Metal pollution status and ecological risk assessment in marine sediments of the inner Izmit Bay

Journal Pre-proof Metal pollution status and ecological risk assessment in marine sediments of the inner Izmit Bay ˙Ibrahim Tan, Ertu˘grul Aslan

PII: DOI: Reference:

S2352-4855(18)30572-3 https://doi.org/10.1016/j.rsma.2019.100850 RSMA 100850

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Regional Studies in Marine Science

Received date : 31 October 2018 Revised date : 24 September 2019 Accepted date : 24 September 2019 Please cite this article as: ˙I. Tan and E. Aslan, Metal pollution status and ecological risk assessment in marine sediments of the inner Izmit Bay. Regional Studies in Marine Science (2019), doi: https://doi.org/10.1016/j.rsma.2019.100850. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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.

© 2019 Published by Elsevier B.V.

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Metal Pollution Status and Ecological Risk Assessment in Marine Sediments of the Inner Izmit Bay İbrahim Tan1*, Ertuğrul Aslan1 1

TUBITAK- Marmara Research Center, Environment and Cleaner Production Institute, Kocaeli, Turkey

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Highlights

● Sediment evaluation at a wide-scale in the Izmit inner bay, Turkey based on quality and degree of contamination. ● Evaluation of the effects of the anthropogenic pressures on sediment quality by using individual and overall contamination assessment methods. ● Sediment quality assessment using a Visual Basic Application (VBA) based software (SedCal). ● Appropriate legal arrangements are required at a national level to improve sediment quality.

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Abstract

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Metal concentrations (Al, Fe, As, Cu, Cr, Cd, Ni, Pb, Zn, and Hg) were determined in the surface sediments of the polluted parts of the inner Izmit Bay (Marmara Sea, Turkey). The dataset was established from 25 sampling points to evaluate contamination, toxicity, and ecological risk levels of metals in the inner bay, using various sediment quality guidelines (SQGs) and contamination assessment methods. The results showed that the concentrations of Zn, Cu, Cr, and Ni exceeded the background levels determined for the Marmara Sea by a factor of 1.2 - 4 whereas the total concentrations of As, Cd, and Pb were comparable with the background values of surface sediments from the less polluted areas. The SQGs analysis of Zn data indicated that surface sediments had been contaminated at all of the observed points of the inner bay. Locally, surface sediments of the northern entrance of the inner bay are more polluted than the other regions of the bay, presumably due to anticlockwise surface current system prevailing in the region. Keywords: Izmit Bay; Marmara Sea; Metal pollution; Sediment quality guidelines; Marine sediment 1

Introduction

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Estuarine sediments play a crucial role in the monitoring of contaminants to indicate both spatial and temporal trends (Asamuddin and Mohamed, 2011; Jayaraju et al., 2011). Moreover, estuaries receive both anthropogenic and natural inputs from the surrounding basins, designating them an important reservoir for organic and inorganic contaminants (Saher and Siddiqui, 2016). Metals are considered as major inorganic pollutants for water and sediment matrices.

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They enter the marine environment from various natural (surface runoff, river discharges, atmospheric deposition) and anthropogenic (industrial and domestic wastewaters, vehicle emissions, agricultural runoff) sources (Saher and Siddiqui, 2016).

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The soluble fraction of some metals (Fe, Cu, Co, and Zn) in the water column are essential for the healthy growth of the marine organisms. However, metals such as Hg, Pb, and Cd are non-essential and have almost no biological role (Jayaraju et al., 2011; Okay et al., 2008). Metals are also nonbiodegradable, persistent, and can affect ecosystems via bio-accumulation or bio-magnification (Manahan, 2000; Buccolieri, 2006). Consequently, metals enter the food chain and may affect human health (Yu et al., 2012; Ergül et al., 2013). As an integrated result of these factors, metals, especially near coastal sediments, need to be monitored due to the variable concentration over locations and time. Grain size distribution is also an essential parameter in determining elemental concentrations in the sediments (Salomons and Förstner, 1984) where a pelitic fraction (< 63 µm) is used for determining the metal concentration in the sediment (Buccolieri, 2006).

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The concentration levels of metals in sediment do not reveal the degree of contamination, especially with regards to the risk of sediment-dwelling organisms and their consumers (Okay et al., 2016). In the literature, several quality guidelines (SQGs) assess the single or overall degree of contamination and ecological risks of metals (Bai et al., 2011; Okay et al., 2016; Saher and Siddiqui, 2016). Okay et al. (2016) showed that different background values in the assessment have an impact on the results. Additionally, the results of their study revealed that calculations considering local background values produced more reliable results.

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The objectives of this study are (1) to investigate the spatial distribution and the levels of the selected eight metals in the surface sediments of the inner Izmit Bay (IB); (2) to estimate the anthropogenic pressures, and assess the pollution status of the studied area; and (3) to evaluate the contamination and ecological risk levels of eight metals in sediments by using numerous assessment methodologies perceived from the literature. Materials and methods Study area

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Izmit Bay is a semi-closed embayment located in the Northeastern Marmara Sea, between 40° 41´ - 40° 47´ N and 29° 21´ - 29° 57´ E (Fig 1). The bay includes three sections (western, central and eastern) connected narrow openings. The eastern (inner) part is 15 km in length, with an average depth of 30 m (Morkoç et al., 2001). The IB is a two-layered stratified water system. The upper layer (10-15 m) is dominated by the less saline (22-24) water originating from the Black Sea and is partially mixed with the lower layer waters of the Marmara Sea. More saline Mediterranean origin waters occupy the entire deeper waters of the bay. A sharp halocline of about 15 m separates these layers, having different physicochemical properties (Oğuz and Sur, 1986; Beşiktepe et al., 1994; Ünlüata et al., 1990).

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Inner bay, being shallow (max depth= 30 m), is usually not very sharply stratified and may exhibit well-mixed water column properties (Oğuz and Sur, 1986; Beşiktepe et al., 1994; Algan et al., 1999). Ventilation and renewal of the inner bay waters are very limited during the year, increasing during the late autumn-winter period when the vertical mixing weakens the halocline under suitable meteorological conditions (Oğuz and Sur, 1986; Algan et al., 1999).

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The eastern part of the IB is subjected to a large amount of domestic, agricultural, and industrial pollution from two main streams; the Sarı and Kullar (Fig 1). The Sarı (Kumla) is located in the northern part of the inner bay which is affected by the 42 Evler urban wastewater treatment plant discharge (UWTP) (Qavg= 35,040 m3day-1) (İSU, 2018), as well as industrial wastewater discharges (textile, metal, chemical). The southern part of the inner bay has freshwater input from the Kullar (Kiraz) river which is affected by discharge from the Kullar UWTP (Qavg = 62,000 m3day-1), agricultural activities and industrial wastewaters.

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The urban, agricultural, commercial and industrial pressures on IB were analyzed by using Corine Land Cover data from 2012 (CLC; European coverage, 1:100,000 scale) which is freely accessible via the European Environment Agency (EEA). The CLC has 44 land cover or land use (LC/LU) classes, and this dataset is organized hierarchically at several LC or LU levels. In this study, datasets are organized in three different hierarchical levels. These three levels are urban (covering class 11 subclass which presents city structure), commercial, and industrial (covering class 12 and 13 subclasses which presents industrial, commercial, transportation, mining, discharge/construction sites), and agricultural areas (covering class 21, 22, 23 and 24 subclasses which also presents arable and mixed agricultural areas, meadows, and sustainable products) (Fig 1). Another notable geographic feature is a wetland in the inner bay of 1 km2 (Fig 1) (Garipağaoğlu and Uzun, 2017).

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Fig 1. Land use of the eastern part of the Izmit Bay (a), and the sampling locations (b). 2.2

Sampling and analysis

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Twenty-five sampling locations were selected to represent the different regions and pressures in the inner bay (Fig 1). The sampling depths were in the range of 1-23.5 m. The sediments were collected through the use of a Van Veen grab sampler (0.1 m2) in July 2017. The upper 2-3 cm of sediment was removed with a polyethylene spoon to avoid contamination. Then, they were kept both in clean polyethylene bags for grain size analysis and in glass bottles for TOC and metal analysis. Glass bottles containing sediments were kept at -20°C until processing. The mechanical analysis was performed according to Folk (1974). The water content of sediments was determined with a AND MX-50 Moisture Analyzer. The total organic carbon (TOC) content of the samples was determined using a CHNS analyzer (Thermo Finnigan Flash EA 1112 Series).

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Wet samples were sieved through a mesh with a pore diameter of 63 µm since metals in sediment generally accumulate in the fine sediment fraction (Salomons and Förstner, 1984). Samples were dried using a freeze dryer; then homogenized using the mortar grinder (Retsch RM200). The modified EPA-3052 method was used for the preparation of the metal analysis. About 0.1 g of the homogenized sediment samples were put into a closed Teflon vessel with 4 mL of HNO3 (Merck (Darmstadt, Germany)), 2 mL of HCl (Merck (Darmstadt, Germany)) and 1 mL of HF acids (Merck (Darmstadt, Germany)) for the complete digestion of the metal samples. A microwave acid

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digestion system (Milestone Ultrawave) was used for the digestion at 120°C for 35 minutes. Teflon vessels were left to cool, and 0.3 g boric acid was added to permit the complexation of fluoride to protect the quartz plasma torch from excess hydrofluoric acid. Then the same microwave digestion procedure was reapplied. After cooling, the vessel contents were filtered and then diluted to 50 mL with deionized water. The diluted samples were preserved in polyethylene bottles for analysis. Sample solutions and blanks were analyzed for the metals (Al, Fe, As, Cu, Cr, Cd, Ni, Pb, Zn) using the ICP-MS instrument (Perkin Elmer Nexion 3000x) utilizing a Kinetic Energy Discrimination (KED) mode. The mercury (Hg) content of the samples was determined using the Milestone DMA-80 Direct Mercury Analyzer. The accuracy and precision of the analytical procedures have been checked by analyzing a certified reference marine sediment IAEA-158 (Campbell et al., 2008). Results indicate good compatibility between the certified and found values (Table 1). Table 1. Metal Concentrations (mg kg-1 dry weight) and recovery values in the certified marine sediment (IAEA-158).

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Standard Deviation 3,400 1,400 1.2 0.039 5.8 4.2 2.9 4.7 9.5 0.014

Found Value 50,794 25,999 11.8 0.371 72.3 51 30.2 39.3 138.7 0.1318

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Certified Value 51,800 26,300 11.5 0.372 74.4 48.3 30.3 39.6 140.6 0.132

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Element Al Fe As Cd Cr Cu Ni Pb Zn Hg

Recovery (%) 98 99 103 100 97 105 100 99 99 100

Methodologies for a quantitative measure of contamination and determination of

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potential risk

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Metal contamination was evaluated by using various indices as given in Table 2. Geoaccumulation index (Igeo) (Müller, 1981), enrichment factor (EF) (Sakan et al., 2009), and contamination factor (CF) (Hakanson, 1980) are the metal pollution indices. Igeo and EF indices are widely used to differentiate the anthropogenic activities from natural sources. Igeo and EF indices were classified into seven groups (uncontaminated/no enrichment to extremely contaminated/enrichment) by Müller (1981) and Sakan et al. (2009), respectively. For Igeo (Table 2), 1.5 is a constant which represents a background value produced by lithogenic effects. The metal reference values were taken from the Earth’s average values of clay from sedimentary rocks (Krauskopf, 1970). Generally, Al (Alkan et al., 2015; Chen et al., 2013; Fernandes et al., 2011; Gao and Chen, 2012; Okay et al., 2008) and Fe (Okay et al., 2016; Saher et al., 2016) are used as the reference elements during the calculation of EF since they are two of the most abundant elements on the earth. During the EF estimations, Al was selected to use as the reference element for geochemical normalization.

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Table 2. Metal pollution indices, their formulations, and descriptions. Index Name Enrichment Factor (EF)1

Formulation (X/Al)sediment\ (X/Al)background

Geoaccumulation Index (Igeo)2

(𝐶𝑛 /1.5𝑅𝑛 ) 𝐶𝑛 /𝑅𝑛 𝑛

Potential ecological risk index for single metal (PERI)3

√𝐶𝐹1 𝑥𝐶𝐹2 𝑥 𝐶𝐹3 𝑥 … 𝑥 𝐶𝐹𝑛 𝐸𝑟𝑛

=

𝑇𝑟𝑛 𝑥(𝐶𝑛 /𝑅𝑛 ) ∑ 𝐸𝑟𝑖

𝐸𝑟𝑛 = The monomial potential ecological risk factor

𝑖=1 𝑛

Contamination Degree (Cd) 3

∑ 𝐶𝑓𝑖

𝑇𝑟𝑛 = The response coefficent for the toxicity of the single metal

Mean probable effect level (m-PEL-Q)6

∑𝑛𝑖=1 𝐶𝑓𝑖 /𝑛 𝑛

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𝑖=1

Modified contamination degree (mCd) 5 Mean effect range medium quotient (mERM-Q) 6

Cn, X: The measured concentration of the examined metal

Rn : Reference value of the examined metal

𝑛

Potential Toxicity Response Index (RI)3

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Contamination Factor (CF)3 Pollution Load Index (PLI) 4

Description

𝐶𝑛 ∑( ) /𝑛 𝐸𝑅𝑀𝑛 𝑖=1 𝑛

𝐶𝑛 ∑( ) 𝑃𝐸𝐿𝑛

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𝑖=1

1

Sakan et al., 2009; 2Müller, 1981; 3Hakanson, 1980; 4Tomlinson et al., 1980; 5Abrahim and Parker, 2008; 6Long and MacDonald, 1998; Gao and Chen, 2012.

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Pollution load index (PLI) (Tomlinson et al., 1980), potential toxicity response index (RI) (Hakanson, 1980; El-Said et al., 2014), modified contamination degree (mCd) (Abrahim and Parker, 2008), mean effect range medium quotient (m-ERM-Q) and mean probable effect level (m-PEL-Q) (Long and MacDonald, 1998; Gao and Chen, 2012) were used for the assessment of overall metal contamination levels. PLI, Cd, and mCd indices are an interpretation of CF index in different forms (Table 2). Cd and mCd indices were calculated using the measured eight metals (As, Cd, Cu, Cr, Ni, Pb, Zn, Hg). In the PERI index, the 𝑇𝑟𝑛 parameter is the response coefficient of the toxicity of each metal (Hg: 40, As: 10, Cd: 30, Cr:2, Cu=Pb=Ni=5, Zn=1) (Hakanson, 1980; Li Xu et al., 2014). PERI index indicates the degree of ecotoxicity of metal pollution (El-Said et al., 2014). In addition, RI is the summations of all PERI index (Table 2). All metal contamination indices are similarly classified. The pollution degree or effect scales up from unpolluted to extreme pollution as the index values increase. Biological risk of toxic substances was assessed by using two sets of sediment quality guidelines (SQGs), i.e., effect range low (ERL) and effect range median (ERM) and probable effects level (PEL) (Long and McDonald, 1998). Division of Fish, Wildlife, and Marine Resources from the Department of Environmental Conservation, New York, USA, has suggested the use of this set of SQGs. ERM and ERL values define three concentration classes for each specific chemical. Concentrations below the ERL imply few biological effects; concentrations between ERL to ERM

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indicate a potential risk in which effects would occur infrequently. Concentrations equal to or above the ERM indicate that adverse biological effects usually occur (Long et al., 1998; MacDonald et al., 2000; Chen et al., 2013).

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Assessment tool for metal calculation

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Additionally, the Ontario Ministry of Environment, Canada, has suggested two levels of risk for each particular chemical. These two values are the lowest effect level (LEL) and severe effect level (SEL), which are comparable to the ERL and ERM, respectively (Chen et al., 2013). In this study, the metal results of the sediment analyses from inner IB were evaluated with the ERL and ERM (Table 3). Besides the comparison of metal results with SQGs, m-ERM-Q, and m-PEL-Q methods (Table 2) have been used to assess the possible biological effects of metals.

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Results and discussions

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A Visual Basic Application (VBA) based software (SedCal) was developed in this study, for the data entry, calculation, charting, and analysis of evaluation indices. The software allows sediment indices to be colorized and graded according to the literature values. The user can enter the background and measured metal concentration data using a graphical user interface. The results of each evaluation method are created and printed on separate worksheets. This software ensures an easy, confident, quick way to evaluate and calculate indices. The most powerful aspect of this software compared to similar programs is the availability of different reference values and the graphical user interface. If requested, the corresponding author can provide the software.

Grain size and organic carbon

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Grain size composition is the most basic analysis of sediment classification. The spatial pattern of TOC content was measured to obtain the general properties of the sediment samples (El-Said et al., 2014; Okay et al., 2016). As shown in Fig 2, the sediments of the inner IB are predominantly composed of mud (< 63 µm; silt+clay) with an average percentage of about 77%, as reported in previous studies in IB (Algan et al.,1999 and Ergin et al. 1991). The fraction of sand (> 63 µm) in the shallow zone of the study area ranged between 4.8-65.2%. The TOC contents varied from 0.65% in sandy sediments to 3.54% by dry weight of the fine sediment samples, an average of 2.60%. TOC values in deep stations (1, 4, 7, 8, 12, 20, 24, 25) were high, while TOC values of shallow stations (2, 5, 9, 18, 19) which are under the effect of freshwater were found to below. Moreover, TOC contents (> 2.5%) were recorded as high at the north and south entrance of the inner IB (Fig 2). The sediment TOC levels of the IB were measured between 0.48 – 1.68% and 1.9-2.6% by Ergin et al. (1991) and Yaşar et al. (2001), respectively. Pekey et al. (2004) reported high concentrations of TOC (> 4.06%) in the north part of the inner bay. Previous studies, along with the current study, have shown that a substantial amount of TOC is discharged from coastal environments.

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Fig 2. The spatial variations of grain size and TOC concentration in marine sediments of the inner Izmit Bay.

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A strong positive correlation was observed between the metal concentrations and TOC (Fig 3). Similar findings were found by Aksu et al. (1998) and Yaşar el al. (2001). According to monitoring results, it is probable that the change in metal concentrations is related to the anthropogenic inputs. These anthropogenic inputs can be traced to the approximately 2 million inhabitants scattered around the inner IB. Three large pulp mills (last operated in 2004), chlor-alkali plants, four pharmaceutical and chemical plants, four petrochemical plants, three steel factories, and a leather factory are also located around the inner IB. Furthermore, domestic, agricultural and industrial effluents are discharged into the bay via Kumla and Sarı Rivers (Fig 1) (Morkoç et al., 2008; Pekey et al., 2006; Yaşar et al., 2001).

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3.2

Distribution of metals

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Fig 3. Correlations between the concentrations of eight metals and TOC in the inner Izmit Bay sediments.

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In order to assess metal pollution in the inner IB, surface sediments and their cumulative effects on sediment quality, the grain size distributions (Fig. 2), TOC and eight metals (As, Cd, Cu, Cr, Ni, Pb, Zn, and Hg) concentrations were determined in the sediment samples (Fig. 3). The concentrations of the metals measured in the replicate sediment samples are depicted in Table 3. The mean concentrations of the metals in the inner bay sediments followed a descending order: Zn> Cu> Cr> Ni> Pb> As> Cd> Hg. Zinc (Zn) showed significantly high concentrations at all stations. The zinc found in the IB can be traced to domestic wastewater and atmospheric emissions. Additionally, ship surface coatings often contain zinc-based primers (Okay et al., 2008).

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As shown in Table 3, similar to the grain size and TOC content of surface sediment, metal concentrations were found higher at the north and south entrances of inner IB. The highest and lowest metal concentrations were detected in stations 25 and 11, respectively. Copper (Cu) concentrations fluctuated from 50 to 105 mg kg-1. Copper is necessary for aquatic life as is Zn, but it can be toxic at higher levels (Saher and Siddiqui, 2016). Domestic and industrial wastewaters, the atmosphere, and runoff are the primary sources of Cu (Mulligan et al., 2001; Lin et al., 2013). The highest concentration of chromium (Cr), ̴ 75 mg kg-1, was recorded in the northern part of the inner IB (Fig 4.). Elevated levels of Cr originate from paint, coating, and leather industries in the IB. These domestic wastewaters are the major source of the Cr pollution in the inner IB. Nickel

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(Ni) and chromium were almost evenly distributed along all the studied area with ranges of 19 – 73 mg kg-1 and 41 – 121 mg kg-1, respectively. Arsenic (As), cadmium (Cd) and lead (Pb) metals have been found the lowest concentration in the marine sediments of the inner IB compared to the other metals.

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Since the waters of the inner IB are poorly renewed (Beşiktepe et al., 1994; Algan et al., 1999; GMK and TUBITAK MRC, 2016; GMK and TUBITAK MRC, 2018), high urban and industrial pressures lead to high-risk of eutrophication and low ecological quality (Tan et al., 2017). Corine, 2012 data supports this finding with 56%, 30%, and 13% land coverage for urban, industrial, and agricultural areas respectively (Fig 1.).

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Analytical results have been elaborated by using the ESRI ArcGIS Desktop 10.0 software, to show the spatial distribution of metal contents by using the kriging interpolation method (Fig 4). The color classification was made by taking into account the minimum and maximum values of each metal. According to the distribution model, the northern part of the inner IB were high polluted with respect to Zn, Hg, Cd, Cr, and Hg (Fig 4 ). Copper and Pb concentrations are high at both parts of the inner IB. The highest concentration of arsenic was recorded at the south entrance (Fig 4.), though the northern coast of IB is more industrialized than the southern side (Morkoç et al., 2008; Taymaz et al., 1984; Yaşar et al., 2001). The high concentration of metals in the sediments stations of the northern region (Fig 4) may be due to the influence of the large pulp mills, chloralkali plants, petrochemical plants, and chemical plants (Morkoç et al., 2008; Pekey et al., 2006; Yaşar et al., 2001). Metals found in the southern part of the inner bay can be attributed to shipyards and chemical plants (Yaşar et al., 2001). The industrial wastewater is discharged into the bay via Kumla and Sarı Rivers (Morkoç et al., 2008; Pekey et al., 2006; Yaşar et al., 2001). Furthermore, rivers (Fig 1), which flow into the inner IB, are directed to the northern part of the bay due to the prevailing anticlockwise current. Therefore, the northern part of the inner IB is expected to be more polluted than the other parts.

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Fig 4. Spatial distributions of As (a), Cu (b), Zn (c), Hg (d), Cd (e), Cr (f), Pb (g) and Ni (h) in surface sediment of the inner Izmit Bay.

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A limited number of studies (Ergin et al., 1991; Morkoç et al., 2008; Pekey et al. 2004; Pekey 2006; Taymaz et al., 1984; Yaşar et al., 2001) have been performed to determine the metal pollution of the sediments in the bay and its effects . The results of sediment studies published in the literature are illustrated in Table 4, including sediment data from various seas. Ergin et al. (1991) determined metal concentrations in the inner IB sediments, showing that they were at the natural background levels in the late 1980s (Table 4). Then, in the early 2000s, Pekey et al. (2004) and Pekey (2006) reported zinc contamination of the inner IB sediments (Table 4), partially resembling the present data obtained in this study.

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The results compiled in Table 4 indicate that Izmit Inner Bay coastal sediments are less polluted compared to the Marmara Sea in terms of As, Cd, and Pb metals. In the inner part of elongated Izmit Bay, Zn concentrations were similar to those measured in the Coastal Bohai Bay China (Gao and Chen, 2012), and the Montevideo Harbour, Uruguay (Muniz et al., 2004). The comparison of data in Table 4 clearly shows that the inner IB sediments are less polluted than the studied coastal and bay regions of Turkey and some of the estuaries worldwide (Table 4). The shallow water sediments in the inner IB are less polluted due to the freshwater input, circulation, and wave effects.

As 10.9±0.61 6.3±0.62 8.3±0.53 9.2±0.79 4.6±0.53 7.6±0.79 8.5±0.53 9.3±0.41 4.8±0.16 4.9±0.49 7.4±0.01 8.6±0.49 5.4±0.60 6.1±0.65 8.4±0.55 4.9±0.72 8.4±0.33 4.4±0.11 5.5±0.33 9.1±0.83 6.5±0.69 7.2±0.25 7.9±0.44 8.5±0.32 9.1±1.03 4.5 10.9 7.3 1.8 0.3

Cd 0.52±0.01 0.20±0.01 0.56±0.02 0.58±0.04 0.17±0.01 0.35±0.02 0.42±0.03 0.46±0.02 0.21±0.02 0.13±0.02 0.32±0.01 0.44±0.03 0.25±0.01 0.29±0.01 0.51±0.00 0.18±0.03 0.50±0.01 0.10±0.01 0.19±0.01 0.50±0.03 0.38±0.03 0.51±0.05 0.37±0.08 0.40±0.05 0.76±0.06 0.10 0.76 0.37 0.17

81 370 52.3 160.4

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Cr 68.8±0.85 40.9±2.58 76.2±3.41 73.6±2.05 53.5±0.67 70.6±2.22 63.9±2.99 69.7±2.09 48.6±0.82 48.9±1.22 59.7±0.25 70.9±0.42 56.2±1.18 61.1±1.43 90.6±0.25 58.7±2.35 96.7±4.37 63.6±1.79 63.9±2.63 116.1±9.56 77.2±0.32 98.6±4.09 110.8±2.81 120.7±10.37 112.8±5.47 40.9 120.8 74.9 22.6

34 270 18.7 108.2

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Cu 98.0±4.92 61.8±3.67 98.7±3.79 91.2±1.32 63.3±1.55 90.0±2.93 78.4±2.48 86.2±5.64 62.8±0.51 55.8±1.26 75.6±1.62 84.7±3.89 68.2±1.07 76.4±8.45 97.7±5.58 62.0±4.97 92.2±6.33 49.9±1.01 67.9±1.15 93.7±3.00 84.6±9.01 94.7±0.93 74.4±2.54 75.2±0.44 105.2±7.24 49.9 105.3 79.6 15.2

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Fe (g kg-1) 58.9±0.83 30.9±3.01 58.8±0.54 50.2±1.12 44.0±1.82 56.3±0.70 46.3±1.32 47.7±1.44 41.9±0.55 45.3±0.50 47.2±0.20 45.9±1.29 43.4±0.59 42.7±0.17 48.6±0.32 46.2±0.17 50.8±0.93 45.6±0.42 55.7±1.13 43.6±0.49 27.8±0.86 36.6±1.04 43.5±1.18 43.2±0.94 41.1±0.66 27.9 58.9 45.7 7.4 10

1.2 9.6 0.68 4.2

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Al (g kg-1) 89.4±1.28 59.4±5.81 83.8±1.85 75.9±1.77 72.2±3.27 82.7±1.37 73.7±2.63 76.5±2.67 66.6±0.35 67.0±1.02 73.5±0.48 73.0±3.05 67.3±0.36 69.4±0.60 73.8±0.35 65.3±1.03 79.5±0.93 56.4±0.39 65.7±0.54 68.2±1.21 51.4±1.01 62.3±1.66 72.9±1.84 71.9±0.70 66.4±1.78 51.5 89.4 70.6 8.5 47

8.2 70 7.2 41.6

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Ni 40.4±0.74 19.3±0.91 44.4±0.65 42.6±0.86 29.0±0.31 41.4±1.04 38.7±1.31 42.0±1.39 28.9±0.92 28.2±0.62 35.9±0.40 40.7±0.77 32.5±0.95 33.8±0.41 50.6±1.06 29.2±0.42 51.9±1.67 31.1±1.04 35.0±1.31 63.4±5.00 44.3±0.25 53.1±1.76 67.6±2.21 72.6±6.05 54.3±2.56 19.39 72.65 42.09 13.06

20

Pb 33.7±0.26 12.6±0.93 27.6±1.66 30.3±0.13 12.1±0.81 21.0±0.39 23.6±1.06 26.6±0.78 12.0±0.48 10.9±0.33 17.7±0.38 23.4±0.04 14.7±0.02 16.2±0.12 25.5±0.35 10.6±0.39 25.6±1.01 10.0±0.23 14.0±0.19 28.4±0.35 26.0±0.86 24.9±1.33 22.7±3.13 24.3±0.38 29.6±2.05 10.0 33.8 21.0 7.2

46.7 218 30.2 112

80

20.9 51.6 15.9 42.8

150 410 124 271

90

Zn 210.7±6.48 124.0±6.79 209.9±7.84 211.5±2.55 136.9±2.59 189.8±5.96 191.5±5.76 217.4±10.52 123.6±3.65 124.1±3.39 179.1±2.42 191.7±6.32 140.6±2.46 182.5±20.71 239.9±14.79 184.6±14.91 261.7±15.42 130.6±4.12 222.0±4.08 288.1±6.64 316.6±31.28 312.0±3.92 300.1±10.49 224.6±3.51 363.2±34.16 123.6 363.2 211.1 66.3

0.15 0.71 NA NA

0.3

Hg 0.545 0.207 0.409 0.469 0.11 0.271 0.346 0.52 0.126 0.078 0.42 0.352 0.168 0.2 0.34 0.081 0.402 0.049 0.084 0.367 0.262 0.263 0.212 0.169 0.748 0.05 0.75 0.29 0.17

NA NA NA NA

NA

TOC (%) 2.93 1.35 3.50 3.36 1.64 3.27 2.89 3.00 2.50 2.79 2.56 3.19 2.02 2.09 3.13 1.02 3.02 0.65 0.93 3.08 3.10 3.54 3.18 2.79 3.29 0.65 3.54 2.60 0.87

Table 3. The concentration levels of eight metals in the inner Izmit Bay sediments (mg kg-1 dry weight; standard deviation). Depth (m) 15 2.5 8 17 1.5 3.8 20 23.5 2.5 2 11.5 20 2 3 12 1.5 8 1.5 2 11.5 1 1.1 2 8.5 8.3 1 23.5 7.6

NA NA NA NA

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Station Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Minimum Maximum Average SD Shale Values1 ERL2 ERM2 TEL2 PEL2

Krauskopf, 1979; 2 Long and McDonald, 1998

S.D: standard deviation; NA: Not Available 1

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3.3

Pollution status and ecological risk assessment

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In order to deal with the sediment pollution problems, the levels of metal contamination need to be determined. The results of the metal concentrations in sediments do not reveal the degree of the contamination, especially the risk to sediment-dwelling organisms and their consumers (Okay et al., 2016). Numerous methods have been developed for the calculation of metal contamination levels in sediment (Abrahim and Parker, 2008; Gao and Chen 2012; Hakanson, 1980; Long and MacDonald, 1998; Müller, 1981; Sakan et al., 2009; Tomlinson et al., 1980). The indices use geochemical background values in order to evaluate the degree of pollution effectively. Okay et al. (2016) showed that the effects of different background values impact the results. The results of their study revealed that taking into account local background values in calculations effectuated more reliable results. There was not any identified local site to be used as a reference point in the inner IB. Consequently, the average shale values (Table 3) were used in indices calculations directly (Krauskopf, 1979).

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The spatial distribution of EF calculations is presented in Fig. 5a. The results show that zinc is significantly enriched (EF>3) in the middle and northern shelf of the inner IB. Moreover, according to the classification system of Sakan et al. (2009), the stations between 15 to 25 have moderate to moderately severe enrichment status (Fig. 5a). The northern shelf stations (20– 25) also contained higher TOC levels (Fig. 2). All other stations demonstrated minor to no enrichment along with most of the metals studied. According to Igeo, most of the data falls under 1, and none of the Igeo values were determined to be more than 2.0 (Fig 5b). Arsenic, Cr, and Ni were found at less than zero, indicating an unpolluted condition with respect to the sites. Igeo values of Cd, Pb, and Hg were observed at a slightly contaminated status in the inner IB.

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(a) (b) Fig 5. Enrichment factors (EF) (a) and Geoaccumulation indices (Igeo) (b) of the metals in the surface sediments of the inner Izmit Bay.

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The pollution load index (PLI), Cd and mCd indices are an interpretation of CF index in different forms. Owing to the results, none of these indices have revealed the distinctive characteristics of pollution in the inner IB (Fig 6 a, b and c). As previously mentioned, PERI values (Fig 7a) were calculated by using the toxic response of eight metals, and RI values (Fig 7b) are the sum of their PERI values. Among those metals, Cd and Hg were found to be the most responsible metals for the biological effects of the sediments. Consequently, RI was observed to be of very high-risk status at most of the stations. In fact, Cd was detected under the ERL value (Fig 8a).

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(a)

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(c) (b) Fig 6. Pollution Load Index (PLI) (a), Contamination Degree (Cd), and Modified Contamination Degree (mCd) (c) of the metals in the surface sediments of the inner Izmit Bay.

(a)

(b)

Fig 7. Potential Ecological Risk Index for single metal (PERI) (a) and Potential Toxicity Response Index (RI) (b) of the metals in the surface sediments of the inner Izmit Bay.

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(a)

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According to SQGs (ERL-ERM and TEL-PEL), biological risk on sedimentary fauna is attributed to contamination mostly from Ni, Cu, Zn, and Hg, and slightly from As. Notwithstanding, indices evaluation showed that solely Zn contamination was found at the north and the south part of the inner IB. The m-ERM-Q assessment shows that metals do not affect biological activity in the inner IB (Fig 8b). As shown in Fig 8c, the m-PEL-Q varied within the range of 0.29-0.75 and all of the inner IB stations have moderately impacted status.

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(b) (c) Fig 8. Assessment of the contamination (ERL – ERM) (a), Mean Effect Range Medium Quotient (mERM-Q) (b) and Mean Probable Effect Level (m-PEL-Q) (c) of the metals in the surface sediments of the inner Izmit Bay.

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Table 4. Sediment metal concentrations of Turkey and other countries (mg kg-1 dry weight; minimum-maximum). Location

As

Cd

Cr

Cu

Ni

Pb

Zn

Hg

Reference

2.78-5.89

4-11

0.10-1

41-121

50-105

19-73

10-34

124-363

0.05-1

This Study

Izmit Bay of the MS

NA

NA

0.12-2.13

NA

NA

NA

5-59.50

NA

0.09-6.10

Taymaz et al., 1984

Izmit Bay of the MS

1.38-4.45

NA

NA

7-89

18-51

43-154

24-83

47-128

NA

Ergin et al., 1991

Izmit Bay of the MS

3.47-6.15

6-30

0.1-1

110-406

20-82

24-108

20-61

25-240

0.4-1.5

Yaşar et al., 2001

Izmit Bay of the MS

NA

20-26.8

3.3-8.9

57.9-116.1

60.6-139

38.4-70.7

23.8-178

510-1190

NA

Pekey et al, 2006 Balkıs and Çağatay, 2001 Algan et al, 2004

0.8-4.6

NA

NA

11-238

3-52

Marmara Sea (MS)

0.6-7.7

NA

NA

11-654

3-107

1.05-4.58

NA

0.005-0.25

35.7-98.8

9.6-43.7

3.23-5.46

NA

0.06-3.94

65-264

20-703

NA

NA

0.12-0.66

601-224.5

3.05

NA

11.05

0.75

NA

NA NA

Nemrut Bay of the Aegean Sea Aliağa Bay of the Aegean Sea Coastal Bohai Bay, China Algericas Bay, Spain Al-Khobar, Arabian Gulf Lima Estuary, Portugal Montevideo Harbour, Uruguay

8-149

19-61

34-272

0.04-3.10

8-1731

10-85

33-410

0.04-3.0

18.1-63.4

22.3-89.4

75-271

1.70-9.60

28-240

91.3-751

86-970

0.32-7.02

20.1-62.9

23.4-52.7

20.9-66.4

55.3-457.3

NA

NA

15

81.5

25.5

75

NA

0.226

NA

182.97

75.10

5.358

52.68

NA

NA

NA

39-110

71-531

18-34

86-89

183-1133

NA

NA

NA

79-253

44-128

174-491

NA

NA: Not available

4

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Erdek Bay of the MS

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Fe (%)

Izmit Bay of the MS

59-13

26-34

Esen et al, 2010 Neşer at al, 2012 Gao and Chen, 2012 Diaz-de Alba et al, 2011 Alharbi and El-Sorogy, 2017 Cardosa et al, 2008 Muniz et al., 2004

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The eastern part of the IB has been receiving large amounts of domestic, agriculture and industrial chemical loads via surface runoff, riverine and wastewater discharges. Coastal pollution of the northern part was further affected by the southern inflow due to the anticlockwise current in the inner IB.

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This study shows that close correlations appeared between TOC and metal concentrations. The highest TOC and metal contents were recorded at north and south entrances of the inner IB. Consequently, the inner IB sediment quality was affected by the river discharges and anticlockwise water circulation. Long residence times and slow water circulation also impacted sediment quality.

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The sediment quality was evaluated using SQGs, individual, and overall contamination assessment methods (EF, Igeo, CF, PLI, RI) to reveal the degree of the contamination. The EF, Igeo, and CF evaluations revealed that the inner IB was contaminated with zinc. Antifouling paints are considered as the main source of Zn contamination. Owing to the SQGs (ERL-ERM), Hg, Zn, Cu, and Ni are grouped into ERL-ERM and may pose adverse biological effects (Fig 8a). On the contrary, it was found that PLI, Cd and mCd indices were not sufficient to reveal the distinctness of pollution between the sites. These results indicate that a comprehensive and meticulous evaluation is required to determine metal pollution and its effects when using metal evaluation indices. An unpolluted local site could not be found due to the anthropogenic pressure in the inner IB. Consequently, the average shale values (Table 3) were used in indices calculations directly

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(Krauskopf, 1979). Metal evaluations applied in this study could be revised if local reference station results were available. Moreover, a core sample could be taken, and the bottom level of the core sample can be used as background metal values in future studies.

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In order to prevent metal contamination, especially from agricultural and industrial metal loadings, there must be appropriate monitoring and remediation strategies to lessen the loadings and the cumulative effect of eight metals in close or nearshore sediments. It is crucial that industrial and wastewater plants develop suitable treatment systems to decrease the loadings. In order to limit further metal pollution in the IB, there is a need for proper sediment quality legislation on a national scale. Acknowledgments

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This study is financially supported by the Kocaeli Municipality (Izmit Bay Environmental Management of Dredge Material, IZTAR, Project No.5178704). The authors wish to thank Kocaeli Municipality for their supports as well as TÜBİTAK MRC Environment and Cleaner Production Institute’s chief senior researcher Dr. Leyla Tolun and researcher Hakan Atabay for their contributions on the measurement and the evaluation of metal findings in the sediments.

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