Assessment of Heavy Metal Pollution Indices in Surface Sediments From Southwestern Bay of Bengal, India

Assessment of Heavy Metal Pollution Indices in Surface Sediments From Southwestern Bay of Bengal, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Heavy Metal Pollution Indices in Surface Sediments From Southwestern Bay of Bengal, India Harikrishnan Sadanandan, Senthil Nathan Dharmalingam, Nitin Agarwal, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-159678/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study of heavy metal distribution in the shelf sediments of Southwestern part of Bay of Bengal is essential in determining the distribution pattern and to understand the consequences of marine pollution beside the coastal environment. The south eastern coastal areas of India are affected by several disturbances and contamination associated with accelerated industrialization and urbanization. Twenty-nine surface sediment samples were collected from shelf region of Southwestern part of Bay of Bengal and analyzed for sediment texture, organic matter and heavy metals. Pollution indices such as Enrichment Factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (CF) as well as multivariate statistical analyses were used to recognize the pollution pattern and probable sources for metal contamination. Comparatively, the concentration of heavy metals in the study area is closely associated with finer fractions and organic matter. The results demonstrate that Cu, Co, Mn, Pb, Zn, Cr and Ni in most of the sites are extremely contaminated in terms of Igeo. The computed values of CF indicate very high contamination of the metals like Pb, Zn and Cr followed by uncontamination to moderate contamination of Cu, Mn, Ni, Co. Based on factor analysis, domestic and industrial activities from adjacent land areas are found to be the major contributors of heavy metals in the shelf sediments. Environmental Engineering Environmental Policy Marine pollution Assessment Heavy metals Shelf sediments Bay of Bengal. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The coastal zones are the areas which act as a major sink for heavy metal contaminants since the industrial revolution. Heavy metals are highly harmful due to their accumulative behavior and non-biodegradability, and are the major cause of marine pollution. Anthropogenic activities play a major role in releasing these heavy metals into the marine environment. Heavy metals viz. Zn, Cu and Pb are sourced from automotive traffic in the urban environment; Ni and V from marine traffic; Cu and Hg from paint industries (Lewan, 1984; Tamim et al., 2016 ; Xianmeng., et al 2018). These heavy metals reside for a longer time in ocean water and are transported to the sea bottom (Xigui et al., 2018). The heavy metal pollutants effectively retain on marine particles and involve themselves in all the physical, chemical, and biological processes of marine organisms. (Yunhai Li et al., 2018 ; Tansel and Rafiuddin, 2016 ). Heavy metals accumulated in sediments become entrained in the food web as contaminants. Thereafter they undergo bio-magnification and accumulation in marine organisms, which when consumed by human beings can pose serious health problems (Wang et al., 2013 ; Mokhtar et al., 2009 ). Heavy metal in the sediments directly influence benthos community (Pan and Wang, 2013), but with high storm and tide, they can be re-suspended, re-dissolved and either re-deposited or transported to affect further oceanic and near shore environments (Xigui Ding et al., 2016). Those heavy metals which are not carried offshore will lead to secondary pollution of coastal areas (Islam et al., 2017 ). The preservation capacity of sediments is perhaps related to its physicochemical properties viz. organic matter and grain size (Ihejirika et al., 2016 ). Moreover, major industrial plants of many countries are established in the cities which are located in the coastal areas and along the banks of major rivers. The effluents released from those industries are dumped into the fluvial or marine environment without any treatment (Sarraf et al., 2016). The present study focuses on the evaluation of heavy metal pollution of sediments from marine environment using indices viz. Enrichment factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (CF). And with the help of multivariate statistical analysis we attempted to probe the source and activities controlling the discharge of heavy metals. 2. Material And Methods From the study area, 29 surface sediment samples were collected from different depths using Van Veen grab sampler in September 2017 (Fig. 1). The collected samples were then preserved by transferring into pre-cleaned polyethylene bag using a plastic spatula. In the laboratory, a representative portion of each sample was used for textural analysis. The remaining portion of each sample was used for chemical analysis. 2.1 Study Area 2.1.1 Geography The study area is located in the South-western part of Bay of Bengal, between the coordinates 11.705213° N 79.798297° E and 11.231771° N 79° 56' 10.248'' E (Fig. 1). There are ephemeral rivers viz. Coleroon, Uppanar, Vellar, and Gadilam drains the continent and finally opens into the Bay of Bengal. 2.1.2 Climate and Rainfall The maximum and minimum temperature recorded in the study area is 32.3ºC and 21.18ºC respectively. The study area receives maximum rainfall due to northeast monsoon with an average annual rainfall of about 1393.3mm (DEIAA, 2018). 2.1.3 Geology The adjacent part of the study area is composed of Precambrian granitic basement overlaid by sedimentary rocks belonging to different geological periods. This Precambrian basement is marked by a series of horst & graben structures (Vasudevan et al., 1998). In this region, sandstone consists of rounded pebbles (fragment and pebbles), lateritic and laterite gravels belonging to Cuddalore formation and are overlaid by red sandy soil (Jayaprakash et al., 2016). 2.1.4 Industries The rivers adjacent to the study area, in addition to flooded water, also carries untreated industrial effluents released by the industries located on its banks such as paint industries, chemical industries, cotton mills, rubber, plastic, petro-products, metal-based industries, electrical machinery, transport equipment companies, tanneries, and oil companies, etc. (Jayaprakash et al., 2016). Besides, Cuddalore port and Thirumullaivasal fishing harbour are located along the estuarine part of the study area. Since 1990 this area is affected by rapid industrialization leading to the degradation of the aquatic system (Jonathan et al., 2008). Several studies regarding marine pollution have been carried out during the past decades in the coastal and marine sediments along the south east coast of India. Moreover, the coastal ecosystem was often affected by several devastating cyclones and rarely by Tsunami in December 2004. 2.2 Textural Analysis The textural analysis was carried out by sieving and pipetting method, first the sediment samples were pre-treated with H 2 O 2 solution for the removal of organic matter. Then they were wet sieved through a 63 µm mesh for 15 min in a sieve shaker. The sample that held on the sieve was weighed and indicated as sand. The mud fraction which includes silt and clay (> 0.063 mm) were determined using the pipet method. The textural classification was determined based on the mud content after Flemming, (2000) and Pejrup, (1988) classification (Fig. 2). (See Fig. 3.) 2.3 Organic matter The sediments were dried in an oven at 50ºC and then standardized. The standardized samples were pulverized into fine powder using FRITSCH Pulverisette 7Agate Ball mill. From each of the powdered samples, 5g was taken and decarbonized with 1N solution of Hydrochloric acid and then washed three times with deionized water and centrifuged to remove absorbed HCl in the sediment. The samples were dried and standardized again for analysis in CHNS analyser (model: Vario el cube Odu). The results of total organic matter in each sample were expressed in terms of percentage (Table 1). 2.4 Heavy metal analysis For heavy metal analysis, the sediments samples were oven-dried at 60ºC and dried samples were crushed into fine powder using FRITSCH pulverisette7 Agate ball mill to use later in the chemical analysis. Approximately 0.01g of the sample was taken in Savillex Teflon pressure decomposition vessel for digestion and these were pre-treated with 1:1 H 2 O 2 to remove the organic matter present in the sample. The samples were digested using 3–4 ml of acid mixture proportion 7:3:1 ratio of HF, HNO 3 and HCl. Further HCl in the ratios of 3:1 were added into the solution and dried frequently till the silicon tetrafluorides were entirely fumed out. After complete digestion, the dried samples were dissolved with 2 ml of 2% HNO 3 and diluted to 100 ml. This final diluted solution is the stock solution and from the stock solution, 2 ml was again diluted up to 10 ml in clean scintillation vials. 2.5 Statistical Analysis Using IBM SPSS (version 20) statistical software, the data were subjected to multivariate statistical analysis viz. Pearson Correlation, Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). PCA was done to group the parameters having identical characteristics in one way or the other and to identify the relation between elements and sampling locations. Cluster analysis was carried out to identify any similarities between sampling locations with regard to organic matter, grain size, depth, and heavy metal concentration. Pearson correlation analysis was carried out to examine the relationship between the variables (grain size, organic matter, and heavy metals) 2.6 Enrichment factor Enrichment factor (EF) is one of the pollution indices, calculated to differentiate the anthropogenic and natural sources for metals enriched in sediments (Abrahim et al., 1998 and Dickinson., 1996). The EF were derived by normalizing the measured trace elements, rare earth elements and actinides concerning metals like Fe, Al or Sc (Ashraf et al., 2016; and Ravichandran et al., 1995). The EF was calculated based on the following equation: EF = (Cx/Fex) sample / (Cref/Fe ref ) where C x is the concentration of an element in the sample, Fe x is the concentration of Fe in the sample; C ref is the concentration of an abundant and common element in the average sediment and Fe ref is the concentration of Fe in the average sediment (Wedepohl and Turekian, 1961). In the present work, Fe was selected as the normalisation element to calculate the enrichment factor. In marine sediments Fe is mainly derived from the natural weathering process thus it is typically used to standardize the metal concentration. The EF values thus obtained are categorized into five tiers, as suggested by (Sutherland, 2000). The elemental ratios show consumption to minimal enrichment when EF 40 extremely enriched. Average crustal abundance values of the heavy metals are frequently used as elemental background concentration for resemblance. In the work average crustal abundance was used as background reference (Taylors, 1964). 2.7 Geo-accumulation index The intensity of pollution for each sampling location is derived by the geo-accumulation index (Igeo). The Igeo is a quantified measure of the degree of the contaminant in sediments (Forstner et al., 1990) and it is calculated by the following equation: Igeo = (C n /1.5*Bn) Where B n is the geochemical background of a provided element and C n is the concentration of elements considered in the sediment. Muller (1979) categorized the sediment based on the Igeo value, as; Igeo > 5 = extremely contaminated, 4 to 5 = strongly to extremely contaminated, 3 to 4 = strongly contaminated, 1to2 = moderately contaminated, 0 to 1 = uncontaminated to moderately contaminated and < 0 = uncontaminated. 2.8 Contamination factor (CF) CF is perceived to be a valuable method of measuring pollution in sediments over time. It is the ratio of each metal in the present sample to the background values in the same metal CF = C heavymetal /C background CF can be classified into four groups (Pekey et al., 2004). If CF values < 1, there is no metal contamination by geogenic or anthropogenic inputs; CF < 3 for a particular metal indicates that sediment is moderately contaminated; CF 6, there is very high contamination for that metal. Taylor's (1964) average crustal abundance values of the trace metal was used as the background reference material. 3. Results A total of 29 surface sediment samples from the study area have been analysed and the result of organic matter, textural class and heavy metal concentrations are given in table 1and 8. 3.1 Sediment properties The textural class of the sediments of the study area is shown in the Table 1. There are three types of sediments- sand, slightly muddy sand, and muddy sand. Textural analysis indicates a good correlation between depth and grain size (Table 1). The surface sediments are dominated by coarse grains in the shallower part and finer sediments in the deeper part, whereas the transect 2 (station number 10) and transect 3 (station number 14, 15 and 16) do not show the above observations. The sandy sediments occur in the stations 3,4,13 and 28 while slightly muddy sand occurs at stations 2, 5, 7, 8, 12, 16, 18, 19, 20, 22, 23, 24, 25 and 26. Samples from the station 1, 9, 17, 27, 11 and 6 are muddy sand and 10, 14, 15 and 21 occur as sandy mud (Fig. 3). 3.2. Heavy metal Distribution Heavy metal analysis of surface sediment samples from the Bay of Bengal and their perceptive values and crustal average (Taylor, 1964) are shown in table 2. In the present study area, C org content in coarse sediments was particularly lower than the finer sediments. The concentration of Cu, Co, Fe, Mn, Pb, Zn, Cr and Ni ranged from 4.89 to 79.23 ppm, 6.38 to 146.28 ppm, 9004.49 to 44483.12ppm, 215.47 to 1036.13 ppm, 134.50 to 15569.84ppm, 324.90 ppm to 1958.14 ppm, 46.41ppm to 582.35 ppm respectively. The mean values of concentration of different heavy metal in the present area are as follows in the descending order: Fe (29712.16 ppm) >Zn (6693.86 ppm) >Pb (4443.13ppm) >Cr (966.98 ppm) >Mn (526.01 ppm) >Ni (258.13) >Co (66.46 ppm) >Cu (30.26 ppm). The higher concentration of Cu, Co and Mn are observed in station no.6 (Transect 1) although Pb, Zn and Cr are encountered in station no.29 (Transect 6) and Fe and Ni is observed in station no.15 (Transect 3) and 18 (Transect 3) respectively (Fig. 4 and 5). The Mn concentrations in the present study area are higher than that of off Karaikal coast surface sediments in the Bay of Bengal; Cu and Zn concentrations are higher than that of, off Cuddalore coast, off Ennore, off Pichavaram, off Tuticorin coast, shelf sediments of Gulf of Mannar in the East Coast of India (table 2). 3.3 Organic matter Organic contents vary between 0.23% and 2.40% with an average of 0.88%. The high values of organic matter are associated in the deeper part samples and low values occur in the shallower part (Fig. 3). High organic matter concentration in the present study is enriched in muddy sediments and low in sandy type sediments except for the transects 1 and 3 (station number 1 and 14. 3.4. Statistical Analysis 3.4.1 Principle Component Analysis The PCA was performed to group the pollutants and identify the influencing factors for the distribution of heavy metals in the study area. The PCA analysis was applied for the organic matter, grain size (sand and mud) and heavy metals. The Kaiser-Meyer-Olkin normalisation technique was used to extract maximum factors that influence the distribution of heavy metals. The technique takes into account only those factors with eigenvalues greater than 1, for each procedure. The Varimax rotation yielded 5 factors. Additionally, factor loading communalities for the first three factors were taken as the percentage of variance and the cumulative percentage of variance was derived (Table 6 and Fig. 6). First principle component shows maximum loadings of Cr (.915), Ni (.814), Zn (.752) and sand (.652) (Fig. 6). The second PC analysis shows significant loadings of Fe (.87), Mn (0.83), Cu (0.63), mud (.61), Co (0.42). Fe and Mn has the highest positive loadings, Cu, mud and Co show medium positive loadings (Fig. 6). The third PC has significant loadings of Organic matter (0.86), Cu (0.48), Ni (0.34) and Cr (0.31). Organic matter exhibits the highest positive loadings. Cu exhibits low positive loadings while Ni and Cr exhibits very low positive loadings (Fig. 6). 3.4.2 Pearson correlation The correlation analysis was performed on the normalized data set to test the relationship between the environmental parameters (table 7). According to the Pearson correlation analysis, Fe shows a strong positive correlation with Mn (r=.732) whereas Zn shows a strong positive correlation with Cr (r=.784) and Ni (r=605) and Cr show strong positive correlation with Ni (r=.700). The heavy metals viz. Cu (-.394) and Mn (-.361) exhibit negative correlation with sand while Zn (.283), Cr (.448), Ni (.466), Pb (.101) show low to moderate correlation with sand. Mud has significant correlation with Fe (.495). Weak positive correlation with Cu (.394) and Mn (.361) while negative moderate correlation with Cr (-.448) and Ni (-.283) and negative weak correlation with Pb (-.283) and Zn (-.101). 3.4.3 Q mode cluster The consequent dendrogram of Q-mode hierarchical cluster analysis provides the grouping of samples according to the heavy metal, organic matter, grain size and depth. The dendrogram exhibits four groups, cluster 1(12, 24, 20, 10, 13, 18, 19, 9, 11, 5, 22 and 7) cluster 2(16, 25, 6, 21, 26, 27, 17, 8) cluster 3(14, 29, 23 and 15) cluster 4 (4, 28, 1, 2 and 3) (Fig. 7). 3.5 Enrichment factor The mean values of EF are as follows Mn > Pb > Cr > Zn > Cu>Co > Ni. Mn is moderately enriched followed by Cu, Pb, Ni, Co and Zn. According to the Muller (1969) Sutherland (2000) classification, the majority of the metals show minimal enrichment to significant enrichment in the sediment sample (Table 3). 3.6 Contamination factor The mean values of CF for the metals in the shelf sediments are shown in table 4. CF in the present study area is as follows Pb<Cu<Co<Ni<Cr<Zn<Mn<Fe. The calculated CF value indicates that all the sediment samples have been very highly contaminated by Fe. There is also significant Zn, Pb and Cr contamination in most of the sampling stations in the study area. 3.7 Geo accumulation index Geo accumulation index shows that most of the samples are extremely contaminated in Pb and Zn. Certain samples are moderate to strongly contaminated in Cr and are uncontaminated to moderately contaminated by Ni and Co. The study area is found to be uncontaminated by Cu and Mn (table 5). 4. Discussion 4.1 Sediment Sediment transportation and deposition are the essential factors which impact the distribution of fine-grained sediments in the marine ecosystem (Tavakoly et al., 2014). The study area is predominantly covered with sandy sediments in the shallower part and can be correlated with incidents of high wave energy condition (Murray, 1963); subsequent erosion (Viveganandan et al., 2013) and the presence of submarine canyon (Narayanan et al., 2015). The higher concentration of mud in the deeper part of the study area is consequent of low energy conditions. Besides, the higher concentration of mud content was found in the shallow depth of transect 1 and 3 suggesting input of freshwater with finer particles from the Coleroon and kollidam river which is then deposited to the sea bottom where the current and wind speed reduces near the shoreline, mostly in the estuarine region (Thomson Becker and Luoma, 1985). The textural characteristics signify that they are mainly dependent on different dynamic processes which affect only the shallow part of the current study area instead of the deeper part; it fluctuates during the November to February (Gopalakrishna and Sastry, 1985) 4.2 Organic matter The significant quantity of organic matter was found to be strongly associated with muddy sediment in the present study area. This implies that the organic matter in the sediments had high adsorption ability and tends to adsorb fine particles (Li et al., 2015 b). Earlier studies in sediments also made a similar observation in the estuaries and offshore areas of both India's East and West Coast (Nobi et al., 2010 ; Magesh et al., 2011 ; Jayaprakash et al., 2014; Chakraborty et al., 2015 ; Chakraborty et al., 2015 a, 2015 b; Kasilingam et al., 2016 ; Gopal et al., 2016; Nethaji et al., 2017 ; and Godson et al., 2018 ). The fluctuation in the concentration of organic matter demonstrates that the textural parameters and dynamic process are dependent on each other. The higher concentration of organic matter in the study area is due to the fact that (i) The distribution of organic matter is provided mainly by terrestrial inputs from the adjacent land area, (ii) Perhaps, in some areas it might be result of direct discharge of domestic waste (iii) Due to sea-grasses, sea-weeds and algae bottom facies, there can be high organic productivity and (iv) The huge rate of sedimentation (Mansour et al., 2013 ). 4.3 Heavy metal The concentration of Cu, Co, Fe, Mn, Pb, Zn, Cr, Ni were in ranges of 4.89–79.23 ppm, 6.38-146.28 ppm, 9004.49-44483.12 ppm, 134.50-15569.84 ppm, 215.17 -1036.13 ppm, 3938.03-9886.03 ppm, 324.90–1958 ppm, 46.41-582.35 ppm with average of 30.26 ppm, 66.46 ppm, 29712.16 ppm, 4443.13 ppm, 526.01 ppm, 6693.86 ppm, 966.98 ppm, 258.13ppm respectively (Figs. 4 and 5). The mean concentration of the study area shows the following decreasing order: Fe > Mn > Zn > Cr > Pb > Ni > Co > Cu. The concentration of Fe and Mn being considerably higher than the other heavy metals in all sampling sites indicates that they originated through fluvial input into the coast of the study area through minor rivers (Sandler et al. 1993). The higher values of Fe and Mn associated with muddy sediment with high organic matter is a consequence of the input of dissolved particles into the water (geogenic and anthropogenic) discharged into the study area. The excess concentration of Fe and Mn in marine sediments are due to industrial effluents which are denoted by the presence of ferrous manganese (Fe vs. Mn: r = .732). These are brought to the open sea by small rivers. (Buckley et al. 1995 ). The heavy metals Cu and Co are higher in the shallower part and are associated with sandy sediments. The dominance of heavy metals such as Co, Cu, Pb and Zn in the surface sediments is caused by the nitrate dominated fertilizers in the agricultural areas of the study area (Liaghati et al.2003 and Jayaprakash 2015), while the anti-fouling paints that seep from the boat/ship are the source of Cu and Zn in the sediment sample (Goh and Chou 1997 ). The enhancement of heavy metals in the sediments is high in the mud fractions as fine particles adsorb soluble metals from the natural waters and carry them to the bottom sediments (Lijklema et al., 1993 ; Maher et al., 1999). Moreover, Cr, Ni and other metals also subsequently join the study area through anthropogenic activities like burning of oil, inorganic sewages, phosphate-containing fertilizers, chemical and industrial waste (Gonnelli and Renella, 2010). The total heavy metal concentration is found to be high from the northern and central part of the study area due to the shipwreck located near the study area which is still lying on the seabed at a depth of 20m ( https://www.facebook.com/mvmothi/ ). Moreover, past records suggests that it is submerged with iron ores on board. 5. Conclusion The present study has been carried out to assess the concentration and understand the spatial distribution of heavy metals in the surface sediments from the south-western part of Bay of Bengal. The relatively higher concentration of Cu, Pb, Zn, Ni and Co in the mud fractions shows that heavy metal concentration was quite dependent on the particle size characteristics. The larger surface area of the sediments helps to bind or adsorb heavy metals easily. The results of the study demonstrate that the mean concentration of Cu and Mn is lower than the background value. The concentration of Co, Pb, Zn, Cr and Ni were also much higher than the background value of surface sediments indicating enrichment of these metals in the study area. Such anomalous behaviour clearly shows the role of human activities in contributing metal toxicity to the environment. The various sediment quality indices used in the current study reveal different aspects of pollution. The values of CF factor and Igeo suggest that the study area is extremely contaminated by Pb and Zn and are sourced from mining activities and effluents released from industrial and agricultural activities. Whereas, Igeo and CF values which focus on the anthropogenic influence suggest that Co, Mn, Cr and Ni, mainly originates from the manmade activities such as ship scrapping, antifouling paints used in boats and ships, industries, metal smelting, dredging and land reclamation action in the coastal areas and sewage effluents. The positive correlation of Fe with various heavy metals (Pb, Zn, Cu, Ni and Cr) is due to the metal scavenging phase of Fe oxyhydroxides. The positive PCA loadings of Zn (0.92), Pb (0.876), Cu (0.788), Fe (0.745) and Cr (0.488) demonstrate the common source i.e. anthropogenic origin. Declarations Ethical Approval : Not Applicable Consent for publication : Not Applicable Availability of Data and Materials: The datasets generated and/or analyzed during the current study are not publicly available due to further investigation of the study but are available from the corresponding author on reasonable request. Funding: This work was supported by Government of Kerala Post metric fellowship. Grant number B3-8502/19. Authors' contributions: 1. Conceptualization, Methodology, Formal analysis, Investigation and Writing: Harikrishnan, S. 2. Writing - review and editing: Nitin Agarwal, M. Sridharan, and N. Anbuselvan 3. Supervision: Senthil Nathan 4. Competing Interest - The authors of this manuscript declare that there are no competing interests. 5. Consent to Participate - Not Applicable Acknowledgement We wholeheartedly thank the University Grants Commission (UGC), New Delhi, for financially supporting the work being done. In addition, we extend our sincere gratitude to Central Instrumentation Facility and the laboratory facilities in Pondicherry University.We would like to use this space to thank the National Institute of Ocean Technology (Ministry of Earth Sciences, Government of India) for tremendous assistance in collecting samples, especially to Mr. D. Rajasekhar (Head of Vessel Management Cell), Mr. K. Ramasundaram (Scientific Officer II), Mr. Thiruvathi Babu and Mr. Mohan Raj for their support and immense participation during sampling. Special thanks to Mr Harikrishnan P R, Research scholar Dept. of Earth sciences, Pondicherry University for his help to use Arc GIS software. References Abraham, J. (1998). 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Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environmental Geology, 39(6), 611–627. doi:10.1007/s002540050473. Tamim, U., Khan, R., Jolly, Y. N., Fatema, K., Das, S., Naher, K., … Hossain, S. M. (2016). Elemental distribution of metals in urban river sediments near an industrial effluent source. Chemosphere, 155, 509–518. doi: 10.1016/j.chemosphere.2016.04.099. Tansel, B., & Rafiuddin, S. (2016). Heavy metal content in relation to particle size and organic content of surficial sediments in Miami River and transport potential. International Journal of Sediment Research, 31(4), 324–329. doi: 10.1016/j.ijsrc.2016.05.004. Tavakoly Sany, S. B., Hashim, R., Rezayi, M., Salleh, A., & Safari, O. (2013). A review of strategies to monitor water and sediment quality for a sustainability assessment of marine environment. Environmental Science and Pollution Research, 21(2), 813–833. doi:10.1007/s11356-013-2217-5. Taylor, S. R. (1964). Abundance of chemical elements in the continental crust: a new table. Geochimica et Cosmochimica Acta, 28(8), 1273–1285. doi:10.1016/0016-7037(64)90129-2. Thomson-Becker, E. A., & Luoma, S. N. (1985). Temporal fluctuations in grain size, organic materials and iron concentrations in intertidal surface sediment of San Francisco Bay. Temporal Dynamics of an Estuary: San Francisco Bay, 91–107. doi:10.1007/978-94-009-5528-8_6. TUREKIAN k., Wedepohl, K.,1961. Distribution of the Elements in Some Major Units of the Earth's Crust. Geol. Soc. Am. Bull. 72, 175-192. Vasudevan K, Silvester JM, Murthy JVSSN, Rangachari V, Anathanaryanan PN (1998) Exploration for stratgraphic and subtle traps in Cauvery basin: lessons learnt and future prespective. In:Proceedings of workshop on integrated exploration for stratigraphic and subtle traps, Dehra Dun. Bull Oil Nat Gas Corp 35:75–92. Wang, S.-L., Xu, X.-R., Sun, Y.-X., Liu, J.-L., & Li, H.-B. (2013). Heavy metal pollution in coastal areas of South China: A review. Marine Pollution Bulletin, 76(1-2), 7–15. doi: 10.1016/j.marpolbul.2013.08.025. Zhao, G., Lu, Q., Ye, S., Yuan, H., Ding, X., & Wang, J. (2016). Assessment of heavy metal contamination in surface sediments of the west Guangdong coastal region, China. Marine Pollution Bulletin, 108(1-2), 268–274. doi: 10.1016/j.marpolbul.2016.04.057. Tables Table 1: Sample number, coordinates, depth, sand (%), mud (%), organic matter (%) and textural class in the study area. Sl.No Latitude Longitude Depth(m) Sand Mud Org Textural class 1 11.705213° 79.798297° 3 64.26 35.74 1.53 Muddy sand 2 11.706511° 79.805263° 7 76.68 23.32 0.434 Slightly muddy sand 3 11.704835° 79.811672° 12 98.72 1.28 0.528 Sandy 4 11.704141° 79.842150° 18 97.24 2.76 0.375 Sandy 5 11.702957° 79.887639° 29 80.2 19.8 0.415 Slightly muddy sand 6 11.699927° 79.978918° 50 52.16 47.84 1.837 Muddy sand 7 11.502285° 79.799394° 5 88.5 11.5 0.367 Slightly muddy sand 8 11.501472° 79.809983° 10 79.64 20.36 0.465 Slightly muddy sand 9 11.501143° 79.821923° 16 73.8 26.2 0.332 Muddy sand 10 11.500621° 79.840273° 22 22.66 77.34 0.391 Sandy mud 11 11.500469° 79.863224° 30 61.16 38.84 0.775 Muddy sand 12 11.498797° 79.910585° 50 94.52 5.48 1.891 Slightly muddy sand 13 11.434539° 79.829538° 4 95.08 4.92 0.425 Sandy 14 11.432874° 79.840041° 10 39.52 60.48 1.837 Sandy mud 15 11.431878° 79.851176° 15 29.42 70.58 0.764 Sandy mud 16 11.432918° 79.862972° 24 14.54 85.46 0.595 Slightly sandy mud 17 11.432094° 79.939082° 52 61.96 38.04 2.219 Muddy sand 18 11.432554° 79.950294° 85 93.12 6.88 2.361 Slightly muddy sand 19 11.345169° 79.850270° 5 90.22 9.78 0.231 Slightly muddy sand 20 11.346099° 79.857946° 11 87.38 12.62 0.244 Slightly muddy sand 21 11.347631° 79.864518° 18 44.56 55.44 0.503 Sandy mud 22 11.345722° 79.891578° 37 87.24 12.76 0.334 Slightly muddy sand 23 11.342831° 79.951158° 53 82.4 17.6 0.726 Slightly muddy sand 24 11.342655° 79.978775° 80 77.76 22.24 2.408 Slightly muddy sand 25 11.238341° 79.867520° 5 80.98 19.02 0.376 Slightly muddy sand 26 11.239205° 79.875840° 10 83.26 16.74 0.47 Slightly muddy sand 27 11.238961° 79.884701° 15 56.1 43.9 0.594 Muddy sand 28 11.238876° 79.907130° 31 97.32 2.68 0.266 Sandy 29 11.231771° 80.001305° 57 85.66 14.34 2.018 Slightly muddy sand Table 2: A comparison of heavy metals concentration: present study, average crustal abundances, different areas of Bay of Bengal and some regions of the world. Metal This study Taylor, 1964 Muthu Raj and Jayaprakash(2007) Jayaprakash et al. (1999) Ramanathan et al. (1999) Devanesan et al.(2017) Jayaraju et al. (2009) Jonathan et al. (2004) Ikram Naifar et al. (2018) Yang-Guang Gu (2017) Brumsack (2006) Average crustal abundance Off Ennore Cuddalore coast Pichavaram Karaikal coast Tuticorin coast Gulf of Mannar Southern coast of Sfax Zhelin Bay, South China Peru margin Fe (ppm) 29712.16 56300 27800 10900 24998 66216 28717 27200 15740 - 21700 Cu (ppm) 30.25 55 506.21 39.54 132.3 - 52 57 359 - 49 Co (ppm) 66.46 25 8.1 7.37 - 23 - 15 - - 6.1 Mn (ppm) 526.18 950 373 291 - 1641 305 - 751.32 - Pb (ppm) 444.122 12.5 32.36 33.92 143.8 50 42 16 39.7 35.69 - Zn (ppm) 67 70 126.83 37.67 106 90 247 73 375 74.95 Cr (ppm) 966 100 194.83 127 617 383 15 177 381 23.07 98 Ni (ppm) 258.12 75 38.61 39.21 252 42 75 24 59.9 7.5 74 Table 3: Enrichment factor of different heavy metals in surface sediments of the study S.No Cu Co Mn Pb Zn Cr Ni 1 0.21 0.20 0.1 2.01 1.32 0.1 0.12 2 1.32 3.50 2.2 0.24 1.12 0.3 0.30 3 2.69 0.10 1.7 4.21 1.11 1.8 0.20 4 1.08 0.10 8.8 2.10 1.74 3.3 0.11 5 1.32 0.30 5.4 0.05 1.65 2.2 0.06 6 0.06 2.80 1.5 3.23 1.32 0.4 0.02 7 0.09 0.07 1.4 4.44 1.21 0.1 0.63 8 1.08 0.40 1.2 2.36 1.25 1.4 0.21 9 0.1 1.30 1 0.02 1.52 0.1 0.44 10 7.68 1.30 8.83 0.01 1.65 0.14 0.79 11 3.6 1.90 8.65 0.01 1.33 7.78 0.56 12 5.32 0.40 1.1 0.02 1.54 1.88 0.10 13 3.2 0.10 1.2 0.02 4.23 3.96 0.20 14 3.65 0.01 1.4 5.62 1.74 0.1 0.36 15 2.36 2.58 9.3 3.50 2.80 5.2 0.37 16 5.36 2.36 9.4 6.22 5.40 0.1 0.21 17 5.03 1.60 8.1 6.30 0.60 1.3 0.41 18 2.06 1.23 0.1 9.60 3.20 1.9 0.22 19 5.04 0.01 9.7 12.40 6.32 3.22 0.32 20 1.56 1.70 9.8 2.50 5.41 2.22 0.10 21 0.03 1.50 9.3 2.70 1.45 7.36 0.41 22 1.60 0.11 8.6 3.54 1.24 0.16 0.23 23 0.21 1.65 7.6 6.03 0.23 4 0.10 24 0.01 0.00 5.6 4.87 5.21 0 0.22 25 0.03 1.36 1.2 6.32 1.23 1.7 0.32 26 0.02 4.32 0.1 6.58 0.01 6.7 0.63 27 0.00 1.70 1.7 5.32 0.36 7.19 0.11 28 4.38 1.40 8.6 4.21 1.01 9.07 0.41 29 0.19 0.07 7.02 0.10 3.50 1.9 0.22 Table 4: Contamination factor of heavy metals in surface sediments of the study area. S.No Cu Co Fe Mn Pb Zn Cr Ni 1 0.32 0.70 2915.19 0.31 607.28 56.26 5.98 1.30 2 0.51 5.63 1599.38 0.36 62.21 102.07 12.85 5.03 3 0.53 0.47 2836.83 0.49 95.25 90.33 10.62 4.07 4 0.09 0.20 2662.70 0.24 821.28 85.23 10.02 2.28 5 0.11 1.49 5099.59 0.29 405.59 112.10 11.80 2.82 6 1.44 5.85 7033.47 1.09 427.13 104.48 11.82 5.44 7 0.50 3.30 4603.90 0.57 423.23 79.77 8.89 1.89 8 0.09 2.26 6256.92 0.76 459.26 92.30 7.77 2.67 9 0.46 1.19 4445.73 0.48 150.19 75.50 5.89 1.64 10 0.82 3.44 5562.22 0.49 16.31 77.17 4.76 2.42 11 0.35 4.21 4556.29 0.39 241.30 65.78 4.88 2.39 12 0.10 1.94 5210.41 0.60 25.02 90.59 8.68 1.94 13 0.23 5.64 5882.40 0.75 -10.76 104.21 7.49 3.33 14 0.84 1.01 7043.56 1.04 847.62 68.24 3.25 0.62 15 0.41 1.82 7901.09 0.74 547.58 134.67 10.83 1.21 16 1.28 3.00 6941.91 0.66 183.08 80.76 7.77 1.12 17 0.86 4.06 6375.48 0.52 169.49 115.65 11.51 3.25 18 1.09 3.29 5643.94 0.61 352.99 132.26 12.92 7.76 19 0.17 0.38 5346.32 0.52 214.01 118.17 11.86 3.29 20 0.12 3.30 5034.91 0.49 101.65 104.01 10.86 3.90 21 0.44 2.93 6038.22 0.60 159.63 75.11 5.53 1.96 22 0.80 5.28 4842.64 0.42 391.17 106.31 12.82 4.69 23 0.21 2.58 5361.42 0.43 1115.06 93.98 11.34 3.84 24 0.58 -0.26 5223.62 0.30 41.36 98.01 13.79 5.02 25 1.25 2.06 6979.64 0.85 235.98 88.12 4.34 4.19 26 0.38 1.39 6127.65 0.73 151.44 83.02 12.46 4.55 27 0.45 4.38 6279.64 0.64 29.44 83.05 6.61 4.71 28 0.24 1.04 2616.38 0.23 798.67 114.82 13.52 5.31 29 1.26 4.51 6625.23 0.47 1245.59 141.23 19.58 7.16 Table 5: Geo accumulation index of heavy metal in surface sediments of the study area S.No Cu Co Fe Mn Pb Zn Cr Ni 1 2.25 1.11 10.92 2.29 8.66 5.23 1.99 0.21 2 1.55 1.91 10.06 2.05 5.37 6.09 3.10 1.75 3 1.49 1.68 10.89 1.62 5.99 5.91 2.82 1.44 4 4.08 2.94 10.79 2.66 9.10 5.83 2.74 0.60 5 3.73 0.01 11.73 2.35 8.08 6.22 2.98 0.91 6 0.06 1.96 12.20 0.46 8.15 6.12 2.98 1.86 7 1.58 1.14 11.58 1.39 8.14 5.73 2.57 0.34 8 4.00 0.59 12.03 0.98 8.26 5.94 2.37 0.83 9 1.70 0.34 11.53 1.64 6.65 5.65 1.97 0.13 10 0.86 1.20 11.86 1.61 3.44 5.69 1.67 0.69 11 2.08 1.49 11.57 1.93 7.33 5.45 1.70 0.67 12 3.94 0.37 11.76 1.33 4.06 5.92 2.53 0.37 13 2.69 1.91 11.94 1.00 2.84 6.12 2.32 1.15 14 0.83 0.57 12.20 0.53 9.14 5.51 1.12 1.28 15 1.86 0.28 12.36 1.02 8.51 6.49 2.85 0.31 16 0.23 1.00 12.18 1.19 6.93 5.75 2.37 0.42 17 0.80 1.44 12.05 1.53 6.82 6.27 2.94 1.12 18 0.47 1.13 11.88 1.29 7.88 6.46 3.11 2.37 19 3.12 1.98 11.80 1.53 7.16 6.30 2.98 1.13 20 3.70 1.14 11.71 1.60 6.08 6.12 2.86 1.38 21 1.75 0.97 11.97 1.32 6.73 5.65 1.88 0.38 22 0.91 1.82 11.66 1.85 8.03 6.15 3.10 1.65 23 2.81 0.78 11.80 1.81 9.54 5.97 2.92 1.36 24 1.37 0.13 11.77 2.34 4.79 6.03 3.20 1.74 25 0.27 0.46 12.18 0.81 7.30 5.88 1.53 1.48 26 1.98 0.11 12.00 1.05 6.66 5.79 3.05 1.60 27 1.73 1.54 12.03 1.23 4.29 5.79 2.14 1.65 28 2.66 0.52 10.77 2.73 9.06 6.26 3.17 1.82 29 0.25 1.59 12.11 1.69 9.70 6.56 3.71 2.26 Table 6: Factor loadings and communality values of heavy metals and sand, mud, organic matter after varimax with kaiser Normalization . Variables Factor1 Factor2 Factor3 Communalities Cu .761 .315 -.016 .678 Co .400 .376 -.607 .670 Fe .821 .017 .045 .676 Mn .730 -.072 -.198 .577 Pb .011 .211 .759 .621 Zn .085 .812 .149 .689 Cr -.170 .851 .266 .825 Ni -.027 .904 -.113 .830 Sand -.742 .519 -.030 .822 Mud .742 -.519 .030 .822 OM .423 .354 .431 .490 The bold values indicate that the values are high and significant. Table 7: Correlation coefficient matrix of sediment texture, organic matter with heavy metals in the study area. Sand mud org Cu Co Fe Mn Pb Zn Cr Ni Sand 1 mud -1.000 1 org -.373 .373 1 Cu -.394 .394 .427 1 Co -.148 .148 .175 .383 1 Fe -.495 .495 .367 .448 .198 1 Mn -.361 .361 .145 .453 .259 .732 1 Pb .101 -.101 .518 .075 -.138 .014 -.092 1 Zn .283 -.283 .116 .163 .204 .206 -.044 .221 1 Cr .448 -.448 .135 .092 .097 -.125 -.330 .321 .784 1 Ni .466 -.466 .043 .340 .330 -.065 -.095 .099 .603 .700 1 Bold values indicate significance at 0.01 level (2 tailed). Italic values indicate significance at 0.05 level (2 tailed). Table 8: Heavy metal concentration (ppm) in the surface sediments of study area. Parameters Minimum Maximum Mean Median SD Cu (ppm) 4.88 79.23 30.25 24.86 21.79 Co (ppm) 6.3 146.27 66.46 64.61 43.58 Fe (ppm) 9004.49 44483.12 29712.2 30184.8 8530.02 Mn (ppm) 215.46 1036.12 526.01 493.28 201.96 Pb (ppm) 134.49 15569.84 4443.12 2949.72 4129.31 Zn (ppm) 3938.03 9886.02 6693.86 6460.93 1439.11 Cr (ppm) 324.9 1958.14 966.97 1061.63 360.48 Ni (ppm) 46.41 582.35 258.12 246.64 130.89 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-159678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":11620425,"identity":"8888689f-3929-469d-9cfc-d5a6f1730392","order_by":0,"name":"Harikrishnan Sadanandan","email":"data:image/png;base64,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","orcid":"","institution":"Pondicherry University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Harikrishnan","middleName":"","lastName":"Sadanandan","suffix":""},{"id":11620426,"identity":"d2f0eb43-4b8e-444d-9a6c-9c525b8822d5","order_by":1,"name":"Senthil Nathan Dharmalingam","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Senthil","middleName":"Nathan","lastName":"Dharmalingam","suffix":""},{"id":11620427,"identity":"e77cbf76-a191-4f4a-9d07-6cff55962ebf","order_by":2,"name":"Nitin Agarwal","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"","lastName":"Agarwal","suffix":""},{"id":11620428,"identity":"d682cf6b-6665-4655-a66b-fe301cc32b61","order_by":3,"name":"Sridharan Mouttoucomarassamy","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sridharan","middleName":"","lastName":"Mouttoucomarassamy","suffix":""},{"id":11620429,"identity":"ed6c16bf-4acd-4ee9-8290-e380fffb4cd6","order_by":4,"name":"Anbuselvan Nagarajan","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anbuselvan","middleName":"","lastName":"Nagarajan","suffix":""}],"badges":[],"createdAt":"2021-01-27 17:25:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-159678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-159678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5962679,"identity":"3b578082-d2e3-4525-b3dc-923aa00d5cd7","added_by":"auto","created_at":"2021-02-15 13:06:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91527,"visible":true,"origin":"","legend":"Study area and sampling locations.\nNote: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/a8710a4c59c6dfb10fdbb762.jpg"},{"id":5962532,"identity":"4af51c71-4ab9-4e9a-b1a4-b3adecd5062a","added_by":"auto","created_at":"2021-02-15 13:03:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49342,"visible":true,"origin":"","legend":"Plots showing sand, organic matter and mud content in each sample.","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/b198942939f21d9e6d22b4d4.jpg"},{"id":5962531,"identity":"11e989ae-f743-4da8-9ffe-534df1a8119e","added_by":"auto","created_at":"2021-02-15 13:03:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54608,"visible":true,"origin":"","legend":"Spatial distribution map of sand, mud and organic matter.","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/9ba39b910c366ccd7deed3d3.jpg"},{"id":5962535,"identity":"a8b8be97-b2e0-4a9b-8e7b-86b4d9c4b63e","added_by":"auto","created_at":"2021-02-15 13:03:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64037,"visible":true,"origin":"","legend":"Spatial distribution map of Fe(ppm), Cu(ppm), Cr(ppm) and Co(ppm)","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/c268ef15c726a081caae2297.jpg"},{"id":5962534,"identity":"9cc1c6bc-4899-4f9a-8434-f5ba1552ce21","added_by":"auto","created_at":"2021-02-15 13:03:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63855,"visible":true,"origin":"","legend":"Spatial distribution map of Mn(ppm), Ni(ppm), Pb(ppm) and Zn(ppm)","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/b9488b5fe072daadc0a41ce5.jpg"},{"id":5962678,"identity":"13d19bb7-f3cb-443c-a71f-95d272a0edc7","added_by":"auto","created_at":"2021-02-15 13:06:09","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30907,"visible":true,"origin":"","legend":"Plots of principal component analysis of heavy metals, sediment textures and organic matter","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/793ce5973db0e3dbe4a3d8e4.jpg"},{"id":5962680,"identity":"83340cd4-8ea0-4f66-8297-c937e7492fd2","added_by":"auto","created_at":"2021-02-15 13:06:09","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":48057,"visible":true,"origin":"","legend":"Dendogram representing sampling stations with similar sediment geochemistry.","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/9c81b7960b7b28e0efb05c9c.jpg"},{"id":13659813,"identity":"c49bdf50-051b-4d1b-81a1-996f875df1f1","added_by":"auto","created_at":"2021-09-17 10:21:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1130106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-159678/v1/fb301877-784f-486a-9b83-00aa8340e0c2.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssessment of Heavy Metal Pollution Indices in Surface Sediments From Southwestern Bay of Bengal, India\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eThe coastal zones are the areas which act as a major sink for heavy metal contaminants since the industrial revolution. Heavy metals are highly harmful due to their accumulative behavior and non-biodegradability, and are the major cause of marine pollution. Anthropogenic activities play a major role in releasing these heavy metals into the marine environment. Heavy metals viz. Zn, Cu and Pb are sourced from automotive traffic in the urban environment; Ni and V from marine traffic; Cu and Hg from paint industries (Lewan, 1984; Tamim et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xianmeng., et al 2018). These heavy metals reside for a longer time in ocean water and are transported to the sea bottom (Xigui et al., 2018). The heavy metal pollutants effectively retain on marine particles and involve themselves in all the physical, chemical, and biological processes of marine organisms. (Yunhai Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tansel and Rafiuddin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Heavy metals accumulated in sediments become entrained in the food web as contaminants. Thereafter they undergo bio-magnification and accumulation in marine organisms, which when consumed by human beings can pose serious health problems (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mokhtar et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Heavy metal in the sediments directly influence benthos community (Pan and Wang, 2013), but with high storm and tide, they can be re-suspended, re-dissolved and either re-deposited or transported to affect further oceanic and near shore environments (Xigui Ding et al., 2016). Those heavy metals which are not carried offshore will lead to secondary pollution of coastal areas (Islam et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The preservation capacity of sediments is perhaps related to its physicochemical properties viz. organic matter and grain size (Ihejirika et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, major industrial plants of many countries are established in the cities which are located in the coastal areas and along the banks of major rivers. The effluents released from those industries are dumped into the fluvial or marine environment without any treatment (Sarraf et al., 2016). The present study focuses on the evaluation of heavy metal pollution of sediments from marine environment using indices viz. Enrichment factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (CF). And with the help of multivariate statistical analysis we attempted to probe the source and activities controlling the discharge of heavy metals.\u003c/p\u003e "},{"header":"2. Material And Methods","content":"\u003cp\u003eFrom the study area, 29 surface sediment samples were collected from different depths using Van Veen grab sampler in September 2017 (Fig.\u0026nbsp;1). The collected samples were then preserved by transferring into pre-cleaned polyethylene bag using a plastic spatula. In the laboratory, a representative portion of each sample was used for textural analysis. The remaining portion of each sample was used for chemical analysis.\u003c/p\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.1.1 Geography\u003c/h2\u003e\n\u003cp\u003eThe study area is located in the South-western part of Bay of Bengal, between the coordinates 11.705213\u0026deg; N 79.798297\u0026deg; E and 11.231771\u0026deg; N 79\u0026deg; 56' 10.248'' E (Fig.\u0026nbsp;1). There are ephemeral rivers viz. Coleroon, Uppanar, Vellar, and Gadilam drains the continent and finally opens into the Bay of Bengal.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.1.2 Climate and Rainfall\u003c/h2\u003e\n\u003cp\u003eThe maximum and minimum temperature recorded in the study area is 32.3\u0026ordm;C and 21.18\u0026ordm;C respectively. The study area receives maximum rainfall due to northeast monsoon with an average annual rainfall of about 1393.3mm (DEIAA, 2018).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.1.3 Geology\u003c/h2\u003e\n\u003cp\u003eThe adjacent part of the study area is composed of Precambrian granitic basement overlaid by sedimentary rocks belonging to different geological periods. This Precambrian basement is marked by a series of horst \u0026amp; graben structures (Vasudevan et al., 1998). In this region, sandstone consists of rounded pebbles (fragment and pebbles), lateritic and laterite gravels belonging to Cuddalore formation and are overlaid by red sandy soil (Jayaprakash et al., 2016).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.1.4 Industries\u003c/h2\u003e\n\u003cp\u003eThe rivers adjacent to the study area, in addition to flooded water, also carries untreated industrial effluents released by the industries located on its banks such as paint industries, chemical industries, cotton mills, rubber, plastic, petro-products, metal-based industries, electrical machinery, transport equipment companies, tanneries, and oil companies, etc. (Jayaprakash et al., 2016). Besides, Cuddalore port and Thirumullaivasal fishing harbour are located along the estuarine part of the study area. Since 1990 this area is affected by rapid industrialization leading to the degradation of the aquatic system (Jonathan et al., 2008). Several studies regarding marine pollution have been carried out during the past decades in the coastal and marine sediments along the south east coast of India. Moreover, the coastal ecosystem was often affected by several devastating cyclones and rarely by Tsunami in December 2004.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.2 Textural Analysis\u003c/h2\u003e\n\u003cp\u003eThe textural analysis was carried out by sieving and pipetting method, first the sediment samples were pre-treated with H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e solution for the removal of organic matter. Then they were wet sieved through a 63 \u0026micro;m mesh for 15 min in a sieve shaker. The sample that held on the sieve was weighed and indicated as sand. The mud fraction which includes silt and clay (\u0026gt;\u0026thinsp;0.063 mm) were determined using the pipet method. The textural classification was determined based on the mud content after Flemming, (2000) and Pejrup, (1988) classification (Fig.\u0026nbsp;2). (See Fig.\u0026nbsp;3.)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.3 Organic matter\u003c/h2\u003e\n\u003cp\u003eThe sediments were dried in an oven at 50\u0026ordm;C and then standardized. The standardized samples were pulverized into fine powder using FRITSCH Pulverisette 7Agate Ball mill. From each of the powdered samples, 5g was taken and decarbonized with 1N solution of Hydrochloric acid and then washed three times with deionized water and centrifuged to remove absorbed HCl in the sediment. The samples were dried and standardized again for analysis in CHNS analyser (model: Vario el cube Odu). The results of total organic matter in each sample were expressed in terms of percentage (Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.4 Heavy metal analysis\u003c/h2\u003e\n\u003cp\u003eFor heavy metal analysis, the sediments samples were oven-dried at 60\u0026ordm;C and dried samples were crushed into fine powder using FRITSCH pulverisette7 Agate ball mill to use later in the chemical analysis. Approximately 0.01g of the sample was taken in Savillex Teflon pressure decomposition vessel for digestion and these were pre-treated with 1:1 H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e to remove the organic matter present in the sample. The samples were digested using 3\u0026ndash;4 ml of acid mixture proportion 7:3:1 ratio of HF, HNO\u003csub\u003e3\u003c/sub\u003e and HCl. Further HCl in the ratios of 3:1 were added into the solution and dried frequently till the silicon tetrafluorides were entirely fumed out. After complete digestion, the dried samples were dissolved with 2 ml of 2% HNO\u003csub\u003e3\u003c/sub\u003e and diluted to 100 ml. This final diluted solution is the stock solution and from the stock solution, 2 ml was again diluted up to 10 ml in clean scintillation vials.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eUsing IBM SPSS (version 20) statistical software, the data were subjected to multivariate statistical analysis viz. Pearson Correlation, Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). PCA was done to group the parameters having identical characteristics in one way or the other and to identify the relation between elements and sampling locations. Cluster analysis was carried out to identify any similarities between sampling locations with regard to organic matter, grain size, depth, and heavy metal concentration. Pearson correlation analysis was carried out to examine the relationship between the variables (grain size, organic matter, and heavy metals)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.6 Enrichment factor\u003c/h2\u003e\n\u003cp\u003eEnrichment factor (EF) is one of the pollution indices, calculated to differentiate the anthropogenic and natural sources for metals enriched in sediments (Abrahim et al., 1998 and Dickinson., 1996). The EF were derived by normalizing the measured trace elements, rare earth elements and actinides concerning metals like Fe, Al or Sc (Ashraf et al., 2016; and Ravichandran et al., 1995). The EF was calculated based on the following equation:\u003c/p\u003e\n\u003cp\u003eEF = (Cx/Fex) \u003csub\u003esample\u003c/sub\u003e / (Cref/Fe\u003csub\u003eref\u003c/sub\u003e)\u003c/p\u003e\n\u003cp\u003ewhere C\u003csub\u003ex\u003c/sub\u003e is the concentration of an element in the sample, Fe\u003csub\u003ex\u003c/sub\u003e is the concentration of Fe in the sample; C\u003csub\u003eref\u003c/sub\u003e is the concentration of an abundant and common element in the average sediment and Fe\u003csub\u003eref\u003c/sub\u003e is the concentration of Fe in the average sediment (Wedepohl and Turekian, 1961). In the present work, Fe was selected as the normalisation element to calculate the enrichment factor. In marine sediments Fe is mainly derived from the natural weathering process thus it is typically used to standardize the metal concentration.\u003c/p\u003e\n\u003cp\u003eThe EF values thus obtained are categorized into five tiers, as suggested by (Sutherland, 2000). The elemental ratios show consumption to minimal enrichment when EF\u0026thinsp;\u0026lt;\u0026thinsp;2 and moderate enrichment if the values fall within 2 and 5. However if the EF\u0026thinsp;=\u0026thinsp;5 to 20, significantly enriched; 20\u0026ndash;40 very highly enriched; and if EF\u0026thinsp;\u0026gt;\u0026thinsp;40 extremely enriched. Average crustal abundance values of the heavy metals are frequently used as elemental background concentration for resemblance. In the work average crustal abundance was used as background reference (Taylors, 1964).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.7 Geo-accumulation index\u003c/h2\u003e\n\u003cp\u003eThe intensity of pollution for each sampling location is derived by the geo-accumulation index (Igeo). The Igeo is a quantified measure of the degree of the contaminant in sediments (Forstner et al., 1990) and it is calculated by the following equation:\u003c/p\u003e\n\u003cp\u003eIgeo = (C\u003csub\u003en\u003c/sub\u003e/1.5*Bn)\u003c/p\u003e\n\u003cp\u003eWhere B\u003csub\u003en\u003c/sub\u003e is the geochemical background of a provided element and C\u003csub\u003en\u003c/sub\u003e is the concentration of elements considered in the sediment. Muller (1979) categorized the sediment based on the Igeo value, as; Igeo\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026thinsp;=\u0026thinsp;extremely contaminated, 4 to 5\u0026thinsp;=\u0026thinsp;strongly to extremely contaminated, 3 to 4\u0026thinsp;=\u0026thinsp;strongly contaminated, 1to2\u0026thinsp;=\u0026thinsp;moderately contaminated, 0 to 1\u0026thinsp;=\u0026thinsp;uncontaminated to moderately contaminated and \u0026lt;\u0026thinsp;0\u0026thinsp;=\u0026thinsp;uncontaminated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.8 Contamination factor (CF)\u003c/h2\u003e\n\u003cp\u003eCF is perceived to be a valuable method of measuring pollution in sediments over time. It is the ratio of each metal in the present sample to the background values in the same metal\u003c/p\u003e\n\u003cp\u003eCF\u0026thinsp;=\u0026thinsp;C\u003csub\u003eheavymetal\u003c/sub\u003e/C\u003csub\u003ebackground\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eCF can be classified into four groups (Pekey et al., 2004). If CF values\u0026thinsp;\u0026lt;\u0026thinsp;1, there is no metal contamination by geogenic or anthropogenic inputs; CF\u0026thinsp;\u0026lt;\u0026thinsp;3 for a particular metal indicates that sediment is moderately contaminated; CF\u0026thinsp;\u0026lt;\u0026thinsp;6 there is considerable contamination; and CF\u0026thinsp;\u0026gt;\u0026thinsp;6, there is very high contamination for that metal. Taylor's (1964) average crustal abundance values of the trace metal was used as the background reference material.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 29 surface sediment samples from the study area have been analysed and the result of organic matter, textural class and heavy metal concentrations are given in table 1and 8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Sediment properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe textural class of the sediments of the study area is shown in the Table 1. There are three types of sediments- sand, slightly muddy sand, and muddy sand. Textural analysis indicates a good correlation between depth and grain size (Table 1). The surface sediments are dominated by coarse grains in the shallower part and finer sediments in the deeper part, whereas the transect 2 (station number 10) and transect 3 (station number 14, 15 and 16) do not show the above observations. The sandy sediments occur in the stations 3,4,13 and 28 while slightly muddy sand occurs at stations 2, 5, 7, 8, 12, 16, 18, 19, 20, 22, 23, 24, 25 and 26. Samples from the station 1, 9, 17, 27, 11 and 6 are muddy sand and 10, 14, 15 and 21 occur as sandy mud (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Heavy metal Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeavy metal analysis of surface sediment samples from the Bay of Bengal and their perceptive values and crustal average (Taylor, 1964) are shown in table 2. In the present study area, C\u003csub\u003eorg\u003c/sub\u003e content in coarse sediments was particularly lower than the finer sediments. The concentration of Cu, Co, Fe, Mn, Pb, Zn, Cr and Ni ranged from 4.89 to 79.23 ppm, 6.38 to 146.28 ppm, 9004.49 to 44483.12ppm, 215.47 to 1036.13 ppm, 134.50 to 15569.84ppm, 324.90 ppm to 1958.14 ppm, 46.41ppm to 582.35 ppm respectively. The mean values of concentration of different heavy metal in the present area are as follows in the descending order: Fe (29712.16 ppm) \u0026gt;Zn (6693.86 ppm) \u0026gt;Pb (4443.13ppm) \u0026gt;Cr (966.98 ppm) \u0026gt;Mn (526.01 ppm) \u0026gt;Ni (258.13) \u0026gt;Co (66.46 ppm) \u0026gt;Cu (30.26 ppm). The higher concentration of Cu, Co and Mn are observed in station no.6 (Transect 1) although Pb, Zn and Cr are encountered in station no.29 (Transect 6) and Fe and Ni is observed in station no.15 (Transect 3) and 18 (Transect 3) respectively (Fig. 4 and 5). The Mn concentrations in the present study area are higher than that of off Karaikal coast surface sediments in the Bay of Bengal; Cu and Zn concentrations are higher than that of, off Cuddalore coast, off Ennore, off Pichavaram, off Tuticorin coast, shelf sediments of Gulf of Mannar in the East Coast of India (table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Organic matter\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOrganic contents vary between 0.23% and 2.40% with an average of 0.88%. The high values of organic matter are associated in the deeper part samples and low values occur in the shallower part (Fig. 3). High organic matter concentration in the present study is enriched in muddy sediments and low in sandy type sediments except for the transects 1 and 3 (station number 1 and 14.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Statistical Analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 Principle Component Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCA was performed to group the pollutants and identify the influencing factors for the distribution of heavy metals in the study area. The PCA analysis was applied for the organic matter, grain size (sand and mud) and heavy metals. The Kaiser-Meyer-Olkin normalisation technique was used to extract maximum factors that influence the distribution of heavy metals. The technique takes into account only those factors with eigenvalues greater than 1, for each procedure. The Varimax rotation yielded 5 factors. Additionally, factor loading communalities for the first three factors were taken as the percentage of variance and the cumulative percentage of variance was derived (Table 6 and Fig. 6).\u003c/p\u003e\n\u003cp\u003eFirst principle component shows maximum loadings of Cr (.915), Ni (.814), Zn (.752) and sand (.652) (Fig. 6). The second PC analysis shows significant loadings of Fe (.87), Mn (0.83), Cu (0.63), mud (.61), Co (0.42). Fe and Mn has the highest positive loadings, Cu, mud and Co show medium positive loadings (Fig. 6). The third PC has significant loadings of Organic matter (0.86), Cu (0.48), Ni (0.34) and Cr (0.31). Organic matter exhibits the highest positive loadings. Cu exhibits low positive loadings while Ni and Cr exhibits very low positive loadings (Fig. 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Pearson correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis was performed on the normalized data set to test the relationship between the environmental parameters (table 7). According to the Pearson correlation analysis, Fe shows a strong positive correlation with Mn (r=.732) whereas Zn shows a strong positive correlation with Cr (r=.784) and Ni (r=605) and Cr show strong positive correlation with Ni (r=.700). The heavy metals viz. Cu (-.394) and Mn (-.361) exhibit negative correlation with sand while Zn (.283), Cr (.448), Ni (.466), Pb (.101) show low to moderate correlation with sand. Mud has significant correlation with Fe (.495). Weak positive correlation with Cu (.394) and Mn (.361) while negative moderate correlation with Cr (-.448) and Ni (-.283) and negative weak correlation with Pb (-.283) and Zn (-.101).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3 Q mode cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe consequent dendrogram of Q-mode hierarchical cluster analysis provides the grouping of samples according to the heavy metal, organic matter, grain size and depth. The dendrogram exhibits four groups, cluster 1(12, 24, 20, 10, 13, 18, 19, 9, 11, 5, 22 and 7) cluster 2(16, 25, 6, 21, 26, 27, 17, 8) cluster 3(14, 29, 23 and 15) cluster 4 (4, 28, 1, 2 and 3) (Fig. 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Enrichment factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean values of EF are as follows Mn \u0026gt; Pb \u0026gt; Cr \u0026gt; Zn \u0026gt; Cu\u0026gt;Co \u0026gt; Ni. Mn is moderately enriched followed by Cu, Pb, Ni, Co and Zn. According to the Muller (1969) Sutherland (2000) classification, the majority of the metals show minimal enrichment to significant enrichment in the sediment sample (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Contamination factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean values of CF for the metals in the shelf sediments are shown in table 4. CF in the present study area is as follows Pb\u0026lt;Cu\u0026lt;Co\u0026lt;Ni\u0026lt;Cr\u0026lt;Zn\u0026lt;Mn\u0026lt;Fe. The calculated CF value indicates that all the sediment samples have been very highly contaminated by Fe. There is also significant Zn, Pb and Cr contamination in most of the sampling stations in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Geo accumulation index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeo accumulation index shows that most of the samples are extremely contaminated in Pb and Zn. Certain samples are moderate to strongly contaminated in Cr and are uncontaminated to moderately contaminated by Ni and Co. The study area is found to be uncontaminated by Cu and Mn (table 5).\u003c/p\u003e"},{"header":"4. Discussion","content":" \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sediment\u003c/h2\u003e \u003cp\u003eSediment transportation and deposition are the essential factors which impact the distribution of fine-grained sediments in the marine ecosystem (Tavakoly et al., 2014). The study area is predominantly covered with sandy sediments in the shallower part and can be correlated with incidents of high wave energy condition (Murray, 1963); subsequent erosion (Viveganandan et al., 2013) and the presence of submarine canyon (Narayanan et al., 2015). The higher concentration of mud in the deeper part of the study area is consequent of low energy conditions. Besides, the higher concentration of mud content was found in the shallow depth of transect 1 and 3 suggesting input of freshwater with finer particles from the Coleroon and kollidam river which is then deposited to the sea bottom where the current and wind speed reduces near the shoreline, mostly in the estuarine region (Thomson Becker and Luoma, 1985). The textural characteristics signify that they are mainly dependent on different dynamic processes which affect only the shallow part of the current study area instead of the deeper part; it fluctuates during the November to February (Gopalakrishna and Sastry, 1985)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Organic matter\u003c/h2\u003e \u003cp\u003eThe significant quantity of organic matter was found to be strongly associated with muddy sediment in the present study area. This implies that the organic matter in the sediments had high adsorption ability and tends to adsorb fine particles (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003eb). Earlier studies in sediments also made a similar observation in the estuaries and offshore areas of both India's East and West Coast (Nobi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Magesh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jayaprakash et al., 2014; Chakraborty et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chakraborty et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003ea, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003eb; Kasilingam et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gopal et al., 2016; Nethaji et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; and Godson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The fluctuation in the concentration of organic matter demonstrates that the textural parameters and dynamic process are dependent on each other. The higher concentration of organic matter in the study area is due to the fact that (i) The distribution of organic matter is provided mainly by terrestrial inputs from the adjacent land area, (ii) Perhaps, in some areas it might be result of direct discharge of domestic waste (iii) Due to sea-grasses, sea-weeds and algae bottom facies, there can be high organic productivity and (iv) The huge rate of sedimentation (Mansour et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Heavy metal\u003c/h2\u003e \u003cp\u003eThe concentration of Cu, Co, Fe, Mn, Pb, Zn, Cr, Ni were in ranges of 4.89\u0026ndash;79.23 ppm, 6.38-146.28 ppm, 9004.49-44483.12 ppm, 134.50-15569.84 ppm, 215.17 -1036.13 ppm, 3938.03-9886.03 ppm, 324.90\u0026ndash;1958 ppm, 46.41-582.35 ppm with average of 30.26 ppm, 66.46 ppm, 29712.16 ppm, 4443.13 ppm, 526.01 ppm, 6693.86 ppm, 966.98 ppm, 258.13ppm respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e and 5). The mean concentration of the study area shows the following decreasing order: Fe\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;Cu.\u003c/p\u003e \u003cp\u003eThe concentration of Fe and Mn being considerably higher than the other heavy metals in all sampling sites indicates that they originated through fluvial input into the coast of the study area through minor rivers (Sandler et al. 1993). The higher values of Fe and Mn associated with muddy sediment with high organic matter is a consequence of the input of dissolved particles into the water (geogenic and anthropogenic) discharged into the study area. The excess concentration of Fe and Mn in marine sediments are due to industrial effluents which are denoted by the presence of ferrous manganese (Fe vs. Mn: r\u0026thinsp;=\u0026thinsp;.732). These are brought to the open sea by small rivers. (Buckley et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The heavy metals Cu and Co are higher in the shallower part and are associated with sandy sediments. The dominance of heavy metals such as Co, Cu, Pb and Zn in the surface sediments is caused by the nitrate dominated fertilizers in the agricultural areas of the study area (Liaghati et al.2003 and Jayaprakash 2015), while the anti-fouling paints that seep from the boat/ship are the source of Cu and Zn in the sediment sample (Goh and Chou \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The enhancement of heavy metals in the sediments is high in the mud fractions as fine particles adsorb soluble metals from the natural waters and carry them to the bottom sediments (Lijklema et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Maher et al., 1999). Moreover, Cr, Ni and other metals also subsequently join the study area through anthropogenic activities like burning of oil, inorganic sewages, phosphate-containing fertilizers, chemical and industrial waste (Gonnelli and Renella, 2010). The total heavy metal concentration is found to be high from the northern and central part of the study area due to the shipwreck located near the study area which is still lying on the seabed at a depth of 20m (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.facebook.com/mvmothi/\u003c/span\u003e\u003c/span\u003e). Moreover, past records suggests that it is submerged with iron ores on board.\u003c/p\u003e \u003c/div\u003e "},{"header":"5. Conclusion","content":" \u003cp\u003eThe present study has been carried out to assess the concentration and understand the spatial distribution of heavy metals in the surface sediments from the south-western part of Bay of Bengal. The relatively higher concentration of Cu, Pb, Zn, Ni and Co in the mud fractions shows that heavy metal concentration was quite dependent on the particle size characteristics. The larger surface area of the sediments helps to bind or adsorb heavy metals easily. The results of the study demonstrate that the mean concentration of Cu and Mn is lower than the background value. The concentration of Co, Pb, Zn, Cr and Ni were also much higher than the background value of surface sediments indicating enrichment of these metals in the study area. Such anomalous behaviour clearly shows the role of human activities in contributing metal toxicity to the environment. The various sediment quality indices used in the current study reveal different aspects of pollution. The values of CF factor and Igeo suggest that the study area is extremely contaminated by Pb and Zn and are sourced from mining activities and effluents released from industrial and agricultural activities. Whereas, Igeo and CF values which focus on the anthropogenic influence suggest that Co, Mn, Cr and Ni, mainly originates from the manmade activities such as ship scrapping, antifouling paints used in boats and ships, industries, metal smelting, dredging and land reclamation action in the coastal areas and sewage effluents. The positive correlation of Fe with various heavy metals (Pb, Zn, Cu, Ni and Cr) is due to the metal scavenging phase of Fe oxyhydroxides. The positive PCA loadings of Zn (0.92), Pb (0.876), Cu (0.788), Fe (0.745) and Cr (0.488) demonstrate the common source i.e. anthropogenic origin.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e: Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e The datasets generated and/or analyzed during the current study are not publicly available due to further investigation of the study but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by Government of Kerala Post metric fellowship. Grant number B3-8502/19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1. Conceptualization, Methodology, Formal analysis, Investigation and Writing:\u003c/em\u003e\u003c/strong\u003e Harikrishnan, S.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2. Writing - review and editing:\u003c/em\u003e\u003c/strong\u003e Nitin Agarwal, M. Sridharan, and N. Anbuselvan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3. Supervision:\u003c/em\u003e\u003c/strong\u003e Senthil Nathan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Competing\u0026nbsp;Interest\u003c/strong\u003e- The authors of this manuscript declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Consent to Participate\u003c/strong\u003e- Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wholeheartedly thank the University Grants Commission (UGC), New Delhi, for financially supporting the work being done. In addition, we extend our sincere gratitude to Central Instrumentation Facility and the laboratory facilities in Pondicherry University.We would like to use this space to thank the \u003cstrong\u003eNational Institute of Ocean Technology\u003c/strong\u003e (Ministry of Earth Sciences, Government of India) for tremendous assistance in collecting samples, especially to \u003cstrong\u003eMr. D. Rajasekhar\u003c/strong\u003e (Head of Vessel Management Cell), \u003cstrong\u003eMr. K. Ramasundaram\u003c/strong\u003e (Scientific Officer II), \u003cstrong\u003eMr. Thiruvathi Babu\u003c/strong\u003e and \u003cstrong\u003eMr. Mohan Raj\u003c/strong\u003e for their support and immense participation during sampling. Special thanks to Mr Harikrishnan P R, Research scholar Dept. of Earth sciences, Pondicherry University for his help to use Arc GIS software.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbraham, J. (1998). Spatial distribution of major and trace elements in shallow reservoir sediments: an example from Lake Waco, Texas. Environmental Geology, 36(3-4), 349\u0026ndash;363. doi:10.1007/s002540050351.\u003c/li\u003e\n\u003cli\u003eBalkhair, K. S., \u0026amp; Ashraf, M. A. (2016). Field accumulation risks of heavy metals in soil and vegetable crop irrigated with sewage water in western region of Saudi Arabia. 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Water Science and Technology, 28(8-9), 1\u0026ndash;12. doi:10.2166/wst.1993.0598.\u003c/li\u003e\n\u003cli\u003eMagesh, N. S., Chandrasekar, N., \u0026amp; Vetha Roy, D. (2011). Spatial analysis of trace element contamination in sediments of Tamiraparani estuary, southeast coast of India. Estuarine, Coastal and Shelf Science, 92(4), 618\u0026ndash;628. doi:10.1016/j.ecss.2011.03.001Maher, B. A., \u0026amp; Thompson, R. (Eds.). (1999). Quaternary Climates, Environments and Magnetism. doi:10.1017/cbo9780511535635.\u003c/li\u003e\n\u003cli\u003eMansour, A. M., Askalany, M. S., Madkour, H. A., \u0026amp; Assran, B. B. (2013). Assessment and comparison of heavy-metal concentrations in marine sediments in view of tourism activities in Hurghada area, northern Red Sea, Egypt. The Egyptian Journal of Aquatic Research, 39(2), 91\u0026ndash;103. doi:10.1016/j.ejar.2013.07.004.\u003c/li\u003e\n\u003cli\u003eMokhtar, M. B., Aris, A. Z., Munusamy, V., \u0026amp; Praveena, S. M. (2009). Assessment level of heavy metals in Penaeus monodon and Oreochromis spp. in selected aquaculture ponds of high densities development area.\u0026nbsp;\u003cem\u003eEuropean Journal of Scientific Research\u003c/em\u003e,\u0026nbsp;\u003cem\u003e30\u003c/em\u003e(3), 348-360.\u003c/li\u003e\n\u003cli\u003eMuller, G. (1980). Schwermetalle in Sedimenten des staugeregelten Neckars. Naturwissenschaften, 67(6), 308\u0026ndash;309. doi:10.1007/bf01153502.\u003c/li\u003e\n\u003cli\u003eNethaji, S., Kalaivanan, R., Arya Viswam, \u0026amp; Jayaprakash, M. (2017). Geochemical assessment of heavy metals pollution in surface sediments of Vellar and Coleroon estuaries, southeast coast of India. Marine Pollution Bulletin, 115(1-2), 469\u0026ndash;479. doi:10.1016/j.marpolbul.2016.11.045.\u003c/li\u003e\n\u003cli\u003eNobi, E. P., Dilipan, E., Thangaradjou, T., Sivakumar, K., \u0026amp; Kannan, L. (2010). Geochemical and geo-statistical assessment of heavy metal concentration in the sediments of different coastal ecosystems of Andaman Islands, India. Estuarine, Coastal and Shelf Science, 87(2), 253\u0026ndash;264. doi:10.1016/j.ecss.2009.12.019.\u003c/li\u003e\n\u003cli\u003ePejrup, M. (1988). The Triangular Diagram Used for Classification of Estuarine Sediments: A New Approach. Tide-Influenced Sedimentary Environments and Facies, 289\u0026ndash;300. doi:10.1007/978-94-015-7762-5_21.\u003c/li\u003e\n\u003cli\u003ePekey, H., Karakaş, D., Ayberk, S., Tolun, L., \u0026amp; Bakoǧlu, M. (2004). Ecological risk assessment using trace elements from surface sediments of İzmit Bay (Northeastern Marmara Sea) Turkey. Marine Pollution Bulletin, 48(9-10), 946\u0026ndash;953. doi:10.1016/j.marpolbul.2003.11.023.\u003c/li\u003e\n\u003cli\u003eRavichandran, M., Baskaran, M., Santschi, P. H., \u0026amp; Bianchi, T. S. (1995). History of Trace Metal Pollution in Sabine-Neches Estuary, Beaumont, Texas. Environmental Science \u0026amp; Technology, 29(6), 1495\u0026ndash;1503. doi:10.1021/es00006a010.\u003c/li\u003e\n\u003cli\u003eS.Viveganandan, C.Lakshumanan, M.Sundararajan, S. Eswaramoorthi \u0026amp; Usha Natesan.(2012). Depositional environment of sediments along the Cuddalore coast of Tamilnadu, India.\u003c/li\u003e\n\u003cli\u003eSandier, A., Halicz, L., \u0026amp; Brenner, I. B. (1993). Distribution of Trace Elements in the Filterable and Acid Extractable Fractions of Waters of the Northern Jordan River. Water Science and Technology, 27(7-8), 405\u0026ndash;412. doi:10.2166/wst.1993.0576.\u003c/li\u003e\n\u003cli\u003eSharifuzzaman, S. M., Rahman, H., Ashekuzzaman, S. M., Islam, M. M., Chowdhury, S. R., \u0026amp; Hossain, M. S. (2016). Heavy Metals Accumulation in Coastal Sediments. Environmental Remediation Technologies for Metal-Contaminated Soils, 21\u0026ndash;42. doi:10.1007/978-4-431-55759-3_2.\u003c/li\u003e\n\u003cli\u003eSutherland, R. A. (2000). Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environmental Geology, 39(6), 611\u0026ndash;627. doi:10.1007/s002540050473.\u003c/li\u003e\n\u003cli\u003eTamim, U., Khan, R., Jolly, Y. N., Fatema, K., Das, S., Naher, K., \u0026hellip; Hossain, S. M. (2016). Elemental distribution of metals in urban river sediments near an industrial effluent source. Chemosphere, 155, 509\u0026ndash;518. doi: 10.1016/j.chemosphere.2016.04.099.\u003c/li\u003e\n\u003cli\u003eTansel, B., \u0026amp; Rafiuddin, S. (2016). Heavy metal content in relation to particle size and organic content of surficial sediments in Miami River and transport potential. International Journal of Sediment Research, 31(4), 324\u0026ndash;329. doi: 10.1016/j.ijsrc.2016.05.004.\u003c/li\u003e\n\u003cli\u003eTavakoly Sany, S. B., Hashim, R., Rezayi, M., Salleh, A., \u0026amp; Safari, O. (2013). A review of strategies to monitor water and sediment quality for a sustainability assessment of marine environment. Environmental Science and Pollution Research, 21(2), 813\u0026ndash;833. doi:10.1007/s11356-013-2217-5.\u003c/li\u003e\n\u003cli\u003eTaylor, S. R. (1964). Abundance of chemical elements in the continental crust: a new table. Geochimica et Cosmochimica Acta, 28(8), 1273\u0026ndash;1285. doi:10.1016/0016-7037(64)90129-2.\u003c/li\u003e\n\u003cli\u003eThomson-Becker, E. A., \u0026amp; Luoma, S. N. (1985). Temporal fluctuations in grain size, organic materials and iron concentrations in intertidal surface sediment of San Francisco Bay. Temporal Dynamics of an Estuary: San Francisco Bay, 91\u0026ndash;107. doi:10.1007/978-94-009-5528-8_6.\u003c/li\u003e\n\u003cli\u003eTUREKIAN k., Wedepohl, K.,1961. Distribution of the Elements in Some Major Units of the Earth's Crust. Geol. Soc. Am. Bull. 72, 175-192.\u003c/li\u003e\n\u003cli\u003eVasudevan K, Silvester JM, Murthy JVSSN, Rangachari V, Anathanaryanan PN (1998) Exploration for stratgraphic and subtle traps in Cauvery basin: lessons learnt and future prespective. In:Proceedings of workshop on integrated exploration for stratigraphic and subtle traps, Dehra Dun. Bull Oil Nat Gas Corp 35:75\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eWang, S.-L., Xu, X.-R., Sun, Y.-X., Liu, J.-L., \u0026amp; Li, H.-B. (2013). Heavy metal pollution in coastal areas of South China: A review. Marine Pollution Bulletin, 76(1-2), 7\u0026ndash;15. doi: 10.1016/j.marpolbul.2013.08.025.\u003c/li\u003e\n\u003cli\u003eZhao, G., Lu, Q., Ye, S., Yuan, H., Ding, X., \u0026amp; Wang, J. (2016). Assessment of heavy metal contamination in surface sediments of the west Guangdong coastal region, China. Marine Pollution Bulletin, 108(1-2), 268\u0026ndash;274. doi: 10.1016/j.marpolbul.2016.04.057.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Sample number, coordinates, depth, sand (%), mud (%), organic matter (%)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand textural class in the study area.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e\u003cstrong\u003eSl.No\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e\u003cstrong\u003eDepth(m)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e\u003cstrong\u003eSand\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cstrong\u003eMud \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cstrong\u003eOrg\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e\u003cstrong\u003eTextural class \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.705213\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.798297\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e64.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e35.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eMuddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.706511\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.805263\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e76.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e23.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.704835\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.811672\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e98.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.704141\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.842150\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e97.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.702957\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.887639\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e80.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e19.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.699927\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.978918\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e52.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e47.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eMuddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.502285\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.799394\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e88.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e11.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.501472\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.809983\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e79.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e20.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.501143\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.821923\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e73.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e26.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eMuddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.500621\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.840273\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e22.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e77.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy mud\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.500469\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.863224\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e61.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e38.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eMuddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.498797\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.910585\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e94.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.434539\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.829538\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e95.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.425\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.432874\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.840041\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e39.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e60.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy mud\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.431878\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.851176\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e29.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e70.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.764\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy mud\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.432918\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.862972\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e14.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e85.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly sandy mud\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.432094\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.939082\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e61.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e38.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eMuddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.432554\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.950294\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e93.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.345169\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.850270\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e90.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e9.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.346099\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.857946\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e87.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e12.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.347631\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.864518\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e44.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e55.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy mud\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.345722\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.891578\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e87.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e12.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.342831\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.951158\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e82.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e17.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.342655\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.978775\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e77.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e22.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.408\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.238341\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.867520\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e80.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e19.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.239205\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.875840\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e83.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e16.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.238961\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.884701\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e56.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e43.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eMuddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.238876\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;79.907130\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e97.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSandy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;11.231771\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;80.001305\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e85.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e14.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSlightly muddy sand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 2: \u003c/strong\u003e\u003cstrong\u003eA comparison of heavy metals concentration: present study, average crustal abundances, different areas of Bay of Bengal and some regions of the world.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e\u003cstrong\u003eMetal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u003cstrong\u003eThis study\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u003cstrong\u003eTaylor, 1964\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e\u003cstrong\u003eMuthu\u0026nbsp;Raj and Jayaprakash(2007)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003eJayaprakash et al. (1999)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u003cstrong\u003eRamanathan et al. (1999)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eDevanesan et al.(2017)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u003cstrong\u003eJayaraju et al. (2009)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eJonathan et al. (2004)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eIkram Naifar et al. (2018)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eYang-Guang Gu (2017)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u003cstrong\u003eBrumsack (2006)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage crustal abundance\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e\u003cstrong\u003eOff Ennore\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003eCuddalore coast\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u003cstrong\u003ePichavaram\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eKaraikal coast\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u003cstrong\u003eTuticorin coast\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eGulf of Mannar \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Southern coast of Sfax\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eZhelin Bay, South China\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u003cstrong\u003ePeru margin\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eFe\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e29712.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e56300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e27800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e10900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e24998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e66216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e28717\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e27200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e15740\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e21700\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eCu\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e30.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e506.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e39.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e132.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eCo\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e66.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e8.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e7.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eMn\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e526.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e751.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003ePb\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e444.122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e12.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e32.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e33.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e143.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e39.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e35.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eZn\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e126.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e37.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e247\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e74.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eCr\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e194.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e617\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e23.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eNi\u0026nbsp; (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e258.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e38.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e39.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e59.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 3: Enrichment factor of different heavy metals in surface sediments of the study \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003eS.No\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eCu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eCo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003eMn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eZn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003eNi\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e2.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e5.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e7.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e8.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e8.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e7.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e5.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"67\"\u003e\n\u003cp\u003e1.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e6.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e7.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e4.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e8.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e9.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"49\"\u003e\n\u003cp\u003e7.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e3.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 4: Contamination factor of heavy metals in surface sediments of the study area.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eS.No\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e2915.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e607.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e56.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e5.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e1599.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e62.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e102.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e12.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e2836.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e95.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e90.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e10.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e2662.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e821.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e85.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e10.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5099.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e405.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e112.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e11.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7033.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e427.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e104.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e11.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e4603.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e423.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e79.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e8.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6256.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e459.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e92.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e7.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e4445.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e150.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e75.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e5.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5562.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e16.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e77.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e4.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e4556.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e241.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e65.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e4.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"47\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e1115.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e93.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e11.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e3.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e-0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5223.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e41.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e98.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e13.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6979.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e235.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e88.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6127.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e151.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e83.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e12.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6279.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e29.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e83.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e6.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e2616.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e798.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e114.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e13.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e4.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6625.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e1245.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e141.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e19.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e7.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 5: Geo accumulation index of heavy metal in surface sediments of the study area\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eS.No\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e2.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e10.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e10.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.75\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e10.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e4.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e10.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e3.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e11.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e12.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e11.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e4.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e12.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"64\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e11.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e11.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e2.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e11.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e11.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e12.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e7.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e12.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e12.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e2.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e10.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e12.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e3.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 6: \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Factor loadings and communality values of heavy metals and sand, mud, organic matter after varimax with kaiser Normalization\u003c/strong\u003e.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eFactor1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eFactor2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eFactor3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eCommunalities\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eCu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.761\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e.315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.678\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eCo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e.400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.670\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eFe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.821\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.676\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eMn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.730\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e-.072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.577\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e.211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.759\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.621\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eZn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.812\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.149\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.689\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.851\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.266\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.825\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eNi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.904\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.830\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eSand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.519\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e-.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.822\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eMud\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.742\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e-.519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.822\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 75px;\"\u003e\n\u003cp\u003eOM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e.423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85px;\"\u003e\n\u003cp\u003e.354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e.431\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 113px;\"\u003e\n\u003cp\u003e.490\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eThe bold values indicate that the values are high and significant.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Correlation coefficient matrix of sediment texture, organic matter with heavy metals in the study area.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eSand\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003emud\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eorg\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eSand\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003emud\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003e-1.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eorg\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cem\u003e-.373\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cem\u003e.373\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cem\u003e-.394\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cem\u003e.394\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cem\u003e.427\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e-.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cem\u003e.383\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003e-.495\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003e.495\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cem\u003e.448\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e-.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cem\u003e.453\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e.732\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003e.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e-.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003e.518\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e-.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd 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width=\"38\"\u003e\n\u003cp\u003e.330\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e-.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003e.603\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e\u003cstrong\u003e.700\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eBold values indicate significance at 0.01 level (2 tailed).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eItalic values indicate significance at 0.05 level (2 tailed).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Heavy metal concentration (ppm) in the surface sediments of study area.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eCu (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e4.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e79.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e30.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e24.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e21.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eCo (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e6.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e146.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e66.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e64.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e43.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eFe (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e9004.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e44483.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e29712.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e30184.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e8530.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eMn (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e215.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1036.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e526.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e493.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e201.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003ePb (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e134.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e15569.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e4443.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e2949.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e4129.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eZn (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e3938.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e9886.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e6693.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e6460.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e1439.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eCr (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e324.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1958.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e966.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1061.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e360.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eNi (ppm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e46.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e582.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e258.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e246.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e130.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Marine pollution, Assessment, Heavy metals, Shelf sediments, Bay of Bengal.","lastPublishedDoi":"10.21203/rs.3.rs-159678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-159678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of heavy metal distribution in the shelf sediments of Southwestern part of Bay of Bengal is essential in determining the distribution pattern and to understand the consequences of marine pollution beside the coastal environment. The south eastern coastal areas of India are affected by several disturbances and contamination associated with accelerated industrialization and urbanization. Twenty-nine surface sediment samples were collected from shelf region of Southwestern part of Bay of Bengal and analyzed for sediment texture, organic matter and heavy metals. Pollution indices such as Enrichment Factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (CF) as well as multivariate statistical analyses were used to recognize the pollution pattern and probable sources for metal contamination. Comparatively, the concentration of heavy metals in the study area is closely associated with finer fractions and organic matter. The results demonstrate that Cu, Co, Mn, Pb, Zn, Cr and Ni in most of the sites are extremely contaminated in terms of Igeo. The computed values of CF indicate very high contamination of the metals like Pb, Zn and Cr followed by uncontamination to moderate contamination of Cu, Mn, Ni, Co. Based on factor analysis, domestic and industrial activities from adjacent land areas are found to be the major contributors of heavy metals in the shelf sediments.\u003c/p\u003e","manuscriptTitle":"Assessment of Heavy Metal Pollution Indices in Surface Sediments From Southwestern Bay of Bengal, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-15 13:03:06","doi":"10.21203/rs.3.rs-159678/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e08ac62b-0404-4e9b-bc01-e9f51309bb0f","owner":[],"postedDate":"February 15th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2401436,"name":"Environmental Engineering"},{"id":2401437,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2021-04-27T06:35:32+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-15 13:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-159678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-159678","identity":"rs-159678","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}