Modeling transport of microplastics in enclosed coastal waters: A case study in the Fethiye Inner Bay

Marine Pollution Bulletin xxx (xxxx) xxxx

Contents lists available at ScienceDirect

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

Modeling transport of microplastics in enclosed coastal waters: A case study in the Fethiye Inner Bay Asli Numanoglu Genca,1, , Nilufer Vuralb,2, Lale Balasc ⁎

a

Civil Engineering Department, TED University, Ziya Gökalp Street, No: 47-48 06420, Kolej, Çankaya, Ankara, Turkey Chemical Engineering Department, Ankara University, Dögol Street 06100, Tandoğan, Ankara, Turkey c Civil Engineering Department, Gazi University, Celal Bayar Avenue, 06570, Maltepe, Ankara, Turkey b

ARTICLE INFO

ABSTRACT

Keywords: Microplastic transport Enclosed waters Numerical modeling HYDROTAM-3D Fethiye Bay

In this study, transport and possible accumulation of microplastic marine litter in enclosed coastal waters are modeled numerically. The model is applied to the Fethiye Inner Bay, located in Fethiye-Göcek Specially Protected Area. In modeling studies, three dimensional coastal hydrodynamics, transport and water quality numerical model HYDROTAM-3D was used. The current climate was prepared by modeling long-term circulation patterns due to wind, wave and density stratifications. Following the hydrodynamic studies, the advection and diffusion of 3 mm size polystyrene particles by the coastal currents in the surface waters of Fethiye Inner Bay were simulated. The coastal regions where the microplastic pollution will be concentrated and transported were determined by the modeling scenarios. It has been found that microplastic accumulation is expected in the southwest and east coastal waters of the Fethiye Inner Bay. The results of the model will contribute to the databases for sustainable protection of the marine environments.

1. Introduction One of the most important pollution aspects in the oceans and seas of the World is the existence of plastic litter. In the oceans, 75% of litter is plastic (Galgani and Paul-Pont, 2015). It has been shown that plastic litter exists at the sea surface, seawater column and the sea bottom from macro to micro size. The adverse effects of plastic litter on the sea fauna, food chain and human health have also been studied (Frias et al., 2014; Galloway, 2015; Koelmans et al., 2015). Swallowing of plastic litter causes the death of marine life. There is evidence that microplastics are consumed at the bottom of the food chain and therefore transferred to the upper part of the food chain (Farrell and Nelson, 2013; Critchell and Lambrechts, 2016). The Mediterranean Sea has a high concentration of plastic litter where 83% of the floating litter is plastic (Lebreton et al., 2012; Galgani et al., 2013; Cozar et al., 2015). However, there is not much data on the distribution, type, amount and source of marine litter. Most of the studies focus on the plastic litter at the beaches and sea bottom (Suaria and Aliani, 2014; Pasquini et al., 2016; Ruiz-Orejon et al., 2016). For the regional seas, there are only few significant studies (Suaria and Aliani, 2014). Tubau et al. (2015) have identified that 73% of marine

litter is plastic between the depths of 140–1731 m. in the underwater canyons at the offshore of Genova and Lyon. Collignon et al. (2014), in their study, which focuses on micro and meso plastics, have shown that, 54% of the plastics in the surface waters are microplastics ranging between 2 and 5 mm in the Calvi Bay of Corsica. Vianello et al. (2013) studied the microplastics in the sediments of a tidal area in the Venice Lagoon and found that the most common microplastics are polyethylene and polypropylene. Suaria and Aliani (2014) have shown that, 93% of the surface marine litter in the middle and west Mediterranean is plastics with Adriatic Sea having the most concentration. Koutsodendris et al. (2008) identified that 56% of marine litter is plastic in the East Mediterranean. Ruiz-Orejon et al. (2016) have sampled the floating marine litter in the Sardinia coast, the Sicilia coast, the Adriatic Sea and the Ion Sea. They concluded that 97% of floating marine litter is plastic. The level of plastic pollution in marine waters has been shown in many studies and this resulted in international action plans. EU Marine Strategy Framework Directive (EU MSFD, 2008/56/EC) aims member countries to reach ‘Good Environmental Status (GES)’ by 2020. Additionally, at the 18th meeting of Contracting Parties to the Barcelona Convention for the Protection of the Marine Environment

Corresponding author. E-mail addresses: [email protected] (A.N. Genc), [email protected] (N. Vural), [email protected] (L. Balas). 1 Aslı Numanoğlu Genç 2 Nilüfer ⁎

https://doi.org/10.1016/j.marpolbul.2019.110747 Received 14 October 2019; Received in revised form 16 November 2019; Accepted 16 November 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Asli Numanoglu Genc, Nilufer Vural and Lale Balas, Marine Pollution Bulletin, https://doi.org/10.1016/j.marpolbul.2019.110747

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 1. The location of the Fethiye Inner Bay. Google Earth, 2019

Fig. 3. The comparison of CFSR wind estimations with ECMWF ERA-Interim wind estimations.

the surface boundary layer. Lebreton et al. (2012) applied a global ocean circulation model coupled with a Lagrangian particle tracking model to simulate the simulation of floating debris in the World's oceans. Mansui et al. (2015) applied a similar simulation for the Mediterranean basin. Ballent et al. (2013) used MOHID modeling system to model the transport and accumulation of non-buoyant plastic pellets in the Nazaré Canyon. Liubartseva et al. (2016) applied a Lagrangian model to predict the sea surface concentrations of plastics and their fluxes onto the coastlines in the Adriatic Sea. Stuparu et al. (2015) used the particle tracking module of Delft3D software to model the distribution and accumulation of plastic litter in the North Sea. In Turkey, there are a few studies on the abundance and distribution of plastic litter along Turkey's coastline. The studies on plastic litter generally focus on the sea bottom sediments and the beaches (Topçu et al., 2013; Eryaşar et al., 2014; Terzi and Seyhan, 2017; Yabanlı et al.,

Fig. 2. The comparison of CFSR wind estimations with ECMWF Operational wind estimations.

and the Coastal Region of the Mediterranean and its Protocols (COP 18, 2013), parties agreed on targeting GES status for 11 ecological objectives. In the report of 18th Meeting, the importance of modeling tools for the evaluation and identification of sources and fate of marine litter is emphasized (Report of Contracting Parties, 2013). In the literature, the abundance and spatial distribution of microplastics in different seas have been studied by various researchers. Tubau et al. (2015) observed the abundance of marine litter in deep submarine canyons of the Northwestern Sea and compared the field data with coastal storms. Kukulka and Brunner (2015) analyzed the effect of equilibrium wind waves on the distribution of microplastic in 2

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 5. Fetch distances of the Fethiye Bay. Table 2 Long term wave statistics.

Fig. 4. Annual wind rose of ECMWF Operational archive for 36.6N, 29.0E coordinate.

Direction

2019; Gündoğdu and Çevik, 2019). Vişne and Bat (2015) have evaluated the situation in the Black Sea within the framework of MSFD. Olguner et al. (2018) in their study focusing on the benthic marine litter on the shelf of Antalya, found that 72% of the marine litter is microplastic. Gündoğdu et al. (2017) found that the Northeast Levantine Coast of Turkey is heavily contaminated with microplastics. Tunçer et al. (2018) studied the microplastic distribution in the surface waters of the Sea of Marmara, and showed that microplastic concentrations are higher compared to other adjacent regions. Although the distribution and abundance of microplastics along Turkish coastline have been shown by a significant number of studies, modeling the transport of microplastics for Turkish coasts have not been investigated yet. Numerical modeling is an important tool in the simulation of physical, chemical and biological features of coastal environments and has become a reliable tool for studying coastal processes. Today there are quite a number of coastal models which focus on hydrodynamics, turbulence, transport processes and water quality of coastal areas. Some of

S SSW SW WSW W

1 h/yr

12 h/yr

1 h/week

Hs(m)

Tm(s)

Hs(m)

Tm(s)

Hs(m)

Tm(s)

3.2 3.0 2.3 2.4 1.1

7.0 7.1 7.2 7.1 6.5

1.9 1.6 1.3 1.4 1.3

6.3 6.3 6.3 6.3 5.9

1.1 0.86 0.72 0.81 0.74

5.6 5.5 5.6 5.6 5.2

the coastal models from the literature can be listed as MIKE by Danish Hydraulic Institute (DHI), Delft3D by Deltares, Flow-3D by Flow Science Inc., MOHID by MARETEC Inc., CORMIX by MixZon Inc. and HYDROTAM-3D by DLTM Inc. HYDROTAM-3D is a three dimensional hydrodynamic, transport and water quality model which simulates the coastal currents induced by wind and waves, density stratification due to changes in seawater temperature and salinity (HYDROTAM-3D, 2019). It has been verified by using several experimental and analytical results published in the literature since 1990 and it's successful use for a

Table 1 Wind rose data. Direction

Max. wind speed

Total hours

0–2 m/s

2–4 m/s

4–7 m/s

7–10 m/s

10–13 m/s

≥13 m/s

N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW Cum. Dur. Cum. %

10.91 12.83 10.23 10.96 8.06 10.47 11.24 12.63 15.24 14.54 10.78 10.1 10.66 8.11 10.28 11.01

11,238 17,058 18,234 14,790 12,402 8634 8352 7362 6828 8256 12,420 11,262 8130 6780 6552 8262 166,560

5688 7170 8004 7914 6150 4686 3468 2910 2964 3138 3384 3894 4032 3732 3636 4560 75,330 45

3738 7224 8322 6294 5130 3054 2922 2304 2070 3228 7332 5220 2796 2154 2106 2508 141,732 85

1638 2436 1788 552 1098 780 1632 1698 1332 1716 1650 2016 1116 834 702 1050 163,770 98

168 210 114 24 24 102 306 384 354 138 48 126 174 60 102 138 166,242 100

6 18 6 6 0 12 24 66 90 30 6 6 12 0 6 6 166,536 100

0 0 0 0 0 0 0 0 18 6 0 0 0 0 0 0 166,560 100

The bold numbers point out the dominat wind directions. 3

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

dense population, intense tourism activities and a high interaction with land-based pollutants are the areas which have a high potential of microplastic pollution. In the analysis, three-dimensional coastal current and circulation patterns due to wind, wave and density changes are studied. After the hydrodynamic studies, the distribution and accumulation of microplastics along the coastal currents in the Fethiye Inner Bay are simulated using the transport submodule of HYDROTAM-3D. The transport submodule used is extended for microplastic pollutant particles with the equations given in Section 2.3. The coastal areas where microplastic pollution will be intensified and spread in the Fethiye Inner Bay are determined by the modeling scenarios studied. In the modeling scenarios, the transportation and accumulation of floating 3 mm size microplastics due to wind and wave induced currents are considered. 2. Methodology In the analysis, the wind climate, wave climate, hydrodynamic, and transport sub-modules of HYDROTAM-3D are used. The mentioned HYDROTAM-3D sub-modules are briefly described below. 2.1. Wind climate sub-module

Fig. 6. Annual wave rose for the Fethiye Inner Bay.

The wind climate, which specifies the wind characteristics affecting a coastal region, is a fundamental element to be considered for almost all coastal and marine activities. In the database of HYDROTAM-3D, the hourly wind data of Meteorological Stations of Turkey from the year of their establishment to present; the analysis 6-hour wind data produced from numerical meteorological model of ECMWF (European Center for Medium-Range Weather Forecasts) Operational archive data with a 0.10 horizontal resolution, ERA (ECMWF Re-analysis) Interim data with a horizontal resolution of 0.250 and NCEP (National Center for Atmospheric Prediction) CFSR (Climate Forecast System Reanalysis) with a horizontal resolution of 0.50 is present. The locations of the databases are shown on a GIS-based map and when the coastal area in concern in selected, the closest databases are shown on the map. The monthly, seasonal and annual wind roses and the long-term and extreme wind statistics can be analyzed in the wind climate sub-module. The annual wind rose shows the rate at which winds blow at different speeds from different directions during the measurement period at meteorological stations. The directions starting from are: North (N) direction, NNE (NorthNorthEast), NE (Northeast), ENE (EastNorthEast), E (East), ESE (EastSouthEast), SE (South East), SSE (SouthSouthEast), S

variety of real-life cases along Turkish coastline has been demonstrated (Balas and Özhan, 2000; Balas, 2001; Balas et al., 2013; Genç et al., 2013; Genç, 2016; Cebe and Balas, 2016, 2018). In this study the transport sub-module of HYDROTAM-3D is extended for the simulation of plastic transport in enclosed coastal areas and then the model is applied to the Fethiye Inner Bay which is located in the coastal area of the Fethiye Bay. The Fethiye Bay is within the Western Mediterranean Basin, and the region is within the boundaries of the Fethiye-Göcek Specially Protected Area (SPA). The Bay is crescent-shaped. The Fethiye Bay is also designated as Sensitive Area according to the Communiqué No: 27271 on Sensitive and Low Sensitive Areas. There are waste treatment facilities in Fethiye, Ölüdeniz and Göcek towns in the Fethiye Bay. However, due to tourism pressure, especially in the summer, these facilities are not efficient. Besides, due to the concentration of olive oil production in Milas and Fethiye districts, which may create industrial pollution load, the basin can be said to be under pollution pressure due to agricultural and olive oil production activities. (Ministry of Environment and Urbanization, 2019). Coastal and transitional waters like the Fethiye Inner Bay, which has a

Fig. 7. The spatial and temporal measurements of the water salinity and temperature (TÜBİTAK 115Y468, 2017). 4

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 8. Bathymetry (water depths) of the modeling area.

(South), SSW (SouthSouthWest), SW (SouthWest), WSW (WestSouthWest), W (West), WNW (WestNorthWest), NW (NorthWest), and NNW (NorthNorthWest). Wind speeds are presented as a scale next to the wind rose. The numbers of wind from any direction are shown in the wind rose. Monthly average and maximum wind speeds are also presented as a graph. Monthly averages of wind speeds are calculated by taking the arithmetic average of all wind speeds in that month from the date of the establishment of meteorological stations to present. The highest, lowest and average maximum values observed for that month during the same periods (the average of the highest values of each year for any month) are given as the monthly maximum values. Monthly

average and maximum wind speeds of all coastal stations are also presented as a graph. The extreme value wind statistics are analyzed by Gumbel distribution. 2.2. The wave climate sub-module In the wave climate sub-module the wave climate is either analyzed by CEM method or by using the 6-hour wave height and periods obtained from ECMWF WAM (WAve Model) numerical model with a 0.10 resolution. The results of the CEM empirical method or the wave estimations of 5

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 9. Annual current rose points. Google Earth, 2019

C C C +u +w t x z

Table 3 Water depths corresponding to rose points. Point

Depth (m)

Latitude

Longitude

N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11

2 5 4.5 11.4 2 14.4 13 19.5 14.5 26.6 17

36° 36° 36° 36° 36° 36° 36° 36° 36° 36° 36°

29° 29° 29° 29° 29° 29° 29° 29° 29° 29° 29°

38.583′N 38.5′N 38.087′N 37.879′N 37.669′N 37.669′N 37.500′N 38.292′N 38.763′N 38.973′N 39.373′N

=

6.914′E 6.914′E 6.914′E 6.432′E 6.673′E 5.975′E 5.679′E 6.273′E 6.432′E 5.556′E 6.449′E

x

Dx

C + (w z

wp )

C z

C C C + Dy + Dz + kp C + S y z z x y

(1)

where C is the microplastic concentration; x, y are the horizontal coordinates and z is the vertical coordinate; u, v, and w are the current velocities in the x, y and z coordinates respectively; wp is the particle settling velocity; t is time; Dx, Dy and Dz are the coefficients of turbulence diffusion in x, y and z coordinates; kp is the reaction coefficient and S is the source concentration. The microplastic concentration C is generally given as either g/m3. If the diameter of microplastic particle is denoted as d, and its density is indicated as ρp, then the microplastic concentration C is given as in Eq. (2) (Zhang, 2017):

WAM model are analyzed for long-term wave statistics. The probability distribution that shows the relation between the significant wave heights generated in a sea state and their probabilities of occurrence are given by the log-linear distribution. Annual and seasonal wave roses are prepared by using calculated deep water significant wave heights and wave periods. The extreme value statistics for the significant wave heights is analyzed by Gumbel distribution.

C=n

p

6

d3

(2) 3

where n is the particle number given as particles/m . There are different approaches to particle settling velocities in the literature. In the transport modeling of microplastics, the settling velocity is important along with the density of the plastic studied. Isachenko et al. (2016) have shown that Eq. (3), proposed by Zhiyao et al. (2008) for natural sediment particles, can be used in the prediction of settling velocity of spherical plastic particles. Therefore in the modeling studies Eq. (3) is applied.

2.3. The hydrodynamic and transport sub-module The transport of microplastics is modeled with the HYDROTAM-3D transport sub-module. In the transport sub-module, the convectivediffusion equation is applied and solved. The three-dimensional pollutant transport equation applied for microplastics is given in Eq. (1).

WS =

d

where; 6

d 3 38.1 + 0.93d

12 7

7 8

(3)

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Annual current velocity for N1 (a) Surface layer (b) Bottom Layer

Annual current velocity for N2 (a) Surface layer (b) Bottom Layer

Annual current velocity for N3 (a) Surface layer (b) Bottom Layer

Annual current velocity for N4 (a) Surface layer (b) Bottom Layer

Fig. 10. Annual current velocities (cm/s) for points listed in Table 3 (N1-N11).

Annual current velocity for N5 (a) Surface layer (b) Bottom Layer

Annual current velocity for N7 (a) Surface layer (b) Bottom Layer

Annual current velocity for N6 (a) Surface layer (b) Bottom Layer

Annual current velocity for N8 (a) Surface layer (b) Bottom Layer

Fig. 10. (continued)

7

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Annual current velocity for N9 (a) Surface layer (b) Bottom Layer

Annual current velocity for N10 (a) Surface layer (b) Bottom Layer

Annual current velocity for N11 (a) Surface layer (b) Bottom Layer Fig. 10. (continued)

d =

g 2

1/3

d and

=

s

1

CFSR. In the literature, there are studies which compare the wind predictions of ECMWF and NCEP with measured data. Kalogeri et al. (2017) have shown that, for the representation of the local wind regime, ERA Interim is slightly more accurate than NCEP CFSR for the European Seas. They also concluded that both NCEP and ERA Interim tend to overestimate the wind speed for lower wind speeds but for intermediate and stronger wind speeds this tendency is gone. Decker et al. (2012) concluded that ERA-Interim datasets appear to be slightly better than all sets about all reanalysis products. ERA- Interim set had the lowest bias in the 6-h wind speed concerning the other databases. Hence, the question is whether to choose the ERA-Interim or ECMWF Operational data. Cavaleri et al. (2018) suggested that in coastal areas where accurate results are required, the spatial resolution should be sufficiently high. As ECMWF Operational data has a 0.10 horizontal resolution, ECMWF Operational 6-h wind predictions for the coordinate of 36.6N-29.0E are used for the wind climate. In Fig. 4 annual wind rose of ECMWF Operational archive for the coordinate of 36.6N, 29.0E is given. The data corresponding to Fig. 4 is given in Table 1. From Fig. 4, it is seen that SSW-W range is a seaward interval of the effective direction of wind speeds of 2 m/s and above. From Table 1, it is seen that W direction has the longest duration for wind speed 10–13 m/s. WSW direction is 10% lower in total duration than SW but it has more duration for wind speed 4–10 m/s. The landward interval is NNW-NE interval where NNE and NE directions are the dominating directions as seen from Table 1.

(4)

where ρs and ρ are the density of microplastic particles and the density of the fluid respectively, and ν is the kinematic viscosity of the fluid. The details of the turbulence sub-module of HYDROTAM-3D are briefly discussed in Balas and Özhan (2000) and Cebe and Balas (2016). 3. Application of numerical model to the Fethiye Inner Bay The modeling studies are carried out for the Fethiye Inner Bay and its location is shown in Fig. 1. Murt River shown in Fig. 1 is a major source for pollution discharges into the Fethiye Inner Bay, which is an enclosed part of the Fethiye Bay (Önal, 2011). 3.1. Wind climate of the Fethiye coastal area In the wind climate of the Fethiye Bay, we analyzed three wind data sources for the Fethiye Bay at 36.5 N, 29.0E coordinate: the 6-hour wind predictions of ECMWF Operational archive (2000–2018), ECMWF ERA Interim archive (1979–2018) and NCEP-CFSR archive (1979–2018). All wind predictions in these data sources are at 10 m height. The over-land hourly measurements of the Fethiye Meteorological Station are not used as it is classified as a Type 4 Station, stating that the measurements are affected by urbanization (Buyruk, 2019). The wind predictions of CFSR are compared with the wind predictions of ECMWF Operational archive for 2000–2018 and are given in Fig. 2. Similarly the wind predictions of CFSR are compared with the wind predictions of ECMWF ERA-Interim archive for 2000–2018 and are given in Fig. 3. It is seen from Figs. 2 and 3 that CFSR wind predictions are approximately 1.06 times higher than the ECMWF Operational wind predictions and 1.1 times higher than the ERA Interim wind predictions. The correlation between wind predictions is not strong with a value of 0.5 for ECMWF Operational-CFSR and 0.4 for ERA Interim –

3.2. Wave climate In the wave climate studies, first the fetch distances of the Fethiye Bay are analyzed to determine the effective wave directions. In Fig. 5, the fetch distances for the Fethiye Bay are shown. The Fethiye Bay is open to waves coming from S-SW direction. In the wave climate analysis the numerical wave estimations of 8

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 11. Micro-plastic release points.

WAM Model for 36.6N-29.0E coordinate for the years 2000–2018 are used. The long term wave statistics are studied using the data for this coordinate. In Table 2, the deep water significant wave heights, Hs corresponding to exceedance probabilities of 1 h/yr, 12 h/yr and 1 week/yr are given. From Table 2, it is seen that S and SSW directions have the highest significant wave height, Hs values. The Fethiye Inner Bay is an enclosed coastal area. Therefore, we analyzed the annual wave rose for the Fethiye Inner Bay area to see the wave height effect in this coastal water. In Fig. 6, the annual wave rose for the Fethiye Inner Bay is presented. The annual wave rose shows that

94.6% of the time wave heights are smaller than 0.20 m. Therefore, for the Fethiye Inner Bay area it can be concluded that wave action is negligible. 3.3. Circulation patterns The use of three-dimensional models is inevitable in coastal water areas where wind driven currents are important. As such currents change direction along with the depth and in the field, their analogy with two- and one-dimensional models results in significant errors. The 9

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 12. Microplastic distribution in the Fethiye Inner Bay – Scenarios 1 (60 particle/s from Entry Point 1).

turbulent coastal currents in the Fethiye Inner Bay are modeled using the hydrodynamic module of HYDROTAM-3D. In the modeling studies, the spatial and temporal measurements of the water salinity and temperature were used as given in Fig. 7. Seawater densities vary between 1025 and 1039 kg/m3 depending on temperature and salinity changes, and no stratification is observed along the depth. The water depths of the modeling area were digitized from the 1:25000 scale Fethiye Bay depth map of Turkish Naval Forces Office of Navigation, Hydrography and Oceanography. A numerical solution area of 7345 m × 8596 m was selected in the coastal water area as shown in Fig. 8. In the modeling water area, 150 × 150 m mesh size is used after the sensitivity study carried out with HYDROTAM-3D. In the simulation of wind and wave-induced currents, a 6-hour current stream time series was obtained using the time series of 6-hour wind and wave data for the 2010–2018 period, obtained from the ECMWF operational archive for the 36.6N-29.0E coordinate. The selected points in the modeling area at different water depths are presented in Fig. 9, and the water depths corresponding to these points are

given in Table 3. Annual current roses for the surface layer and bottom layer obtained at these points along the depth are presented in Fig. 10. Although the study area varies from region to region, surface layer waters are often drifted towards NNE-NE direction and SSW-SW direction with an average velocity of 15 cm/s and bottom layer waters are drifted to SE direction and NE direction with an average velocity of 5 cm/s. The circulations in the coastal areas are generally irregular and turbulent. In the model, the relation between turbulent motion and average motion is provided by vertical and horizontal eddy viscosities, and mass displacement generated by vertical and horizontal eddy diffusion. In semi-enclosed or enclosed coastal waters like bays, where horizontal distances are larger than the water depth, the turbulence intensities in the horizontal and vertical directions also vary significantly. These differences in vertical and horizontal create a nonisotropic situation. Consequently, in the model, different horizontal and vertical eddy viscosity values are used. The vertical eddy viscosity values are calculated by the isotropic k-ε model. In the horizontal eddy 10

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 13. Microplastic distribution in the Fethiye Inner Bay – Scenario 2 (30 particle/s from Entry Point 1).

viscosity calculations a sub grid scale sub-turbulence model is used.

(Corsica), found that 54% of the plastics found in surface waters are large-scale microplastics of 2–5 mm dimensions. Gündoğdu and Çevik (2017) conducted a study in the Gulf of İskenderun and the Gulf of Mersin. They measured the mean size of microplastics in the surface waters of the Gulf of İskenderun as 2.77 mm and in the Gulf of Mersin as 3.01 mm. Tunçer et al. (2018) in their study in the Sea of Marmara, have concluded that the size distribution of microplastics show a peak between 2 and 4 mm. In this study, microplastic particle sizes are assumed to be 3 mm in size and polystyrene is taken as the microplastic type. The average annual distribution of microplastic particles in the Fethiye Inner Bay due to six-hour annual winds and currents is modeled and the annual average microplastic distribution is presented. In the numerical modeling studies, it was assumed that the coastal area had a negligible microplastic concentration (Co = 0.0) at the initial time of modeling (t = 0). The separation of the particles from the bottom to the suspension state occurs when the bottom shear stress calculated by the hydrodynamic model exceeds the critical shear stress of the suspension

3.4. Microplastic transport modeling In the microplastic transport modeling studies, it is assumed that the plastic particles follow the velocity field (Rodi, 1993). The transport of microplastics is modeled by wind, wave and density induced currents. The density of most plastics is less than the density of seawater, consequently they are generally considered as buoyant (Wu et al., 2020). Polystrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terafitalate (PET) and polyvinyl chloride (PVC) are the most common polymers seen in environmental pollution (Hidalgo-Ruz et al., 2012). In the Western Mediterranean Sea, de Haan et al. (2019) studied the floating microplastics. They found that the most commonly used three polymers which are low and high density PE, PP and PS are present in the microplastics existing in the surface waters of the North Catalan coast and the southern coast of Spain. Collignon et al. (2014), in their research on micro and mesoplastic (5–200 mm) wastes in Calvi Bay 11

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 14. Microplastic distribution in the Fethiye Inner Bay – Scenario 3 (30 particle/s from Entry Point 2).

state. In the same way, the microplastic particle settles when the bottom shear stress calculated by the hydrodynamic model falls below the settling shear stress. In modeling studies, possible reactions of microplastics with seawater were neglected. It is also assumed that the circulation pattern is not affected by the microplastic concentration. The dispersion and distribution of microplastic particles in the Fethiye Inner Bay, are modeled using the HYDROTAM-3D numerical model transport module. Several modeling studies are carried out. Among these seven scenarios which are close to the microplastic concentration measurements, 0.01–0.055 particles/m2 as given by Güven et al., 2017 and Hamid et al., 2018, are given below. The point sources of microplastics used in the scenarios are shown in Fig. 11. In Scenario 1, it is assumed that microplastics are released from the mouth of the Murt River (point 1) continuously at a rate of 60 particles/ s during modeling. Sea water densities vary between 1025 and 1039 kg/m3 depending on temperature and salinity changes. The modeling was run for a year with the simulated currents. In Scenario 2, all conditions are kept the same as Scenario 1, and the expected

distribution at the end of one year is studied when the Murt Mouth particle concentration is 30 particles/s continuously. The annual microplastic distributions for Scenario 1 and Scenario 2 are presented in Figs. 12 and 13 respectively. In Scenario 1, microplastic concentrations accumulate at the levels of 0.05–0.4 particles/m2 in the Fethiye Inner Bay in the direction of the NE-SW axis as a result of continuous microplastic entry of 60 particles/s from Murt mouth (entry point 1) for one year. The highest concentration is reached at the Murt mouth in an area of 600 × 600 m at levels of 0.45 particles/m2. In Scenario 2, microplastic concentrations accumulate at 0.05–0.3 particle/m2 in the Fethiye Inner Bay. The highest concentration is reached at the Murt mouth in an area of about 400 × 400 m at 0.32 particles/m2. Although the input concentration decreased by 50%, there was a 29% reduction in microplastic concentration values in coastal waters of the Fethiye Inner Bay as shown in Figs. 12 and 13. In Scenario 3, microplastic concentrations of 0.67 particles/m2 are observed in the Northeastern area of the Fethiye Inner Bay when there 12

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 15. Microplastic distribution in the Fethiye Inner Bay – Scenario 4 (30 particle/s from Entry Point 3).

is a 30 particles/s microplastic inflow from the coastal waters of Letoonia Marina (entry point 2) for one year as shown in Fig. 14. The highest concentration was reached in an area of 300 × 300 m2 in the exit zone and 450 × 900 m2 in the shallow NE zone. Continuous microplastic input from point 2 has the potential to generate increasing microplastic pollution in the Fethiye Inner Bay under the influence of the dominant circulation pattern. Scenario 4 has a continuous microplastic input of 30 particles/s from the eastern coastal waters of the Fethiye Inner Bay (entry point 3), Annual microplastic distribution on the surface is observed 1.4 particle/ m2 levels in the northeastern shallow area of the Fethiye Inner Bay as seen in Fig. 15. Microplastic concentrations in the coastal waters near the entrance reach concentrations of 0.4–1.44 particles/m2 in an area of approximately 400 × 1200 m2. Continuous microplastic entry from point 3 causes the accumulation in the Fethiye Inner Bay under the influence of the dominant circulation scheme, and it has the potential to generate microplastic pollution in increasing proportions. In scenario 5, continuous microplastic input of 30 particles/s from

the eastern coastal waters of the Fethiye Inner Bay (entry point 4) is simulated. Annual microplastic distribution on the surface shows microplastic concentrations of 1.65 particles/ m2 in the eastern shallow zone of the Fethiye Inner Bay as given in Fig. 16. Microplastic concentrations in the coastal waters near the entrance reach high concentrations between 0.4 and 1.65 particles/m2 in an area of approximately 750 × 2700 m2. Under the influence of dominant circulation patterns, continuous microplastic input from point 4, causes accumulation along the eastern coastal waters in the Fethiye Inner Bay and has the potential to generate increasing microplastic pollution. In Scenario 6, under a continuous input of 30 particles/s microplastic particles from the SW coastal waters of the Fethiye Inner Bay (entry point 5), the annual microplastic distribution on the surface will reach to a concentration of 3.2 particles/m2 Fig. 17. In coastal waters near the entrance, microplastic concentrations reach levels of 1–3.2 particles/m2 in an area of approximately 1000 × 600 m2. Continuous microplastic entry from point 5 causes accumulation along the southern and eastern coastal waters in the Fethiye Inner Bay under the influence 13

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 16. Microplastic distribution in the Fethiye Inner Bay – Scenario 5 (30 particle/s from Entry Point 4).

of dominant fluctuations and has the potential to generate microplastic pollution in increasing proportions. In scenario 7, the case where pulse loadings of 5000 microplastic particles from all of the point sources at the same time are simulated. The distribution of microplastic particles after one day of simulation is shown in Fig. 18. This scenario allows us to investigate the transport of microplastic particles and thus understand the accumulation regions of microplastics. According to this simulation, microplastic accumulation is expected in the south-west coastal waters of the Fethiye Inner Bay where coastal circulations are weak and in coastal waters east of the Murt River mouth.

been an important issue in recent years. Modeling the transport and circulation of microplastic marine litter in coastal areas is important for determining the sensitive areas where they concentrate and the land resources for microplastics along these coasts. In the analysis, three dimensional coastal hydrodynamic, transport and water quality numerical model HYDROTAM-3D has been adapted to the Fethiye Inner Bay for the simulation of microplastic transport. Long term wind and wave statistics have been determined. Wind data of ECMWF operational archive, ERA Interim archive and CFSR archive were analyzed in wind forecast data. Studies have shown that the dominant wind directions are SSW-W and N-NE. According to the results of the modeling of wave climate, the dominant wave direction for the Fethiye Bay is the S-SSW range. Significant wave heights approaching from the dominant wave propagation direction range are expected to vary between 1 and 3 m and wave period between 1 and 7 s. The Fethiye Inner Bay is separated from the Fethiye Bay by an island. Wave heights in the Fethiye Inner Bay are significantly reduced due to the effects of wave diffraction and rotation, and 94.6% of the

4. Conclusions In this study, the numerical modeling of transport and possible accumulation of microplastic particles in the coastal waters of the Fethiye-Göcek Special Environmental Protection Area is analyzed. Sensitivities to plastic pollution in coastal waters under protection have 14

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 17. Microplastic distribution in the Fethiye Inner Bay – Scenario 6 (30 particle/s from Entry Point 5).

spread in the Fethiye Inner Bay have been determined by loading modeling scenarios. In order to investigate the transport of microplastic particles and thus understand the accumulation regions of microplastics, an instantaneous release of 5000 microplastic particles/s from all of the point sources at the same time is simulated. According to this simulation, microplastic accumulation is expected in the south-west coastal waters of the Fethiye Inner Bay where coastal circulations are weak and in coastal waters east of the Murt River mouth. The modeling results presented for the Fethiye Inner Bay will contribute to the determination of the maximum permissible daily pollutant load in similar enclosed coastal waters. By identifying areas sensitive to plastic pollution, a basic data source is created for future measures and plans. The results of hydrodynamics and transport modeling will also make important contribution to the quality classification studies of coastal waters, and will contribute to the databases for the sustainable protection of the marine environment.

year significant wave heights are of 20 cm. and less. Wind and wave climate studies show that the circulations in the Fethiye Inner Bay are wind-driven. The turbulent coastal currents in the Fethiye Inner Bay are modeled using the hydrodynamic module of HYDROTAM-3D. The wind and wave climate results and the spatial and temporal measurements of water salinity and temperatures in the Fethiye Bay were used as input in the model studies. In the simulations of wind and wave-induced currents, 6-hour-hour wind and wave data from the ECMWF operational archive for the coordinate 36.6 N-29.0E is used to obtain the current pattern time series, and current roses are formed. Though it varies from region to region in the study area, surface layer waters are often drifted towards the North-West direction with average velocities of 15 cm/s, and bottom layer waters with average velocities of 5 cm/s. After the hydrodynamic studies, the distribution and accumulation of microplastics along the coastal currents in the Fethiye Inner Bay were simulated using the extended transport submodule of HYDROTAM-3D. The coastal areas where microplastic pollution will be intensified and 15

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al.

Fig. 18. Microplastic distribution at the surface of the Fethiye Inner Bay (5000 particles/s from Entry all Points –pulse loading).

CRediT authorship contribution statement

Acknowledgements

Asli Numanoglu Genc: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Project administration, Supervision, Funding acquisition, Writing - review & editing. Nilufer Vural: Investigation, Writing - original draft, Writing - review & editing. Lale Balas: Conceptualization, Methodology, Formal analysis, Software, Validation, Resources, Data curation, Writing - review & editing.

This research was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK), under the grant number CAYDAG-117Y500 (“Transport Modeling of Microplastics and an Application to Fethiye Bay”). The authors would also like to thank to DLTM Inc. for providing HYDROTAM-3D software. References Balas, L., 2001. Simulation of pollutant transport in Marmaris Bay. China Ocean Eng. 15 (4), 565–578 Nanjing Hydraulics Research Institute (NHRI). Balas, L., Özhan, E., 2000. An implicit three dimensional numerical model to simulate transport processes in coastal water bodies. Int. J. Numer. Methods Fluids 34, 307–339 John Wiley and Sons, USA. Balas, L., İnan, A., Genç, A.N., 2013. Modelling of dilution of thermal discharges in enclosed coastal waters. Res. J. Chem. Environ. 17 (10), 82–89. Ballent, A., Pando, S., Purser, A., Juliano, M.F., Thomsen, L., 2013. Modelled transport of benthic marine microplastic pollution in the Nazare Canyon. Biogeosciences 10, 2957–7970. https://doi.org/10.5194/bg-10-7957-2013.

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

16

Marine Pollution Bulletin xxx (xxxx) xxxx

A.N. Genc, et al. Buyruk, T., 2019. GIS Based Electronic Atlas of Wind and Wave Climate and Energy Potential on Turkish Coasts. unpublished Ph.D. Thesis. Gazi University, Ankara (204 pp). Cavaleri, L., Abdalla, S., Benetazzo, A., Bertotti, L., Bidlot, J.-R., Breivik, Ø., Carniel, S., Jensen, R.E., Portilla-Yandun, J., Rogers, W.E., Roland, A., Sanchez-Archilla, A., Smith, J.M., Staneva, J., Toledo, Y., van Vledder, G.P., van der Westhuysen, A.J., 2018. Wave modelling in coastal and inner seas. Prog. Oceanogr. 167, 164–233. https://doi.org/10.1016/j.pocean.2018.03.010. Cebe, K., Balas, L., 2016. Water quality modelling in Kaş Bay. Appl. Math. Model. 40 (3), 1887–1913. https://doi.org/10.1016/j.apm.2015.09.037. Cebe, K., Balas, L., 2018. Monitoring and modeling land-based marine pollution. Reg. Stud. Mar. Sci. 24, 23–39. Collignon, A., Hecq, J.H., Galgani, F., Collard, F., Goffart, A., 2014. Annual variation in neustonic micro- and meso-plastic particles and zooplankton in the Bay of Calvi (Mediterranean-Corsica). Mar. Pollut. Bull. 79, 293–298. https://doi.org/10.1016/j. marpolbul.2013.11.023. COP 18, 2013. 18th meeting of Contarcting Parties to Barcelona Convention. https://sdg. iisd.org/news/barcelona-convention-parties-adopt-declaration-on-marineenvironment/. Cozar, A., Sanz-Martin, M., Marti, E., Gonzalez-Gordillo, J.I., Ubeda, B., Galvez, J.A., Irigoien, X., Duarte, C.M., 2015. Plastic accumulation in the Mediterranean Sea. PLoS ONE. https://doi.org/10.1371/journal.pone.0121762. Critchell, K., Lambrechts, J., 2016. Modelling accumulation of marine plastics in the coastal zone; what are the dominant physical processes? Estuar. Coast. Shelf Sci. 171, 111–122. 105. https://doi.org/10.1016/j.ecss.2016.01.036. de Haan, W.P., Sanchez-Vidal, A., Canals, M., 2019. Floating microplastics and aggregate formation in the Western Mediterranean Sea. Mar. Pollut. Bull. 140, 523–535. https://doi.org/10.1016/j.marpolbul.2019.03.044. Decker, M., Brunke, M.A., Wang, Z., Sakaguchi, K., Zeng, X., Bosilovich, M.G., 2012. Evaluation of the reanalysis products from GFSC, NCEP, and ECMWF using flux tower observations. J. Clim. 25, 1916–1944. https://doi.org/10.1175/JCLI-D-11-00004.1. Eryaşar, A.R., Özbilgin, H., Gücü, A.C., Sakınan, S., 2014. Marine debris in the bottom crawl catches and their effects on the selectivity grids in the north eastern Mediterranean. Mar. Pollut. Bull. 81, 80–84. https://doi.org/10.1016/j.marpolbul. 2014.02.017. Farrell, P., Nelson, K., 2013. Trophic level transfer of microplastic: Mytilus edulis (L.) to Carcinus maenas (L.). Environ. Pollut. 177, 1–3. https://doi.org/10.1016/j.envpol. 2013.01.046. Frias, J.P.G.L., Otero, V., Sobral, P., 2014. Evidence of microplastics in samples of zooplankton from Portuguese coastal waters. Mar. Environ. Res. 95, 89–95. https://doi. org/10.1016/j.marenvres.2014.01.001. Galgani, F., Paul-Pont, I., 2015. Chapter 11: protected shores contaminated with plastic: from knowledge to action. In: Baztan, J., Chouinard, O., Jorgensen, B., Tett, P., Vanderlinden, J.P., Vasseur, L.H. (Eds.), Coastal Zones. Elsevier. Galgani, F., Hanke, G., Werner, S., De Vrees, L., 2013. Marine litter within the European Marine Strategy Framework Directive. ICES J. Mar. Sci. 70 (6), 1055–1064. https:// doi.org/10.1093/icesjms/fst122. Galloway, T.S., Bergmann, M., Gutow, L., Klages, M., 2015. Part II: micro-and-nanoplastics and human health. In: Marine Anthropogenic Litter. Springer Open, Almanya. Genç, A.N., 2016. Modeling of sediment transport processes in Alara, Turkey. J. Fac. Eng. Archit. Gazi Univ. 31 (3), 545–553. https://doi.org/10.17341/gummfd.22374. Genç, A.N., İnan, A., Yılmaz, N., Balas, L., 2013. Modelling of erosion at Göksu coasts. J. Coast. Res. 65 (2), 2155–2160. https://doi.org/10.2112/SI65-364.1. Google Earth, 2019. The Fethiye Inner Bay. Image by Maxar Technologies. Google Earth Pro 7.3.2.5776. Gündoğdu, S., Çevik, C., 2017. Micro- and mesoplastics in Northeast Levantine coast of Turkey: the preliminary results from surface examples. Mar. Pollut. Bull. https://doi. org/10.1016/j.marplbul.2017.03.002. Gündoğdu, S., Çevik, C., 2019. Mediterranean dirty edge: high level of meso and macroplastics pollution on the Turkish coast. Environ. Pollut. 255, 113351. https://doi. org/10.1016/j.envpol.2019.113351. Gündoğdu, S., Çevik, C., Karaca, S., 2017. Fouling assemblage of benthic plastic debris collected from Mersin Bay, NE Levantine coast of Turkey. Mar. Pollut. Bull. 124 (1), 147–154. https://doi.org/10.1016/j.marpolbul.2017.07.023. Güven, O., Gökdağ, K., Jovanovic, B., Kıdeyş, A.E., 2017. Microplastic litter composition of the Turkish territorial waters of the Mediterranean Sea, and its occurrence in the gastrointestinal tract of fish. Environ. Pollut. 223 (286–294), 107. https://doi.org/10. 1016/j.envpol.2017.01.025. Hamid, F.S., Bhatti, M.S., Anuar, N., Anuar, N., Mohan, P., Periathamby, A., 2018. Worldwide distribution and abundance of microplastic: how dire is the situation? Waste Manag. Res. 36 (10), 873–897. https://doi.org/10.1177/0734242X18785730. Hidalgo-Ruz, V., Gutow, L., Thompson, R.C., Thiel, M., 2012. Microplastics in the marine environment: a review of the methods used for identification and quantification. Environ. Sci. Technol. 46 (6), 3060–3075. https://doi.org/10.1021/es2031505. HYDROTAM-3D, 2019. HYDROTAM-3D Hakkında (About HYDROTAM-3D). https:// hydrotam3d.com. Isachenko, I., Khatmulina, L., Chubarenko, I., 2016. Settling velocity of marine microplastic particles: laboratory tests. In: Geophysical Research Abstracts Vol. 18, EGU2016-6553, 2016 EGU General Assembly 2016, Vienna, Austria. Kalogeri, C., Galanis, G., Spyrou, C., Diamantis, D., Baladima, F., Koukoula, M., Kallos, G., 2017. Assessing the European Offshore wind and wave energy resource for combined exploitation. Renew. Energy 101, 244–264. https://doi.org/10.1016/j.renene.2016. 08.010.

Koelmans, A.A., Besseling, E., Shin, W.J., 2015. Part III: nanoplastics in the aquatic environment. Critical review. In: Bergmann, M., Gutow, L., Klages, M. (Eds.), Marine Anthropogenic Litter. Springer Open, Germany, pp. 325–342. Koutsodendris, A., Papatheodorou, G., Kougiourouki, O., Georgiadis, M., 2008. Benthic marine litter in four gulfs in Greece, Eastern Mediterranean; abundance, composition and source identification. Estuar. Coast. Shelf Sci. 77, 501–512. https://doi.org/10. 1016/j.ecss.2007.10.011. Kukulka, T., Brunner, K., 2015. Passive buoyant tracers in the ocean surface boundary layer: 1. Influence of equilibrium wind-waves on vertical distributions. J. Geophys. Res. Oceans 120, 3837–3858. https://doi.org/10.1002/2014JC010487. Lebreton, L.C.-M., Greer, S.D., Borrero, J.C., 2012. Numerical modelling of floating debris in the world’s oceans. Mar. Pollut. Bull. 64, 653–661. https://doi.org/10.1016/j. marpolbul.2011.10.027. Liubartseva, S., Coppini, G., Lecci, R., Creti, S., 2016. Regional approach to modeling the transport of floating plastic debris in the Adriatic Sea. Mar. Pollut. Bull. 103, 115–127. https://doi.org/10.1016/j.marpolbul.2015.12.031. Mansui, J., Molcard, A., Ourmieres, Y., 2015. Modelling the transport and accumulation of floating marine debris in the Mediterranean Basin. Mar. Pollut. Bull. 91, 249–257. https://doi.org/10.1016/j.marpolbul.2014.11.037. Ministry of Environment and Urbanization, 2019. Batı Akdeniz Kirlilik Önleme Eylem Planı (action plan on the prevention of marine litter in the Western Mediterranean). https://www.csb.gov.tr/db/cygm/editordosya/Bati_Akdeniz%20_Havzasi.pdf. Olguner, M.T., Olguner, C., Mutlu, E., Deval, M.C., 2018. Distribution and composition of benthic marine litter on the shelf of Antalya in the eastern Mediterranean. Mar. Pollut. Bull. 136, 171–176. https://doi.org/10.1016/j.marpolbul.2018.09.020. Önal, Z., 2011. Sedimentological, Mineralogical and Geochemical Characteristics of Beaches of the Fethiye Gulf. M.Sc. Thesis. Ankara University, Ankara, Turkey. Pasquini, G., Ronchi, F., Strafella, P., Scacella, G., Fortibuoni, T., 2016. Seabed litter composition, distribution and sources in the Northern and Central Adriatic Sea (Mediterranean). Waste Manag. 58, 41–51. https://doi.org/10.1016/j.wasman.2016. 08.038. Report of Contracting Parties, 2013. Report of the 18th ordinary meeting of the contracting parties to the convention for the protection of the marine environment and the coastal region of the Mediterranean and its protocols. https://wedocs.unep.org/ bitstream/handle/20.500.11822/5995/13ig21_9_eng.pdf?sequence=1& isAllowed=y. Rodi, W., 1993. Turbulence Models and Their Application in Hydraulics, 3rd edn. IAHR Series A.A. Balkema, Delft, Rotterdam, The Netherlands IAHR. (116 pp). Ruiz-Orejon, L.F., Sarda, R., Ramis-Pujol, J., 2016. Floating marine debris in the Central and Western Mediterranean Sea. Mar. Environ. Res. 120, 136–144. https://doi.org/ 10.1016/j.marenvres.2016.08.001. Stuparu, D., van der Meulen, M., Kelissen, F., Vethaak, D., el Serafy, G., 2015. Developing a transport model for plastic distribution in the North Sea. In: E-proceedings of the 36th IAHR World Congress, pp. 1–5. Suaria, G., Aliani, S., 2014. Floating debris in the Mediterranean Sea. Mar. Pollut. Bull. 86, 494–504. https://doi.org/10.1016/j.marpolbul.2014.06.025. Terzi, Y., Seyhan, K., 2017. Seasonal and spatial variations of marine litter on the southeastern Black Sea coast. Mar. Pollut. Bull. 120, 154–158. https://doi.org/10.1016/j. marpolbul.2017.04.041. Topçu, E.N., Tonay, A.M., Dede, A., Öztürk, A.A., Öztürk, B., 2013. Origin and abundance of marine litter along sandy beaches of the Turkish Western Black Sea Coast. Mar. Environ. Res. 85, 21–28. https://doi.org/10.1016/j.marenvres.2012.12.006. Tubau, X., Canals, M., Lastras, G., Rayo, X., Rivera, J., Amblas, D., 2015. Marine litter on the floor of deep submarine canyons of the Northwestern Mediterranean Sea: the role of hydrodynamic processes. Prog. Oceanogr. 134, 379–403. https://doi.org/10. 1016/j.pocean.2015.03.013. TÜBİTAK 115Y468, 2017. Final Report of TÜBİTAK 115Y468 Project, Monitoring and Evaluation of Water Quality Parameters in Coastal Waters. 127 p. Gazi University, Ankara, Turkey. Tunçer, S., Artüz, O.B., Demirkol, M., Artüz, M.L., 2018. First report of occurence, distribution, and composition of microplastics in surface waters of the Sea of Marmara, Turkey. Mar. Pollut. Bull. 135, 283–289. https://doi.org/10.1016/j.marpolbul.2018. 06.054. Vianello, A., Boldrin, A., Guerriero, P., Moschino, V., Rella, R., Sturaro, A., Da Ros, L., 2013. Microplastic particles in sediments of Lagoon of Venice, Italy: first observations on occurence, spatial patterns and identification. Estuar. Coast. Shelf Sci. 130, 54–61. https://doi.org/10.1016/j.ecss.2013.03.022. Vişne, A., Bat, L., 2015. Evaluation of marine litter on the marine strategy framework directive and current status in the Black Sea. J. Aquac. Eng. Fish. Res. 1 (3), 104–115. https://doi.org/10.3153/JAEFR15011. Wu, f, Pennings, S.C., Tong, C., Xu, Y., 2020. Variation of microplastics composition at small spatial and temporal scales in a tidal flat of the Yangtze Estuary, China. Sci. Total Environ. 699, 134252. https://doi.org/10.1016/j.scitotenv.2019.134252. Yabanlı, M., Yozukmaz, A., Şener, İ., Ölmez, Ö., 2019. Microplastic pollution at the intersection of the Aegean and Mediterranean Seas: a study of the Datça Peninsula (Turkey). Mar. Pollut. Bull. 145, 47–55. https://doi.org/10.1016/j.marpolbul.2019. 05.003. Zhang, H., 2017. Transport of microplastics in coastal seas. Estuar. Coast. Shelf Sci. 199, 74–86. https://doi.org/10.1016/j.ecss.2017.09.032. Zhiyao, S., Tingting, W., Fumin, X., Ruijie, L., 2008. A simple formula for predicting settling velocity of sediment particles. Water Sci. Eng. 1 (1), 37–43 ISSN 1674–2370, http://kkb.hhu.edu.cn, e-mail: [email protected].

17