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Regional Studies in Marine Science
journal homepage:www.elsevier.com/locate/rsma
Assessment of heavy metal contamination in the surface sediments of the Vedaranyam coast, Southern India
Veeramalai Gopal
a, Ramasamy Ramasamy Krishnamurthy
a, Ravichandran Vignesh
a, Chellamuthu Sabari Nathan
a, Raju Anshu
b, Rajaram Kalaivanan
c, Perumal Mohana
d, Nochyil Sivan Magesh
e, Karuppasamy Manikanda Bharath
f,g, Armel Zacharie Ekoa Bessa
h,∗, Kamal Abdelrahman
i, Mohamed Abioui
j,k,∗aDepartment of Applied Geology, University of Madras, Guindy Campus, Chennai, Tamil Nadu 600085, India
bDepartment of Geology, University of Madras, Guindy Campus, Chennai, Tamil Nadu, 600085, India
cDepartment of Coastal Disaster Management, Port Blair Campus, Pondicherry University, Port Blair, Andaman and Nicobar Islands 744112, India
dCentre for Remote Sensing and Geo-informatics (A Joint Initiative of ISRO and Sathyabama University), Sathyabama University, Chennai, Tamil Nadu 600119, India
eCentre for Water Resources Development and Management, Kozhikode, Kerala 673571, India
fInstitute for Ocean Management, Anna University, Chennai, Tamil Nadu 600 025, India
gNational Institute of Technical Teachers Training and Research, Government of India, Ministry of Education, Department of Higher Education, Taramani, Chennai, Tamil Nadu 600 113, India
hInstitute of Earth Sciences (ISTE), University of Lausanne, Lausanne 1015, Switzerland
iDepartment of Geology & Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
jGeosciences, Environment and Geomatics Laboratory (GEG), Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, Agadir 80000, Morocco
kMARE-Marine and Environmental Sciences Centre - Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra 3004 531, Portugal
a r t i c l e i n f o
Article history:
Received 18 December 2022 Received in revised form 6 June 2023 Accepted 24 June 2023
Available online 1 July 2023 Keywords:
Coastal sediments Heavy metals Environmental indices Multivariate statistics Southern India
a b s t r a c t
The marine sediments of the Vedaranyam coast were examined for their heavy metal contamination largely contributed by natural and anthropogenic activities. Sediment samples were collected and assessed for metals such as chromium (Cr), copper (Cu), manganese (Mn), iron (Fe), Nickel (Ni), zinc (Zn), and cobalt (Co). The individual toxic risk index (TRIi) of metals in this study is significantly variable for Cr (0.88–1.10), Cu (1.78–2.73), Zn (2.33–4.81) and Ni (1.10–4.62). TRIi in the sediments is below 5 for Cr, Cu, Zn, and Ni suggesting the absence of any toxic risk in the aquatic life. To assess the natural and anthropogenic contamination as well as to know the sediment quality, various indices such as the geo-accumulation index, enrichment factor, pollution load index, and contamination factor were computed. The enrichment factor result shows that the heavy metals are primarily from anthropogenic sources. There is a high positive correlation among the heavy metals, indicating a similar origin.
©2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
1. Introduction
Heavy metal contamination in the aquatic ecosystem is an emerging risk due to its toxicity towards species living in water and the possibility to transfer into the human food chain, creating health problems (Sin et al.,2001; Yuan et al.,2011; Zhu et al., 2013; Ahmed et al.,2015; Suresh et al., 2015; Malvandi,2017;
Chatterjee et al., 2007). Generally, many heavy metals adhere to the sediment in the aquatic system; however, they can re- suspend in the water column under favourable pH conditions
∗ Corresponding authors.
E-mail addresses: [email protected](A.Z. Ekoa Bessa), [email protected](M. Abioui).
(Varol, 2011). Heavy metals in flowing water are periodically redistributed, whereas, in sediments, they accumulate and reside for a more extended period, potentially becoming importunate contaminants (Ghrefat and Yusuf,2006). Due to global population increase and rising industrial and agricultural production, large quantities of harmful chemicals, particularly heavy metals, have been dumped into rivers worldwide (Shikazono et al.,2012;Liu et al.,2016).
Riverine sediments play an essential role in the deposition and movement of metals (Antizar-Ladislao et al., 2015). Sediments can be utilized to assess anthropogenic metal contamination in the aquatic environment because sediment can act as a carrier and a secondary source for heavy metals in the water (Ma et al., 2016). The distribution of heavy metals can be assessed based
https://doi.org/10.1016/j.rsma.2023.103081
2352-4855/©2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Fig. 1. Study area map showing the sampling locations.
on the extent of sediment contamination, potential risk, heavy metal mobility, and bioavailability (Caeiro et al.,2005). According to Kaushik et al. (2008), industrial and domestic activities in India heavily rely on the riverine system for discharging waste, thus avoiding treatment or recycling costs. Fast urbanization and industrialization along the coastal zones are significant sources of metal pollutants (Gutiérrez-Mosquera et al.,2018).
The geo-accumulation index (Igeo) and the pollution load index (PLI) show the overall quality of the sediments as well as the level of pollution in the area (Tomlinson et al., 1980).
The enrichment factor (EF) is used to identify if a concerning element’s source is naturally occurring or artificial (Zhang and Liu,2002). The contamination factor (CF) indicates the sediments’
hazardous level due to the presence of heavy metals (Kadhum et al.,2015;Omwene et al.,2018).
Coastal pollution has negative consequences such as endan- gering human health, harming living resources and ecosystems, and impeding marine activities. The study’s primary goal is to identify significant metal pollutants in the sediments and com- prehend the relationship with the sediment texture, organic mat- ter, and calcium carbonate. This study helps us learn about heavy metals’ behaviour, association, and pollution in the coastal envi- ronment of Vedaranyam, Southeast Coast of India.
2. Study area
The study was conducted on the Vedaranyam coastline in Nagapattinam district, Tamil Nadu, India (Fig. 1). The coastline receives substantial sediment from the tributaries of the major rivers, such as the Cauvery, and through the canals, generating a delta zone. River Cauvery originates from the Western Ghats of India and passes through Karnataka and Tamil Nadu and finally
ends up in the Bay of Bengal. Due to the northeast monsoon’s intense rainfall and storms, the study area is significantly more vulnerable to extreme flooding (Gopal et al.,2020,2021;LEAPS, 2021;Hamza and Reji,2022). The discharge of effluents from the tobacco processing plants, bromine extraction units, and a few aquaculture farms influence the region which drains into the Bay of Bengal, impacting the marine flora and fauna.
3. Materials and methods
Twenty-five off-shore surface sediment samples (> 2 me- tres) were collected using a Van-Veen grab sampler in January 2019, after the monsoon season, and packed into pre-numbered sample bags immediately onboard to avoid contamination. The sampling locations were fixed using a hand-held GPS (Garmin, eTrex), and the recorded coordinates were exported to ArcGIS 10.3 for map preparation. The collected sediments were oven- dried at temperatures below 40◦C for further investigation in the laboratory.
The oven-dried sediments were pulverized and sieved (2 mm sieve) to eliminate coarser sediments. Textural studies of the sediment such as sand, silt, and clay were performed following the method of Ingram (1970). Calcium carbonate (CaCO3) was estimated by the method proposed byLoring and Rantala(1992), and the procedure ofGaudette et al. (1974) was used to esti- mate the organic matter in the sediment. In an agate mortar, a portion of the sediment was pulverized to a grain size of 0.063 mm. A mixture of concentrated HClO4 (2 ml, analytical grade, Merck, Germany) and HF (10 ml, analytical grade, Merck, Germany) was used to digest 0.5 g of the homogenized material until white vapours started to form (Smith and Arsenault,1996).
The remaining sample was dissolved in concentrated HCl and
Table 1
Certified and observed analytical results of CRM MESS-2 showing RSD%.
Elements Certified Observed RSD%
Fe 3.1±0.38 2.93 93.16
Mn 513±25 475 91
Cr 71±1.1 68.8 97.3
Cu 25.11±3.88 21.9 90.19
Ni 29.5±2.7 28.3 94.23
Co 10.8±1.9 9.9 91.66
Pb 34±6.1 31.8 93.73
Zn 191±17 173.3 90.73
then diluted to a volume of 25 ml using Milli-Q water (18 M cm−1). The acid solution was filtered using Whatman (Grade A) filter paper, and the concentration of heavy metals (Fe, Mn, Cr, Cu, Zn, Ni, and Co) was assessed using PerkinElmer AA-700 in a graphite furnace atomic absorption spectroscopy (GF-AAS). The whole analytic process is carried out using millipore water (18 M cm−1). The accuracy of the analysis was crosschecked using a certified reference material (MESS-2) procured from NIST, USA (Table 1).
3.1. Geoaccumulation index
Muller(1981) proposed an equation to establish the level of metal contamination in the sediment:
Igeo=log2Cn/1.5Bn (1)
Where Bnis the geochemical value of the earth’s crust (Wedepohl, 1995) and Cn is the measured elemental concentration in the sediment.
3.2. Enrichment factor
The enrichment factor (EF) is measured by standardizing the tested element against the reference element to estimate the abundance of metals in the sediment. EF helps to identify whether the enrichment is caused by natural or anthropogenic activities in the sediment. Many reference elements were used by different researchers based on their natural occurrences. The commonly used reference metals for normalization are iron and aluminium due to their abundance in the earth’s crusts. This study used iron as a reference element to distinguish the sources, whether they were of natural or anthropogenic origin. Various authors have successfully used this method (Goher et al.,2014;Kalpana et al., 2016).
EF= (M
Fe
) sample (M
Fe
) background
Where (M/Fe) is the metal-to-Fe concentration ratio of the sample and background.Sutherland(2000) identified five contamination index categories: EF < 2, depletion to mineral enrichment: EF≥ 2 and≤5, moderate enrichment; EF≥5 and≤20, significant enrichment; EF≥20 and≤40, very high enrichment; and EF >
40, extremely high enrichment.
3.3. Contamination factor
The contamination factor was proposed byHakanson(1980) and determined experimentally, as the ratio between the elemen- tal concentration of the sample and its background:
CF= C metal C background
The CF values were further classified as follows: CF < 1, low con- tamination factor; CF≥1≤3, moderate contamination factors;
CF≥3≤6, considerable contamination factors; and CF > 6, very high contamination factor.
3.4. Pollution load index
Tomlinson et al.(1980) proposed PLI, for estimating the impact of metal pollution on a concerned site. The classification of PLI is similar to that of the Igeo:
PLI=n√
(CF1×CF2×CF3× · · ·CFn)
Where n is the number of metals, CF is the contamination factor and a value < 1 indicates no pollution (Gopal et al.,2016).
3.5. Modified hazard quotient (mHQ)
In the present study, a new index is developed and sug- gested for assessing sediment pollution based on the level of contamination by certain heavy metals. By contrasting the metal concentration in sediment with the synoptic metal concentration, this innovative method permits the assessment of contamination.
Distributions of harmful ecological consequences for three some- what different threshold values (TEL, PEL, and SEL), as previously described (MacDonald et al.,2000; Benson et al., 2018). An es- sential assessment tool that clarifies the level of risk posed by each heavy metal to the aquatic environment and the biota is the determination of the modified hazard quotient (mHQ) of metals, which is calculated using the formula below:
mHQ= [Ci ( 1
TELi+ 1 PELi+ 1
SELi )
]1/2
Where, TELi, PELi, and SELi are acronyms for the threshold effect level, likely effect level, and severe impact level for each metal, respectively. Ci is the observed concentration of heavy metals in the sediment samples. The square root is inserted into the equation as a drawdown function for mathematical and ranking purposes. Modified Hazard Quotient classified as follows: mHQ
>3.5, Extreme severity of contamination; 3.0≤mHQ<3.5, Very high severity of contamination; 2.5≤mHQ<3.0, High severity of contamination; 2.0 ≤ mHQ < 2.5, Considerable severity of contamination; 1.5≤mHQ<2.0, Moderate severity of contam- ination; 1.0 ≤mHQ< 1.5, Low severity of contamination; 0.5
≤mHQ<1.0, Very low severity of contamination; mHQ<0.5, Nil to very low severity of contamination (Benson et al.,2018;
MacDonald et al.,2000).
3.6. Potential ecological risk index (PERI)
By Hakanson (1980), a potential ecological risk index was developed. Through ecological analysis, PERI evaluates the effects of heavy metals on ecosystems (Stamatis et al., 2019; Ustaoğlu and Tepe,2019;Ustaoğlu et al.,2020a,b; Tepe et al.,2022). The following formulas can be used to determine the risk index (RI).
PRFi=TRi×Cmi Cbi PERI=
n
∑
i=1
(PRFi)
where PRFi was the potential risk factor of a given pollutant;
TRi, the toxic response of metals (Wang et al., 2015); Cmi, the concentration of metal ‘i’ in the sediment; and Cbi, the regional background of the metal ‘i’ in sediments. The following thresholds were used to interpret the PERI and PRFi values (Gan et al.,2000).
The PERI was expressed using the above formula. The PRFi and PERI are defined as having the following five categories: PRFi
<30; PERI<100 (low risk), 30 < PRFi<50; 100 < PERI<150 (moderate risk), 50 < PRFi<100; 150 < PERI<200 (Considerable risk), 100 < PRFi<150; 200 < PERI<300 (very high risk), PRFi
>150; PERI>300 (disastrous risk) (Gan et al.,2000).
Table 2
Values of the analysed parameters in the sediments off Vedaranyam coast.
Sample No.
Sand
%
Silt
%
Clay
%
OM
%
CaCO3
%
Fe (ppm)
Mn (ppm)
Cr (ppm)
Cu (ppm)
Zn (ppm)
Ni (ppm)
Co (ppm)
1 2.4 97 0.6 0.73 18.5 68712 636 51.52 118.3 672 54 45
2 24.4 75 0.6 0.7 18.82 56512 580 48 114.6 549.2 26 51
3 94 5.5 0.5 0.71 22.33 64000 614 43.7 110.5 509 35 20
4 5.5 94 0.5 0.66 18.67 62400 609 48.5 108.3 701 47 99
5 77.4 22 0.6 0.66 20.88 57112 580 49.6 117.5 627 43 49
6 85 15 0 0.61 22.33 59100 589 42.7 102.8 417.1 38 62
7 93.2 6.4 0.4 0.6 21.57 68630 614 43.5 102 378 26 60
8 83.4 16 0.6 0.72 20.2 57400 739 45 88.5 470 32 35
9 8.4 91 0.6 0.69 17.93 77400 620 53 124 779 98 92
10 42.6 57 0.4 0.69 19.64 71112 700 47.1 108 575 33 66
11 1.4 98 0.6 0.78 18.19 72412 680 53.6 122 716 94 89
12 21.4 78 0.6 0.68 20 68612 746 50.4 120.6 621 92 80
13 2.4 97 0.6 0.87 18.56 76712 694 52.04 114.5 682 96 78
14 2.5 97 0.5 0.68 18.37 70012 595 50.04 105.7 627 71 94
15 9.4 90 0.6 0.8 17.5 62012 684 49.37 112.9 588 82 97
16 2.4 97 0.6 0.89 17.89 73500 656 53.2 122 726 105.3 47
17 5.4 94 0.6 0.75 18.52 65200 663 49.7 121.8 605 98.9 43
18 1.8 98 0.2 0.88 19.04 70012 645 45 109.4 675 25 52
19 9.4 90 0.6 0.75 19.5 71312 645 51.4 122 565 77 91
20 8.4 91 0.6 0.88 18.5 60900 703 47.5 125.7 655 89 98
21 10.4 89 0.6 0.73 18 65440 624 46.4 135.6 778 84 86
22 8.5 91 0.5 0.78 18.72 53912 591 50.3 105.7 692 56 93
23 10.5 89 0.5 0.74 18.07 66812 660 46.3 130.3 583.2 102 86
24 46.6 53 0.4 0.69 20.12 76000 720 49.9 120 674 64 71
25 2.4 97 0.6 0.83 19.38 53912 580 51.6 115.4 713 81 93
Min 1 6 0 1 17.5 53912 580 42.7 88.5 378 25 20
Max 94 98 1 1 22.3 77400 746 53.6 135.6 779 105 99
Avg 26 73 1 1 19.2 65966 647 48.8 115.1 623 66 71
3.7. Toxic risk index (TRI)
To evaluate the biological risks, the TRI takes into account the threshold effect level (TEL) and likely effect level (PEL) of metals.
The following equation is used to construct the individual toxic risk index (TRIi):
TRIi=
√(Ci/TELi)2+(Ci/PELi)2 2
TRI=
n
∑
i=1
TRIi
where Ci stands for the measured concentration of the target heavy metal i, n for the number of target heavy metals, TELi for the target heavy metal is TEL value, and PELi for the target heavy metal is PEL value. The classification of TRI’s pollution levels is classified as follows: TRI≤5, no toxic risk; 5 <TRI≤10, low toxic risk; 10 <TRI≤15, moderate toxic risk; 15<TRI≤20, considerable toxic risk; TRI>20, very high toxic risk (Pedersen et al.,1998).
4. Results and discussion
In an environmental investigation, the detection of sources of trace metals is of particular importance. The pollution indices such as Igeo, EF, CF, PLI, mHQ, PERI and TRI were computed to estimate the overall sediment quality and heavy metal pollution level in the sediments of Vedaranyam. Environmental indices such as Igeo, EF, CF, mHQ and PERI are used as the most com- mon ecological risk assessment tactics by an individual element, whereas PLI and TRI assess the environmental risk posed by mixed elements (Caeiro et al.,2005;Hou et al.,2013;Wang et al., 2014;Zhao et al.,2016;Iqbal et al.,2016).
4.1. Textural parameters and their distribution
Textural parameters of sediments in aquatic systems help interpret depositional environments, provenances, and energy
conditions of the medium and study basin processes controlling metal transportation and deposition (Gao and Li,2012;Jayaprakash et al.,2014; Gopal et al., 2016). Srinivas et al.(2017) reported that the sediment distribution controls the local hydrodynamic conditions that cause the winnowing in fine-grain sediments. The sand content ranged from 1.4 to 94%, whereas the silt and clay content ranged from 5.5 to 98%and 0 to 0.6%, respectively (Ta- ble 2). The Trefethen trilinear diagram shows that most samples fall under the sand, silt, sandy silt, and silty clay category (Fig. 2).
The silt accumulation indicates that it has been carried to the ocean’s depths by the river flow.Figs. 3a, b, and c show the spatial distribution of sand, silt, and clay.
4.2. Organic matter and calcium carbonate
Table 2shows the concentration of organic matter in the study that varies from 0.6 to 0.89%. The spatial distribution of organic matter shows high concentrations at sample locations 13, 16, 18, and 20 (Fig. 3d). In these samples, the dominance of mud and OM indicates that the fine particles are weak and often trapped.
The high concentration of OM in the study area is also due to its high productivity and low currents mediated by terrestrial inputs from natural and anthropogenic origins. The concentra- tion of CaCO3 in the study area varies between 17.5 and 22.3%
(Table 2). The spatial distribution of CaCO3 (Fig. 3e) indicates more calcium carbonate concentration levels in sample locations 3, 6, and 7 as tertiary limestones and sandstones cover them.
Higher sand fractions within the study area are primarily due to the distribution of calcareous sand within the coastal region and low energy distribution in the coastal water. Organic matter is a significant component and an excellent environmental index for depositing sediments. Organic matter in sediments reflects the aquatic ecosystems’ biological activity, fertility, and pollution (Alagarsamy,1991;Ilayaraja and Krishnamurthy,2010).
4.3. Concentration of heavy metals and their distribution
The Fe concentration in the study area ranged from 53912 to 77400 ppm and was probably derived from terrestrial sources
Fig. 2. Trefethen trilinear diagram showing the relative percentage of sand, silt, and clay fractions of the Vedaranyam coastal sediments.
(Fig. 3f). The average Fe concentration is relatively higher (65965 ppm) than the continental crust average (UCC value is 30890 ppm, Wedepohl, 1995). Fe is mainly originated from crustal weathering and can be converted to complex hydroxyl compounds that may eventually precipitate into the sediments (Riley and Chester, 1971). The Mn concentration ranged from 580 to 746 ppm (Fig. 4a). Mn comes from a terrestrial source (UCC value is 570 ppm;Wedepohl,1995) through crustal weath- ering that can be converted to complex hydroxyl manganese compounds, which eventually precipitate into the sediments.
The Cr concentration ranges from 42.7 to 53.6 ppm (Fig. 4b), higher than the continental crust average (UCC value is 35 ppm;
Wedepohl,1995). It indicates that Cr is derived from both ter- restrial and anthropogenic sources, such as shipping and other activities associated with fishing harbors. The Cu concentration ranges from 88.5 to 135.6 ppm (Fig. 4c) due to recent external or anthropogenic input (UCC value is 14.3 ppm; Wedepohl, 1995) from the source areas. The Zn concentration ranges from 378 to 779 ppm (Fig. 4d), indicating significant anthropogenic activities such as industrial inputs and effluent discharge (UCC value is 52 ppm;Wedepohl,1995). The Ni concentration ranges from 25 to 105.3 ppm (Fig. 4e), indicating an anthropogenic source prob- ably from metal processing industries (UCC value is 18.6 ppm;
Wedepohl,1995). The Co concentration ranges from 20 to 99 ppm (Fig. 4f), indicating non-geogenic and recent anthropogenic activ- ities in the area (UCC value is 11.6 ppm;Wedepohl,1995). It was discovered that the research area’s trace element content was higher than the geochemical background value. The average trace element concentration in coastal sediments around the world is compared to background values inTable 5.
4.4. Assessment of environmental indices
Most researchers have used the Igeo for trace element studies in sediments (Arunachalam et al.,2014;Gopal et al.,2016). The range of Igeo for the Vedaranyam coastal sediments has been calculated and presented inFig. 5a. It shows that Mn and Cr are uncontaminated, Fe and Ni are moderately contaminated, and Cu, Zn, and Co are moderate to strongly contaminated. The range of EF for Vedaranyam coastal sediment is shown inFig. 5b. The calculated values indicate no enrichment in Mn and Cr; Ni is slightly enriched, whereas Cu and Co are minor to moderately en- riched; and Zn shows moderate to moderately severe enrichment, indicating the anthropogenic presence.
Metals from the Vedaranyam coastal sediments show that Fe, Mn, and Cr have moderate contamination, whereas Ni has low to considerable contamination levels. Co, Cu, and Zn have moderate to high contamination. The range of CF in the study area is shown inFig. 5c. According to the obtained mHQs, Zn>Ni>Cu>Cr was the sequence in which the four heavy metals caused the most severe sediment-associated pollution. That pattern is in sync with other contamination patterns discovered during the creation of pollution assessment indices that were previously reported for similar ecosystems in other research papers (Ruiz et al.,2006;
Maanan et al., 2015). Results indicated that Zn recorded high severity of contamination followed by Ni and Cu with severity ranking characterized by considerable severity of contamination to moderate severity of contamination. However, Cr generally showed low severity of contamination (Fig. 5d). The PLI values of all the studied samples are showing a value above 1 indicating polluted sediments (Fig. 5e). Metal contamination of the surface sediment indicates that the entire study area has metals con- taminated by fluvial input that transports untreated sewage to the marine environment (Gopal et al., 2016). Nearly all of the
Fig. 3. Spatial distribution of sand, silt, clay, OM, CaCO3, and Fe of Vedaranyam coastal sediments.
samples are classified as low risk according to the risk index (PERI) categorization (Fig. 5e). The individual toxic risk index (TRIi) of metals in this study is significantly variable for Cr (0.88–
1.10), Cu (1.78–2.73), Zn (2.33–4.81) and Ni (1.10–4.62). TRIi in the sediments is below 5 for Cr, Cu, Zn, and Ni suggesting the absence of any toxic risk in the aquatic life. The ecological toxicity classification based on integrated toxic risk (TRI) values is shown in Fig. 5e, which summarized the toxic risk index to provide a better picture of toxicity risks. The∑
TRI in the study area varies from 6.42 to 12.69, and 48% of the area has∑
TRI5 < TRI≤10 indicating low toxic risk and the remaining 52% of the study area has a moderate toxic risk. Spatially the∑
TRI of the heavy metals is highest in the northern part of the study area.
4.5. Multivariate statistics
Multivariate statistical methods were used to infer the rela- tionship among the studied variables, including Pearson’s correla- tion matrix and factor analysis. The correlation analysis is carried
out using a statistical package for social science (SPSS) software to identify inter-element relationships. The inter-element relation- ship between sand and CaCO3 has a strong positive correlation (r=0.89) indicating carbonate sources derived from shells. Silt shows a strong positive correlation with OM (r = 0.60) and heavy metals such as Cr, Cu, Zn, Ni, and Co (Table 3) indicating a common source originated from terrestrial runoff bound with organic matter. Moreover, OM in the sediments and have high hu- mic acids with high metal adsorption capacity and control metal abundance (Kargar et al.,2013;Kalpana et al.,2016). Similar ob- servations are also noticed among heavy metals with clay which indicates the role of mud (silt+clay) in transporting heavy metals to the coastal sediments. A positive correlation between Fe and Mn (r=0.40) indicates the association of these elements in the form of Fe–Mn oxyhydroxides. A significant positive correlation among Cr, Cu, Zn, and Ni highlights a possible anthropogenic source (Table 3).
While the characterization and interpretation of different geo- chemical parameters are often complex, factor analysis provides a
Fig. 4. Spatial distribution of Mn, Cr, Cu, Zn, Ni, and Co off Vedaranyam coastal sediments.
Table 3
Correlation matrix among the studied parameters in the sediments off Vedaranyam coast.
Parameters Sand Silt Clay OM CaCO3 Fe Mn Cr Cu Zn Ni Co
Sand 1
Silt −1.00** 1
Clay −.388 .385 1
OM −.606** .606** .289 1
CaCO3 .894** −.894** −.520** −.550** 1
Fe −.254 .254 .008 .090 −.235 1
Mn −.063 .062 .184 .253 −.147 .405* 1
Cr −.674** .672** .615** .378 −.612** .351 .095 1
Cu −.526** .525** .401* .287 −.489* .326 .067 .424* 1
Zn −.757** .756** .422* .499* −.708** .274 .004 .700** .594** 1
Ni −.638** .637** .509** .451* −.625** .373 .299 .653** .702** .564** 1
Co −.575** .575** .156 .136 −.512** .072 .001 .384 .314 .438* .488* 1
*Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
Fig. 5. Box plot showing the range of (a) Igeo, (b) EF, (c) CF, (d) mHQ and (f) a bar graph showing PLI, TRI and PERI values for Vedaranyam coastal sediments.
reliable way to identify the similarities between all the variables contained in the geochemical data (Mohana and Velmurugan, 2020). Thus, the varimax rotating factor was applied in this study to describe the factors and was presented inTable 4. Two factors were derived based on the eigenvalue above 1. The cumulative percentage of variance explained is 63.5%, with F1 contributing 51.7%, followed by 11.8% for F2. The significant positive loading of silt (0.94), clay (0.54), OM (0.59), Cr (0.76), Cu (0.63), Zn (0.85), Ni (0.73), and Co (0.64) suggests that the role of mud on heavy metal binding probably derived from anthropogenic origin. The negative loadings for sand (−0.94), and CaCO3 (−0.89) indicate the presence of biogenic calcareous matter in the form of shell fragments. The positive loadings of the Fe–Mn factor (Fe0.75, Mn 0.84) suggest the co-precipitation of Fe–Mn oxyhydroxides which
play an essential role in absorbing and binding these metals (Kalpana et al.,2016).
5. Conclusions
The coastal sediments off Vedaranyam show various pollution levels that indicate the quality, characteristics, and level of con- tamination, together with inter-elemental relationships between the sediment texture, CaCO3, OM, and metals. The results show that the study area is dominated by sand, silt, sandy silt, and silty clay. The presence of organic matter also contributes to the adsorption of metals and serves as a binding agent. The results of Igeo, EF, CF, and PLI indicate metals from anthropogenic sources and are supported by statistical analyses. To give readers a clearer
Table 4
Factor analysis showing factor loading based on principal component analysis.
Parameters Principal component analysis
PC1 PC2
Sand −.945 −.040
Silt .944 .039
Clay .544 .214
OM .588 .198
CaCO3 −.893 −.106
Fe .181 .751
Mn −.015 .840
Cr .764 .237
Cu .630 .281
Zn .847 .074
Ni .733 .432
Co .636 −.148
Eigenvalues 5.89 1.72
% of variance 49.15 14.31
Cumulative % 49.15 63.46
understanding of the toxicity concerns, Fig. 5e summarizes the toxic risk index and presents the ecological toxicity categoriza- tion based on integrated toxic risk (TRI) values. The research region’s TRI ranges from 6.42 to 12.69, with TRI values of 5 or 10 indicating low toxic risk in 48% of the area and TRI values of 5 or 10 indicating moderate toxic risk in the remaining 52%.
Geographically, the northerly region of the study area has the largest concentrations of the heavy metals TRI. The high con- centration of Cu, Zn, and Co in the sediment revealed that the contributing factors could be anthropogenic origin along with dispersal or lithogenic influx from the terrestrial areas. When the results of environmental indices were interpreted, Cu, Zn, and Co were identified as the priority pollutants of concern. However, Fe and Cr could not be ignored because they indicated enrichment in the study area. The primary sources of these anthropogenic sources are farming, untreated households, traffic pollution, and boating activities. In addition, the monsoon seasons in this region play a vital role in managing the distribution of metals as sig- nificant flushing occurs during river overflows. Therefore, regular surveillance and remedial measures for contaminant discharge may reduce the metal content in aquatic systems, thus ensuring future restructuring of the coastal environment in the region. The limitations of this study include seasonal high-density sampling and contaminant transport modelling. The work can be improved by including the above-mentioned factors in the future.
CRediT authorship contribution statement
V. Gopal: Study’s conception and design, the first draft of the manuscript was written and the manuscript was edited and reviewed. R.R. Krishnamurthy, R. Vignesh, C. Sabari Nathan, R. Anshu, R. Kalaivanan, P. Mohana, N.S. Magesh, and K. Manikanda Bharath:Study’s conception and design, Material preparation, Data collection, and Analysis. A.Z. Ekoa Bessa, K.
Abdelrahman, and M. Abioui: Study’s conception and design, and the manuscript was edited and reviewed. All authors read and approved the final manuscript.
Declaration of competing interest
The authors declare that they have no known competing finan- cial interests or personal relationships that could have appeared to influence the work reported in this paper. The funding body did not influence the content of this study.
Data availability
Data will be made available on request
Acknowledgments
We thankRegional Studies in Marine ScienceAssociate Editor Prof. Dr. Tieyu Wang and two anonymous reviewers for their con- structive comments on an early version of the manuscript. This research was funded by Researchers Supporting Project number (RSP2023R351), King Saud University, Riyadh, Saudi Arabia. In ad- dition, Prof. M. Abioui is thankful for the support of national funds through Fundação para a Ciência e Tecnologia, I. P (FCT), under the projects UIDB/04292/2020, UIDP/04292/2020, granted to MARE, and LA/P/0069/2020, granted to the Associate Laboratory ARNET.
Finally, Prof. M. Abioui dedicates this research paper to the late Dr. Saadia Asouam, who passed away on June 29, 2023, just five days after the acceptance of this paper.
Funding
Open access funding is provided by University of Lausanne.
Table 5
A comparative data of trace element concentration of different coastal regions of India and world along with average crustal concentrations in sediments.
Study area Fe Mn Cr Cu Zn Ni Co References
Off Vedharanyam Coast, India 65966 647 49 115 623 66 71 Present study
Point Calimere, India 73,884 606.5 59.2 115.5 738 32 43 Gopal et al.(2020)
Atlantic coast, South West of Cameroon 18,095.66 303.79 59.33 106.66 102.65 216.66 104 Ekoa Bessa et al.(2021)
Rosetta coast, Egypt 1,09.560 553 0.18 24.57 183.2 480.8 69.8 Nour et al.(2019)
Off Pushpavanam coastal sediment, India 77,529 361.7 43.4 86.8 474 59 62.4 Gopal et al.(2019) Shalateen coastal area, Red Sea, Egypt 8,452 198.7 DNA 9.43 44.2 17.5 3.9 Nour et al.(2019)
Cuddalore, SE coast of India 10,982 291 127 40 37.7 39.2 DNA Jayaprakash et al.(2016)
Egyptian Mediterranean coast 17175.2 553.2 DNA 11.3 27.2 DNA DNA El Baz and Khalil(2018)
Surface sediments, Van Island, Gulf of Mannar, 31,218.90 162.5 108.73 57.81 52.5 110.04 DNA Krishnakumar et al.(2017)
India 30,890 527 35 14.3 52 18.6 11.6 Wedepohl(1995)
Crustal average 56,300 950 100 55 70 75 25 Taylor(1964)
DNA:Data Not Available.
References
Ahmed, M.K., Shaheen, N., Islam, M.S., Habibullah-al Mamun, M., Islam, S., Mohiduzzaman, M., Bhattacharjee, L., 2015. Dietary intake of trace elements from highly consumed cultured fish (Labeorohita, Pangasius pangasiusand Oreochromis mossambicus) and human health risk implications in Bangladesh.
Chemosphere 128, 284–292. http://dx.doi.org/10.1016/j.chemosphere.2015.
02.016.
Alagarsamy, R., 1991. Organic carbon in the sediments of Mandovi estuary, Goa.
Indian J. Geo. Mar. Sci. 20, 221–222.
Antizar-Ladislao, B., Mondal, P., Mitra, S., Sarkar, S.K., 2015. Assessment of trace metal contamination level and toxicity in sediments from coastal regions of West Bengal, eastern part of India. Mar. Pollut. Bull. 101 (2), 886–894.
http://dx.doi.org/10.1016/j.marpolbul.2015.11.014.
Arunachalam, K.D., Sathesh Kumar, A., Kamesh, B., Savan, R., Sanjay, J., Sreedevi, 2014. Spatial and multivariate analysis of trace elements in the surface water and deep sediments of freshwater aquatic ecosystem. Am. J. Environ. Sci. 10 (2), 102–122.http://dx.doi.org/10.3844/ajessp.2014.102.122.
Benson, N.U., Adedapo, A.E., Fred-Ahmadu, O.H., Williams, A.B., Udosen, E.D., Ayejuyo, O.O., Olajire, A.A., 2018. A new method for assessment of sediment- associated contamination risks using multivariate statistical approach.
MethodsX 5, 268–276.http://dx.doi.org/10.1016/j.mex.2018.03.005.
Caeiro, S., Costa, M.H., Ramos, T.B., Fernandes, F., Silveira, N., Coimbra, A., Medeiros, G., Painho, M., 2005. Assessing heavy metal contamination in Sado Estuary sediment: An index analysis approach. Ecol. Indic. 5 (2), 151–169.
http://dx.doi.org/10.1016/j.ecolind.2005.02.001.
Chatterjee, M., Silva Filho, E.V., Sarkar, S.K., Sella, S.M., Bhattacharya, A., Satpathy, K.K., Prasad, M.V.R., Chakraborty, S., Bhattacharya, B.D., 2007.
Distribution and possible source of trace elements in the sediment cores of a tropical macrotidal estuary and their ecotoxicological significance. Environ.
Int. 33 (3), 346–356.http://dx.doi.org/10.1016/j.envint.2006.11.013.
Ekoa Bessa, A.Z., Ngueutchoua, G., Kwewouo Janpou, A., El-Amier, Y.A., Mbella Nguetnga, O.A.N.N., Kankeu Kayou, U.R., Bertrant Bisse, S., Ngo Ma- puna, E.C., Armstrong-Altrin, J.S., 2021. Heavy metal contamination and its ecological risks in the beach sediments along the Atlantic ocean (Limbe coastal fringes, Cameroon). Earth Syst. Environ. 5, 433–444. http://dx.doi.
org/10.1007/s41748-020-00167-5.
El Baz, S.M., Khalil, M.M., 2018. Assessment of trace metals contamination in the coastal sediments of the Egyptian Mediterranean coast. J. Afr. Earth Sci.
143, 195–200.http://dx.doi.org/10.1016/j.jafrearsci.2018.03.029.
Gan, J.L., Jia, X.P., Lin, Q., Li, C.H., Wang, Z.H., Zhou, G.J., Wang, X.P., Cai, W.G., Lu, X.Y., 2000. A primary study on ecological risk caused by the heavy metals in coastal sediments. J. Fish. China 24, 533–538.
Gao, X., Li, P., 2012. Concentration and fractionation of trace metals in surface sediments of intertidal Bohai Bay, China. Mar. Pollut. Bull. 64 (8), 1529–1536.
http://dx.doi.org/10.1016/j.marpolbul.2012.04.026.
Gaudette, H.E., Flight, W.R., Toner, L., Folger, D.W., 1974. An inexpensive titration method for the determination of organic carbon in recent sediments. J.
Sediment. Pet. 44 (1), 249–253. http://dx.doi.org/10.1306/74D729D7-2B21- 11D7-8648000102C1865D.
Ghrefat, H., Yusuf, N., 2006. Assessing Mn, Fe, Cu, Zn, and Cd pollution in bottom sediments of Wadi Al-Arab Dam, Jordan. Chemosphere 65 (11), 2114–2121.
http://dx.doi.org/10.1016/j.chemosphere.2006.06.043.
Goher, M.E., Farhat, H.I., Abdo, M.H., Salem, S.G., 2014. Metal pollution assess- ment in the surface sediment of Lake Nasser, Egypt. Egypt J. Aquat. Res. 40 (3), 213–224.http://dx.doi.org/10.1016/j.ejar.2014.09.004.
Gopal, V., Krishnakumar, S., Peter, T., Nethaji, S., Sureshkumar, K., Jayaprakash, M., Magesh, N.S., 2016. Assessment of trace element accumulation in surface sediments off Chennai coast after a major flood event. Mar. Pollut. Bull. 114 (2), 1063–1071. http://dx.doi.org/10.1016/j.
marpolbul.2016.10.019.
Gopal, V., Krishnamurthy, R.R., Chakraborty, P., Magesh, N.S., Jayaprakash, M., 2019. Trace element contamination in marine sediments along the southeast Indian shelf following cyclone Gaja. Mar. Pollut. Bull. 149, 110520. http:
//dx.doi.org/10.1016/j.marpolbul.2019.110520.
Gopal, V., Krishnamurthy, R.R., Sakthi Kiran, D.R., Magesh, N.S., Jayaprakash, M., 2020. Trace metal contamination in the marine sediments off Point Calimere, Southeast coast of India. Mar. Pollut. Bull. 161 (B), 111764.http://dx.doi.org/
10.1016/j.marpolbul.2020.111764.
Gopal, V., Krishnamurthy, R.R., Sreeshma, T., Chakraborty, P., Nathan, C.Sabari, Kalaivanan, R., Anshu, R., Magesh, N.S., Jayaprakash, M., 2021. Effect of a tropical cyclone on the distribution of heavy metals in the marine sediments off Kameswaram, Southeast coast of India. Mar. Pollut. Bull. 171, 112741.
http://dx.doi.org/10.1016/j.marpolbul.2021.112741.
Gutiérrez-Mosquera, H., Shruti, V.C., Jonathan, M.P., Roy, P.D., Rivera-Rivera, D.M., 2018. Metal concentrations in the beach sediments of Bahia Solano and Nuquí along the Pacific coast of Chocó, Colombia: A baseline study. Mar.
Pollut. Bull. 135, 1–8.http://dx.doi.org/10.1016/j.marpolbul.2018.06.060.
Hakanson, L., 1980. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 14 (8), 975–1001.http://dx.doi.org/
10.1016/0043-1354(80)90143-8.
Hamza, V., Reji, M.J.K., 2022. Features of regional Indian monsoon rainfall extremes. In: Ongoma, V., Tabari, H. (Eds.), Climate Impacts on Extreme Weather: Current to Future Changes on a Local to Global Scale. Elsevier, Am- sterdam, pp. 83–100.http://dx.doi.org/10.1016/B978-0-323-88456-3.00014- 9.
Hou, D., He, J., Ren, L., Qingyun, F., Jinghua, W., Zhilei, X., 2013. Distribution characteristics and potential ecological risk assessment of heavy metals (Cu, Pb, Zn, Cd) in water and sediments from Lake Dalinouer, China. Ecotoxicol.
Environ. Safety 93, 135–144.http://dx.doi.org/10.1016/j.ecoenv.2013.03.012.
Ilayaraja, K., Krishnamurthy, R.R., 2010. Sediment characterisation of the 26 december 2004 Indian ocean tsunami in Andaman group of islands, Bay of Bengal, India. J. Coast Conserv. 14, 215–230. http://dx.doi.org/10.1007/
s11852-010-0087-2.
Ingram, R.L., 1970. Procedures in Sedimentary Petrology. Wiley, New York.
Iqbal, M.A., Ping, Q., Abid, M., Muhammad Muslim Kazmi, S., Rizwan, M., 2016.
Assessing risk perceptions and attitude among cotton farmers: A case of Punjab province, Pakistan. Int. J. Disaster Risk Reduct. 16, 68–74.
Jayaprakash, M., Gopal, V., Anandasabari, K., Kalaivanan, R., Sujitha, S.B., Jonathan, M.P., 2016. Enrichment and toxicity of trace metals in near-shore bottom sediments of Cuddalore, SE coast of India. Environ. Earth Sci. 75 (1303),http://dx.doi.org/10.1007/s12665-016-6082-7.
Jayaprakash, M., Viswam, A., Gopal, V., Muthuswamy, S., Kalaivanan, R., Girid- haran, L., Jonathan, M.P., 2014. Bioavailable trace metals in micro-tidal Thambraparani estuary, Gulf of Mannar, SE coast of India. Estuar. Coast. Shelf Sci. 146, 42–48.http://dx.doi.org/10.1016/j.ecss.2014.05.009.
Kadhum, S.A., Ishak, M.Y., Zulkifli, S.Z., Hashim, R.B., 2015. Evaluation of the status and distributions of heavy metal pollution in surface sediments of the Langat river basin in Selangor Malaysia. Mar. Pollut. Bull. 101 (1), 391–396.
http://dx.doi.org/10.1016/j.marpolbul.2015.10.012.
Kalpana, G., Shanmugasundharam, A., Nethaji, S., Viswam, A., Kalaivanan, R., Gopal, V., Jayaprakash, M., 2016. Evaluation of total trace metal (TTMs) enrichment from estuarine sediments of Uppanar, southeast coast of India.
Arab. J. Geosci. 9 (34),http://dx.doi.org/10.1007/s12517-015-2048-4.
Kargar, M., Jutras, P., Clark, O.G., Hendershot, W.H., Prasher, S.O., 2013. Trace metal contamination influenced by land use, soil age, and organic matter in Montreal tree pit soil. J. Environ. Qual. 42 (5), 1527–1533.http://dx.doi.org/
10.2134/jeq2013.02.0055.
Kaushik, A., Kansal, A., Santosh, Meena, Kumari, S., Kaushik, C.P., 2008. Heavy metal contamination of river Yamuna, Haryana, India: Assessment by metal enrichment factor of the sediments. J. Hazard. Mater. 164 (1), 265–270.
http://dx.doi.org/10.1016/j.jhazmat.2008.08.031.
Krishnakumar, S., Ramasamy, S., Chandrasekar, N., Simon Peter, T., God- son Prince, S., Gopal, V., Magesh, N.S., 2017. Spatial risk assessment and trace element concentration in reef associated sediments of Van island, southern part of the Gulf of Mannar, India. Mar. Poll. Bull. 115 (1–2), 444–450.
http://dx.doi.org/10.1016/j.marpolbul.2016.10.067.
LEAPS (Loyal Environmental Awareness & Protection Society), 2021.
Impact Study on Response to Gaja Cyclone. LEAPS, India, p. 25, https://www.mphasis.com/content/dam/mphasis-com/global/en/csr/impact- report-uw-be-gaja-response.pdf.
Liu, J., Yin, P., Chen, B., Gao, F., Song, H., Li, M., 2016. Distribution and contamination assessment of heavy metals in surface sediments of the Luanhe River Estuary, northwest of the Bohai Sea. Mar. Pollut. Bull. 109 (1), 633–639.http://dx.doi.org/10.1016/j.marpolbul.2016.05.020.
Loring, D.H., Rantala, R.T.T., 1992. Manual for the geochemical analyses of marine sediments and suspended particulate matter. Earth Sci. Rev. 32, 235–283.
http://dx.doi.org/10.1016/0012-8252(92)90001-A.
Ma, X., Zuo, H., Tian, M., Zhang, L., Meng, J., Zhou, X., Min, N., Chang, X., Liu, Y., 2016. Assessment of heavy metals contamination in sediments from three adjacent regions of the Yellow river using metal chemical fractions and multivariate analysis techniques. Chemosphere 144, 264–272.http://dx.doi.
org/10.1016/j.chemosphere.2015.08.026.
Maanan, M., Saddik, M., Maanan, M., Chaibi, M., Assobhei, O., Zourarah, B., 2015.
Environmental and ecological risk assessment of heavy metals in sediments of Nador lagoon, Morocco. Ecol. Indic. 48, 616–626.http://dx.doi.org/10.1016/
j.ecolind.2014.09.034.
MacDonald, D.D., Ingersoll, C.G., Berger, T.A., 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosys- tems. Arch. Environ. Contam. Toxicol. 39, 20–31.http://dx.doi.org/10.1007/
s002440010075.
Malvandi, H., 2017. Preliminary evaluation of heavy metal contamination in the Zarrin-Gol river sediments, Iran. Mar. Pollut. Bull. 117 (1–2), 547–553.
http://dx.doi.org/10.1016/j.marpolbul.2017.02.035.
Mohana, P., Velmurugan, P.M., 2020. Evaluation and characterization of ground- water using chemometric and spatial analysis. Environ. Dev. Sustain. 23, 309–330.http://dx.doi.org/10.1007/s10668-019-00581-4.
Muller, G., 1981. Die Schwermetallbelastung der Sedimenten des Neckars und Seiner Nebenflusse. Chem. Ztg. 105 (6), 157–164.
Nour, H.E., El-Sorogy, A.S., El-Wahab, M.A., Nouh, E.S., Mohamaden, M., Al- Kahtany, K., 2019. Contamination and ecological risk assessment of heavy metals pollution from the Shalateen coastal sediments, Red sea, Egypt. Mar.
Pollut. Bull. 144, 167–172.http://dx.doi.org/10.1016/j.marpolbul.2019.04.056.
Omwene, P.I., Öncel, M.S., Çelen, M., Kobya, M., 2018. Heavy metal pollution and spatial distribution in surface sediments of Mustafakemalpaşa stream located in the world’s largest borate basin (Turkey). Chemosphere 208, 782–792.
http://dx.doi.org/10.1016/j.chemosphere.2018.06.031.
Pedersen, F., Bjørnestad, E., Andersen, H.V., Kjølholt, J., Poll, C., 1998. Character- ization of sediments from Copenhagen harbor by use of biotests. Water Sci.
Technol. 37 (6–7), 233–240.http://dx.doi.org/10.1016/S0273-12239800203- 0.
Riley, J.P., Chester, R., 1971. Introduction to Marine Chemistry. Academic Press, London and New York.
Ruiz, F., Abad, M., Olias, M., Galan, E., Gonzalez, I., Aguila, E., Hamoumi, N., Pulido, I., Cantano, M., 2006. The present environmental scenario of the Nador Lagoon (Morocco). Environ. Res. 102 (2), 215–229.http://dx.doi.org/
10.1016/j.envres.2006.03.001.
Shikazono, N., Tatewaki, K., Mohiuddin, K.M., Nakano, T., Zakir, H.M., 2012.
Sources, spatial variation and speciation of heavy metals in sediments of the Tamagawa river in central Japan. Environ. Geochem. Health 34, 13–26.
http://dx.doi.org/10.1007/s10653-011-9409-z.
Sin, S.N., Chua, H., Lo, W., Ng, L.M., 2001. Assessment of heavy metal cations in sediments of ShingMun river, Hong Kong. Environ. Int. 26 (5–6), 297–301.
http://dx.doi.org/10.1016/S0160-4120(01)00003-4.
Smith, F.E., Arsenault, E.A., 1996. Microwave-assisted sample preparation in analytical chemistry. Talanta 43 (8), 1207–1268.http://dx.doi.org/10.1016/
0039-9140(96)01882-6.
Srinivas, R., Shynu, R., Sreeraj, M.K., Ramachandran, K.K., 2017. Trace metal pollution assessment in the surface sediments of nearshore area off Calicut, southwest coast of India. Mar. Pollut. Bull. 120 (1–2), 370–375.http://dx.doi.
org/10.1016/j.marpolbul.2017.05.028.
Stamatis, N., Nikolaos, K., Pelagia, P., Georgios, S., Emmanouil, K., 2019. Quality indicators and possible ecological risks of heavy metals in the sediments of three semi-closed East Mediterranean Gulfs. Toxics 7 (2), 30.http://dx.doi.
org/10.3390/toxics7020030.
Suresh, G., Ramasamy, V., Sundarrajan, M., Paramasivam, K., 2015. Spatial and vertical distributions of heavy metals and their potential toxicity levels in various beach sediments from high-background radiation area, Kerala, India.
Mar. Pollut. Bull. 91 (1), 389–400.http://dx.doi.org/10.1016/j.marpolbul.2014.
11.007.
Sutherland, R.A., 2000. Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environ. Geol. 39, 611–627.http://dx.doi.org/10.1007/
s002540050473.
Taylor, S.R., 1964. Abundance of chemical elements in the continental crust: a new table. Geochim. Cosmochim. Acta 28, 1273–1285.http://dx.doi.org/10.
1016/0016-7037(64)90129-2.
Tepe, Y., Şimşek, A., Ustaoğlu, F., Taş, B., 2022. Spatial–temporal distribution and pollution indices of heavy metals in the Turnasuyu stream sediment, Turkey. Environ. Monit. Assess. 194, 818.http://dx.doi.org/10.1007/s10661- 022-10490-1.
Tomlinson, D.L., Wilson, J.G., Harris, C.R., Jeffrey, D.W., 1980. Problems in the assessment of heavy metal levels in estuaries and the formation of a pollution index. Helgol. Mar. Res. 33, 566–575. http://dx.doi.org/10.1007/
BF02414780.
Ustaoğlu, F., Tepe, Y., 2019. Water quality and sediment contamination assess- ment of Pazarsuyu stream, Turkey using multivariate statistical methods and pollution indicators. Int. Soil Water Conserv. Res. 7 (1), 47–56. http:
//dx.doi.org/10.1016/j.iswcr.2018.09.001.
Ustaoğlu, F., Tepe, Y., Aydin, H., 2020a. Heavy metals in sediments of two nearby streams from southeastern Black Sea coast: Contamination and ecological risk assessment. Environ. Forensic. 21 (2), 145–156. http://dx.doi.org/10.
1080/15275922.2020.1728433.
Ustaoğlu, F., Tepe, Y., Taş, B., 2020b. Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index. Ecol. Indic. 113, 105815.http://dx.doi.org/
10.1016/j.ecolind.2019.105815.
Varol, M., 2011. Assessment of heavy metal contamination in sediments of the Tigris river (Turkey) using pollution indices and multivariate statistical tech- niques. J. Hazard. Mater. 195, 355–364.http://dx.doi.org/10.1016/j.jhazmat.
2011.08.051.
Wang, H., Long, L., Kumar, A., Wang, W., Schemm, J.E., Zhao, M., Vecchi, G.A., Lar- row, T.E., Lim, Y., Schubert, S.D., Shaevitz, D.A., Camargo, S.J., Henderson, N., Kim, D., Jonas, J.A., Walsh, K.J.E., 2014. How well do global climate models simulate the variability of Atlantic tropical cyclones associated with ENSO? J.
Clim. 27 (15), 5673–5692.http://dx.doi.org/10.1175/JCLI-D-13-00625.1.
Wang, Z., Wang, Y., Chen, L., Yan, C., Yan, Y., Chi, Q., 2015. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull.
99 (1–2), 43–53.http://dx.doi.org/10.1016/j.marpolbul.2015.07.064.
Wedepohl, K.H., 1995. The composition of the continental crust. Geochim. Cos- mochim. Acta 59 (7), 1217–1239.http://dx.doi.org/10.1016/0016-7037(95) 00038-2.
Yuan, G.L., Liu, C., Chen, L., Yang, Z., 2011. Inputting history of heavy metals into the inland lake recorded in sediment profiles: Poyang lake in China. J. Hazard.
Mater. 185 (1), 336–345.http://dx.doi.org/10.1016/j.jhazmat.2010.09.039.
Zhang, J., Liu, C.L., 2002. Riverine composition and estuarine geochemistry of particulate metals in China - weathering features, anthropogenic impact and chemical fluxes. Estuar. Coast. Shelf Sci. 54 (6), 1051–1070.http://dx.doi.org/
10.1006/ecss.2001.0879.
Zhao, G.M., Lu, Q.Y., Ye, S.Y., Yuan, H.M., Ding, X.G., Wang, J., 2016. Assessment of heavy metal contamination in surface sediments of the west Guangdong coastal region, China. Mar. Pollut. Bull. 108 (1–2), 268–274.http://dx.doi.org/
10.1016/j.marpolbul.2016.04.057.
Zhu, X., Ji, H., Chen, Y., Qiao, M., Tang, L., 2013. Assessment and sources of heavy metals in surface sediments of Miyun reservoir, Beijing. Environ. Monit.
Assess. 185, 6049–6062.http://dx.doi.org/10.1007/s10661-012-3005-2.
