Microplastic Contamination Hotspots in the Sakarya, a Major Anatolian River: Evidence from Water and Sediment

Microplastic Contamination Hotspots in the Sakarya, a Major Anatolian River: Evidence from Water and Sediment | 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 Microplastic Contamination Hotspots in the Sakarya, a Major Anatolian River: Evidence from Water and Sediment Akif Er, Cantekin Dursun, Nagihan Demirci, Yusuf Ceylan, Serkan Gül This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8967489/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 Rivers are important pathways for the transport of microplastics from terrestrial environments to aquatic ecosystems; however, data from major freshwater systems in Türkiye remain limited. This study aimed to determine the abundance, characteristics, and transport potential of microplastics in the Sakarya River, one of the largest freshwater systems in Türkiye. Surface water and sediment samples were collected from 10 stations along approximately 800 km of the river. Microplastics were quantified and classified according to polymer type. Annual transport was estimated using instantaneous concentrations and long-term average flow rates. Microplastic abundance ranged from 0–166.7 particles/m³ in surface water and 0–40 particles/kg in sediment. Inputs from tributaries draining densely populated areas significantly increased microplastic pollution. Ten polymer types were identified, with PET and PVC being dominant. Annual microplastic transport was estimated at approximately 10¹¹ particles. The Sakarya River exhibits significant microplastic contamination. These findings provide critical information for developing environmentally sound waste management practices and long-term environmental planning strategies. Environmental monitoring Freshwater Polymer Pollution River pollution Sustainability Water resources Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction On a global scale, the rapid increase in plastic production and use causes plastic waste to break down and enter the ecosystem as MPs, posing a constant threat to the environment (Ding et al., 2019 ; Choong et al., 2021 ) and thus, they can persist in the environment for an extended period of time due to their strength (Rodrigues et al., 2018 ). They are a significant pollutant in freshwater and saltwater habitats due to their lengthy lives in aquatic systems (Scherer et al., 2020 ; Villabona-González et al., 2024 ). Rivers play a crucial role in the environmental cycle of MPs by transporting plastics from the land to the ocean (Gerolin et al., 2020 ; Singh et al., 2021 ). Numerous micro plastics were found in water and sediment samples from the West River, the most prevalent of which were fibrous particles (Huang et al., 2021 ). Studies in the Ganges River have shown that polyethylene-type plastics are prevalent in water and sediments (Singh et al., 2021 ). The concentrations of MPs in the sediments of the Elbe River in Europe were found to be hundreds of thousands of times higher than those in the water column (Scherer et al., 2020 ). This indicates that sediments provide MPs with a significant accumulating substrate. MPs are permanent and widespread pollutants on a global scale, as evidenced by similar results found in river studies conducted on other continents, including Citarum (Sembiring et al., 2020 ), Antuã (Rodrigues et al., 2018 ), and Subarnarekha and Kharkai (Patidar et al., 2024 ). The morphological and chemical diversity of microplastics further makes complex their impact on ecosystems. Various studies have shown that the most common types of MPs are fibers, films, particles, and foams, while polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) are prominent in terms of polymer types (Akdoğan et al., 2023; Patidar et al., 2024 ; Khedre et al., 2024 ). Fiber-formed particles, in particular, are thought to originate from textiles, fishing nets, and industrial discharges (Adjornor et al., 2024 ; Du et al., 2025 ). Furthermore, blue and black polyester fibers have been reported to predominate in many river ecosystems (Khedre et al., 2024 ). These findings indicate that MPs are not only an environmental problem but also offer distinctive characteristics for monitoring. The distribution of MPs in aquatic environments is shaped not only by physical processes but also by anthropogenic pressures. Population density, industrial activities, agricultural activities, and inadequate waste management directly increase the microplastic load in rivers (Zhang et al., 2024 ; Salikova et al., 2024 ). In the case of the Wei River, the abundance of MPs was found to be significantly higher in urban areas than in agricultural and mountainous areas (Zhang et al., 2024 ). A study conducted in the Yesil River in Kazakhstan determined that microplastic concentration in aquatic environments is related to water quality parameters such as turbidity (Salikova et al., 2024 ). These findings highlight the importance of investigating microplastic pollution in rivers such as the Sakarya River, which are subject to high population density and industrial pressure. The biological impacts of microplastics are not negligible in freshwater ecosystems. Studies have indicated that MPs can be ingested by fish, invertebrates, and even planktonic organisms, potentially causing to their transfer into the food chain (Sembiring et al., 2020 ; Khedre et al., 2024 ). Furthermore, microplastics pose a secondary source of pollution to the ecosystem by adsorbing heavy metals and organic pollutants on their surfaces (Du et al., 2025 ; Villabona-González et al., 2024 ). This process poses critical risks to both ecosystem health and human health. In light of these global findings, studies conducted on the Sakarya River are of great importance in filling existing knowledge gaps both in Türkiye and on a regional scale. The Sakarya River, a system with significant industrial and agricultural activities, is a suitable model area for understanding the distribution of MP pollution in both water and sediment samples. The main objectives of this study are to determine the abundance, morphological characteristics, and potential sources of MP in the water and sediments of the Sakarya River, and to determine the variation of MP pollution from the source to the river mouth and the current discharge load based on the obtained data. Therefore, this study will provide a scientific basis for the development of sustainable waste management and ecosystem protection strategies, in addition to determining the current pollution levels. Materials and Methods Fieldwork and sampling The Sakarya River, Türkiye's third longest river, flows for 824 km until it empties into the Black Sea. Its source is surrounded by high plateaus where settlements are sparse and agricultural activities, livestock farming, and rural population density are relatively low. In its middle and lower reaches, the Porsuk and Ankara streams are important tributaries that significantly affect the river's water regime and pollution load due to high population pressure in their basins. The river supports agricultural activities in many provinces and is also used to meet Istanbul's fresh water needs through a high-capacity bulk water line. In the last 100 km before it flows into the Black Sea, dense population, agricultural activities, and industrialization become increasingly pronounced. Therefore, water quality varies significantly from source to mouth, leading to a gradual deterioration in overall water quality. (Savuca et al., 2022 ; URL-1). When selecting sampling stations, factors such as tributaries, dams, and areas where population density has begun to increase were considered to reveal changes in the area from the source to the river network. The hydrodynamic structure of the river, sediment accumulation areas and existing monitoring infrastructure allow both transport and accumulation mechanisms to be examined together (Arslan & Mercan, 2020 ). Thus, 10 stations were determined on the river and both water and sediment sampling were performed (Fig. 1 ). Surface water samples were collected from the surface to a depth of 20 cm by filtering at least 30 litres of water using a steel bucket and a 50-micron plankton net (25 cm diameter). The plankton net was washed from the outside to ensure that any remaining samples were transferred to the collector. The collected samples were transported to the laboratory in 250-ml glass jars (Nkosi et al., 2023 ). Sediment samples were taken with a Van Veen type (20 × 20 cm) grapple. Samples were collected from a depth of 0–5 cm until 500 ml aluminium foil containers were filled, and the sample containers were stored by covering them with foil and transferred to the laboratory. The annual microplastic load carried by the Sakarya River to the Black Sea was estimated using the average microplastic concentrations measured at the downstream Ferizli and Karasu stations and the river’s long-term mean discharge, according to the following equation: $$\begin{array}{c}L=CxQxT \left(1\right)\end{array}$$ where, L: represents the annual microplastic load (p ieces year⁻¹), C: is the mean microplastic concentration measured at the downstream stations (pieces/m³), Q: denotes the long-term mean annual discharge of the Sakarya River (m³ s⁻¹), T represents the time in seconds per year (Lebreton et al., 2017 ; Schmidt et al. 2017 ) The results were calculated based on the long-term mean annual discharge of the Sakarya River (203 m³/s), as reported by the Republic of Türkiye Ministry of Agriculture and Forestry General Directorate of Water Management, and were evaluated using minimum and maximum concentration scenarios (Anonymous, 2022 ) Microplastic Characterization For water samples, MPs were obtained using a potent oxidizing agent, 30% hydrogen peroxide (H₂O₂, w/w). For the process, water samples were placed into a glass beaker with 250 ml volume. These beakers were set inside a batch reactor operating at a constant temperature of 65°C for two days. Water samples were treated with 50 mL of H₂O₂ solution. To reduce both evaporation and potential contamination, the beakers were covered with watch glasses during the digestion phase. For sediment samples, the obtained materials were covered with an aluminium pot and dried in an oven at 65°C for 5–6 days to vapour water and to stabilize the weight. A total of 100 g dried sediment from each station was taken into different beakers and mixed with 400 mL of saturated ZnCl₂ solution (1.65 g cm⁻3) using a glass rod. The mixtures were left to sit for 2 hours. Later, the supernatant was extracted, and the remaining materials were collected using different-sized mesh sieves. The material was washed using filtered distilled water and rinsed into fresh beakers. This process was replicated two times to acquire maximum MP items. Organic materials were removed by the digestion process of adding 50 mL of 30% H₂O₂ in each beaker and incubating at 65°C for one day. After all samples were fully digested, the remaining material was filtered under vacuum using Whatman Grade 4 qualitative filter paper, which has a pore size of 20–25 µm. The filters were then placed in glass Petri dishes for microscopic examination. Potential microplastic particles were visually identified with a Leica S6D® stereomicroscope (Wetzlar, Germany) based on their colour and shape. To verify polymer composition, Fourier Transform Infrared Spectroscopy (FT-IR) was applied using a PerkinElmer Spectrum 100 spectrometer, equipped with an Attenuated Total Reflectance (ATR) module (PerkinElmer, Waltham, MA, USA). Spectra were recorded across the 4000–650 cm⁻¹ range, using 12 scans per particle at a resolution of 2 cm⁻¹. Each spectrum was compared against entries in the PerkinElmer SEARCH Plus ATR Polymer database. The threshold was set up at least 70% to accept the particles as MP with the spectral match (Fig. 2 ). Microplastic size was calculated using ImageJ software v1.46r (Aragón-Sánchez et al., 2017 ). To determine the lengths of particles, a constant calibrated reference scale (1 mm at 4× magnification) was used. Throughout the procedures, contamination control protocols were rigorously applied. Personnel wore lab coats made of 100% cotton and disposable nitrile gloves. All tools and containers, made from either glass or stainless steel, were thoroughly rinsed with distilled water before use. Microplastic abundance in surface water samples was calculated in pieces per cubic meter (pieces/m³) by standardizing the number of microplastics detected according to the total volume of filtered water. In sediment samples, it is expressed in units of pieces/kg according to dry weight (Jiang et al., 2018 ; Rami et al., 2023 ). Statistical Analyses Descriptive statistics of microplastic (MP) features in different categories were calculated using the psych package (Revelle, 2022 ). Normality assumption was tested using the olssr package (Hebbali, 2020 ). Differences in MP sizes across various MP patterns were assessed using the Kruskal–Wallis and Wilcoxon Rank Sum tests. Categorical variables, including MP type, colour, and shape, were analysed using Pearson’s chi-square test based on frequency distributions for environment and stations, separately. All analyses were conducted using the stats package (R Core Team, 2025 ). The proportion of each MP characteristic was visualized with doughnut charts, and a network diagram was drawn to show connections between MP features using the lessR package (Gerbing, 2021 ). Stacked bar plots were constructed to demonstrate the percentage of subcategories within MP features using ggplot2 package (Wickham, 2016 ). The distribution of MP size across different categories was shown using box plots using the ggpubr package (Kassambara, 2020 ). Additionally, a tree map was created to nest MP categories using the treemap package (Tennekes, 2023 ). Analyses and visualizations were carried out in R Programming Language v4.5 (R Core Team, 2025 ). Results MP concentrations along the Sakarya River varied significantly among stations in both surface water and sediment. MP abundance in surface water samples ranged from 0 to 166.7 pieces/m³, and even at Station 1, the source of the river, 66 pieces/m³ of microplastics were found. The highest value in surface water was found at Dümrek (Station 6) with 166.66 pieces/m³. However, MP abundance in sediment ranged from 0 to 40 pieces/kg, with the highest concentration also observed in Dümrek. After the confluence of the Porsuk and Ankara Streams with the Sakarya River, microplastic concentrations increased significantly, especially in the Karacaahmet (Station 5) and Dümrek (Station 6) regions. When all stations were taken into consideration, the average amount of MP in surface water and sediment was calculated as 91.66 ± 42.72 pieces/m³ and 15.71 ± 11.33 pieces/kg, respectively (Fig. 3 A-B). In addition, the instantaneous average particle count detected in the surface water at the last two stations (Ferizli, Karasu) downstream of the river was determined to be 83.3 pieces/m³. Based on this concentration and the long-term mean discharge of the Sakarya River, the annual microplastic transport to the Black Sea was estimated to be 5.3 × 10¹¹ pieces year⁻¹. Descriptive statistics based on different MP features were presented in Table 1 . Since the size of MPs did not follow a normal distribution, non-parametric tests were used in downstream analyses (W = 0.80; p < 0.001). The average MP size was calculated 232.27 ± 33.37 µm for 33 identified items. The minimum MP size was 48.08 µm and the maximum was 767.69 µm. In water, the number of detected MPs (N = 22) was two times more than sediment (N = 11). The mean MP size in water was 264.05 ± 46.52 µm whereas it was 168.69 ± 31.51 µm MPs from sediment. However, there was not a significant difference between mean MP size from these environments (W = 106; p = 0.58). Besides, there was not a significant difference between environments in terms of the number of MP colour (χ2 = 12.60; df = 9; p = 0.18), MP type (χ2 = 16.90; df = 9; p = 0.05), and MP shape (χ2 = 0.01, df = 1, p = 0.90; Fig. 4 ). Nested MP features from water and sediment environments were presented in Fig. 5 . Table 1 Descriptive statistics of detected MPs based on different features. MP Size (µm) % Mean ± SE Minimum Maximum All items 100 232.27 33.37 48.08 767.69 Environment N Mean ± SE Minimum Maximum Water 66.67 264.05 ± 46.52 57.45 767.69 Sediment 33.33 168.69 ± 31.51 48.08 421.79 Type % Mean ± SE Minimum Maximum ABS 3.03 122.95 - - PA 6.06 85.25 ± 37.17 48.08 122.42 PAN 6.06 409.85 ± 11.95 397.9 421.79 PE 6.06 6.06 79.51 115.2 PET 13 346.13 ± 64.66 114.75 767.69 PMMA 3.03 70.66 - - PP 3.03 99.36 - - PS 3.03 135.37 - - PVA 9.09% 168.98 ± 35.21 106.17 227.97 PVC 21.21% 149.28 ± 42.45 57.45 343.04 Colour % Mean ± SE Minimum Maximum Black 6.25 118.85 ± 4.1 114.75 122.95 Blue 18.75 291.4 ± 87.88 70.66 649.47 Gray 6.25 201.06 ± 26.91 174.16 227.97 Green 3.13 135.37 Navy Blue 15.63 229.08 ± 55.82 106.17 413.23 Pink 25.00 152.22 ± 36.88 57.45 343.04 Red 6.25 256.55 ± 141.35 115.2 397.9 Transparent 9.38 638.54 ± 109.04 421.79 767.69 White 6.25 89.95 ± 21.97 48.08 122.42 Yellow 3.13 79.51 - - Shape N Mean ± SE Minimum Maximum Fiber 48.48 342.46 ± 54.1 106.17 767.69 Fragment 51.52 128.56 ± 18.8 48.08 343.04 Station N Mean ± SE Minimum Maximum 1 6.06 68.38 ± 10.93 57.45 79.31 2 3.03 120.84 - - 3 9.09 274.19 ± 75.5 172.81 421.79 4 15.15 107.29 ± 24.69 48.08 174.16 5 27.27 270.55 ± 70.77 79.51 726.15 6 6.06 392.42 ± 257.05 135.37 649.47 7 12.12 214.3 ± 58.14 113.71 343.04 8 9.09 228.45 ± 99.81 70.66 413.23 9 12.12 321.48 ± 155.13 106.17 767.69 Two different MP shape namely fiber (N = 17) and fragment (N = 16) were found from all environments. The proportion of each type was almost equal (Fig. 5 ). Fiber MPs were approximately three times larger than fragments, and the mean size difference was statistically significant (W = 234; p < 0.001). A total of 10 different polymer type was found in observed MP items. The most abundant polymer type was PET and PVC while only single ABS, PMMA, PP and PS were found from all environments. The largest MP size was found in PAN (409.85 ± 11.95 µm) and PET (346.13 ± 64.66 µm), and the smallest items were in PA (85.25 ± 37.17 µm) and PMMA (70.66 µm; Fig. 6 ). However, no significant difference was found in terms of mean polymer size (χ2 = 16.883; df = 9; p = 0.05). MPs varied from black to yellow regarding 10 different colours. Pink colour dominated the scale with 8 items, followed by blue (N = 6) and navy blue (N = 5). The largest mean MPs were transparent (638.54 ± 109.04 µm) while the smallest mean MP size with a single yellow item (79.51 µm). The longest item was transparent, and the smallest item was white. However, no significant difference was found in terms of mean colour (χ2 = 14.754; df = 9; p = 0.09). A total of 9 from 10 stations were contaminated with MPs (90.00%). For sediment, MPs were found in 8 stations (80.00%) which is identical to water samples. Station 2 included MPs only in sediment while Station 1 and Station 9 included MPs only in water. The mean MP item per station was 3.66 for contaminated stations, and 3.33 for all stations (Fig. 6 ). The most abundant MPs were observed in Station 5 (N = 9). A single MP was found in Station 2. The largest mean MP size was recorded in Station 6 (392.42 ± 257.05 µm), and the smallest mean MP size was recorded in Station 1 (68.38 ± 10.93 µm). There was not a significant difference between stations in terms of mean MP size (chi-squared = 10.169; df = 8; p = 0.25). Besides, there was not a significant difference between stations in terms of the number of MP colour (χ2 = 75.532, df = 72, p = 0.36), MP type (χ2 = 81.593, df = 72, p = 0.20), and MP shape (χ2 = 10.557, df = 8, p = 0.22) (Fig. 7 ). The proportion of each MP items based on nested MP categories was presented in Supplementary Material S1. The proportional connection between MP features was demonstrated with network graphic in Fig. 8 . Discussion In freshwater ecosystems, microplastic pollution is considered a critical environmental problem, especially in rivers that serve as transport channels between terrestrial and marine environments (Castañeda et al., 2014 ; Li et al., 2023 ; Singh et al., 2025 ). Rivers, loaded with microplastics from various sources including discarges of wastewater, urban runoff, agricultural activities, and industrial discharges, serve as important transport ways for these particles (de Carvalho et al., 2021 ; Wang et al., 2021 ; Dai et al., 2022 ; Idowu et al., 2024 ). The continuous flow and turbulent mixing conditions facilitate the dispersion of MPs throughout the water column, while also enabling their deposition in riverbed sediments. Over time, these particles may accumulate within sediments and can be remobilised during high-flow events (Bonyadi et al., 2022 ; Mutlu et al., 2024a , b ). Furthermore, MPs pose important ecological risks due to their potential ingestion by aquatic organisms and their potential to serve as carriers of other environmental pollutants. Therefore, assessing the current concentrations and spatial distribution of MPs in water and sediments of rivers is a fundamental step toward assessing their ecological impacts and developing effective mitigation strategies. The average microplastic concentrations determined in the Sakarya River were 91.67 items/m³ in surface water and 15.71 items/kg in sediment. These values indicate a moderate level of pollution when compared with the results of different reported studies. This is lower than the average values of 293 ± 59 items/m³ reported in rivers flowing into Mersin Bay in Türkiye (Özgüler et al., 2021 ) and 735 items/m³ measured in the Yellow River in China (Feng et al., 2021 ). However, it is similar to the 80 ± 60 items/m³ determined in the Chao Phraya River in Southeast Asia (Ta et al., 2024 ). The values measured in sediment are also lower than the results from the Çoruh River in Türkiye (> 400 items/kg; Mutlu et al., 2024). These findings indicate that the Sakarya River has a moderate pollution level on a global scale and a relatively low level on a national scale in terms of microplastic accumulation. A noteworthy point in this study is the low MP load immediately following the source, evidence of anthropogenic influence. There are two tourist facilities and a picnic area near the source, and field observations indicated a lack of attention to waste management. When interpreting the mechanisms underlying the relatively low and variable microplastic concentrations in the Sakarya River, it is necessary to consider the significant impacts reported in the literature on microplastic transport, including population density, dams, and industrial activities. For example, dams are known to facilitate the migration of microplastics from water to sediment due to their lower water flow. MP accumulation has been observed behind dam reservoirs, and MP loads have decreased significantly after the dams were constructed (Watkins et al., 2019 ; Shen et al., 2023 ). The MP load reached its highest level at stations 5 and 6, but showed a significant decrease at station 7, after the Sarıyar dam. The variability in MP load strengthens the interpretation that irrigation dams play a role in facilitating MP sedimentation by extending their flow durations. The increase in microplastic density in the Sakarya River, particularly in the middle and lower basins (Stations 5 and 6), is largely due to the Porsuk and Ankara streams, which contribute significant amounts of water and waste to the river and flow through populated and industrialized areas before flowing into the Sakarya River. Population density, industrial production, and inadequate waste management are highlighted as the primary factors increasing the microplastic load in river systems (Chen et al., 2020). Therefore, anthropogenic pressure from the tributaries of the Sakarya River explains the increasing microplastic load in the lower and middle basins, and the decreasing trend toward the lower basin (Sarıyar Dam Lake before Station 7, Doğançay HEPP Lake before Station 8) due to the partial retention capacity of the dams. The increasing trend at the last stations (stations 9 and 10) is related to the population and industrial density in Sakarya province. This is because the river basin has the highest population and industrial density in approximately the last 100 km of the river (Fig. 1 ). Studies conducted in rivers of varying sizes and geographical locations have reported annual particle loads ranging from 100 million to 10 trillion (Lebreton et al., 2017 ; Zhao et al., 2021 ; Mai et al., 2020 ; Castillo et al., 2020 ). Considering these amounts, it can be concluded that the Sakarya River has a slightly above-average level of MP load. The contamination of rivers with plastics can depend on many factors (industrialization, population, waste management, etc.). Furthermore, the mesh size of the sampling device is a significant factor affecting particle counts. Therefore, generally accepted standard methods should be developed to describe this type of contamination. In this study, two MP shapes fiber and fragment were observed in detected items with nearly equal proportions. Mutlu et al. ( 2024a ) detected three different shapes of MPs in Çoruh basin, and fragment was the most abundant shape followed by fiber and film. Mutlu et al. ( 2024b ) reported the presence of fiber (N = 116) and fragment (N = 257) MPs from Çoruh basin. Contrary to our findings, fragments were dominant in the rivers in these studies, and fragment items were significantly larger than fibers in terms of MP size. Özgüler et al. ( 2021 ) investigated 8 different rivers flowing into the Mersin Bay, Türkiye and 83.5% of MPs were fiber, followed by fragments and film items. Akdogan et al. ( 2023 ) reported 6 different MP shapes from the Ergene river dominated by fiber and fragments. Kılıç et al. ( 2024 ) assessed MP pollution with water and sediment samples from Orontes basin, Hatay, Türkiye. The MPs were mostly fiber and fragment, but fibers were larger than fragments in terms of mean size as in our findings. Besides, the mean MP size from water was 1011 µm and from sediment 1084 µm. However, MPs obtained from water were larger than sedimental items in this study. Due to continuously introduced from multiple diffuse sources, fiber and fragment MPs are mostly observed shapes in river waters. Their physical properties also facilitate the transportation in freshwater environments. Fibers largely occur from synthetic clothing during washing processes, as well as degradation of larger plastic materials. Fragment MPs also arise from the weathering and degradation of larger materials and penetrate different ecosystems. Rivers also carry out industrial discharges, sewage overflows and stormwater runoff causing transportation of debris including MPs. From this aspect, the continuous cycle of these pathways can be the main reason for high relative abundance for these resistant MP materials. A total of 10 different colours were observed in MPs, and pink items were the most common polymers followed by blue and navy blue. Akdoğan et al. (2023) detected more than 10 colours for MP items from the Ergene river dominated by black and blue polymers. Almas et al. ( 2022 ) tracked MPs in Susurluk basin, Türkiye and most of the MPs were black, blue and red from 7 different colours. Güven (2022) also reported identical colour domination in 13 different colours for MPs from Manavgat River, and Kılıç et al. ( 2024 ) from Orontes Basin in 9 different colours. However, in the study of Baycan et al. ( 2025 ), and Terzi et al. ( 2025 ) white and transparent items were frequently observed contrary to previous studies. Our findings were similar with literature in terms of blue domination, but pink items were not observed often in other riverine systems. Black and blue MP items are more related to anthropogenic activities and insufficient waste management, and their sources are generally tire wear particles, textile fibers and plastic bottles entering freshwater environments. Pink items mostly occur from cosmetic and consumer products, and packaging materials. The detection of only pink microplastics (originating from cosmetics and personal care products and packaging materials) at the source station (station 1) suggests that the contamination originated from day visitors to the surrounding tourist facilities and picnic areas, despite the absence of settlements in the area. At subsequent stations, a significant variation in microplastic colours was observed due to different anthropogenic influences. For polymers, 10 different types were revealed from Sakarya river. PET was the most abundant type (39.00%) and PVC items followed them (21.00%). PET was reported as the primary MP from eastern Black Sea basin (Mutlu et al., 2024a ), southern Black Sea basin (Terzi et al., 2025 ) and Gediz river (Baycan et al., 2025 ). However, PE materials, which were limited in this study, were the main contaminant reported from Çoruh basin (Mutlu et al. 2024), Ergene river (Akdogan et al., 2023 ), Orontes basin (Kılıç et al., 2024 ), Manavgat River (Guven, 2022), and rivers flowing to Mersin Bay (Özgüler et al., 2022 ). Contrary to our findings, PVC covered either much lower proportions or was absent in these studies. The prevalence of PET items in different environments is regarding extensive usage of the material in manufacture of beverage bottles, food packaging, and textile fibers. They can pass to freshwater systems and persist for long periods because of their high chemical stability and resistance to degradation. PVC items can be found in river waters largely due to the extensive application in construction, agriculture, and various industrial sectors. In production, PVC materials are exposed to weathering and mechanical abrasion allowing them to be smaller. They can penetrate water systems with wastewater or anthropogenic waste management processes. In terms of plastic diversity, Baycan et al. ( 2025 ) and Terzi et al. ( 2025 ) observed more polymer types than this study, but other studies demonstrated less diversity in terms of total polymer types. In conclusion, this study highlights microplastic contamination and its characteristics in the Sakarya River for the first time, demonstrating that MPs are present in both water and sediment samples. The dominance of PET and PVC polymers, combined with the high relative abundance of fiber and particle morphotypes, suggests that anthropogenic sources have a significant impact on the pollution. Future studies could include more stations along the river, and it would be beneficial to determine the MP load caused by small streams and creeks flowing into the Sakarya River. An effective waste management system, increased plastic recycling, and measures to reduce plastic use could significantly reduce microplastic pollution in the natural environment (land, aquatic environments, etc.). Furthermore, developing and implementing informed community programs is critical to reducing microplastic input into large water systems like the Sakarya River. Discharges from connecting tributaries, in particular, demonstrate the need for a holistic approach to combating pollution in river ecosystems. Declarations Funding This research has not received any external funding. Author Contributions Conceptualization: Yusuf Ceylan; Data curation: Cantekin Dursun and Serkan Gül; Formal analysis: Nagihan Demirci; Investigation: Yusuf Ceylan and Akif Er; Methodology:Yusuf Ceylan, Akif Er and Serkan Gül; Supervision: Serkan Gül; Validation: Yusuf Ceylan, Akif Er and Cantekin Dursun; Visualization: Cantekin Dursun; Writing – original draft: Cantekin Dursun, Serkan Gül and Yusuf Ceylan. Conflict of interest The authors declare no conflict of interest. Declaration of use of AI The authors declare that no generative artificial intelligence tools or large language models (LLMs) were used in the preparation of this manuscript. Data availability All data set used during the current study are available from the corresponding author on reasonable request. 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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-8967489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606431287,"identity":"2c30cef1-14ad-44ad-bd27-287fd20d3104","order_by":0,"name":"Akif Er","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Akif","middleName":"","lastName":"Er","suffix":""},{"id":606431288,"identity":"3c0b7a99-8bf7-4f1c-ad16-9ebac9c29d4e","order_by":1,"name":"Cantekin Dursun","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Cantekin","middleName":"","lastName":"Dursun","suffix":""},{"id":606431289,"identity":"de3404f2-621b-4ff0-9425-740079e064ef","order_by":2,"name":"Nagihan Demirci","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Nagihan","middleName":"","lastName":"Demirci","suffix":""},{"id":606431290,"identity":"8ed1dc2c-e472-45c2-b974-c80a8f33d683","order_by":3,"name":"Yusuf Ceylan","email":"data:image/png;base64,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","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":true,"prefix":"","firstName":"Yusuf","middleName":"","lastName":"Ceylan","suffix":""},{"id":606431291,"identity":"ed840ce4-7d6c-45a0-bbb2-37fb93848bc3","order_by":4,"name":"Serkan Gül","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Serkan","middleName":"","lastName":"Gül","suffix":""}],"badges":[],"createdAt":"2026-02-25 11:56:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8967489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8967489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104876919,"identity":"22a6a5af-b07e-4e56-9118-d68a79bdd5fa","added_by":"auto","created_at":"2026-03-18 08:44:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354873,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover and habitat types around the Sakarya River according to the CORINE classification representing current changes in land cover and land use between 2000 and 2020 (Potapov et al., 2022). The map shows the spatial distribution of sampling points (black dots) located on this CORINE classification.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/e83a14335a4acdfa80f85c5e.jpeg"},{"id":104876914,"identity":"9ec3bfcd-f197-4362-9eb9-e171138e8ce6","added_by":"auto","created_at":"2026-03-18 08:44:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219722,"visible":true,"origin":"","legend":"\u003cp\u003eFT-IR spectra of detected MP types in this study. The photographs demonstrate each MP type with the highest percentage in observations.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/c902996afb618caa56cd5711.jpeg"},{"id":104876707,"identity":"ddd52f2d-8300-4811-bb5a-14c3d0fefeb9","added_by":"auto","created_at":"2026-03-18 08:43:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1122203,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of microplastic abundance in the Sakarya River. (A) Microplastic abundance in water samples (number m⁻³), (B) Microplastic abundance in sediment samples (number kg⁻¹ dry weight). Warm colours represent high abundances; cool colours represent low abundances.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/af90700714e16e1ea0ec058f.jpeg"},{"id":104876891,"identity":"fbc716e6-6175-44f5-b931-78967f0489f9","added_by":"auto","created_at":"2026-03-18 08:43:59","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61871,"visible":true,"origin":"","legend":"\u003cp\u003eStacked bar plots representing the percentage of observed MP patterns in type, shape, and colour. Groups were separately illustrated for water and sediment.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/daa6082e658b4b1043c4f92e.jpeg"},{"id":104877008,"identity":"ef3f8022-d925-450d-9f12-7be0b441b463","added_by":"auto","created_at":"2026-03-18 08:44:29","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":144982,"visible":true,"origin":"","legend":"\u003cp\u003eTree maps of the microplastic patterns in water and sediment. The size of rectangles are congruent with the observation frequencies of the patterns.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/524abae8c45e3a1712bb6501.jpeg"},{"id":104876985,"identity":"238a7f80-2884-4892-b5b9-6cc4cd2bcd0f","added_by":"auto","created_at":"2026-03-18 08:44:18","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233305,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of observed MP items in each categorical variable illustrated using doughnut graphics.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/a03284dc54159cb77780f881.jpeg"},{"id":104876828,"identity":"e9eae541-d114-4227-9c96-6b8d0732b10b","added_by":"auto","created_at":"2026-03-18 08:43:47","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":138295,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots demonstrating MP size distribution in each MP pattern as well as stations. Each MP was represented with a single dot. The lines in boxes are showing median values. The lines positioned under and on the box are whiskers. The minimum, maximum values, and interquartile range were represented with jitters.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/6622b543dd3304b0e303d0ff.jpeg"},{"id":104876685,"identity":"475ab50a-e524-4ebf-8a12-d6868168305c","added_by":"auto","created_at":"2026-03-18 08:43:23","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":140287,"visible":true,"origin":"","legend":"\u003cp\u003eThe network visualization map of MP patterns. Connected lines demonstrate the relationship of subcategories. Circle sizes are corresponded to number of the observations\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/0fae197050dfc16e3bacf7f3.jpeg"},{"id":104877015,"identity":"0b070790-e2cd-49f0-883d-f16d1a5d9763","added_by":"auto","created_at":"2026-03-18 08:44:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3096247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/44718ad4-1ccf-4ca7-8f47-e63db321bac0.pdf"},{"id":104876752,"identity":"6f3a62b7-49ce-44ec-ae1c-497a7de6fd6e","added_by":"auto","created_at":"2026-03-18 08:43:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18365,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8967489/v1/7750fe1252dac25e1d5dac13.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microplastic Contamination Hotspots in the Sakarya, a Major Anatolian River: Evidence from Water and Sediment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOn a global scale, the rapid increase in plastic production and use causes plastic waste to break down and enter the ecosystem as MPs, posing a constant threat to the environment (Ding et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Choong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and thus, they can persist in the environment for an extended period of time due to their strength (Rodrigues et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). They are a significant pollutant in freshwater and saltwater habitats due to their lengthy lives in aquatic systems (Scherer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Villabona-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rivers play a crucial role in the environmental cycle of MPs by transporting plastics from the land to the ocean (Gerolin et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Numerous micro plastics were found in water and sediment samples from the West River, the most prevalent of which were fibrous particles (Huang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies in the Ganges River have shown that polyethylene-type plastics are prevalent in water and sediments (Singh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The concentrations of MPs in the sediments of the Elbe River in Europe were found to be hundreds of thousands of times higher than those in the water column (Scherer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This indicates that sediments provide MPs with a significant accumulating substrate. MPs are permanent and widespread pollutants on a global scale, as evidenced by similar results found in river studies conducted on other continents, including Citarum (Sembiring et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Antu\u0026atilde; (Rodrigues et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and Subarnarekha and Kharkai (Patidar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe morphological and chemical diversity of microplastics further makes complex their impact on ecosystems. Various studies have shown that the most common types of MPs are fibers, films, particles, and foams, while polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) are prominent in terms of polymer types (Akdoğan et al., 2023; Patidar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khedre et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Fiber-formed particles, in particular, are thought to originate from textiles, fishing nets, and industrial discharges (Adjornor et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, blue and black polyester fibers have been reported to predominate in many river ecosystems (Khedre et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings indicate that MPs are not only an environmental problem but also offer distinctive characteristics for monitoring. The distribution of MPs in aquatic environments is shaped not only by physical processes but also by anthropogenic pressures. Population density, industrial activities, agricultural activities, and inadequate waste management directly increase the microplastic load in rivers (Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salikova et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the case of the Wei River, the abundance of MPs was found to be significantly higher in urban areas than in agricultural and mountainous areas (Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A study conducted in the Yesil River in Kazakhstan determined that microplastic concentration in aquatic environments is related to water quality parameters such as turbidity (Salikova et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings highlight the importance of investigating microplastic pollution in rivers such as the Sakarya River, which are subject to high population density and industrial pressure. The biological impacts of microplastics are not negligible in freshwater ecosystems. Studies have indicated that MPs can be ingested by fish, invertebrates, and even planktonic organisms, potentially causing to their transfer into the food chain (Sembiring et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Khedre et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, microplastics pose a secondary source of pollution to the ecosystem by adsorbing heavy metals and organic pollutants on their surfaces (Du et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Villabona-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This process poses critical risks to both ecosystem health and human health.\u003c/p\u003e \u003cp\u003eIn light of these global findings, studies conducted on the Sakarya River are of great importance in filling existing knowledge gaps both in T\u0026uuml;rkiye and on a regional scale. The Sakarya River, a system with significant industrial and agricultural activities, is a suitable model area for understanding the distribution of MP pollution in both water and sediment samples. The main objectives of this study are to determine the abundance, morphological characteristics, and potential sources of MP in the water and sediments of the Sakarya River, and to determine the variation of MP pollution from the source to the river mouth and the current discharge load based on the obtained data. Therefore, this study will provide a scientific basis for the development of sustainable waste management and ecosystem protection strategies, in addition to determining the current pollution levels.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eFieldwork and sampling\u003c/p\u003e \u003cp\u003eThe Sakarya River, T\u0026uuml;rkiye's third longest river, flows for 824 km until it empties into the Black Sea. Its source is surrounded by high plateaus where settlements are sparse and agricultural activities, livestock farming, and rural population density are relatively low. In its middle and lower reaches, the Porsuk and Ankara streams are important tributaries that significantly affect the river's water regime and pollution load due to high population pressure in their basins. The river supports agricultural activities in many provinces and is also used to meet Istanbul's fresh water needs through a high-capacity bulk water line. In the last 100 km before it flows into the Black Sea, dense population, agricultural activities, and industrialization become increasingly pronounced. Therefore, water quality varies significantly from source to mouth, leading to a gradual deterioration in overall water quality. (Savuca et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; URL-1). When selecting sampling stations, factors such as tributaries, dams, and areas where population density has begun to increase were considered to reveal changes in the area from the source to the river network. The hydrodynamic structure of the river, sediment accumulation areas and existing monitoring infrastructure allow both transport and accumulation mechanisms to be examined together (Arslan \u0026amp; Mercan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, 10 stations were determined on the river and both water and sediment sampling were performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurface water samples were collected from the surface to a depth of 20 cm by filtering at least 30 litres of water using a steel bucket and a 50-micron plankton net (25 cm diameter). The plankton net was washed from the outside to ensure that any remaining samples were transferred to the collector. The collected samples were transported to the laboratory in 250-ml glass jars (Nkosi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sediment samples were taken with a Van Veen type (20 \u0026times; 20 cm) grapple. Samples were collected from a depth of 0\u0026ndash;5 cm until 500 ml aluminium foil containers were filled, and the sample containers were stored by covering them with foil and transferred to the laboratory.\u003c/p\u003e \u003cp\u003eThe annual microplastic load carried by the Sakarya River to the Black Sea was estimated using the average microplastic concentrations measured at the downstream Ferizli and Karasu stations and the river\u0026rsquo;s long-term mean discharge, according to the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}L=CxQxT \\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, L: represents the annual microplastic load (p\u003c/p\u003e \u003cp\u003eieces year⁻\u0026sup1;), C: is the mean microplastic concentration measured at the downstream stations (pieces/m\u0026sup3;), Q: denotes the long-term mean annual discharge of the Sakarya River (m\u0026sup3; s⁻\u0026sup1;), T represents the time in seconds per year (Lebreton et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe results were calculated based on the long-term mean annual discharge of the Sakarya River (203 m\u0026sup3;/s), as reported by the Republic of T\u0026uuml;rkiye Ministry of Agriculture and Forestry General Directorate of Water Management, and were evaluated using minimum and maximum concentration scenarios (Anonymous, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMicroplastic Characterization\u003c/p\u003e \u003cp\u003eFor water samples, MPs were obtained using a potent oxidizing agent, 30% hydrogen peroxide (H₂O₂, w/w). For the process, water samples were placed into a glass beaker with 250 ml volume. These beakers were set inside a batch reactor operating at a constant temperature of 65\u0026deg;C for two days. Water samples were treated with 50 mL of H₂O₂ solution. To reduce both evaporation and potential contamination, the beakers were covered with watch glasses during the digestion phase.\u003c/p\u003e \u003cp\u003eFor sediment samples, the obtained materials were covered with an aluminium pot and dried in an oven at 65\u0026deg;C for 5\u0026ndash;6 days to vapour water and to stabilize the weight. A total of 100 g dried sediment from each station was taken into different beakers and mixed with 400 mL of saturated ZnCl₂ solution (1.65 g cm⁻3) using a glass rod. The mixtures were left to sit for 2 hours. Later, the supernatant was extracted, and the remaining materials were collected using different-sized mesh sieves. The material was washed using filtered distilled water and rinsed into fresh beakers. This process was replicated two times to acquire maximum MP items. Organic materials were removed by the digestion process of adding 50 mL of 30% H₂O₂ in each beaker and incubating at 65\u0026deg;C for one day. After all samples were fully digested, the remaining material was filtered under vacuum using Whatman Grade 4 qualitative filter paper, which has a pore size of 20\u0026ndash;25 \u0026micro;m. The filters were then placed in glass Petri dishes for microscopic examination.\u003c/p\u003e \u003cp\u003ePotential microplastic particles were visually identified with a Leica S6D\u0026reg; stereomicroscope (Wetzlar, Germany) based on their colour and shape. To verify polymer composition, Fourier Transform Infrared Spectroscopy (FT-IR) was applied using a PerkinElmer Spectrum 100 spectrometer, equipped with an Attenuated Total Reflectance (ATR) module (PerkinElmer, Waltham, MA, USA). Spectra were recorded across the 4000\u0026ndash;650 cm⁻\u0026sup1; range, using 12 scans per particle at a resolution of 2 cm⁻\u0026sup1;. Each spectrum was compared against entries in the PerkinElmer SEARCH Plus ATR Polymer database. The threshold was set up at least 70% to accept the particles as MP with the spectral match (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Microplastic size was calculated using ImageJ software v1.46r (Arag\u0026oacute;n-S\u0026aacute;nchez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To determine the lengths of particles, a constant calibrated reference scale (1 mm at 4\u0026times; magnification) was used. Throughout the procedures, contamination control protocols were rigorously applied. Personnel wore lab coats made of 100% cotton and disposable nitrile gloves. All tools and containers, made from either glass or stainless steel, were thoroughly rinsed with distilled water before use. Microplastic abundance in surface water samples was calculated in pieces per cubic meter (pieces/m\u0026sup3;) by standardizing the number of microplastics detected according to the total volume of filtered water. In sediment samples, it is expressed in units of pieces/kg according to dry weight (Jiang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rami et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStatistical Analyses\u003c/p\u003e \u003cp\u003eDescriptive statistics of microplastic (MP) features in different categories were calculated using the \u003cem\u003epsych\u003c/em\u003e package (Revelle, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Normality assumption was tested using the \u003cem\u003eolssr\u003c/em\u003e package (Hebbali, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Differences in MP sizes across various MP patterns were assessed using the Kruskal\u0026ndash;Wallis and Wilcoxon Rank Sum tests. Categorical variables, including MP type, colour, and shape, were analysed using Pearson\u0026rsquo;s chi-square test based on frequency distributions for environment and stations, separately. All analyses were conducted using the \u003cem\u003estats\u003c/em\u003e package (R Core Team, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe proportion of each MP characteristic was visualized with doughnut charts, and a network diagram was drawn to show connections between MP features using the \u003cem\u003elessR\u003c/em\u003e package (Gerbing, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Stacked bar plots were constructed to demonstrate the percentage of subcategories within MP features using \u003cem\u003eggplot2\u003c/em\u003e package (Wickham, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The distribution of MP size across different categories was shown using box plots using the \u003cem\u003eggpubr\u003c/em\u003e package (Kassambara, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, a tree map was created to nest MP categories using the \u003cem\u003etreemap\u003c/em\u003e package (Tennekes, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Analyses and visualizations were carried out in R Programming Language v4.5 (R Core Team, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMP concentrations along the Sakarya River varied significantly among stations in both surface water and sediment. MP abundance in surface water samples ranged from 0 to 166.7 pieces/m\u0026sup3;, and even at Station 1, the source of the river, 66 pieces/m\u0026sup3; of microplastics were found. The highest value in surface water was found at D\u0026uuml;mrek (Station 6) with 166.66 pieces/m\u0026sup3;. However, MP abundance in sediment ranged from 0 to 40 pieces/kg, with the highest concentration also observed in D\u0026uuml;mrek. After the confluence of the Porsuk and Ankara Streams with the Sakarya River, microplastic concentrations increased significantly, especially in the Karacaahmet (Station 5) and D\u0026uuml;mrek (Station 6) regions. When all stations were taken into consideration, the average amount of MP in surface water and sediment was calculated as 91.66\u0026thinsp;\u0026plusmn;\u0026thinsp;42.72 pieces/m\u0026sup3; and 15.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.33 pieces/kg, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). In addition, the instantaneous average particle count detected in the surface water at the last two stations (Ferizli, Karasu) downstream of the river was determined to be 83.3 pieces/m\u0026sup3;. Based on this concentration and the long-term mean discharge of the Sakarya River, the annual microplastic transport to the Black Sea was estimated to be 5.3 \u0026times; 10\u0026sup1;\u0026sup1; pieces year⁻\u0026sup1;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDescriptive statistics based on different MP features were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Since the size of MPs did not follow a normal distribution, non-parametric tests were used in downstream analyses (W\u0026thinsp;=\u0026thinsp;0.80; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average MP size was calculated 232.27\u0026thinsp;\u0026plusmn;\u0026thinsp;33.37 \u0026micro;m for 33 identified items. The minimum MP size was 48.08 \u0026micro;m and the maximum was 767.69 \u0026micro;m. In water, the number of detected MPs (N\u0026thinsp;=\u0026thinsp;22) was two times more than sediment (N\u0026thinsp;=\u0026thinsp;11). The mean MP size in water was 264.05\u0026thinsp;\u0026plusmn;\u0026thinsp;46.52 \u0026micro;m whereas it was 168.69\u0026thinsp;\u0026plusmn;\u0026thinsp;31.51 \u0026micro;m MPs from sediment. However, there was not a significant difference between mean MP size from these environments (W\u0026thinsp;=\u0026thinsp;106; p\u0026thinsp;=\u0026thinsp;0.58). Besides, there was not a significant difference between environments in terms of the number of MP colour (χ2\u0026thinsp;=\u0026thinsp;12.60; df\u0026thinsp;=\u0026thinsp;9; p\u0026thinsp;=\u0026thinsp;0.18), MP type (χ2\u0026thinsp;=\u0026thinsp;16.90; df\u0026thinsp;=\u0026thinsp;9; p\u0026thinsp;=\u0026thinsp;0.05), and MP shape (χ2\u0026thinsp;=\u0026thinsp;0.01, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.90; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nested MP features from water and sediment environments were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\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\u003eDescriptive statistics of detected MPs based on different features.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMP Size (\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232.27 33.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264.05\u0026thinsp;\u0026plusmn;\u0026thinsp;46.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168.69\u0026thinsp;\u0026plusmn;\u0026thinsp;31.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e421.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.25\u0026thinsp;\u0026plusmn;\u0026thinsp;37.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e421.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346.13\u0026thinsp;\u0026plusmn;\u0026thinsp;64.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168.98\u0026thinsp;\u0026plusmn;\u0026thinsp;35.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.28\u0026thinsp;\u0026plusmn;\u0026thinsp;42.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e343.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291.4\u0026thinsp;\u0026plusmn;\u0026thinsp;87.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e649.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201.06\u0026thinsp;\u0026plusmn;\u0026thinsp;26.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNavy Blue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229.08\u0026thinsp;\u0026plusmn;\u0026thinsp;55.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.22\u0026thinsp;\u0026plusmn;\u0026thinsp;36.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e343.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256.55\u0026thinsp;\u0026plusmn;\u0026thinsp;141.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e397.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransparent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638.54\u0026thinsp;\u0026plusmn;\u0026thinsp;109.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.95\u0026thinsp;\u0026plusmn;\u0026thinsp;21.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShape\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e342.46\u0026thinsp;\u0026plusmn;\u0026thinsp;54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e343.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.38\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274.19\u0026thinsp;\u0026plusmn;\u0026thinsp;75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e421.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.29\u0026thinsp;\u0026plusmn;\u0026thinsp;24.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270.55\u0026thinsp;\u0026plusmn;\u0026thinsp;70.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e726.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e392.42\u0026thinsp;\u0026plusmn;\u0026thinsp;257.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e649.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214.3\u0026thinsp;\u0026plusmn;\u0026thinsp;58.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e343.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228.45\u0026thinsp;\u0026plusmn;\u0026thinsp;99.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321.48\u0026thinsp;\u0026plusmn;\u0026thinsp;155.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwo different MP shape namely fiber (N\u0026thinsp;=\u0026thinsp;17) and fragment (N\u0026thinsp;=\u0026thinsp;16) were found from all environments. The proportion of each type was almost equal (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Fiber MPs were approximately three times larger than fragments, and the mean size difference was statistically significant (W\u0026thinsp;=\u0026thinsp;234; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA total of 10 different polymer type was found in observed MP items. The most abundant polymer type was PET and PVC while only single ABS, PMMA, PP and PS were found from all environments. The largest MP size was found in PAN (409.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95 \u0026micro;m) and PET (346.13\u0026thinsp;\u0026plusmn;\u0026thinsp;64.66 \u0026micro;m), and the smallest items were in PA (85.25\u0026thinsp;\u0026plusmn;\u0026thinsp;37.17 \u0026micro;m) and PMMA (70.66 \u0026micro;m; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, no significant difference was found in terms of mean polymer size (χ2\u0026thinsp;=\u0026thinsp;16.883; df\u0026thinsp;=\u0026thinsp;9; p\u0026thinsp;=\u0026thinsp;0.05). MPs varied from black to yellow regarding 10 different colours. Pink colour dominated the scale with 8 items, followed by blue (N\u0026thinsp;=\u0026thinsp;6) and navy blue (N\u0026thinsp;=\u0026thinsp;5). The largest mean MPs were transparent (638.54\u0026thinsp;\u0026plusmn;\u0026thinsp;109.04 \u0026micro;m) while the smallest mean MP size with a single yellow item (79.51 \u0026micro;m). The longest item was transparent, and the smallest item was white. However, no significant difference was found in terms of mean colour (χ2\u0026thinsp;=\u0026thinsp;14.754; df\u0026thinsp;=\u0026thinsp;9; p\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 9 from 10 stations were contaminated with MPs (90.00%). For sediment, MPs were found in 8 stations (80.00%) which is identical to water samples. Station 2 included MPs only in sediment while Station 1 and Station 9 included MPs only in water. The mean MP item per station was 3.66 for contaminated stations, and 3.33 for all stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The most abundant MPs were observed in Station 5 (N\u0026thinsp;=\u0026thinsp;9). A single MP was found in Station 2. The largest mean MP size was recorded in Station 6 (392.42\u0026thinsp;\u0026plusmn;\u0026thinsp;257.05 \u0026micro;m), and the smallest mean MP size was recorded in Station 1 (68.38\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93 \u0026micro;m). There was not a significant difference between stations in terms of mean MP size (chi-squared\u0026thinsp;=\u0026thinsp;10.169; df\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.25). Besides, there was not a significant difference between stations in terms of the number of MP colour (χ2\u0026thinsp;=\u0026thinsp;75.532, df\u0026thinsp;=\u0026thinsp;72, p\u0026thinsp;=\u0026thinsp;0.36), MP type (χ2\u0026thinsp;=\u0026thinsp;81.593, df\u0026thinsp;=\u0026thinsp;72, p\u0026thinsp;=\u0026thinsp;0.20), and MP shape (χ2\u0026thinsp;=\u0026thinsp;10.557, df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;=\u0026thinsp;0.22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The proportion of each MP items based on nested MP categories was presented in Supplementary Material S1. The proportional connection between MP features was demonstrated with network graphic in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn freshwater ecosystems, microplastic pollution is considered a critical environmental problem, especially in rivers that serve as transport channels between terrestrial and marine environments (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Rivers, loaded with microplastics from various sources including discarges of wastewater, urban runoff, agricultural activities, and industrial discharges, serve as important transport ways for these particles (de Carvalho et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Idowu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The continuous flow and turbulent mixing conditions facilitate the dispersion of MPs throughout the water column, while also enabling their deposition in riverbed sediments. Over time, these particles may accumulate within sediments and can be remobilised during high-flow events (Bonyadi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mutlu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Furthermore, MPs pose important ecological risks due to their potential ingestion by aquatic organisms and their potential to serve as carriers of other environmental pollutants.\u003c/p\u003e \u003cp\u003eTherefore, assessing the current concentrations and spatial distribution of MPs in water and sediments of rivers is a fundamental step toward assessing their ecological impacts and developing effective mitigation strategies. The average microplastic concentrations determined in the Sakarya River were 91.67 items/m\u0026sup3; in surface water and 15.71 items/kg in sediment. These values indicate a moderate level of pollution when compared with the results of different reported studies. This is lower than the average values of 293\u0026thinsp;\u0026plusmn;\u0026thinsp;59 items/m\u0026sup3; reported in rivers flowing into Mersin Bay in T\u0026uuml;rkiye (\u0026Ouml;zg\u0026uuml;ler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and 735 items/m\u0026sup3; measured in the Yellow River in China (Feng et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, it is similar to the 80\u0026thinsp;\u0026plusmn;\u0026thinsp;60 items/m\u0026sup3; determined in the Chao Phraya River in Southeast Asia (Ta et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The values measured in sediment are also lower than the results from the \u0026Ccedil;oruh River in T\u0026uuml;rkiye (\u0026gt;\u0026thinsp;400 items/kg; Mutlu et al., 2024). These findings indicate that the Sakarya River has a moderate pollution level on a global scale and a relatively low level on a national scale in terms of microplastic accumulation.\u003c/p\u003e \u003cp\u003eA noteworthy point in this study is the low MP load immediately following the source, evidence of anthropogenic influence. There are two tourist facilities and a picnic area near the source, and field observations indicated a lack of attention to waste management. When interpreting the mechanisms underlying the relatively low and variable microplastic concentrations in the Sakarya River, it is necessary to consider the significant impacts reported in the literature on microplastic transport, including population density, dams, and industrial activities. For example, dams are known to facilitate the migration of microplastics from water to sediment due to their lower water flow. MP accumulation has been observed behind dam reservoirs, and MP loads have decreased significantly after the dams were constructed (Watkins et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The MP load reached its highest level at stations 5 and 6, but showed a significant decrease at station 7, after the Sarıyar dam. The variability in MP load strengthens the interpretation that irrigation dams play a role in facilitating MP sedimentation by extending their flow durations.\u003c/p\u003e \u003cp\u003eThe increase in microplastic density in the Sakarya River, particularly in the middle and lower basins (Stations 5 and 6), is largely due to the Porsuk and Ankara streams, which contribute significant amounts of water and waste to the river and flow through populated and industrialized areas before flowing into the Sakarya River. Population density, industrial production, and inadequate waste management are highlighted as the primary factors increasing the microplastic load in river systems (Chen et al., 2020). Therefore, anthropogenic pressure from the tributaries of the Sakarya River explains the increasing microplastic load in the lower and middle basins, and the decreasing trend toward the lower basin (Sarıyar Dam Lake before Station 7, Doğan\u0026ccedil;ay HEPP Lake before Station 8) due to the partial retention capacity of the dams. The increasing trend at the last stations (stations 9 and 10) is related to the population and industrial density in Sakarya province. This is because the river basin has the highest population and industrial density in approximately the last 100 km of the river (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies conducted in rivers of varying sizes and geographical locations have reported annual particle loads ranging from 100\u0026nbsp;million to 10 trillion (Lebreton et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mai et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Castillo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Considering these amounts, it can be concluded that the Sakarya River has a slightly above-average level of MP load. The contamination of rivers with plastics can depend on many factors (industrialization, population, waste management, etc.). Furthermore, the mesh size of the sampling device is a significant factor affecting particle counts. Therefore, generally accepted standard methods should be developed to describe this type of contamination.\u003c/p\u003e \u003cp\u003eIn this study, two MP shapes fiber and fragment were observed in detected items with nearly equal proportions. Mutlu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) detected three different shapes of MPs in \u0026Ccedil;oruh basin, and fragment was the most abundant shape followed by fiber and film. Mutlu et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) reported the presence of fiber (N\u0026thinsp;=\u0026thinsp;116) and fragment (N\u0026thinsp;=\u0026thinsp;257) MPs from \u0026Ccedil;oruh basin. Contrary to our findings, fragments were dominant in the rivers in these studies, and fragment items were significantly larger than fibers in terms of MP size. \u0026Ouml;zg\u0026uuml;ler et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated 8 different rivers flowing into the Mersin Bay, T\u0026uuml;rkiye and 83.5% of MPs were fiber, followed by fragments and film items. Akdogan et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported 6 different MP shapes from the Ergene river dominated by fiber and fragments. Kılı\u0026ccedil; et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) assessed MP pollution with water and sediment samples from Orontes basin, Hatay, T\u0026uuml;rkiye. The MPs were mostly fiber and fragment, but fibers were larger than fragments in terms of mean size as in our findings. Besides, the mean MP size from water was 1011 \u0026micro;m and from sediment 1084 \u0026micro;m. However, MPs obtained from water were larger than sedimental items in this study. Due to continuously introduced from multiple diffuse sources, fiber and fragment MPs are mostly observed shapes in river waters. Their physical properties also facilitate the transportation in freshwater environments. Fibers largely occur from synthetic clothing during washing processes, as well as degradation of larger plastic materials. Fragment MPs also arise from the weathering and degradation of larger materials and penetrate different ecosystems. Rivers also carry out industrial discharges, sewage overflows and stormwater runoff causing transportation of debris including MPs. From this aspect, the continuous cycle of these pathways can be the main reason for high relative abundance for these resistant MP materials.\u003c/p\u003e \u003cp\u003eA total of 10 different colours were observed in MPs, and pink items were the most common polymers followed by blue and navy blue. Akdoğan et al. (2023) detected more than 10 colours for MP items from the Ergene river dominated by black and blue polymers. Almas et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) tracked MPs in Susurluk basin, T\u0026uuml;rkiye and most of the MPs were black, blue and red from 7 different colours. G\u0026uuml;ven (2022) also reported identical colour domination in 13 different colours for MPs from Manavgat River, and Kılı\u0026ccedil; et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) from Orontes Basin in 9 different colours. However, in the study of Baycan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Terzi et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) white and transparent items were frequently observed contrary to previous studies. Our findings were similar with literature in terms of blue domination, but pink items were not observed often in other riverine systems. Black and blue MP items are more related to anthropogenic activities and insufficient waste management, and their sources are generally tire wear particles, textile fibers and plastic bottles entering freshwater environments. Pink items mostly occur from cosmetic and consumer products, and packaging materials. The detection of only pink microplastics (originating from cosmetics and personal care products and packaging materials) at the source station (station 1) suggests that the contamination originated from day visitors to the surrounding tourist facilities and picnic areas, despite the absence of settlements in the area. At subsequent stations, a significant variation in microplastic colours was observed due to different anthropogenic influences.\u003c/p\u003e \u003cp\u003eFor polymers, 10 different types were revealed from Sakarya river. PET was the most abundant type (39.00%) and PVC items followed them (21.00%). PET was reported as the primary MP from eastern Black Sea basin (Mutlu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), southern Black Sea basin (Terzi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Gediz river (Baycan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, PE materials, which were limited in this study, were the main contaminant reported from \u0026Ccedil;oruh basin (Mutlu et al. 2024), Ergene river (Akdogan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Orontes basin (Kılı\u0026ccedil; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Manavgat River (Guven, 2022), and rivers flowing to Mersin Bay (\u0026Ouml;zg\u0026uuml;ler et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Contrary to our findings, PVC covered either much lower proportions or was absent in these studies. The prevalence of PET items in different environments is regarding extensive usage of the material in manufacture of beverage bottles, food packaging, and textile fibers. They can pass to freshwater systems and persist for long periods because of their high chemical stability and resistance to degradation. PVC items can be found in river waters largely due to the extensive application in construction, agriculture, and various industrial sectors. In production, PVC materials are exposed to weathering and mechanical abrasion allowing them to be smaller. They can penetrate water systems with wastewater or anthropogenic waste management processes. In terms of plastic diversity, Baycan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Terzi et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) observed more polymer types than this study, but other studies demonstrated less diversity in terms of total polymer types.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights microplastic contamination and its characteristics in the Sakarya River for the first time, demonstrating that MPs are present in both water and sediment samples. The dominance of PET and PVC polymers, combined with the high relative abundance of fiber and particle morphotypes, suggests that anthropogenic sources have a significant impact on the pollution. Future studies could include more stations along the river, and it would be beneficial to determine the MP load caused by small streams and creeks flowing into the Sakarya River. An effective waste management system, increased plastic recycling, and measures to reduce plastic use could significantly reduce microplastic pollution in the natural environment (land, aquatic environments, etc.). Furthermore, developing and implementing informed community programs is critical to reducing microplastic input into large water systems like the Sakarya River. Discharges from connecting tributaries, in particular, demonstrate the need for a holistic approach to combating pollution in river ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has not received any external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Yusuf Ceylan; Data curation: Cantekin Dursun and Serkan Gül; Formal analysis:\u0026nbsp;Nagihan Demirci; Investigation: Yusuf Ceylan and Akif Er; Methodology:Yusuf Ceylan, Akif Er and Serkan Gül; Supervision: Serkan Gül; Validation: Yusuf Ceylan, Akif Er and Cantekin Dursun; Visualization: Cantekin Dursun; Writing – original draft: Cantekin Dursun, Serkan Gül and Yusuf Ceylan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of use of AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no generative artificial intelligence tools or large language models (LLMs) were used in the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data set used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdjornor, B. 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Science of the Total Environment, 773, 145537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.145537\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.145537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Environmental monitoring, Freshwater, Polymer, Pollution, River pollution, Sustainability, Water resources","lastPublishedDoi":"10.21203/rs.3.rs-8967489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8967489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRivers are important pathways for the transport of microplastics from terrestrial environments to aquatic ecosystems; however, data from major freshwater systems in T\u0026uuml;rkiye remain limited.\u003c/p\u003e \u003cp\u003eThis study aimed to determine the abundance, characteristics, and transport potential of microplastics in the Sakarya River, one of the largest freshwater systems in T\u0026uuml;rkiye. Surface water and sediment samples were collected from 10 stations along approximately 800 km of the river. Microplastics were quantified and classified according to polymer type. Annual transport was estimated using instantaneous concentrations and long-term average flow rates.\u003c/p\u003e \u003cp\u003eMicroplastic abundance ranged from 0\u0026ndash;166.7 particles/m\u0026sup3; in surface water and 0\u0026ndash;40 particles/kg in sediment. Inputs from tributaries draining densely populated areas significantly increased microplastic pollution. Ten polymer types were identified, with PET and PVC being dominant. Annual microplastic transport was estimated at approximately 10\u0026sup1;\u0026sup1; particles.\u003c/p\u003e \u003cp\u003eThe Sakarya River exhibits significant microplastic contamination. These findings provide critical information for developing environmentally sound waste management practices and long-term environmental planning strategies.\u003c/p\u003e","manuscriptTitle":"Microplastic Contamination Hotspots in the Sakarya, a Major Anatolian River: Evidence from Water and Sediment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:42:03","doi":"10.21203/rs.3.rs-8967489/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":"db9d0a1b-d91d-40d1-a821-c832a5a3e600","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-05T09:31:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T15:17:17+00:00","index":61,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T13:54:52+00:00","index":60,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T09:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:42:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8967489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8967489","identity":"rs-8967489","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}