap the metals either in
physical form (as fine material) or in chemical form
(through the precipitation of metals from solution) (Harb-
ison 1986). Association between metal ions and organic
matter takes place with the ligands of organic matter in the
sediment through the functional groups like –NH2, –OH
and –COOH. This generates stable organic metal com-
plexes (Riffaldi et al. 1983), which may explain the high
concentration of heavy metals in the vicinity of the mouths
of the rivers that flow into the study area. Mean value of
heavy metal concentration indicated relatively low con-
tamination of almost all the heavy metals at Mundra (S1)
revealing its relatively pristine environment.
Statistical analysis
Correlation analysis
The Pearson correlation matrix reveals that Co and Ni have
a very strong correlation with Fe, while Co has a significant
correlation with Mn. This suggests that these metals may
be adsorbed onto the oxyhydroxides of Fe and Mn. An
insignificant correlation of Cr, Ni and Cd with most of the
metals indicates that external inputs may be operating in
the mangrove sediments. Zinc shows strong positive cor-
relation with Cu and significant correlation with Pb, Fe and
Mn (Table 3). The abnormally high concentration of Zn in
Fe
Mn
Pb
Cd
Zn
Cu
Ni
Co
Cr
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Mod
erat
e en
richm
ent
Min
or e
nric
hmen
t
N
o en
richm
ent
Enrichment Factors (EF)
Hea
vy M
etal
s S1 S2 S3 S4 S5 S6 S7 S8 S9
Fig. 2 Enrichment factor (EF) of various metals in surface sediments
of Gulf of Kachchh
0
50
100
150
200
250
300
-2 -1 0 1 2
Con
cent
ratio
n (m
g/K
g)
Igeo values
Cr Co Ni Cu Zn Cd Pb Mn**
Unc
onta
min
ated
Unc
onta
min
ated
to m
oder
atel
y co
ntam
inat
ed
Mod
erat
elyc
onta
min
ated
Fig. 3 Heavy metal
concentrations (average) versus
Igeo plot for surface sediments
of mangrove ecosystem of Gulf
of Kachchh (Mn unit is g/Kg)
Environ Earth Sci
123
the offshore sediment samples is related to anthropogenic
activities such as industrial tailing and wastes (Donazzolo
et al. 1984). The correlation of Zn with Fe (r = 0.55), Mn
(r = 0.59) and Pb (r = 0.70) clearly indicates that the high
levels of these elements are related to the presence of
diffused anthropogenic activities (viz. municipal discharge,
city runoff and vehicular emission).
Factor analysis
Two factors with an Eigen value[1 were identified, which
explain about 75.38 % of the total variance (Table 4).
Factor 1 accounts for 52.65 % variance in the dataset and
shows a high loading of almost all elements except Fe. This
factor explains a contribution from industrial activities,
surface runoff and waste disposal. Factor 2 accounts for
22.73 % variance in the dataset and shows a strong loading
of Zn, Pb and Fe, which may be a good indicator of natural
and marine in situ processes like reduction of Fe by man-
grove roots along with some anthropogenic contribution.
Association of Cu, Cd, Pb and Zn with each other suggests
anthropogenic influence (Bloemen et al. 1995; Davies
1997). These metals often associate in urban systems, and
their relationship in the mangrove ecosystem reflects the
influence of rapid urbanization and industrialization.
Enrichment factor (EF)
Enrichment Factor is a good tool to differentiate the sources
of heavy metals in the environment such as anthropogenic
and natural inputs (Morillo et al. 2004; Vald’es et al. 2005).
By applying this statistical tool, the concentrations of the
metals were normalized with Al, a predominant element
found in the form of aluminasilicates in the marine sedi-
ments. The mean EF values of the metals were studied with
respect to crust average background concentration (Turekian
and Wedepohl 1961). The highest EF value of Cr (3.49) was
found in the sediments of Jodiya (S2) near the mouth of Und
River which received a high volume of effluents from the
industries and sewage discharges in the neighborhood. It is
also supported by the presence of high clay content in the
surface sediments (Table 1).
Sediments in the vicinity of the confluence of the rivers
Ghi Nadi (main influence), Jambudi Nadi and Api Nadi,
i.e. Jam-Salaya (S8), contain a higher value of organic
carbon in the surface sediment and show a high degree of
metal contamination. The EF values were 3.87 for Cd, 3.78
for Cu and 1.49 for Mn. Cd had the highest EF value
amongst the nine metals at Jam- Salaya (S8). The highest
EF value of Co, Cu, Pb and Fe was reported at Sikka (S6)
which may be due to waste water discharge, the disman-
tling of old ships and fall out deposition. The EF values
were greater than 3 (3.49 for Cr, 3.87 for Cd and 3.78 for
Cu) which indicate moderate contamination at Jodiya (S2)
and Jam- Salaya (S8). The lowest value of Cr, Co, Ni, Cu,
Mn and Fe were reported at Mundra (Fig. 2). The result
indicates moderate enrichment of Cr, Cd and Cu at the
various locations.
Index of geo-accumulation (Igeo)
Muller’s (1979) expression was applied to calculate the Igeo
values for the metals studied. Our results show that
Table 3 Pearson correlation
matrixes for heavy metals
* Significant correlation
** Strong to very strong
correlation
Element Cr Co Ni Cu Zn Cd Pb Fe Mn
Cr 1.00
Co 0.49 1.00
Ni 0.52* 0.99** 1.00
Cu 0.31 0.55* 0.49 1.00
Zn -0.02 0.36 0.27 0.86** 1.00
Cd 0.41 0.37 0.34 0.72* 0.49 1.00
Pb 0.11 0.54* 0.49 0.56* 0.70* 0.36 1.00
Fe 0.31 0.96** 0.92** 0.66* 0.55* 0.40 0.67* 1.00
Mn 0.23 0.60* 0.56* 0.74* 0.59* 0.33 0.33 0.66* 1.00
Table 4 Principal and varimax rotated R-mode factor loading matrix
Variables Factor 1 Factor 2 Communities
Cr 0.752 – 0.568
Co 0.929 – 0.872
Ni 0.928 – 0.863
Cu 0.796 0.453 0.839
Zn 0.574 0.717 0.844
Cd 0.747 0.345 0.677
Pb 0.609 0.616 0.750
Fe – 0.811 0.664
Mn 0.742 0.394 0.706
Eigen value 4.739 2.046 –
% of variance 52.652 22.730 –
% of cumulative variance 52.652 75.382 –
Italics indicate high loading values
Environ Earth Sci
123
sediments near the mouths of all the rivers were more
polluted than the other locations for heavy metals. The Igeo
index, when computed for Gulf of Kachchh, shows that few
metals (viz. Cr, Cu and Cd) fall within Class 1 and Class 2
of Muller’s grade scale (Fig. 3). Cu was ranked moderate
(Igeo, class = 2) for sediments at Old Bedi Port (S5) and
Jam-Salaya (S8). Cd at Jam-Salaya (S8) was also ranked
moderate (Igeo, class = 2) for sediments. This might indi-
cate that the Gulf of Kachchh has a higher accumulation of
Cu and Cd metals which apparently come from the
catchment area. Influence of anthropogenic activities can
be assessed by means of the geoaccumulation index (Igeo)
(Muller 1979) in the tropical estuarine system. High
accumulation of Cu at Old Bedi Port (S5) and Jam-Salaya
(S8) and Cd at at Jam-Salaya (S8) shows anthropogenic
influences.
Pollution Load Index (PLI)
The PLI values of the analyzed samples ranged from 0.78
to 1.57 which confirmed that the sediments of the study
area are progressively deteriorating (Table 5). A higher
value of PLI was reported at Narara (S7) contributed by Cd
and Fe in the sediments, whereas the presence of Fe, Cd
and Cr contributed to the higher value of PLI at New Bedi
Port (S4). The PLI provided a clear view of the deterio-
ration of the estuarine quality in the mangrove ecosystem
of area studied. Higher concentration of heavy metals may
be caused by external distinct anthropogenic sources like
agricultural runoff, industrial waste water discharge and
atmospheric dry deposition (Fernandes and Nayak 2012). It
also provides valuable information to the policy makers on
the pollution levels in the study area which is part of a
National Park.
Comparative discussion
Heavy metal pollution in the mangrove sediments is caused
by both natural as well as anthropogenic factors. The
Pichavaram mangrove was reported as being relatively
unpolluted and only a minor anthropogenic effect was
observed which acts as a sink for heavy metals (Ramana-
than et al. 1999). The Ulhas estuary has been reported as a
moderately polluted ecosystem due to the impact of
industrial and domestic waste inputs (Fernandes and Nayak
2012). The metal pollution in the mangrove sediment in the
Gulf of Kachchh is mainly due to the anthropogenic factors
in the form of drained water of seasonal rivers and indus-
trial wastes. The effluents from the metallurgy-based fac-
tories directly or indirectly cause high concentration of
heavy metals in the mangrove sediments. The metal con-
centration in the surface sediments is higher than the ASV
at most of the sampling locations (Table 2). This shows Ta
ble
5A
ver
age
con
cen
trat
ion
s(A
),co
nce
ntr
atio
nfa
cto
rs(B
)an
dP
oll
uti
on
Lo
adIn
dex
(PL
I)o
fh
eav
ym
etal
sin
surf
ace
sed
imen
ts
Cr
Co
Ni
Cu
Zn
Cd
Pb
Fe
Mn
PL
I
AB
AB
AB
AB
AB
AB
AB
AB
AB
AS
V*
90
19
50
45
95
0.3
20
46
,00
08
50
S1
11
9.7
31
.33
17
.50
0.9
25
1.1
41
.02
43
.93
0.9
88
5.9
80
.91
0.3
31
.10
15
.31
0.7
75
6,9
10
1.2
48
51
1.0
01
.27
S2
25
4.8
32
.83
22
.88
1.2
06
9.6
81
.39
70
.87
1.5
78
3.4
50
.88
0.4
01
.33
13
.94
0.7
06
9,6
20
1.5
11
,00
01
.18
1.0
2
S3
13
5.4
71
.51
22
.74
1.2
06
8.7
71
.38
71
.08
1.5
81
02
.77
1.0
80
.17
0.5
81
7.9
70
.90
73
,14
01
.59
1,1
30
1.3
31
.19
S4
14
1.3
61
.57
22
.27
1.1
76
6.7
61
.34
62
.10
1.3
88
5.6
30
.90
0.4
11
.36
17
.60
0.8
87
2,9
70
1.5
91
,04
01
.22
1.5
6
S5
14
4.9
11
.61
22
.80
1.2
06
6.2
31
.32
13
7.8
83
.06
15
9.9
51
.68
0.4
61
.53
25
.16
1.2
67
8,3
80
1.7
01
,11
01
.31
1.2
4
S6
18
2.0
32
.02
25
.39
1.3
47
7.0
61
.54
89
.28
1.9
81
23
.20
1.3
00
.43
1.4
23
3.9
21
.70
82
,34
01
.79
1,0
00
1.1
81
.30
S7
13
2.0
91
.47
22
.54
1.1
96
8.0
41
.36
65
.35
1.4
51
03
.62
1.0
90
.46
1.5
41
6.4
50
.82
72
,23
01
.57
98
01
.15
1.5
7
S8
19
9.0
52
.21
25
.76
1.3
67
8.3
91
.57
15
2.8
43
.40
12
9.9
61
.37
0.7
02
.32
23
.09
1.1
51
,14
00
.02
1,1
40
1.3
41
.08
S9
16
3.7
61
.82
25
.24
1.3
37
6.7
31
.53
65
.10
1.4
58
7.1
50
.92
0.2
90
.98
18
.11
0.9
19
80
0.0
29
80
1.1
50
.78
*A
SV
aver
age
shal
ev
alu
es
**
Wo
rld
geo
chem
ical
bac
kg
rou
nd
val
ue
(mg
/kg
)in
aver
age
shal
e(T
ure
kia
nan
dW
edep
oh
l1
96
1)
Environ Earth Sci
123
that the sediments are contaminated with heavy metals in
the study area. The mean values of Mn, Cu, Cd, Cr, Ni, Pb
and Zn were observed as 701, 32, 6.96 141.2, 62, 11.2 and
89 mg/kg, respectively, in the Pichavaram mangrove (Ra-
manathan et al. 1999) (Table 6). In another study done by
Fernandes and Nayak (2012) at the Ulhas Estuary, the
mean values of Mn, Cu, Co, Cr, Ni, Pb and Zn were 2,077,
173, 79, 239, 190, 82 and 180 mg/kg, respectively.
Our results, with mean concentrations of Mn (1,025), Cu
(84), Cd (0.41) Co (23), Cr (163), Ni (69), Pb (20) and Zn
(107) (mg/kg), respectively, show a higher concentration of
all metals than at Pichavaram mangrove and slightly less
than that at the Ulhas estuary. Zinc and Pb concentration at
the Godavari estuary were lesser than the current study.
The concentration of Pb and Cu was higher in our study
area than at the Krishna estuary, while the concentration of
other metals like Mn, Cu, Ni and Zn was comparatively
higher. The comparative study indicates that the study area
is moderately polluted with heavy metals.
Conclusions
The statistical analysis and comparative study of the sedi-
ments show that the study area was minor to moderately
contaminated with heavy metals. Clay and organic carbon
content are crucial in the accumulation of heavy metals in
the sediments. Statistical analysis reveals a very strong
correlation of Co and Ni with Fe and a significant corre-
lation of Co with Mn. This indicates the adsorbing char-
acter of metals onto the oxyhydroxides of Fe and Mn.
Further, a good correlation of Mn, Fe and Pb with Zn
indicates the presence of diffused anthropogenic activities.
The EF values of various heavy metals show moderate
enrichment of Cr, Cd and Cu at various locations. How-
ever, Igeo values indicate that sediments in the vicinity of
the mouths of all the rivers were more polluted than those
in the other locations with heavy metals. Moderate con-
tamination was observed for the Cu and Cd with the help of
Igeo computed for all metals. PLI values were observed to
be greater than one in most of the locations, indicating
progressively deteriorating estuarine environment. All
statistical analyses reveal anthropogenic contribution to the
pollution load of heavy metals in Gulf of Kachchh. The
findings of the study would enable the formulation of more
effective watershed and estuary management guidelines
and thus help regulate the metal discharges into the estuary.
Acknowledgments This research was supported by the UGC-CSIR,
GOI for providing Junior Research Fellowship (JRF). The authors are
thankful to the Forest Department, Gujarat, for granting permission to
carry out current research work in the reserved forest area. Authors
are also thankful to School of Environmental Sciences, Jawaharlal
Nehru University, for providing the lab facilities for the analysis.
Thanks are also due to the valued reviewers for making the manu-
script more effective with the help of their appropriate comments and
suggestions.
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