Microplastics in Turkish Coastal Lagoons: Unveiling the Hidden Threat to Wetland Ecosystems

Microplastics in Turkish Coastal Lagoons: Unveiling the Hidden Threat to Wetland Ecosystems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microplastics in Turkish Coastal Lagoons: Unveiling the Hidden Threat to Wetland Ecosystems Sedat Gündoğdu, Cem Çevik, Yahya Terzi, Kenan Gedik, Ferhat Büyükdeveci, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6201957/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transitional ecosystems like coastal lagoons provide numerous ecosystem services. However, they are increasingly threatened by plastic pollution, particularly microplastics (MPs). Despite growing concerns, the occurrence and distribution of MPs in Türkiye’s lagoon systems remain largely unknown. This study aims to assess the abundance, composition, and seasonal variability of MPs in the surface water and sediments of five lagoons located in the northeastern Mediterranean region of Türkiye. Additionally, potential MP sources and their environmental implications are addressed. Water and sediment samples were collected from Akyatan, Tuzla, Ağyatan, Çamlık, and Yelkoma Lagoons during the November and June periods. MPs were extracted using density separation and digestion techniques, quantified via stereo microscopy, and characterized through µ-Raman spectroscopy to identify polymer composition. A total of 15,526 MPs were recovered, with significantly higher concentrations in water (47.5 ± 4.02 MPs/L) during November compared to June (17.0 ± 2.57 MPs/L; p < 0.05). MP concentrations varied among lagoons, with Yelkoma and Tuzla exhibiting the highest levels in water, while Akyatan showed the highest sediment contamination. Fibers were the dominant MP type, followed by fragments and films. polymer analysis identified polyethylene (PE), polypropylene (PP), and polyester (PES) as the most common polymers, indicating agricultural runoff, fishing activities, and mismanaged plastic waste as primary MP sources. This study provides the first comprehensive assessment of MP pollution in Turkish lagoons, highlighting seasonal and spatial differences in contamination levels. The results highlight the pressing need for improved waste management policies and conservation strategies to mitigate MP pollution in these ecologically and economically significant coastal systems. Çukurova Delta microplastic polymer coastal lagoon seasonal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights - First study on microplastics in Turkish Mediterranean coastal lagoons. - Microplastic pollution varies by season, with higher levels in November. - Fibers dominate, with polyethylene, polypropylene, and polyester most common. - Agricultural runoff and urban/industrial waste are key sources of microplastic pollution. - Findings stress the need for improved waste management and conservation. 1. Introduction Plastics are synthetic organic compounds that are used in all areas of modern life due to their versatility. However, their widespread use has led to significant environmental concerns, particularly the accumulation of plastic waste in marine and coastal ecosystems. Plastics production has skyrocketed since the first mass production in the 1950s, reaching approximately 414 million metric tons per year globally by 2023 (Plastic Europe, 2024). Assessments indicate that if no restrictions are imposed, plastic production is projected to triple by 2100 (Bergmann et al., 2022), inevitably leading to a corresponding increase in plastic waste generation (Stegmann et al., 2022). Among the various pollutants affecting marine environments, microplastics (MPs) have emerged as a critical environmental issue, now recognized as an emerging class of contaminants Defined as plastic particles smaller than 5 mm in diameter, MPs originate from the fragmentation of larger plastic debris (secondary MPs) or are manufactured at a microscopic scale for industrial applications, such as cosmetics and textiles (primary MPs) (GESAMP, 2016). The persistence, buoyancy, and ability of MPs to disperse widely in aquatic environments exacerbate their environmental impact, making them a major global concern (Ali et al., 2025). Globally, an estimated 19–23 million metric tons of plastics entered aquatic ecosystems in 2016, and projections suggest these levels could reach 53 million metric tons by 2030 (Borrelle et al., 2020). MP pollution is now documented not only in heavily impacted coastal regions but also in remote glaciers, deep-sea sediments, and surface waters of the Pacific, Atlantic, Arctic, and Antarctic Oceans (Aydın et al., 2023; S. Gündoğdu et al., 2024; Tsuchiya et al., 2024). Numerous marine MP hotspots have been identified, particularly in areas of oceanic convergence, such as gyres, where plastics accumulate due to ocean currents. One of the most affected regions is the Mediterranean Sea (Gedik et al., 2022; Terzi et al., 2024). The Mediterranean Sea has been identified as one of the most polluted marine ecosystems in the world, exhibiting some of the highest concentrations of floating plastic debris (Cincinelli et al., 2019; Liubartseva et al., 2018; Terzi et al., 2024). This is primarily attributed to its semi-enclosed nature, limited water exchange, densely populated coastal areas, and intense anthropogenic activities, including tourism, fishing, and industrial discharges (Suaria et al., 2016). Estimates suggest that between 5% and 10% of the total global plastic mass accumulates in the Mediterranean (van Sebille et al., 2015). A major yet often overlooked contributor to this pollution is agricultural plastic use, which has seen a dramatic increase across the Mediterranean basin, including Türkiye (Akca et al., 2024; R. Gündoğdu et al., 2022). The application of plastics in agriculture—such as greenhouse films, mulch films, irrigation pipes, and plastic containers—contributes significantly to environmental MP pollution. Agricultural plastics undergo physical degradation, chemical aging, and biological breakdown, releasing MP fragments into the soil and water systems (Ng et al., 2018; Rezaei et al., 2022). These MPs are then transported to coastal and lagoon environments through surface runoff, irrigation drainage, and atmospheric deposition, further exacerbating the contamination of aquatic ecosystems. Coastal lagoons, which serve as transitional zones between terrestrial and marine ecosystems, are particularly vulnerable to MP pollution (Garcés-Ordóñez et al., 2022). These ecosystems provide critical services, including serving as breeding, feeding, and nursery grounds for numerous marine species (Newton et al., 2018). Their ecological significance makes them valuable conservation areas; however, their semi-enclosed nature and restricted water circulation render them highly susceptible to pollutant accumulation. Furthermore, lagoons support extensive fishing and aquaculture activities, making them economically significant while simultaneously exposing them to increased anthropogenic pressures. The presence of MPs in these environments can have cascading effects on aquatic biodiversity, food web dynamics, and human health due to their potential for bioaccumulation and biomagnification in marine organisms (Bhattacharjee et al., 2025). Despite growing concerns about plastic pollution in coastal environments, no prior research has been conducted on MP contamination in these lagoons. Previous studies in the region have primarily focused on heavy metal contamination, leaving a significant gap in understanding the extent of MP pollution. Given the widespread use of agricultural plastics in surrounding rural areas and the potential for plastic waste to enter lagoon ecosystems via connected freshwater channels, assessing MP pollution in these environments is crucial for effective conservation and management strategies. In Türkiye, five major lagoons—Akyatan, Tuzla, Ağyatan, Çamlık, and Yelkoma—located along the northeastern Mediterranean coast within Adana Province, are critical biodiversity hotspots. Among them, Yumurtalık/Çamlık and Yelkoma lagoons hold special conservation status, with Yumurtalık Lagoons designated as a Natural Conservation Area (1993), a Wildlife Protection Area (1994), and a Ramsar Site (2005), while Yelkoma Lagoon is protected as a Nature Reserve. These protected areas provide refuge for migratory birds, fish, and other aquatic species; however, they are increasingly threatened by human activities, particularly extensive agricultural and fishing operations. This study aims to assess the occurrence, distribution, and potential sources of MP pollution in the surface water and sediments of five lagoons located along the northeastern Mediterranean coast of Türkiye. Specifically, the objectives are i) to seasonally quantify and characterize MPs in the surface waters and sediments of Akyatan, Tuzla, Ağyatan, Çamlık, and Yelkoma lagoons, ii) to identify potential sources of MPs in these lagoons, with a focus on agricultural runoff, mismanaged plastic waste, and urban influences. To compare MP contamination levels in these lagoons with those reported in similar ecosystems worldwide. To provide baseline data that can inform future monitoring programs and contribute to the development of mitigation strategies for reducing MP pollution in coastal lagoon environments. By addressing these objectives, this study will contribute to the growing body of knowledge on MP pollution in semi-enclosed coastal ecosystems and provide valuable insights for policymakers, conservationists, and local stakeholders working toward the protection of these ecologically and economically significant habitats. 2. Materials and Methods 2.1. Sampling Location This study was conducted in five lagoon systems within the Adana province, located in the Çukurova Delta, a wetland of international significance and a critical ecological hotspot in Türkiye. The delta contains diverse coastal lagoons that provide key ecological functions, including biodiversity conservation, hydrological regulation, and fisheries support. Yelkoma Lagoon, Akyatan Lagoon, Tuzla Lagoon, Çamlık Lagoon, and Ağyatan (Hurmaboğazı) Lagoon were chosen based on their ecological importance and susceptibility to MP pollution (Bayrak, 2023) (Fig. 1 ). Yelkoma Lagoon, located in the eastern sector of the Çukurova Delta, features a unique wetland system characterized by a mix of brackish and freshwater habitats. It provides a critical habitat for diverse ichthyofauna, migratory avifauna, and aquatic vegetation. Samples were collected from seven strategically distributed stations within the lagoon to assess MP contamination (Bayrak, 2023). Akyatan Lagoon, the largest lagoon in Türkiye, covers an area of approximately 7,420 hectares and holds the status of a RAMSAR site due to its global significance for bird conservation. Its hydrology is influenced by both saline and freshwater inputs, which fluctuate seasonally. To investigate the spatial distribution of MP pollution, samples were from nine sampling stations across the lagoon (Satar, 2018). Tuzla Lagoon, situated in proximity to the Karataş district, is an ecologically important wetland with shallow waters and high biological productivity. Spanning approximately 550 hectares, it supports a wide range of aquatic species. Samples were collected from ten stations to assess MP contamination (Bayrak & Ekinci, 2015). Çamlık Lagoon, located near the Ceyhan River, is a relatively smaller wetland system yet remains ecologically significant. It consists of interconnected water bodies and marshlands that provide habitat for fish populations and migratory bird species. MP sampling was conducted at seven stations within this lagoon (Bayrak, 2023). Ağyatan (Hurmaboğazı) Lagoon, positioned in a dynamic coastal environment, undergoes seasonal fluctuations in salinity and water depth. It plays a vital role in local fisheries and avian biodiversity conservation (Table 1 ). Ten sampling stations were designated within this lagoon to evaluate MP contamination levels (Bayrak, 2023). All lagoons included in this study are integral components of the Çukurova Delta, a region subject to increasing anthropogenic pressures, including agricultural runoff (S. Gündoğdu et al., 2018), industrial pollution (Yücel & Çam, 2021), and habitat degradations (Fig. 1 A). Field observations indicate that plastic pollution is widespread in these wetland ecosystems (Fig. 1 B,C,D). Table 1 Characteristics of the sampling locations Characteristics Total Area (ha) Average Depth (m) Protection Status Water Source Biodiversity Seasonal Variability Economic Activities Water Quality Geological & Hydrological Features Ecosystem Threats Utilization Purposes Akyatan Lagoon 14 1.5–2.5 Ramsar Site, Wildlife Development Area Former mouth of Seyhan River, drainage waters Birds: Flamingo, cormorant, kingfisher, crane, and waterfowl. Fish: Mullet, sea bass, eel, gilthead seabream, Other: Green sea turtle (* Chelonia mydas *), blue crab Shrinks in summer expands in winter Fisheries, agriculture, tourism Salinity fluctuates seasonally Part of the Seyhan Delta, extensive dune areas Agriculture, drainage channels, water pollution Biodiversity conservation, tourism, fisheries Tuzla Lagoon 2,8 0.5–2 Wildlife Protection Area, Strictly Protected Zone Rainwater, Seyhan River Important bird area, migratory birds, mullet, sea bass, gilthead seabream Water level rises in winter Fisheries, agriculture Slightly saline, varies with freshwater inflows Extension of the Seyhan River, marsh ecosystems Agriculture, urbanization Irrigation, fisheries, nature conservation Yumurtalık Lagoon 19,5 1–2 Strictly Protected Zone, National Park Ceyhan River, rainwater Waterfowl, marsh ecosystems, blue crab, mullet, sea bass, gilthead seabream Expands in winter Fisheries, ecotourism Mixture of salt and freshwater Formed by alluvial deposits from the Ceyhan River Environmental pollution, expansion of agricultural lands Ecotourism, nature conservation Çamlık Lagoon Not specified 0.5–1.5 Strictly Protected Zone, National Park Ceyhan River Salt marshes, reedbeds, Other: Green sea turtle ( Chelonia mydas ), blue crab Water level rises in winter Fisheries, agriculture Presence of saline marshes Surrounded by saline marshes Agricultural pressures Wetland conservation, fisheries Ağyatan (Hurmaboğazı) Lagoon 2,2 1–2 Strictly Protected Zone Freshwater inflow from Ceyhan River and direct connection to the sea Rich biodiversity, migratory birds stopover site, Other: Green sea turtle ( Chelonia mydas ), blue crab, mullet, sea bass, gilthead seabream Fluctuates based on seasonal precipitation Fisheries, limited agriculture Mix of fresh and saline water, seasonal variability Direct connection to the sea, extensive marsh ecosystems Agricultural expansion, water quality changes Ecotourism, biodiversity conservation, fisheries 2.2. Sampling and MP Extraction Surface water and sediment samples were collected from the lagoons in November 2021 and June 2022. Due to the shallow nature of the study area, the use of a manta net or WP net was deemed unsuitable for surface water sampling. Instead, 5-liter of triplicate water samples were collected using sterile glass bottles. The collected water samples were immediately filtered through filters with a pore size of 0.45 µm and the filters were subsequently placed in sterile petri dishes for microscopic and spectroscopic analysis. Sediment samples were obtained from the same stations where water samples were collected. A Van Veen Grab sampler was used to extract bottom sediment from an area of 250 cm², reaching an average sediment depth of 10 cm. The collected sediment was carefully transferred into a metal container. A subsample of approximately 400 g was then taken from the upper 5 cm of the sediment, which represents the sediment layer in direct contact with water, using a wooden spatula. The subsamples were stored in sterile glass jars for further processing. Sediment samples were subjected to density separation to extract MPs. The extraction of MPs from sediment matrices, which have a high organic content, is a complex process (Bläsing & Amelung, 2018; Hurley et al., 2018). In the laboratory, sediment samples were placed in a density separation apparatus as proposed by Coppock et al. (2017). A 4 Molar potassium carbonate solution with a fixed density of 1.8 g/mL was added to cover the samples by 3–5 cm fully. The samples were allowed to stand for 24 hours to ensure complete density separation. Following this, the settled sediment was separated from the supernatant, and the floating material was transferred into a beaker. Since density separation alone is insufficient to remove all organic matter, a 30% hydrogen peroxide (H₂O₂) solution was added to the separated material to facilitate organic matter degradation. The samples were then placed on a hot plate set at 50°C and left until all organic material was degraded entirely, a process that typically took 2–4 days. The remaining material was vacuum-filtered through 1,2 µm filters (GF/C), and the filter papers were stored in closed Petri dishes for subsequent microscopic and spectroscopic analyses. 2.3. MP Characterization and Validation MPs were analyzed using a stereo microscope (SZX16, Olympus Co., Tokyo, Japan) positioned within an enclosed cabinet to prevent contamination. The size, shape (categorized as fiber/filament or fragment), and color of the MPs were determined. A Touptek XCAM1080PHD camera, connected to the microscope, was used for size measurements. The classification of MP color and morphology followed the methodology outlined by Koelmans et al. (2019). Following the initial characterization, a randomly selected subset of MP particles underwent µ-Raman spectroscopy for polymer identification. The filter papers containing MP samples were analyzed using the Renishaw InVia Qontor Confocal Raman Microscopy System (Renishaw Plc., New Mills, Wotton-under-Edge, Gloucestershire, UK), equipped with 532 nm and 785 nm lasers. Particles were focused under 50x magnification using a Leica microscope, and spectral measurements were conducted with two accumulations across a spectral range of 300–3200 cm⁻¹. Exposure times were set at 10 seconds, with grating configurations of 600 l/mm and 1200 l/mm. The obtained Raman spectra were compared against reference spectra from the ST-Japan MP library. A polymer identification threshold of ≥ 70% spectral match was applied, as recommended by previous studies (S. Gündoğdu et al., 2021; Kim et al., 2018; Woodall et al., 2014) 2.4. Contamination Control To mitigate contamination risks throughout the study, all equipment was subjected to a standardized cleaning protocol. Prior to use, instruments were washed three times with ultrapure water, followed by acetone rinsing (Beer et al., 2018). Acetone, a non-polar solvent, was utilized due to its efficiency in removing grease, oil, cosmetic residues, and solidified oils, thereby preventing particle adhesion and minimizing contamination from the surrounding environment. Additionally, acetone facilitates the removal of residual water from glassware surfaces, aiding in the elimination of potential contaminants. All cleaned equipment was stored in a sealed cabinet to maintain sterility. Furthermore, all solutions used in the digestion, separation, and purification processes were vacuum-filtered through 1.2 µm GF/C Whatman filter paper before use to ensure the removal of extraneous particulates. All analyses were conducted within an ESCO-brand enclosed laminar flow cabinet. To further prevent contamination, all sample containers were sealed with aluminum foil during waiting periods. Work surfaces were cleaned with acetone before and after each analysis session to maintain a contamination-free environment. 2.5. Statistical Analysis The normal distribution of data was tested using the Shapiro-Wilk test. Concentration and size data showed a non-normal distribution. Thus, non-parametric tests; Mann Whitney U and Kruskal-Wallis were used to determine the differences between seasons and lagoons. Dunns’ test was applied to determine differing groups following a significant difference determined by Kruskal-Wallis. Nonmetric multidimensional scaling (NMDS) with Bray curtis dissimilarity was applied to construct composition plots. Comparison of shape, polymer type and color compositions among lagoons, and sampling periods were done using ANOSIM. All statistical analyses were performed using R (ver 4.4.3) (R Core Team, 2025). Shapiro-Wilk, Mann Whitney U, Kruskal-Wallis and Dunns’ tests were performed using rstatix (ver. 0.7.2) package (Kassambara, 2023). NMDS, and ANOSIM analyses were done using vegan (ver. 2.6-8) package (Oksanen et al., 2024). Maps and data visualizations were prepared using QGIS (3.34) (QGIS Development Team, 2025) and ggplot2 (ver. 3.5.1) (Wickham, 2016), respectively. The data were expressed as the mean ± standard error of the mean. The confidence level for statistical analyses were set to 95%. 3. Results A total of 15526 MPs were extracted from the water (14104) and sediment (1422) samples. In the water samples, the overall average MP concentration during November was 47.5 ± 4.02 MPs/L, which was significantly higher than the June average of 17.0 ± 2.57 MPs/L (Mann-Whitney U, p < 0.05) (Fig. 2 A). In addition, MP concentrations in each lagoon were significantly higher during November compared to June (Mann-Whitney U, p < 0.05). The lagoons showed significant variability in MP concentrations in both sampling periods (KW test, p < 0.05). From highest to lowest, the average water MP concentrations in the November sampling period were as follows: Yelkoma (62.68 ± 12.76 MPs/L), Tuzla (61.38 ± 9.63 MPs/L), Çamlık (48.40 ± 7.60 MPs/L), Ağyatan (45.46 ± 5.86 MPs/L), and Akyatan (24.24 ± 3.16 MPs/L). The highest MP concentrations during this month were recorded at Y2 (114 MPs/L), T6 (111 MPs/L), Y1 (99.4 MPs/L), and T7 stations (92.4 MPs/L) (Fig. 3 ). Akyatan had a significantly lower MP concentration than Yelkoma and Tuzla in November, while concentrations were similar among the other lagoons (Dunn’s test, p < 0.05) (Fig. 2 A). During June sampling period, Tuzla (31.68 ± 8.54 MPs/L) showed the highest average concentration, followed by Çamlık (19.83 ± 3.73 MPs/L), Akyatan (13.44 ± 2.26 MPs/L), Ağyatan (11.94 ± 2.92 MPs/L), and Yelkoma (5.20 ± 1.54 MPs/L). Among the stations, T2 (101.6 MPs/L), T1 (42.0 MPs/L), T3 (39.6 MPs/L), and C3 (38.4 MPs/L) had the highest MP concentrations (Fig. 3 ). The concentration in Tuzla was significantly higher than that in Yelkoma but similar to the rest of the sampled lagoons (Dunn’s test p < 0.05) (Fig. 2 A). In the sediment samples, the overall average MP concentrations in the November (47.4 ± 7.56 MPs/kg) and June (34.1 ± 5.38 MPs/kg) periods were statistically similar (Mann-Whitney U, p > 0.05) (Fig. 2 B). Likewise, each lagoon’s sediment concentrations did not differ significantly between these two sampling periods (Mann-Whitney U, p > 0.05). However, there was a significant difference in sediment MP concentrations across the lagoons during both sampling periods (Kruskal–Wallis test, p < 0.05). During November, Akyatan had the highest average MP concentration (85.25 ± 27.96 MPs/kg), followed by Tuzla (61.75 ± 8.05 MPs/kg), Yelkoma (36.79 ± 5.92 MPs/kg), Çamlık (22.5 ± 2.73 MPs/kg), and Ağyatan (20.25 ± 3.79 MPs/kg). Within this period, Tuzla’s MP concentration was significantly higher than those of Çamlık and Ağyatan (Dunn’s test, p 0.05). The most contaminated stations in November were Ak3 (225 MPs/kg), Ak2 (215 MPs/kg), Ak1 (195 MPs/kg), and T9 (122.5 MPs/kg) (Fig. 3 ). In June, the highest average sediment MP concentration was again observed in Akyatan (61.11 ± 16.84 MPs/kg), followed by Tuzla (44.25 ± 12.61 MPs/kg), Yelkoma (22.5 ± 4.97 MPs/kg), Ağyatan (19.0 ± 5.75 MPs/kg), and Çamlık (18.53 ± 4.96 MPs/kg). Station Ak1 (182.5 MPs/kg) showed the highest contamination during this period, followed by T10 (115 MPs/kg), T7 (112.5 MPs/kg), and Ak6 (77.5 MPs/kg). Overall, MP concentrations among lagoons were similar (Dunn’s test, p > 0.05), with the exception of Akyatan, which had significantly higher levels than Ağyatan (Dunn’s test, p < 0.05) (Fig. 2 B). The overall average size of the MPs in water samples were 1.11 ± 0.03 mm in November, and 0.92 ± 0.04 mm in June (Fig. 4 A). MP size in each lagoon were similar between the sampling periods except for Yelkoma. The MPs in this lagoon were significantly larger in the November period (Mann-Whitney U, p < 0.05). The overall average size of the MPs in sediment samples were 0.74 ± 0.04 mm in November and 0.80 ± 0.06 mm in June (Fig. 4 B). The MP size in sediment at Çamlık was found to be significantly higher in June (Mann-Whitney U, p < 0.05). The average sizes of MPs (MPs) by shape were as follows: fibers measured as 1.33 ± 0.04 mm, films 0.91 ± 0.11 mm, foams 2.31 ± 0.64 mm, and fragments 0.51 ± 0.02 mm. The shape composition of MPs in water was predominantly fibers in both November (75.6%) and June (76.1%), followed by fragments, which accounted for 22.1% in November and 18.9% in June (Fig. 5 A). In November, the average proportion of fiber-shaped MPs by lagoon, from highest to lowest, was as follows: Çamlık (83.12 ± 2.04%), Tuzla (80.77 ± 3.18%), Yelkoma (79.09 ± 3.64%), Ağyatan (67.72 ± 5.17%), and Akyatan (52.70 ± 4.70%). Fragments were most prevalent in Akyatan (42.05 ± 4.52%), followed by Ağyatan (30.31 ± 4.85%), Yelkoma (18.85 ± 3.38%), Tuzla (16.19 ± 2.82%), and Çamlık (15.83 ± 1.77%). The average percentage of film-shaped MPs varied from 2.44 ± 0.27% in Çamlık to 10.50 ± 4.68% in Akyatan. In June, fiber-shaped MPs were most abundant in Akyatan (76.80 ± 5.84%), followed by Tuzla (75.00 ± 3.95%), Çamlık (73.63 ± 5.72%), Ağyatan (68.64 ± 5.66%), and Yelkoma (53.22 ± 8.07%). The distribution of fragment-shaped MPs, from highest to lowest, was Yelkoma (46.55 ± 8.11%), Ağyatan (40.08 ± 9.00%), Çamlık (22.82 ± 5.21%), Akyatan (18.78 ± 5.77%), and Tuzla (15.99 ± 3.10%). Films were varied between 4.29 ± 0.96% (Ağyatan) to 12.43 ± 2.15% (Çamlık). Foams were only observed at Tuzla (1.72%) and Yelkoma (1.61%). Similar to water, the most abundant shape in sediment was fibers, accounting for 54.3% in November and 62.0% in June (Fig. 5 B). In November, fragment-shaped MPs were most abundant in Çamlık (70.48 ± 5.51%), followed by Tuzla (63.14 ± 5.51%), Akyatan (57.73 ± 6.16%), Yelkoma (50.00 ± 5.19%), and Ağyatan (48.95 ± 7.26%). Fiber-shaped MPs showed a different distribution pattern, with the highest abundance in Yelkoma (45.96 ± 7.88%), followed by Ağyatan (44.55 ± 7.88%), Çamlık (35.82 ± 4.37%), Akyatan (31.57 ± 7.91%), and Tuzla (24.96 ± 6.28%). Film-shaped MPs ranged from 13.03 ± 2.72% in Çamlık to 31.67 ± 1.66% in Yelkoma. Foams were only detected in Tuzla, with a proportion of 5%. In sediment samples collected in June, fragment-shaped MPs were the most abundant in Tuzla (77.57 ± 7.16%), followed by Akyatan (73.06 ± 6.86%), Ağyatan (67.70 ± 9.52%), Çamlık (58.99 ± 16.83%), and Yelkoma (48.93 ± 11.92%). Fiber-shaped MPs showed a different distribution pattern, with the highest abundance in Çamlık (53.94 ± 17.16%), followed by Ağyatan (48.31 ± 10.18%), Yelkoma (37.07 ± 14.72%), Tuzla (30.56 ± 5.59%), and Akyatan (9.97 ± 3.27%). Film-shaped MPs were most prevalent in Çamlık (47.32 ± 18.50%) and Yelkoma (46.58 ± 7.35%), with lower percentages observed in Ağyatan (40.96 ± 12.93%), Akyatan (28.78 ± 4.16%), and Tuzla (13.65 ± 4.92%). NMDS plots indicated a certain degree of separation in shape composition between water and sediment samples across the lagoons (Fig. 5 C). ANOSIM analysis confirmed statistically significant differences in overall shape compositions between June and November in both sediment and water (p < 0.05). In water samples, Akyatan displayed a significantly different shape composition from the other lagoons in November (ANOSIM, p < 0.05). In June, Yelkoma’s shape composition was significantly different from Akyatan, Tuzla, and Çamlık (ANOSIM, p < 0.05). For sediment samples, Tuzla exhibited a significantly different shape composition compared to Ağyatan and Çamlık in June (ANOSIM, p < 0.05). Additionally, Akyatan’s shape composition was distinct from Ağyatan and Çamlık, while Tuzla differed significantly from both Yelkoma and Çamlık in June (ANOSIM, p < 0.05). A total of 1882 particles extracted from water (1488) and sediment (394) samples were analyzed using µ-Raman. In water samples, 4.46% of the analyzed particles were identified as cellulose, whereas no cellulose particles were detected in sediment samples. A total of ten distinct polymers were detected in water samples, whereas eight polymers were identified in sediment samples (Fig. 6 A, B). The analysis of MP polymer types in water and sediment samples collected in November and June revealed distinct compositions. In water samples, the most common polymers in November were polyethylene (PE) at 31.45%, polypropylene (PP) at 30.81%, polyester/polyethylene terephthalate (Polyester/PET) at 10.87%, and acrylonitrile butadiene styrene (ABS) at 9.28%. In June, the polymer distribution was similar, with PE increasing to 37.55%, PP at 28.97%, Polyester/PET at 11.80%, and ABS at 8.80%. In sediment samples, PE was also the dominant polymer in both months, accounting for 29.07% in November and 29.94% in June. In November, PP (22.47%), Polyester/PET (22.03%), and polyvinyl chloride (PVC) (13.66%) were also prevalent. In June, the proportion of Polyester/PET increased to 28.74%, while PP decreased to 18.56%, and ABS reached 10.18%. ANOSIM analysis indicated a significant difference in polymer composition between water and sediment (p < 0.05) A total of eleven colors were determined in water and sediment (Fig. 7 A, B). The color composition was different between water and sample (ANOSIM, p < 0.05). In water samples, blue particles were the most dominant color in November (35.57%), followed by transparent (27.33%) and black (23.64%). In June, the order shifted, with black particles being the most prevalent (28.62%), followed by transparent (26.57%) and blue (26.30%).In sediment samples, transparent particles accounted for 56.05% of the total, followed by black (12.93%) and blue (12.10%) particles in November. NMDS plots indicate differences in color composition in water and sediment samples among the lagoons (Fig. 7 C). The proportion of transparent particles increased to 59.62% in June, while blue particles made up 17.04% and black particles 11.24%. In November, the color composition of MPs in water at Akyatan differed significantly from those at Ağyatan, Tuzla, and Çamlık (ANOSIM, p < 0.05). In June, the color composition at Akyatan was distinct from both Tuzla and Yelkoma (ANOSIM, p < 0.05). Additionally, Tuzla showed a different color composition compared to Ağyatan and Yelkoma, while Yelkoma and Çamlık also exhibited significant differences in their color compositions (ANOSIM, p < 0.05). In sediment samples, the color composition at Tuzla differed significantly from Ağyatan, Yelkoma, and Çamlık in November. In June, the color composition of sediments at Akyatan and Ağyatan also showed distinct differences (ANOSIM, p < 0.05). 4. Discussion This comprehensive study on MPs in Turkish coastal lagoons aligns with and expands upon the growing literature on plastic pollution in semi-enclosed water bodies. The results reveal significant MP contamination in the Çukurova Delta lagoons, with notable differences in abundance, size, shape, and polymer composition between locations and seasons. When compared to other studies worldwide, these findings are consistent with previous research on MPs in lagoon environments, reinforcing the idea that semi-enclosed coastal ecosystems are hotspots for plastic pollution due to their role as transitional zones between terrestrial and marine systems. These results also align with studies conducted in lagoons across different geographic regions (Table 2 ), emphasizing that MP pollution is largely driven by anthropogenic pressures and hydrodynamic processes. The presence of MPs in the study area demonstrates that even wetlands of high ecological significance, such as the lagoons in the Çukurova Delta, are not exempt from plastic contamination. This trend has been observed in other coastal and estuarine ecosystems (Table 2 ). For example, a study conducted in Sontecomapan Lagoon, Mexico, reported MPs in surface water, sediments, and zooplankton, with concentrations of 7.5 ± 5.3 MPs/L in water and 8.5 ± 12.5 MPs/kg in sediments (Sánchez-Campos et al., 2024), which closely align with findings from the Çukurova Delta. Like Yumurtalık Lagoon, Sontecomapan Lagoon is located within a Ramsar-designated site in the Los Tuxtlas Biosphere Reserve, yet it still exhibits MP contamination, demonstrating that conservation status alone does not prevent plastic pollution. Similarly, studies conducted in Bizerte Lagoon, Tunisia (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2020b, 2020a), have reported significant seasonal variations in MP concentrations, with the highest levels recorded in summer and the lowest in winter, a pattern also observed in the Çukurova lagoons. Table 2 MP levels found in different matrices of lagoons across the globe Country Lagoon Coverage (km2) Source Abundance Size range (mm) Polymer Shape Detection Method Ref Brazil Acarai 101.8 Surface water 0.001–0.018 mp/m3 0.05-2 PE, PET, PB, PS NA FTIR (Lorenzi et al., 2021) Colombia Ciénaga Grande de Santa Marta 1321 Sediment 0-3.08 mp/kg NA PP, PE, HDPE, PS fiber, fragment, film, foam, granule FTIR-ATR (Garcés-Ordóñez et al., 2022) Colombia Ciénaga Grande de Santa Marta 1321 Surface water 0–30 mp/L NA PP, PE, HDPE, PS fiber, fragment, film, foam, granule FTIR-ATR Ghana Ghanaian NA Sediment 1.6–2.5 mp/cm3 NA NA NA Nile-red (Chico-Ortiz et al., 2020) Greece Korissia 6 Surface water 3-158 mp/L NA PP, PET fragment Raman (Simantiris et al., 2025) French Polynesia Ahe, Manihi, Takaroa 145, 165, 89 Surface water 3.30 mp/m3 0.02-1 PE, PU, PS, PE, PP, PES, Acrylic fragment, fiber FTIR (Gardon et al., 2021) French Polynesia Ahe, Manihi, Takaroa 145, 165, 89 Water column 3613 mp/m3 0.02-1 PE, PS, PVC fragment, fiber FTIR India Chilika 1165 Surface water 26-1107 mp/L < 1–5 PVC, PVA, PS, PP, PE, PES, PA foam, fragment, filament, film FTIR-ATR (Singh et al., 2023) India Chilika 1165 Sediment 7-25.2 mp/kg < 1–5 PVC, PVA, PS, PP, PE, PES, PA filament, fragment, film, foam FTIR-ATR Italy Antinioti 1.87 Surface water 40–50 mp/L NA Nylon, PS NA Raman (Simantiris et al., 2025) Italy Lesina 600 Surface water 0.11 mp/L NA PP, PE filament, fragment, sphere, film µFTIR (Specchiulli et al., 2023) Italy Lesina 600 Sediment 39.91 mp/kg NA PP, PE filament, fragment, sphere, film µFTIR Italy Venice 550 Surface water 0.7–3.5 mp/L 0.03-5 NA filament, fiber Stereomicroscope (Cecchi et al., 2024) Mexico Rio Lagartos 94 Sediment 0-200 mp/kg 0.3-2 NA fiber, fragment NA (Quesadas-Rojas et al., 2021) Mexico Rio Lagartos 94 Surface water 0.6 mp/m3 0.3-2 NA fiber, fragment NA Mexico Laguna de Terminos 2007 Sediment 201.1 mp/g 0.065-5 PE, PET, PVC, CE fiber, fragment FTIR-ATR (Celis-Hernandez et al., 2023) Mexico Laguna de Terminos 2007 Surface water 12.8 mp/L 0.065-5 PE, PET, PVC, CE fiber, fragment FTIR-ATR Mexico Sontecomapan 8.9 Surface water 7.5 ± 5.3 mp/L 0.02–4.7 PES, PE, PS, NL, rayon, acrylic fiber, fragment, foam FTIR (Sánchez-Campos et al., 2024) Mexico Sontecomapan 8.9 Sediment 8.5 mp/kg 0.01–2.6 PES, acrylic, NL fiber, fragment FTIR Spain Mar Menor 135 Sediment 53.1 ± 7.6 mp/kg 0.1-5 LDPE, HDPE, PVE, PP, NL, PES fragment, fiber, film, pellet, foam FTIR (Bayo et al., 2019) Thailand Songkhla 1040 Surface water 0.34–0.43 mp/L 1 PET, rayon, PP, PE, fiber, fragment FTIR (Pradit, Noppradit, Sornplang, Jitkaew, Jiwarungrueangkul, et al., 2024) Thailand Songkhla 1040 Sediment 1-5.4 mp/g NA PP, PES, PET fiber, fragment FTIR (Pradit, Noppradit, Sornplang, Jitkaew, Kobketthawin, et al., 2024) Tunisia Bizerte 150 Sediment 7960 ± 6840 mp/kg 0.3-5 NA fiber, fragment Stereomicroscope (Abidli et al., 2017) Tunisia Bizerte 150 Surface water 0.4 ± 0.2 mp/L NA PE, PP, CE fiber, film, fragment FTIR-ATR (Wakkaf et al., 2020a) Tunisia Bizerte 150 Surface water 453.0 ± 335.mp/m3 NA PE, PP, PET, CP, NL, PS fiber, fragment, film FTIR-ATR (Wakkaf et al., 2020b) Tunisia Bizerte 150 Sediment 33.2-109.6 mp/kg NA PE, PP, PVC, PET fiber, fragment, film FTIR-ATR (Wakkaf et al., 2022) Tunisia Bizerte 150 Sediment 106 ± 65 mp/kg 0.07–0.2 PP, PET, PVC fragment FTIR-ATR (Jaouani et al., 2022) Türkiye Küçükçekmece 16 Surface water 33 mp/L NA PE fragment, fiber, film FTIR (Çullu et al., 2021) Türkiye Küçükçekmece 16 Sediment 2922 ± 517 mp/kg NA NA fiber, fragment, film, bead Stereomicroscope (İşlek et al., 2023) USA Barnes Sound 703 Surface water 40-76000 mp/L 0.03–0.19 PS NA FTIR (Badylak et al., 2021) NL:Nylon, CP:Cellophane, PE:Polyethylene, PP:Polypropylene, PET: Polyethylene Terephthalate, PS: polystyrene, PB: polybutadiene, PES: polyester, PAN: poly-acrylonitrile, PEUU: polyester urethane, PA:polyamide, HDPE: high-density polyethylene, LDPE: low-density polyethylene, PVE: polyvinyl ester, PA: polyarcylic, EVA: ethylene-vinyl acetate, FTIR: Fourier-transform infrared spectroscopy, ATR: Attenuated total reflectan Seasonal variations have played a crucial role in MP concentrations within these lagoons, with significantly higher levels observed during the dry season (Fig. 2 , 3 ). This seasonal pattern suggests that reduced water input and slower sedimentation rates during dry periods contribute to higher MP accumulation in surface waters. During the rainy season, an increased inflow of freshwater occurs through drainage channels, which may facilitate the transport of agricultural MPs into the lagoon system. Indeed, a study by Gündoğdu et al. (2022) conducted near the lagoons reported a substantial presence of MPs in soil from seasonal greenhouse areas where agricultural plastics are used. Similarly, Akça et al. (2024) found that agricultural soils exhibited extremely high levels of MP contamination. Therefore, it is highly likely that residues from agricultural plastic applications in these soils are transported into the lagoons through drainage channels. To better understand this phenomenon, it is crucial to monitor the seasonal and spatial distribution of MPs transported through these channels and implement necessary mitigation measures. When comparing different lagoons within the Çukurova Delta, it is evident that MP contamination levels vary significantly between locations (Fig. 2 , 3 ). Spatial variations in MP pollution across different lagoons in the Çukurova Delta are likely influenced by hydrodynamic conditions, proximity to pollution sources, and lagoon morphology. Lagoons with lower water flow and higher sedimentation rates tend to accumulate more MPs, while those with higher water exchange rates are expected to have lower MP concentrations due to dilution effects. For example, Akyatan Lagoon exhibited relatively high MP concentrations, likely due to its proximity to intensive fishing activities and agricultural areas. This finding is consistent with research from Chilika Lake in India, where extensive fishing and tourism were identified as major contributors to MP pollution (Singh et al., 2023) (Table 2 ). Among the study sites, Tuzla Lagoon showed particularly high levels of plastic accumulation, especially in sediments. This observation is comparable to findings from Bizerte Lagoon, where sediments were identified as long-term sinks for MPs (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2022). The high levels of sediment-bound plastics in Tuzla may be attributed to its relatively low hydrodynamic activity, which allows MPs to settle rather than being transported away. In contrast, Ağyatan Lagoon exhibited lower MP concentrations compared to Akyatan and Tuzla, although contamination was still present. The seasonal variations observed in Ağyatan Lagoon align with findings from Acaraí Lagoon in Brazil (Lorenzi et al., 2021), where higher MP concentrations were detected during the dry season due to limited water exchange, while in the wet season, increased water flow transported MPs toward the lagoon’s mouth (Table 2 ). A similar mechanism may be at play in Ağyatan Lagoon, where seasonal hydrological changes influence MP transport and retention. Yelkoma Lagoon, located in the eastern part of the Çukurova Delta, exhibited moderate levels of MP contamination compared to other lagoons. This intermediate contamination level may be attributed to a mix of freshwater and brackish water inputs, leading to variable MP transport dynamics (Doto et al., 2025). This pattern resembles findings from pearl-farming lagoons in French Polynesia, where MP concentrations fluctuate based on hydrological conditions and aquaculture activities (Gardon et al., 2021). MPs can exhibit dynamic distribution patterns between the central and estuarine regions of lagoons (Doto et al., 2025). Furthermore, differences in MP distribution across northern, central, and southern sectors of the lagoons may be linked to variations in hydrodynamic conditions within the system (Doto et al., 2025). Additionally, stronger currents are typically observed in areas near the lagoon’s water inflow and outflow points. This hydrodynamic behavior explains the observed differences in MP concentrations between stations and aligns with findings from studies conducted by Bortolin et al. (2020) and Doto et al. (2025) regarding the formation of sediment deposition centers. Given that the lagoons in the study area have two main water inflow directions, it is expected that the distribution and accumulation of MPs are influenced by these hydrodynamic patterns. The findings indicate that MP accumulation in both water and sediment follows distinct spatial trends: MPs released in the northern section of the lagoon tend to become trapped within the system, while those introduced in the central region primarily remain within the estuarine area, with some fraction being transported toward the coastal region. This movement pattern may be governed by the interaction between estuarine geometry, river discharge, and barotropic pressure gradients generated by wind-driven tidal currents (Doto et al., 2025; Elisei Schicchi et al., 2023; Moller et al., 2001). The MP size distribution in the Çukurova lagoons exhibited a dominance of smaller particles, particularly those below 1 mm, similar to findings from Chilika Lake in India and Sontecomapan Lagoon in Mexico (Sánchez-Campos et al., 2024; Singh et al., 2023). In these studies, sediment samples predominantly contained MPs within the 0.1–1 mm range, with the smallest fractions exhibiting the highest concentrations (Table 2 ). The prevalence of smaller particles increases the likelihood of ingestion by aquatic organisms, as demonstrated in Lagos Lagoon, Nigeria, where MPs of similar sizes were detected in fish species. The high proportion of small MPs is likely attributed to fragmentation processes driven by UV exposure, wave action, and mechanical degradation. Additionally, considering that freshwater inflows into the region’s lagoons are heavily influenced by wastewater discharges from intensive agricultural activities, plastic recycling facilities, and the textile industry, it is possible that smaller MPs are directly introduced into the lagoons at these sizes (R. Gündoğdu et al., 2022). Moreover, sampling methodology plays a crucial role in determining the size distribution of MPs within a sample. Several studies have indicated that sampling nets exhibit size selectivity for MPs, typically using mesh size as a threshold to determine the smallest MP size that can be captured (Barrows et al., 2017; Kang et al., 2015; Yu et al., 2025). This approach assumes that the net defines the lower size limit of MPs that can be retained and that MP concentrations are reported accordingly. However, Yu et al. (2025) noted that this assumption may overlook the complex morphological characteristics of MPs, which can affect their likelihood of passing through or being retained by a net. While net-based surface water sampling tends to capture larger particles rather than smaller fragments, the bulk sampling method used in this study allows for a broader size range of particles to be captured, as it relies on the pore size of the filter rather than the selectivity of a net. Therefore, the higher proportion of smaller particles in the MP size distribution observed in this study can be attributed to this sampling method. The dominant MP shapes in the Çukurova lagoons were fibers and fragments, with films and pellets appearing in smaller proportions. However, as the identification of fragment and film structures becomes increasingly difficult for smaller particles through visual inspection, the distinction between fibers and other MP shapes becomes particularly important. The substantial presence of fibers, likely originating from textile waste, aligns with the types of polymers predominantly used in the textile industry, indicating that wastewater discharges from urban areas and textile production facilities significantly contribute to MP contamination. Gündoğdu et al. (2018) revealed in their study that the high proportion of fibers is likely influenced by wastewater discharges from freshwater inflows feeding the lagoons and by the coastal currents supplied by the Ceyhan River. Both the Ceyhan River and the surface water channels that drain into the lagoons appear to be significant contributors to the predominance of fibers in these ecosystems. In addition, the pattern observed in the Çukurova lagoons aligns with studies conducted in Bizerte Lagoon, Tunisia (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2020b, 2020a, 2022), and Acaraí Lagoon, Brazil (Lorenzi et al., 2021), where fibers accounted for more than 70% of the detected MPs (Table 2 ). This is further supported by findings from Sontecomapan Lagoon, where polyester and acrylic fibers were widely detected in both water and sediment samples (Sánchez-Campos et al., 2024). Fragments, the second most common shape, followed by films, likely originate from the degradation of larger plastic debris such as agricultural plastics and consumer plastics. Additionally, the presence of these shapes may also be linked to MP-rich wastewater effluents from plastic recycling facilities. Despite the fact that the lagoons in this study are actively used for fishing, the presence of foam-like polystyrene fragments was notably lower compared to Chilika Lake, which experiences similar fishing activities. This discrepancy requires further investigation to determine why polystyrene foams are less prevalent in the Çukurova lagoons. Polymer analysis has revealed that polyethylene (PE) and polypropylene (PP) are the most abundant MP types found in the Çukurova lagoons. In a study conducted in the region including our study area, Gündoğdu et al., (2022) stated that the high presence of PE and PP, especially in areas near agricultural lands, suggests that agricultural films, plastic mulching, and single-use drip irrigation systems play a significant role in MP pollution. Consequently, it can be asserted that this MP source is also efficacious in lagoons. Furthermore, these findings align with studies conducted in various lagoon environments worldwide, such as the Bizerte Lagoon (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2020b, 2020a, 2022), Ciénaga Grande de Santa Marta (Garcés-Ordóñez et al., 2022), and the Lesina Lagoon (Specchiulli et al., 2023), all of which have reported PE and PP as the dominant polymer types (Table 2 ). The widespread presence of these polymers is primarily attributed to their global production scale and extensive use across multiple applications, including agricultural activities. In contrast, studies in Chilika Lake (Singh et al., 2023) and the Sontecomapan Lagoon (Sánchez-Campos et al., 2024) have reported significant concentrations of polyester/PET and polyvinyl chloride (PVC), which are less common in the Çukurova lagoons. The lower detection of PVC in water samples can be explained by its higher density, which causes it to sink and become embedded in sediments, whereas PE and PP, being less dense (Hidalgo-Ruz et al., 2012), tend to remain suspended or accumulate on the water surface. The variation in polymer compositions across different lakes and lagoons is closely linked to land use patterns, human activities surrounding these ecosystems, and the sources of inflowing water. Conclusion This study highlights significant MP contamination in the Mediterranean lagoons of Türkiye, with seasonal variations and anthropogenic activities influencing pollution levels. Higher MP concentrations in water during November suggest increased input from rainfall and surface runoff, while sediment accumulation, particularly in Akyatan Lagoon, indicates long-term retention due to reduced water exchange. The dominance of fibers and polymers like PE, PP, and PES/PET points to agricultural runoff, fisheries-related plastic debris, and urban waste as key sources. The ecological risks of MP pollution in the Çukurova Delta are concerning, given its role as a critical habitat for fish, migratory birds, and aquatic vegetation. MPs pose a threat to biodiversity, food security, and public health through bioaccumulation in the food web. Despite conservation efforts, plastic pollution remains a major issue, necessitating improved wastewater treatment, stricter agricultural plastic regulations, and better waste management. Given that lagoons act as long-term sinks for MPs, ongoing monitoring and targeted mitigation strategies are essential to preserving their ecological integrity. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This study was supported by the Scientific Research Projects Unit of Çukurova University under project numbers FBA-2021-13403, FBA-2021-13410 and FBA-2023-15165. Data availability statement Data supporting this study are included within the article. Competing Interests Statement 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. Author’ Contributions SG Conceptualized the study, performed the investigation, administered the project and received funding, and was a major contributor in writing the manuscript. 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C., Sanchez-Vidal, A., Canals, M., Paterson, G. L. J., Coppock, R., Sleight, V., Calafat, A., Rogers, A. D., Narayanaswamy, B. E., & Thompson, R. C. (2014). The deep sea is a major sink for microplastic debris. Royal Society Open Science , 1 (4), 140317. https://doi.org/10.1098/rsos.140317 Yu, M., Herrmann, B., Liang, H., Sistiaga, M., Zhu, Z., Brčić, J., Tang, L., Liu, C., & Tang, Y. (2025). Size selection in sampling nets leads to underestimation of microplastic pollution. Environmental Pollution , 372 , 126007. https://doi.org/10.1016/j.envpol.2025.126007 Yücel, Y., & Çam, A. R. (2021). Assessment of industrial pollution effects in coastal seawater Northeastern Mediterranean Sea) with chemometric approach. International Journal of Environmental Analytical Chemistry , 101 (1), 95–112. https://doi.org/10.1080/03067319.2019.1660877 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6201957","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427904723,"identity":"68a757bf-ded0-4b1c-aab1-13a6f71a0af1","order_by":0,"name":"Sedat Gündoğdu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLACHgYGxgb2BhCTmRQtPAegWtiI1iKRQKQW+f41Zg/eVNTKbrj5+JkEQ4V1YoN87wO8WgxuvDE3nHPmuPGG22lmEgxn0hMb2NgN8GuROGMmzdt2LHHD7Rw2Cca2w0AtBFwmPwOm5eYZoJZ/RGhhON8D0lKTuOEGD1BLAxFaDG6wlUnOOXPAeOaZNGOLhGPpxm1saQQc1n94m8SbijrZvuOHH974UGMt2898jIDDINFxGMIBsQnHJP8BEFlHUN0oGAWjYBSMYAAAmoxF4QuwjGQAAAAASUVORK5CYII=","orcid":"","institution":"Cukurova University","correspondingAuthor":true,"prefix":"","firstName":"Sedat","middleName":"","lastName":"Gündoğdu","suffix":""},{"id":427904724,"identity":"3202623d-cd34-4dbe-9597-9efe9d7f4ac3","order_by":1,"name":"Cem Çevik","email":"","orcid":"","institution":"Cukurova University","correspondingAuthor":false,"prefix":"","firstName":"Cem","middleName":"","lastName":"Çevik","suffix":""},{"id":427904725,"identity":"b58b5fa4-3bfd-4115-808f-37ef4b68cda6","order_by":2,"name":"Yahya Terzi","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"","lastName":"Terzi","suffix":""},{"id":427904726,"identity":"b92603e9-8c75-4323-a91c-078f67b97b06","order_by":3,"name":"Kenan Gedik","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Kenan","middleName":"","lastName":"Gedik","suffix":""},{"id":427904727,"identity":"3d3da4fe-9958-48a2-b854-026c473ce2ad","order_by":4,"name":"Ferhat Büyükdeveci","email":"","orcid":"","institution":"Directorate of Provincial Food, Agriculture and Livestock","correspondingAuthor":false,"prefix":"","firstName":"Ferhat","middleName":"","lastName":"Büyükdeveci","suffix":""},{"id":427904728,"identity":"396f5878-14cb-475c-89a0-8843e9f2db3d","order_by":5,"name":"Rafet Çağrı Öztürk","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"prefix":"","firstName":"Rafet","middleName":"Çağrı","lastName":"Öztürk","suffix":""}],"badges":[],"createdAt":"2025-03-11 10:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6201957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6201957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78649640,"identity":"62ef36cc-2cc8-4470-ad94-1351a25b520d","added_by":"auto","created_at":"2025-03-17 08:10:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":493950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLand cover details of surrounding environments of the lagoons and sampling locations (A).A pile of illegally dumped shredded agricultural plastics near Tuzla Lagoon (B); improperly disposed of plastic waste near Akyatan Lagoon (C); and a site of single-use plastic waste accumulation near Yumurtalık Lagoon (D).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/7f97ecc87418120776cd6d6a.jpeg"},{"id":78648437,"identity":"92e6c651-8720-4c3f-94fd-eede5b48f3db","added_by":"auto","created_at":"2025-03-17 08:02:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119086,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of MP concentrations in water (A) and sediment (B) samples during November and June periods. Dashed lines along the plots indicate the overall average in each sampling period. Red lines located in boxplots show the average MP concentration in the lagoons (For a detailed version of this figure, please see the online version of the publication).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/129b3b76535cf00ac4406262.jpeg"},{"id":78648442,"identity":"202de220-7fd4-4d54-89e5-19c7a5a51923","added_by":"auto","created_at":"2025-03-17 08:02:50","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":516951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMP concentration in water and sediment at each sampling stations during November and June\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/746e90cbb5af6c035bdb33d1.jpeg"},{"id":78649641,"identity":"5e73f7ca-54a6-4fff-b4b7-226cd86147db","added_by":"auto","created_at":"2025-03-17 08:10:50","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMP size distribution in water (A) and in sediment (B) samples during November and June periods. Dashed lines along the plots indicate the overall average in each sampling period. Red lines located in boxplots show the average MP size in the lagoons (For a detailed version of this figure, please see the online version of the publication).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/6523845cb1ba68389dcbbd27.jpeg"},{"id":78649642,"identity":"5e47c92e-2a3a-43f7-be23-57f86abf0a63","added_by":"auto","created_at":"2025-03-17 08:10:50","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":805479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMP shape composition by station in water (A) and sediment (B) samples during the sampling periods. Non-metric multidimensional scaling (NMDS) plot constructed with Bray-Curtis metrics based on the shape data (C ).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/c39bdfcffa6f5a9a8ccde206.jpeg"},{"id":78648439,"identity":"dbc41ac4-8605-4567-b538-25b63517cce6","added_by":"auto","created_at":"2025-03-17 08:02:50","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":134663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eShape and polymer composition of MPs in water (A) and sediment (B) samples. (PET: polyethylene terephthalate, PVC: polyvinyl chloride, PS: polystyrene, PP: polypropylene, POM: polyoxymethylene, PE: polyester, PC: polycarbonate, PA: polyamide, ABS: Acrylonitrile butadiene styrene)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/3a9cc8dd6ed652deaad90aed.jpeg"},{"id":78649648,"identity":"03ec977a-1ae2-4846-9c3e-07b2c422d4ff","added_by":"auto","created_at":"2025-03-17 08:10:51","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":705370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eColor composition of MPs by station in water (A) and sediment (B) samples. Non-metric multidimensional scaling (NMDS) plot constructed with Bray-Curtis metrics based on the color data (C ).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/044782b09afa58d75215f8f0.jpeg"},{"id":78700549,"identity":"98c8cff5-f2a2-44cf-92a3-d1f53ce5603a","added_by":"auto","created_at":"2025-03-17 18:55:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3997654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6201957/v1/2f898bd0-e739-44fe-8b24-d4edd29de5db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microplastics in Turkish Coastal Lagoons: Unveiling the Hidden Threat to Wetland Ecosystems","fulltext":[{"header":"Highlights","content":"\u003cp\u003e- First study on microplastics in Turkish Mediterranean coastal lagoons.\u003c/p\u003e\u003cp\u003e- Microplastic pollution varies by season, with higher levels in November.\u003c/p\u003e\u003cp\u003e- Fibers dominate, with polyethylene, polypropylene, and polyester most common.\u003c/p\u003e\u003cp\u003e- Agricultural runoff and urban/industrial waste are key sources of microplastic pollution.\u003c/p\u003e\u003cp\u003e- Findings stress the need for improved waste management and conservation.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003ePlastics are synthetic organic compounds that are used in all areas of modern life due to their versatility. However, their widespread use has led to significant environmental concerns, particularly the accumulation of plastic waste in marine and coastal ecosystems. Plastics production has skyrocketed since the first mass production in the 1950s, reaching approximately 414\u0026nbsp;million metric tons per year globally by 2023 (Plastic Europe, 2024). Assessments indicate that if no restrictions are imposed, plastic production is projected to triple by 2100 (Bergmann et al., 2022), inevitably leading to a corresponding increase in plastic waste generation (Stegmann et al., 2022). Among the various pollutants affecting marine environments, microplastics (MPs) have emerged as a critical environmental issue, now recognized as an emerging class of contaminants Defined as plastic particles smaller than 5 mm in diameter, MPs originate from the fragmentation of larger plastic debris (secondary MPs) or are manufactured at a microscopic scale for industrial applications, such as cosmetics and textiles (primary MPs) (GESAMP, 2016). The persistence, buoyancy, and ability of MPs to disperse widely in aquatic environments exacerbate their environmental impact, making them a major global concern (Ali et al., 2025).\u003c/p\u003e \u003cp\u003eGlobally, an estimated 19\u0026ndash;23\u0026nbsp;million metric tons of plastics entered aquatic ecosystems in 2016, and projections suggest these levels could reach 53\u0026nbsp;million metric tons by 2030 (Borrelle et al., 2020). MP pollution is now documented not only in heavily impacted coastal regions but also in remote glaciers, deep-sea sediments, and surface waters of the Pacific, Atlantic, Arctic, and Antarctic Oceans (Aydın et al., 2023; S. G\u0026uuml;ndoğdu et al., 2024; Tsuchiya et al., 2024). Numerous marine MP hotspots have been identified, particularly in areas of oceanic convergence, such as gyres, where plastics accumulate due to ocean currents. One of the most affected regions is the Mediterranean Sea (Gedik et al., 2022; Terzi et al., 2024). The Mediterranean Sea has been identified as one of the most polluted marine ecosystems in the world, exhibiting some of the highest concentrations of floating plastic debris (Cincinelli et al., 2019; Liubartseva et al., 2018; Terzi et al., 2024). This is primarily attributed to its semi-enclosed nature, limited water exchange, densely populated coastal areas, and intense anthropogenic activities, including tourism, fishing, and industrial discharges (Suaria et al., 2016). Estimates suggest that between 5% and 10% of the total global plastic mass accumulates in the Mediterranean (van Sebille et al., 2015). A major yet often overlooked contributor to this pollution is agricultural plastic use, which has seen a dramatic increase across the Mediterranean basin, including T\u0026uuml;rkiye (Akca et al., 2024; R. G\u0026uuml;ndoğdu et al., 2022). The application of plastics in agriculture\u0026mdash;such as greenhouse films, mulch films, irrigation pipes, and plastic containers\u0026mdash;contributes significantly to environmental MP pollution. Agricultural plastics undergo physical degradation, chemical aging, and biological breakdown, releasing MP fragments into the soil and water systems (Ng et al., 2018; Rezaei et al., 2022). These MPs are then transported to coastal and lagoon environments through surface runoff, irrigation drainage, and atmospheric deposition, further exacerbating the contamination of aquatic ecosystems.\u003c/p\u003e \u003cp\u003eCoastal lagoons, which serve as transitional zones between terrestrial and marine ecosystems, are particularly vulnerable to MP pollution (Garc\u0026eacute;s-Ord\u0026oacute;\u0026ntilde;ez et al., 2022). These ecosystems provide critical services, including serving as breeding, feeding, and nursery grounds for numerous marine species (Newton et al., 2018). Their ecological significance makes them valuable conservation areas; however, their semi-enclosed nature and restricted water circulation render them highly susceptible to pollutant accumulation. Furthermore, lagoons support extensive fishing and aquaculture activities, making them economically significant while simultaneously exposing them to increased anthropogenic pressures. The presence of MPs in these environments can have cascading effects on aquatic biodiversity, food web dynamics, and human health due to their potential for bioaccumulation and biomagnification in marine organisms (Bhattacharjee et al., 2025).\u003c/p\u003e \u003cp\u003eDespite growing concerns about plastic pollution in coastal environments, no prior research has been conducted on MP contamination in these lagoons. Previous studies in the region have primarily focused on heavy metal contamination, leaving a significant gap in understanding the extent of MP pollution. Given the widespread use of agricultural plastics in surrounding rural areas and the potential for plastic waste to enter lagoon ecosystems via connected freshwater channels, assessing MP pollution in these environments is crucial for effective conservation and management strategies. In T\u0026uuml;rkiye, five major lagoons\u0026mdash;Akyatan, Tuzla, Ağyatan, \u0026Ccedil;amlık, and Yelkoma\u0026mdash;located along the northeastern Mediterranean coast within Adana Province, are critical biodiversity hotspots. Among them, Yumurtalık/\u0026Ccedil;amlık and Yelkoma lagoons hold special conservation status, with Yumurtalık Lagoons designated as a Natural Conservation Area (1993), a Wildlife Protection Area (1994), and a Ramsar Site (2005), while Yelkoma Lagoon is protected as a Nature Reserve. These protected areas provide refuge for migratory birds, fish, and other aquatic species; however, they are increasingly threatened by human activities, particularly extensive agricultural and fishing operations.\u003c/p\u003e \u003cp\u003eThis study aims to assess the occurrence, distribution, and potential sources of MP pollution in the surface water and sediments of five lagoons located along the northeastern Mediterranean coast of T\u0026uuml;rkiye. Specifically, the objectives are i) to seasonally quantify and characterize MPs in the surface waters and sediments of Akyatan, Tuzla, Ağyatan, \u0026Ccedil;amlık, and Yelkoma lagoons, ii) to identify potential sources of MPs in these lagoons, with a focus on agricultural runoff, mismanaged plastic waste, and urban influences. To compare MP contamination levels in these lagoons with those reported in similar ecosystems worldwide. To provide baseline data that can inform future monitoring programs and contribute to the development of mitigation strategies for reducing MP pollution in coastal lagoon environments. By addressing these objectives, this study will contribute to the growing body of knowledge on MP pollution in semi-enclosed coastal ecosystems and provide valuable insights for policymakers, conservationists, and local stakeholders working toward the protection of these ecologically and economically significant habitats.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sampling Location\u003c/h2\u003e \u003cp\u003eThis study was conducted in five lagoon systems within the Adana province, located in the \u0026Ccedil;ukurova Delta, a wetland of international significance and a critical ecological hotspot in T\u0026uuml;rkiye. The delta contains diverse coastal lagoons that provide key ecological functions, including biodiversity conservation, hydrological regulation, and fisheries support. Yelkoma Lagoon, Akyatan Lagoon, Tuzla Lagoon, \u0026Ccedil;amlık Lagoon, and Ağyatan (Hurmaboğazı) Lagoon were chosen based on their ecological importance and susceptibility to MP pollution (Bayrak, 2023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eYelkoma Lagoon, located in the eastern sector of the \u0026Ccedil;ukurova Delta, features a unique wetland system characterized by a mix of brackish and freshwater habitats. It provides a critical habitat for diverse ichthyofauna, migratory avifauna, and aquatic vegetation. Samples were collected from seven strategically distributed stations within the lagoon to assess MP contamination (Bayrak, 2023). Akyatan Lagoon, the largest lagoon in T\u0026uuml;rkiye, covers an area of approximately 7,420 hectares and holds the status of a RAMSAR site due to its global significance for bird conservation. Its hydrology is influenced by both saline and freshwater inputs, which fluctuate seasonally. To investigate the spatial distribution of MP pollution, samples were from nine sampling stations across the lagoon (Satar, 2018). Tuzla Lagoon, situated in proximity to the Karataş district, is an ecologically important wetland with shallow waters and high biological productivity. Spanning approximately 550 hectares, it supports a wide range of aquatic species. Samples were collected from ten stations to assess MP contamination (Bayrak \u0026amp; Ekinci, 2015). \u0026Ccedil;amlık Lagoon, located near the Ceyhan River, is a relatively smaller wetland system yet remains ecologically significant. It consists of interconnected water bodies and marshlands that provide habitat for fish populations and migratory bird species. MP sampling was conducted at seven stations within this lagoon (Bayrak, 2023). Ağyatan (Hurmaboğazı) Lagoon, positioned in a dynamic coastal environment, undergoes seasonal fluctuations in salinity and water depth. It plays a vital role in local fisheries and avian biodiversity conservation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ten sampling stations were designated within this lagoon to evaluate MP contamination levels (Bayrak, 2023).\u003c/p\u003e \u003cp\u003eAll lagoons included in this study are integral components of the \u0026Ccedil;ukurova Delta, a region subject to increasing anthropogenic pressures, including agricultural runoff (S. G\u0026uuml;ndoğdu et al., 2018), industrial pollution (Y\u0026uuml;cel \u0026amp; \u0026Ccedil;am, 2021), and habitat degradations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Field observations indicate that plastic pollution is widespread in these wetland ecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB,C,D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the sampling locations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Area (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Depth (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtection Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBiodiversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeasonal Variability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEconomic Activities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWater Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGeological \u0026amp; Hydrological Features\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eEcosystem Threats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUtilization Purposes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAkyatan Lagoon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRamsar Site, Wildlife Development Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFormer mouth of Seyhan River, drainage waters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBirds: Flamingo, cormorant, kingfisher, crane, and waterfowl. Fish: Mullet, sea bass, eel, gilthead seabream, Other: Green sea turtle (*\u003cem\u003eChelonia mydas\u003c/em\u003e*), blue crab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eShrinks in summer expands in winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFisheries, agriculture, tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSalinity fluctuates seasonally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePart of the Seyhan Delta, extensive dune areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAgriculture, drainage channels, water pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBiodiversity conservation, tourism, fisheries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTuzla Lagoon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWildlife Protection Area, Strictly Protected Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRainwater, Seyhan River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImportant bird area, migratory birds, mullet, sea bass, gilthead seabream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWater level rises in winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFisheries, agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSlightly saline, varies with freshwater inflows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExtension of the Seyhan River, marsh ecosystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAgriculture, urbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eIrrigation, fisheries, nature conservation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYumurtalık Lagoon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrictly Protected Zone, National Park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCeyhan River, rainwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWaterfowl, marsh ecosystems, blue crab, mullet, sea bass, gilthead seabream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExpands in winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFisheries, ecotourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMixture of salt and freshwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFormed by alluvial deposits from the Ceyhan River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eEnvironmental pollution, expansion of agricultural lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEcotourism, nature conservation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Ccedil;amlık Lagoon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrictly Protected Zone, National Park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCeyhan River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt marshes, reedbeds, Other: Green sea turtle (\u003cem\u003eChelonia mydas\u003c/em\u003e), blue crab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWater level rises in winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFisheries, agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePresence of saline marshes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSurrounded by saline marshes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAgricultural pressures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eWetland conservation, fisheries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAğyatan (Hurmaboğazı) Lagoon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrictly Protected Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFreshwater inflow from Ceyhan River and direct connection to the sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRich biodiversity, migratory birds stopover site, Other: Green sea turtle (\u003cem\u003eChelonia mydas\u003c/em\u003e), blue crab, mullet, sea bass, gilthead seabream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFluctuates based on seasonal precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFisheries, limited agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMix of fresh and saline water, seasonal variability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDirect connection to the sea, extensive marsh ecosystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAgricultural expansion, water quality changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEcotourism, biodiversity conservation, fisheries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling and MP Extraction\u003c/h2\u003e \u003cp\u003eSurface water and sediment samples were collected from the lagoons in November 2021 and June 2022. Due to the shallow nature of the study area, the use of a manta net or WP net was deemed unsuitable for surface water sampling. Instead, 5-liter of triplicate water samples were collected using sterile glass bottles. The collected water samples were immediately filtered through filters with a pore size of 0.45 \u0026micro;m and the filters were subsequently placed in sterile petri dishes for microscopic and spectroscopic analysis.\u003c/p\u003e \u003cp\u003eSediment samples were obtained from the same stations where water samples were collected. A Van Veen Grab sampler was used to extract bottom sediment from an area of 250 cm\u0026sup2;, reaching an average sediment depth of 10 cm. The collected sediment was carefully transferred into a metal container. A subsample of approximately 400 g was then taken from the upper 5 cm of the sediment, which represents the sediment layer in direct contact with water, using a wooden spatula. The subsamples were stored in sterile glass jars for further processing. Sediment samples were subjected to density separation to extract MPs.\u003c/p\u003e \u003cp\u003eThe extraction of MPs from sediment matrices, which have a high organic content, is a complex process (Bl\u0026auml;sing \u0026amp; Amelung, 2018; Hurley et al., 2018). In the laboratory, sediment samples were placed in a density separation apparatus as proposed by Coppock et al. (2017). A 4 Molar potassium carbonate solution with a fixed density of 1.8 g/mL was added to cover the samples by 3\u0026ndash;5 cm fully. The samples were allowed to stand for 24 hours to ensure complete density separation. Following this, the settled sediment was separated from the supernatant, and the floating material was transferred into a beaker.\u003c/p\u003e \u003cp\u003eSince density separation alone is insufficient to remove all organic matter, a 30% hydrogen peroxide (H₂O₂) solution was added to the separated material to facilitate organic matter degradation. The samples were then placed on a hot plate set at 50\u0026deg;C and left until all organic material was degraded entirely, a process that typically took 2\u0026ndash;4 days. The remaining material was vacuum-filtered through 1,2 \u0026micro;m filters (GF/C), and the filter papers were stored in closed Petri dishes for subsequent microscopic and spectroscopic analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. MP Characterization and Validation\u003c/h2\u003e \u003cp\u003eMPs were analyzed using a stereo microscope (SZX16, Olympus Co., Tokyo, Japan) positioned within an enclosed cabinet to prevent contamination. The size, shape (categorized as fiber/filament or fragment), and color of the MPs were determined. A Touptek XCAM1080PHD camera, connected to the microscope, was used for size measurements. The classification of MP color and morphology followed the methodology outlined by Koelmans et al. (2019).\u003c/p\u003e \u003cp\u003eFollowing the initial characterization, a randomly selected subset of MP particles underwent \u0026micro;-Raman spectroscopy for polymer identification. The filter papers containing MP samples were analyzed using the Renishaw InVia Qontor Confocal Raman Microscopy System (Renishaw Plc., New Mills, Wotton-under-Edge, Gloucestershire, UK), equipped with 532 nm and 785 nm lasers. Particles were focused under 50x magnification using a Leica microscope, and spectral measurements were conducted with two accumulations across a spectral range of 300\u0026ndash;3200 cm⁻\u0026sup1;. Exposure times were set at 10 seconds, with grating configurations of 600 l/mm and 1200 l/mm. The obtained Raman spectra were compared against reference spectra from the ST-Japan MP library. A polymer identification threshold of \u0026ge;\u0026thinsp;70% spectral match was applied, as recommended by previous studies (S. G\u0026uuml;ndoğdu et al., 2021; Kim et al., 2018; Woodall et al., 2014)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Contamination Control\u003c/h2\u003e \u003cp\u003eTo mitigate contamination risks throughout the study, all equipment was subjected to a standardized cleaning protocol. Prior to use, instruments were washed three times with ultrapure water, followed by acetone rinsing (Beer et al., 2018). Acetone, a non-polar solvent, was utilized due to its efficiency in removing grease, oil, cosmetic residues, and solidified oils, thereby preventing particle adhesion and minimizing contamination from the surrounding environment. Additionally, acetone facilitates the removal of residual water from glassware surfaces, aiding in the elimination of potential contaminants. All cleaned equipment was stored in a sealed cabinet to maintain sterility. Furthermore, all solutions used in the digestion, separation, and purification processes were vacuum-filtered through 1.2 \u0026micro;m GF/C Whatman filter paper before use to ensure the removal of extraneous particulates. All analyses were conducted within an ESCO-brand enclosed laminar flow cabinet. To further prevent contamination, all sample containers were sealed with aluminum foil during waiting periods. Work surfaces were cleaned with acetone before and after each analysis session to maintain a contamination-free environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe normal distribution of data was tested using the Shapiro-Wilk test. Concentration and size data showed a non-normal distribution. Thus, non-parametric tests; Mann Whitney U and Kruskal-Wallis were used to determine the differences between seasons and lagoons. Dunns\u0026rsquo; test was applied to determine differing groups following a significant difference determined by Kruskal-Wallis. Nonmetric multidimensional scaling (NMDS) with Bray curtis dissimilarity was applied to construct composition plots. Comparison of shape, polymer type and color compositions among lagoons, and sampling periods were done using ANOSIM. All statistical analyses were performed using R (ver 4.4.3) (R Core Team, 2025). Shapiro-Wilk, Mann Whitney U, Kruskal-Wallis and Dunns\u0026rsquo; tests were performed using rstatix (ver. 0.7.2) package (Kassambara, 2023). NMDS, and ANOSIM analyses were done using vegan (ver. 2.6-8) package (Oksanen et al., 2024). Maps and data visualizations were prepared using QGIS (3.34) (QGIS Development Team, 2025) and ggplot2 (ver. 3.5.1) (Wickham, 2016), respectively. The data were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean. The confidence level for statistical analyses were set to 95%.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 15526 MPs were extracted from the water (14104) and sediment (1422) samples. In the water samples, the overall average MP concentration during November was 47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02 MPs/L, which was significantly higher than the June average of 17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57 MPs/L (Mann-Whitney U, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In addition, MP concentrations in each lagoon were significantly higher during November compared to June (Mann-Whitney U, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The lagoons showed significant variability in MP concentrations in both sampling periods (KW test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). From highest to lowest, the average water MP concentrations in the November sampling period were as follows: Yelkoma (62.68\u0026thinsp;\u0026plusmn;\u0026thinsp;12.76 MPs/L), Tuzla (61.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63 MPs/L), \u0026Ccedil;amlık (48.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60 MPs/L), Ağyatan (45.46\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86 MPs/L), and Akyatan (24.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16 MPs/L). The highest MP concentrations during this month were recorded at Y2 (114 MPs/L), T6 (111 MPs/L), Y1 (99.4 MPs/L), and T7 stations (92.4 MPs/L) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Akyatan had a significantly lower MP concentration than Yelkoma and Tuzla in November, while concentrations were similar among the other lagoons (Dunn\u0026rsquo;s test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). During June sampling period, Tuzla (31.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54 MPs/L) showed the highest average concentration, followed by \u0026Ccedil;amlık (19.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73 MPs/L), Akyatan (13.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26 MPs/L), Ağyatan (11.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92 MPs/L), and Yelkoma (5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 MPs/L). Among the stations, T2 (101.6 MPs/L), T1 (42.0 MPs/L), T3 (39.6 MPs/L), and C3 (38.4 MPs/L) had the highest MP concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The concentration in Tuzla was significantly higher than that in Yelkoma but similar to the rest of the sampled lagoons (Dunn\u0026rsquo;s test p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sediment samples, the overall average MP concentrations in the November (47.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56 MPs/kg) and June (34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38 MPs/kg) periods were statistically similar (Mann-Whitney U, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Likewise, each lagoon\u0026rsquo;s sediment concentrations did not differ significantly between these two sampling periods (Mann-Whitney U, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, there was a significant difference in sediment MP concentrations across the lagoons during both sampling periods (Kruskal\u0026ndash;Wallis test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). During November, Akyatan had the highest average MP concentration (85.25\u0026thinsp;\u0026plusmn;\u0026thinsp;27.96 MPs/kg), followed by Tuzla (61.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.05 MPs/kg), Yelkoma (36.79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92 MPs/kg), \u0026Ccedil;amlık (22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73 MPs/kg), and Ağyatan (20.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79 MPs/kg). Within this period, Tuzla\u0026rsquo;s MP concentration was significantly higher than those of \u0026Ccedil;amlık and Ağyatan (Dunn\u0026rsquo;s test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the remaining lagoons were statistically similar (Dunn\u0026rsquo;s test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The most contaminated stations in November were Ak3 (225 MPs/kg), Ak2 (215 MPs/kg), Ak1 (195 MPs/kg), and T9 (122.5 MPs/kg) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In June, the highest average sediment MP concentration was again observed in Akyatan (61.11\u0026thinsp;\u0026plusmn;\u0026thinsp;16.84 MPs/kg), followed by Tuzla (44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61 MPs/kg), Yelkoma (22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97 MPs/kg), Ağyatan (19.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75 MPs/kg), and \u0026Ccedil;amlık (18.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96 MPs/kg). Station Ak1 (182.5 MPs/kg) showed the highest contamination during this period, followed by T10 (115 MPs/kg), T7 (112.5 MPs/kg), and Ak6 (77.5 MPs/kg). Overall, MP concentrations among lagoons were similar (Dunn\u0026rsquo;s test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with the exception of Akyatan, which had significantly higher levels than Ağyatan (Dunn\u0026rsquo;s test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall average size of the MPs in water samples were 1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mm in November, and 0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mm in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). MP size in each lagoon were similar between the sampling periods except for Yelkoma. The MPs in this lagoon were significantly larger in the November period (Mann-Whitney U, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The overall average size of the MPs in sediment samples were 0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mm in November and 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 mm in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The MP size in sediment at \u0026Ccedil;amlık was found to be significantly higher in June (Mann-Whitney U, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The average sizes of MPs (MPs) by shape were as follows: fibers measured as 1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mm, films 0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 mm, foams 2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 mm, and fragments 0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe shape composition of MPs in water was predominantly fibers in both November (75.6%) and June (76.1%), followed by fragments, which accounted for 22.1% in November and 18.9% in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In November, the average proportion of fiber-shaped MPs by lagoon, from highest to lowest, was as follows: \u0026Ccedil;amlık (83.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04%), Tuzla (80.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18%), Yelkoma (79.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64%), Ağyatan (67.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17%), and Akyatan (52.70\u0026thinsp;\u0026plusmn;\u0026thinsp;4.70%). Fragments were most prevalent in Akyatan (42.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52%), followed by Ağyatan (30.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85%), Yelkoma (18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38%), Tuzla (16.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82%), and \u0026Ccedil;amlık (15.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77%). The average percentage of film-shaped MPs varied from 2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27% in \u0026Ccedil;amlık to 10.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68% in Akyatan. In June, fiber-shaped MPs were most abundant in Akyatan (76.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84%), followed by Tuzla (75.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95%), \u0026Ccedil;amlık (73.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72%), Ağyatan (68.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66%), and Yelkoma (53.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.07%). The distribution of fragment-shaped MPs, from highest to lowest, was Yelkoma (46.55\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11%), Ağyatan (40.08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00%), \u0026Ccedil;amlık (22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.21%), Akyatan (18.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77%), and Tuzla (15.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10%). Films were varied between 4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96% (Ağyatan) to 12.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15% (\u0026Ccedil;amlık). Foams were only observed at Tuzla (1.72%) and Yelkoma (1.61%).\u003c/p\u003e \u003cp\u003eSimilar to water, the most abundant shape in sediment was fibers, accounting for 54.3% in November and 62.0% in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In November, fragment-shaped MPs were most abundant in \u0026Ccedil;amlık (70.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51%), followed by Tuzla (63.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51%), Akyatan (57.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16%), Yelkoma (50.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19%), and Ağyatan (48.95\u0026thinsp;\u0026plusmn;\u0026thinsp;7.26%). Fiber-shaped MPs showed a different distribution pattern, with the highest abundance in Yelkoma (45.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88%), followed by Ağyatan (44.55\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88%), \u0026Ccedil;amlık (35.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37%), Akyatan (31.57\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91%), and Tuzla (24.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.28%). Film-shaped MPs ranged from 13.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72% in \u0026Ccedil;amlık to 31.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66% in Yelkoma. Foams were only detected in Tuzla, with a proportion of 5%. In sediment samples collected in June, fragment-shaped MPs were the most abundant in Tuzla (77.57\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16%), followed by Akyatan (73.06\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86%), Ağyatan (67.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52%), \u0026Ccedil;amlık (58.99\u0026thinsp;\u0026plusmn;\u0026thinsp;16.83%), and Yelkoma (48.93\u0026thinsp;\u0026plusmn;\u0026thinsp;11.92%). Fiber-shaped MPs showed a different distribution pattern, with the highest abundance in \u0026Ccedil;amlık (53.94\u0026thinsp;\u0026plusmn;\u0026thinsp;17.16%), followed by Ağyatan (48.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18%), Yelkoma (37.07\u0026thinsp;\u0026plusmn;\u0026thinsp;14.72%), Tuzla (30.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59%), and Akyatan (9.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27%). Film-shaped MPs were most prevalent in \u0026Ccedil;amlık (47.32\u0026thinsp;\u0026plusmn;\u0026thinsp;18.50%) and Yelkoma (46.58\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35%), with lower percentages observed in Ağyatan (40.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93%), Akyatan (28.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16%), and Tuzla (13.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92%).\u003c/p\u003e \u003cp\u003eNMDS plots indicated a certain degree of separation in shape composition between water and sediment samples across the lagoons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). ANOSIM analysis confirmed statistically significant differences in overall shape compositions between June and November in both sediment and water (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In water samples, Akyatan displayed a significantly different shape composition from the other lagoons in November (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In June, Yelkoma\u0026rsquo;s shape composition was significantly different from Akyatan, Tuzla, and \u0026Ccedil;amlık (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For sediment samples, Tuzla exhibited a significantly different shape composition compared to Ağyatan and \u0026Ccedil;amlık in June (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, Akyatan\u0026rsquo;s shape composition was distinct from Ağyatan and \u0026Ccedil;amlık, while Tuzla differed significantly from both Yelkoma and \u0026Ccedil;amlık in June (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 1882 particles extracted from water (1488) and sediment (394) samples were analyzed using \u0026micro;-Raman. In water samples, 4.46% of the analyzed particles were identified as cellulose, whereas no cellulose particles were detected in sediment samples. A total of ten distinct polymers were detected in water samples, whereas eight polymers were identified in sediment samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). The analysis of MP polymer types in water and sediment samples collected in November and June revealed distinct compositions. In water samples, the most common polymers in November were polyethylene (PE) at 31.45%, polypropylene (PP) at 30.81%, polyester/polyethylene terephthalate (Polyester/PET) at 10.87%, and acrylonitrile butadiene styrene (ABS) at 9.28%. In June, the polymer distribution was similar, with PE increasing to 37.55%, PP at 28.97%, Polyester/PET at 11.80%, and ABS at 8.80%. In sediment samples, PE was also the dominant polymer in both months, accounting for 29.07% in November and 29.94% in June. In November, PP (22.47%), Polyester/PET (22.03%), and polyvinyl chloride (PVC) (13.66%) were also prevalent. In June, the proportion of Polyester/PET increased to 28.74%, while PP decreased to 18.56%, and ABS reached 10.18%. ANOSIM analysis indicated a significant difference in polymer composition between water and sediment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of eleven colors were determined in water and sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). The color composition was different between water and sample (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In water samples, blue particles were the most dominant color in November (35.57%), followed by transparent (27.33%) and black (23.64%). In June, the order shifted, with black particles being the most prevalent (28.62%), followed by transparent (26.57%) and blue (26.30%).In sediment samples, transparent particles accounted for 56.05% of the total, followed by black (12.93%) and blue (12.10%) particles in November. NMDS plots indicate differences in color composition in water and sediment samples among the lagoons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The proportion of transparent particles increased to 59.62% in June, while blue particles made up 17.04% and black particles 11.24%. In November, the color composition of MPs in water at Akyatan differed significantly from those at Ağyatan, Tuzla, and \u0026Ccedil;amlık (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In June, the color composition at Akyatan was distinct from both Tuzla and Yelkoma (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, Tuzla showed a different color composition compared to Ağyatan and Yelkoma, while Yelkoma and \u0026Ccedil;amlık also exhibited significant differences in their color compositions (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In sediment samples, the color composition at Tuzla differed significantly from Ağyatan, Yelkoma, and \u0026Ccedil;amlık in November. In June, the color composition of sediments at Akyatan and Ağyatan also showed distinct differences (ANOSIM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis comprehensive study on MPs in Turkish coastal lagoons aligns with and expands upon the growing literature on plastic pollution in semi-enclosed water bodies. The results reveal significant MP contamination in the Çukurova Delta lagoons, with notable differences in abundance, size, shape, and polymer composition between locations and seasons. When compared to other studies worldwide, these findings are consistent with previous research on MPs in lagoon environments, reinforcing the idea that semi-enclosed coastal ecosystems are hotspots for plastic pollution due to their role as transitional zones between terrestrial and marine systems. These results also align with studies conducted in lagoons across different geographic regions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), emphasizing that MP pollution is largely driven by anthropogenic pressures and hydrodynamic processes. The presence of MPs in the study area demonstrates that even wetlands of high ecological significance, such as the lagoons in the Çukurova Delta, are not exempt from plastic contamination. This trend has been observed in other coastal and estuarine ecosystems (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, a study conducted in Sontecomapan Lagoon, Mexico, reported MPs in surface water, sediments, and zooplankton, with concentrations of 7.5 ± 5.3 MPs/L in water and 8.5 ± 12.5 MPs/kg in sediments (Sánchez-Campos et al., 2024), which closely align with findings from the Çukurova Delta. Like Yumurtalık Lagoon, Sontecomapan Lagoon is located within a Ramsar-designated site in the Los Tuxtlas Biosphere Reserve, yet it still exhibits MP contamination, demonstrating that conservation status alone does not prevent plastic pollution. Similarly, studies conducted in Bizerte Lagoon, Tunisia (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2020b, 2020a), have reported significant seasonal variations in MP concentrations, with the highest levels recorded in summer and the lowest in winter, a pattern also observed in the Çukurova lagoons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMP levels found in different matrices of lagoons across the globe\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLagoon\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage (km2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbundance\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSize range (mm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePolymer\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShape\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDetection Method\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcarai\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001–0.018 mp/m3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05-2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PET, PB, PS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Lorenzi et al., 2021)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColombia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCiénaga Grande de Santa Marta\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1321\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0-3.08 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PE, HDPE, PS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment, film, foam, granule\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Garcés-Ordóñez et al., 2022)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColombia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCiénaga Grande de Santa Marta\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1321\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0–30 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PE, HDPE, PS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment, film, foam, granule\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhanaian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6–2.5 mp/cm3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNile-red\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Chico-Ortiz et al., 2020)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorissia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3-158 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRaman\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Simantiris et al., 2025)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrench Polynesia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAhe, Manihi, Takaroa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145, 165, 89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.30 mp/m3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02-1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PU, PS, PE, PP, PES, Acrylic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efragment, fiber\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Gardon et al., 2021)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrench Polynesia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAhe, Manihi, Takaroa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145, 165, 89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater column\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3613 mp/m3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02-1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PS, PVC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efragment, fiber\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChilika\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26-1107 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 1–5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePVC, PVA, PS, PP, PE, PES, PA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efoam, fragment, filament, film\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Singh et al., 2023)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChilika\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7-25.2 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 1–5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePVC, PVA, PS, PP, PE, PES, PA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efilament, fragment, film, foam\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntinioti\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40–50 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNylon, PS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRaman\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Simantiris et al., 2025)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLesina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efilament, fragment, sphere, film\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eµFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Specchiulli et al., 2023)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLesina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.91 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efilament, fragment, sphere, film\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eµFTIR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVenice\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7–3.5 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03-5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efilament, fiber\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStereomicroscope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Cecchi et al., 2024)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRio Lagartos\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0-200 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3-2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Quesadas-Rojas et al., 2021)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRio Lagartos\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 mp/m3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3-2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaguna de Terminos\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201.1 mp/g\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065-5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PET, PVC, CE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Celis-Hernandez et al., 2023)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaguna de Terminos\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065-5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PET, PVC, CE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSontecomapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5 ± 5.3 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02–4.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePES, PE, PS, NL, rayon, acrylic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment, foam\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Sánchez-Campos et al., 2024)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSontecomapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01–2.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePES, acrylic, NL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMar Menor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.1 ± 7.6 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1-5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLDPE, HDPE, PVE, PP, NL, PES\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efragment, fiber, film, pellet, foam\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Bayo et al., 2019)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSongkhla\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34–0.43 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.5-\u0026gt;1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePET, rayon, PP, PE,\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Pradit, Noppradit, Sornplang, Jitkaew, Jiwarungrueangkul, et al., 2024)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSongkhla\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1-5.4 mp/g\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PES, PET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Pradit, Noppradit, Sornplang, Jitkaew, Kobketthawin, et al., 2024)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBizerte\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7960 ± 6840 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3-5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStereomicroscope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Abidli et al., 2017)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBizerte\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4 ± 0.2 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PP, CE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, film, fragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Wakkaf et al., 2020a)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBizerte\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e453.0 ± 335.mp/m3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PP, PET, CP, NL, PS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment, film\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Wakkaf et al., 2020b)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBizerte\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.2-109.6 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE, PP, PVC, PET\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment, film\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Wakkaf et al., 2022)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBizerte\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106 ± 65 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07–0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP, PET, PVC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efragment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR-ATR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Jaouani et al., 2022)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTürkiye\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKüçükçekmece\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efragment, fiber, film\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Çullu et al., 2021)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTürkiye\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKüçükçekmece\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2922 ± 517 mp/kg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efiber, fragment, film, bead\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStereomicroscope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(İşlek et al., 2023)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarnes Sound\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e703\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40-76000 mp/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03–0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFTIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(Badylak et al., 2021)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNL:Nylon, CP:Cellophane, PE:Polyethylene, PP:Polypropylene, PET: Polyethylene Terephthalate, PS: polystyrene, PB: polybutadiene, PES: polyester, PAN: poly-acrylonitrile, PEUU: polyester urethane, PA:polyamide, HDPE: high-density polyethylene, LDPE: low-density polyethylene, PVE: polyvinyl ester, PA: polyarcylic, EVA: ethylene-vinyl acetate, FTIR: Fourier-transform infrared spectroscopy, ATR: Attenuated total reflectan\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSeasonal variations have played a crucial role in MP concentrations within these lagoons, with significantly higher levels observed during the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This seasonal pattern suggests that reduced water input and slower sedimentation rates during dry periods contribute to higher MP accumulation in surface waters. During the rainy season, an increased inflow of freshwater occurs through drainage channels, which may facilitate the transport of agricultural MPs into the lagoon system. Indeed, a study by Gündoğdu et al. (2022) conducted near the lagoons reported a substantial presence of MPs in soil from seasonal greenhouse areas where agricultural plastics are used. Similarly, Akça et al. (2024) found that agricultural soils exhibited extremely high levels of MP contamination. Therefore, it is highly likely that residues from agricultural plastic applications in these soils are transported into the lagoons through drainage channels. To better understand this phenomenon, it is crucial to monitor the seasonal and spatial distribution of MPs transported through these channels and implement necessary mitigation measures.\u003c/p\u003e \u003cp\u003eWhen comparing different lagoons within the Çukurova Delta, it is evident that MP contamination levels vary significantly between locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Spatial variations in MP pollution across different lagoons in the Çukurova Delta are likely influenced by hydrodynamic conditions, proximity to pollution sources, and lagoon morphology. Lagoons with lower water flow and higher sedimentation rates tend to accumulate more MPs, while those with higher water exchange rates are expected to have lower MP concentrations due to dilution effects. For example, Akyatan Lagoon exhibited relatively high MP concentrations, likely due to its proximity to intensive fishing activities and agricultural areas. This finding is consistent with research from Chilika Lake in India, where extensive fishing and tourism were identified as major contributors to MP pollution (Singh et al., 2023) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the study sites, Tuzla Lagoon showed particularly high levels of plastic accumulation, especially in sediments. This observation is comparable to findings from Bizerte Lagoon, where sediments were identified as long-term sinks for MPs (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2022). The high levels of sediment-bound plastics in Tuzla may be attributed to its relatively low hydrodynamic activity, which allows MPs to settle rather than being transported away. In contrast, Ağyatan Lagoon exhibited lower MP concentrations compared to Akyatan and Tuzla, although contamination was still present. The seasonal variations observed in Ağyatan Lagoon align with findings from Acaraí Lagoon in Brazil (Lorenzi et al., 2021), where higher MP concentrations were detected during the dry season due to limited water exchange, while in the wet season, increased water flow transported MPs toward the lagoon’s mouth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A similar mechanism may be at play in Ağyatan Lagoon, where seasonal hydrological changes influence MP transport and retention. Yelkoma Lagoon, located in the eastern part of the Çukurova Delta, exhibited moderate levels of MP contamination compared to other lagoons. This intermediate contamination level may be attributed to a mix of freshwater and brackish water inputs, leading to variable MP transport dynamics (Doto et al., 2025). This pattern resembles findings from pearl-farming lagoons in French Polynesia, where MP concentrations fluctuate based on hydrological conditions and aquaculture activities (Gardon et al., 2021).\u003c/p\u003e \u003cp\u003eMPs can exhibit dynamic distribution patterns between the central and estuarine regions of lagoons (Doto et al., 2025). Furthermore, differences in MP distribution across northern, central, and southern sectors of the lagoons may be linked to variations in hydrodynamic conditions within the system (Doto et al., 2025). Additionally, stronger currents are typically observed in areas near the lagoon’s water inflow and outflow points. This hydrodynamic behavior explains the observed differences in MP concentrations between stations and aligns with findings from studies conducted by Bortolin et al. (2020) and Doto et al. (2025) regarding the formation of sediment deposition centers. Given that the lagoons in the study area have two main water inflow directions, it is expected that the distribution and accumulation of MPs are influenced by these hydrodynamic patterns. The findings indicate that MP accumulation in both water and sediment follows distinct spatial trends: MPs released in the northern section of the lagoon tend to become trapped within the system, while those introduced in the central region primarily remain within the estuarine area, with some fraction being transported toward the coastal region. This movement pattern may be governed by the interaction between estuarine geometry, river discharge, and barotropic pressure gradients generated by wind-driven tidal currents (Doto et al., 2025; Elisei Schicchi et al., 2023; Moller et al., 2001).\u003c/p\u003e \u003cp\u003eThe MP size distribution in the Çukurova lagoons exhibited a dominance of smaller particles, particularly those below 1 mm, similar to findings from Chilika Lake in India and Sontecomapan Lagoon in Mexico (Sánchez-Campos et al., 2024; Singh et al., 2023). In these studies, sediment samples predominantly contained MPs within the 0.1–1 mm range, with the smallest fractions exhibiting the highest concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The prevalence of smaller particles increases the likelihood of ingestion by aquatic organisms, as demonstrated in Lagos Lagoon, Nigeria, where MPs of similar sizes were detected in fish species. The high proportion of small MPs is likely attributed to fragmentation processes driven by UV exposure, wave action, and mechanical degradation. Additionally, considering that freshwater inflows into the region’s lagoons are heavily influenced by wastewater discharges from intensive agricultural activities, plastic recycling facilities, and the textile industry, it is possible that smaller MPs are directly introduced into the lagoons at these sizes (R. Gündoğdu et al., 2022).\u003c/p\u003e \u003cp\u003eMoreover, sampling methodology plays a crucial role in determining the size distribution of MPs within a sample. Several studies have indicated that sampling nets exhibit size selectivity for MPs, typically using mesh size as a threshold to determine the smallest MP size that can be captured (Barrows et al., 2017; Kang et al., 2015; Yu et al., 2025). This approach assumes that the net defines the lower size limit of MPs that can be retained and that MP concentrations are reported accordingly. However, Yu et al. (2025) noted that this assumption may overlook the complex morphological characteristics of MPs, which can affect their likelihood of passing through or being retained by a net. While net-based surface water sampling tends to capture larger particles rather than smaller fragments, the bulk sampling method used in this study allows for a broader size range of particles to be captured, as it relies on the pore size of the filter rather than the selectivity of a net. Therefore, the higher proportion of smaller particles in the MP size distribution observed in this study can be attributed to this sampling method.\u003c/p\u003e \u003cp\u003eThe dominant MP shapes in the Çukurova lagoons were fibers and fragments, with films and pellets appearing in smaller proportions. However, as the identification of fragment and film structures becomes increasingly difficult for smaller particles through visual inspection, the distinction between fibers and other MP shapes becomes particularly important. The substantial presence of fibers, likely originating from textile waste, aligns with the types of polymers predominantly used in the textile industry, indicating that wastewater discharges from urban areas and textile production facilities significantly contribute to MP contamination. Gündoğdu et al. (2018) revealed in their study that the high proportion of fibers is likely influenced by wastewater discharges from freshwater inflows feeding the lagoons and by the coastal currents supplied by the Ceyhan River. Both the Ceyhan River and the surface water channels that drain into the lagoons appear to be significant contributors to the predominance of fibers in these ecosystems. In addition, the pattern observed in the Çukurova lagoons aligns with studies conducted in Bizerte Lagoon, Tunisia (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2020b, 2020a, 2022), and Acaraí Lagoon, Brazil (Lorenzi et al., 2021), where fibers accounted for more than 70% of the detected MPs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is further supported by findings from Sontecomapan Lagoon, where polyester and acrylic fibers were widely detected in both water and sediment samples (Sánchez-Campos et al., 2024). Fragments, the second most common shape, followed by films, likely originate from the degradation of larger plastic debris such as agricultural plastics and consumer plastics. Additionally, the presence of these shapes may also be linked to MP-rich wastewater effluents from plastic recycling facilities. Despite the fact that the lagoons in this study are actively used for fishing, the presence of foam-like polystyrene fragments was notably lower compared to Chilika Lake, which experiences similar fishing activities. This discrepancy requires further investigation to determine why polystyrene foams are less prevalent in the Çukurova lagoons.\u003c/p\u003e \u003cp\u003ePolymer analysis has revealed that polyethylene (PE) and polypropylene (PP) are the most abundant MP types found in the Çukurova lagoons. In a study conducted in the region including our study area, Gündoğdu et al., (2022) stated that the high presence of PE and PP, especially in areas near agricultural lands, suggests that agricultural films, plastic mulching, and single-use drip irrigation systems play a significant role in MP pollution. Consequently, it can be asserted that this MP source is also efficacious in lagoons. Furthermore, these findings align with studies conducted in various lagoon environments worldwide, such as the Bizerte Lagoon (Abidli et al., 2017; Jaouani et al., 2022; Wakkaf et al., 2020b, 2020a, 2022), Ciénaga Grande de Santa Marta (Garcés-Ordóñez et al., 2022), and the Lesina Lagoon (Specchiulli et al., 2023), all of which have reported PE and PP as the dominant polymer types (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The widespread presence of these polymers is primarily attributed to their global production scale and extensive use across multiple applications, including agricultural activities. In contrast, studies in Chilika Lake (Singh et al., 2023) and the Sontecomapan Lagoon (Sánchez-Campos et al., 2024) have reported significant concentrations of polyester/PET and polyvinyl chloride (PVC), which are less common in the Çukurova lagoons. The lower detection of PVC in water samples can be explained by its higher density, which causes it to sink and become embedded in sediments, whereas PE and PP, being less dense (Hidalgo-Ruz et al., 2012), tend to remain suspended or accumulate on the water surface. The variation in polymer compositions across different lakes and lagoons is closely linked to land use patterns, human activities surrounding these ecosystems, and the sources of inflowing water.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights significant MP contamination in the Mediterranean lagoons of Türkiye, with seasonal variations and anthropogenic activities influencing pollution levels. Higher MP concentrations in water during November suggest increased input from rainfall and surface runoff, while sediment accumulation, particularly in Akyatan Lagoon, indicates long-term retention due to reduced water exchange. The dominance of fibers and polymers like PE, PP, and PES/PET points to agricultural runoff, fisheries-related plastic debris, and urban waste as key sources. The ecological risks of MP pollution in the Çukurova Delta are concerning, given its role as a critical habitat for fish, migratory birds, and aquatic vegetation. MPs pose a threat to biodiversity, food security, and public health through bioaccumulation in the food web. Despite conservation efforts, plastic pollution remains a major issue, necessitating improved wastewater treatment, stricter agricultural plastic regulations, and better waste management. Given that lagoons act as long-term sinks for MPs, ongoing monitoring and targeted mitigation strategies are essential to preserving their ecological integrity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Scientific Research Projects Unit of \u0026Ccedil;ukurova University under project numbers FBA-2021-13403, FBA-2021-13410 and FBA-2023-15165.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting this study are included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSG\u0026nbsp;\u003c/strong\u003eConceptualized the study, performed the investigation, administered the project and received funding, and was a major contributor in writing the manuscript. \u003cstrong\u003eC\u0026Ccedil;\u003c/strong\u003e played part in investigation, project administration and funding acquisition. \u003cstrong\u003eYT\u003c/strong\u003e performed statistical analysis, draw figures, and was a major contributor in writing the manuscript. \u003cstrong\u003eKG\u003c/strong\u003e was a major contributor in writing the manuscript. \u003cstrong\u003eFB\u003c/strong\u003e played part in investigation. \u003cstrong\u003eR\u0026Ccedil;\u0026Ouml;\u003c/strong\u003e was a major contributor in writing the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbidli, S., Toumi, H., Lahbib, Y., \u0026amp; Trigui El Menif, N. 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Assessment of industrial pollution effects in coastal seawater Northeastern Mediterranean Sea) with chemometric approach. \u003cem\u003eInternational Journal of Environmental Analytical Chemistry\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(1), 95\u0026ndash;112. https://doi.org/10.1080/03067319.2019.1660877\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Çukurova Delta, microplastic, polymer, coastal lagoon, seasonal","lastPublishedDoi":"10.21203/rs.3.rs-6201957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6201957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransitional ecosystems like coastal lagoons provide numerous ecosystem services. However, they are increasingly threatened by plastic pollution, particularly microplastics (MPs). Despite growing concerns, the occurrence and distribution of MPs in T\u0026uuml;rkiye\u0026rsquo;s lagoon systems remain largely unknown. This study aims to assess the abundance, composition, and seasonal variability of MPs in the surface water and sediments of five lagoons located in the northeastern Mediterranean region of T\u0026uuml;rkiye. Additionally, potential MP sources and their environmental implications are addressed. Water and sediment samples were collected from Akyatan, Tuzla, Ağyatan, \u0026Ccedil;amlık, and Yelkoma Lagoons during the November and June periods. MPs were extracted using density separation and digestion techniques, quantified via stereo microscopy, and characterized through \u0026micro;-Raman spectroscopy to identify polymer composition. A total of 15,526 MPs were recovered, with significantly higher concentrations in water (47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02 MPs/L) during November compared to June (17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57 MPs/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). MP concentrations varied among lagoons, with Yelkoma and Tuzla exhibiting the highest levels in water, while Akyatan showed the highest sediment contamination. Fibers were the dominant MP type, followed by fragments and films. polymer analysis identified polyethylene (PE), polypropylene (PP), and polyester (PES) as the most common polymers, indicating agricultural runoff, fishing activities, and mismanaged plastic waste as primary MP sources. This study provides the first comprehensive assessment of MP pollution in Turkish lagoons, highlighting seasonal and spatial differences in contamination levels. The results highlight the pressing need for improved waste management policies and conservation strategies to mitigate MP pollution in these ecologically and economically significant coastal systems.\u003c/p\u003e","manuscriptTitle":"Microplastics in Turkish Coastal Lagoons: Unveiling the Hidden Threat to Wetland Ecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 08:02:45","doi":"10.21203/rs.3.rs-6201957/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3ccf300-08b7-4998-89f9-2a380422b692","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T18:47:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 08:02:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6201957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6201957","identity":"rs-6201957","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}