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


 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.


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 tra c in the urban environment; Ni and V from marine tra c; Cu and Hg from paint industries (Lewan, 1984; Tamim 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 e uents released from those industries are dumped into the uvial 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.

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.

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 nally opens into the Bay of Bengal.

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).

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).

Textural Analysis
The textural analysis was carried out by sieving and pipetting method, rst 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 classi cation was determined based on the mud content after Flemming, (2000) and Pejrup, (1988) classi cation (Fig. 2). (See Fig. 3.)

Organic matter
The sediments were dried in an oven at 50ºC and then standardized. The standardized samples were pulverized into ne 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).

Heavy metal analysis
For heavy metal analysis, the sediments samples were oven-dried at 60ºC and dried samples were crushed into ne powder using FRITSCH pulverisette7 Agate ball mill to use later in the chemical analysis. Approximately 0.01g of the sample was taken in Savillex Te on 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 tetra uorides 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 nal diluted solution is the stock solution and from the stock solution, 2 ml was again diluted up to 10 ml in clean scintillation vials.

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)

Enrichment factor
Enrichment factor (EF) is one of the pollution indices, calculated to differentiate the anthropogenic and natural sources for metals enriched in sediments 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 ve tiers, as suggested by (Sutherland, 2000). The elemental ratios show consumption to minimal enrichment when EF < 2 and moderate enrichment if the values fall within 2 and 5. However if the EF = 5 to 20, signi cantly enriched; 20-40 very highly enriched; and if 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).

Geo-accumulation index
The intensity of pollution for each sampling location is derived by the geo-accumulation index (Igeo). The Igeo is a quanti ed measure of the degree of the contaminant in sediments (Forstner et al., 1990) and it is calculated by the following equation: 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.

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 classi ed 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 considerable contamination; and 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.

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.

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 ner 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).

Heavy metal Distribution
Heavy metal analysis of surface sediment samples from the Bay of Bengal and their perceptive values and crustal average (Taylor, 1964)

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.

Principle Component Analysis
The PCA was performed to group the pollutants and identify the in uencing 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 in uence 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 rst three factors were taken as the percentage of variance and the cumulative percentage of variance was derived (Table 6 and Fig. 6). Cu exhibits low positive loadings while Ni and Cr exhibits very low positive loadings (Fig. 6).

Pearson correlation
The correlation analysis was performed on the normalized data set to test the relationship between the environmental parameters (table 7)

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) classi cation, the majority of the metals show minimal enrichment to signi cant enrichment in the sediment sample (Table 3).

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 signi cant Zn, Pb and Cr contamination in most of the sampling stations in the study area.

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)

Organic matter
The signi cant 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 ne particles (Li et

Heavy metal
The concentration of Cu, Co, Fe, Mn, Pb, Zn, Cr, Ni were in ranges of 4.89-79. 23  The concentration of Fe and Mn being considerably higher than the other heavy metals in all sampling sites indicates that they originated through uvial 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 e uents 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 andJayaprakash 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 ne 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.

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 e uents released from industrial and agricultural activities. Whereas, Igeo and CF values which focus on the anthropogenic in uence 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 e uents. 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.     The bold values indicate that the values are high and signi cant. Italic values indicate signi cance at 0.05 level (2 tailed).