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Assessment of heavy metal pollution, distribution and source apportionment in the sediment from Feni River estuary, Bangladesh
Accepted Manuscript Assessment of heavy metal pollution, distribution and source apportionment in the sediment from Feni River estuary, Bangladesh Md. Saiful Islam, M. Belal Hossain, Abdul Matin, Md. Shafiqul Islam Sarker PII:
S0045-6535(18)30491-0
DOI:
10.1016/j.chemosphere.2018.03.077
Reference:
CHEM 21021
To appear in:
ECSN
Received Date: 2 July 2017 Revised Date:
17 February 2018
Accepted Date: 11 March 2018
Please cite this article as: Islam, M.S., Hossain, M.B., Matin, A., Islam Sarker, M.S., Assessment of heavy metal pollution, distribution and source apportionment in the sediment from Feni River estuary, Bangladesh, Chemosphere (2018), doi: 10.1016/j.chemosphere.2018.03.077. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT 1
Assessment of heavy metal pollution, distribution and source
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apportionment in the sediment from Feni River estuary, Bangladesh Md. Saiful Islama, M Belal Hossaina,*, Abdul Matina, Md. Shafiqul Islam Sarkerb
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a: Department of Fisheries and Marine Science, Noakhali Science and Technology
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University, Noakhali-3814, Bangladesh.
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b: Forensic Science Laboratory, Rapid Action Battalions Headquarters, Dhaka-1229,
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Bangladesh.
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*Corresponding author’s email: [email protected]
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Note: First two authors contributed equally.
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Assessment of heavy metal pollution, distribution and source
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apportionment in the sediment from Feni River estuary, Bangladesh ABSTRACT
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Heavy metal pollution in sediment resources may pose serious threat to ecosystem and
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human health through food web. In this study, surface sediment samples of 10 stations along
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the Feni River estuary were analyzed to profile the accumulation, sources and pollution levels
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of heavy metals. The results revealed that the average contents (µg g-1) of eight selected
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heavy metals followed the order of Mn (37.85) > Cr (35.28) > Ni (33.27) > Co (31.02) > Pb
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(6.47) > Ag (1.09) > As (0.85) > Hg (0.71), and the concentrations varied spatially and
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seasonally with relatively higher levels at upward stations and during the rainy season.
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According to sediment quality guidelines (SQGs), the sediment samples were heavily
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contaminated with Ag and Hg, and moderately with Co. Threshold effect concentration
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(TEC) and probable effect concentration (PEC) values indicated that the concentration of
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only Ni and Cr were likely to occasionally exhibit adverse effects on the ecosystem.
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Enrichment factor (EF), geo-accumulation index (Igeo) and contamination factor (CF)
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analyses revealed that Ag, Co and Hg were at moderate to high pollution levels and the rests
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(As, Cr, Ni, Pb and Mn) were at no to low pollution levels. Potential ecological risk index
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(PERI) also showed that Ag, Co and Hg were the most potential ecological risk factor being
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determined in this studied area. Correlation matrix combined with multivariate principal
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component analysis and cluster analysis suggest that Ag, Co, Ni and Hg originated from
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anthropogenic sources (agrochemicals, silver nanoparticles anti-microbial agent, silver
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plating), whereas As, Cr, Pb and Mn primarily originated from natural geological
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background.
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Keywords: Heavy metals, Estuarine sediments, Multivariate analysis, Potential ecological
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risk assessments, Feni River estuary.
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1. Introduction Heavy metal pollution is considered to be a serious threat to any aquatic ecosystems
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because of its toxicity, persistent nature, omnipresent, and ability to non-biodegradability and
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be bio-accumulated into the food chain (Duman et al., 2007). Heavy metals enter the aquatic
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ecosystems through point sources such as industrial, municipal and domestic waste water
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effluents as well as diffuse sources which include surface runoff, erosion, and atmospheric
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deposition (Akcay et al., 2003; Demirak et al., 2006; Armstrong-Altrin et al., 2015; Ramos-
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Vázquez et al., 2017). These metals that are introduced into the aquatic environment are
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ultimately incorporated into aquatic sediments (Zhang et al., 2017). Thus, sediments are one
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of the possible media in monitoring the health of aquatic ecosystems (Baran et al., 2002).
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Sediments serve as the largest pool of metals in any aquatic environments (Zhang et
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al., 2017), and more than 90% of the heavy metal load in the aquatic ecosystems has been
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found to be related to suspended particulate matter and sediments (Zheng et al., 2008; Amin
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et al., 2009). Metals in suspended particulates settle down and lay up in sediments
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(Kucuksezgin et al., 2008), while the dissolved metals adsorb onto fine particles which may
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carry them to bottom sediments (Singh et al., 2005) and persist in the sediment for a long
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time. Their distribution are influenced by the mineralogical and chemical composition of
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suspended material, anthropogenic influences, deposition, sorption, enrichment in organism
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(Jain et al., 2007), and various physico-chemical characteristics (Singh et al., 2005).
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Sediments are ecologically important components of the aquatic habitat and play a
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significant role in maintaining the trophic status of any water body (Zhang et al., 2017; Singh
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et al., 2005) and provides a site for biogeochemical cycling and the foundation of the food
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and biological processes and are an integral component for functioning of ecological
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integrity. In addition, sediments act as a sink of organic as well as inorganic pollutants and
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provide a history of anthropogenic pollutant input (Bermejo et al., 2003; Shuhaimi, 2008) and
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ecological changes (Shomar et al., 2005).
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The contamination of sediments with heavy metals leads to serious environmental
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(Loizidouet al., 1992) and worldwide problems (Yoon et al., 2006; Fernandes et al., 2008;
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Kucuksezgin et al., 2008). Therefore, sediment has widely been studied for anthropogenic
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impacts on the aquatic environments (Sayadi et al., 2010). Various studies have reported the
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distribution and contamination of heavy metals and quantification of pollution load in
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sediments of different rivers and estuaries such as the Yangtze River estuary, China (Wang et
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al. 2015a), the River Gomti, India (Singh et al., 2005), the Luanhe River estuary, China (Liu
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et al. 2016)), and the Karnaphuli River estuary, Bangladesh (Ali et al., 2016). However,
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estuarine sediment contamination by heavy metals has meagerly been investigated in
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Bangladesh in general and no information is available for the Feni River estuary and its
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associated tributaries. Nationally, the Feni River estuary plays a very significant role in
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providing a huge amount of fish supplies to the local and national markets as well as
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livelihood to the local inhabitants. The estuary has been subjected to heavy metal pollution
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due to a rapid increase in population and unplanned human settlements in its catchment area,
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agricultural activities, fish farming, fishing, industrial and medical waste discharge,
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recreational activities, washing activities, poultry waste discharge, dumping of solid waste
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and direct and/or indirect discharge of untreated domestic effluents. Therefore, the present
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study aimed to (1) determine the spatial and seasonal distributional trends of heavy metals in
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the sediments of Feni River estuary, (2) assess ecosystem risk of heavy metals following
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sediment quality guidelines viz., threshold effect level/probable effect level (TEL/PEL), (3)
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ACCEPTED MANUSCRIPT quantify the extent of metal pollution using enrichment factor (EF), geo-accumulation indices
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(Igeo) and potential ecological risk index (PERI), and (4) identify the natural and/or
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anthropogenic sources of these metals using correlation matrix and multivariate statistical
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techniques.
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2. Materials and Methods
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2.1. Study area
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Located in the central coast of Bangladesh, Feni River estuary (also known as Little
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Feni River estuary; Latitude 22° 46'44" N and Longitude 91°22'42" E) originates in the
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South Tripura district (India) and flows a distance of 116 km through different cities and
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municipalities in India and Bangladesh, and finally empties into the Bay of Bengal. The
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estuary is heavily used for irrigation, fishing, agriculture, aquaculture, washing, livestock
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farming, recreation, dumping domestic waste, sewage disposal and water based transport.
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Rainfall in this area is mostly seasonal, with a rainy season between June and November and
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a dry period between December and May. An average annual rainfall is 3,302 mm and annual
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temperature varies from a maximum of 34.3 °C to a minimum of 14.4 °C (Miah et al., 2015).
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The salinity of the estuary ranged from 4.20 -7.50 ppt with a mean value of 5.78 ±1.32 ppt.
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Due to the heavy rainfall and annual flooding, the region is composed of fertile alluvial plains
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resulting from the underlying forces of the Feni river. Therefore, most of the catchment areas
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are suitable for temporary or permanent crops such as rice, wheat, beans, vegetables, red
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pepper, potato, tomato, bananas and sugarcane. However, to increase the production of these
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crops, indiscriminate and long-term repeated application of fertilizers and metal-containing
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pesticides, herbicides and fungicides have been gradually accumulated to potentially harmful
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levels in the soils.
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2.2. Sediment sampling
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ACCEPTED MANUSCRIPT In total, sixty samples of surface sediments (0–5 cm) from 10 stations (S1 to S10) in
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the Feni River estuary (Fig. 1) were collected using an Ekman dredge during the wet (June
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2016) and dry (January 2017) seasons. Samples that showed no evidence of surface
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disturbance were retained. Triplicate samples were collected at each station in each season.
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All samples were stored in a cooler at 4 °C, and then were frozen at −20 °C after being
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transferred to the laboratory immediately for further analysis (Lasorsa and Casas, 1996).
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Before analysis, the sediments were defrosted at room temperature, dried at 50 °C to constant
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weight (~24 h) (Hyun et al., 2007), and ground in an agate mortar (Zhao et al., 2016). The
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fraction smaller than 63 µm was used for analyses in this study due to strong association of
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metals with fine-grained sediments (Tam and Wong, 2000). Global Positioning System
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(GPS) was used to locate the sites.
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2.3. Determination of metals
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For the measurement of total metal concentrations, acid digests of each sediment
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sample were prepared using USEPA method 3051. Each sediment sample measuring 0.5 g
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was digested in 10 ml of ultrapure HNO3 using Micro Wave Digestion System (WX-6000,
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Preekem, Shangai), and then filtered and diluted. Total metal concentrations of Ag, Co, Ni,
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Hg, Cr, Pb, As and Mn were determined in triplicate using Inductively Coupled Plasma-Mass
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Spectrometry (ICP-MS) (SPECTRO MS, SPECTRO GmbH, Germany) at Forensic Science
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Laboratory, Rapid Action Battalions Headquarters, Dhaka, Bangladesh. All the reagents used
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were of super quality and of analytical grade. All solutions were prepared using ultrapure
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water (Ultra Clear TM TWF UV UF Type IP23, EVOQUA Water Technologies, Germany).
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All acid proof plastic and Teflon apparatus were soaked in HNO3 (10%) for at least 24 h and
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rinsed repeatedly with ultrapure water. Analytical blanks and standard reference material
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were run in the same way as the samples, and heavy metal concentrations were determined
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using standard solutions prepared in the same acid matrix. Certified reference material (CRM
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ACCEPTED MANUSCRIPT 320) supplied by Merck KGaA (Germany) was used (N=3) to ensure the validation of data
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and the accuracy and precision of analytical method. Analytical results of the selected metals
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indicated a good agreement between the reference and analytical values of the reference
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materials. Percentages of recoveries were between 95% and 105% for the all metals. The
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results also showed that there was no contamination during analysis, and the relative standard
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deviation (RSD) of all replicate samples was <= 10%. The limit of detection (LOD) of the
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metals varied (As = Ag = Ni = Pb = Mn = 0.001, Cu = Co = 0.0007, Hg = 0.0005, Cr = 0.003
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and Zn = 0.002 ppm).
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2.4. Assessment of sediment pollution
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2.4.1. Enrichment factor (EF)
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Normalized enrichment factor is applied (Salati and Moore, 2010) to differentiate
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metal source originating from anthropogenic and natural means (Sayadi et al., 2010). This
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involves normalization of the sediment with respect to reference elements such as Al, and Fe
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(Amin et al., 2009; Karbassi et al., 2008), Mn, Ti and Sc (Salati and Moore, 2010), and Li
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and Cs (Pereira et al., 2007). Normalized EF of metals in Feni River estuary sediments from
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each site was calculated using Eq. (1). Manganese (Mn) was used as a reference element to
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calculate anthropogenic metal enrichments as described by Loska et al. (1997). World
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average concentration of metals reported for the shale by Turekian and Wedepohl (1961) was
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used as background values for heavy metals.
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EF =
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where (Cn/CMn) is the ratio of concentration of the element of concern (Cn) to that of Mn
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(CMn) in the sediment sample (µg g-1 dry weight) and (Cn/CMn) is the same ratio in an
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unpolluted reference sample.
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( ⁄ ) ( ⁄ )
…….
(1)
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states deficiency to moderate enrichment, EF = 5–10 moderately severe enrichment, EF =
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10–25 severe enrichment, EF= 25-50 very severe enrichment and EF >50 extremely high
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enrichment (Birch and Olmos, 2008).
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2.4.2. Geo-accumulation index (Igeo)
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Geo-accumulation index (Igeo) was developed by Müller (1979) and had widely been
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used in trace metal studies of sediments and soils (Amin et al., 2009). To quantify the degree
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of heavy metal pollution in Feni River sediments, Igeo was calculated according to Muller
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(1979) and is given in Eq. (2).
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I = log (C ⁄1.5B ) …….
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where Cn is the concentration of the examined metal in the sediment, Bn is the geochemical
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background value of a given metal in the shale (Turekian and Wedepohl, 1961) and the factor
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1.5 is used to account the possible variations in the background values.
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(2)
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The geo-accumulation index consists of seven grades or classes: Class 0 (practically
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uncontaminated): Igeo< 0; Class 1 (uncontaminated to moderately contaminated): 0
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Class 2 (moderately contaminated): 1
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2
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contaminated): 4
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concentrations in Class 6 may be hundredfold greater than the geochemical background value
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(Bhuiyan et al., 2010).
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2.4.3. Contamination Factor (CF)
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The level of contamination of sediment by metal is expressed in terms of a contamination factor (CF) calculated as:
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CF =
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where Cn Sample is the concentration of a given metal in river sediment, and Bn is the
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geochemical background value of a given metal in the shale (Turekian and Wedepohl, 1961).
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CF values were interpreted as follows: low contamination at CF < 1; moderate contamination
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at 1 ≤ CF < 3; considerable contamination at 3 ≤ CF < 6; and very high contamination at CF
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>6, according to Hakanson, 1980.
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2.4.4. Potential ecological risk index (PERI)
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The PERI was used to assess the comprehensive potential ecological risk of heavy metals in
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sediment and was initially introduced by Hakanson (1980). The potential ecological risk
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factor of a given metal (E ) is defined as
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E = T × $
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Equation (3) was used to calculate the risk index (RI) of sampling sites as follows:
%
………….
(4)
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RI = '
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where Ci is the concentration of metal i in sediment, C0 is the concentration of the same
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element in background sediment, T is the biological toxicity factor of an individual element,
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which was determined for Cu = Pb = Ni= 5, Zn = 1, As = 10, Cr = 2 and Cd = 30 (Suresh et
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al., 2011), E is the potential ecological risk factor of a single metal, and RI is the
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comprehensive potential ecological risk index of the metals. The PERI of heavy metals was
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categorized according to five levels, as shown in Table S1.
%
(5)
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………
EP
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T × $
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(3)
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the relationship and interdependency among the variables and their relative weights
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(Bartolomeo et al., 2004). Correlation and principal components analyses, the most common
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multivariate statistical methods, were used to check for significant relationships among heavy
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metals in the sediment samples. The various statistical methods were performed with a 95%
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confidence interval (significance p < 0.05). Factor analysis based on principal component
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analysis (PCA) was used to ascertain sources of contamination (natural and anthropogenic).
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Cluster analysis was used to identify spatial variability among the sites. Euclidean distance
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was used as dissimilarity matrix, whereas Ward's method was used as a linkage method. All
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the statistical analyses were done using free statistical software, PAST (Hammer et al., 2001).
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3. Results and discussion
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3.1. Seasonal and spatial variation in the surface sediment of the study area
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Concentrations of heavy metals in the study area are summarized in Table 1. The
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mean concentrations (µg g-1) of the selected metals followed similar decreasing trend during
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both seasons: Mn (37.85) > Cr (35.28) > Ni (33.27) > Co (31.02) > Pb (6.47) > Ag (1.09) >
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As (0.85) > Hg (0.71). Co, Cr, Ni and Pb were significantly different between seasons (p ≤
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0.01) and greater at sampling sites viz., S3, S4, S5, S6 and S7 located in close vicinity of
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agricultural land, urban, semi-urban and tourist areas with anthropogenic activities (Table 1).
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In wet season concentrations of all metals were higher than dry season in sediment sample
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(Fig. 2). However, lower concentrations of metals in dry season could be attributed to the
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decreased dilution of runoff and unpolluted water. Sometimes, the variations in metal
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concentrations may also be influenced by changes in lithological inputs, hydrological effects,
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geological features, cultural influences and type of vegetation cover (Jain et al., 2007).
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ACCEPTED MANUSCRIPT During wet season the concentrations of Ag were higher in all the sites which ranged from
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0.09 µg/g to 2.07 µg g-1. The highest concentration of Ag was recorded in the site S3 (2.07
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µg/g) in sediment samples. On the average basis of both seasons, the higher concentrations of
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Mn, Cr, Ni, Co, Pb, Ag, As and Hg at the sampling sites S1, S3, S5, S6 and S7 may be
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attributed to several anthropogenic activities (Fig. 2 and Table 1). For example, the site S6
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and S7 were located in highly agricultural land with semi-urbanized area, and the estuary at
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this site directly receives untreated domestic sewage, agricultural runoff, urban runoff, and
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wastes from construction of residential and commercial areas.
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The heavy metal concentrations in sediment of the Feni river estuary were compared with
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other selected estuaries (Table 2). The mean concentrations of Cr, As and Pb were higher in
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this estuary than the values reported by Ali et al. (2016) in the Karnaphuli River estuary. The
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mean concentrations of Cr, Pb and As in the Yangtze River estuary were found higher but Ni
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was lower as recorded by Wang et al. (2015a) than present measured concentrations. The
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mean concentrations of Cr and Pb were found to be higher but Ni was lower in the Ganges
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estuary (Subramanian et al., 1988) and Luanhe River estuary (Liu et al., 2016) than in this
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study.
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3.2. Sediment quality guidelines (SQGs)
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Sediment quality guidelines (SQGs) are useful to screen sediment contamination by
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comparing sediment contaminant concentration with the corresponding quality guidelines
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(Caeiro et al., 2005), which evaluate the degree to which the sediments associated chemical
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status might adversely affect aquatic organisms and are designed to assist the interpretation of
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sediment quality (Wenning et al., 2005). Threshold effect level (TEL) refers to the
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concentration below which adverse biological effects are expected to occur rarely, and
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Probable effect level (PEL) indicates the concentration above which adverse effects are
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ACCEPTED MANUSCRIPT expected to occur frequently (Long and Morgan, 1990). Table 1 furnishes the heavy metal
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concentrations in sediment from the Feni River, as well as the average shale values and
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sediment quality guidelines (SQGs) used in this study. When comparing the average values
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of heavy metals with the average shale values, the results indicated that Ag, Co and Hg were
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higher than the average shale value in the wet and dry seasons. This indicates that the
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anthropogenic activities had a direct effect on the concentration of these metals in sediment
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(Chai et al., 2014). The concentration of heavy metals in the sediment samples were
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contrasted with the consensus-based threshold effect concentration (TEC) and probable effect
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concentration (PEC) values (Table 1). The results show that Ni and Cr were between TEC
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and PEC for 70% and 20% indicating that the concentration of Ni and Cr were likely to
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occasionally exhibit adverse effects on the ecosystem.
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3.3 Pollution level of heavy metals
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3.3.1. Enrichment factor (EF)
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A comparison of metal concentration in sediments with background reference values
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is generally used to assess metal enrichment (Tuna et al., 2007). Mean EF values of Ag, As,
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Co, Cr, Ni, Pb and Hg were greater in the wet season, and followed the order: Ag > Co >
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Hg> Ni > Cr >Pb> As (Fig. S1). Lower values of metal EF in dry season can be related to
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decrease in the anthropogenic activities. Total EF values followed the order of S9 > S10 >
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S3 > S8 > S6 > S7 > S4 > S5 > S2 > S1. The EF values of Ag ranged from 53.90 (S1) to
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841.22 (S9) which corresponded to extremely severe enrichment. Severe enrichment with Ag
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may be attributed to urban hospital and clinic waste, some upstream silver plating industries,
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runoff from agricultural land and rive bank erosion. Co and Ni are commonly used in
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household products such as stainless steel, alloys, batteries, and car bearings and thus, there is
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plenty chance of increased input of Co and Ni from urban areas (Barałkiewicz and Siepak,
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ACCEPTED MANUSCRIPT 1999). An increased amount of Ni may also be connected to silty sedimentary rock in the
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upstream. Some anthropogenic sources e.g., fossil fuel burning and coal burning in brick-
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fields, and natural sources may contribute to enrichment of Hg in the study area.
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3.3.2. Geo-accumulation index (Igeo)
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Igeo values have been used to explain sediment quality (Karbassi et al., 2008). The Igeo
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of sampling sites during the wet and dry seasons are displayed in Fig. S2. Among the metals
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Ag showed the highest accumulation in wet season. Igeo values of Ag ranged from 1.17 (site
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S9) to 4.30 (site S3) which corresponded to class 2 of moderately polluted and class 5 of
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strongly to extremely polluted sediment samples.
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The Index of geo-accumulation (Igeo) shows that Feni river estuary is not polluted with As,
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Cr, Mn and Pb (Igeo< 0), unpolluted to moderately polluted with Co and Hg (Igeo< 1), and
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moderately to highly polluted with Ag (Igeo> 2). The average highest Igeo value showed Ag
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(2.93) which corresponded to class 4 and indicated Feni river estuary was highly polluted by
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Ag.
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3.3.3. Contamination factor (CF) and potential ecological risk index (PERI)
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The highest CF values for all metals studied were found at site-S3, which receives a huge
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amount of agricultural discharge (Fig. 3). Total contamination factors followed the order of
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site S3 > S6 > S9 > S7 > S10 > S8 > S4 > S2 > S1. The average CF value for Ag was 15.6
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(Fig. 3) which revealed that Feni River estuary was very highly polluted with Ag.
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The results of the potential ecological risk index (PERI) for only four heavy metals (Ni, Pb,
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Cr and As) in the surface sediment of the Feni River estuary are depicted in Fig. S3. The
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biological toxicity factors (Tri) for the rest metals (Ag, Co, Mn and Hg) were not available to
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calculate PER value. From the PERI calculation, heavy metal pollution by a single element
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was obtained, and the degree of pollution from the four heavy metals decreased in the
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ACCEPTED MANUSCRIPT following sequence: Ni >Pb> Cr > As. In comparison with the other elements, the PERI for
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Ni was higher than other metals. However, the Eir values of Ni indicated pollution in the
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sediment samples of Feni River estuary. The increased amount of Ni might be originated
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from local household products e.g., stainless steel, batteries.
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3.4. Pollution source identification based on Pearson Correlation, CA and PCA
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Correlations among heavy metals may reflect the origin and migration of these elements
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(Suresh et al., 2011). If no correlation exists among the elements, then the metals are not
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controlled by a single factor (Kukrer et al., 2014). The concentration of Ag was not
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significantly correlated with any of the heavy metals except for a negative correlation with Pb
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(Table 3). However, positive correlations (p < 0.01) were found between several element
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pairs: As-Pb (0.84), Co-Cr (0.90), Co-Mn (0.98), Co-Ni (0.90), Cr-Mn (0.98), Cr-Ni (0.98)
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Cr-Hg (0.95) Mn-Ni (0.98), Mn-Hg (0.92) and Ni-Hg (0.93). Three element pairs exhibited a
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significantly positive correlation of p < 0.05.
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One group of elements comprised of As, Cr, Mn, Pb and Hg. The concentrations of these five
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metals were slightly lower or close to the average shale values. In addition, the results of EF,
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Igeo, CF and PERI assessment indicated that the contents of these five metals presented a low
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or zero potential ecological risk. These findings suggested that this group of elements might
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originate from natural sources, such as river bank erosion, weathering and atmospheric
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precipitation (Wu et al., 2014; Wang et al., 2015b).
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A second group, including Ag, Ni and Co, could be ascribed to an anthropogenic factor. The
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mean concentrations of these metals were higher than their average shale values. The element
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of Ag, Ni and Co had similar spatial distribution trends, with higher concentrations appearing
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in landward sites: S3, S4, S5, S6, S7 and S9 . Various human activities e.g., using fertilizer,
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metal-containing pesticides, fungicides, herbicides in agriculture and aquaculture, waste
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ACCEPTED MANUSCRIPT disposal, dredging in and around the Feni River estuary, may greatly contribute to the
330
concentration of Ag, Ni and Co. Domestic and commercial use of stainless steel, alloyes,
331
batteries may contribute to the increase of Co and Ni. Recent increasing use of silver-
332
nanoparticle antibacterial and antifungal agent in wound care products, medical devices,
333
cosmetics and textiles may also be associated with high concentration of Ag in the study area
334
(Landsdown, 2010). Metallic silver may be inert; however, biologically active Ag+ binds
335
strongly to metallothionein, albumins, and macroglobulins of human body which can cause
336
skin allergy, Argyria and argyrosis disease (Lansdown, 2015).
337
Cluster analysis (CA) was used to group the similar sampling sites (spatial variability) and to
338
identify specific areas of contamination (Simeonov et al., 2000). Spatial CA rendered a
339
dendrogram (Fig. S4) where all ten sampling sites on the river were grouped into two
340
statistically significant clusters/groups at (Dlink/Dmax) × 100 < 15. Group 1 consisted of
341
three sites (S8, S9 & S10) with lowest metal concentrations in sediment samples and group 2
342
consisted of seven sites (S5, S6, S7, S3, S4, S2 & S1) with highest metal concentrations
343
which were surrounded by semi urban and agricultural areas. Group 1 corresponded to
344
weakly contaminated sites and group 2 corresponded to highly contaminated sites. Group 2
345
sites were situated upstream of the catchment areas, and sediments at these sites were
346
polluted with anthropogenic activities such as waste-water discharges from urban areas,
347
dumping of solid waste, raw sewage, automobile washing and auto-workshops near these
348
sites.
349
PCA leads to a reduction of the initial dimension of the data set to two principle components
350
(PCs) which explain 91.89% of the data variation (Fig. 4). A total of two significant PCs
351
were extracted with eigenvalues > 1. PC1 was heavily loaded with As, Cr, Mn, Pb and Hg
352
which explained 68.95% of the total variance and exhibited an eigenvalue of 5.52. PC2
353
explained 22.94% of total variance and was strongly correlated with Ag (0.52) and Co (0.41).
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ACCEPTED MANUSCRIPT A positive correlation existed between Ag and Co; hence, Ag and Co may exist from a
355
common origin (Wang et al., 2015a). High concentrations of Ag and Co are possibly caused
356
by anthropogenic inputs from flooding of agricultural lands, medical waste, domestic waste,
357
upstream silver plating industries, and atmospheric deposition, which were widespread
358
throughout the catchment area of the estuary.
359
Conclusions
360
The results of the current study provide information on the characteristics of pollution from
361
heavy metals of the Feni River estuary for the first time. The results showed that the degree
362
of pollution from eight selected heavy metals followed the decreasing order of Mn > Cr > Ni
363
> Co > Pb > Ag > As > Hg. The concentrations of heavy metal were greater in the wet season
364
than in the dry season which can be associated with heavy rainfall during monsoon and the
365
subsequent flooding of agricultural lands causing dissolution of heavy metals. The mean
366
concentrations of Ag and Co were higher than the average world values. Calculations of EF,
367
Igeo and CF demonstrated that Ag, Co and Hg were at moderate to high pollution levels and
368
As, Cr, Ni, Pb and Mn were at no to low pollution levels. Potential ecological risk index
369
(PERI) also revealed that Ag, Co and Hg were the most potential ecological risk factors.
370
Major possible sources of Ag can be medical waste and upstream silver plating industries.
371
Household uses of stainless steel, batteries, alloys, and burning fossil fuel can be connected to
372
enhanced accumulation of Co and Hg. Therefore, Ag, Co and Hg were the most important
373
factors affecting the ecological environment of the Feni River estuary. The results of the PCA
374
analysis suggest that Ag, Co and Ni mainly originated from anthropogenic sources (due to
375
excessive use of fertilizers, agrochemicals, metal-containing pesticides, silver-nanoparticle
376
antimicrobial agent, upstream silver plating industries) and As, Cr, Mn, Pb and Hg mainly
377
originated from natural sources (geological rock). The generated information can be a basis
378
for effectively targeting policies to protect the sediment of Feni river from long-term
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ACCEPTED MANUSCRIPT accumulation of heavy metals and for ensuring land and water based food quality, and
380
protecting human health. Important strategies should be implemented to reduce or ban the use
381
of agrochemicals, pesticides, inclusion of wastewater treatment and safe disposal of silver
382
containing medical devices.
383
Conflict of interest
384
The authors declare that there is no conflict of interest.
385
Acknowledgements
386
The authors are deeply thankful to the laboratory in Forensic Analysis Department, Rab HQ,
387
Bangladesh for providing all necessary research facilities. They are also delighted to express
388
their gratitude and sincere thanks to editor and four anonymous reviewers for their useful
389
comments and suggestions to improve the quality of article.
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References
392
Akcay, H., Oguz, A., Karapire, C., 2003. Study of heavy metal pollution and speciation in
393
Buyak Menderes and Gediz river sediments. Water. Res. 37(4), 813-822.
394
Ali, M. M., Ali, M. L., Islam, M. S., Rahman, M. Z., 2016. Preliminary assessment of heavy
395
metals in water and sediment of Karnaphuli River, Bangladesh. Environ. Nanotechnol.
396
Monit. Manage. 5, 27-35.
397
Amin, B., Ismail, A., Arshad, A., Yap, C. K., Kamarudin, M. S., 2009. Anthropogenic
398
impacts
399
Indonesia. Environ. Monit. Assess. 148(1), 291-305.
AC C
EP
391
on
heavy
metal
concentrations
in
the
coastal
sediments
of
Dumai,
ACCEPTED MANUSCRIPT Armstrong-Altrin, J. S., Machain-Castillo, M. L., Rosales-Hoz, L., Carranza-Edwards, A.,
401
Sanchez-Cabeza, J. A., & Ruíz-Fernández, A. C., 2015. Provenance and depositional history
402
of continental slope sediments in the Southwestern Gulf of Mexico unraveled by geochemical
403
analysis. Cont. Shelf Res. 95, 15-26.
404
Barałkiewicz, D., & Siepak, J., 1999. Chromium, nickel and cobalt in environmental samples
405
and existing legal norms. Polish J. Env. Stud. 8(4), 201-208.
406
Baran, S., Oleszczuk, P., Lesiuk, A., & Baranowska, E., 2002. Trace metals and polycyclic
407
aromatic hydrocarbons in surface sediment samples from the Narew River (Poland). Polish J.
408
Env. Stud. 11(4), 299-306.
409
Bermejo, J. S., Beltrán, R., Ariza, J. G., 2003. Spatial variations of heavy metals
410
contamination in sediments from Odielriver (Southwest Spain). Environ. Int. 29(1), 69-77.
411
Bhuiyan, M. A., Parvez, L., Islam, M. A., Dampare, S. B., Suzuki, S., 2010. Heavy metal
412
pollution of coal mine-affected agricultural soils in the northern part of Bangladesh. J. hazard.
413
Mater. 173(1), 384-392.
414
Birch, G. F., Olmos, M. A., 2008. Sediment-bound heavy metals as indicators of human
415
influence and biological risk in coastal water bodies. ICES. J. Mar. Sci. 65(8), 1407-1413.
416
Burton Jr, A. G., Baudo, R., Beltrami, M., Rowland, C., 2001. Assessing sediment
417
contamination using six toxicity assays. J. Limnol. 60(2), 263-267.
418
Caeiro, S., Costa, M.N., Ramos, T.B., Fernandes, F., Silveira, N., 2005. Assessing heavy
419
metal contamination in Sado Estuary sediment: an index analysis approach. Ecol. Ind. 5,
420
151–169.
AC C
EP
TE D
M AN U
SC
RI PT
400
ACCEPTED MANUSCRIPT Chai, M., Shi, F., Li, R., Shen, X., 2014. Heavy metal contamination and ecological risk in
422
Spartinaalterniflora marsh in intertidal sediments of Bohai Bay, China. Mar. Pollut. Bull.
423
84(1), 115-124.
424
Demirak, A., Yilmaz, F., Tuna, A. L., Ozdemir, N., 2006.Heavy metals in water, sediment
425
and tissues of Leuciscuscephalus from a stream in southwestern Turkey. Chemosphere. 63(9),
426
1451-1458.
427
Duman, F., Aksoy, A., Demirezen, D., 2007.Seasonal variability of heavy metals in surface
428
sediment of Lake Sapanca, Turkey. Environ. Monit. Assess. 133(1-3), 277-283.
429
Fernandes, C., Fontaínhas-Fernandes, A., Cabral, D., Salgado, M. A., 2008. Heavy metals in
430
water, sediment and tissues of Liza saliens from Esmoriz–Paramos lagoon, Portugal. Environ.
431
Monit. Assess. 136(1), 267-275.
432
Hakanson, L., 1980. An ecological risk index for aquatic pollution control. A
433
sedimentological approach. Water. Res. 14(8), 975-1001.
434
Hammer, Ø., Harper, D. A., Ryan, P. D., 2001. PAST: paleontological statistics software
435
package for education and data analysis. Palaeo. Elect. 4(1), 9.
436
Hyun, S., Lee, C., Lee, T., Choi, J., 2007. Anthropogenic contributions to heavy metal
437
distributions in the surface sediments of Masan Bay, Korea. Mar. Pollut. Bull. 54, 1031–
438
1071.
439
Jain, C. K., Malik, D. S., Yadav, R., 2007. Metal fractionation study on bed sediments of
440
Lake Nainital, Uttaranchal, India. Environ.Monit. Assess. 130(1-3), 129-139.
441
Karbassi, A. R., Monavari, S. M., Bidhendi, G. R. N., Nouri, J., & Nematpour, K., 2008.
442
Metal pollution assessment of sediment and water in the Shur River. Env. Mon. Asses. 147(1-
443
3), 107.
AC C
EP
TE D
M AN U
SC
RI PT
421
ACCEPTED MANUSCRIPT Kucuksezgin, F., Uluturhan, E., Batki, H., 2008. Distribution of heavy metals in water,
445
particulate matter and sediments of Gediz River (Eastern Aegean). Environ. Monit.
446
Assess. 141(1), 213-225.
447
Kükrer, S., Şeker, S., Abacı, Z. T., Kutlu, B., 2014. Ecological risk assessment of heavy
448
metals in surface sediments of northern littoral zone of Lake Çıldır, Ardahan,
449
Turkey. Environ. Monit. Assess. 186(6), 3847-3857.
450
Lansdown, A. B., 2010. A pharmacological and toxicological profile of silver as an
451
antimicrobial agent in medical devices. Adv. Pharmacol. Sci. 16.
452
Larrose, A., Coynel, A., Schäfer, J., Blanc, G., Massé, L., Maneux, E., 2010. Assessing the
453
current state of the Gironde Estuary by mapping priority contaminant distribution and risk
454
potential in surface sediment. Appl. Geochem. 25, 1912–1923.
455
Lasorsa, B.M., Casas, A., 1996. A comparison of sample handling and analytical methods for
456
determination of acid volatile sulfide in sediment. Mar. Chem. 52, 211–220.
457
Liu, J., Yin, P., Chen, B., Gao, F., Song, H., Li, M., 2016. Distribution and contamination
458
assessment of heavy metals in surface sediments of the Luanhe River Estuary, northwest of
459
the Bohai Sea. Mar. Pollut. Bull. 109(1), 633-639.
460
Loizidou, M., Haralambous, K. J., Sakellarides, P. O., 1992. Environmental study of the
461
marinas Part II.A study on the removal of metals from the marinas sediments. Environ.
462
Technol. 13(3), 245-252.
463
Long, E.R., Morgan, L.G., 1990. The potential for biological effects of sediment-sorbed
464
contaminants tested in the national status and trends program. Technical Memorandum.
465
NOAA, Maryland, USA.
AC C
EP
TE D
M AN U
SC
RI PT
444
ACCEPTED MANUSCRIPT Loska, K., Cebula, J., Pelczar, J., Wiechuła, D., Kwapuliński, J., 1997. Use of enrichment,
467
and contamination factors together with geoaccumulation indexes to evaluate the content of
468
Cd, Cu, and Ni in the Rybnik water reservoir in Poland. Water, Air, and Soil Poll. 93(1-4),
469
347-365.
470
MacDonald, D. D., Ingersoll, C., Berger, T. A., 2000. Development and evaluation of
471
consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ.
472
Contam. Toxicol. 39(1), 20–31.
473
Miah, M. Y., Robel, F. N., Bhowmik, S., Bhattacharjee, S., Paul, S. C., Hossain, M. J.,
474
Hossain, M. Z., 2015. Assessment of the Coastal Area Water Quality in Noakhali,
475
Bangladesh. Int. J. Sci. Eng. Res. 6(2).
476
Muller, G., 1979. Schwermetalle in den sedimenten des Rheins-Veranderungen seit 1971.
477
Umschau, 79, 778-783.
478
Ramos-Vázquez, M. A., Armstrong-Altrin, J. S., Rosales-Hoz, L., Machain-Castillo, M. L.,
479
& Carranza-Edwards, A., 2017. Geochemistry of deep-sea sediments in two cores retrieved at
480
the mouth of the Coatzacoalcos River delta, western Gulf of Mexico, Mexico. Arab. J.
481
Geosci, 10(6), 148.
482
Salati, S., & Moore, F., 2010. Assessment of heavy metal concentration in the Khoshk River
483
water and sediment, Shiraz, Southwest Iran. Env. Mon. Assess. 164(1), 677-689.
484
Sayadi, M. H., Sayyed, M. R., Kumar, S., 2010. Short-term accumulative signatures of heavy
485
metals in river bed sediments in the industrial area, Tehran, Iran. Environ. Monit. Assess.
486
162(1), 465-473.
AC C
EP
TE D
M AN U
SC
RI PT
466
ACCEPTED MANUSCRIPT Shomar, B. H., Müller, G., Yahya, A., 2005. Seasonal variations of chemical composition of
488
water and bottom sediments in the wetland of Wadi Gaza, Gaza Strip. Wetl. Ecol. Manage.
489
13(4), 419-431.
490
Shuhaimi-Othman, M., 2008. Metals concentrat ions in the sed-iments of Richard Lake,
491
Sudbury, Canada and sediment toxicity in an amphipod Hyalellaazteca. J. Environ. Sci.
492
Technol. 1(1), 34-41.
493
Simeonov, V., Massart, D. L., Andreev, G., Tsakovski, S. 2000. Assessment of metal
494
pollution based on multivariate statistical modeling of ‘hot spot’ sediments from the Black
495
Sea. Chemosphere. 41(9), 1411-1417.
496
Singh, K. P., Malik, A., Sinha, S., Singh, V. K., Murthy, R. C., 2005. Estimation of source of
497
heavy metal contamination in sediments of Gomti River (India) using principal component
498
analysis. Water. Air. Soil. Pollut. 166(1), 321-341.
499
Singh, V. K., Singh, K. P., Mohan, D., 2005. Status of heavy metals in water and bed
500
sediments of river Gomti–A tributary of the Ganga river, India. Environ. Monit. Assess.
501
105(1), 43-67.
502
Subramanian, V., Itta, P.K., Griekan, R.V., 1988. Heavy metals in the Ganges Estuary. Mar.
503
Pollut. Bull. 19 (6), 290-293.
504
Sun, Z., Mou, X., Tong, C.,Wang, C., Xie, Z., Song, H., Sun,W., Lv, Y., 2015. Spatial
505
variations and bioaccumulation of heavy metals in intertidal zone of the Yellow River
506
estuary, China. Catena 126, 43–52.
507
Suresh, G., Ramasamy, V., Meenakshisundaram, V., Venkatachalapathy, R., Ponnusamy, V.,
508
2011. Influence of mineralogical and heavy metal composition on natural radionuclide
509
concentrations in the river sediments. Appl. Radiat. Isot. 69(10), 1466-1474.
AC C
EP
TE D
M AN U
SC
RI PT
487
ACCEPTED MANUSCRIPT Tam, N. F. Y., Wong, Y. S., 2000. Spatial variation of heavy metals in surface sediments of
511
Hong Kong mangrove swamps. Environ. Pollut. 110(2), 195-205.
512
Tuna, A. L., Yilmaz, F., Demirak, A., Ozdemir, N., 2007. Sources and distribution of trace
513
metals in the saricay stream basin of southwestern turkey. Environ. Monit. Assess. 125(1),
514
47-57.
515
Wang, H., Wang, J., Liu, R., Yu, W., Shen, Z., 2015a. Spatial variation, environmental risk
516
and biological hazard assessment of heavy metals in surface sediments of the Yangtze River
517
estuary. Mar. pollut. Bull. 93(1), 250-258.
518
Wang, Y., Yang, L., Kong, L., Liu, E., Wang, L., Zhu, J., 2015b. Spatial distribution,
519
ecological risk assessment and source identification for heavy metals in surface sediments
520
from Dongping Lake, Shandong, East China. Catena. 125, 200-205.
521
Wenning, R., Batley, G., Ingersoll, C., Moore, D. (Eds.), 2005. Use of Sediment Quality
522
Guidelines and Related Tools for the Assessment of Contaminated Sediments. SETAC Press,
523
USA.
524
Wu, J. Y., Zhou, J. L., Liu, Z. T., Wang, X. N., Gao, A. F., 2014. Spatial distribution and risk
525
assessment of heavy metals in the surface sediments of the Liao River. Environ. Sci. Technol.
526
37(6N), 268-273.
527
Yoon, J., Cao, X., Zhou, Q., & Ma, L. Q., 2006. Accumulation of Pb, Cu, and Zn in native
528
plants growing on a contaminated Florida site. Sci. Total Environ. 368(2), 456-464.
529
Zhang, G., Bai, J., Xiao, R., Zhao, Q., Jia, J., Cui, B., & Liu, X., 2017. Heavy metal fractions
530
and ecological risk assessment in sediments from urban, rural and reclamation-affected rivers
531
of the Pearl River Estuary, China. Chemosphere. 184, 278-288.
AC C
EP
TE D
M AN U
SC
RI PT
510
ACCEPTED MANUSCRIPT Zhao, G.M., Lu, Q.Y., Ye, S.Y., Yuan, Y.M., Ding, X.G., Wang, J., 2016. Assessment of
533
heavy metal contamination in surface sediments of the west Guangdong coastal region,
534
China. Mar. Pollut. Bull. 108, 268–274.
535
Zheng, N. A., Wang, Q., Liang, Z., Zheng, D., 2008. Characterization of heavy metal
536
concentrations in the sediments of three freshwater rivers in Huludao City, Northeast
537
China. Environ. Pollut. 154(1), 135-142.
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ACCEPTED MANUSCRIPT FIGURE LEGENDS: Fig. 1. Sampling sites in the Feni River estuary. Fig. 2. Seasonal (a) and spatial (b) variations of metal concentrations in the sediments during wet and dry season. Error bars indicate standard error of Mean.
horizontal axis indicates the level of contamination degree).
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Fig. 3. Contamination factor (CF) selected metals in sediment of Feni River. (Dot line of the
Fig 4. PCA plot showing the loading of two components influencing variation of heavy
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metals in the sediments from Feni River estuary.
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Fig. 1. Sampling sites in the Feni River estuary.
45 40 35 30 25 20 15 10 5 0
a
Wet season Dry season
Ag
As
Co
Cr
Mn
Ni
Pb
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Metals concentrations (µg/g)
ACCEPTED MANUSCRIPT
Hg
50
Ag
As
S2
S3
Co
Cr
Mn
SC
b
Ni
Pb
Hg
M AN U
Metals concentrations (µg/g)
Metals
40 30 20 10 0 S4
S5
S6
TE D
S1
S7
S8
S9 S10
Sampling sites
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Fig. 2. Seasonal (a) and spatial (b) variations of metal concentrations in the sediments during
AC C
wet and dry season. Error bars indicate standard error of Mean.
18 16 14 12 10
Wet season
Dry season
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Very high pollution
8 6 4 2 0
Considerable pollution
Moderate pollution Low pollution
As
Co
Cr Mn Metals
Ni
Pb
Hg
M AN U
Ag
SC
CF value
ACCEPTED MANUSCRIPT
Fig. 3. Contamination factor (CF) selected metals in sediment of Feni River. (Dot line of the
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horizontal axis indicates the level of contamination degree).
ACCEPTED MANUSCRIPT
16
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Pb
8 4
-4 -8
Co
-12 -16 -20 -48
-32 -16 0 16 32 PC1 (68.95 % variation)
TE D
-64
SC
Ni AsCr HgMn Ag
0
M AN U
PC2 (22.94 % variation)
12
48
64
Fig 4. PCA plot showing the loading of two components influencing variation of heavy
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metals in the sediments from Feni river estuary.
ACCEPTED MANUSCRIPT Table 1 Metal concentrations (µg/g) in sediments of the Feni river estuary during wet and dry season (n=30 for each season). As
Co
Cr
Mn
Ni
Pb
Hg
1.17
0.93
32.30
36.82
39.55
35.02
7.09
0.79
Range
0.09-
0.13-
14.48-
17.77-
23.46-
13.54-
0.67-
0.87-
(wet
2.07
2.79
45.84
46.09
48.73
45.71
17.03
1.57
1.01
0.78
29.75
33.74
36.15
Range
0.26-
0.13-
11.11-
(dry
1.86
2.27
42.84
1.09
0.85
0.07
13.00
Mean (wet
Mean
8.56-
0.36-
0.09-
41.74
46.01
41.86
14.04
1.04
31.02
35.28
37.85
33.27
6.47
0.71
19.00
90.00
850.00
68.00
20.00
0.40
9.79
N.A.
43.40
NA
22.7.
35.80
NA
33.00
N.A.
111.00
NA
48.60
128.00
NA
(Feni river
PECb a
NA
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TECb
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estuary)
TE D
season)
(Shalea)
NA
0.63
17.92-
season)
Aver.
5.86
13.91-
(dry
Aver.
31.52
M AN U
season)
SC
season)
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Ag
Turekian and Wedepohl(1961). b MacDonald et al. (2000).
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Table 2 Heavy metal concentrations (µg/g) in sediment samples from the Feni river estuary and other selected estuary from the references.
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Mean concentrations (µg/g) of heavy metal Estuary
Cr
As
Mn
Pb
Ni
Ag
Feni river
35.28
0.85
37.85
6.47
33.27
1.09 0.71
Karnaphuli
11.56-
37.23-
NA
21.98-
NA
river estuary
35.48
160.32
Ganges
21-
NA
estuary
100
(Bangladesh)
(Bangladesh) 254800
(India) 9.6-
3.4-
estuary
35.6
13.5
79.1
9.1
(China) The Yangtze
(China)
44.09
NA
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Yellow River
NA
EP
River estuary
NA
12-
NA
8-57
References
31.02
This study
NA
NA
NA
NA
NA
115
22.6-
3.5-
43.7
35.8
23.8
31.9
TE D
Luanhe River
NA
M AN U
73.42
Co
SC
estuary
Hg
43.05
NA
NA
Ali et al. (2016)
Subramanian et al. (1988)
NA
Liu et al. (2016)
NA
NA
NA
Wang et al. (2015a)
Na
NA
NA
NA
Sun et al. (2015)
Estuary (China)
Gironde estuary
(France)
78.4
18.4
NA
46.8
NA
NA
NA
NA
Larrose et al. (2010)
ACCEPTED MANUSCRIPT Table 3 Pearson correlation analysis of heavy metals in the Feni river estuary. Ag
As
Co
Cr
Mn
Ni
Pb
Hg
Ag -0.63
Co
-0.05
0.02
Cr
-0.35
0.39
0.90**
Mn
-0.22
0.28
0.93**
0.98**
Ni
-0.28
0.40
0.90**
0.98**
0.98**
Pb
-0.64*
0.84**
0.29
0.67*
0.59
Hg
-0.31
0.53
0.78*
0.95**
0.92**
SC
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As
M AN U
0.64
0.93**
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*Significant at p < 0.05 levels. ** Significant at p < 0.01 levels.
0.76*
ACCEPTED MANUSCRIPT
HIGHLIGHTS • EF, Igeo, CF and PERI were used to profile pollution levels of heavy metals in sediments of the
• Ag, Co, Hg and Ni enrichment was more in sediments.
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Feni River estuary.
• Ag and Co were above average shale values; however, Ni exceeded the TEC values.
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SC
• Ag, Co and Ni threats to aquatic ecosystem should not be ignored.
S0045-6535(18)30491-0
DOI:
10.1016/j.chemosphere.2018.03.077
Reference:
CHEM 21021
To appear in:
ECSN
Received Date: 2 July 2017 Revised Date:
17 February 2018
Accepted Date: 11 March 2018
Please cite this article as: Islam, M.S., Hossain, M.B., Matin, A., Islam Sarker, M.S., Assessment of heavy metal pollution, distribution and source apportionment in the sediment from Feni River estuary, Bangladesh, Chemosphere (2018), doi: 10.1016/j.chemosphere.2018.03.077. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT 1
Assessment of heavy metal pollution, distribution and source
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apportionment in the sediment from Feni River estuary, Bangladesh Md. Saiful Islama, M Belal Hossaina,*, Abdul Matina, Md. Shafiqul Islam Sarkerb
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a: Department of Fisheries and Marine Science, Noakhali Science and Technology
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University, Noakhali-3814, Bangladesh.
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b: Forensic Science Laboratory, Rapid Action Battalions Headquarters, Dhaka-1229,
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Bangladesh.
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*Corresponding author’s email: [email protected]
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Note: First two authors contributed equally.
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Assessment of heavy metal pollution, distribution and source
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apportionment in the sediment from Feni River estuary, Bangladesh ABSTRACT
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Heavy metal pollution in sediment resources may pose serious threat to ecosystem and
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human health through food web. In this study, surface sediment samples of 10 stations along
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the Feni River estuary were analyzed to profile the accumulation, sources and pollution levels
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of heavy metals. The results revealed that the average contents (µg g-1) of eight selected
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heavy metals followed the order of Mn (37.85) > Cr (35.28) > Ni (33.27) > Co (31.02) > Pb
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(6.47) > Ag (1.09) > As (0.85) > Hg (0.71), and the concentrations varied spatially and
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seasonally with relatively higher levels at upward stations and during the rainy season.
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According to sediment quality guidelines (SQGs), the sediment samples were heavily
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contaminated with Ag and Hg, and moderately with Co. Threshold effect concentration
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(TEC) and probable effect concentration (PEC) values indicated that the concentration of
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only Ni and Cr were likely to occasionally exhibit adverse effects on the ecosystem.
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Enrichment factor (EF), geo-accumulation index (Igeo) and contamination factor (CF)
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analyses revealed that Ag, Co and Hg were at moderate to high pollution levels and the rests
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(As, Cr, Ni, Pb and Mn) were at no to low pollution levels. Potential ecological risk index
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(PERI) also showed that Ag, Co and Hg were the most potential ecological risk factor being
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determined in this studied area. Correlation matrix combined with multivariate principal
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component analysis and cluster analysis suggest that Ag, Co, Ni and Hg originated from
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anthropogenic sources (agrochemicals, silver nanoparticles anti-microbial agent, silver
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plating), whereas As, Cr, Pb and Mn primarily originated from natural geological
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background.
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Keywords: Heavy metals, Estuarine sediments, Multivariate analysis, Potential ecological
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risk assessments, Feni River estuary.
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1. Introduction Heavy metal pollution is considered to be a serious threat to any aquatic ecosystems
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because of its toxicity, persistent nature, omnipresent, and ability to non-biodegradability and
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be bio-accumulated into the food chain (Duman et al., 2007). Heavy metals enter the aquatic
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ecosystems through point sources such as industrial, municipal and domestic waste water
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effluents as well as diffuse sources which include surface runoff, erosion, and atmospheric
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deposition (Akcay et al., 2003; Demirak et al., 2006; Armstrong-Altrin et al., 2015; Ramos-
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Vázquez et al., 2017). These metals that are introduced into the aquatic environment are
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ultimately incorporated into aquatic sediments (Zhang et al., 2017). Thus, sediments are one
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of the possible media in monitoring the health of aquatic ecosystems (Baran et al., 2002).
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Sediments serve as the largest pool of metals in any aquatic environments (Zhang et
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al., 2017), and more than 90% of the heavy metal load in the aquatic ecosystems has been
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found to be related to suspended particulate matter and sediments (Zheng et al., 2008; Amin
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et al., 2009). Metals in suspended particulates settle down and lay up in sediments
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(Kucuksezgin et al., 2008), while the dissolved metals adsorb onto fine particles which may
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carry them to bottom sediments (Singh et al., 2005) and persist in the sediment for a long
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time. Their distribution are influenced by the mineralogical and chemical composition of
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suspended material, anthropogenic influences, deposition, sorption, enrichment in organism
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(Jain et al., 2007), and various physico-chemical characteristics (Singh et al., 2005).
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Sediments are ecologically important components of the aquatic habitat and play a
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significant role in maintaining the trophic status of any water body (Zhang et al., 2017; Singh
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et al., 2005) and provides a site for biogeochemical cycling and the foundation of the food
ACCEPTED MANUSCRIPT web (Burton et al., 2001). Therefore, sediments provide an essential link between chemical
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and biological processes and are an integral component for functioning of ecological
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integrity. In addition, sediments act as a sink of organic as well as inorganic pollutants and
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provide a history of anthropogenic pollutant input (Bermejo et al., 2003; Shuhaimi, 2008) and
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ecological changes (Shomar et al., 2005).
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The contamination of sediments with heavy metals leads to serious environmental
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(Loizidouet al., 1992) and worldwide problems (Yoon et al., 2006; Fernandes et al., 2008;
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Kucuksezgin et al., 2008). Therefore, sediment has widely been studied for anthropogenic
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impacts on the aquatic environments (Sayadi et al., 2010). Various studies have reported the
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distribution and contamination of heavy metals and quantification of pollution load in
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sediments of different rivers and estuaries such as the Yangtze River estuary, China (Wang et
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al. 2015a), the River Gomti, India (Singh et al., 2005), the Luanhe River estuary, China (Liu
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et al. 2016)), and the Karnaphuli River estuary, Bangladesh (Ali et al., 2016). However,
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estuarine sediment contamination by heavy metals has meagerly been investigated in
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Bangladesh in general and no information is available for the Feni River estuary and its
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associated tributaries. Nationally, the Feni River estuary plays a very significant role in
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providing a huge amount of fish supplies to the local and national markets as well as
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livelihood to the local inhabitants. The estuary has been subjected to heavy metal pollution
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due to a rapid increase in population and unplanned human settlements in its catchment area,
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agricultural activities, fish farming, fishing, industrial and medical waste discharge,
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recreational activities, washing activities, poultry waste discharge, dumping of solid waste
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and direct and/or indirect discharge of untreated domestic effluents. Therefore, the present
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study aimed to (1) determine the spatial and seasonal distributional trends of heavy metals in
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the sediments of Feni River estuary, (2) assess ecosystem risk of heavy metals following
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sediment quality guidelines viz., threshold effect level/probable effect level (TEL/PEL), (3)
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ACCEPTED MANUSCRIPT quantify the extent of metal pollution using enrichment factor (EF), geo-accumulation indices
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(Igeo) and potential ecological risk index (PERI), and (4) identify the natural and/or
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anthropogenic sources of these metals using correlation matrix and multivariate statistical
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techniques.
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2. Materials and Methods
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2.1. Study area
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Located in the central coast of Bangladesh, Feni River estuary (also known as Little
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Feni River estuary; Latitude 22° 46'44" N and Longitude 91°22'42" E) originates in the
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South Tripura district (India) and flows a distance of 116 km through different cities and
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municipalities in India and Bangladesh, and finally empties into the Bay of Bengal. The
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estuary is heavily used for irrigation, fishing, agriculture, aquaculture, washing, livestock
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farming, recreation, dumping domestic waste, sewage disposal and water based transport.
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Rainfall in this area is mostly seasonal, with a rainy season between June and November and
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a dry period between December and May. An average annual rainfall is 3,302 mm and annual
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temperature varies from a maximum of 34.3 °C to a minimum of 14.4 °C (Miah et al., 2015).
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The salinity of the estuary ranged from 4.20 -7.50 ppt with a mean value of 5.78 ±1.32 ppt.
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Due to the heavy rainfall and annual flooding, the region is composed of fertile alluvial plains
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resulting from the underlying forces of the Feni river. Therefore, most of the catchment areas
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are suitable for temporary or permanent crops such as rice, wheat, beans, vegetables, red
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pepper, potato, tomato, bananas and sugarcane. However, to increase the production of these
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crops, indiscriminate and long-term repeated application of fertilizers and metal-containing
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pesticides, herbicides and fungicides have been gradually accumulated to potentially harmful
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levels in the soils.
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2.2. Sediment sampling
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ACCEPTED MANUSCRIPT In total, sixty samples of surface sediments (0–5 cm) from 10 stations (S1 to S10) in
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the Feni River estuary (Fig. 1) were collected using an Ekman dredge during the wet (June
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2016) and dry (January 2017) seasons. Samples that showed no evidence of surface
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disturbance were retained. Triplicate samples were collected at each station in each season.
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All samples were stored in a cooler at 4 °C, and then were frozen at −20 °C after being
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transferred to the laboratory immediately for further analysis (Lasorsa and Casas, 1996).
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Before analysis, the sediments were defrosted at room temperature, dried at 50 °C to constant
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weight (~24 h) (Hyun et al., 2007), and ground in an agate mortar (Zhao et al., 2016). The
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fraction smaller than 63 µm was used for analyses in this study due to strong association of
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metals with fine-grained sediments (Tam and Wong, 2000). Global Positioning System
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(GPS) was used to locate the sites.
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2.3. Determination of metals
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For the measurement of total metal concentrations, acid digests of each sediment
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sample were prepared using USEPA method 3051. Each sediment sample measuring 0.5 g
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was digested in 10 ml of ultrapure HNO3 using Micro Wave Digestion System (WX-6000,
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Preekem, Shangai), and then filtered and diluted. Total metal concentrations of Ag, Co, Ni,
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Hg, Cr, Pb, As and Mn were determined in triplicate using Inductively Coupled Plasma-Mass
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Spectrometry (ICP-MS) (SPECTRO MS, SPECTRO GmbH, Germany) at Forensic Science
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Laboratory, Rapid Action Battalions Headquarters, Dhaka, Bangladesh. All the reagents used
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were of super quality and of analytical grade. All solutions were prepared using ultrapure
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water (Ultra Clear TM TWF UV UF Type IP23, EVOQUA Water Technologies, Germany).
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All acid proof plastic and Teflon apparatus were soaked in HNO3 (10%) for at least 24 h and
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rinsed repeatedly with ultrapure water. Analytical blanks and standard reference material
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were run in the same way as the samples, and heavy metal concentrations were determined
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using standard solutions prepared in the same acid matrix. Certified reference material (CRM
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ACCEPTED MANUSCRIPT 320) supplied by Merck KGaA (Germany) was used (N=3) to ensure the validation of data
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and the accuracy and precision of analytical method. Analytical results of the selected metals
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indicated a good agreement between the reference and analytical values of the reference
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materials. Percentages of recoveries were between 95% and 105% for the all metals. The
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results also showed that there was no contamination during analysis, and the relative standard
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deviation (RSD) of all replicate samples was <= 10%. The limit of detection (LOD) of the
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metals varied (As = Ag = Ni = Pb = Mn = 0.001, Cu = Co = 0.0007, Hg = 0.0005, Cr = 0.003
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and Zn = 0.002 ppm).
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2.4. Assessment of sediment pollution
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2.4.1. Enrichment factor (EF)
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Normalized enrichment factor is applied (Salati and Moore, 2010) to differentiate
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metal source originating from anthropogenic and natural means (Sayadi et al., 2010). This
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involves normalization of the sediment with respect to reference elements such as Al, and Fe
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(Amin et al., 2009; Karbassi et al., 2008), Mn, Ti and Sc (Salati and Moore, 2010), and Li
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and Cs (Pereira et al., 2007). Normalized EF of metals in Feni River estuary sediments from
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each site was calculated using Eq. (1). Manganese (Mn) was used as a reference element to
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calculate anthropogenic metal enrichments as described by Loska et al. (1997). World
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average concentration of metals reported for the shale by Turekian and Wedepohl (1961) was
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used as background values for heavy metals.
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EF =
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where (Cn/CMn) is the ratio of concentration of the element of concern (Cn) to that of Mn
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(CMn) in the sediment sample (µg g-1 dry weight) and (Cn/CMn) is the same ratio in an
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unpolluted reference sample.
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( ⁄ ) ( ⁄ )
…….
(1)
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states deficiency to moderate enrichment, EF = 5–10 moderately severe enrichment, EF =
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10–25 severe enrichment, EF= 25-50 very severe enrichment and EF >50 extremely high
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enrichment (Birch and Olmos, 2008).
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2.4.2. Geo-accumulation index (Igeo)
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Geo-accumulation index (Igeo) was developed by Müller (1979) and had widely been
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used in trace metal studies of sediments and soils (Amin et al., 2009). To quantify the degree
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of heavy metal pollution in Feni River sediments, Igeo was calculated according to Muller
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(1979) and is given in Eq. (2).
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I = log (C ⁄1.5B ) …….
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where Cn is the concentration of the examined metal in the sediment, Bn is the geochemical
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background value of a given metal in the shale (Turekian and Wedepohl, 1961) and the factor
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1.5 is used to account the possible variations in the background values.
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The geo-accumulation index consists of seven grades or classes: Class 0 (practically
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uncontaminated): Igeo< 0; Class 1 (uncontaminated to moderately contaminated): 0
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Class 2 (moderately contaminated): 1
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2
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contaminated): 4
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concentrations in Class 6 may be hundredfold greater than the geochemical background value
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(Bhuiyan et al., 2010).
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2.4.3. Contamination Factor (CF)
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The level of contamination of sediment by metal is expressed in terms of a contamination factor (CF) calculated as:
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CF =
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where Cn Sample is the concentration of a given metal in river sediment, and Bn is the
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geochemical background value of a given metal in the shale (Turekian and Wedepohl, 1961).
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CF values were interpreted as follows: low contamination at CF < 1; moderate contamination
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at 1 ≤ CF < 3; considerable contamination at 3 ≤ CF < 6; and very high contamination at CF
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>6, according to Hakanson, 1980.
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2.4.4. Potential ecological risk index (PERI)
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The PERI was used to assess the comprehensive potential ecological risk of heavy metals in
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sediment and was initially introduced by Hakanson (1980). The potential ecological risk
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factor of a given metal (E ) is defined as
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E = T × $
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Equation (3) was used to calculate the risk index (RI) of sampling sites as follows:
%
………….
(4)
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RI = '
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where Ci is the concentration of metal i in sediment, C0 is the concentration of the same
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element in background sediment, T is the biological toxicity factor of an individual element,
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which was determined for Cu = Pb = Ni= 5, Zn = 1, As = 10, Cr = 2 and Cd = 30 (Suresh et
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al., 2011), E is the potential ecological risk factor of a single metal, and RI is the
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comprehensive potential ecological risk index of the metals. The PERI of heavy metals was
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categorized according to five levels, as shown in Table S1.
%
(5)
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………
EP
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T × $
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the relationship and interdependency among the variables and their relative weights
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(Bartolomeo et al., 2004). Correlation and principal components analyses, the most common
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multivariate statistical methods, were used to check for significant relationships among heavy
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metals in the sediment samples. The various statistical methods were performed with a 95%
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confidence interval (significance p < 0.05). Factor analysis based on principal component
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analysis (PCA) was used to ascertain sources of contamination (natural and anthropogenic).
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Cluster analysis was used to identify spatial variability among the sites. Euclidean distance
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was used as dissimilarity matrix, whereas Ward's method was used as a linkage method. All
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the statistical analyses were done using free statistical software, PAST (Hammer et al., 2001).
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3. Results and discussion
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3.1. Seasonal and spatial variation in the surface sediment of the study area
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Concentrations of heavy metals in the study area are summarized in Table 1. The
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mean concentrations (µg g-1) of the selected metals followed similar decreasing trend during
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both seasons: Mn (37.85) > Cr (35.28) > Ni (33.27) > Co (31.02) > Pb (6.47) > Ag (1.09) >
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As (0.85) > Hg (0.71). Co, Cr, Ni and Pb were significantly different between seasons (p ≤
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0.01) and greater at sampling sites viz., S3, S4, S5, S6 and S7 located in close vicinity of
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agricultural land, urban, semi-urban and tourist areas with anthropogenic activities (Table 1).
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In wet season concentrations of all metals were higher than dry season in sediment sample
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(Fig. 2). However, lower concentrations of metals in dry season could be attributed to the
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decreased dilution of runoff and unpolluted water. Sometimes, the variations in metal
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concentrations may also be influenced by changes in lithological inputs, hydrological effects,
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geological features, cultural influences and type of vegetation cover (Jain et al., 2007).
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ACCEPTED MANUSCRIPT During wet season the concentrations of Ag were higher in all the sites which ranged from
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0.09 µg/g to 2.07 µg g-1. The highest concentration of Ag was recorded in the site S3 (2.07
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µg/g) in sediment samples. On the average basis of both seasons, the higher concentrations of
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Mn, Cr, Ni, Co, Pb, Ag, As and Hg at the sampling sites S1, S3, S5, S6 and S7 may be
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attributed to several anthropogenic activities (Fig. 2 and Table 1). For example, the site S6
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and S7 were located in highly agricultural land with semi-urbanized area, and the estuary at
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this site directly receives untreated domestic sewage, agricultural runoff, urban runoff, and
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wastes from construction of residential and commercial areas.
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The heavy metal concentrations in sediment of the Feni river estuary were compared with
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other selected estuaries (Table 2). The mean concentrations of Cr, As and Pb were higher in
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this estuary than the values reported by Ali et al. (2016) in the Karnaphuli River estuary. The
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mean concentrations of Cr, Pb and As in the Yangtze River estuary were found higher but Ni
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was lower as recorded by Wang et al. (2015a) than present measured concentrations. The
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mean concentrations of Cr and Pb were found to be higher but Ni was lower in the Ganges
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estuary (Subramanian et al., 1988) and Luanhe River estuary (Liu et al., 2016) than in this
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study.
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3.2. Sediment quality guidelines (SQGs)
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Sediment quality guidelines (SQGs) are useful to screen sediment contamination by
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comparing sediment contaminant concentration with the corresponding quality guidelines
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(Caeiro et al., 2005), which evaluate the degree to which the sediments associated chemical
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status might adversely affect aquatic organisms and are designed to assist the interpretation of
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sediment quality (Wenning et al., 2005). Threshold effect level (TEL) refers to the
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concentration below which adverse biological effects are expected to occur rarely, and
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Probable effect level (PEL) indicates the concentration above which adverse effects are
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ACCEPTED MANUSCRIPT expected to occur frequently (Long and Morgan, 1990). Table 1 furnishes the heavy metal
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concentrations in sediment from the Feni River, as well as the average shale values and
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sediment quality guidelines (SQGs) used in this study. When comparing the average values
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of heavy metals with the average shale values, the results indicated that Ag, Co and Hg were
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higher than the average shale value in the wet and dry seasons. This indicates that the
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anthropogenic activities had a direct effect on the concentration of these metals in sediment
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(Chai et al., 2014). The concentration of heavy metals in the sediment samples were
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contrasted with the consensus-based threshold effect concentration (TEC) and probable effect
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concentration (PEC) values (Table 1). The results show that Ni and Cr were between TEC
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and PEC for 70% and 20% indicating that the concentration of Ni and Cr were likely to
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occasionally exhibit adverse effects on the ecosystem.
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3.3 Pollution level of heavy metals
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3.3.1. Enrichment factor (EF)
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A comparison of metal concentration in sediments with background reference values
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is generally used to assess metal enrichment (Tuna et al., 2007). Mean EF values of Ag, As,
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Co, Cr, Ni, Pb and Hg were greater in the wet season, and followed the order: Ag > Co >
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Hg> Ni > Cr >Pb> As (Fig. S1). Lower values of metal EF in dry season can be related to
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decrease in the anthropogenic activities. Total EF values followed the order of S9 > S10 >
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S3 > S8 > S6 > S7 > S4 > S5 > S2 > S1. The EF values of Ag ranged from 53.90 (S1) to
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841.22 (S9) which corresponded to extremely severe enrichment. Severe enrichment with Ag
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may be attributed to urban hospital and clinic waste, some upstream silver plating industries,
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runoff from agricultural land and rive bank erosion. Co and Ni are commonly used in
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household products such as stainless steel, alloys, batteries, and car bearings and thus, there is
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plenty chance of increased input of Co and Ni from urban areas (Barałkiewicz and Siepak,
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ACCEPTED MANUSCRIPT 1999). An increased amount of Ni may also be connected to silty sedimentary rock in the
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upstream. Some anthropogenic sources e.g., fossil fuel burning and coal burning in brick-
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fields, and natural sources may contribute to enrichment of Hg in the study area.
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3.3.2. Geo-accumulation index (Igeo)
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Igeo values have been used to explain sediment quality (Karbassi et al., 2008). The Igeo
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of sampling sites during the wet and dry seasons are displayed in Fig. S2. Among the metals
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Ag showed the highest accumulation in wet season. Igeo values of Ag ranged from 1.17 (site
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S9) to 4.30 (site S3) which corresponded to class 2 of moderately polluted and class 5 of
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strongly to extremely polluted sediment samples.
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The Index of geo-accumulation (Igeo) shows that Feni river estuary is not polluted with As,
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Cr, Mn and Pb (Igeo< 0), unpolluted to moderately polluted with Co and Hg (Igeo< 1), and
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moderately to highly polluted with Ag (Igeo> 2). The average highest Igeo value showed Ag
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(2.93) which corresponded to class 4 and indicated Feni river estuary was highly polluted by
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Ag.
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3.3.3. Contamination factor (CF) and potential ecological risk index (PERI)
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The highest CF values for all metals studied were found at site-S3, which receives a huge
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amount of agricultural discharge (Fig. 3). Total contamination factors followed the order of
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site S3 > S6 > S9 > S7 > S10 > S8 > S4 > S2 > S1. The average CF value for Ag was 15.6
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(Fig. 3) which revealed that Feni River estuary was very highly polluted with Ag.
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The results of the potential ecological risk index (PERI) for only four heavy metals (Ni, Pb,
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Cr and As) in the surface sediment of the Feni River estuary are depicted in Fig. S3. The
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biological toxicity factors (Tri) for the rest metals (Ag, Co, Mn and Hg) were not available to
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calculate PER value. From the PERI calculation, heavy metal pollution by a single element
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was obtained, and the degree of pollution from the four heavy metals decreased in the
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ACCEPTED MANUSCRIPT following sequence: Ni >Pb> Cr > As. In comparison with the other elements, the PERI for
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Ni was higher than other metals. However, the Eir values of Ni indicated pollution in the
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sediment samples of Feni River estuary. The increased amount of Ni might be originated
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from local household products e.g., stainless steel, batteries.
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3.4. Pollution source identification based on Pearson Correlation, CA and PCA
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Correlations among heavy metals may reflect the origin and migration of these elements
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(Suresh et al., 2011). If no correlation exists among the elements, then the metals are not
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controlled by a single factor (Kukrer et al., 2014). The concentration of Ag was not
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significantly correlated with any of the heavy metals except for a negative correlation with Pb
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(Table 3). However, positive correlations (p < 0.01) were found between several element
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pairs: As-Pb (0.84), Co-Cr (0.90), Co-Mn (0.98), Co-Ni (0.90), Cr-Mn (0.98), Cr-Ni (0.98)
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Cr-Hg (0.95) Mn-Ni (0.98), Mn-Hg (0.92) and Ni-Hg (0.93). Three element pairs exhibited a
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significantly positive correlation of p < 0.05.
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One group of elements comprised of As, Cr, Mn, Pb and Hg. The concentrations of these five
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metals were slightly lower or close to the average shale values. In addition, the results of EF,
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Igeo, CF and PERI assessment indicated that the contents of these five metals presented a low
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or zero potential ecological risk. These findings suggested that this group of elements might
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originate from natural sources, such as river bank erosion, weathering and atmospheric
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precipitation (Wu et al., 2014; Wang et al., 2015b).
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A second group, including Ag, Ni and Co, could be ascribed to an anthropogenic factor. The
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mean concentrations of these metals were higher than their average shale values. The element
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of Ag, Ni and Co had similar spatial distribution trends, with higher concentrations appearing
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in landward sites: S3, S4, S5, S6, S7 and S9 . Various human activities e.g., using fertilizer,
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metal-containing pesticides, fungicides, herbicides in agriculture and aquaculture, waste
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ACCEPTED MANUSCRIPT disposal, dredging in and around the Feni River estuary, may greatly contribute to the
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concentration of Ag, Ni and Co. Domestic and commercial use of stainless steel, alloyes,
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batteries may contribute to the increase of Co and Ni. Recent increasing use of silver-
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nanoparticle antibacterial and antifungal agent in wound care products, medical devices,
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cosmetics and textiles may also be associated with high concentration of Ag in the study area
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(Landsdown, 2010). Metallic silver may be inert; however, biologically active Ag+ binds
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strongly to metallothionein, albumins, and macroglobulins of human body which can cause
336
skin allergy, Argyria and argyrosis disease (Lansdown, 2015).
337
Cluster analysis (CA) was used to group the similar sampling sites (spatial variability) and to
338
identify specific areas of contamination (Simeonov et al., 2000). Spatial CA rendered a
339
dendrogram (Fig. S4) where all ten sampling sites on the river were grouped into two
340
statistically significant clusters/groups at (Dlink/Dmax) × 100 < 15. Group 1 consisted of
341
three sites (S8, S9 & S10) with lowest metal concentrations in sediment samples and group 2
342
consisted of seven sites (S5, S6, S7, S3, S4, S2 & S1) with highest metal concentrations
343
which were surrounded by semi urban and agricultural areas. Group 1 corresponded to
344
weakly contaminated sites and group 2 corresponded to highly contaminated sites. Group 2
345
sites were situated upstream of the catchment areas, and sediments at these sites were
346
polluted with anthropogenic activities such as waste-water discharges from urban areas,
347
dumping of solid waste, raw sewage, automobile washing and auto-workshops near these
348
sites.
349
PCA leads to a reduction of the initial dimension of the data set to two principle components
350
(PCs) which explain 91.89% of the data variation (Fig. 4). A total of two significant PCs
351
were extracted with eigenvalues > 1. PC1 was heavily loaded with As, Cr, Mn, Pb and Hg
352
which explained 68.95% of the total variance and exhibited an eigenvalue of 5.52. PC2
353
explained 22.94% of total variance and was strongly correlated with Ag (0.52) and Co (0.41).
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ACCEPTED MANUSCRIPT A positive correlation existed between Ag and Co; hence, Ag and Co may exist from a
355
common origin (Wang et al., 2015a). High concentrations of Ag and Co are possibly caused
356
by anthropogenic inputs from flooding of agricultural lands, medical waste, domestic waste,
357
upstream silver plating industries, and atmospheric deposition, which were widespread
358
throughout the catchment area of the estuary.
359
Conclusions
360
The results of the current study provide information on the characteristics of pollution from
361
heavy metals of the Feni River estuary for the first time. The results showed that the degree
362
of pollution from eight selected heavy metals followed the decreasing order of Mn > Cr > Ni
363
> Co > Pb > Ag > As > Hg. The concentrations of heavy metal were greater in the wet season
364
than in the dry season which can be associated with heavy rainfall during monsoon and the
365
subsequent flooding of agricultural lands causing dissolution of heavy metals. The mean
366
concentrations of Ag and Co were higher than the average world values. Calculations of EF,
367
Igeo and CF demonstrated that Ag, Co and Hg were at moderate to high pollution levels and
368
As, Cr, Ni, Pb and Mn were at no to low pollution levels. Potential ecological risk index
369
(PERI) also revealed that Ag, Co and Hg were the most potential ecological risk factors.
370
Major possible sources of Ag can be medical waste and upstream silver plating industries.
371
Household uses of stainless steel, batteries, alloys, and burning fossil fuel can be connected to
372
enhanced accumulation of Co and Hg. Therefore, Ag, Co and Hg were the most important
373
factors affecting the ecological environment of the Feni River estuary. The results of the PCA
374
analysis suggest that Ag, Co and Ni mainly originated from anthropogenic sources (due to
375
excessive use of fertilizers, agrochemicals, metal-containing pesticides, silver-nanoparticle
376
antimicrobial agent, upstream silver plating industries) and As, Cr, Mn, Pb and Hg mainly
377
originated from natural sources (geological rock). The generated information can be a basis
378
for effectively targeting policies to protect the sediment of Feni river from long-term
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ACCEPTED MANUSCRIPT accumulation of heavy metals and for ensuring land and water based food quality, and
380
protecting human health. Important strategies should be implemented to reduce or ban the use
381
of agrochemicals, pesticides, inclusion of wastewater treatment and safe disposal of silver
382
containing medical devices.
383
Conflict of interest
384
The authors declare that there is no conflict of interest.
385
Acknowledgements
386
The authors are deeply thankful to the laboratory in Forensic Analysis Department, Rab HQ,
387
Bangladesh for providing all necessary research facilities. They are also delighted to express
388
their gratitude and sincere thanks to editor and four anonymous reviewers for their useful
389
comments and suggestions to improve the quality of article.
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References
392
Akcay, H., Oguz, A., Karapire, C., 2003. Study of heavy metal pollution and speciation in
393
Buyak Menderes and Gediz river sediments. Water. Res. 37(4), 813-822.
394
Ali, M. M., Ali, M. L., Islam, M. S., Rahman, M. Z., 2016. Preliminary assessment of heavy
395
metals in water and sediment of Karnaphuli River, Bangladesh. Environ. Nanotechnol.
396
Monit. Manage. 5, 27-35.
397
Amin, B., Ismail, A., Arshad, A., Yap, C. K., Kamarudin, M. S., 2009. Anthropogenic
398
impacts
399
Indonesia. Environ. Monit. Assess. 148(1), 291-305.
AC C
EP
391
on
heavy
metal
concentrations
in
the
coastal
sediments
of
Dumai,
ACCEPTED MANUSCRIPT Armstrong-Altrin, J. S., Machain-Castillo, M. L., Rosales-Hoz, L., Carranza-Edwards, A.,
401
Sanchez-Cabeza, J. A., & Ruíz-Fernández, A. C., 2015. Provenance and depositional history
402
of continental slope sediments in the Southwestern Gulf of Mexico unraveled by geochemical
403
analysis. Cont. Shelf Res. 95, 15-26.
404
Barałkiewicz, D., & Siepak, J., 1999. Chromium, nickel and cobalt in environmental samples
405
and existing legal norms. Polish J. Env. Stud. 8(4), 201-208.
406
Baran, S., Oleszczuk, P., Lesiuk, A., & Baranowska, E., 2002. Trace metals and polycyclic
407
aromatic hydrocarbons in surface sediment samples from the Narew River (Poland). Polish J.
408
Env. Stud. 11(4), 299-306.
409
Bermejo, J. S., Beltrán, R., Ariza, J. G., 2003. Spatial variations of heavy metals
410
contamination in sediments from Odielriver (Southwest Spain). Environ. Int. 29(1), 69-77.
411
Bhuiyan, M. A., Parvez, L., Islam, M. A., Dampare, S. B., Suzuki, S., 2010. Heavy metal
412
pollution of coal mine-affected agricultural soils in the northern part of Bangladesh. J. hazard.
413
Mater. 173(1), 384-392.
414
Birch, G. F., Olmos, M. A., 2008. Sediment-bound heavy metals as indicators of human
415
influence and biological risk in coastal water bodies. ICES. J. Mar. Sci. 65(8), 1407-1413.
416
Burton Jr, A. G., Baudo, R., Beltrami, M., Rowland, C., 2001. Assessing sediment
417
contamination using six toxicity assays. J. Limnol. 60(2), 263-267.
418
Caeiro, S., Costa, M.N., Ramos, T.B., Fernandes, F., Silveira, N., 2005. Assessing heavy
419
metal contamination in Sado Estuary sediment: an index analysis approach. Ecol. Ind. 5,
420
151–169.
AC C
EP
TE D
M AN U
SC
RI PT
400
ACCEPTED MANUSCRIPT Chai, M., Shi, F., Li, R., Shen, X., 2014. Heavy metal contamination and ecological risk in
422
Spartinaalterniflora marsh in intertidal sediments of Bohai Bay, China. Mar. Pollut. Bull.
423
84(1), 115-124.
424
Demirak, A., Yilmaz, F., Tuna, A. L., Ozdemir, N., 2006.Heavy metals in water, sediment
425
and tissues of Leuciscuscephalus from a stream in southwestern Turkey. Chemosphere. 63(9),
426
1451-1458.
427
Duman, F., Aksoy, A., Demirezen, D., 2007.Seasonal variability of heavy metals in surface
428
sediment of Lake Sapanca, Turkey. Environ. Monit. Assess. 133(1-3), 277-283.
429
Fernandes, C., Fontaínhas-Fernandes, A., Cabral, D., Salgado, M. A., 2008. Heavy metals in
430
water, sediment and tissues of Liza saliens from Esmoriz–Paramos lagoon, Portugal. Environ.
431
Monit. Assess. 136(1), 267-275.
432
Hakanson, L., 1980. An ecological risk index for aquatic pollution control. A
433
sedimentological approach. Water. Res. 14(8), 975-1001.
434
Hammer, Ø., Harper, D. A., Ryan, P. D., 2001. PAST: paleontological statistics software
435
package for education and data analysis. Palaeo. Elect. 4(1), 9.
436
Hyun, S., Lee, C., Lee, T., Choi, J., 2007. Anthropogenic contributions to heavy metal
437
distributions in the surface sediments of Masan Bay, Korea. Mar. Pollut. Bull. 54, 1031–
438
1071.
439
Jain, C. K., Malik, D. S., Yadav, R., 2007. Metal fractionation study on bed sediments of
440
Lake Nainital, Uttaranchal, India. Environ.Monit. Assess. 130(1-3), 129-139.
441
Karbassi, A. R., Monavari, S. M., Bidhendi, G. R. N., Nouri, J., & Nematpour, K., 2008.
442
Metal pollution assessment of sediment and water in the Shur River. Env. Mon. Asses. 147(1-
443
3), 107.
AC C
EP
TE D
M AN U
SC
RI PT
421
ACCEPTED MANUSCRIPT Kucuksezgin, F., Uluturhan, E., Batki, H., 2008. Distribution of heavy metals in water,
445
particulate matter and sediments of Gediz River (Eastern Aegean). Environ. Monit.
446
Assess. 141(1), 213-225.
447
Kükrer, S., Şeker, S., Abacı, Z. T., Kutlu, B., 2014. Ecological risk assessment of heavy
448
metals in surface sediments of northern littoral zone of Lake Çıldır, Ardahan,
449
Turkey. Environ. Monit. Assess. 186(6), 3847-3857.
450
Lansdown, A. B., 2010. A pharmacological and toxicological profile of silver as an
451
antimicrobial agent in medical devices. Adv. Pharmacol. Sci. 16.
452
Larrose, A., Coynel, A., Schäfer, J., Blanc, G., Massé, L., Maneux, E., 2010. Assessing the
453
current state of the Gironde Estuary by mapping priority contaminant distribution and risk
454
potential in surface sediment. Appl. Geochem. 25, 1912–1923.
455
Lasorsa, B.M., Casas, A., 1996. A comparison of sample handling and analytical methods for
456
determination of acid volatile sulfide in sediment. Mar. Chem. 52, 211–220.
457
Liu, J., Yin, P., Chen, B., Gao, F., Song, H., Li, M., 2016. Distribution and contamination
458
assessment of heavy metals in surface sediments of the Luanhe River Estuary, northwest of
459
the Bohai Sea. Mar. Pollut. Bull. 109(1), 633-639.
460
Loizidou, M., Haralambous, K. J., Sakellarides, P. O., 1992. Environmental study of the
461
marinas Part II.A study on the removal of metals from the marinas sediments. Environ.
462
Technol. 13(3), 245-252.
463
Long, E.R., Morgan, L.G., 1990. The potential for biological effects of sediment-sorbed
464
contaminants tested in the national status and trends program. Technical Memorandum.
465
NOAA, Maryland, USA.
AC C
EP
TE D
M AN U
SC
RI PT
444
ACCEPTED MANUSCRIPT Loska, K., Cebula, J., Pelczar, J., Wiechuła, D., Kwapuliński, J., 1997. Use of enrichment,
467
and contamination factors together with geoaccumulation indexes to evaluate the content of
468
Cd, Cu, and Ni in the Rybnik water reservoir in Poland. Water, Air, and Soil Poll. 93(1-4),
469
347-365.
470
MacDonald, D. D., Ingersoll, C., Berger, T. A., 2000. Development and evaluation of
471
consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ.
472
Contam. Toxicol. 39(1), 20–31.
473
Miah, M. Y., Robel, F. N., Bhowmik, S., Bhattacharjee, S., Paul, S. C., Hossain, M. J.,
474
Hossain, M. Z., 2015. Assessment of the Coastal Area Water Quality in Noakhali,
475
Bangladesh. Int. J. Sci. Eng. Res. 6(2).
476
Muller, G., 1979. Schwermetalle in den sedimenten des Rheins-Veranderungen seit 1971.
477
Umschau, 79, 778-783.
478
Ramos-Vázquez, M. A., Armstrong-Altrin, J. S., Rosales-Hoz, L., Machain-Castillo, M. L.,
479
& Carranza-Edwards, A., 2017. Geochemistry of deep-sea sediments in two cores retrieved at
480
the mouth of the Coatzacoalcos River delta, western Gulf of Mexico, Mexico. Arab. J.
481
Geosci, 10(6), 148.
482
Salati, S., & Moore, F., 2010. Assessment of heavy metal concentration in the Khoshk River
483
water and sediment, Shiraz, Southwest Iran. Env. Mon. Assess. 164(1), 677-689.
484
Sayadi, M. H., Sayyed, M. R., Kumar, S., 2010. Short-term accumulative signatures of heavy
485
metals in river bed sediments in the industrial area, Tehran, Iran. Environ. Monit. Assess.
486
162(1), 465-473.
AC C
EP
TE D
M AN U
SC
RI PT
466
ACCEPTED MANUSCRIPT Shomar, B. H., Müller, G., Yahya, A., 2005. Seasonal variations of chemical composition of
488
water and bottom sediments in the wetland of Wadi Gaza, Gaza Strip. Wetl. Ecol. Manage.
489
13(4), 419-431.
490
Shuhaimi-Othman, M., 2008. Metals concentrat ions in the sed-iments of Richard Lake,
491
Sudbury, Canada and sediment toxicity in an amphipod Hyalellaazteca. J. Environ. Sci.
492
Technol. 1(1), 34-41.
493
Simeonov, V., Massart, D. L., Andreev, G., Tsakovski, S. 2000. Assessment of metal
494
pollution based on multivariate statistical modeling of ‘hot spot’ sediments from the Black
495
Sea. Chemosphere. 41(9), 1411-1417.
496
Singh, K. P., Malik, A., Sinha, S., Singh, V. K., Murthy, R. C., 2005. Estimation of source of
497
heavy metal contamination in sediments of Gomti River (India) using principal component
498
analysis. Water. Air. Soil. Pollut. 166(1), 321-341.
499
Singh, V. K., Singh, K. P., Mohan, D., 2005. Status of heavy metals in water and bed
500
sediments of river Gomti–A tributary of the Ganga river, India. Environ. Monit. Assess.
501
105(1), 43-67.
502
Subramanian, V., Itta, P.K., Griekan, R.V., 1988. Heavy metals in the Ganges Estuary. Mar.
503
Pollut. Bull. 19 (6), 290-293.
504
Sun, Z., Mou, X., Tong, C.,Wang, C., Xie, Z., Song, H., Sun,W., Lv, Y., 2015. Spatial
505
variations and bioaccumulation of heavy metals in intertidal zone of the Yellow River
506
estuary, China. Catena 126, 43–52.
507
Suresh, G., Ramasamy, V., Meenakshisundaram, V., Venkatachalapathy, R., Ponnusamy, V.,
508
2011. Influence of mineralogical and heavy metal composition on natural radionuclide
509
concentrations in the river sediments. Appl. Radiat. Isot. 69(10), 1466-1474.
AC C
EP
TE D
M AN U
SC
RI PT
487
ACCEPTED MANUSCRIPT Tam, N. F. Y., Wong, Y. S., 2000. Spatial variation of heavy metals in surface sediments of
511
Hong Kong mangrove swamps. Environ. Pollut. 110(2), 195-205.
512
Tuna, A. L., Yilmaz, F., Demirak, A., Ozdemir, N., 2007. Sources and distribution of trace
513
metals in the saricay stream basin of southwestern turkey. Environ. Monit. Assess. 125(1),
514
47-57.
515
Wang, H., Wang, J., Liu, R., Yu, W., Shen, Z., 2015a. Spatial variation, environmental risk
516
and biological hazard assessment of heavy metals in surface sediments of the Yangtze River
517
estuary. Mar. pollut. Bull. 93(1), 250-258.
518
Wang, Y., Yang, L., Kong, L., Liu, E., Wang, L., Zhu, J., 2015b. Spatial distribution,
519
ecological risk assessment and source identification for heavy metals in surface sediments
520
from Dongping Lake, Shandong, East China. Catena. 125, 200-205.
521
Wenning, R., Batley, G., Ingersoll, C., Moore, D. (Eds.), 2005. Use of Sediment Quality
522
Guidelines and Related Tools for the Assessment of Contaminated Sediments. SETAC Press,
523
USA.
524
Wu, J. Y., Zhou, J. L., Liu, Z. T., Wang, X. N., Gao, A. F., 2014. Spatial distribution and risk
525
assessment of heavy metals in the surface sediments of the Liao River. Environ. Sci. Technol.
526
37(6N), 268-273.
527
Yoon, J., Cao, X., Zhou, Q., & Ma, L. Q., 2006. Accumulation of Pb, Cu, and Zn in native
528
plants growing on a contaminated Florida site. Sci. Total Environ. 368(2), 456-464.
529
Zhang, G., Bai, J., Xiao, R., Zhao, Q., Jia, J., Cui, B., & Liu, X., 2017. Heavy metal fractions
530
and ecological risk assessment in sediments from urban, rural and reclamation-affected rivers
531
of the Pearl River Estuary, China. Chemosphere. 184, 278-288.
AC C
EP
TE D
M AN U
SC
RI PT
510
ACCEPTED MANUSCRIPT Zhao, G.M., Lu, Q.Y., Ye, S.Y., Yuan, Y.M., Ding, X.G., Wang, J., 2016. Assessment of
533
heavy metal contamination in surface sediments of the west Guangdong coastal region,
534
China. Mar. Pollut. Bull. 108, 268–274.
535
Zheng, N. A., Wang, Q., Liang, Z., Zheng, D., 2008. Characterization of heavy metal
536
concentrations in the sediments of three freshwater rivers in Huludao City, Northeast
537
China. Environ. Pollut. 154(1), 135-142.
AC C
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ACCEPTED MANUSCRIPT FIGURE LEGENDS: Fig. 1. Sampling sites in the Feni River estuary. Fig. 2. Seasonal (a) and spatial (b) variations of metal concentrations in the sediments during wet and dry season. Error bars indicate standard error of Mean.
horizontal axis indicates the level of contamination degree).
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Fig. 3. Contamination factor (CF) selected metals in sediment of Feni River. (Dot line of the
Fig 4. PCA plot showing the loading of two components influencing variation of heavy
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metals in the sediments from Feni River estuary.
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Fig. 1. Sampling sites in the Feni River estuary.
45 40 35 30 25 20 15 10 5 0
a
Wet season Dry season
Ag
As
Co
Cr
Mn
Ni
Pb
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Metals concentrations (µg/g)
ACCEPTED MANUSCRIPT
Hg
50
Ag
As
S2
S3
Co
Cr
Mn
SC
b
Ni
Pb
Hg
M AN U
Metals concentrations (µg/g)
Metals
40 30 20 10 0 S4
S5
S6
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S1
S7
S8
S9 S10
Sampling sites
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Fig. 2. Seasonal (a) and spatial (b) variations of metal concentrations in the sediments during
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wet and dry season. Error bars indicate standard error of Mean.
18 16 14 12 10
Wet season
Dry season
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Very high pollution
8 6 4 2 0
Considerable pollution
Moderate pollution Low pollution
As
Co
Cr Mn Metals
Ni
Pb
Hg
M AN U
Ag
SC
CF value
ACCEPTED MANUSCRIPT
Fig. 3. Contamination factor (CF) selected metals in sediment of Feni River. (Dot line of the
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horizontal axis indicates the level of contamination degree).
ACCEPTED MANUSCRIPT
16
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Pb
8 4
-4 -8
Co
-12 -16 -20 -48
-32 -16 0 16 32 PC1 (68.95 % variation)
TE D
-64
SC
Ni AsCr HgMn Ag
0
M AN U
PC2 (22.94 % variation)
12
48
64
Fig 4. PCA plot showing the loading of two components influencing variation of heavy
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metals in the sediments from Feni river estuary.
ACCEPTED MANUSCRIPT Table 1 Metal concentrations (µg/g) in sediments of the Feni river estuary during wet and dry season (n=30 for each season). As
Co
Cr
Mn
Ni
Pb
Hg
1.17
0.93
32.30
36.82
39.55
35.02
7.09
0.79
Range
0.09-
0.13-
14.48-
17.77-
23.46-
13.54-
0.67-
0.87-
(wet
2.07
2.79
45.84
46.09
48.73
45.71
17.03
1.57
1.01
0.78
29.75
33.74
36.15
Range
0.26-
0.13-
11.11-
(dry
1.86
2.27
42.84
1.09
0.85
0.07
13.00
Mean (wet
Mean
8.56-
0.36-
0.09-
41.74
46.01
41.86
14.04
1.04
31.02
35.28
37.85
33.27
6.47
0.71
19.00
90.00
850.00
68.00
20.00
0.40
9.79
N.A.
43.40
NA
22.7.
35.80
NA
33.00
N.A.
111.00
NA
48.60
128.00
NA
(Feni river
PECb a
NA
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TECb
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estuary)
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season)
(Shalea)
NA
0.63
17.92-
season)
Aver.
5.86
13.91-
(dry
Aver.
31.52
M AN U
season)
SC
season)
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Ag
Turekian and Wedepohl(1961). b MacDonald et al. (2000).
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Table 2 Heavy metal concentrations (µg/g) in sediment samples from the Feni river estuary and other selected estuary from the references.
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Mean concentrations (µg/g) of heavy metal Estuary
Cr
As
Mn
Pb
Ni
Ag
Feni river
35.28
0.85
37.85
6.47
33.27
1.09 0.71
Karnaphuli
11.56-
37.23-
NA
21.98-
NA
river estuary
35.48
160.32
Ganges
21-
NA
estuary
100
(Bangladesh)
(Bangladesh) 254800
(India) 9.6-
3.4-
estuary
35.6
13.5
79.1
9.1
(China) The Yangtze
(China)
44.09
NA
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Yellow River
NA
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River estuary
NA
12-
NA
8-57
References
31.02
This study
NA
NA
NA
NA
NA
115
22.6-
3.5-
43.7
35.8
23.8
31.9
TE D
Luanhe River
NA
M AN U
73.42
Co
SC
estuary
Hg
43.05
NA
NA
Ali et al. (2016)
Subramanian et al. (1988)
NA
Liu et al. (2016)
NA
NA
NA
Wang et al. (2015a)
Na
NA
NA
NA
Sun et al. (2015)
Estuary (China)
Gironde estuary
(France)
78.4
18.4
NA
46.8
NA
NA
NA
NA
Larrose et al. (2010)
ACCEPTED MANUSCRIPT Table 3 Pearson correlation analysis of heavy metals in the Feni river estuary. Ag
As
Co
Cr
Mn
Ni
Pb
Hg
Ag -0.63
Co
-0.05
0.02
Cr
-0.35
0.39
0.90**
Mn
-0.22
0.28
0.93**
0.98**
Ni
-0.28
0.40
0.90**
0.98**
0.98**
Pb
-0.64*
0.84**
0.29
0.67*
0.59
Hg
-0.31
0.53
0.78*
0.95**
0.92**
SC
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As
M AN U
0.64
0.93**
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*Significant at p < 0.05 levels. ** Significant at p < 0.01 levels.
0.76*
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HIGHLIGHTS • EF, Igeo, CF and PERI were used to profile pollution levels of heavy metals in sediments of the
• Ag, Co, Hg and Ni enrichment was more in sediments.
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Feni River estuary.
• Ag and Co were above average shale values; however, Ni exceeded the TEC values.
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SC
• Ag, Co and Ni threats to aquatic ecosystem should not be ignored.