Microplastics in FLOW: Seasonal Patterns in Major Latvian Rivers

Microplastics in FLOW: Seasonal Patterns in Major Latvian Rivers | 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 Case Report Microplastics in FLOW: Seasonal Patterns in Major Latvian Rivers Marta Barone, Sanda Svipsta, Jānis Bikše, Inta Dimante-Deimantovica This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5955650/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 considered key pathways for microplastics, transporting these pollutants from inland sources to marine environments. In this study, we investigated the seasonal fluctuations of microplastics in the surface waters of four major Latvian rivers (Daugava, Lielupe, Gauja, and Salaca) that flow into the Gulf of Riga. Sampling was conducted from spring 2022 to summer 2023 to represent distinct seasonal and environmental conditions. Using a Manta net (100 µm mesh size) samples were collected in triplicate and analysed for microplastic concentration, shape, size, colour, and polymer composition. Microplastic concentrations ranged from 0.63 to 132.88 particles/m³, with the highest levels observed in the River Salaca. Fibres and fragments were the predominant microplastic shapes, with polyethylene, polypropylene, and ethylene propylene diene monomer being the most abundant polymers. We found significant spatial and seasonal variations in microplastic concentrations in some rivers, with the River Salaca showing the most extreme seasonal fluctuations. However, overall, no significant correlation was observed between the suspended material and river discharge variables. Replicate sampling revealed variability between samples, highlighting the need to include replicas. These findings highlight the complexity of microplastic pollution dynamics and the need for careful consideration of seasonal factors when assessing environmental contamination. This article is the first to contribute data from Latvian rivers on the growing pool of information on microplastics contamination in waterways. Marine and Freshwater Ecology Microplastic river pollution seasonality estuaries Gulf of Riga replica samples Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights Microplastics in Latvian rivers vary by season, peaking in spring Seasonal peaks lack correlation with suspended solids or river discharge The highest microplastic level ranged up to 49.75 ±41.56 particles/m³ Fibres and fragments - dominant shapes; PE, PP, and EPDM - dominant polymers High variability among replicate samples was found 1. Introduction Microplastics (MPs) are plastic particles up to 5 mm in size and have been recognised as widespread contaminants across various aquatic ecosystems. They have a high ability to accumulate in food webs and potentially harm living organisms (Ahmed et al., 2023 ; Rossatto et al., 2023 ). Although initial focus on MPs has been in the marine environment, interest in their effects on freshwater environments has been growing rapidly in recent years (Cera et al., 2020 ; Waldschläger et al., 2020 ; Wang et al., 2023 ). Of all freshwater habitats, riverine systems play a unique central role as dynamic connectors between terrestrial, inland, groundwater, and marine environments. MP pollution primarily originates from land-based sources, e.g. spills of raw material, pellets, personal care products, industrial and household wastewater, plastic debris, artificial turfs and farming films, construction, transport, and medicine related plastic products (An et al., 2020 ). These materials end up in marine environments, with rivers acting as critical pathways. Through this process, MPs often get trapped in riverbanks, sediments, and floodplains (Lahon and Handique, 2023 ; Waldschläger et al., 2020 ). The role of rivers in transporting and contributing MPs to coastal areas, marine waters, and sediments has been highlighted in several studies (Lebreton et al., 2017 ; Strokal et al., 2023 ; Waldschläger et al., 2020 ; Weiss et al., 2021 ). Meijer et al. ( 2021 ) estimated that more than 1000 rivers are responsible for 80% of global annual MP emissions. They contribute between 0.8 and 2.7 million metric tons each year, with some of the smaller urban rivers identified as the highest emitters (Meijer et al., 2021 ). Similarly, Büngener et al. ( 2024 ) concluded that MP concentrations in small rivers can be comparable to or higher than larger rivers. This is likely because the distribution and abundance of MPs from river systems depends on land use and population density in the associated river catchment and reflects household, urban, industrial, and agricultural waste tendencies of that area (Gonzalez-Saldias et al., 2024 ; Zhang et al., 2024 ). Hence, MPs in riverine systems can be more variable and informative than standing waters such as lakes and ponds. Small urban rivers act as significant concentrators of land-based MPs, while river estuaries serve as critical pathways for their dispersal into the marine environment (Dris et al., 2020 ; Purwiyanto et al., 2022 ). However, estuaries can act as filters by trapping MPs in benthic sediments (Fulfer and Walsh, 2023 ). Simulation studies showed a similar pattern, with 5 mm plastic particles of varying buoyancy being trapped at different points in the estuary (López et al., 2021 ). Additionally, MPs are also removed from river flows through flocculation as they cross into saline water (Laursen et al., 2023 ). Seasonal changes in precipitation, snowmelt, and sediment infiltration rates influence river flows, discharge, flood, and drought frequency, and ultimately, fluctuations in MP pollution transport (Balla et al., 2024 ; Barthelemy et al., 2024 ; Forrest et al., 2022 ; Wei et al., 2022 ). For instance, Gündoğdu et al. ( 2018 ) measured a 14-fold increase in MP pollution levels during periods of heavy rain and floods, while (Forrest et al., 2022 ) measured 11- to 114-fold increases during spring snowmelt. Yonkos et al. ( 2014 ) also observed an increase in MP concentrations in river flows shortly after major rain events, while de Carvalho et al. ( 2022 ) found that floods can alter the physicochemical profile of particles, posing additional risks to animal and human health. Conversely, drought conditions such as flash droughts can concentrate contaminants by reducing water levels (Wontor et al., 2024 ). Despite the existing case studies described above and the increasing attention to fluvial system MP pollution, the seasonal patterns of riverine MP pollution remain poorly understood (Huang et al., 2023 ). Studies investigating seasonal differences in MP pollution are mostly conducted in regions where seasons are characterised by distinct precipitation changes (dry versus wet season). However, there is a significant lack of studies on MP seasonal patterns in regions with temperature-driven seasons, such as the four-season year in mid-latitudes (Waldschläger et al., 2020 ). The lack of knowledge about season-specific trends in MP transport for a given geographical area creates ambiguity, making it difficult to draw conclusions or compare existing data. This challenge is compounded by the fact that most data are collected during single sampling campaigns, despite variability caused by environmental and anthropogenic factors, as well as differences in sample preparation and analysis methods (Hidalgo-Ruz et al., 2012 ; Mani and Burkhardt-Holm, 2020 ). In this study, we document for the first time seasonal variations in MP abundances and characteristics (shape, size, colour, and polymer type) in four major Latvian rivers (Lielupe, Daugava, Gauja, and Salaca) that flow into the Gulf of Riga. These rivers contribute 86% of the total river runoff in the gulf, with the majority flowing into its southeastern part (Yurkovskis et al., 1993 ). Moreover, river Daugava, which has the fifth-largest catchment area among rivers flowing into the Baltic Sea (HELCOM, 2021 ), has only been studied through model-based assessments of MP transport into the estuary (Frishfelds et al., 2022 ). Our study applied replicate sampling to gain insight into representativeness and variations among samples. This study also investigates whether river characteristics, such as suspended material and river discharge, affect MP pollution concentrations. 2. Methods and Materials 2.1. Sampling area We collected surface water samples from the mouths of the four largest Latvian rivers flowing into the Gulf of Riga (Daugava, Lielupe, Gauja, Salaca) from the spring of 2022 until the summer of 2023 every two months to observe seasonal changes. These rivers represent three catchment basins, each with distinct dominant land uses (Fig. 1). The River Lielupe catchment basin is shared between Latvia and Lithuania. It flows predominantly north through central Latvia, which is home to 11.6% of the population. This region features a combination of lowlands and wetlands and is one of the most intensively farmed areas in Latvia, with extensive crop cultivation and livestock farming. Higher temperatures and decreased precipitation are typical for this area. In addition to agriculture, hydromorphological transformations contribute to local anthropomorphic pressure. The river flows through several urban areas with significant industrial activities, where both treated and untreated wastewater is discharged, contributing to pollution and adversely affecting water quality. This includes litter, stormwater runoff, and other pollutants from various sources. As a slow-flowing potamal river, the Lielupe’s slow flow velocity intensifies its susceptibility to pollution, allowing contaminants to accumulate and persist in the water (LVĢMC, 2023a ). The River Daugava, one of the major rivers in Latvia, has a catchment area that spans parts of Russia, Belarus, and Latvia. Because of this, aquatic ecosystems within the catchment are subject to various regulations and land practices. The catchment basin includes significant urban infrastructure and industrial zones that contribute to both domestic and industrial pollution (approx. 59% of the country's population inhabit the area). The river receives significant urban runoff containing microplastics from sources such as tire wear, road dust, and plastic waste. Daugava is known for its hydroelectric dams, which are crucial for Latvia's energy supply. Although not a direct source, dams can accumulate and then release microplastics downstream during water discharge events. The countries’ largest city Riga acquires 52% of its raw drinking water from the River Daugava. In addition to the impact of agriculture and hydromorphological regulations, the prevalence of decentralised sewage systems remains a significant environmental concern, although the situation has improved in recent decades. The largest port in Latvia (freeport of Riga) is located in the estuary of the River Daugava (LVĢMC, 2024a ). The Gauja and Salaca Rivers share a common catchment basin, with a small part of it located in Estonia. The region's landscape is characterised by a combination of lowlands and plains with highlands and hills. These variations in terrain, along with the region's location in northern Latvia explain distinct climatic features. As the rivers near the Gulf of Riga there is increased humidity and a notably more moderate temperature regime. Overall, the Gauja and Salaca River basins experience significant rainfall. Almost 13% of the population live in the Salaca catchment area. A notable part of the River Gauja catchment area is within the Gauja National Park, while River Salaca - in the North Vidzeme Biosphere Reserve, i.e. Latvia's largest reserve with a special protection status (LVĢMC, 2023b ; UNESCO, 2024 ). Unlike other three rivers studied, the Gauja River is of the ritral type, characterised by its relatively fast flow and greater self-purification capacity. The Gauja catchment basin consists of rural and agricultural lands with traditional farming practices, although the share of agricultural land is slender compared to other river basins. The greatest anthropogenic pressure occurs from hydromorphological regulations. Additionally, the northernmost port in Latvia (Salacgriva port) is located at the mouth of the Salaca River (Salacgriva Port Authority, S.a.). 2.2. Sampling approach Sampling was performed by trawling a Manta net (length 2 m, 100 µm mesh aperture, 0.15 × 0.30 m frame opening, HYDRO-BIOS) against the river current. Transects were run for 30 minutes outside the wake zone of the boat and approximately 300 m upstream of the river's convergence with the sea. For each sampling campaign, samples were collected in consecutive triplicates. To avoid the influence of sea water, sampling was conducted when the wind currents were not blowing from the sea against the river flow. Before and after the start and end of each sampling, the measurement of HYDRO-BIOS mechanical flow metre was recorded to calculate the filtered surface water volume ( V , m 3 ) by applying the following formula: V = r × a × 0.3 (1) where r is the number of flow metre revolutions calculated from registered start and end measurement values, a is the submerged net frame opening area (0.021 m 2 ), and 0.3 is the coefficient for one flow metre revolution. After trawling, the net was rinsed from the outside with river water to concentrate the sample at the cod end of the net and then transferred to a metal bowl. Larger non-plastic objects (insects, leaves, etc.) were rinsed with filtered water over the sample and discarded. The sample was then concentrated through a 50 µm sieve and transferred to a labelled glass jar for further treatment. 2.3. Samples treatment and analysis MPs were extracted from samples as determined by an adaptable multistep treatment protocol (Fig. 2). First, samples were oxidised with 30% hydrogen peroxide (H 2 O 2 , sample:solution ratio 1:1), incubating in a shaking water bath (100 rpm, 50°C, 48 hours). Then 10% sodium hydroxide (NaOH, sample:solution ratio 1:3) was added and samples were incubated under the same conditions as for H 2 O 2 treatment. This was followed with Fenton oxidation and density separation (TC-Tungsten Compounds, sodium polytungstate solution, density 1.75 g/ml). After each treatment step, the previous solution was removed by filtering samples through a stainless-steel sieve (Retsch, mesh size 50 µm, ⌀10 cm) and thoroughly rinsing with water. Samples were transferred from the sieve to the beaker using either water or the solution intended for the next treatment step (e.g., SPT solution). Finally, samples were filtered on glass fibre (GF) filters (Whatman, pore size 1.2 µm, ⌀ 47 mm) for visual analysis. During visual analysis, a stereomicroscope (ZEISS SteREO Discovery V8, Axiocam 208 camera, Labscope v3.4 software) was used to determine particle shape (fragment, fibre, film, foam, bead), size (length and width) and colour. To determine whether particles too small for manual transfer were of synthetic origin, they were subjected to a hot-needle test as described by Cutroneo et al. ( 2020 ) and Prata et al. ( 2024 ). Particles large enough for manual handling were subjected to chemical composition analysis using attenuated total reflection-Fourier transform infrared spectroscopy (Thermo Fisher Scientific Nicolet iS20 spectrometer, OMNIC 9 software; 32 scans, spectral resolution 4 cm − 1 and energy range 4000 − 400 cm − 1 ). Data were considered reliable when the particle spectrum matched a database entry with a percentage higher than 70%, however, all spectra were manually verified. 2.4. Quality assurance and control All solutions and water were filtered through GF filters (Whatman, pore size 1.2 µm, ⌀ 47 mm) that were pretreated in a muffle furnace at 500°C for six hours prior use. All equipment and labware were made of glass and metal whenever possible and thoroughly rinsed with filtered water before use. When equipment was made of plastic (Manta net (nylon), rinsing bottles (Telfon), laboratory gloves (nitrile)), the polymer composition was recorded and excluded from the dataset during chemical analysis. Cotton coats and nitrile gloves were always used in the laboratory, and sample treatment was performed in a laminar flow cabinet. The trays containing the samples were covered with aluminium foil at all times when not in use. To minimise potential particle loss, the same beaker was used for all treatment steps. To assess the potential for sample contamination during fieldwork, a negative control sample was created by placing an opened jar near each sample. Afterwards, it was filtered on GF filters and analysed in the same manner as its paired sample. To assess laboratory contamination, negative control samples were created in the laboratory as well. The negative control samples were performed in triplicate with each sample batch, subjected to the longest sample treatment protocol and analysed in the same manner as filed samples. Positive control samples were also performed in triplicate; each sample contained 100 red polystyrene beads (⌀ 100 µm, density 1.05 g/cm³; Sigma‒Aldrich, product no. 56969-10ML-F) and was treated following the longest treatment protocol in the same manner. In total, 21 laboratory negative control samples were created, yielding on average 6.76 ± 0.73 fibres per sample (min. 2 ± 0.33, max 10 ± 1.20 fibres); no other shapes were present. After a hot-needle test, 10% of all fibres found in control samples were deemed synthetic, while the remaining 90% were counted as non-synthetic fibres. The negative control results indicate potential procedural contamination of up to 3.23% if only synthetic fibres are counted, or up to 9.66% if both synthetic and non-synthetic fibres are counted. The positive control results indicate a recovery rate of 92 ± 1.53%. The acquired data on microplastic abundance in environmental samples were not corrected according to the quality control results. 2.5. Hydrological data and suspended material The river stage and discharge data were obtained from the Latvian Environment, Geology and Meteorology Centre (LEGMC). Hydrological monitoring data were retrieved for each river from one station closest to the sea: at station "Kalnciems" located on the Lielupe 48 km from the river's mouth, at station "Jekabpils" located on the Daugava 165 km upstream from the river's mouth, at station "Sigulda" located on the Gauja 55 km from its mouth, at station "Lagaste" located on the Salaca 20 km from its mouth. For the station "Kalnciems" (Lielupe River), automatic discharge measurements were available from LEGMC. For the other three stations, river discharge was estimated based on river stage measurements using the power-law model implemented in the R package bdrc (Hrafnkelsson et al., 2022 ). The plm function, which includes a power-law relationship with stage-dependent log-error variance, was applied to model the discharge-stage relationship using a Bayesian hierarchical model. This approach accounts for the heteroscedasticity commonly observed in discharge data, where variance tends to increase with river stage. The model was calibrated using observed discharge and stage measurements and predicted for all time series. Suspended material samples were obtained in parallel with MP samples from the upper 20 cm layer of the river using a clean 5L plastic bottle that was pre-rinsed with river water three times to minimize contamination. In the laboratory, the water sample was thoroughly shaken to ensure homogeneity and filtered through a pre-weighed 0.45 µm pore-size membrane filter (Cytiva Whatman™) using a vacuum filtration system until the filtered was clogged. The volume of filtered water (0.4–1.1 L) was recorded. Following filtration, the membrane filter was dried at 50°C for 24 hours to remove residual moisture and weighed to determine the mass of suspended material. 2.6. Statistical analyses Data (i.e., concentration, shape, size, colour, and polymer composition of MPs) are reported as the mean of three replicas ± standard error of the mean. One-way ANOVA was performed to assess differences in MP concentrations between sampling seasons. Significant differences were further analysed using pairwise comparisons. Shapiro-Wilk and Levene’s tests were used to check for normality and homogeneity of variance, respectively. Linear regression was employed to understand temporal trends in MP concentrations (seasonal changes) and polymer share, and to compare MP concentrations with river discharge and suspended material. The R-squared values were calculated to determine the proportion of variance explained by the river factor. Significant results were followed by Tukey’s HSD post-hoc test. Kruskal-Wallis tests were conducted when normality assumptions were violated, and significant results were followed by Dunn’s test with Bonferroni correction. To enable pooling sample data for general correlation analysis, the min-max normalisation technique was applied for river discharge data. 3. Results and discussion 3.1. Hydrological data and suspended material Rivers’ discharge regime, the same as suspended material, varies among studied rivers (Fig. 3). The discharge data for the “Kalnciems” station on the river Lielupe sometimes showed negative discharge, implying reverse river flow. This is due to the influence of the Baltic Sea and the very low gradient of Lielupe (0.1 m/km). Thus, the average river stage at the “Kalnciems” station was 0.35 m above sea level (asl), while the 1st and 3rd quartiles were 0.16 and 0.51 m asl, respectively. The April 2023 sampling campaigns were characterised by the highest discharges, with average values of 2305, 144, 134, and 57 m³/s for Daugava, Lielupe, Gauja and Salaca Rivers, respectively. The discharges were ranked as the second highest in April 2022. In contrast, the lowest discharges were noted during the August 2022 campaign, with average values of 163, 16, 31, and 7 m³/s for the Daugava, Lielupe, Gauja, and Salaca Rivers, respectively. During October 2022, the Daugava River recorded a discharge of 236 m³/s, while the other rivers demonstrated even lower values of 1.8, 29, and 6.5 m³/s for the Lielupe, Gauja, and Salaca Rivers, respectively. The Daugava and Gauja rivers showed moderate positive correlations between river discharge and suspended material (r = 0.6428 and r = 0.6571, respectively), whereas in Lielupe a weak positive correlation (r = 0.2594) was observed. However, these relationships lacked statistical significance (p = 0.1193, p = 0.1561 and p = 0.5742, respectively). Conversely, the River Salaca showed a strong, statistically significant positive correlation (r = 0.8696, p = 0.0243). These findings further support the hypothesis that higher flow rates mobilise sediments more effectively, especially during peak discharge events (Bisantino et al., 2011 ). The lack of significant correlations for the three rivers can be partially attributed to the large distances between sampling locations and river monitoring stations (48–165 km), while the river Salaca sampling point was the closest to its station (20 km). Moreover, the mean water stage of the Lielupe River at the Kalnciems station, at approximately 0.4 m above sea level, makes river discharge highly susceptible to sea level fluctuations, which can even induce flow reversal (Fig. 3A), potentially exacerbating the observed lack of significance. Furthermore, additional factors may influence these relationships, potentially indicating variations in sediment type, particle characteristics, or flow dynamics across different river systems. 3.2. Seasonal variation of microplastics Microplastics concentration MPs were found in all surface water samples among the studied rivers and seasons (Fig. 4). Single measurements not considering replicates ranged from 0.63 to 132.88 particles/m 3 . Considering replicates, the highest mean MP abundances were found at the outlet of the Salaca River (min 2.83 ± 0.36, max 49.75 ± 41.56, mean 20.27 ± 7.70 particles/m 3 ) located in the northeast part of the Gulf of Riga. Moderate pollution was observed in the rivers Gauja (min 1.86 ± 0.65, max 19.61 ± 10.84, mean 6.51 ± 2.67 particles/m 3 ) in the southeast part of the Gulf of Riga and Daugava (min 1.17 ± 0.25, max 10.71 ± 0.05, mean 5.20 ± 1.49 particles/m 3 ) in the south part of the Gulf of Riga. The lowest MP concentrations were identified in the Lielupe River (min 1.49 ± 0.24, max 3.30 ± 0.49, mean 2.23 ± 0.26 particles/m 3 ) located in the southern part of the Gulf of Riga. The mean MP concentrations between rivers showed significant differences (p = 0.0168). Pairwise comparisons of mean MP concentrations between rivers, conducted using Tukey's HSD post-hoc test, revealed significant differences for Salaca vs. Daugava (p = 0.0498) and Salaca vs. Lielupe (p = 0.0150). The MP concentrations were relatively low in the River Lielupe with no significant differences (p = 0.085) across months. The river exhibited its lowest concentration in April 2022 at 1.49 ± 0.24 particles/m 3 and peaked in April 2023 at 3.30 ± 0.49 particles/m 3 . Throughout the study period, this river had the most consistent pollution levels while showing a minor gradual increase over the observed period. The River Daugava exhibited moderate MP concentrations, with the lowest in autumn (October 2022) at 1.17 ± 0.25 particles/m 3 . Significantly higher concentrations were observed in the early summer the following year, June 2023, at 10.71 ± 0.05 particles/m 3 (p = 0.049). MP concentrations were relatively low in the River Gauja, with the lowest observed pollution in April 2022 at 1.86 ± 0.65 particles/m 3 , which was followed by a significant concentration increase (p = 0.0128) during the next sampling campaign in June 2022, when the MP concentration rose to 19.61 ± 10.84 particles/m 3 . The following months were characterised by more stable and moderate pollution levels, ranging between 3.50 ± 0.74 and 5.21 ± 0.49 particles/m 3 . The River Salaca showed extreme fluctuations in MP concentrations across seasons, with the highest values occurring in spring, April 2023 and April 2022, at 49.75 ± 41.56 and 34.50 ± 4.36 particles/m 3 , respectively. This was followed by a significant decrease in early summer, with concentrations dropping to 2.83 ± 0.36 particles/m³ in June 2022 (p = 0.027). The MP concentrations in other months varied but were relatively low compared to April 2023. These trends highlight seasonal variability in MP concentrations, potentially driven by environmental factors such as runoff patterns, water flow changes, and human activities. The pollution concentration spikes in certain months for specific rivers suggest that local events or seasonal activities affect MP levels, however no evident seasonal trends in MP concentrations among rivers nor within particular river were found. Our findings align closely with the research done by Mani and Burkhardt-Holm ( 2020 ), who sampled MPs in one of the major European rivers - the Rhine River with a 300 µm mesh manta net (opposed to 100 µm mesh manta net used in our study) and detected no distinct seasonal patterns in MP concentrations. Sampling of MPs in rivers using a Manta net with a 100 µm mesh size is less common than using larger mesh sizes i.e. 300 µm (Pasquier et al., 2022 ). This is due to concerns about net clogging and hence - sampling efficiency and data accuracy. Some studies where finer mesh size (i.e. 100 µm as in our study) Manta nets are used are from regions with distinct dry and wet seasons. A higher level of pollution was detected in the Houjin River, Taiwan (Huang et al., 2023 ) and Tanchon stream of Han River in South Korea (Park et al., 2020 ) than in the rivers of Latvia. Results of South Korea revealed water MP concentration ranging between 5.3 and 87.3 particles/m 3 with the downstream being the most polluted; besides, MPs were more abundant in the rainy season than in the dry season (Park et al., 2020 ). While the Houjin River in Taiwan was more polluted during dry season reaching 183.3 particles/m 3 (Huang et al., 2023 ). Disregarding seasonality this can be explained by geographical location, since both rivers are flowing through densely populated and industrialised regions in Taiwan and South Korea, simultaneously receiving household and industrial wastewater, while the urbanisation level of Latvia is considerably lower. Taiwan is among the most densely populated countries, i.e. 673 inhabitants/km 2 ., in South Korea there are 530 inhabitants/km 2 ., while in Latvia this number is 30 inhabitants/km 2 only (United Nations, 2017 ). Zhang et al. ( 2024 ) also concluded MP concentration in the water is related to population density and per capita GDP, as well as to environmental variables such as water temperature, ammonia, nitrogen and oxidation-reduction potential. On the contrary, in the Ottawa River, Canada, a study was carried out to assess MP pollution levels above and below the city's wastewater treatment plant. The results revealed MP concentrations lower than those reported here (identical mesh size Manta net was used). MP pollution in the Ottawa River was 2.8 times greater below the effluent output (1.9 particles/m 3 ) than above it (0.7 particles/m 3 ) (Vermaire et al., 2017 ). Shape, size and colour of microplastics Across all rivers, fibres and fragments were the predominant particle shapes (Fig. 5). Fibres were the most prevalent microplastic shape in Lielupe, constituting 68.02% of all particles. The other rivers were mostly dominated by fragments – 86.22% in Salaca, 52.99% in Daugava and 47.34% in Gauja. Films, foam and beads were generally rare across all rivers, with beads being absent in all cases except for October 2022 when only one bead was identified in the river Lielupe. The presence and distribution of fibres is known to be dependent on several factors such as land use, human activity, river characteristics and fibres physical properties. For instance, Zhang et al. ( 2024 ) found that the overall abundance of MPs in the Wei River, China, is higher in urban areas compared to agricultural (moderately populated) and mountainous (sparsely populated) regions. This suggests that areas with greater human activity, such as cities, contribute more fibres, likely from textiles, while films and fragments primarily originate from the weathering and degradation of mulch used in agriculture. In the review on major European rivers Gao et al. ( 2024 ) highlighted fibres from synthetic textiles as common secondary MPs. There is a considerable connection between particle morphology and river hydrodynamic characteristics. Particle morphology is crucial – heavier particles like fragments are more likely to settle while lighter particles as fibres remain in the water column for longer due to larger surface area and slower settling velocity. Simultaneously faster currents and increased discharge during floods tend to transport MP particles, disturb and resuspend them from sediments (Adjornor et al., 2024 ; Bai et al., 2022 ; Bhan et al., 2025 ; Hübner et al., 2020 ; Mani et al., 2015 ). We do not have data on MP in sediments of studied rivers, still the interplay of factors mentioned above might explain the expressed dominance of fibres in slow flowing Lielupe river compared to other rivers. Across all five size classes (Fig. 6), particles within the 300 to 999 µm size range were the most abundant for all rivers (52.45%), followed by the size range 1000 to 4999 µm (27.62%). The least represented size class is particles larger than 5 mm (0.93%). Overall, there is no seasonal variation in particle size distribution among the rivers, but with a few notable exceptions. Specifically, in the River Lielupe in June 2022 and the River Salaca in April and June 2023, a higher proportion of particles in the 100 to 199 µm size range is observed. Not considering seasonal differences, a study of the Houjin River found that small MPs (0.1-2 mm) were more abundant than larger MPs (> 2 mm) (Huang et al., 2023 ). The mesh size of the filtering apparatus influences the abundances of the collected MPs. By collecting samples using two types of mesh sizes (50 and 330 µm), Song et al. ( 2014 ) revealed that the smaller mesh size captured 1143 ± 3353 particles/m 3 , whereas the larger mesh size collected 47 ± 192 particles/m 3 , suggesting that the larger mesh size retained only about 4% of potential MP particles. Additionally, half of all detected particles collected with 50 µm mesh were found to be smaller than 100 µm. This indicates that the application of larger mesh sizes can lead to significant underestimation of actual MP pollution (Zhao et al., 2024 ). Typically, the number of detected particles increases as particle size decreases (Aigars et al., 2021 ; Waldschläger et al., 2020 ); however, this pattern was not observed in our study. Instead, particles in the size fractions just above the used mesh size (100–199 µm and 200–299 µm) were not predominant. Rather, particles in the 300–999 µm range were more abundant. This pattern was also noted by Sadri and Thompson ( 2014 ), who found that particles in the 1–3 mm size range were more abundant than those smaller than 1 mm. A likely explanation for this deviation is the dominance of fibres in the sample. Due to the substantial difference between their length and width, even long but thin fibres can pass through the mesh under the pressure of flowing water. For example, fragments in the River Salaca accounted for nearly 90% of all particles, and a considerable increase in the proportion of particles in the 100–199 and 200–299 µm size ranges was observed. Thus, results obtained using the Manta net should be interpreted cautiously, especially when considering small particles compared to mesh size. Most of the collected particles were black, transparent, and blue in colour, with black being the most common particle colour in the Salaca River (Fig. 7. D) and transparent in the Gauja River (Fig. 7. C). The River Lielupe exhibited the most consistent particle colour distribution, except in August 2022, when yellow particles comprised 19.25% (Fig. 7. A). In the River Daugava, a higher proportion of grey particles (51.36%) was identified in June 2023 than in the other sampling periods (Fig. 7. B). Colours that constituted less than 3% of the total colour percentage were categorised as "other" (purple, orange, brown). Microplastics polymers The predominant polymers were polyethylene (PE), polypropylene (PP), and ethylene propylene diene monomer (EPDM), with PE generally being the most dominant (Fig. 8). PE and PP dominance aligns with the majority of already existing studies (Gallo et al., 2018 ; Gao et al., 2024 ; Li et al., 2020 ) and those are also most common plastic polymers produced worldwide. EPDM constituted a notable portion of all particles in the river Salaca across all seasons (on average 94.74%) and in the river Daugava in August 2022 (94.41%). Near the mouths of both rivers important regional and international ports are located, i.e. Salacgriva port in river Salaca and the port of Riga in Daugava. EPDM, being a synthetic rubber, is widely used in various industrial and construction applications, such as sealants, liners, water gaskets etc. The extensive use of EPDM suggests that ports may act as significant point sources of EPDM particles, contributing to their dominance in rivers Daugava and Salaca. Simultaneously rivers with no ports (Lielupe, Gauja) were dominated by PE and PP. This is consistent with study by Moses et al. ( 2023 ) suggesting that activities taking place in the river and its catchment play a role in determining the characteristics of pollution. The presence of other polymers like polyester (PES), polystyrene (PS), nylon, and polyvinyl chloride (PVC) varied, with some considerable spikes in certain months. Polymers that constituted less than 1% of the total share (polyoxymethylene, polyethylene terephthalate, polyurethane, polytetrafluoroethylene, ethylene-vinyl acetate) were pooled in the category “Other”. The linear regression analysis revealed no significant variation of polymers during the changing seasons in the river Lielupe. PE showed a moderate positive trend (p = 0.152). PES exhibited a negative insignificant trend (p = 0.144) throughout the study period. The remaining polymers (PP, PS, Ny, EPDM, PVC, Other) showed negligible trends, with p-values between 0.297 and 0.894. In the Daugava River no significant temporal trend in polymer concentration was observed. P-values were mostly > 0.05, and R-squared values were low, indicating that polymer abundance variations cannot be explained by temporal changes. In the river Gauja, PE showed a declining but insignificant trend (p = 0.107), whereas PP significantly increased (p = 0.033). PS and Ny showed no significant trends (p = 0.880 and p = 0.524), with PS varying slightly and Ny remaining stable. EPDM, PVC and other polymers also showed no significant trends (p = 0.415, p = 0.207, p = 0.633, respectively). The River Salaca was dominated by particles consisting of EPDM rubber. PE, PP, PS, and Ny exhibited no significant trends. EPDM displayed no significant temporal changes despite its significant abundance. PVC and Other polymers showed negligible variation over time. Of the fibres subjected to hot needle test, 80.78% were confirmed to be synthetic, while the remaining 19.22% did not show characteristic of synthetic particles. This aligns with observations made by Suaria et al. ( 2020 ) and Genchi et al. ( 2023 ) emphasizing the significant part of natural origin fibres in the samples. According to Suaria et al. ( 2020 ) only 8.2% of oceanic fibres are synthetic while the rest are cellulosic and animal origin. Genchi et al. ( 2023 ) reported more than 70% of environmental microfibers to be of natural origin. This finding aligns with logical expectations, as plant fibres – primarily composed of cellulose along with other components like lignin, hemicellulose, and animal-based keratin or silk – are expected to remain dominant across various ecosystems, regardless of decomposition rates. Therefore, studies relying on microscopy or manual particle selection should supplement their methods with traditional techniques, such as the hot needle test or various chemical analyses, to prevent misclassifying all fibre-like particles as synthetic. In our study (riverine systems before entering the sea) a higher synthetic fibre proportion most likely results from concentrated industrial and domestic effluent input. 3.3. Correlation between MP concentration, river discharge and suspended material The correlation analyses showed a moderate negative relationship between river discharge and MP concentration in the rivers Daugava (r = -0.5357) and Salaca (r = -0.6377), and a weak negative relationship (r = -0.2000) in Gauja, although it was not statistically significant (p = 0.2152, p = 0.1730 and p = 0.7040, accordingly). The lack of significance may be due to the small sample size (seven sampling campaigns per site), which limits the interpretability of the findings. In contrast, the Lielupe River exhibited a strong positive relationship (r = 0.8153, p = 0.0253) between river discharge and MP concentration (Fig. 9), similarly as in the study by Tamminga et al. ( 2022 ). For instance, Moses et al. ( 2023 ) found 80% of all detected MPs in River Weser during high discharge period. Conversely, during low discharge conditions, MP concentrations might increase, possibly due to reduced dilution and accumulation in certain areas (Bailey et al., 2021). In our study, in the River Lielupe, higher discharge levels directly contribute to increased MP concentrations, potentially due to runoff bringing more plastics into the waterway. Another aspect is the size of studied MP particles. For instance, Moses et al. ( 2023 ) found a positive correlation between small MP particles (10–500 µm) and both discharge and suspended particulate matter, whereas no such correlation was observed for large particles (500–5000µm). The results of correlation analysis of suspended material and MP concentration across the studied river systems showed that the Lielupe and Salaca Rivers displayed a strong positive correlation (r = 0.714, and r = 0.7714, accordingly), while the Daugava showed a weak positive correlation (r = 0.1428). In contrast, the Gauja River indicated a weak negative correlation (r = -0.0857) between these two variables. However, all of the observed relationships lacked statistical significance (p = 0.0713, p = 0.0723, p = 0.7599 and p = 0.8717, respectively), suggesting a need for extended investigation. Most likely the limited data pool (n = 6 for Gauja and Salaca, n = 7 for Lielupe and Daugava) undermined the reliability of the estimate, leaving insufficient evidence to establish statistical significance. For this reason, we pooled normalised river discharge data from all rivers to increase the data pool size (n = 26) and to test whether there is a general correlation between MP concentration, suspended material, and river discharge. No correlation was observed for variables MP concentration-river discharge (p = 0.1453) and river discharge-suspended material (p = 0.1990). However, MP concentration-suspended material showed significant positive correlation (p = 0.0319). Our study therefore agrees with Moses et al. ( 2023 ) who emphasized that factors responsible for MP dynamics might be resuspension and precipitation-driven runoff. In other words, MP particles may become adhered to fine sediment particles, i.e. when sediments are suspended in the water column also previously settled MP become resuspended, consequently MP concentration in the water is increasing. Resuspension typically occurs due to currents, waves, anthropogenic activities such as dredging and boating. Heavy rainfalls and storm events generate runoff hence introducing additional sediments and MPs from terrestrial sources. Moreover, strong runoff can promote the resuspension of settled sediments and MPs. As demonstrated by (Hurley et al., 2018 ), flooding can reduce MP pollution in riverbeds by flushing away MP particles. Although Latvia is not characterised by distinct differences in river water discharge across seasons, a slight increase can be observed twice a year - in spring after the snowmelt and in autumn if consistent precipitation occurs (LVĢMC, 2024b ). Consequently, higher MP concentrations observed in spring and early summer could possibly occur due to increased runoff from agricultural lands or urban areas inflicted by the melting of snow and ice cover. Previous research has emphasised the need for temporal sampling during various weather events to capture the dynamics of MP concentrations effectively (Forrest et al., 2022 ). Snow has been reported to act as a vector for MP removal from the atmosphere, depositing it in certain areas (Bergmann et al., 2019 ; Evangeliou et al., 2020 ). Later, the pollution gets transported across the environment through surface runoff due to increased temperatures resulting from changing seasons (Werbowski et al., 2021 ). For instance, a significant increase in MP pollution was noted during spring snowmelt in Canada, indicating that seasonal changes driven by temperature-induced surface runoff are critical for understanding microplastic pollution in river environments (Forrest et al., 2022 ). The extent of studies focusing on pollution changes in regions where temperature plays a key role in defining seasonal differences (e.g., ice-covered season in northern Europe) falls short. In regions with precipitation-driven seasons, more notable variations have been reported with regard to MP concentrations, e.g., in Lake Manipal, India, MP concentrations were found to be more than twofold higher during the monsoon season (423.00 particles/m 3 ) in comparison to the post-monsoon period (117.00 particles/m 3 ) (Warrier et al., 2022 ). The evaluation of seasonal MP variation in the Houjin River, Taiwan, revealed higher MP pollution in the dry season (183.33 ± 128.95 particles/m 3 ) than in the wet season (102.08 ± 45.80 particles/m 3 ) (Huang et al., 2023 ). However, the opposite pattern was observed in the Yangtze River, China, with the dry season (0.70 ± 0.28 particles/m 3 ) exhibiting almost two times lower MP pollution than the wet season (1.32 ± 1.09 particles/m 3 ) (Wu et al., 2024 ). It is worth highlighting that the acute impacts of rainfall and runoff are often more easily observable and measurable than the subtler effects of temperature fluctuations. This wording keeps the contrast between precipitation-driven and temperature-driven seasonal differences, showing how discharge or precipitation can serve as relevant seasonal indicators depending on the regional climate characteristics. Further research considering MP size fractions, concentration changes and hydrological variability would provide understanding of the fluvial system discharge, sediment dynamics, and MP pollution interconnectedness. 3.4. Importance of replicate sampling In addition to reporting the abundance and seasonal changes in MP pollution, we also investigated the usefulness of replicate sampling. The variability of MP concentrations between replicates was found to be relatively high (Fig. 10), resulting in the standard error within one river’s sampling campaign being greater than or equal to the standard error between monthly mean concentrations. The greatest variability between replicate samples was observed for data collected from the Salaca River. Specifically, the three replicates from April 2023 indicated concentrations of 8.01, 8.35, and 132.88 particles/m 3 . The variability observed in our replicates aligns with findings from Barone et al. ( 2024 ), who emphasized the critical role of replicate sampling in capturing the spatial and temporal heterogeneity of MP pollution in surface waters, i.e. MP concentrations can vary by more than an order of magnitude between trawling events, particularly in environments with low pollution levels. Pasquier et al. ( 2022 ) noted that only 21% of MP studies include replicate sampling. Hence, it turns out to be a methodological gap that significantly affects data robustness because variability of MP concentrations can be affected by weather, local hydrodynamics and particle characteristics. Depending on the sampled matrix, suggested minimum number of replicates can vary (Brander et al., 2020 ), still Barone et al. ( 2024 ) recommended a minimum of three replicate trawlings per site for surface water samples. Conclusion This study provides the first comprehensive analysis of microplastic (MP) pollution seasonality in four major Latvian rivers – Daugava, Lielupe, Gauja, and Salaca – that flow into the Gulf of Riga. The results reveal significant spatial and seasonal variations in MP concentrations, with the River Salaca exhibiting the highest levels and extreme fluctuations, while Daugava and Gauja had moderate levels, and Lielupe had relatively low microplastic pollution level. Our study also highlights a significant gap in research on pollution dynamics in regions characterised by temperature gradients across the year, such as northern Europe. Notably, we observed increased MP concentrations in spring, coinciding with the melt of snow and ice cover. Despite the observed seasonal changes in MP concentrations, no clear correlation was found between MP levels and river discharge or suspended material in most cases. The exception was the River Salaca, which demonstrated a strong positive correlation between MP concentrations and suspended material, likely due to localised hydrodynamic conditions. These findings emphasize the need for future studies that integrate hydrological and meteorological factors to better understand the mechanisms driving MP transport in riverine systems. Regarding particle shapes, fibers dominated in River Lielupe, whereas fragments were more prevalent in the other rivers. Polymer analysis revealed that PE and PP were the most common in Lielupe, Daugava, and Gauja, while EPDM particles dominated in River Salaca. As to commonly applied sampling methodology, we conclude that collecting a single sample per site is insufficient to obtain representative data. To ensure reliable results with low variability, we recommend collecting samples consecutively at least three times. This is particularly important for riverine systems due to their dynamic flow regimes which can redistribute particles unevenly across surface layers. Although this study spanned more than a year and documented pollution levels across different seasons, limited information was collected about the conditions potentially influencing changes in MP pollution levels during the sampling campaigns. Key factors such as wind patterns, river runoff, surface and sub-surface water currents, the impact of resuspension and detailed analyses of anthropogenic activities within the rivers' catchment basins were not comprehensively addressed. This underscores the need for a more extensive and detailed investigation into both natural and human-driven factors affecting riverine MP pollution and its seasonal variations. Still, given the critical role of rivers as flow flume for MP transport to marine ecosystems, this research provides valuable insights into MP pollution dynamics in the Baltic Sea region. Declarations Funding This work was supported by the Administration of Latvian Environmental Protection Fund project No. 1–08/37/2022, and ESF project No. 8.2.2.0/20/I/003. Acknowledgements We are grateful to the research assistants, students, and visiting scientists at the Latvian Institute of Aquatic Ecology for their efforts in the fieldwork. References Adjornor, B.Y., Han, B., Zahran, E.M., Pichtel, J., Wood, R., 2024. Transport and Deposition of Microplastics at the Water–Sediment Interface: A Case Study of the White River near Muncie, Indiana. Hydrology 11, 141. https://doi.org/10.3390/hydrology11090141 Ahmed, A.S.S., Billah, M.M., Ali, M.M., Bhuiyan, M.K.A., Guo, L., Mohinuzzaman, M., Hossain, M.B., Rahman, M.S., Islam, M.S., Yan, M., Cai, W., 2023. Microplastics in aquatic environments: A comprehensive review of toxicity, removal, and remediation strategies. Sci. 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Pollut. 314, 120326. https://doi.org/10.1016/j.envpol.2022.120326 Weiss, L., Ludwig, W., Heussner, S., Canals, M., Ghiglione, J.-F., Estournel, C., Constant, M., Kerhervé, P., 2021. The missing ocean plastic sink: Gone with the rivers. Science 373, 107–111. https://doi.org/10.1126/science.abe0290 Werbowski, L.M., Gilbreath, A.N., Munno, K., Zhu, X., Grbic, J., Wu, T., Sutton, R., Sedlak, M.D., Deshpande, A.D., Rochman, C.M., 2021. Urban Stormwater Runoff: A Major Pathway for Anthropogenic Particles, Black Rubbery Fragments, and Other Types of Microplastics to Urban Receiving Waters. ACS EST Water 1, 1420–1428. https://doi.org/10.1021/acsestwater.1c00017 Wontor, K., Olubusoye, B.S., Cizdziel, J.V., 2024. Microplastics in the Mississippi River System during Flash Drought Conditions. Environments 11, 141. https://doi.org/10.3390/environments11070141 Wu, P., Fan, Y., Zhang, X., Wu, W., Zhang, Z., Wu, Y., Wang, J., Xu, J., Chen, T., Gao, B., 2024. Seasonal dynamics, tidal influences, and anthropogenic impacts on microplastic distribution in the Yangtze River estuary: A comprehensive characterization and comparative analysis. J. Hazard. Mater. 476, 135167. https://doi.org/10.1016/j.jhazmat.2024.135167 Yonkos, L.T., Friedel, E.A., Perez-Reyes, A.C., Ghosal, S., Arthur, C.D., 2014. Microplastics in Four Estuarine Rivers in the Chesapeake Bay, U.S.A. Environ. Sci. Technol. 48, 14195–14202. https://doi.org/10.1021/es5036317 Yurkovskis, A., Wulff, F., Rahm, L., Andruzaitis, A., Rodriguez-Medina, M., 1993. A Nutrient Budget of the Gulf of Riga; Baltic Sea. Estuarine, Coastal and Shelf Science 37, 113–127. https://doi.org/10.1006/ecss.1993.1046 Zhang, L., Li, X., Li, Q., Xia, X., Zhang, H., 2024. The effects of land use types on microplastics in river water: A case study on the mainstream of the Wei River, China. Environ. Monit. Assess. 196, 349. https://doi.org/10.1007/s10661-024-12430-7 Zhao, W., Li, J., Liu, M., Wang, R., Zhang, B., Meng, X.-Z., Zhang, S., 2024. Seasonal variations of microplastics in surface water and sediment in an inland river drinking water source in southern China. Sci. Total Environ. 908, 168241. https://doi.org/10.1016/j.scitotenv.2023.168241 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5955650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":410891718,"identity":"a3b15130-fe8f-4b7d-a472-abdd97417556","order_by":0,"name":"Marta Barone","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYPCCAxDqQ8EBBjYG5gZCyhkbYFoYZxiAtDCSoIWZx+AAVAAP0G0/+/zBD4Y78ubtvQcf2xjckeNjYGyTwKfF7Ey6YWMPwzPDOWfOJRvnGDwzZiOo5UAaYwMPw2HGGRI5ZtI5BocT2xgYmw3wajn/jLHxD8Nhe6AW898WBofrCWu5kcbYDLQlEWQLM4PB4QSgwxof4NfyjHG2jMGz5Bk8Z4wlewwOG7YxE9JyPo3h45uKO7Yz2HsMP/yoOCwv39584AA+LRCA4nZmwupHwSgYBaNgFBAAAJERSlWbBbyOAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6501-572X","institution":"Latvian Institute of Aquatic Ecology","correspondingAuthor":true,"prefix":"","firstName":"Marta","middleName":"","lastName":"Barone","suffix":""},{"id":410891719,"identity":"6395bd28-542e-4fd2-b38f-b57c4d03543e","order_by":1,"name":"Sanda Svipsta","email":"","orcid":"","institution":"Latvian Institute of Aquatic Ecology","correspondingAuthor":false,"prefix":"","firstName":"Sanda","middleName":"","lastName":"Svipsta","suffix":""},{"id":410891720,"identity":"02d16f54-b844-4daf-b5a1-99405962155a","order_by":2,"name":"Jānis Bikše","email":"","orcid":"https://orcid.org/0000-0003-2380-2690","institution":"University of Latvia, Faculty of Geography and Earth Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jānis","middleName":"","lastName":"Bikše","suffix":""},{"id":410891721,"identity":"d9abd95b-2747-43f2-9b70-2dbdf28ea8d5","order_by":3,"name":"Inta Dimante-Deimantovica","email":"","orcid":"https://orcid.org/0000-0002-2655-8198","institution":"Latvian Institute of Aquatic Ecology","correspondingAuthor":false,"prefix":"","firstName":"Inta","middleName":"","lastName":"Dimante-Deimantovica","suffix":""}],"badges":[],"createdAt":"2025-02-04 07:00:02","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5955650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5955650/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75519887,"identity":"4acef4b4-1144-4b2d-95af-6c7a0b95ace2","added_by":"auto","created_at":"2025-02-05 11:55:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1501263,"visible":true,"origin":"","legend":"\u003cp\u003eSampling area in the Baltic Sea region. Represented catchment basins within Latvia territory are highlighted as follows: Lielupe – yellow; Daugava – orange; Gauja and Salaca – green (A). Sampling sites are marked with red dots in the mouths of the four largest Latvian rivers (Lielupe, Daugava, Gauja, Salaca) flowing in the Gulf of Riga (B).\u003c/p\u003e","description":"","filename":"01.Mapofsamplingpoints.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/152f8108de5bfe16cf9bb402.png"},{"id":75519898,"identity":"e7cf0a76-0ad4-4e4c-9aa5-3cc90efa5497","added_by":"auto","created_at":"2025-02-05 11:55:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":578244,"visible":true,"origin":"","legend":"\u003cp\u003eSamples preparation scheme for microplastics extraction, GF – glass fibre.\u003c/p\u003e","description":"","filename":"02.Treatment.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/9771662b60d28263f4d0989a.png"},{"id":75519892,"identity":"ac7dab72-8137-4b7d-9b8f-c955d3954cd3","added_by":"auto","created_at":"2025-02-05 11:55:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1986658,"visible":true,"origin":"","legend":"\u003cp\u003eRiver discharge vs. suspended material in studied rivers surface water throughout the study period (April 2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"03.DischSusp.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/d125337dff0cc9c8bb1598ab.png"},{"id":75519902,"identity":"3d0275aa-44bc-4e24-bb53-187471cbe082","added_by":"auto","created_at":"2025-02-05 11:55:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":875239,"visible":true,"origin":"","legend":"\u003cp\u003eSurface water microplastic (MP) pollution by season (average MP concentrations of three replicates) in four Latvian rivers (Lielupe, Daugava, Gauja, Salaca). The dashed lines indicate average MP concentrations per river. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"04.Concentration.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/f4ca9ea0a6c6dd73e04a12d0.png"},{"id":75519899,"identity":"6bbcdbd5-1a7e-47de-b730-e1af20966fc1","added_by":"auto","created_at":"2025-02-05 11:55:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1277552,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic particle shape in studied rivers surface water throughout the study period (April 2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"05.Shape.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/ad7f28523b4a0e4170dd8079.png"},{"id":75519890,"identity":"65073185-1ff6-49ba-9fe1-ea2bf57cede5","added_by":"auto","created_at":"2025-02-05 11:55:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1382181,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic particle size in studied rivers surface water throughout the study period (April 2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"06.Size.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/40e02b8827da47cb0f2890d2.png"},{"id":75520311,"identity":"a278f820-ad86-4e27-8bd4-71ea4b574aa1","added_by":"auto","created_at":"2025-02-05 12:03:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2221890,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic particle colour in studied rivers surface water throughout the study period (April 2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"07.Colour.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/2e68f03cf18ae17edef527df.png"},{"id":75519903,"identity":"272331d4-36d3-478f-9296-6260f7a1da14","added_by":"auto","created_at":"2025-02-05 11:55:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1622712,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic particle polymers in studied rivers surface water throughout the study period (April 2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. PE - polyethylene, PP - polypropylene, PES - polyester, EPDM - ethylene propylene diene monomer, PVC - polyvinyl chloride. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"08.Polymer.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/88f721f781529462096f2b2f.png"},{"id":75519897,"identity":"e79e5435-7aef-404c-b1e0-6791e240e66e","added_by":"auto","created_at":"2025-02-05 11:55:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1980433,"visible":true,"origin":"","legend":"\u003cp\u003eRiver discharge vs. microplastic (MP) concentration in studied rivers surface water throughout the study period (April 2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. Samples were not taken in December 2022 for rivers Gauja and Salaca.\u003c/p\u003e","description":"","filename":"09.DischConc.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/bd43385396faf14bbacff276.png"},{"id":75520312,"identity":"231a1c69-d242-4405-ab75-1bb3ad1cf53f","added_by":"auto","created_at":"2025-02-05 12:03:11","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":990844,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic (MP) samples replicate concentration variability in studied rivers surface water throughout the study period (April2022 - June 2023): (A) Lielupe; (B) Daugava; (C) Gauja; (D) Salaca. Grey dots represent the MP concentration values of individual replicates.\u003c/p\u003e","description":"","filename":"10.Replicatevariabilitydotplot.png","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/4f9916f0c00b442f3505afc6.png"},{"id":75521401,"identity":"d372a4fb-65fc-413b-a9fb-c88755200e13","added_by":"auto","created_at":"2025-02-05 12:11:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15151269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5955650/v1/58f81b38-4b79-45af-9ae1-9380332a863b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMicroplastics in FLOW: Seasonal Patterns in Major Latvian Rivers\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eMicroplastics in Latvian rivers vary by season, peaking in spring\u003c/li\u003e\n \u003cli\u003eSeasonal peaks lack correlation with suspended solids or river discharge\u003c/li\u003e\n \u003cli\u003eThe highest microplastic level ranged up to 49.75 ±41.56 particles/m³\u003c/li\u003e\n \u003cli\u003eFibres and fragments - dominant shapes; PE, PP, and EPDM - dominant polymers\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHigh variability among replicate samples was found\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eMicroplastics (MPs) are plastic particles up to 5 mm in size and have been recognised as widespread contaminants across various aquatic ecosystems. They have a high ability to accumulate in food webs and potentially harm living organisms (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rossatto et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although initial focus on MPs has been in the marine environment, interest in their effects on freshwater environments has been growing rapidly in recent years (Cera et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Waldschl\u0026auml;ger et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Of all freshwater habitats, riverine systems play a unique central role as dynamic connectors between terrestrial, inland, groundwater, and marine environments.\u003c/p\u003e \u003cp\u003eMP pollution primarily originates from land-based sources, e.g. spills of raw material, pellets, personal care products, industrial and household wastewater, plastic debris, artificial turfs and farming films, construction, transport, and medicine related plastic products (An et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These materials end up in marine environments, with rivers acting as critical pathways. Through this process, MPs often get trapped in riverbanks, sediments, and floodplains (Lahon and Handique, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Waldschl\u0026auml;ger et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The role of rivers in transporting and contributing MPs to coastal areas, marine waters, and sediments has been highlighted in several studies (Lebreton et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Strokal et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Waldschl\u0026auml;ger et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Weiss et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meijer et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) estimated that more than 1000 rivers are responsible for 80% of global annual MP emissions. They contribute between 0.8 and 2.7\u0026nbsp;million metric tons each year, with some of the smaller urban rivers identified as the highest emitters (Meijer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, B\u0026uuml;ngener et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) concluded that MP concentrations in small rivers can be comparable to or higher than larger rivers. This is likely because the distribution and abundance of MPs from river systems depends on land use and population density in the associated river catchment and reflects household, urban, industrial, and agricultural waste tendencies of that area (Gonzalez-Saldias et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, MPs in riverine systems can be more variable and informative than standing waters such as lakes and ponds.\u003c/p\u003e \u003cp\u003eSmall urban rivers act as significant concentrators of land-based MPs, while river estuaries serve as critical pathways for their dispersal into the marine environment (Dris et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Purwiyanto et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, estuaries can act as filters by trapping MPs in benthic sediments (Fulfer and Walsh, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Simulation studies showed a similar pattern, with 5 mm plastic particles of varying buoyancy being trapped at different points in the estuary (L\u0026oacute;pez et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, MPs are also removed from river flows through flocculation as they cross into saline water (Laursen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeasonal changes in precipitation, snowmelt, and sediment infiltration rates influence river flows, discharge, flood, and drought frequency, and ultimately, fluctuations in MP pollution transport (Balla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Barthelemy et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Forrest et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, G\u0026uuml;ndoğdu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) measured a 14-fold increase in MP pollution levels during periods of heavy rain and floods, while (Forrest et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) measured 11- to 114-fold increases during spring snowmelt. Yonkos et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also observed an increase in MP concentrations in river flows shortly after major rain events, while de Carvalho et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that floods can alter the physicochemical profile of particles, posing additional risks to animal and human health. Conversely, drought conditions such as flash droughts can concentrate contaminants by reducing water levels (Wontor et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite the existing case studies described above and the increasing attention to fluvial system MP pollution, the seasonal patterns of riverine MP pollution remain poorly understood (Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies investigating seasonal differences in MP pollution are mostly conducted in regions where seasons are characterised by distinct precipitation changes (dry versus wet season). However, there is a significant lack of studies on MP seasonal patterns in regions with temperature-driven seasons, such as the four-season year in mid-latitudes (Waldschl\u0026auml;ger et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The lack of knowledge about season-specific trends in MP transport for a given geographical area creates ambiguity, making it difficult to draw conclusions or compare existing data. This challenge is compounded by the fact that most data are collected during single sampling campaigns, despite variability caused by environmental and anthropogenic factors, as well as differences in sample preparation and analysis methods (Hidalgo-Ruz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mani and Burkhardt-Holm, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we document for the first time seasonal variations in MP abundances and characteristics (shape, size, colour, and polymer type) in four major Latvian rivers (Lielupe, Daugava, Gauja, and Salaca) that flow into the Gulf of Riga. These rivers contribute 86% of the total river runoff in the gulf, with the majority flowing into its southeastern part (Yurkovskis et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Moreover, river Daugava, which has the fifth-largest catchment area among rivers flowing into the Baltic Sea (HELCOM, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), has only been studied through model-based assessments of MP transport into the estuary (Frishfelds et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our study applied replicate sampling to gain insight into representativeness and variations among samples. This study also investigates whether river characteristics, such as suspended material and river discharge, affect MP pollution concentrations.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sampling area\u003c/h2\u003e \u003cp\u003eWe collected surface water samples from the mouths of the four largest Latvian rivers flowing into the Gulf of Riga (Daugava, Lielupe, Gauja, Salaca) from the spring of 2022 until the summer of 2023 every two months to observe seasonal changes. These rivers represent three catchment basins, each with distinct dominant land uses (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe River Lielupe catchment basin is shared between Latvia and Lithuania. It flows predominantly north through central Latvia, which is home to 11.6% of the population. This region features a combination of lowlands and wetlands and is one of the most intensively farmed areas in Latvia, with extensive crop cultivation and livestock farming. Higher temperatures and decreased precipitation are typical for this area. In addition to agriculture, hydromorphological transformations contribute to local anthropomorphic pressure. The river flows through several urban areas with significant industrial activities, where both treated and untreated wastewater is discharged, contributing to pollution and adversely affecting water quality. This includes litter, stormwater runoff, and other pollutants from various sources. As a slow-flowing potamal river, the Lielupe\u0026rsquo;s slow flow velocity intensifies its susceptibility to pollution, allowing contaminants to accumulate and persist in the water (LVĢMC, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe River Daugava, one of the major rivers in Latvia, has a catchment area that spans parts of Russia, Belarus, and Latvia. Because of this, aquatic ecosystems within the catchment are subject to various regulations and land practices. The catchment basin includes significant urban infrastructure and industrial zones that contribute to both domestic and industrial pollution (approx. 59% of the country's population inhabit the area). The river receives significant urban runoff containing microplastics from sources such as tire wear, road dust, and plastic waste. Daugava is known for its hydroelectric dams, which are crucial for Latvia's energy supply. Although not a direct source, dams can accumulate and then release microplastics downstream during water discharge events. The countries\u0026rsquo; largest city Riga acquires 52% of its raw drinking water from the River Daugava. In addition to the impact of agriculture and hydromorphological regulations, the prevalence of decentralised sewage systems remains a significant environmental concern, although the situation has improved in recent decades. The largest port in Latvia (freeport of Riga) is located in the estuary of the River Daugava (LVĢMC, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Gauja and Salaca Rivers share a common catchment basin, with a small part of it located in Estonia. The region's landscape is characterised by a combination of lowlands and plains with highlands and hills. These variations in terrain, along with the region's location in northern Latvia explain distinct climatic features. As the rivers near the Gulf of Riga there is increased humidity and a notably more moderate temperature regime. Overall, the Gauja and Salaca River basins experience significant rainfall. Almost 13% of the population live in the Salaca catchment area. A notable part of the River Gauja catchment area is within the Gauja National Park, while River Salaca - in the North Vidzeme Biosphere Reserve, i.e. Latvia's largest reserve with a special protection status (LVĢMC, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unlike other three rivers studied, the Gauja River is of the ritral type, characterised by its relatively fast flow and greater self-purification capacity. The Gauja catchment basin consists of rural and agricultural lands with traditional farming practices, although the share of agricultural land is slender compared to other river basins. The greatest anthropogenic pressure occurs from hydromorphological regulations. Additionally, the northernmost port in Latvia (Salacgriva port) is located at the mouth of the Salaca River (Salacgriva Port Authority, S.a.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling approach\u003c/h2\u003e \u003cp\u003eSampling was performed by trawling a Manta net (length 2 m, 100 \u0026micro;m mesh aperture, 0.15 \u0026times; 0.30 m frame opening, HYDRO-BIOS) against the river current. Transects were run for 30 minutes outside the wake zone of the boat and approximately 300 m upstream of the river's convergence with the sea. For each sampling campaign, samples were collected in consecutive triplicates. To avoid the influence of sea water, sampling was conducted when the wind currents were not blowing from the sea against the river flow. Before and after the start and end of each sampling, the measurement of HYDRO-BIOS mechanical flow metre was recorded to calculate the filtered surface water volume (\u003cem\u003eV\u003c/em\u003e, m\u003csup\u003e3\u003c/sup\u003e) by applying the following formula:\u003c/p\u003e \u003cp\u003e \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003er\u003c/em\u003e \u0026times; \u003cem\u003ea \u0026times; 0.3\u003c/em\u003e (1)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003er\u003c/em\u003e is the number of flow metre revolutions calculated from registered start and end measurement values, \u003cem\u003ea\u003c/em\u003e is the submerged net frame opening area (0.021 m\u003csup\u003e2\u003c/sup\u003e), and 0.3 is the coefficient for one flow metre revolution.\u003c/p\u003e \u003cp\u003eAfter trawling, the net was rinsed from the outside with river water to concentrate the sample at the cod end of the net and then transferred to a metal bowl. Larger non-plastic objects (insects, leaves, etc.) were rinsed with filtered water over the sample and discarded. The sample was then concentrated through a 50 \u0026micro;m sieve and transferred to a labelled glass jar for further treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Samples treatment and analysis\u003c/h2\u003e \u003cp\u003eMPs were extracted from samples as determined by an adaptable multistep treatment protocol (Fig.\u0026nbsp;2). First, samples were oxidised with 30% hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, sample:solution ratio 1:1), incubating in a shaking water bath (100 rpm, 50\u0026deg;C, 48 hours). Then 10% sodium hydroxide (NaOH, sample:solution ratio 1:3) was added and samples were incubated under the same conditions as for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e treatment. This was followed with Fenton oxidation and density separation (TC-Tungsten Compounds, sodium polytungstate solution, density 1.75 g/ml). After each treatment step, the previous solution was removed by filtering samples through a stainless-steel sieve (Retsch, mesh size 50 \u0026micro;m, ⌀10 cm) and thoroughly rinsing with water. Samples were transferred from the sieve to the beaker using either water or the solution intended for the next treatment step (e.g., SPT solution). Finally, samples were filtered on glass fibre (GF) filters (Whatman, pore size 1.2 \u0026micro;m, ⌀ 47 mm) for visual analysis.\u003c/p\u003e \u003cp\u003eDuring visual analysis, a stereomicroscope (ZEISS SteREO Discovery V8, Axiocam 208 camera, Labscope v3.4 software) was used to determine particle shape (fragment, fibre, film, foam, bead), size (length and width) and colour. To determine whether particles too small for manual transfer were of synthetic origin, they were subjected to a hot-needle test as described by Cutroneo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Prata et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Particles large enough for manual handling were subjected to chemical composition analysis using attenuated total reflection-Fourier transform infrared spectroscopy (Thermo Fisher Scientific Nicolet iS20 spectrometer, OMNIC 9 software; 32 scans, spectral resolution 4 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and energy range 4000\u0026thinsp;\u0026minus;\u0026thinsp;400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Data were considered reliable when the particle spectrum matched a database entry with a percentage higher than 70%, however, all spectra were manually verified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quality assurance and control\u003c/h2\u003e \u003cp\u003eAll solutions and water were filtered through GF filters (Whatman, pore size 1.2 \u0026micro;m, ⌀ 47 mm) that were pretreated in a muffle furnace at 500\u0026deg;C for six hours prior use. All equipment and labware were made of glass and metal whenever possible and thoroughly rinsed with filtered water before use. When equipment was made of plastic (Manta net (nylon), rinsing bottles (Telfon), laboratory gloves (nitrile)), the polymer composition was recorded and excluded from the dataset during chemical analysis. Cotton coats and nitrile gloves were always used in the laboratory, and sample treatment was performed in a laminar flow cabinet. The trays containing the samples were covered with aluminium foil at all times when not in use. To minimise potential particle loss, the same beaker was used for all treatment steps.\u003c/p\u003e \u003cp\u003eTo assess the potential for sample contamination during fieldwork, a negative control sample was created by placing an opened jar near each sample. Afterwards, it was filtered on GF filters and analysed in the same manner as its paired sample. To assess laboratory contamination, negative control samples were created in the laboratory as well. The negative control samples were performed in triplicate with each sample batch, subjected to the longest sample treatment protocol and analysed in the same manner as filed samples. Positive control samples were also performed in triplicate; each sample contained 100 red polystyrene beads (⌀ 100 \u0026micro;m, density 1.05 g/cm\u0026sup3;; Sigma‒Aldrich, product no. 56969-10ML-F) and was treated following the longest treatment protocol in the same manner.\u003c/p\u003e \u003cp\u003eIn total, 21 laboratory negative control samples were created, yielding on average 6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 fibres per sample (min. 2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33, max 10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20 fibres); no other shapes were present. After a hot-needle test, 10% of all fibres found in control samples were deemed synthetic, while the remaining 90% were counted as non-synthetic fibres. The negative control results indicate potential procedural contamination of up to 3.23% if only synthetic fibres are counted, or up to 9.66% if both synthetic and non-synthetic fibres are counted. The positive control results indicate a recovery rate of 92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53%. The acquired data on microplastic abundance in environmental samples were not corrected according to the quality control results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Hydrological data and suspended material\u003c/h2\u003e \u003cp\u003eThe river stage and discharge data were obtained from the Latvian Environment, Geology and Meteorology Centre (LEGMC). Hydrological monitoring data were retrieved for each river from one station closest to the sea: at station \"Kalnciems\" located on the Lielupe 48 km from the river's mouth, at station \"Jekabpils\" located on the Daugava 165 km upstream from the river's mouth, at station \"Sigulda\" located on the Gauja 55 km from its mouth, at station \"Lagaste\" located on the Salaca 20 km from its mouth.\u003c/p\u003e \u003cp\u003eFor the station \"Kalnciems\" (Lielupe River), automatic discharge measurements were available from LEGMC. For the other three stations, river discharge was estimated based on river stage measurements using the power-law model implemented in the R package bdrc (Hrafnkelsson et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The plm function, which includes a power-law relationship with stage-dependent log-error variance, was applied to model the discharge-stage relationship using a Bayesian hierarchical model. This approach accounts for the heteroscedasticity commonly observed in discharge data, where variance tends to increase with river stage. The model was calibrated using observed discharge and stage measurements and predicted for all time series.\u003c/p\u003e \u003cp\u003eSuspended material samples were obtained in parallel with MP samples from the upper 20 cm layer of the river using a clean 5L plastic bottle that was pre-rinsed with river water three times to minimize contamination. In the laboratory, the water sample was thoroughly shaken to ensure homogeneity and filtered through a pre-weighed 0.45 \u0026micro;m pore-size membrane filter (Cytiva Whatman\u0026trade;) using a vacuum filtration system until the filtered was clogged. The volume of filtered water (0.4\u0026ndash;1.1 L) was recorded. Following filtration, the membrane filter was dried at 50\u0026deg;C for 24 hours to remove residual moisture and weighed to determine the mass of suspended material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analyses\u003c/h2\u003e \u003cp\u003eData (i.e., concentration, shape, size, colour, and polymer composition of MPs) are reported as the mean of three replicas\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean. One-way ANOVA was performed to assess differences in MP concentrations between sampling seasons. Significant differences were further analysed using pairwise comparisons. Shapiro-Wilk and Levene\u0026rsquo;s tests were used to check for normality and homogeneity of variance, respectively. Linear regression was employed to understand temporal trends in MP concentrations (seasonal changes) and polymer share, and to compare MP concentrations with river discharge and suspended material. The R-squared values were calculated to determine the proportion of variance explained by the river factor. Significant results were followed by Tukey\u0026rsquo;s HSD post-hoc test. Kruskal-Wallis tests were conducted when normality assumptions were violated, and significant results were followed by Dunn\u0026rsquo;s test with Bonferroni correction. To enable pooling sample data for general correlation analysis, the min-max normalisation technique was applied for river discharge data.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Hydrological data and suspended material\u003c/h2\u003e\n \u003cp\u003eRivers\u0026rsquo; discharge regime, the same as suspended material, varies among studied rivers (Fig.\u0026nbsp;3). The discharge data for the \u0026ldquo;Kalnciems\u0026rdquo; station on the river Lielupe sometimes showed negative discharge, implying reverse river flow. This is due to the influence of the Baltic Sea and the very low gradient of Lielupe (0.1 m/km). Thus, the average river stage at the \u0026ldquo;Kalnciems\u0026rdquo; station was 0.35 m above sea level (asl), while the 1st and 3rd quartiles were 0.16 and 0.51 m asl, respectively.\u003c/p\u003e\n \u003cp\u003eThe April 2023 sampling campaigns were characterised by the highest discharges, with average values of 2305, 144, 134, and 57 m\u0026sup3;/s for Daugava, Lielupe, Gauja and Salaca Rivers, respectively. The discharges were ranked as the second highest in April 2022. In contrast, the lowest discharges were noted during the August 2022 campaign, with average values of 163, 16, 31, and 7 m\u0026sup3;/s for the Daugava, Lielupe, Gauja, and Salaca Rivers, respectively. During October 2022, the Daugava River recorded a discharge of 236 m\u0026sup3;/s, while the other rivers demonstrated even lower values of 1.8, 29, and 6.5 m\u0026sup3;/s for the Lielupe, Gauja, and Salaca Rivers, respectively.\u003c/p\u003e\n \u003cp\u003eThe Daugava and Gauja rivers showed moderate positive correlations between river discharge and suspended material (r\u0026thinsp;=\u0026thinsp;0.6428 and r\u0026thinsp;=\u0026thinsp;0.6571, respectively), whereas in Lielupe a weak positive correlation (r\u0026thinsp;=\u0026thinsp;0.2594) was observed. However, these relationships lacked statistical significance (p\u0026thinsp;=\u0026thinsp;0.1193, p\u0026thinsp;=\u0026thinsp;0.1561 and p\u0026thinsp;=\u0026thinsp;0.5742, respectively). Conversely, the River Salaca showed a strong, statistically significant positive correlation (r\u0026thinsp;=\u0026thinsp;0.8696, p\u0026thinsp;=\u0026thinsp;0.0243). These findings further support the hypothesis that higher flow rates mobilise sediments more effectively, especially during peak discharge events (Bisantino et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The lack of significant correlations for the three rivers can be partially attributed to the large distances between sampling locations and river monitoring stations (48\u0026ndash;165 km), while the river Salaca sampling point was the closest to its station (20 km). Moreover, the mean water stage of the Lielupe River at the Kalnciems station, at approximately 0.4 m above sea level, makes river discharge highly susceptible to sea level fluctuations, which can even induce flow reversal (Fig.\u0026nbsp;3A), potentially exacerbating the observed lack of significance. Furthermore, additional factors may influence these relationships, potentially indicating variations in sediment type, particle characteristics, or flow dynamics across different river systems.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Seasonal variation of microplastics\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eMicroplastics concentration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMPs were found in all surface water samples among the studied rivers and seasons (Fig.\u0026nbsp;4). Single measurements not considering replicates ranged from 0.63 to 132.88 particles/m\u003csup\u003e3\u003c/sup\u003e. Considering replicates, the highest mean MP abundances were found at the outlet of the Salaca River (min 2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36, max 49.75\u0026thinsp;\u0026plusmn;\u0026thinsp;41.56, mean 20.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.70 particles/m\u003csup\u003e3\u003c/sup\u003e) located in the northeast part of the Gulf of Riga. Moderate pollution was observed in the rivers Gauja (min 1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65, max 19.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84, mean 6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67 particles/m\u003csup\u003e3\u003c/sup\u003e) in the southeast part of the Gulf of Riga and Daugava (min 1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25, max 10.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, mean 5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 particles/m\u003csup\u003e3\u003c/sup\u003e) in the south part of the Gulf of Riga. The lowest MP concentrations were identified in the Lielupe River (min 1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24, max 3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49, mean 2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 particles/m\u003csup\u003e3\u003c/sup\u003e) located in the southern part of the Gulf of Riga. The mean MP concentrations between rivers showed significant differences (p\u0026thinsp;=\u0026thinsp;0.0168). Pairwise comparisons of mean MP concentrations between rivers, conducted using Tukey\u0026apos;s HSD post-hoc test, revealed significant differences for Salaca vs. Daugava (p\u0026thinsp;=\u0026thinsp;0.0498) and Salaca vs. Lielupe (p\u0026thinsp;=\u0026thinsp;0.0150).\u003c/p\u003e\n \u003cp\u003eThe MP concentrations were relatively low in the River Lielupe with no significant differences (p\u0026thinsp;=\u0026thinsp;0.085) across months. The river exhibited its lowest concentration in April 2022 at 1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 particles/m\u003csup\u003e3\u003c/sup\u003e and peaked in April 2023 at 3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 particles/m\u003csup\u003e3\u003c/sup\u003e. Throughout the study period, this river had the most consistent pollution levels while showing a minor gradual increase over the observed period.\u003c/p\u003e\n \u003cp\u003eThe River Daugava exhibited moderate MP concentrations, with the lowest in autumn (October 2022) at 1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 particles/m\u003csup\u003e3\u003c/sup\u003e. Significantly higher concentrations were observed in the early summer the following year, June 2023, at 10.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 particles/m\u003csup\u003e3\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e\n \u003cp\u003eMP concentrations were relatively low in the River Gauja, with the lowest observed pollution in April 2022 at 1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65 particles/m\u003csup\u003e3\u003c/sup\u003e, which was followed by a significant concentration increase (p\u0026thinsp;=\u0026thinsp;0.0128) during the next sampling campaign in June 2022, when the MP concentration rose to 19.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84 particles/m\u003csup\u003e3\u003c/sup\u003e. The following months were characterised by more stable and moderate pollution levels, ranging between 3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 and 5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 particles/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe River Salaca showed extreme fluctuations in MP concentrations across seasons, with the highest values occurring in spring, April 2023 and April 2022, at 49.75\u0026thinsp;\u0026plusmn;\u0026thinsp;41.56 and 34.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36 particles/m\u003csup\u003e3\u003c/sup\u003e, respectively. This was followed by a significant decrease in early summer, with concentrations dropping to 2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 particles/m\u0026sup3; in June 2022 (p\u0026thinsp;=\u0026thinsp;0.027). The MP concentrations in other months varied but were relatively low compared to April 2023.\u003c/p\u003e\n \u003cp\u003eThese trends highlight seasonal variability in MP concentrations, potentially driven by environmental factors such as runoff patterns, water flow changes, and human activities. The pollution concentration spikes in certain months for specific rivers suggest that local events or seasonal activities affect MP levels, however no evident seasonal trends in MP concentrations among rivers nor within particular river were found.\u003c/p\u003e\n \u003cp\u003eOur findings align closely with the research done by Mani and Burkhardt-Holm (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), who sampled MPs in one of the major European rivers - the Rhine River with a 300 \u0026micro;m mesh manta net (opposed to 100 \u0026micro;m mesh manta net used in our study) and detected no distinct seasonal patterns in MP concentrations. Sampling of MPs in rivers using a Manta net with a 100 \u0026micro;m mesh size is less common than using larger mesh sizes i.e. 300 \u0026micro;m (Pasquier et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is due to concerns about net clogging and hence - sampling efficiency and data accuracy. Some studies where finer mesh size (i.e. 100 \u0026micro;m as in our study) Manta nets are used are from regions with distinct dry and wet seasons. A higher level of pollution was detected in the Houjin River, Taiwan (Huang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Tanchon stream of Han River in South Korea (Park et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) than in the rivers of Latvia. Results of South Korea revealed water MP concentration ranging between 5.3 and 87.3 particles/m\u003csup\u003e3\u003c/sup\u003e with the downstream being the most polluted; besides, MPs were more abundant in the rainy season than in the dry season (Park et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). While the Houjin River in Taiwan was more polluted during dry season reaching 183.3 particles/m\u003csup\u003e3\u003c/sup\u003e (Huang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Disregarding seasonality this can be explained by geographical location, since both rivers are flowing through densely populated and industrialised regions in Taiwan and South Korea, simultaneously receiving household and industrial wastewater, while the urbanisation level of Latvia is considerably lower. Taiwan is among the most densely populated countries, i.e. 673 inhabitants/km\u003csup\u003e2\u003c/sup\u003e., in South Korea there are 530 inhabitants/km\u003csup\u003e2\u003c/sup\u003e., while in Latvia this number is 30 inhabitants/km\u003csup\u003e2\u003c/sup\u003e only (United Nations, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) also concluded MP concentration in the water is related to population density and per capita GDP, as well as to environmental variables such as water temperature, ammonia, nitrogen and oxidation-reduction potential. On the contrary, in the Ottawa River, Canada, a study was carried out to assess MP pollution levels above and below the city\u0026apos;s wastewater treatment plant. The results revealed MP concentrations lower than those reported here (identical mesh size Manta net was used). MP pollution in the Ottawa River was 2.8 times greater below the effluent output (1.9 particles/m\u003csup\u003e3\u003c/sup\u003e) than above it (0.7 particles/m\u003csup\u003e3\u003c/sup\u003e) (Vermaire et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eShape, size and colour of microplastics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAcross all rivers, fibres and fragments were the predominant particle shapes (Fig.\u0026nbsp;5). Fibres were the most prevalent microplastic shape in Lielupe, constituting 68.02% of all particles. The other rivers were mostly dominated by fragments \u0026ndash; 86.22% in Salaca, 52.99% in Daugava and 47.34% in Gauja. Films, foam and beads were generally rare across all rivers, with beads being absent in all cases except for October 2022 when only one bead was identified in the river Lielupe. The presence and distribution of fibres is known to be dependent on several factors such as land use, human activity, river characteristics and fibres physical properties. For instance, Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that the overall abundance of MPs in the Wei River, China, is higher in urban areas compared to agricultural (moderately populated) and mountainous (sparsely populated) regions. This suggests that areas with greater human activity, such as cities, contribute more fibres, likely from textiles, while films and fragments primarily originate from the weathering and degradation of mulch used in agriculture. In the review on major European rivers Gao et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlighted fibres from synthetic textiles as common secondary MPs.\u003c/p\u003e\n \u003cp\u003eThere is a considerable connection between particle morphology and river hydrodynamic characteristics. Particle morphology is crucial \u0026ndash; heavier particles like fragments are more likely to settle while lighter particles as fibres remain in the water column for longer due to larger surface area and slower settling velocity. Simultaneously faster currents and increased discharge during floods tend to transport MP particles, disturb and resuspend them from sediments (Adjornor et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bai et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bhan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; H\u0026uuml;bner et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mani et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). We do not have data on MP in sediments of studied rivers, still the interplay of factors mentioned above might explain the expressed dominance of fibres in slow flowing Lielupe river compared to other rivers.\u003c/p\u003e\n \u003cp\u003eAcross all five size classes (Fig.\u0026nbsp;6), particles within the 300 to 999 \u0026micro;m size range were the most abundant for all rivers (52.45%), followed by the size range 1000 to 4999 \u0026micro;m (27.62%). The least represented size class is particles larger than 5 mm (0.93%). Overall, there is no seasonal variation in particle size distribution among the rivers, but with a few notable exceptions. Specifically, in the River Lielupe in June 2022 and the River Salaca in April and June 2023, a higher proportion of particles in the 100 to 199 \u0026micro;m size range is observed.\u003c/p\u003e\n \u003cp\u003eNot considering seasonal differences, a study of the Houjin River found that small MPs (0.1-2 mm) were more abundant than larger MPs (\u0026gt;\u0026thinsp;2 mm) (Huang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The mesh size of the filtering apparatus influences the abundances of the collected MPs. By collecting samples using two types of mesh sizes (50 and 330 \u0026micro;m), Song et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) revealed that the smaller mesh size captured 1143\u0026thinsp;\u0026plusmn;\u0026thinsp;3353 particles/m\u003csup\u003e3\u003c/sup\u003e, whereas the larger mesh size collected 47\u0026thinsp;\u0026plusmn;\u0026thinsp;192 particles/m\u003csup\u003e3\u003c/sup\u003e, suggesting that the larger mesh size retained only about 4% of potential MP particles. Additionally, half of all detected particles collected with 50 \u0026micro;m mesh were found to be smaller than 100 \u0026micro;m. This indicates that the application of larger mesh sizes can lead to significant underestimation of actual MP pollution (Zhao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Typically, the number of detected particles increases as particle size decreases (Aigars et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Waldschl\u0026auml;ger et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); however, this pattern was not observed in our study. Instead, particles in the size fractions just above the used mesh size (100\u0026ndash;199 \u0026micro;m and 200\u0026ndash;299 \u0026micro;m) were not predominant. Rather, particles in the 300\u0026ndash;999 \u0026micro;m range were more abundant. This pattern was also noted by Sadri and Thompson (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), who found that particles in the 1\u0026ndash;3 mm size range were more abundant than those smaller than 1 mm. A likely explanation for this deviation is the dominance of fibres in the sample. Due to the substantial difference between their length and width, even long but thin fibres can pass through the mesh under the pressure of flowing water. For example, fragments in the River Salaca accounted for nearly 90% of all particles, and a considerable increase in the proportion of particles in the 100\u0026ndash;199 and 200\u0026ndash;299 \u0026micro;m size ranges was observed. Thus, results obtained using the Manta net should be interpreted cautiously, especially when considering small particles compared to mesh size.\u003c/p\u003e\n \u003cp\u003eMost of the collected particles were black, transparent, and blue in colour, with black being the most common particle colour in the Salaca River (Fig.\u0026nbsp;7. D) and transparent in the Gauja River (Fig.\u0026nbsp;7. C). The River Lielupe exhibited the most consistent particle colour distribution, except in August 2022, when yellow particles comprised 19.25% (Fig.\u0026nbsp;7. A). In the River Daugava, a higher proportion of grey particles (51.36%) was identified in June 2023 than in the other sampling periods (Fig.\u0026nbsp;7. B). Colours that constituted less than 3% of the total colour percentage were categorised as \u0026quot;other\u0026quot; (purple, orange, brown).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMicroplastics polymers\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe predominant polymers were polyethylene (PE), polypropylene (PP), and ethylene propylene diene monomer (EPDM), with PE generally being the most dominant (Fig. 8). PE and PP dominance aligns with the majority of already existing studies (Gallo et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and those are also most common plastic polymers produced worldwide. EPDM constituted a notable portion of all particles in the river Salaca across all seasons (on average 94.74%) and in the river Daugava in August 2022 (94.41%). Near the mouths of both rivers important regional and international ports are located, i.e. Salacgriva port in river Salaca and the port of Riga in Daugava. EPDM, being a synthetic rubber, is widely used in various industrial and construction applications, such as sealants, liners, water gaskets etc. The extensive use of EPDM suggests that ports may act as significant point sources of EPDM particles, contributing to their dominance in rivers Daugava and Salaca.\u003c/p\u003e\n \u003cp\u003eSimultaneously rivers with no ports (Lielupe, Gauja) were dominated by PE and PP. This is consistent with study by Moses et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggesting that activities taking place in the river and its catchment play a role in determining the characteristics of pollution.\u003c/p\u003e\n \u003cp\u003eThe presence of other polymers like polyester (PES), polystyrene (PS), nylon, and polyvinyl chloride (PVC) varied, with some considerable spikes in certain months. Polymers that constituted less than 1% of the total share (polyoxymethylene, polyethylene terephthalate, polyurethane, polytetrafluoroethylene, ethylene-vinyl acetate) were pooled in the category \u0026ldquo;Other\u0026rdquo;.\u003c/p\u003e\n \u003cp\u003eThe linear regression analysis revealed no significant variation of polymers during the changing seasons in the river Lielupe. PE showed a moderate positive trend (p\u0026thinsp;=\u0026thinsp;0.152). PES exhibited a negative insignificant trend (p\u0026thinsp;=\u0026thinsp;0.144) throughout the study period. The remaining polymers (PP, PS, Ny, EPDM, PVC, Other) showed negligible trends, with p-values between 0.297 and 0.894.\u003c/p\u003e\n \u003cp\u003eIn the Daugava River no significant temporal trend in polymer concentration was observed. P-values were mostly\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and R-squared values were low, indicating that polymer abundance variations cannot be explained by temporal changes.\u003c/p\u003e\n \u003cp\u003eIn the river Gauja, PE showed a declining but insignificant trend (p\u0026thinsp;=\u0026thinsp;0.107), whereas PP significantly increased (p\u0026thinsp;=\u0026thinsp;0.033). PS and Ny showed no significant trends (p\u0026thinsp;=\u0026thinsp;0.880 and p\u0026thinsp;=\u0026thinsp;0.524), with PS varying slightly and Ny remaining stable. EPDM, PVC and other polymers also showed no significant trends (p\u0026thinsp;=\u0026thinsp;0.415, p\u0026thinsp;=\u0026thinsp;0.207, p\u0026thinsp;=\u0026thinsp;0.633, respectively).\u003c/p\u003e\n \u003cp\u003eThe River Salaca was dominated by particles consisting of EPDM rubber. PE, PP, PS, and Ny exhibited no significant trends. EPDM displayed no significant temporal changes despite its significant abundance. PVC and Other polymers showed negligible variation over time.\u003c/p\u003e\n \u003cp\u003eOf the fibres subjected to hot needle test, 80.78% were confirmed to be synthetic, while the remaining 19.22% did not show characteristic of synthetic particles. This aligns with observations made by Suaria et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Genchi et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasizing the significant part of natural origin fibres in the samples. According to Suaria et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) only 8.2% of oceanic fibres are synthetic while the rest are cellulosic and animal origin. Genchi et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported more than 70% of environmental microfibers to be of natural origin. This finding aligns with logical expectations, as plant fibres \u0026ndash; primarily composed of cellulose along with other components like lignin, hemicellulose, and animal-based keratin or silk \u0026ndash; are expected to remain dominant across various ecosystems, regardless of decomposition rates. Therefore, studies relying on microscopy or manual particle selection should supplement their methods with traditional techniques, such as the hot needle test or various chemical analyses, to prevent misclassifying all fibre-like particles as synthetic. In our study (riverine systems before entering the sea) a higher synthetic fibre proportion most likely results from concentrated industrial and domestic effluent input.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Correlation between MP concentration, river discharge and suspended material\u003c/h2\u003e\n \u003cp\u003eThe correlation analyses showed a moderate negative relationship between river discharge and MP concentration in the rivers Daugava (r = -0.5357) and Salaca (r = -0.6377), and a weak negative relationship (r = -0.2000) in Gauja, although it was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.2152, p\u0026thinsp;=\u0026thinsp;0.1730 and p\u0026thinsp;=\u0026thinsp;0.7040, accordingly). The lack of significance may be due to the small sample size (seven sampling campaigns per site), which limits the interpretability of the findings. In contrast, the Lielupe River exhibited a strong positive relationship (r\u0026thinsp;=\u0026thinsp;0.8153, p\u0026thinsp;=\u0026thinsp;0.0253) between river discharge and MP concentration (Fig.\u0026nbsp;9), similarly as in the study by Tamminga et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, Moses et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) found 80% of all detected MPs in River Weser during high discharge period. Conversely, during low discharge conditions, MP concentrations might increase, possibly due to reduced dilution and accumulation in certain areas (Bailey et al., 2021). In our study, in the River Lielupe, higher discharge levels directly contribute to increased MP concentrations, potentially due to runoff bringing more plastics into the waterway. Another aspect is the size of studied MP particles. For instance, Moses et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) found a positive correlation between small MP particles (10\u0026ndash;500 \u0026micro;m) and both discharge and suspended particulate matter, whereas no such correlation was observed for large particles (500\u0026ndash;5000\u0026micro;m).\u003c/p\u003e\n \u003cp\u003eThe results of correlation analysis of suspended material and MP concentration across the studied river systems showed that the Lielupe and Salaca Rivers displayed a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.714, and r\u0026thinsp;=\u0026thinsp;0.7714, accordingly), while the Daugava showed a weak positive correlation (r\u0026thinsp;=\u0026thinsp;0.1428). In contrast, the Gauja River indicated a weak negative correlation (r = -0.0857) between these two variables. However, all of the observed relationships lacked statistical significance (p\u0026thinsp;=\u0026thinsp;0.0713, p\u0026thinsp;=\u0026thinsp;0.0723, p\u0026thinsp;=\u0026thinsp;0.7599 and p\u0026thinsp;=\u0026thinsp;0.8717, respectively), suggesting a need for extended investigation.\u003c/p\u003e\n \u003cp\u003eMost likely the limited data pool (n\u0026thinsp;=\u0026thinsp;6 for Gauja and Salaca, n\u0026thinsp;=\u0026thinsp;7 for Lielupe and Daugava) undermined the reliability of the estimate, leaving insufficient evidence to establish statistical significance. For this reason, we pooled normalised river discharge data from all rivers to increase the data pool size (n\u0026thinsp;=\u0026thinsp;26) and to test whether there is a general correlation between MP concentration, suspended material, and river discharge. No correlation was observed for variables MP concentration-river discharge (p\u0026thinsp;=\u0026thinsp;0.1453) and river discharge-suspended material (p\u0026thinsp;=\u0026thinsp;0.1990). However, MP concentration-suspended material showed significant positive correlation (p\u0026thinsp;=\u0026thinsp;0.0319). Our study therefore agrees with Moses et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) who emphasized that factors responsible for MP dynamics might be resuspension and precipitation-driven runoff. In other words, MP particles may become adhered to fine sediment particles, i.e. when sediments are suspended in the water column also previously settled MP become resuspended, consequently MP concentration in the water is increasing. Resuspension typically occurs due to currents, waves, anthropogenic activities such as dredging and boating. Heavy rainfalls and storm events generate runoff hence introducing additional sediments and MPs from terrestrial sources. Moreover, strong runoff can promote the resuspension of settled sediments and MPs. As demonstrated by (Hurley et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), flooding can reduce MP pollution in riverbeds by flushing away MP particles.\u003c/p\u003e\n \u003cp\u003eAlthough Latvia is not characterised by distinct differences in river water discharge across seasons, a slight increase can be observed twice a year - in spring after the snowmelt and in autumn if consistent precipitation occurs (LVĢMC, \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Consequently, higher MP concentrations observed in spring and early summer could possibly occur due to increased runoff from agricultural lands or urban areas inflicted by the melting of snow and ice cover. Previous research has emphasised the need for temporal sampling during various weather events to capture the dynamics of MP concentrations effectively (Forrest et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Snow has been reported to act as a vector for MP removal from the atmosphere, depositing it in certain areas (Bergmann et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Evangeliou et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Later, the pollution gets transported across the environment through surface runoff due to increased temperatures resulting from changing seasons (Werbowski et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, a significant increase in MP pollution was noted during spring snowmelt in Canada, indicating that seasonal changes driven by temperature-induced surface runoff are critical for understanding microplastic pollution in river environments (Forrest et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe extent of studies focusing on pollution changes in regions where temperature plays a key role in defining seasonal differences (e.g., ice-covered season in northern Europe) falls short. In regions with precipitation-driven seasons, more notable variations have been reported with regard to MP concentrations, e.g., in Lake Manipal, India, MP concentrations were found to be more than twofold higher during the monsoon season (423.00 particles/m\u003csup\u003e3\u003c/sup\u003e) in comparison to the post-monsoon period (117.00 particles/m\u003csup\u003e3\u003c/sup\u003e) (Warrier et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The evaluation of seasonal MP variation in the Houjin River, Taiwan, revealed higher MP pollution in the dry season (183.33\u0026thinsp;\u0026plusmn;\u0026thinsp;128.95 particles/m\u003csup\u003e3\u003c/sup\u003e) than in the wet season (102.08\u0026thinsp;\u0026plusmn;\u0026thinsp;45.80 particles/m\u003csup\u003e3\u003c/sup\u003e) (Huang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the opposite pattern was observed in the Yangtze River, China, with the dry season (0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 particles/m\u003csup\u003e3\u003c/sup\u003e) exhibiting almost two times lower MP pollution than the wet season (1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 particles/m\u003csup\u003e3\u003c/sup\u003e) (Wu et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is worth highlighting that the acute impacts of rainfall and runoff are often more easily observable and measurable than the subtler effects of temperature fluctuations. This wording keeps the contrast between precipitation-driven and temperature-driven seasonal differences, showing how discharge or precipitation can serve as relevant seasonal indicators depending on the regional climate characteristics.\u003c/p\u003e\n \u003cp\u003eFurther research considering MP size fractions, concentration changes and hydrological variability would provide understanding of the fluvial system discharge, sediment dynamics, and MP pollution interconnectedness.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Importance of replicate sampling\u003c/h2\u003e\n \u003cp\u003eIn addition to reporting the abundance and seasonal changes in MP pollution, we also investigated the usefulness of replicate sampling. The variability of MP concentrations between replicates was found to be relatively high (Fig.\u0026nbsp;10), resulting in the standard error within one river\u0026rsquo;s sampling campaign being greater than or equal to the standard error between monthly mean concentrations. The greatest variability between replicate samples was observed for data collected from the Salaca River. Specifically, the three replicates from April 2023 indicated concentrations of 8.01, 8.35, and 132.88 particles/m\u003csup\u003e3\u003c/sup\u003e. The variability observed in our replicates aligns with findings from Barone et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), who emphasized the critical role of replicate sampling in capturing the spatial and temporal heterogeneity of MP pollution in surface waters, i.e. MP concentrations can vary by more than an order of magnitude between trawling events, particularly in environments with low pollution levels. Pasquier et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) noted that only 21% of MP studies include replicate sampling. Hence, it turns out to be a methodological gap that significantly affects data robustness because variability of MP concentrations can be affected by weather, local hydrodynamics and particle characteristics. Depending on the sampled matrix, suggested minimum number of replicates can vary (Brander et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), still Barone et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) recommended a minimum of three replicate trawlings per site for surface water samples.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first comprehensive analysis of microplastic (MP) pollution seasonality in four major Latvian rivers \u0026ndash; Daugava, Lielupe, Gauja, and Salaca \u0026ndash; that flow into the Gulf of Riga. The results reveal significant spatial and seasonal variations in MP concentrations, with the River Salaca exhibiting the highest levels and extreme fluctuations, while Daugava and Gauja had moderate levels, and Lielupe had relatively low microplastic pollution level. Our study also highlights a significant gap in research on pollution dynamics in regions characterised by temperature gradients across the year, such as northern Europe. Notably, we observed increased MP concentrations in spring, coinciding with the melt of snow and ice cover. \u0026nbsp;Despite the observed seasonal changes in MP concentrations, no clear correlation was found between MP levels and river discharge or suspended material in most cases. The exception was the River Salaca, which demonstrated a strong positive correlation between MP concentrations and suspended material, likely due to localised hydrodynamic conditions. These findings emphasize the need for future studies that integrate hydrological and meteorological factors to better understand the mechanisms driving MP transport in riverine systems.\u003c/p\u003e\n\u003cp\u003eRegarding particle shapes, fibers dominated in River Lielupe, whereas fragments were more prevalent in the other rivers. Polymer analysis revealed that PE and PP were the most common in Lielupe, Daugava, and Gauja, while EPDM particles dominated in River Salaca. As to commonly applied sampling methodology, we conclude that collecting a single sample per site is insufficient to obtain representative data. To ensure reliable results with low variability, we recommend collecting samples consecutively at least three times. This is particularly important for riverine systems due to their dynamic flow regimes which can redistribute particles unevenly across surface layers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough this study spanned more than a year and documented pollution levels across different seasons, limited information was collected about the conditions potentially influencing changes in MP pollution levels during the sampling campaigns. Key factors such as wind patterns, river runoff, surface and sub-surface water currents, the impact of resuspension and detailed analyses of anthropogenic activities within the rivers\u0026apos; catchment basins were not comprehensively addressed. This underscores the need for a more extensive and detailed investigation into both natural and human-driven factors affecting riverine MP pollution and its seasonal variations.\u003c/p\u003e\n\u003cp\u003eStill, given the critical role of rivers as flow flume for MP transport to marine ecosystems, this research provides valuable insights into MP pollution dynamics in the Baltic Sea region.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Administration of Latvian Environmental Protection Fund project No. 1\u0026ndash;08/37/2022, and ESF project No. 8.2.2.0/20/I/003.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful to the research assistants, students, and visiting scientists at the Latvian Institute of Aquatic Ecology for their efforts in the fieldwork.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdjornor, B.Y., Han, B., Zahran, E.M., Pichtel, J., Wood, R., 2024. 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Total Environ. 908, 168241. https://doi.org/10.1016/j.scitotenv.2023.168241\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"5c2b49bb-b6ce-4f13-bd75-b34cc33ca3ad","identifier":"10.13039/501100015040","name":"Latvijas Vides Aizsardzības Fonda Administrācijas","awardNumber":"project No. 1-08/37/2022","order_by":0},{"identity":"2ac2b02e-3e36-4392-95d4-43820f6e66a6","identifier":"10.13039/501100004895","name":"European Social Fund","awardNumber":"8.2.2.0/20/I/003","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Latvian Institute of Aquatic Ecology","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":"Microplastic, river, pollution, seasonality, estuaries, Gulf of Riga, replica samples","lastPublishedDoi":"10.21203/rs.3.rs-5955650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5955650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRivers are considered key pathways for microplastics, transporting these pollutants from inland sources to marine environments. In this study, we investigated the seasonal fluctuations of microplastics in the surface waters of four major Latvian rivers (Daugava, Lielupe, Gauja, and Salaca) that flow into the Gulf of Riga. Sampling was conducted from spring 2022 to summer 2023 to represent distinct seasonal and environmental conditions. Using a Manta net (100 \u0026micro;m mesh size) samples were collected in triplicate and analysed for microplastic concentration, shape, size, colour, and polymer composition. Microplastic concentrations ranged from 0.63 to 132.88 particles/m\u0026sup3;, with the highest levels observed in the River Salaca. Fibres and fragments were the predominant microplastic shapes, with polyethylene, polypropylene, and ethylene propylene diene monomer being the most abundant polymers. We found significant spatial and seasonal variations in microplastic concentrations in some rivers, with the River Salaca showing the most extreme seasonal fluctuations. However, overall, no significant correlation was observed between the suspended material and river discharge variables. Replicate sampling revealed variability between samples, highlighting the need to include replicas. These findings highlight the complexity of microplastic pollution dynamics and the need for careful consideration of seasonal factors when assessing environmental contamination. This article is the first to contribute data from Latvian rivers on the growing pool of information on microplastics contamination in waterways.\u003c/p\u003e","manuscriptTitle":"Microplastics in FLOW: Seasonal Patterns in Major Latvian Rivers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-05 11:55:05","doi":"10.21203/rs.3.rs-5955650/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":"38249b27-86ac-496e-ad89-6121ac6b1cae","owner":[],"postedDate":"February 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43806925,"name":"Marine and Freshwater Ecology"}],"tags":[],"updatedAt":"2025-02-05T11:55:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-05 11:55:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5955650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5955650","identity":"rs-5955650","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}