Heavy metal distribution and contamination status in the sedimentary environment of Cochin estuary

Heavy metal distribution and contamination status in the sedimentary environment of Cochin estuary

Marine Pollution Bulletin 119 (2017) 191–203 Contents lists available at ScienceDirect Marine Pollution Bulletin journal homepage: www.elsevier.com/...

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Marine Pollution Bulletin 119 (2017) 191–203

Contents lists available at ScienceDirect

Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Baseline

Heavy metal distribution and contamination status in the sedimentary environment of Cochin estuary

MARK

P.M. Salas⁎, C.H. Sujatha, C.S. Ratheesh Kumar, Eldhose Cheriyan Department of Chemical Oceanography, School of Marine Sciences, Cochin University of Science and Technology, Cochin 682016, Kerala, India

A R T I C L E I N F O

A B S T R A C T

Keywords: Heavy metals Pollution indices Contamination Estuarine sediment

Heavy metals (Fe, Mn, Cr, Zn, Ni, Pb, Cu, Co and Cd) in the surface sediments of Cochin estuary, Southwest coast of India were analyzed to understand the spatio-temporal variation and contamination status via six sampling campaigns. Pollution indices like enrichment factor, geoaccumulation index and pollution load index inferred that the sediments of the northern arm of the estuary exhibited severe trace metal accumulation. Numerical sediment quality guidelines were applied to assess adverse biological effects of the trace metals, suggesting that occasional biological effect may occur due to Cr, Cu, Ni and Pb. Correlations between metals, organic carbon, silt and clay suggested that both fine grained sediment and organic matter were important carriers for these metals. Multivariate statistics indicated that the sources of Cu and Ni resulted primarily from natural weathering processes, whereas enriched levels of Cd, Cr, Zn and Pb were mainly attributed to anthropogenic activities.

Industrialization and urbanization have created a strong risk of heavy metal contamination in estuaries and coastal ecosystems of tropical and subtropical countries (Bryan et al., 1980; Langston, 1982). Assessment of heavy metal accumulation in aquatic environment arising from anthropogenic activities is of particular concern owing to its toxicity, persistence and biomagnification effects. Enrichment of heavy metals in sediments induces toxic effects on living organisms when they exceed certain concentration limits (Macfarlane and Burchett, 2000). Trace metals once discharged into the estuarine system, undergo several processes such as dissolution, precipitation, sorption, complexation with inorganic or organic ligands and particulate matter and which settles to the bottom sediments, creating a potential source of metal pollution (Lim et al., 2012a, 2012b) which can alter the environmental quality. The fate and transport of trace elements in estuaries are controlled by a variety of factors such as redox potential, ionic strength, abundance of adsorbing surfaces, pH and organic matter (Wen et al., 1999; Mounier et al., 2001). Furthermore, the spatial variation of heavy metal content in surface sediments of urbanized estuaries has often been attributed to mixing of sediments from different origins and point/non-point pollution sources (Forstner, 1981). The concentration of trace metals in sediments usually exceed the overlying water column by three to five orders of magnitude (Zabetoglou et al., 2002) and they may be transferred to higher trophic levels in the food web through biomagnification. Cochin estuarine system (CES), the largest estuarine system in the Southwest coast of India, forms the part of the Vembanad-Kol wetlands.



This tropical ecosystem is under the profound influence of anthropogenic activities like intertidal land reclamation, industrial effluent discharges, harbour development, dredging and urbanization (Gopalan et al., 1983; Menon et al., 2000). In addition to the existing industrial establishments at the upper reaches of the estuary, new projects which have been constructed in the lower reaches of the estuary along the coast include Vallarpadam container terminal, Mareena Park and LNG (Liquefied Natural Gas) terminal and moored buoy terminal may induce alterations in the dynamics and ecology of CES. Lack of proper planning in developmental activities has created an imbalance in the ecosystem, ultimately reducing the carrying capacity of this natural buffering zone. The objectives of the present study were to determine the total concentration and spatio-temporal variation of heavy metals in the surface sediments of Cochin estuary, to evaluate the extent of anthropogenic influences on their distribution pattern. The investigation also aimed to assess the accumulation, contamination status and ecotoxicological effects of heavy metals in the sedimentary environment employing pollution indices and sediment quality guidelines. Cochin estuarine system, situated at Latitude: 9° 40′ & 10° 12′ N and Longitude: 76° 10′ & 76° 30′ E, has been ranked as one of the most productive estuarine systems (Qasim, 2003). It forms the part of Vembanad Kol wetland constitute a complex, network of shallow brackish water (250 km2) running parallel to the coast, with two perennial openings to the Arabian Sea with a tidal amplitude less than or equal to 1.0 m. This tropical estuary is a well known ‘Ramsar site’

Corresponding author. E-mail addresses: [email protected] (P.M. Salas), [email protected] (E. Cheriyan).

http://dx.doi.org/10.1016/j.marpolbul.2017.04.018 Received 2 December 2016; Received in revised form 6 April 2017; Accepted 10 April 2017 Available online 25 April 2017 0025-326X/ © 2017 Elsevier Ltd. All rights reserved.

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Fig. 1. Location map of the sampling stations.

estuary appears to be primarily due to the processing of metal containing materials at the FACT plant, IRE and Merchem. The effluents from these industries contain organics, alcohols, ammonia, nitrates, phosphorous, heavy metals such as Cd, Hg, Cr, Zn and rare earth element products, suspended solids, radiologicals, chlorides of metals etc. The industrial units consume about 189,343 m3 water per day from the river and in turn discharge routinely about 75% as treated water along with large quantity of effluents and pollutants. It is estimated that nearly 260 million liters of industrial effluents reach the Periyar River daily from the Kochi industrial belt (Green Peace, 2003). According to the report of Green Peace (2003), the lower Periyar has been described as a cesspool of toxins, which have alarming levels of contaminants and pollutants especially toxic metals viz., Pb, Cd, Hg, Cr, Ni, Co and Zn. Previous literature suggest that the industries have effluent treatment plants (ETP) which directly emptying their treated effluents to the lower reaches of River Periyar extending to Cochin estuary (Dsikowitzky et al., 2014). Hence, the lower reaches of the Periyar River are heavily polluted. Pollution of the river and surrounding wetlands may cause a serious threat to aquatic flora and fauna and consumption of polluted river water pose serious health problems (Ciji and Nandan, 2014). The central part of the estuary has been polluted by the release of waste oil, paints, metal and paint scrapings from the Cochin port, Cochin Shipyard and domestic sewage drains. The discharge of industrial effluents along with the restricted flow due to indiscriminate sand mining in the upper reaches of the River Periyar and dredging operations in shallow regions of ship channel in the estuary has resulted in the accumulation of contaminants enriched with heavy metals (Balachandran et al., 2005; George et al., 2012). Surface sediment samples from fifteen stations (Fig. 1) located along Cochin estuary were collected in six sampling campaigns viz., January 2009 (post monsoon, POM09), April 2009 (pre monsoon, PRM09), August 2009 (monsoon, MON09), January 2010 (post monsoon, POM10), April 2010 (pre monsoon, PRM10) and September 2012 (monsoon, MON12). As the estuary has been continuously subjected to severe deterioration, on account of urbanization and industrialization, a regular monitoring of the physicochemical variables is an essential requirement to assess the variability in the parameters and ecological health. Usually, the monsoon brings heavy rainfall and the

(No. 1214), which is under the profound influence of monsoon, contributing to about 71% of the annual rainfall (Jayaprakash, 2002). Distinct circulation patterns in the northern and southern arms of the estuary and high flushing during monsoon transforms the estuary into a freshwater habitat and six major rivers discharge about 20,000 × 106 m3 of fresh water into the estuary annually (Srinivas, 2000; Balachandran et al., 2008). Variation in the river discharge induces a salinity gradient, which is responsible for the diverse biotopes of plankton communities in the estuary. Existing seasonal conditions in the study area can be categorized as monsoon (June–September), postmonsoon (October–January) and pre-monsoon (February–May). Usually southwest monsoon bring about a rainfall exceeding 300 cm and surplus of fresh water and huge loads of sediment into the estuary, whereas in the non-monsoon season, the river influx reduces and tidal influence gains momentum with an increase in salinity longitudinally leading to the mixed type of estuarine conditions in the Vembanad wetland (Rasheed et al., 1995; Priju and Narayana, 2007). The occurrence of tides with mixed semi-diurnal type and maximum spring tide range of about 1 m have been established (Srinivas et al., 2003). Constant mixing with seawater through tidal exchanges has given it the characteristics of a tropical estuary (Balchand and Nair, 1994; Ajith and Balchand, 1996). Circulation patterns in the northern and southern arms of the estuary are distinctly different, owing to the peculiar topography. The north-western part frequently develops flow-restrictions due to converging tides entering from two adjacent inlets, whereas the southern arm experiences tidal amplification (Balachandran et al., 2008). The banks of river Periyar and Chithrapuzha encompasses approximately 70% industrial establishments which constitute chemical, engineering, food, drug, paper, rayon, rubber, textiles, and plywood industries. Important chemical industries located at Eloor, like Fertilizers and Chemicals Travancore Limited (FACT), Indian Rare Earths Limited (IRE), Hindustan Insecticides Limited (HIL), Periyar Chemicals, United Catalysts, Merchem and Cominco Binani Zinc are the main point sources of pollution. Similar industrial units located at Ambalamugal, on the banks of Chithrapuzha River include Fertilizer Plant (FACT), Petroleum Refinery (BPCL-KR) and Hindustan Organic Chemicals Limited (HOCL). Heavy metal contamination in Cochin 192

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Table 1 Location of sampling sites and description of study area. Station code

Sampling site

Depth (m)

Latitude

Longitude

Description

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

Karippadam Murinjapuzha Enadhi Murinjapuzha Bharamamangalam Murnijapuzha Perumbalam Aroor-Kumbalam Thevara Bridge Marine Science Jetty Bolgatty Mulavukad Chenoor Cheranellur Eloor Edayar FACT-Kalamassery

4.3 4.4 4.9 4.6 3.2 5.6 2.7 3.3 2.5 2.2 1.9 2.1 4.4 2.5 2.8

9° 9° 9° 9° 9° 9° 9° 9° 9° 9° 9° 9° 9° 9° 9°

076° 076° 076° 076° 076° 076° 076° 076° 076° 076° 076° 076° 076° 076° 076°

Thickly populated area with outflow of domestic wastes Disposal of domestic wastes Disposal of domestic wastes Disposal of domestic wastes Disposal of domestic and fish processing wastes Fishing and processing unit operations Sewage outfall Sulphur Jetty input and sewage. Industrial pollution Inland navigation and other tourism operations-waste disposal Disposal of domestic sewages and fish wastes Domestic sewages out fall Disposal of domestic sewage and wastes Industrial region Industrial region Industrial region

47.646′N 47.887′N 48.495′N 49.508′N 49.793′N 53.105′N 55.070′N 57.77′N 59.213′N 01.857′N 03.255′N 03.999′N 05.656′N 05.502′N 04.993′N

25.708′E 24.607′E 24.019′E 23.359′E 21.430′E 18.409′E 18.253′E 16.919′E 16.084′E 15.789′E 16.043′E 16.924′E 17.049′E 17.744′E 17.906′E

tion of heavy metal in the samples was analyzed using flame Atomic Absorption Spectrometer (Perkin Elmer 3110) after calibration with suitable elemental standards. The precision and accuracy of the analytical procedure were checked using BCSS-1 (standard reference material for marine and estuarine sediments). Triplicate analysis of BCSS- 1 showed a good accuracy and the recovery rate ranged between 95.2% for Mn and 102.4% for Zn. Elemental standards were purchased from Merck (Germany) and diluted properly as per instrument (AAS) calibration range. All glasswares and plastic bottles were washed with nitric acid and cleaned thoroughly using Milli-Q water. Hydrodynamic conditions in Cochin estuary is mainly governed by the ingression of seawater associated with tides, influx of fresh water from rivers and precipitation processes (Joseph, 2002). Both salinity and pH gradient in the estuary favours the interaction of metals with fine particles through flocculation and coagulation (Bouezmarni and Wollast, 2005). Wide fluctuation in pH was recorded for surface and bottom waters, ranging from 6.40 to 8.10 and 6.20 to 8.40 respectively. The observed pH values (surface layer) were maximum at S8 during PRM10 and S8 (bottom layer) during PRM09; exhibiting significant spatial and seasonal variation (p ≪ 0.01). Lower values of pH were recorded in the river influenced upstream with a remarkable transition to the alkaline condition at estuarine stations, due to seawater influx from the Arabian Sea. Heavy metals release rates were affected to a much greater extent in the lower pH (4–7) than in high pH (8–10) conditions (Li et al., 2013). The acidic environment of the metal hydroxide minerals alters its solubility and significantly increases the availability and toxicity of metal ions (Cunningham et al., 2010). So low pH values observed in the estuary may induce metal mobility and becomes toxic to marine organisms. Cd and Zn tend to be bioavailable at a higher pH than Fe and Cu and consequently, they are likely to involve in biological processes which directly impacts aquatic life (Salomons, 1995). Further, Pb is more soluble at acidic pH while increasing pH affects the bioavailability of Hg. Surface waters exhibit minimum dissolved oxygen content at S7 (3.32 mg/L, POM10) and maximum at S6 (7.92 mg/L, MON12). Meanwhile, in bottom water, it varied from 2.24 mg/L (S13, MON09) to 7.92 mg/L (S13, MON12) with strong spatio-temporal variations (p ≪ 0.01). Dissolved oxygen displayed its maximum at S6 (surface; MON12), and S13 (bottom; MON12); attributed to the freshwater discharge from the rivers. The low DO at S7 is probably due to the degradation of organic matter received from sewage outfalls and municipal effluents from adjacent land. In addition to this, the TOC% during POM10 is about 3% which explains the load of organic matter export to the bottom. The low water column depth induces oxygen minimum condition during organic matter degradation at station S7. Highly significant spatio-temporal variations (p ≪ 0.01) was exhibited by salinity which ranged from 0.03 (S3, MON12) to 31.99 (S9, POM09); and 0.03 (S3, MON12) to 33.72

terrestrial run off, which hand over huge loads of allochthonous materials to the estuary, causing wide deviations in metallic pollutions. Hence, to upgrade the information on the metal pollution in the CES, sampling during monsoon 2012 (MON12) was also done. It primarily aims to compare the metal levels to ascertain any significant difference from the earlier metal enrichment pattern. The location of the sampling stations and their characteristic features are depicted in Fig. 1 and Table 1 respectively. Most of the stations from S1–S12 are either under the influence of domestic or sewage discharges. On the other hand, S13–S15 are under the influence of industrial discharges. These stations are located on the lower reaches of river Periyar in the immediate vicinity of the industrial hub. Water samples (both surface and bottom layers) were collected using a Niskin sampler (General Oceanics, USA) and surface sediment samples (0–5 cm) were collected using Van-Veen Grab (0.042 m2), subsampled using a teflon-coated spatula and packed in polyethylene bottles and kept at 4 °C until analysis. Water samples for general hydrography and nutrients were sub sampled immediately into clean plastic bottles. pH of the water and fresh wet sediment samples were determined in situ using portable pH meter. Sediment samples were oven dried at 50 °C, powdered using an agate mortar and pestle, sieved through 63 μm sieve and analyzed for organic carbon, total carbon, total nitrogen, total sulphur and heavy metals. The redox potential of the sediments was measured by portable Eh meter which was calibrated with Zobell solution. After the determination of ‘in situ’ variables, the samples were kept in ice box, transported to the laboratory without delay and kept frozen at −20 °C until analysis. Salinity and dissolved oxygen were measured by Mohr-Knudson and Winkler methods respectively (Grasshoff et al., 1983). Water samples were analyzed immediately for nutrients (ammonia, nitrate, nitrite, phosphate and silicate) following standard spectrophotometric procedures (Grasshoff et al., 1983), while alkalinity was measured using Koroleff method (Anderson et al., 1999) without delay. The texture of the sediments was determined by pipette analysis after removing the inorganic carbonates using 10% HCl and the organic matter using H2O2 (Folk, 1974). Total nitrogen (TN) and total sulphur (TS) were determined using CHNS Analyzer (Vario EL III CHNS Analyzer). Total phosphorus (TP) was reported as the sum of the fractions obtained from sequential extraction method (Golterman, 1996). Total organic carbon was estimated by TOC analyzer (VARIO TOC SELECT-Elementar), after removing inorganic carbon with 10% HCl (Bouillon et al., 2004). For the heavy metal analysis (total), about 1 g of dried and finely powdered sediment was digested in Teflon vessels using Microwave reaction system (Anton Paar Multiwave 3000) with 1:5 mixture of HClO4 and HNO3 respectively. Complete digestion was ensured by repeating the acidification until a clear solution was obtained and brought into solution in 0.5 M HCl using Milli-Q water. The concentra193

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mouths of the Rivers Muvattupuzha and Periyar. The sediments were mainly dominated by mud (silt + clay), except at stations near the river mouths and the bar mouth. The majority of the stations, during POM10, displayed the dominance of silt and clay, while S3 and S4 showed sandy nature. As can be seen from the Fig. 2, during POM09, all stations displayed the dominance of mud except stations S2, S4, S13 and S14 which exhibited higher sand content. Sand dominated sediments were observed at S3, S4, S5, S11, S13, S14 and S15 during PRM09. The occurrence of mud (silt + clay) as the major grain size fraction at S6, S7, S8, S9, S10 and S12 was also noticed. Whereas, during PRM10, the stations S1, S2, S6, S8, S12 and S14 exhibited higher mud content and at the other stations dominance of sand was observed. It was observed during MON12 sand content were lower at S6, S7, S8 and S9 (purely estuarine station), while at riverine region both north and southern stations greater sand content was observed especially > 70%. River dominated stations recorded sand as the major fraction especially during the monsoon season, due to the high energy condition existing in the river mouth. On the other hand, silt content is high at all stations except S6, S7, S8 and S9, but higher clay content was observed at S6, S8 and S12. During monsoon, the accumulation of alluvium as bed sediment in the southern arm, and the sediment transport in this system is dominant through suspension (Laluraj et al., 2008). TOC content ranged from 0.16% (S13) to 6.39% (S12) displaying significant spatial variation (p ≪ 0.01) with higher concentration in mud enriched sediments. Highly significant spatial variation was observed for TOC and the maximum was noted at S12 during MON09. Higher surface area of finer fractions (silt and clay) provides active sites for adsorption of heavy metals; thereby control the distribution of organic matter (Keil et al., 1994; Rodríguez-Barroso et al., 2010). The observed strong correlation of TOC with fine textural components implied similar distributional characteristics and the effect of hydrodynamic processes governing their concentration in the sediments (Valdes et al., 2005). Remarkable spatial variation was recorded by total sulphur, with its minimum concentration at S13 (MON09) and maximum at S7 (PRM10). Meanwhile, total nitrogen varied from 0.02% (S13, MON09) to 1.07% (S10, PRM10) and exhibited significant seasonal variations. Concentration of total phosphorous fluctuated between 199.88 mg/kg (S3, POM10) and 12,900.59 mg/kg (S12, POM09). The spatial distribution pattern and variation of the estimated heavy metals in the sediments is shown in Fig. 3. Trace metal concentration in sediments of CES varied as follows: (mg/kg):1.35–146.60 (Cu),

(S8, POM09) in surface and bottom waters respectively. It gradually decreased towards the river influenced stations and increased towards the estuarine region with its maximum was recorded at S9 (surface) and S8 (bottom) during POM09 due to the influence of Arabian Sea. Alkalinity of surface water varied from 6.40 mgCaCO3/L (S3, POM10) to 139.50 mgCaCO3/L (S6, POM09) and in bottom layer it fluctuated between 6.40 mgCaCO3/L (S8, POM10) and 145.08 mgCaCO3/L (S8, POM09), exhibiting only seasonal variation (p ≪ 0.01). Lower values of alkalinity were found at the northern part of the estuary due to the discharge of industrial effluents. Phosphate displayed a minimum of 0.01 μmol/L (S3; PRM10) and a maximum of 3.06 μmol/L (S9, PRM09) in the surface, while in bottom layers; it ranged between 0.02 μmol/L (S2, PRM09) and 4.16 μmol/L (S7, PRM09). Silicate in surface water found to vary from 8.53 μmol/L (S13, MON09) to 147.20 μmol/L (S6, POM10) and 3.95 μmol/L (S11, PRM10) to 118.90 μmol/L (S13, POM10) in surface and bottom layers of the water column respectively. Nitrate in surface water varied between 0.93 μmol/L (S5, PRM10) to 54.20 μmol/L (S3, MON09), meanwhile in the bottom it ranged from 0.53 μmol/L (S12, POM09) to 41.89 μmol/L (S3, MON09). Nitrite in surface layer recorded a variation from 0.018 μmol/L (S15, POM10) to 1.02 μmol/L (S14, POM09) and in bottom layers, it ranged from 0.01 μmol/L (S11, POM09) to 1.26 μmol/L (S5, PRM10). The observed content of ammonia found to vary from 0.02 μmol/L (S1, MON12) to 410.50 μmol/L (S7, PRM10) and 0.02 μmol/L (S13, POM09) to 177.70 μmol/L (S15, POM10) in surface and bottom layers respectively. The higher concentration of ammonia at northern arm stations may be due to localized sources caused by the discharge of ammonia and ammonium components along with the effluent from a fertilizer factory (Devi et al., 1991). The concentration and distribution of sedimentary parameters are represented in Table 2. Both grain size and organic matter content are important factors that govern the geochemical behaviour of heavy metals in sedimentary environments (Gao and Chen, 2012; Gao and Li, 2012; Hu et al., 2013a, 2013b; Dou et al., 2013). Eh recorded a marked variation from − 345 (S9, PRM09) to 21 (S2, PRM10). Lower values of pH were recorded during PRM10 and higher values during PRM09 and it ranged from 6.65 (S12, PRM10) to 8.30 (S8, PRM09). Texture analysis (Fig. 2) revealed that during MON09, most of the stations exhibited the dominance of mud (silt + clay) except at S13. Clay exhibited both spatial and temporal variations (ANOVA, p « 0.01) whereas sand and silt recorded spatial variation (Table 2). Grain size generally increased towards the bar mouth region and also at the

Table 2 Concentration of sedimentary variables and heavy metals in sediments with spatiotemporal variation. Parameters

pH Eh Sand (%) Silt (%) Clay (%) TOC (%) TS (%) TN (%) TP (mg/kg) Cu (mg/kg) Pb (mg/kg) Cd (mg/kg) Co (mg/kg) Ni (mg/kg) Zn (mg/kg) Mn (mg/kg) Cr (mg/kg) Fe (mg/kg) a

Minimum

6.65 −345 0.38 0.24 0.09 0.16 0.16 0.02 199.88 1.35 0.2 0.06 0.48 3.12 3.4 45.54 10.33 2664.73

Average ± SDa

Maximum

8.3 21 99.14 92.59 51.42 6.39 3.1 1.07 12,900.59 146.6 95.64 64.4 30.18 74.26 4655 921.25 681.25 74,531.25

7.5 ± 0.32 − 109.62 ± 72.51 40.27 ± 32.56 40 ± 23.16 19.73 ± 13.84 2.55 ± 1.67 1.2 ± 0.7 0.25 ± 0.17 2586.51 ± 2367 26.74 ± 26.44 21.91 ± 15.54 5.07 ± 9.02 13.75 ± 6.85 31.12 ± 15.64 386.08 ± 636.85 273.78 ± 170.12 133.7 ± 173.93 31,783.96 ± 16,540.5

SD-standard deviation.

194

ANOVA (p value) Spatial

Temporal

0.41 ≪0.01 ≪0.01 ≪0.01 ≪0.01 ≪0.01 ≪0.01 0.01 ≪0.01 ≪0.01 0.002 ≪0.01 ≪0.01 ≪0.01 ≪0.01 ≪0.01 ≪0.01 ≪0.01

0.17 ≪0.01 0.237 0.0294 ≪0.01 0.992 0.677 ≪0.01 ≪0.01 0.09 0.28 0.25 0.6 ≪0.01 0.15 0.45 < 0.01 0.99

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Fig. 2. Distribution of grain size and TOC in sediments.

Cochin estuary than the values recorded in the southern arm, which is mainly from anthropogenic sources. Processing of phosphorous minerals nearby factory plant, contribute metallic contaminants like Cd, Cr, Cu, Hg, Ni, Pb, and Zn. Besides, elemental processes involving mineral monazite, manufacturing compounds of rare earth elements and discharge effluents may lead to an increase in the concentration of Pb and Zn. In addition to this, various chemical manufacturing units including Zn compounds can act as point sources of contamination in the estuary. Metals recorded higher concentrations at stations dominated with fine-grained sediments and lower concentrations were found in the sandy sediments, suggesting the cohesive dependence on texture. It has been established that the metal scavenging ability of sediments increases as the particle size decreases (Unnikrishnan and Nair, 2004; Casey et al., 2007). The distribution of all heavy metals in the study

0.20–95.64 (Pb), 0.06–64.40 (Cd), 0.48–30.18 (Co), 3.12–74.26 (Ni), 3.40–4655 (Zn), 45.54–921.25 (Mn), 10.33–681.25 (Cr), 2664.73–74,531.25 (Fe). Heavy metal concentration decreased in the following order: Fe > Zn > Mn > Cr > Ni > Cu > Pb > Co > Cd. ANOVA revealed that all the estimated heavy metals exhibit a strong spatial variation (p « 0.01) and no significant seasonal variation except Cr and Ni (Table 2). The concentration of Pb exhibited spatial variability; with higher content observed at stations located in the industrial belt (S11 to S15) and lower levels at non industrial area (S1 to S6). Cu and Ni showed similar distribution pattern with less variation among the stations. The distribution pattern of Cr revealed higher concentration in the northern arm of the estuary and near the port region of the southern arm compared with other regions indicating anthropogenic inputs. Zn and Cd enriched in the northern arm of the 195

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Fig. 3. Spatial distribution and seasonal variation of heavy metals in sediments.

196

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39–181 22–55 23.5–89.3 17.6–48 5.1–93.27 2.08–58.20 – 24.65–77.59 21.9–46.5 57–304 6–30 8–79 2.9–103.7 – 2.60–34.90 3.12–74.26

16–199 586–5261 77.6–3113 47.6–154 4–134.6 51.93–741.93 23.2–77.5 12.98–212.58 100–289 199–5349 – 29.8–973.7 10–2233.2 49.84–149.19 6.30–1.90 3.40–4655

– 235–425 329–6020 413–1112 9.29–334.12 19.10–252.93 445–6661 481.0–1033.4 0.06–0.14 – 166–426 93–1086 370–1946 – – 45.54–921.25

40–233 61–157 20.7–400 36.9–173 3.73–97.56 0.15–84.18 32.9–132 59.0–127.4 74.1–123 65–160 – 20.7–200.9 7–379.6 52.34–311.69 6.54–78.40 10.33–681.25

– 26,500–73,700 16,690–79,464 20,538–49,627 3400–75,200 3300–51,100 16,200–66,600 32,100–54,100 32,400–41,400 34,060–92,620 – 12,000–98,000 2200–48,600 15,670–36,010 – 2664–74,531

Padmalal et al., 1997 Ruiz and Saiz-Salinas, 2000 Edeltrauda et al., 2005 Zhang et al., 2009 Renjith and Chandramohanakumar, 2009 Kumar et al., 2010 Demina et al., 2010 Qi et al., 2010 Yu et al., 2010 Zhang et al., 2011 Rabee et al., 2011 Selvam et al., 2012 Martin et al., 2012 Barakat et al., 2012 Hu et al., 2013c Present study

area indicated that higher concentration (especially in stations S11 to S15) can be attributed to the anthropogenic processes such as industrial, shipping and other port operations. The heavy metal content in the sediments was comparable with previously reported studies (Table 3). Pb, Cd, Cr and Zn exhibited weak correlation with textural components (Table 4) indicating the fact that texture had no influence on their dispersal pattern. On the other hand, heavy metals viz., Cu, Co, Ni, Fe and Mn recorded positive correlation with both silt, clay, TOC, TS and TN indicating natural origin by weathering process and the influence of sediment grain size on their distribution. Good to strong correlations between metals, TOC as well as fine grained fraction (silt + clay) suggests that fine grained sediments and organic matter are important carriers for these metals (Bastami et al., 2012; Yang et al., 2012; Yuan et al., 2012; Dou et al., 2013). Significant correlation of TOC with metals revealed the formation of organic complexes with heavy metals as a ligand by flocculation and subsequently influences their distributions, due to its high specific surface area (Marchand et al., 2006; Zourarah et al., 2008; Sreekanth et al., 2015). The redox sensitive metal Fe exhibited strong correlation with heavy metals viz., Cu, Ni, Cr, Co, Zn, Mn and Pb together with TOC, which revealed its key control over the linkage of these metals with organic matrix by association as Fe oxyhydroxides (Rubio et al., 2000; Balachandran et al., 2005; Cheriyan et al., 2015). Strong interrelationships among the estimated metals indicated their similarity and identical behaviour. Multivariate statistical analysis has been proved to be an effective tool for providing information on sources and pathways on heavy metals (Varol, 2011; Hu et al., 2013a, 2013b). Principal component analysis revealed (Fig. 4) two components with eigen values > 1, which explained 70.20% of the total variance in the original data set. The first principal component can be termed as the lithogenic factor which was characterized by highly significant positive loading for silt, clay, TOC, TS, Fe, Mn, Ni and Co. This factor clearly illustrated the origin of heavy metals by natural weathering and the granulometric dependence of metals with TOC and Fe oxyhydroxides as dominant factors controlling its distribution. The adsorption of metals onto Fe-Mn oxyhydroxides and Fe pyrites is commonly observed in marine sediments (Calvert, 1976). They are formed as coatings on the clay minerals or as individual particles and are normally observed in highly weathered environments (Santos et al., 2005). The sand provides high porosity thereby favouring oxygenation and remineralization of organic carbon. The elevated metal contents in the estuary and association of elements with Fe suggest coprecipitation of iron hydroxide along with scavenging, a probable mechanism for the accumulation of metals in the estuary. The second component was constituted by variables like Cu, Pb, Cd, Zn and Cr implying anthropogenic input mainly from different industrial effluents, domestic sewage and shipping operation. Cluster analysis was carried out for better interpretation and understanding of the sampling stations with respect to analyzed geochemical parameters and especially the sources of metal concentrations (Fig. 5). Dendrogram shows three clusters with regard to sampling stations. Cluster 1 includes stations S2, S10, S7, S4 and S11. Low to moderate enrichment of metals were observed at these sites. Cluster 2 contains six sampling sites viz., S6, S9, S3, S5, S8 and S13. It can be described as stations having moderate to high enrichment of metals. Cluster 3 comprises of the remaining stations S15, S1, S14 and S12. The majority of stations in this cluster have been influenced by severe anthropogenic activities showing very high enrichment of heavy metals. Cluster analysis has helped to better classify the sampling stations, according to the observed metal levels. The enrichment factor (EF) was calculated for each metal, using iron as normalizing element following the equation;

*BDL - below detectable level.

0–46 7–50 – – 3.19–28.07 3.90–21.58 9.2–28.5 16.00–31.61 – 21–103 – 5.4–40.7 3.4–58.4 – – 0.48–30.18 2–8 3–112 1.22–21.7 0.12–0.75 – BDL-11 0.15–3.65 – – 1.85–25.20 0.3–1.3 0.07–10.5 0.2–40.7 0.6–6.27 0.01–0.12 0.06–64.40 9–63 118–1785 34.1–298 6.87–49.7 0.7–41.52 0.28–41.80 6.25–28.94 38.66–187.35 18.9–87.2 42–49 5–55 4.6–59.2 3.6–123.5 32.69–740.75 0.70–14.90 1.35–146.60 Cochin estuary Bilbao Estuary, Spain Odra River, Germany Yangtze River, China Cochin estuary Cochin estuary Ob River Estuary-Kara Sea Section Pearl River Estuary, Southern China Pearl River Estuary Fe-smelting plant in urban river, China Tigris River Cochin estuary Cochin estuary Day River, Morocco Changhua River Estuary Cochin estuary

– 126–1112 29.0–427 18.3–44.1 11.1–71.38 *BDL-34.50 4.45–33.97 39.00–69.34 40.9–92.4 46–1735 – 17.3–18.5 6.8–99.6 72.93–140.36 10.4–36.7 0.20–95.64

Co Cd Pb Cu Study region

Table 3 Heavy metal content (mg/kg) in riverine/estuarine sediments from different regions of the world.

Ni

Zn

Mn

Cr

Fe

References

P.M. Salas et al.

EF = Metal Fesediment Metal Fecrust Fe has been widely employed as a normalizer to compensate for the granulometric and mineralogical variability of metal concentrations in 197

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Table 4 Pearson correlation matrix for the analyzed variables in sediments. pH

Eh

Sand

Clay

Silt

pH Eh Sand Clay

1 − 0.19 − 0.07 0.13

1 0.19 − 0.13

0.51 (**) 0.35 (**) 0.07

1

0.08

1 − 0.79 (**) − 0.93 (**) − 0.33 (**) − 0.09

Silt

0.02

− 0.19

Cu

0.06

0.00

Pb

0.06

Cd

0.04

0.01

− 0.11

0.15

0.07

Co

0.01

− 0.05

Ni

0.03

0.03

Zn

0.04

0.04

− 0.36 (**) − 0.50 (**) − 0.09

0.37 (**) 0.60 (**) 0.16

0.28 (**) 0.35 (**) 0.06

Mn

− 0.12

0.16

Cr

0.01

0.16

− 0.39 (**) − 0.15

0.39 (**) 0.14

0.32 (**) 0.13

Fe

0.05

0.01

TN

-0.13

0.17

− 0.38 (**) − 0.19

0.27 (**) 0.13

TP

0.04

-0.08

− 0.02

TOC

-0.06

0.08

TS

-0.04

0.01

− 0.33 (**) − 0.45 (**)

0.45 (**) 0.21 (*) 0.24 (*) 0.25 (*) 0.41 (**)

0.25 (*) 0.09

− 0.10 0.31 (**) 0.39 (**)

Cu

Pb

Cd

Co

Ni

Zn

Mn

Cr

Fe

TN

TP

TOC

TS

1 0.50 (**) 0.76 (**) 0.68 (**) 0.57 (**) 0.71 (**) 0.48 (**) 0.59 (**) 0.71 (**) 0.44 (**) 0.40 (**) 0.57 (**) 0.42 (**)

1 0.47 (**) 0.36 (**) 0.39 (**) 0.56 (**) 0.027 (**) 0.51 (**) 0.23 (*) 0.25 (*) 0.27 (**) 0.26 (*) 0.23 (*)

1 0.45 (**) 0.37 (**) 0.96 (**) 0.26 (*) 0.65 (**) 0.45 (**) 0.25 (*) 0.37 (**) 0.44 (**) 0.26 (*)

1 0.67 (**) 0.42 (**) 0.62 (**) 0.37 (**) 0.68 (**) 0.51 (**) 0.25 (*) 0.72 (**) 0.58 (**)

1 0.35 (**) 0.61 (**) 0.32 (**) 0.57 (**) 0.42 (**) 0.29 (**) 0.45 (**) 0.62 (**)

1 0.24 (*) 0.67 (**) 0.39 (**) 0.22 (*) 0.31 (**) 0.38 (**) 0.23 (*)

1 0.38 (**) 0.71 (**) 0.52 (**) 0.12

1

0.55 (**) 0.56 (**)

0.34 (**) 0.30 (**)

0.49 (**) 0.31 (**) 0.16

1 0.45 (**) 0.32 (**) 0.57 (**) 0.55 (**)

1 0.08

1

0.40 (**) 0.59 (**)

0.30 (**) 0.15

1 0.59 (**)

1

Note: **Correlation is significant at the 0.01 level,*correlation is significant at the 0.05 level.

poses moderately severe enrichment, 10–25 implies severe enrichment, 25–50 indicates very severe and greater than 50 (> 50) suggests extremely severe enrichment. The estimated enrichment factor for the studied metals ranged as follows: Cu = 0.14 (S4; PRM09) to 4.14 (S15, MON12), Pb = 0.03 (S13, PRM10) to 41.24 (S15, MON12), Cd = 0.36 (S6, POM09) to 215.44 (S14, MON12), Zn = 0.12 (S3, MON09) to 68.19 (S15, MON12), Mn = 0.12 (S15, POM09) to 1.75 (S2, PRM10), Ni = 0.15 (S13, PRM10) to 3.06 (S8, MON12), Co = 0.09 (S13, PRM10) to 4.99 (S15, PRM10), Cr = 0.10 (S14, POM10) to 51.31 (S13, MON09). The mean EF values of heavy metals from CES followed the trend: Cd (22.99) > Zn (6.16) > Cr (2.67) > Pb (2.08) > Co (1.17) > Cu (0.85) > Ni (0.76) > Mn (0.51). The observed EF values revealed the fact that in the northern arm of the estuary, major portion of the trace metals are delivered from industrial effluents compared to the southern arm. Estimated EF values fluctuated between negligible to minor enrichment for Cu, while a moderate enrichment was noticed during MON12 (S15). However increasing trend was shown by Pb, Zn and Cd recorded from southern to northern arm (industrial region) of the estuary (Fig. 6). The toxic trace metal Cd displayed minor enrichment from southern arm towards very severe enrichment at northern arm - the industrial area, while during MON12, stations S14 and S15 displayed a severe enrichment character and these results were comparable with the previous studies (Kumar et al., 2010; Selvam et al., 2012). Zn fluctuated between negligible to severe enrichment, but during MON12 (S15) and POM10 (S13), it showed extremely severe enrichment (Fig. 6). Earlier investigations have already established the anthropogenic input of Zn into the northern parts of the estuary (Paul and Pillai, 1983; Shibu et al., 1990; Kumar et al., 2010). Co and Cr in most of the stations fluctuated between negligible to minor enrichment, but latter recorded extremely severe enrichment at S13 (MON09) and severe

Fig. 4. Principal component analysis-factor loadings for the variables.

sediments (Schiff and Weisberg, 1999; Neto et al., 2000; Chapman and Wang, 2001). Present investigation used shale averages as background level, due to the difficulty in obtaining a pristine environment value because of the fluctuating sediment texture and diverse anthropogenic influences occurring in the study region. Enrichment factor (EF) < ˂1.5 implies that the origin of trace metals mainly from crustal materials through natural weathering processes (Zhang and Liu, 2002; Feng et al., 2004). However, an EF value > 1.5 indicates that a significant portion of the trace metal is delivered from non-crustal materials or non-natural weathering processes and also contributed by other sources (Feng et al., 2004). EF values were interpreted as the levels of trace metal pollution as suggested by Birch and Olmos (2008), where EF values less than 1 (< 1) indicates no enrichment, less than 3 (< 3) poses minor enrichment, 3–5 shows moderate enrichment; 5–10 198

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Fig. 5. Dendrogram showing the cluster of sampling stations based on analyzed variables.

input of effluent discharges. On the other hand, the west coast of India experiences tropical climatic conditions which induce intense rainfall and fresh water influx during southwest monsoon (June–September). Due to the intense freshwater influx, the pollutants may be brought down to the central estuary and finally flushed out to the Sea while an increase occurs in the exchange volume of the estuary. So, the restricted flow in the estuary cause accumulation of trace metals, whereas, the overall metal contamination status may be obtained on average as unpolluted. Zn and Cd with high Igeo values towards the northern side of the estuary reflected industrial activities. Pollution Load Index (PLI) proposed by Tomlinson et al. (1980) was calculated in terms of Contamination Factor (CF) of each metal with respect to the background value in the sediment (Angulo, 1996), by applying the following equation.

enrichment at S15 (MON12). Irrespective of seasons, EF of Cu (except S15; MON12), Mn, Ni (except S8; MON12) and Co (except S15, PRM10) were < 1.5 pointing the origin of these metals mainly from crustal weathering. EF of Zn and Cd (> 1.5) in the northern arm of the study region was attributed to the effluent discharge from nearby chemical industries (fertilizers, heavy metal processing, pesticides, insecticides, petroleum refining, chemical and allied industries) and urban activities occurring along the banks of River Periyar. Sources of Pb in sediments include exhaust from shipping activities, paints and indirect sources like atmospheric deposition as well as land run off (Gajghate and Bhanarkar, 2005). The source of Cr which is mainly enriched in the northern arm near industrial belt was due to the municipal waste and the leather effluent discharge from industries. Geoaccumulation index (Igeo), introduced by Müller (1979) was used to assess metal pollution in sediments. The extent of heavy metal pollution in sediment by anthropogenic activities can also be assessed by Igeo (Ridgway and Shimmield, 2002).

1

PLI = CF1 × CF2 × CF3 × … … … .×CFnn where CF = Cmetal / Cbackground and n = number of metals. PLI, a summative indication for the overall level of heavy metal pollution in sediments has been widely employed to evaluate the contamination status (Tomlinson et al., 1980; Ray et al., 2006; Badr et al., 2009). Present investigation revealed PLI values ranging from 0.13–4.14. Most of the stations exhibited low PLI and high values (Fig. 6) were recorded in the northern part of the estuary clearly indicating the accumulation of metals originating from agricultural, domestic discharges and industrial operations. Accumulation of severe levels of toxic trace metals represent longterm sources of contamination to higher trophic level and impart direct risk to detrital and deposit-feeding benthic organisms (Mendil and Uluozlu, 2007). Earlier reports suggest that the fish living in polluted waters contain a considerable amount of toxic metals deposited preferably in fish tissues (Jezierska and Witeska, 2006; El-Moselhy et al., 2014; Velusamy et al., 2014; Leung et al., 2014; Baharom and Ishak, 2015). Ragi et al. (2017) observed that there is significant concentration of toxic metals in gastropods and bivalves collected from a major fish landing centre along South India. So there is evidence to show metal accumulation causing long term contamination to marine organisms and always expected to occur in polluted regions such as Cochin estuary. Recurrent events of effluent discharge from the industrial region in the past few decades has created ecological

Igeo = log2 [C n 1.5Bn ] where Cn = measured concentration of heavy metal in sediment, Bn = geochemical background value in average shale (Wedepohl, 1995) of element n, 1.5 is the background matrix correction factor due to lithogenic effects. According to Igeo classification, extent of pollution can be categorized as: very strongly polluted (Igeo > 5), strongly to very strongly polluted (Igeo = 4–5), strongly polluted (Igeo = 3–4), moderately to strongly polluted (Igeo = 2–3), moderately polluted (Igeo = 1–2), unpolluted to moderately polluted (Igeo = 0–1) and unpolluted (Igeo < 0). On average, most of the trace metals exhibited depleted values (Igeo < 0), indicating unpolluted condition (Table 5). The present study utilized samples from all the three parts viz., northern, central and southern arms of the estuary. The southern and northern arms show flow restrictions while the central estuary maintains an effective flushing through the perennial channel (Balachandran et al., 2008). Martin et al. (2011) reported that the anthropogenic activities aggravated by the weak flushing have increased the phytoplankton production in the northern part of the estuary. Consequently, the export of organic matter to the bottom sediments may scavenge and thereby preserve the metals during high 199

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Fig. 6. Enrichment factor for metals viz., cadmium, chromium, lead and zinc and pollution load index.

metals in the present study can be categorized as follows: Cu and Ni moderately polluted, Pb - not polluted, Zn and Cr - heavily polluted. The ecotoxicological sense of heavy metal contamination in sediments was determined using sediment quality guidelines developed for marine and estuarine ecosystem (Bakan and Ozkoc, 2007). The toxicological effects applied were: a) effect range low (ERL)/effect range median (ERM) b) threshold effect level (TEL)/probable effect level (PEL). ERM and PEL are concentrations above which adverse effects would occur frequently on sediment dwelling fauna while ERL and TEL denote chemical concentration below which adverse effects rarely occurs to biota. It was found that average metal concentration of Cu, Zn, Cr, Cd and Ni (Table 6) exceeded TEL. Cu and Pb were below the ERL target value but Cr, Pb and Ni exhibited values below PEL and ERM. Zn and Cd fluctuated between PEL and ERM target value denoting accumulation of these trace metals in sediments which can induce toxic effects on benthic organisms. Fish and sediment dwelling organisms viz., gastropods, bivalves etc. accounts for the major fishery resource in the Cochin backwaters (Raveenderan and Sujatha, 2011). According to Martin et al., 2011, the northern arm of the Cochin estuary (Industrial Area) recorded a decrease in species numbers of bivalves and gastropods. Previous research by Ciji and Nandan (2014), reported the presence of extremely high concentration of metals in water and sediments collected from the lower reaches of River Periyar. Further, the study by Dsikowitzky et al. (2014), reported the absence of benthic

Table 5 Geoaccumulation index estimated for the heavy metals in the sediment. Metals

Cu Pb Cd Co Ni Zn Mn Cr Fe

Geoaccumulation index Minimum

Maximum

Average

− 5.65 − 7.24 − 2.88 − 5.88 − 5.03 − 5.39 − 4.81 − 3.71 − 4.73

1.12 3.03 7.16 0.08 − 0.46 5.03 − 0.47 2.34 0.07

−1.86 −0.83 1.88 −1.32 −1.95 0.14 −2.46 −0.78 −1.36

imbalance thereby affecting the species composition and diversity of benthic organisms in Cochin estuary. The disappearance of benthic population and the appearance of tolerant species have already been reported in the estuary (Martin et al., 2010). SQGs have been regarded as an efficient tool to evaluate and categorize the relative quality of sediments (Long et al., 1998, 2006) and also make an initial assessment of sediment toxicity in the absence of direct biological effects data (Birch and Taylor, 2002). According to SQGs (Table 6) (US National Oceanic and Atmospheric Administration - NOAA), the estimated

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Table 6 Sediment quality guideline for trace metals (MacDonald et al., 2000; Bakan and Ozkoc, 2007). Elements

Average (mg/kg)

SQG not polluted

SQG moderate polluted

SQG heavily polluted

TEL

PEL

ERL

ERM

Cu Zn Cr Cd Pb Ni

26.75 386.09 133.71 5.07 21.91 31.13

< 25 < 90 < 25 – < 40 < 20

25–50 90–200 25–75 – 40–60 20–50

> 50 > 200 > 75 – > 60 > 50

18.7 124 52.3 0.68 30.2 15.9

110 270 160 4.2 110 43

34 150 81 1.2 46.7 20.9

270 410 370 9.6 218 51.6

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species at a station nearby the industrial hub, possibly due to the high concentration of toxic pollutants. These results suggest that the persistent exposure, even to low concentrations of toxic organic/ inorganics may result in bioaccumulation, causing changes in metabolic activities and alterations in the community structure of marine biota in the region. Obviously, metal results in the present study indicate that the trace hazardous element concentrations exceeded the toxicity thresholds for benthic species and may accumulate in marine organisms in and around the region of anthropogenic inputs in the Cochin estuary. The heavy metal content in the estuarine sediments decreased in the following order: Fe > Zn > Mn > Cr > Ni > Cu > Pb > Co > Cd. Distribution of heavy metals in the sediments were governed by the association of Fe oxyhydroxides, organic carbon and sediment texture. ANOVA revealed spatial variation in metal concentration, while other sedimentary variables exhibited prominent spatial as well as seasonal variation. PLI values revealed that the sediment was heavily contaminated in northern arm compared to southern arm of the estuary. Multivariate statistics provided useful information on the sources of metals and the influence of granulometry, TOC and Fe oxyhydroxides on metal distribution. High metal enrichment factors of Cd, Zn, Pb and Cr in the northern arm of the estuary suggest point sources, reflecting the intensity of anthropogenic inputs associated with industrial discharge. A comparison of the present data with sediment quality guidelines was also made, which testified the build-up of toxic levels of heavy metals that may induce negative effects on benthic organisms. Acknowledgements The authors gratefully acknowledge the financial support of the Ministry of Earth Sciences (MoES), Government of India by Grant number MoES/11-MRDF/1/38/P/08 and also thank the Head, Department of Chemical Oceanography, School of Marine Sciences, Cochin University of Science and Technology (Ac.B3/UJRF/2010-11 and Ac.B3/007201-SRF/13-14) for providing the necessary facilities to carry out the work. We thank the Editor and anonymous Reviewer for their insightful comments to improve the clarity of the manuscript. References Ajith, J.K., Balchand, A.N., 1996. Morphodynamic behaviour of a tropical coastal Seainlet estuary system. In: Proceedings of the 8th International Conference on Physics of Estuaries and Coastal Seas, Netherlands. 42–43. Anderson, L.G., et al., 1999. In: Grasshoff, K. (Ed.), In Methods of Seawater Analysis. Wiley-VCH, Germany. Angulo, E., 1996. The Tomlinson pollution load index applied to heavy metal ‘MusselWatch’ data: a useful index to assess coastal pollution. Sci. Total Environ. 187, 49–56. Badr, N.B.E., El-Fiky, A.A., Mostafa, A.R., Al-Mur, B.A., 2009. Metal pollution records in core sediments of some Red Sea coastal areas, Kingdom of Saudi Arabia. Environ. Monit. Assess. 155, 509–526. Baharom, Z.S., Ishak, M.Y., 2015. Determination of heavy metal accumulation in fish species in Galas River, Kelantan and Beranang mining pool, Selangor. Procedia Environ. Sci. 30, 320–325. Bakan, G., Ozkoc, H.B., 2007. An ecological risk assessment of the impact of heavy metals in surface sediments on biota from the mid-Black Sea coast of Turkey. Int. J. Environ. Stud. 64, 45–57. Balachandran, K.K., Laluraj, C.M., Nair, M., Joseph, T., Sheeba, P., Venugopal, P., 2005. Heavy metal accumulation in a flow restricted, tropical estuary. Estuar. Coast. Shelf

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