Assessment of heavy metal pollution from the sediment of Tupilipalem Coast, southeast coast of India

Assessment of heavy metal pollution from the sediment of Tupilipalem Coast, southeast coast of India

Author’s Accepted Manuscript Assessment of heavy metal pollution from the sediment of Tupilipalem Coast, southeast coast of India Sreenivasulu Ganugap...

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Author’s Accepted Manuscript Assessment of heavy metal pollution from the sediment of Tupilipalem Coast, southeast coast of India Sreenivasulu Ganugapenta, Jayaraju Nadimikeri, Sundara Raja Reddy Balam Chinnapolla, Lakshmanna Ballari, Rajasekhar Madiga, Nirmala K, Lakshmi Prasad Tella

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To appear in: International Journal of Sediment Research Received date: 16 November 2016 Revised date: 30 November 2017 Accepted date: 14 February 2018 Cite this article as: Sreenivasulu Ganugapenta, Jayaraju Nadimikeri, Sundara Raja Reddy Balam Chinnapolla, Lakshmanna Ballari, Rajasekhar Madiga, Nirmala K and Lakshmi Prasad Tella, Assessment of heavy metal pollution from the sediment of Tupilipalem Coast, southeast coast of India, International Journal of Sediment Research, https://doi.org/10.1016/j.ijsrc.2018.02.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Assessment of heavy metal pollution from the sediment of Tupilipalem Coast, southeast coast of India Sreenivasulu Ganugapentaa, Jayaraju Nadimikeria,*, Sundara Raja Reddy Balam Chinnapollab, Lakshmanna Ballaria, Rajasekhar Madigaa, Nirmala Kc, Lakshmi Prasad Tellad a

Department of Geology, Yogi Vemana University, Kadapa, Andhra Pradesh, India. Department of Geology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India. c Institute of Ocean Management, Anna University, Chennai, Tamil Nadu, India. d Department of Earth Sciences, Yogi Vemana University, Kadapa, Andhra Pradesh, India *Corresponding Author, Email: [email protected] b

Abstract Sediment from twelve stations was sampled from the Tupilipalem Coast, southeast coast of India, and the presence of a set of heavy metals was established including iron (Fe), manganese (Mn), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn) and cadmium (Cd). The heavy metals were assessed by factor analysis, the results of which showed positive and/or negative correlations among Fe, Mn, Cr, Cu, Ni, Pb, Zn, and Cd. Factor analysis also indicated that heavy metals in the sediments of the study area have different natural and anthropogenic sources. Similarly, a sediment pollution assessment was done using the Geoaccumulation Index (Igeo), Enrichment Factor (EF), and Pollution Load Index (PLI). The Geoaccumulation Index indicated that the surface sediment of the Tupilipalem Coast was extremely contaminated with Fe, Mn, Cr, Cu, Ni, Pb, and Zn. The calculation of enrichment factors showed a significant enrichment with respect to Pb, Zn, and Cd and a moderate enrichment with Cr, Cu, and Ni. The falling trend of average contents’ enrichment factors is Cd> Pb> Zn> Cu> Cr> Ni> Mn> Fe. The PLI values of the Cd show higher (>1) values due to the influence of distinct external sources like agricultural runoff, industrial activities, and other anthropogenic inputs. Ninety two percent of heavy metals under study 1

showed the highest concentrations at station TP-5 where the Buckingham Canal and other agricultural and aquacultural effluents connect with the Bay of Bengal. This location is the second inlet which is periodically closed and it seemed that these parts of the study area are heavily affected by anthropogenic pollution. Key words: Factor analysis, Geo Accumulation Index, Enrichment Factor, Pollution Load Index, Heavy metal pollution, East coast of India.

1. Introduction Heavy metals are considered the foremost anthropogenic contaminants in coastal and marine environments worldwide (Naser, 2013). They pose a serious threat to human health, natural ecosystems, and living organisms because of their toxicity, persistence, and bioaccumulation characteristics (DeForest et al., 2007;Jarup, 2003). Numerous substantial metal ions are known to be toxic or carcinogenic to humans (Jayaraju et al., 2009; Fu & Wang, 2011) and also play a significant role as sensitive indicators for monitoring contaminants in aquatic systems (Ozkan & Buyukisik, 2012). Several studies have demonstrated that marine sediments are highly polluted by heavy metals (Fatoki & Mathabatha, 2001; Glasby et al., 1988; Jayaraju et al., 2011; Pande & Nayak, 2013); therefore, the evaluation of metal distribution in surface sediment is useful to assess pollution in the marine environment along the east coast of India. Industrial activities, economic development, and urbanization in metropolitan urban centers all over the globe have risen very quickly in recent years and substantial quantities of contaminants are introduced into rivers and estuarine and coastal regions (Ravichandran & Manickam, 2012). Estuarine sediment contamination is getting increasing attention from the 2

scientific community, since it is known as a major source of ecosystem health stress (Riba et al., 2002). Sediment which provides habitats for many aquatic organisms are polluted with various forms of hazardous and toxic substances, including heavy metals (Baran & Tarnawski, 2015). As a result, they are considered to be important carriers as well as a sink of heavy metals in the hydrological cycle (Ho et al., 2010) and reflect the existing quality of the system in addition to providing information on the impact of the sources of pollution and function as natural archives of recent environmental changes (Kruopiene, 2007). This paper reflects an effort to measure the levels of heavy metals from the sediment of the Tupilipalem Coast, southeast coast of India. There is a dearth of studies dealing with heavy metals from the sediment of the current study area. A few studies have focused on specific areas such as physico-chemical parameters (Sreenivasulu et al., 2015) and sea mouth dynamics (Nanda Kumar et al., 2009, 2010). Hence, it is important to understand the spatial distribution and concentration of metals in the study area. In this study, the authors investigated the distribution of heavy metals and the pollution status using environmental assessment indices: Geoaccumulation Index (Igeo) (Muller, 1960, 1979; Rubio et al., 2000; Satpathy et al., 2012; Yu et al., 2008), Enrichment Factor (EF) (Abrahim & Parker, 2008; Ra et al., 2013; Shakeri et al., 2014) and Pollution Load Index (PLI) (Goher et al., 2014; Ozseker & Eruz, 2011; Rabee et al., 2011). This work would be of assistance to the relevant authorities in ensuring a good and standard heavy metal status of the neighboring water bodies and would function as a foundation for further work for the study area. The paper attempts to elucidate coastal management by providing primary data sets for decision-making policy holders. In addition, the baseline information will form a flatform for the stake holders to monitor the coastal pollution by inexpensive biomarkers, and also to manage and conserve

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the study area from further pollution hazards. Further, the pollution levels for the study area can be constanstly monitored in order to safeguard the fishing community and biodiversity.

2. Study area Tupilipalem is about 20 km from Dugarajapatnam (southeast coast of India) and more than 120 km from Pulicat Lake and the Sriharikota High Altitude Range (SHAR) center at Sriharikota. Tupilipalem is the proposed site for constructing a major port to be named Dugarajapatnam Port. The study area is geographically located in the southeastern part of Nellore district, Andhra Pradesh, India, lying between the latitudes 14°0’10” – 14°02’30” N and longitudes 80°08’20”– 80°19’00”E and is connected to Pulicat Lake. The Buckingham Canal (navigation channel) is part of the lagoon on its western side. It is connected to the sea through Rayadoruvu and Pulilcat Lake from north to south. A subtropical climate prevails over the study area with an average annual rainfall of 1041 mm. The mean minimum and maximum temperatures are 20 and 39.6°C, respectively. However, the currents in the study area are also affected by the tidal cycles, the action of waves, the shore line geography, and by the presence of different water masses, assuming predominantly the SW- NE direction. The government of India is planning and proposing a huge port in the study area which will form a gateway to Southeast Asian countries in the future. With this background, the present investigation was carried out inorder to provide first hand information to the beneficiaries.

Fig. 1. Sampling location map of the study area

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3. Materials and methods Twelve samples were randomly collected from the upper 0-2 cm of undisturbed bottom sediment using a mud grab sampler and the actual coordinates of the sampling points were recorded using a Global Positioning System (GPS) tracker (Shaari et al., 2015). The sampling locations are shown in Fig. 1. Then, sediment samples were stored in clean selfsealing plastic bags for further analysis (Obaidy et al., 2014). About 1g of dried sediment samples were digested (at 110°C for 90 min) with 14 ml of aqua-regia solution (HNO3:HCl). After cooling, 14 ml of aqua-regia was added and heated again at 110°C for 30 min. The digested samples were filtered through 0.45 μm membrane and the concentrations of heavy metals [iron (Fe), manganese (Mn), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn), and Cadmium (Cd)] were measured by inductively coupled plasma-optical emission spectrometry (ICP-OES) (Alomary & Belhadj, 2007; Ashraf et al., 2012; Moukhchan et al., 2013; Ali et al., 2014). Maps were prepared of digitized contour lines using ArcGIS 9.1 software to investigate the sediment quality, and, thereby, most polluted points were determined (Saadet et al., 2014, 2016).

3.1. Metal enrichment assessment To understand the existing environmental condition and the amount of heavy metal contamination with respect to the natural environment, other approaches should also be applied. The anthropogenic contribution of the selected heavy metals in marine sediment can be estimated from the metal enrichment relative to background levels. Various methods have been suggested for quantifying metal enrichment in surface sediment.

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3.1.1. Geoaccumulation index (Igeo) A common principle to assess heavy metal pollution in sediment is the Geoaccumulation index (Igeo), which was initially defined by Muller (1979) to determine metal contamination in sediments, by comparing current concentrations to preindustrial levels and it can be calculated using the following equation:

 C  Igeo  log 2  n  1.5Bn 

(1)

where Cn is the concentration of element ‘n’, and Bn is the geochemical background value. According to the world surface rock average given by Martin and Meybeck (1979), the geochemical background values are 46700 ppm for Fe, 850 ppm for Mn, 90 ppm for Cr, 28 ppm for Cu, 68 ppm for Ni, 20 ppm for Pb, 95 ppm for Zn, and 0.3 ppm for Cd. 1.5 is a factor for potential variation in background data due to lithogenic effects.

3.1.2. Enrichment factor (EF) The enrichment factor can be used to assess metal contamination in the studied sediment in a more comprehensive way (Tesfamariam et al., 2016). This method normalizes the measured heavy metal concentration with respect to a reference metal such as Fe or Al (Veerasingam et al., 2012). According to Nirmala et al. (2016), in the case of Fe, particularly the redox sensitive iron hydroxide (Fe(OH)3) and oxide under oxidation condition constitute significant sinks of heavy metals in aquatic systems. Even a low percentage of Fe(OH)3, in an aquatic system, has a controlling influence on heavy metal distribution. Hence, Fe was used as a conservative tracer to differentiate natural from anthropogenic components

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(Bentum et al., 2011; Ghrefat & Yusuf, 2006; Ibrahim et al., 2015). The EF is calculated using the following equation:

(2) where Mx is sediment sample concentration of the heavy metal, Fex is the Fe concentration in the sediment, and Mb and Feb are their concentrations in a suitable background or baseline reference material.

3.1.3. Pollution load index (PLI) The Pollution load index (PLI) for a particular site as well as a zone is evaluated as per Tomlinson et al. (1980). It is a possible tool in heavy metal pollution evaluation (Ogbeibu et al., 2014). The index is calculated as:

(3) where n is the number of metals and contamination factors, CF is given by CF = Cmetal / Cbackground (Cmetal is the corresponding metal concentration of the sample and Cbackground metal concentration in the back ground). (4) 3.2. Statistical analysis In order to study the concentrations of heavy metals’ content in sediment from the Tupilipalem Coast, southeast coast of India, these data were subjected to Correlation Analysis, Principal Component Factor Analysis, and Hierarchical Cluster Analysis with 7

XLSTAT 2016 to determine association as well as the differences in the concentrations between different locations. Principal Component Factor Analysis is the most common multivariate statistical method used in environmental studies to reduce data and to extract a small number of latent factors for analyzing relations among the observed variables (Chen et al., 2007; Oliva & Espinosa, 2007). Hierarchical Cluster Analysis (HCA) characterizes similarities among samples by examining interpoint distances representing all possible sample pairs in high-dimensional space (Ameh, 2013, 2014). The sample similarities are represented on two dimensional diagrams called dendrograms (Sarkar et al., 2014). 4. Results and discussion

Table 1. Heavy metal concentrations (ppm) for the study area.

The variations of heavy metals (Fe, Mn, Cr, Cu, Ni, Pb, Zn, and Cd) concentrations at the different locations along the Tupilipalem Coast are listed in Table 1 and shown in Fig. 2. The range and average concentrations (given in parenthesis) (ppm) were 943.30-6813.41 (2779.81) for Fe, 18.46-169.24 (54.84) for Mn, 4.89-18.09 (9.25) for Cr, 2.23-5.97 (3.55) for Cu, 3.52-9.51 (6.43) for Ni, 3.64-7.24 (5.67) for Pb, 11.57-24.75 (14.19) for Zn and 0.411.09 (0.71) for Cd. These results indicate that greater variation was observed in Fe, Mn, and Cr concentrations, whereas other elements showed a gradual trend between the various sampling sites. The decreasing trend of average contents of heavy metals was Fe> Mn> Zn> Cr> Ni> Pb> Cu> Cd. Fe had the highest values and Cd had the lowest at all the stations. The highest concentrations of all the heavy metals under study except Cd were observed at Station TP-5 which was located in the brackish environment where the Buckingham Canal and other 8

aquacultural effluents were connected to the Bay of Bengal. This location, being the second inlet, which is periodically closed, showed the extent to which pollutants were being moved inside the channel. It seemed that these parts of the study area were heavily affected by anthropogenic sources of pollution. The lowest concentration (ppm) of Fe (943.3), Cr (4.89) and Cu (2.23) was observed at station TP-7 which is situated in the southern inlet. Mn (18.46) and Ni (3.52) showed the lowest concentration (ppm) and Cd (1.09) showed the highest concentration (ppm) at station TP-8. Stations TP-2 and TP-3, at the beach environment, showed the lowest concentrations (ppm) of Cd (0.41) and Pb (3.64). Finally, Zn (11.57) showed the highest concentration (ppm) at stations TP-10 and TP-11.

Fig. 2. Contour map showing the heavy metal concentrations for the study area.

4.1. Hierarchical clustering analysis Hierarchical clustering analysis was done for different sampling stations. Four groups were formed (Fig. 3). Cluster 1, formed by stations TP-1, TP-4, TP-9, TP-11, and TP-12 indicated the influence of river loads at both the inlets (northern and southern) of the study area, is explained by its nearness to these stations. In Cluster 2, TP-2, TP-6, TP-7, and TP-10 showed moderate contamination with all the studied heavy metals. Further, the stations from Cluster 2 were distributed at the central part of the both inlets. TP-3 and TP-8 formed Cluster 3, which showed the highest concentration of Cd, which was toxic at very low concentrations (1.08 ppm). Moreover, Cluster 4, formed with station TP-5, showed the highest concentrations of all the studied heavy metals except Cd in a brackish environment at northern inlet. Fig. 3. Dendrogram of grouping of sampling stations. 9

4.2. Factor analysis: The selected heavy metal data were subjected to factor analysis. The analysis yielded three factors, namely, Factor 1 (64.40%), Factor 2 (15.53%), and Factor 3 (12.68%) (Figs. 4 and 5). Factor 1 was represented by Mn (0.982), Fe (0.952), Cr (0.938), Zn (0.863), Ni (0.830), and Cu (0.820). These elements originate naturally in the earth's crust from lithological units on the surface (Ramesh et al., 2000). Therefore, there were high concentrations of these elements near the inlets because of the natural abundance of them in the earth’s crust. Factor 2 was composed of Cd (-0.925) only. Anthropogenic activities have long been regarded as the main factors associated with elevated Cd incidences in the marine environment (Mwashote, 2003). However, the two stations (TP-3 and TP-8) found with elevated concentrations of Cd can be associated with localities characterized by the concentration of anthropogenic activities. In addition, Factor 3 was represented by Pb (0.848). According to Jayaraju et al., (2007), Pb may come from different sources like paint, cement, paper, rubber and also from sewage and industrial effluents. The highest concentration of Pb was found at station TP-5. Finally, a cross plot of Factors 1&2 and Factors 3&4 gave distinct information about heavy metals’ distribution and set clusters of similar factors. Fig. 4. Factor loadings of selected heavy metals (Factor 1 and Factor 2). Fig. 5. Factor loadings of selected heavy metals (Factor 1 and Factor 3).

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The relation between metal concentrations was investigated by Pearson’s correlation matrix (Table 2). The analysis yielded positive correlations among Fe, Mn, Cr, Ni, Zn, Cu, and Pb. Positive correlations among heavy metals signified that metals had common sources, mutual dependence, and identical behavior during transport (Bastami et al., 2015). Moreover, Cd negatively correlated with Ni, suggesting that they had similar geochemical behaviors. These results of correlation can be useful in understanding the relations of metal concentrations from the sediments (Shirneshan et al., 2013). Table 2. Pearson correlation matrix.

4.3. Geoaccumulation index (Igeo), enrichment factor (EF), and pollution load index (PLI) The effects of the geoaccumulation index (Igeo) are graphically presented in Fig. 6. Based on the Muller’s classification, this index includes 7 classes: Igeo ≤ 0: practically unpolluted; 0 < Igeo < 1: Unpolluted to moderately polluted; 1 < Igeo < 2: Moderately polluted; 2 < Igeo < 3: Moderately to strongly polluted; 3 < Igeo < 4: Strongly polluted; 4 < Igeo < 5: Strongly to extremely polluted; and 5 < Igeo: Extremely polluted (Varol, 2011). The Igeo values are in the following ranges: Fe (25-28), Mn (13-17), Cr (8-10), Cu (5-7), Ni (7-9), Pb (6-7), Zn (10-11) and Cd (1-2). The formula resulting from the analysis of large amounts of data concerning Tupilipalem Coast, southeast coast of India, revealed that Fe, Mn, Cr, Cu, Ni, Pb, and Zn had a higher degree of pollution and that the study area was unpolluted with Cd according to Igeo values.

Fig. 6. Geoaccumulation index (Igeo) for heavy metals in sediments of the Tupilipalem Coast. 11

The enrichment factor (EF) is a suitable measure of geochemical trends and is used for making comparisons between areas. Because of the natural basis of metal elements, the gross concentrations of metal elements don’t specifically demonstrate the anthropogenic contribution (Zubair & Begum, 2014). Several authors have successfully used Fe to normalize heavy metal contaminants (Uduma & Awagu, 2013). In the current study, Fe was also used as a conservative tracer to differentiate natural from anthropogenic components. The results of these calculations for the Tupilipalem Coast sediment are plotted in Fig. 7. According to Sutherland (2000), five contamination categories are reported on the basis of the enrichment factor, which are- EF < 2 deficiency to minimal enrichment, EF = 2-5 moderate enrichment, EF = 5-20 significant enrichment, EF = 20-40 very high enrichment, and EF > 40 extremely high enrichment. The enrichment factor (EF) represents the EF values of all the heavy metals measured in the coastal sediments. The decreasing trend of average contents’ enrichment factors was Cd> Pb> Zn> Cu> Cr> Ni> Mn> Fe. When applying the foregoing contamination categories, Cd indexed extremely high enrichment at stations TP-7 and TP-8, 42% of the stations showing very high enrichment. Pb showed significant enrichment for 50% of the stations. The remaining 50% of the stations showed moderate enrichment. Zn showed significant enrichment at the station TP-7 and moderate enrichment for 70% of the stations. Cu showed moderate enrichment in 70%, of the stations. Cr and Ni showed moderate enrichment in 25% of the stations. Moreover, the remaining heavy metals (Fe and Mn) showed deficiency to minimal enrichment at all the stations. Fig. 7. Enrichment factor (EF) for heavy metals in sediments of the Tupilipalem Coast.

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The values of Pollution Load Index (PLI) for a particular station ranged between 0.21 and 0.43 ppm (Fig. 8). According to Tomlinson et al. (1980), PLI values from all the sampling stations were less than one indicating that all the studied heavy metals at all the stations were within the baseline level. The PLI values for the zone varied from 0.05 to 2.30 (Fig. 9). Cd showed higher (>1) values due to the influence of direct external sources like agricultural runoff, industrial activities, and other anthropogenic inputs. According to the GESAMP (1985), Cd comes from contaminated agricultural soils, mining waste, municipal sewage effluents and sludges, and also is derived from erosion of sulfide ores, phosporites, hydrothermally mineralized rocks, and black shale deposits. Fe, Mn, Cr, Cu, Ni, Pb, and Zn recorded lower levels, indicating no pollution when compared to their concentrations in the world wide sediment. The difference in indices results due to the difference in sensitivity of these indices towards the sediment pollutants (Praveena et al., 2007). Fig. 8. The pollution load index for sampling stations in the sediment of the Tupilipalem Coast. Fig. 9. The pollution load index for the zone in the sediment of the Tupilipalem Coast.

5. Conclusions The analysis of total concentrations of heavy metals and their distribution show that sediment from the Tupilipalem Coast are contaminated with heavy metals, which is an effect of intensive anthropogenic stress in the area. The falling trend of heavy metals is Fe> Mn> Zn> Cr> Ni> Pb> Cu> Cd with a average concentrations of 2779.81> 51.84> 14.19> 9.25> 6.43> 5.67> 3.55> 0.71 ppm, respectively. The highest concentrations of studied heavy metals are observed at station TP-5 which is situated at the second inlet (northern side of the study area) and this area is connected with Buckingham Canal and other agricultural and 13

aquacultural effluents. Multivariate analyses, including the Correlation Matrix Analysis and Factor Analysis, used in this study, provide important tools for a better understanding the complex dynamics of the pollutants. The correlation analysis of concentration data showed positive and negative correlations among Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd and it also indicated that these metals had complicated geochemical behaviors. Factor analysis summarized the set of normal data into three main components which were associated with different potential sources of contribution. Three factors explained 92% of the total variance: First factor included “Mn, Fe, Cr, Zn, Ni, and Cu", second factor was "Cd" and third factor was "Pb". Sediment pollution in the current study was assessed using the Geoaccumulation Index, Enrichment Factor, and Pollution Load Index values. Igeo denoted that the sediments were extremely polluted with Fe, Mn, Cr, Cu, Ni, Pb, and Zn. The Enrichment Factor indexed that Cd had extremely high enrichment at stations TP-7 amd TP-8 and, had very high concentration at 40% of the stations. Pb and Zn showed significant enrichment and Cu, Cr, and Ni showed moderate enrichment. Moreover, the PLI values of the Cd showed higher (>1) values due to the influence of direct external sources like agricultural runoff, industrial activities, and other anthropogenic inputs. It is a stock pollutant and is extremely harmful in various concentration levels, to marine life. According to the current study, the impact of anthropogenic input, as a source of heavy metals in the study area, is alarming. To prevent severe heavy metal contamination of the study area, especially in the surrounding area of the second inlet exposed to agricultural and industrial metal loadings, it is crucial to implement timely monitoring and remediation strategies to improve the loadings and cumulative concentrations of heavy metals in the sediments.

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Acknowledgements G. Sreenivasulu, is thankful to the Department of Science and Technology (DST), New Delhi, India for the financial support in the form of DST-INSPIRE Fellowship (No. DST/INSPIRE Fellowship/2012/344- IF120311). We thank Mrs. Jyothi Hymavathi Devi, Centre for English Language Studies, University of Hyderabad, for editing the English language of the paper to its present form. Editor and anonymous reviewers were thanked for their comments and editing to improve the quality of the manuscript substantially.

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Table 1. Heavy metal concentrations (ppm) for the study area. Sample ID

Fe

Mn

Cr

Cu

Ni

Pb

Zn

Cd

TP-1

2030.69

51.93

7.04

5.41

6.16

7.23

14.33

0.85

TP-2

2047.96

26.63

8.65

3.17

4.92

4.43

14.56

0.41

TP-3

3056.79

59.98

11.23

3.37

6.97

3.64

17.37

1.08

TP-4

2554.86

42.7

9.50

3.36

6.83

6.57

13.75

0.54

TP-5

6813.41

169.24

18.09

5.97

9.51

7.24

24.75

0.88

TP-6

2304.66

27.52

7.32

2.83

4.69

4.20

11.88

0.69

TP-7

943.3

26.93

4.89

2.23

5.69

4.96

11.61

0.53

TP-8

1431.78

18.46

6.86

3.20

3.52

5.39

13.06

1.09

TP-9

1875.2

45.27

6.73

3.18

6.16

7.13

12.68

0.76

TP-10

2437.43

29.45

7.93

2.90

6.84

4.79

11.57

0.67

TP-11

3516.57

68.96

10.26

3.79

7.01

6.86

11.57

0.63

TP-12

4345.16

91.02

12.60

3.28

8.97

5.64

13.26

0.50

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Table 2. Pearson correlation matrix. Variables Fe

Fe

Mn

Cr

Cu

Ni

Pb

Zn

Cd

1

Mn

0.957

1

Cr

0.978

0.926

1

Cu

0.663

0.746

0.633

1

Ni

0.841

0.851

0.806

0.497

1

Pb

0.337

0.476

0.251

0.642

0.387

1

Zn

0.774

0.823

0.826

0.743

0.552

0.227

1

Cd

0.092

0.148

0.131

0.352

-0.117

0.007

0.385

1

Highlights  





Sediment pollution assessment was done using the Geoaccumulation Index (Igeo), Enrichment Factor (EF), and Pollution Load Index (PLI). The concentrations of heavy metals in sediment from the Tupilipalem Coast, Southeast Coast of India were subjected to correlation analysis, Principal Component Factor Analysis, and Hierarchical Cluster Analysis with XLSTAT 2016. The PLI values of cadmium show higher (>1) values due to the influence of external, distinct sources like agricultural runoff, industrial activities, and other anthropogenic inputs. It is crucial to implement timely monitoring and remediation strategies to improve the loadings and cumulative concentrations of heavy metals in the sediment.

30